KR20210116863A - AptaSSN selection method and apparatus for classifying a sample, molecular identification method and apparatus coupled thereto, target molecule analysis method and apparatus using AptaSSN population, and biological meaning determination support system - Google Patents

Abstract

The present invention relates to a method, a reagent, a kit and a device designed for development of an AptaSSN group that binds to a molecular group constituting a sample composed of various types of molecules and a molecular group that binds thereto, selection of an AptaSSN capable of classifying samples, multiplex AptaSSN-based multiplex analysis, target molecule analysis, and performance improvement of a biological meaning determination support system.

Description

AptaSSN selection method and apparatus capable of classifying samples, molecular identification method and apparatus binding thereto, target molecule analysis method and apparatus using AptaSSN population, and biological meaning determination support system {AptaSSN selection method and apparatus for classifying a sample, molecular identification method and apparatus coupled thereto, target molecule analysis method and apparatus using AptaSSN population, and biological meaning determination support system}

The present invention is a method designed to simultaneously develop biomarkers and AptaSSN (molecularly coupled single-stranded nucleic acid) by analyzing a molecular (M) population constituting a sample composed of various types of molecules, and to improve the performance of multiple AptaSSN-based assays , reagents, kits and devices, and internal quality control and biological significance determination support systems. These methods are widely useful in the design and development of tools for the discovery and study of biomarkers and the development of therapeutics as well as diagnostic applications.

Selection of single-stranded nucleic acid (SSN) populations with significant binding differences to molecular (M) populations in samples composed of various types of molecules and identification of corresponding molecules, development of aptamers for specific molecules, and quantitation of multiple AptaSSN-based molecules Analytical methods are important tools in scientific research and health care.

The development of a binding SSN for a molecular population constituting a sample composed of various types of molecules is provided in Korean Patent Registration No. 10-0923048 and Korean Patent Application No. 10-2017-0145181. Analysis of the aptamer population eluted from the complex formed by contacting multiple aptamer populations and molecular populations is performed by mass spectrometry, QPCR, microarray immobilized on one or more aptamers immobilized on a solid support, and complementary to aptamers. Various nucleic acid analysis techniques, including microarrays and NGS equipment in which nucleotide sequences are fixed, are used.

Each of the aptamers can bind to molecules in a very specific manner and with very high affinity. Once the population of aptamers is in contact with the sample, the aptamers bind to individual molecules present in the sample, thereby determining the absence, presence, amount and/or concentration of molecules in the sample.

The binding SSN selection, target molecule identification, aptamer development, and AptaSSN multiplex assay are solution-based molecular interactions and separation steps designed to remove specific components of the reaction mixture and sample from molecules to be detected during separation of the M-AptaSSN complex. In order to isolate components of , one or more specific capture steps and internal quality control techniques are required in aptamer multiple analysis and a biological meaning determination support system using the same.

In the present invention, there is provided an SSN library in which a first tag is attached to a site consisting of an arbitrary nucleotide sequence for selecting one or more types of AptaSSN having a significant binding difference to a molecular population in a sample composed of various types of molecules.

The present invention provides a method and apparatus for efficiently securing only true AptaSSN from AptaSSN that occurs due to non-specific binding by selecting AptaSSN having a significant binding difference in a molecular population of a sample composed of various types of molecules. .

In the present invention, one of the main causes of non-specific binding in AptaSSN selection and aptamer development for a specific molecule, which has a significant binding difference to molecules constituting a sample composed of various types of molecular groups, is unexpected non-target/ A method and apparatus are provided for minimizing SSN interactions and the likelihood of SSN/SSN interactions.

In the AptaSSN multiplex analysis method, due to the large dynamic and complexity of the biological sample, the present invention removes the background signal through various washing processes and maintains the target/AptaSSN interaction while maintaining the target/AptaSSN interaction. Provided are an AptaSSN multi-analysis method and apparatus having sensitivity and specificity, and an internal quality control method and apparatus therefor.

The present invention provides an internal quality control and biological meaning determination support system for classifying the sample based on the biological meaning by using molecular data on the molecular group constituting the sample composed of various types of molecules.

The present invention includes methods, devices, reagents and kits designed to improve the performance of AptaSSN and corresponding target molecules having a significant binding difference in the molecular population constituting the sample, and the performance of the AptaSSN multiplex analysis method. The disclosed methods, devices, reagents and kits provide highly sensitive assays for the detection and/or quantification of molecules in a sample by reducing or eliminating background signals.

The singular forms include the plural and are used interchangeably with "at least one" and "one or more" unless the content clearly dictates otherwise. Thus, reference to “aptamers” includes mixtures of aptamers and the like.

"binds" and any variations thereof are due to the interaction or complex formation between the tag and the capture moiety, e.g., the unbound component of the sample from the tag-capture complex under given complex formation or reaction conditions. It refers to the formation of a complex that is sufficiently stable to permit dissociation of an "unbound" molecule.

The tag and the capture component have specificity and can directly bind to each other by interacting and binding. The tag and capture moiety may also be linked indirectly to each other, such as when complex formation is mediated by a linker.

In the present invention, a “single-stranded nucleic acid (molecular binding SSN, AptaSSN)” that specifically binds to a molecule using a plurality of molecules constituting a sample, and a “pressure, a structure capable of amplification,” developed by various known SELEX methods "tamer" can be used interchangeably to refer to a non-naturally occurring nucleic acid having a desired action on a molecule, and these are collectively referred to as "AptaSSN". A desired action is binding to a molecule, catalytically changing the molecule, reacting the molecule or a functional activity with the molecule in a way that modifies or alters the functional activity of the molecule, covalently binds to the molecule, and the interaction between the molecule and another molecule. including, but not limited to, facilitating the reaction.

The above action is a three-dimensional chemical structure different from that of a polynucleotide that binds to a nucleic acid ligand through a mechanism independent of Watson/Crick base pairing or triple helix formation. specific binding affinity, wherein the aptamer is not a nucleic acid with a known physiological function that is bound to the molecule.

An aptamer for a given molecule is defined as (a) contacting the molecule with a candidate mixture, wherein a nucleic acid having an increased affinity for the molecule relative to other nucleic acids in the candidate mixture is a candidate mixture if the aptamer is a ligand of the molecule. can be separated from the rest of; (b) isolating the nucleic acid of increased affinity from the remainder of the candidate mixture; and (c) a nucleic acid identified from a candidate mixture of nucleic acids by a method comprising amplifying the nucleic acid of increased affinity to obtain a mixture of ligand-enriched nucleic acids against which the aptamer of the molecule is to be identified. .

Affinity interactions are a matter of degree, but the "specific binding affinity" of an aptamer for its molecule is generally higher than the degree to which the aptamer binds to other non-molecular components in a mixture or sample. is recognized to mean that it binds to its molecule with an affinity of

Aptamers may comprise any suitable number of nucleotides. Different aptamers may have the same or different number of nucleotides. Aptamers may be DNA or RNA, and may be single-stranded, double-stranded, or contain a double-stranded or triple-stranded region. Aptamers can be designed to have any combination of nucleotides with the desired base modified.

Aptamers can be identified using any known method, including the SELEX method. See, eg, US Patent No. 5,475,096 ("Nucleic Acid Ligands"). Once identified, the aptamer can be prepared or synthesized according to any known method, including chemical synthesis methods and enzymatic synthesis methods.

In the present invention, identification of binding SSNs and corresponding molecules that are significantly different in the amount of binding to molecules constituting biological samples and other samples, development of aptamers for specific molecules, and "M-SSN complex" in the AptaSSN multiplex assay " or "M-AptaSSN complex" is used interchangeably herein to refer to a non-covalent complex formed by the interaction of an SSN or AptaSSN with a target molecule. "M-AptaSSN complex" refers to a set of one or more such complexes. An “M-SSN complex” or M-AptaSSN complex can generally be converted or dissociated by a change in ambient conditions, eg, an increase in temperature, an increase in salt concentration, or addition of a denaturant. That is, “binding SSN” and “aptamer” and “molecule” and “target” can be used interchangeably in the present invention.

If a non-covalent complex of an aptamer and a target molecule is provided, wherein the aptamer has a Kd for the molecule of less than about 100 nM, wherein the dissociation of the aptamer from the molecule (given as _{the half-life of the complex: t 1/2 )} rate is greater than or equal to about 30 minutes; wherein one, several or all pyrimidines in the nucleic acid sequence of the aptamer are changed at the 5-position of the base.

Since an aptamer can be identified as any chemical or biological molecule of virtually any size, any chemical or biological molecule of virtually any size can be a suitable target. The target may also be altered to enhance the likelihood or strength of an interaction between the target and AptaSSN. Molecules may also be modified to include tags as described above. In an exemplary embodiment, the molecule is a protein. See US Pat. No. 6,376,190 (“Modified SELEX Processes Without Purified Protein”) for a method in which the SELEX molecule is a peptide.

"Non-specific complex" refers to non-covalent binding between an aptamer and two or more non-target molecules other than a target molecule. Since the non-specific complex is not selected based on the affinity interaction between the aptamer and the non-target molecule, but represents the interaction between the aptamer and the non-target molecule, the components of the non-specific complex will show a lower affinity for each other on average, It will have a relatively higher dissociation rate than aptamers and non-target molecules. Nonspecific complexes include aptamers and non-targets, aptamers and other aptamers, competitor and non-target, competitor and target, aptamer and competitor, and complexes formed between molecules and non-molecules, as well as aptamers, molecules, non-targets, surfaces and Contains higher-order aggregates of competitors.

"Non-target molecule" and "non-target" are used interchangeably to refer to a molecule contained in a sample capable of forming a non-specific complex with AptaSSN. It will be seen that a non-target to the first AptaSSN can be a target to the second AptaSSN. Likewise, a target for a first AptaSSN may be a non-target for a second AptaSSN.

"Nucleotide refers to ribonucleotides or deoxyribonucleotides or their modified forms as well as analogs thereof. Nucleotides are purines (e.g., adenine, hypok hypoxanthine, guanine and derivatives and analogs thereof) as well as pyrimidines (e.g. cytosine, uracil, thymine and derivatives and analogs thereof) include types that include

"nucleic acid", "oligonucleotide" and "polynucleotide" are used interchangeably to refer to a polymer of nucleotides, including DNA, RNA, DNA/RNA hybrids, and nucleic acids of this kind; Modifications of oligonucleotides and polynucleotides, including binding of various entities or parts to nucleotide units at any position."Polynucleotide", "oligonucleotide" and "nucleic acid" "Nucleic acid" includes double-stranded or single-stranded molecules as well as multi-stranded (ie, triple-helixed) molecules. Nucleic acid, oligonucleotide and polynucleotide are broader terms than the term aptamer, so the terms nucleic acid, oligo Nucleotides and polynucleotides include polymers of nucleotides that are aptamers, but the terms nucleic acids, oligonucleotides and polynucleotides are not limited to aptamers.

"Single-stranded nucleic acid library" is a single-stranded nucleic acids (single stranded nucleic acid, SSN) the case where the number of a base 40 constituting a random nucleotide sequence as SSN group comprising at least a base of about 10 ²⁴ sequences composed of any nucleotide sequence This can lead to high diversity.

“Molecules”, “target molecule”, and “target” are used interchangeably herein to refer to any molecule of interest that may be present in a sample. A "molecule of interest" is, for example, in the case of a protein, a minor change in the amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation ), or any minor variation of a particular molecule, such as any other manipulation or modification, such as binding to a labeling component that does not substantially alter the identity of the molecule. ) is included. Molecules with a major M, or M, are proteins, polypeptides, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, other than nucleic acids. Receptors, antigens, antibodies, affibodies, antibody mimics, viruses, antibody mimics, toxic substances, substrates , metabolites, transition state analogs, cofoactors, inhibitors, drugs, dyes, nutrients, growth factors, cells (cells), tissues, and any fragment or portion of any of the foregoing.

"Polypeptide", "peptide" and "protein" are used interchangeably herein to refer to a polymer of amino acids of any length. The polymer may be straight or branched, may contain altered amino acids, and may be interrupted by non-amino acids. The terms may also be altered in nature or be associated with, for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation or binding to a labeled component. amino acid polymers by modification such as any other manipulation or alteration. Also included within the definition are polypeptides comprising one or more analogs of amino acids (including, for example, unnatural amino acids, etc.), as well as other modifications known in the art, for example. Polypeptides may be single chain or linked chain.

"Molecular data" refers to substantial information on all measurable characteristics of a sample composed of a plurality of molecules. Such characteristics or variables include, but are not limited to, the level of the cell population and one or more molecules associated therewith, the level of one or more lytic factors, the level of one or more other molecules, genotypic information, gene expression levels, and gene mutations. Such characteristics or variables include all hourly data and current data. For example, complete molecular data of an organism includes all of these features at any point in the past, as well as all measurable features at the present time.

"Soluble factor" means any measurable component of a biological sample or tissue that is not a cell population or cell-associated molecule. Solubility factors include, but are not limited to, soluble proteins, carbohydrates, lipids, glycoproteins, steroids, other small molecules including metallic, inorganic, ionic, organometallic species, and cytokines and soluble receptors of such components. Included are any complex antibody with antigen and drug complexed with any. Soluble factors can be autologous, such as cytokines, antibodies, acute-phase proteins, and the like, and foreign, such as chemicals, products of infectious diseases, intestinal bacteria and animals. Soluble factors may be endogenous produced by the organism or they may be exogenous, such as viruses, drugs, or environmental toxins. The solubility factor may be a small molecule compound such as a prostaglandin, a vitamin, a metabolite (eg, iron, sugar, amino acid, etc.).

The present invention provides molecular data for a sample, methods for obtaining such molecular data, and methods for using such molecular data, including identifying biomarkers.

"Sample" refers to any molecule, solution, or mixture comprising multiple molecules, and may include at least one molecule. The sample includes a biological sample as defined below and a sample that can be used for environmental or toxicological testing, such as, for example, contaminated or potentially contaminated water and industrial wastewater. A sample may also be a final product, an intermediate product, or a by-product from a preparation process, such as, for example, a manufacturing process. A sample may comprise any suitable assay medium, buffer or diluent to which a molecule, solution or mixture obtained from a living organism or some other source (eg, an environmental or industrial source) has been added.

"Biological sample" refers to any molecule, solution or mixture obtained from an organism. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, plasma and serum), sputum, breath, urine, semen ( semen, saliva, meningeal fluid, amniotic fluid, glandular fluid, lymph fluid, nipple aspirate, bronchial aspirate, synovial fluid (synovial fluid), joint aspirate, cells, cell extracts and cerebrospinal fluid. This also includes, for the most part, separate fragments of all of the foregoing. The term "biological sample" also includes a mixture comprising molecules, solutions or homogenized solid molecules from, for example, such as a stool sample, tissue sample or tissue biopsy. The term "biological sample" also includes molecules, solutions or mixtures derived from tissue processing, cellular processing, bacterial processing or viral processing or a cell free biological system (eg, IVTT).

A test sample, a “comparison sample,” may be compared to a control sample. "Control sample" as used herein refers to any molecule, solution or mixture that contains a plurality of molecules and is known to contain at least one molecule. The exact amount or concentration of any M present in the control sample may also be well known. The control sample and the comparison sample were collectively referred to as “analytical sample”. The term control sample includes biological samples as defined herein and samples that can be used for environmental or toxin testing, such as, for example, contaminated or potentially contaminated water and industrial wastewater. A control sample may also be an end product, an intermediate product, or a by-product of a manufacturing process, such as a preparation process. A control sample may include any suitable assay medium, buffer or diluent that may be added to a molecule, solution or mixture obtained from a biological or any other source (eg, an environmental or industrial source).

“Biological meaning” means (a) the physiological change of the sample or the person who took the sample, and corresponding to the above clinical examination value, prevention, diagnosis, treatment, alleviation and treatment for health management purposes making) necessary information, or (b) “classification” that supports the process of making decisions about biological significance for the purpose of diagnosing, treating, mitigating, treating, or preventing a disease or other condition; Means “score” and “index”, and after collecting the sample whose biological meaning is determined or the clinical information of the person who took the sample and the produced multiple molecular values, statistical techniques or machine learning algorithms are used. It is a specific result produced so that the disease can be inferred or discriminated from the information collected above.

The present invention provides a method for selecting an SSN that can classify samples because there is a difference in the amount of significant binding to biological samples and molecular groups constituting other samples.

1 is a single-stranded nucleic acid (SSN) library prepared from a starting library including an arbitrary nucleotide sequence region. Single-stranded nucleic acid having a specific and significantly different binding amount to a molecular population in a sample composed of various types of molecules. A diagram showing a method for selecting (SSN) populations and corresponding molecules.

A SSN-FSS complex library is prepared using a single-stranded nucleic acid with a first tag added to the SSN prepared in the starting library and a first solid support (FSS) in a suspended state to capture molecules and perform a first separation, the harvested complex By adding a second tag to the molecule of the immobilized state forms a complex with the second solid support. The second separation of the formed complex may be carried out by repeating the selection and amplification steps of amplifying the eluted bound SSN and additionally comprising an analysis step.

The present invention can provide a method for developing a biomarker and a ligand while performing Nontarget Proteomics using the molecular data production technology, as shown in FIG. 1 .

2 is a diagram providing a method for developing a biomarker by performing Target Proteomics with an AptaSSN group.

3 is a diagram of preparing an AptaSSN-FSS complex library using a first suspended solid support (FSS) by adding a first tag to the AptaSSN population prepared in the AptaSSN multiplex analysis for samples composed of various types of molecules. . A first separation is performed by capturing a molecule with the prepared complex, and a second tag is added to the harvested complex molecule to form a complex with a second immobilized solid support (SSS). It can be carried out by a method comprising an analysis step of isolating the formed complex and eluting the bound AptaSSN and amplifying it.

I. AptaSSN selection

1. Departure library

In the present specification, a single-stranded nucleic acid group consisting of an arbitrary nucleotide sequence is referred to as a “starting library”.

In the present invention, the starting library may be a nucleic acid population extracted from a biological sample containing at least a nucleic acid or organically synthesized by a known method. Extraction of the nucleic acid population is performed differently depending on the type of nucleic acid, that is, DNA and RNA, and can be performed by a known method (Maniatis, Tom, EF Fritsch, and Joseph Sambrook. Molecular Cloning: A Laboratory Manual. Cold Spring Harbor, NY). : Cold Spring Harbor Laboratory, 1982.).

As used herein, the term “nucleic acid” has a meaning comprehensively including DNA (gDNA and cDNA) and RNA molecules. analogues) (Scheit, Nucleotide Analogs, John Wiley, New York (1980); Uhlman and Peyman, Chemical Reviews, 90:543-584 (1990)).

The nucleic acid population is single-stranded by a known method from any one selected from the group comprising genomic DNA and cDNA derived from genes or fragments thereof, DNA, mRNA, rRNA, tRNA and other non-coding RNAs. Nucleic acid libraries can be prepared.

In the present invention, a starting library for analyzing a nucleic acid population in a sample composed of various types of molecular populations may be prepared by fragmentation from a nucleic acid population extracted from a biological sample by a known method. The fragmentation method can be performed by known physical and biological methods (Maniatis, Tom, EF Fritsch, and Joseph Sambrook. Molecular Cloning: A Laboratory Manual. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory, 1982). .).

Figure 4 is a diagram of a starting library for the molecular types except for the nucleic acid molecule.

The structure of the starting library is a single-stranded nucleic acid (SSN), an oligonucleotide group, in which Structural Formula (1) is a fixed sequence-any nucleotide sequence-fixed sequence structure (Structure 1) or an arbitrary nucleotide sequence structure (Structure Formula 2).

5'-5' Fixed sequence (C1)-Variable sequence-3' Fixed sequence (C2)-3' (Structural formula 1)

5'-variable sequence-3' (Structural Formula 2)

In the present invention, the starting single-stranded nucleic acid may include a random sequence and a stationary sequence necessary for efficient amplification. Typically, the starting single-stranded nucleic acid may have a fixed 5' terminal sequence (C1) and a 3' terminal fixed sequence (C2) flanked by an internal region consisting of 15 to 60 random nucleotides.

A single-stranded (SS) or double-stranded (DS) library, an RNA library, a binding SSN library, and an equivalent or differential binding SSN library can be prepared from the starting single-stranded nucleic acid.

The variable sequence of the starting single-stranded nucleic acid typically provides each molecular binding site for the molecule, and the variable sequence is either completely randomized (ie, the probability of finding a base at any position is 1 in 4). Partially randomized (ie, the probability of finding a base at any position can be chosen at any level between 1 and 100 percent). An oligonucleotide in which at least 15 to 100 or less arbitrary nucleotide sequences of the variable sequence having diversity between the fixed sequences of both sides exist.

A mixture of all four nucleotides for synthesizing the random sequence constituting the variable sequence of the starting single-stranded nucleic acid is added to each nucleotide addition step during the synthesis process, so that nucleotides can be randomly incorporated. The oligonucleotides may be modified or unmodified DNA, RNA or DNA/RNA hybrids. The random single-stranded nucleic acid may include an entirely random sequence or a partially random sequence. A partially random sequence can be prepared by adding 4 nucleotides in different molar ratios in each addition step. Typically, the single-stranded nucleic acid has constant 5' and 3' fixed state sequences flanked by an internal region consisting of 15 to 60 random nucleotides.

Random nucleotides can be produced by a number of methods, including chemical synthesis and size-specific selection of randomly cleaved intracellular nucleic acids. Sequence variations in nucleic acids can also be introduced or augmented by mutagenesis before or during repeated selection/amplification procedures. The random sequence portion of the oligonucleotide may be of any length and may include ribonucleotide-deoxyribonucleotides, and may also include modified or non-natural nucleotides or nucleotide analogs.

Random oligonucleotides can be synthesized from phosphodiester-linked nucleotides using solid-phase oligonucleotide synthesis techniques well known in the art. Random oligonucleotides can also be synthesized using a solution-phase method such as a triester synthesis method. As a result of a typical synthesis performed using automated DNA synthesis equipment, 10 ¹⁴ to 10 ¹⁶ individual molecules (sufficient number for most Celex experiments) are produced. A sufficiently large region of random sequences in sequence design increases the likelihood that each synthetic molecule will represent a unique sequence.

Typically, the constant sequence of <Structural Formula 1> is selected to aid in the amplification steps described below or to enhance the likelihood of a given structural arrangement of single-stranded nucleic acids in the starting library.

The fixed sequence of <Structural Formula 1> is a sequence common to oligonucleotides present in a population incorporated for preselection, for example, a CpG motif described below, a hybridization site for a PCR primer, an RNA polymerase (e.g., cloning-sequencing of promoter sequences, restriction sites or homopolymer sequences for T3, T4, T7 and SP6), e.g. poly A or poly T tracks, catalytic cores, sites for selective binding to affinity columns and oligonucleotides of interest and other sequences that promote

The fixed sequence of <Structural Formula 1> occupies about half of the entire base sequence. Such a constant sequence may have a non-specific interaction with a variable sequence having an arbitrary base sequence, thereby limiting the complexity of the oligonucleotide library.

In order to overcome the above problem, "primer free" selex technology, a technology for developing a binding SSN to a target molecule using a library consisting of a starting single-stranded nucleic acid without a fixed state sequence produced by various methods such as organic synthesis has been proposed and is the structure of <Structural Formula 2>.

Since the differential or equivalent process proposed in the present invention requires a fixed sequence, for this purpose, the necessary process can be performed by artificially adding a nucleotide sequence capable of the above process to the starting single-stranded nucleic acid of the "primer free" Selex result product. .

The present invention provides a single-stranded nucleic acid (SSN) of the <Structural Formula 1> structure by addition reaction of the 5' fixed sequence (C1) and the 3' fixed sequence (C2) of <Structural Formula 1> to the oligonucleotide of the <Structural Formula 2>; It can also be prepared as a library consisting of oligonucleotides.

In the present invention, <Structural Formula 2> the base of the 5' fixed sequence (C1) of <Structural Formula 1> to the binding SSN of the binding SSN library separated from the M-linked SSN complex group formed by contacting the starting library and the molecular population constituting the sample A binding SSN library can be prepared by adding an oligonucleotide prepared with the sequence and the base sequence of the 3' fixed sequence (C2).

In the present invention, the 5' end portion of the starting single-stranded nucleic acid of <Structural Formula 1> is composed of a 5' fixed sequence, and the 3' end portion is composed of a 3' fixed sequence. It must be manufactured so as to provide a position where it can be combined.

5 is a diagram of a primer for preparing a DS oligonucleotide library from the starting single-stranded nucleic acid <Structural Formula 1>.

As shown in FIG. 5 , the 5' and 3' fixed sequences of the starting single-stranded nucleic acid <Structural Formula 1> are primer pairs (DS primers C1 and C2') essential for the DS oligonucleotide library production process.

6 is a diagram of primers for SSN library preparation.

Referring to FIG. 6 , it is the same as the primer pair (T7 primer and DS primer C2') essential for the production of DS oligonucleotide, which is a template for RNA oligonucleotide library production.

In the present invention, the single-stranded nucleic acid library prepared from the starting library comprises a single-stranded nucleic acid comprising an arbitrary base sequence in the central region and a fixed sequence at both ends and a single-stranded nucleic acid having a first tag at any one selected from both ends. to provide.

Preferably, in the present invention, in the case of RNA SSN library preparation, the single-stranded nucleic acid library is prepared using a PCR process using the DS primer C2', the T7 promoter base sequence, and the T7 primer including the 5' fixed sequence to prepare the DS DNA library and in vivo. An RNA SSN library can be prepared by an external transcription reaction.

Preferably, in the case of preparing a DS DNA library from an RNA SSN library in the present invention, reverse transcription is performed using DS primer C2' to prepare cDNA, and DS DNA can be prepared by a PCR process using DS primers C1 and C2'.

The binding SSN library prepared by contacting the single-stranded nucleic acid library prepared above with a biological sample composed of a molecular population has too high diversity and dynamic range, so there are many difficulties in biological samples.

In order to overcome the above problems, a normalization process to constant the frequency of the binding SSNs constituting the binding SSN library prepared with respect to the biological sample, the binding SSN library for the control group and the test group differentially to any one group A differential screening process that selects a binding SSN population that exists as a can

7, 8 and 9 show equivalent (normalization, Curr Protoc Mol Biol. 2010 Apr; Chapter 5: Unit 5.12.1-27.) and differential screening (Biotechniques. 1997 Dec; 23) in the binding SSN library. 6):1084-6.) and Suppression Subtractive Hybridization (Proc. Natl. Acad. Sci. USA Vol. 93, pp. 6025-6030, June 1996) were prepared by ligation method and PCR method. It can be carried out, and it is a drawing of a primer and an adapter necessary for this.

7 and 8, when the equivalent, differential, and suppression subtractive hybridization process of the AptaSSN library is performed by the PCR method, the AptaSSN can provide the binding position of the primer pair necessary for the equivalent, differential, and suppression subtractive hybridization process. there should be

The PCR method is a primer prepared by adding a specific sequence to the DS primers C1 and C2' (a sequence specific primer T1-1/T1-2 for tester 1 and a specific sequence primer T2-1/T2-2 for tester 2) ) and using the DS primers C1 and C2' depending on the situation, testers 1 and 2 can be manufactured.

Referring to FIG. 9 , in the ligation method for performing the equivalent, differential and suppression subtractive hybridization process of the AptaSSN library, tester 1 and tester 2 can be prepared by ligating an adapter containing a specific sequence to the DS DNA AptaSSN of the test sample. have.

The specific sequence used for preparing the tester by the ligation or PCR method has ITRs (Inverted Terminal Repeats) at both ends of the SS oligonucleotide and corresponds to a specific site between the ITRs. It may play a role in suppressing PCR amplification of oligonucleotides. The principle of PS (PCR suppression) is to design oligonucleotides to adopt a hairpin shape. PS allows amplification of desired sequences and simultaneously inhibits amplification of undesired sequences. A pan-handle-type stem-loop oligonucleotide structure for PS can be constructed by linking a specific sequence rich in long GCs to an SS oligonucleotide.

2. Single-stranded nucleic acid library

10 is a sample with various types of molecules, binding SSN from a specific type of molecular population such as nucleic acid and/or protein, and SSN library, binding SSN profile for experimental group and control group, differential binding SSN evaluation and selection, and identification of target molecule It is a drawing that provides a method.

In the present invention, the single-stranded nucleic acid library prepared from the starting library comprises a single-stranded nucleic acid comprising an arbitrary base sequence in the central region and a fixed sequence at both ends and a single-stranded nucleic acid having a first tag at any one selected from both ends. to provide.

An SSN library can be prepared from the starting library as follows. The present invention provides a method for preparing an SSN library from the starting library having the structure <Structural Formula 1> or <Structural Formula 2>.

A method for enzymatically preparing an SSN library from a starting library consisting of a starting single-stranded nucleic acid of <Structural Formula 1> is as follows. The starting library may be synthesized and used by a general organic synthesis method.

An SS RNA or SS DNA library can be prepared from the starting library consisting of the starting single-stranded nucleic acid of <Structural Formula 1> used in the present invention.

Preferably, in the method for preparing an SS RNA library from a starting library consisting of a starting single-stranded nucleic acid having the structure of <Structural Formula 1> of FIG. 4, PCR is performed with the T7 primer P3 and the DS primer C2' using the single-stranded nucleic acid as a template. A double-stranded DNA fragment with the T7 promoter is prepared. By ex vivo transcription of the prepared double-stranded DNA fragment as a template to prepare an SS RNA oligonucleotide and attaching a tag to the 3' end, the SS RNA library as the base sequence structure (1) can be obtained.

Preferably, in the case of preparing the SS DNA library having the structure of <Structural Formula 1> of FIG. 4, one way-PCR process is performed using the DS primers C1 and C2' to obtain SS DNA oligonucleotides and biotin is attached to the 3' end By doing so, an SS DNA library may be prepared or the SS DNA library, which is the base sequence construct (2), may be prepared by binding biotin to one of the primer ends in the DS primers C1 and C2'-PCR process.

DNA or RNA polymerase may be selected from wild type or mutant type depending on the purpose.

To prepare the oligonucleotide constituting the SSN library, all four nucleotides or a mixture of modified nucleotides are added to each nucleotide addition step during the synthesis process, and the SSN variable sequence of the SSN library contains nucleotides containing modified bases. includes Any modified SSN can be used in all of the methods, devices and kits mentioned above. SS oligonucleotides comprising nucleotides with modified bases differ significantly in properties from standard SS oligonucleotides comprising naturally occurring nucleotides (ie, unmodified nucleotides). Methods for modifying nucleotides include the use of amide linkages. However, other suitable modification methods may be used.

The modified SSN structure is not entirely consistent with the structure predicted by the standard base-pairing model. This phenomenon can be inferred from the fact that the measured melting point of the SS oligonucleotide does not match the melting point predicted by the model. As is known, there is no correlation between the measured and predicted melting points of SSNs. On average, the calculated melting point (Tm) is 6°C lower than the measured Tm. The measured melting point predicts that SS oligonucleotides comprising these modified nucleotides are more stable and have a novel second structure, and the possibility exists. When the modified nucleotides are used to produce an SSN library, the SS oligonucleotides for the molecule are better separated. As used herein, "modified nucleic acid" refers to a nucleic acid sequence comprising one or more nucleotides. The present invention can also be practiced with single-stranded nucleic acids containing modified nucleotides.

Chemical modifications of nucleotides include sugar modifications at the 2'-position, pyrimidine modifications at the 5-position (eg, 5-(N-benzylcarboxyamide)-2'-deoxyuridine, 5-(N-iso Butylcarboxyamide)-2'-deoxyuridine, 5-(N-[2-(1H-indol-3yl)ethyl]carboxyamide)-2'-deoxyuridine, 5-(N-[ 1-(3-trimethylammonium)propyl]carboxyamide)-2'-deoxyuridine chloride, 5-(N-naphthylcarboxyamide)-2'-deoxyuridine, or 5-(N-[1 -(2,3-dihydroxypropyl)]carboxyamide)-2'-deoxyuridine), purine modification at 8-position, modification at exocyclic amines, of 4-thiouridine Substitution, substitution of 5-bromo- or 5-iodo-uracil, backbone modifications, methylation, abnormal base pair combinations such as isobases isocytidine and isoguanidine (unusual base-pairing combinations) may be included alone or in combination. Modifications may also include 3' and 5' modifications, such as capping or pegylation.

Other modifications include substitution of one or more naturally occurring nucleotides with analogs, methyl phosphonates with uncharged linkage, phosphotriesters, phosphoamidates, and acridine, psoralen, including carbamates, etc., charged linkage phosphorothioates, phosphorodithioates, etc., and intercalators (psoralen) and the like, including metals, radioactive metals, boron, metal oxides, etc., containing chelators, those containing alkylators, and internucleotide modifications such as modified linked alpha anomeric nucleic acids and the like. In addition, any hydroxyl group normally present in sugars may be substituted by a phosphonate group or a phosphate group and may be protected by standard protecting groups or additional nucleotides or additional nucleotides on a solid support. It can be activated to make a bond.

The OH groups at the 5' and 3' ends can be phosphorylated, or can be amines, organic capping group moieties from about 1 to about 20 carbon atoms, or from about 1 to about 20 polyethylene glycol (PEG) polymers. or organic capping moieties from other hydrophilic or hydrophobic biological or synthetic polymers. If present, the nucleotide structure may be modified before or after polymer combination. The sequence of nucleotides may be interrupted by non-nucleotide components. Polynucleotides can be further modified, such as by conjugation with labeling of components after polymerization.

Oligonucleotides are also 2'-O-methyl-(2'-O-methyl-), 2'-O-allyl (2'-O-allyl), 2'-[0072]fluoro-(2'-fluoro -) or 2'-azido-ribose, carbocyclic sugar analogs, α-anomeric sugars, arabinose, xylose or lysose Epimeric sugars such as (lyxoses), pyranose sugars, furanose sugars, sedoheptulose, acyclic analogs and methyl riboside It may include analog forms of ribose or deoxyribose that are generally well known to those skilled in the art, including basic nucleotide analogs such as

As noted above, one or more phosphodiester linkages may be replaced with an alternative linking group. These alternative linkages include those in which the phosphate is P(O)S (“thioate”), P(S)S (“dithioate”), (O)NR2 (“amidate”) ), P(O)R, P(O)OR', CO or CH2 ("formacetal"), each R or R' is independently H or an ether (-O-) bond, aryl , substituted or unsubstituted alkyl (1-20C) optionally including alkenyl, cycloalkyl, cycloalkenyl or araldyl.

Not all bonds of a polynucleotide need be identical. Substitution of similar forms of sugars, purines and pyrimidines may be advantageous in designing the final product, for example, with alternative backbone structures such as polyamide backbones.

3. Selection of the binding SSN

* Referring to FIG. 10, in the present invention, for the development of an SSN that binds to a specific type of molecular population in a sample composed of various types of molecules, a sample composed of various molecules is formed by contacting an SSN library composed of an arbitrary nucleotide sequence. The binding SSN population is separated from the M-SSN complex group to be used, analyzed by nucleic acid analysis technology, and the SSN having a significant quantitative difference between the two samples is determined by comparing the analysis results of the experimental sample and the control sample, and successively the determined Using the SSN, it is possible to isolate and identify a molecule that binds to a sample with a corresponding molecule.

The molecular population may be composed of heterogeneous molecules or homologous molecules, and the molecular population constituting a biological sample has high diversity and a large dynamic range. Therefore, the binding SSN library for a biological sample can perform normalization, differentiation, and suppression subtractive hybridization processes.

The present invention provides a method for developing an aptamer that binds to a specific molecule using a molecular population composed of a specific homologous molecule.

Referring to FIG. 10 , in the present invention, the development of the SSN population binding to the molecular population constituting the sample includes a nucleotide sequence construct with a first tag added to the SSN, SSN-FSS complex library formation, capture, first separation, and second It can be carried out by a method comprising the repeated steps of selection and amplification consisting of the addition of the tag, the second separation and elution, and the identification step.

A specific SSN constituting a single-stranded nucleic acid (SSN) library composed of an arbitrary nucleotide sequence may have high affinity and specificity for a specific molecule.

The SSN constituting the SSN library may have a first tag capable of binding to a first capture element. The SSN library is an SSN library consisting of an arbitrary nucleotide sequence consisting of modified nucleotides, and the first tag may be biotin. The present invention provides an SSN library including a first tag.

The SSN library having a specific affinity for the molecule and comprising the first tag is contacted with the first capture component of the first solid support (FSS) in suspension in the reaction solution prior to equilibration with the sample, so that the SSN-FSS complex library to form

The FSS is a magnetic bead such as DynaBeads MyOne Streptavidin C1, and the first capture component is streptavidin.

The formation of the suspension SSN-FSS complex library is achieved by contacting the SSN with the FSS, and directly or indirectly binding the first tag included in the SSN to the first capture component of the suspension FSS.

After formation of the as-developed SSN-FSS complex library, the assay background is reduced by washing with a solution buffered to pH 11 to remove SSN/SSN aggregates.

It has a specific affinity for the molecules constituting the prepared sample, and in contact with the suspended SSN-FSS complex library, a suspended M-SSN-FSS complex population can be formed, which is referred to as a 'first reaction solution'. .

In another method of the present invention, a sample is contacted with an SSN comprising a first tag having a specific binding affinity for a molecule in the sample and having an affinity for a first capture component, and thereby extracting an M-SSN complex from the molecule in the sample. is formed and a suspension mixture can be prepared, and FSS including a first capture component is added to the mixture, whereby the first tag binds to the first capture component to form a suspension M-SSN-FSS complex A reaction solution is prepared.

The M-SSN-FSS complex and the free SSN-FSS complex suspended in the first reaction solution may be simultaneously separated.

In the suspended state, the M-SSN-FSS complex and the free SSN-FSS complex are separated from the remainder of the first reaction solution, and all other components except the M-SSN-FSS complex and the free SSN-FSS complex are separated in the first reaction solution. Molecules, ie components of the mixture that do not bind to FSS, are removed.

Following the first separation, the isolated suspension M-SSN-FSS complex along with any suspension free SSN-FSS complex is isolated using an appropriate method.

The first separation of the present invention is completed by separation of the suspended M-SSN-FSS complex and the free SSN-FSS complex from the first reaction solution.

The separation is achieved by centrifugation of the suspended M-SSN-FSS complex and the free SSN-FSS complex or by a magnetic bead harvesting method using a magnet. The M-SSN-FSS complex may be eluted or collected for later use in an assay, or may be contacted with SSS to perform the remaining steps of the assay.

The first reaction solution may optionally use a strict washing solution method. The stringent wash solution method helps to reduce any non-specific binding between the SSN and molecules. 10 mM dextran sulfate is added to the M-SSN complex, and the mixture is treated for about 15 minutes. Other competitors include, but are not limited to, competing nucleic acids. The stringent wash solution is initiated by performing a first separation in the presence of 10 mM dextran sulfate. The stringent washing solution is performed after the equilibration step and before the second separation. The stringent wash solution is carried out by dilution.

M of the suspension M-SSN-FSS complex is treated with a reagent that introduces a second tag. The molecule is a protein or a peptide, and biotin can be added by treating the molecule with NHS-PEG 4-biotin, which is referred to as a 'second reaction solution'.

The second tag introduced into the molecule may be the same as or different from the first tag. If the second tag is identical to the first tag, the free first capture component in the suspended FSS may be blocked prior to initiation of this tagging step. The SSN-FSS complex is washed with free biotin prior to initiation of the molecular tag.

This is done to remove the free SSN-FSS complex in suspension. A second tag used for a second separation may be added to the molecule of the suspended M-SSN-FSS complex. A second tag may be added to the molecule at another point in the assay prior to initiation of the second separation.

The second separation is completed by harvesting of the suspended second tagged M-SSN-FSS complex from a second reaction solution comprising the suspended free SSN-FSS complex and the second tagged M-SSN-FSS complex. second separation step).

A second reaction solution comprising the M-SSN-FSS complex including the second tag in the suspended state is contacted with immobilized SSS, and the SSS has high affinity and specificity for the second tag of the M-SSN-FSS complex, , having a second capture component attached to the surface of the SSS.

The second tag introduced into the molecule may be the same as or different from the first tag. If the second tag is identical to the first tag, the first capture component of the FSS may be blocked prior to initiation of this tag step. The M-SSN-FSS complex was washed with free biotin prior to initiation of molecular tags.

The SSS is a well surface on which magnetic beads such as DynaBeads MyOne Streptavidin C1 of a microtiter plate are immobilized, and the second capture component is streptavidin.

The second separation was performed to remove the free SSN-FSS complex in suspension. As described above, the second tag used in the second separation may be added to the molecule, while the second tag may be added to the molecule at another point in the assay prior to the initiation of the second separation.

The first reaction solution has a second capture component and is in contact with the SSS immobilized on the surface of the reaction sphere to form a second reaction solution, and the second capture component of the stationary state SSS is the suspension of the M-SSN-FSS complex. It can bind with high affinity and specificity to the second tag in the molecule.

The immobilized state SSS of the present invention is a well surface coated with magnetic beads such as DynaBeads MyOne Streptavidin C1 in a microtiter plate, and the second capture component is streptavidin. The coated magnetic beads provide a convenient method for separation of the separated components of the second reaction solution.

A second tag on a molecule of the suspended state M-SSN-FSS complex in a second reaction solution interacts with a second capture component of the immobilized state SSS to form a fixed state SSS-M-SSN-FSS complex .

The immobilized state SSS-M-SSN-FSS complex is obtained by washing the fixed state SSS-M-SSN-FSS complex with a buffer solution containing a buffer containing an organic solvent including, but not limited to, glycerol. separated from

After performing the second separation, the liquid was carefully removed from the wells of the plate with a pipette, and washed with 300ul of buffer PB1 supplemented with 25% Propylene Glycol or 30% glycerol. Wash the wells 5 times with 300ul of SB17T buffer and remove the final wash.

The tube is centrifuged to remove the liquid and the tube is washed with 300ul of SB17T buffer supplemented with 10mM glycerin. Add 100ul of SB17T buffer supplemented with 1mM dextran sulfate.

In order to preferentially select sequences having a slow dissociation rate of the complex, the following stringent washing solution (Kinetic challenge) was performed. This is in the immobilized state SSS-M-SSN-FSS complex solution, 20-fold SB17T 15 minutes before capture in rounds 2-5, 400-fold SB17T 60 minutes before capture in rounds 6-7, and 10 mM in round 8 60 minutes before capture. This was achieved by adding 400 times SB17T containing dextran sulfate.

Cellex is terminated and the elution step for analysis is performed by adding 100 ul of CAPSO elution buffer and shaking the stationary SSS-M-SSN-FSS complex for 5 minutes. The SSN-FSS population is obtained by manually transferring 90 ul of the solution from the wells of the plate to the wells of the PCR plate containing 10 uL of neutralization buffer.

Alternatively, the wells of the plate with the immobilized SSS-M-SSN-FSS complex population may be heated for 5 minutes to elute the bound SSN.

After the round is finished, the elution step for analysis is that the SSN of the fixed state SSS-M-SSN-FSS complex is sodium perchlorate, lithium chloride, sodium chloride and magnesium chloride ( M-SSN complexes or bound SSN populations were selectively eluted using a buffer containing a chaotropic salt from the group including but not limited to magnesium chloride), high temperature, or high or low pH.

An amplified SSN population is prepared from the eluted bound SSN population, and the step of elution of the bound SSN from the fixed-state SSS-M-SSN-FSS complex is repeated one or more times from the first step.

Ultimately, the eluted SSN population can be analyzed by various nucleic acid analysis techniques. After performing the above steps for a sample composed of various molecules or the same molecule, the eluted SSN group obtained in the final round is analyzed with NGS equipment to analyze the nucleotide sequence produced to determine the type and frequency of occurrence of a read for a specific sample.

The present invention comprises the steps of (a) forming an M-SSN complex between the SSN library, which is the nucleotide sequence structure (1), and the molecular (M) population in the sample; (b) washing the M-SSN complex with at least one selected from the group consisting of a buffer solution with the organic solvent and the stringent washing solution method; (c) repeating steps (a) and (b) with the single-stranded nucleic acid group isolated from the washed and M-SSN complex; and (d) analyzing the single-stranded nucleic acid population from the washed M-SSN complex obtained in step (c).

Preferably, the method may further include binding the molecular population in the sample to a solid support before step (a).

In the present invention, “binding SSN” refers to an oligonucleotide capable of binding to a molecule and includes at least an aptamer.

According to the present invention, the binding SSN can be said to be a ligand capable of producing information on a molecular group having a quantitative difference between a test sample group and a control sample group comprising one or more analysis samples.

In the present invention, the binding SSN population or the molecular population separated from the binding SSN and molecular complex population can be analyzed by various techniques. The result of the analysis is that the nucleotide sequence and frequency of the binding SSN population or the identification and frequency of the molecular group are molecular data that reflects the behavior of the binding SSN group and the molecular group that bind to the molecules constituting the sample. can do. A binding profile produced by analyzing the binding SSN was referred to as a “binding SSN profile”, and a binding profile produced by analyzing a molecule was referred to as a “molecular profile”.

In the present invention, the biologically meaningful binding SSN and target molecule are selected by reacting the sample and the binding SSN library with the analytical sample group composed of the samples composed of the molecular group without any information on the binding SSN and the target molecule in advance. The binding SSN population or molecular population isolated from the complex population formed by the above-described method can be used to generate a profile for the presence pattern.

A reference database for the binding SSN group and the target molecule group is constructed in the biological meaning determination support system. The reference database can be constructed by dividing the case in which the nucleotide sequence and target molecule of the binding SSN are not confirmed, the nucleotide sequence is confirmed but the target molecule is unconfirmed, and the nucleotide sequence and the target molecule are both confirmed.

In <Structural Equation 1> of the SSN, a reference database is composed of a variable region and 5' and 3' fixed regions. This is essential in the case of a binding SSN group separated from a complex group in which the SSN library of which the base sequence is not confirmed and the molecular group constituting the sample are formed.

The operation process of the algorithm is briefly described as follows. The NGS result of the separated SSN group is produced as read data in Fastaq format, which is converted into a document format that can be easily used in the system of the present invention.

The present invention provides a method for determining the nucleotide sequence and frequency of binding SSN constituting the binding SSN library prepared from the complex.

Preferably, the first step is a step of constructing a reference database with the read data converted into an appropriate document format, and the base sequence of the converted read of the 5' and 3' constant regions in <Structural Formula 1> of the starting library structure After mapping the nucleotide sequence to the number of nucleotide sequences in the variable region, one binding SSN is identified for a converted read having a nucleotide sequence in which the nucleotide sequence of the two constant regions and the number of nucleotides in the variable region match 100%. A reference database consisting of the nucleotide sequence of the binding SSN identified as described above is constructed.

Second, in the step of analyzing the characteristics of the converted read data by comparing the nucleotide sequence and the variable region of two fixed regions of the nucleotide sequence of the binding SSN constituting the constructed reference database, first, two fixed regions for each binding SSN By comparing the nucleotide sequences of the region and the variable region, the frequency of matched converted reads is determined to determine the frequency of occurrence of the corresponding binding SSN, and the frequency of the matched converted reads is analyzed by comparing it with the variable region of the next binding SSN. Estimate the frequency of the combined SSN. In other words, it is possible to predict the type and frequency of occurrence of the bound SSN by sequencing of the fixed region and the converted read, and the structure of the bound SSN predicted here is used as the structure of the binding SSN in the next step.

In the same manner as described above, a binding SSN library separated from the complex group formed by reacting the SSN library of the present invention with the molecular population constituting the sample can be constructed. That is, it is possible to determine the type and frequency of appearance of the binding SSN by analyzing the constant region and the variable region of <Structural Formula 1> for the prepared binding SSN library. Molecular data and binding profiles can be produced by determining the nucleotide sequence and frequency of occurrence of the binding SSN constituting the separated binding SSN library.

In the present invention, molecular data and binding profiles for a molecular population constituting a specific sample can be prepared using the SSN library of which the nucleotide sequence is not confirmed.

In the present invention, a reference database may be constructed using the base sequence of the binding SSN, which is basically selected as a reference sequence. It is mainly used in the case of a binding SSN group separated from a complex group formed by the binding SSN group whose nucleotide sequence is confirmed and the molecular group constituting the sample.

It is necessary to convert the read data in Fastaq format, which is the NGS result of the separated binding SSN population, to enable further analysis. In the first step, after mapping the converted read data to the combined SSN reference database, the characteristics of the converted read data mapped to the fixed and variable regions of the combined SSN of the reference database are analyzed. That is, the presence or absence of two fixed regions of the binding SSN in the converted read data and the length and similarity of the variable region are checked. Then, the frequency of occurrence of the converted read data mapped to the nucleotide sequence in the region between the fixed sequences of the converted read data and the variable region for each binding SSN is measured, and the type and frequency of occurrence of the binding SSN in the read data are produced. That is, it is possible to produce molecular data and binding profiles by determining the type and frequency of occurrence of binding SSNs in the read data by mapping the fixed region and analyzing the variable region. used as a structure. Alternatively, molecular data and a binding profile can be prepared by determining the type and frequency of occurrence of a binding SSN with respect to a molecular group constituting a specific sample by using a binding SSN group whose nucleotide sequence is known through the above process.

Quantitative differential screening that selects and evaluates a molecular group that is significantly different between groups and a binding SSN group that binds thereto with large-scale molecular data and binding profiles produced for the molecular group of the sample constituting the analysis sample group in the present invention technology can be provided. A method for producing raw data necessary for preparing the molecular data is described below.

A database is constructed based on the biological state (biological meaning) of the biological samples, for example, a disease group and a non-disease group, and a feature selection step representing the two groups is performed using various statistical methods.

The correlation between the selected feature and the biological state may be evaluated by supervised machine learning by a database evaluation method constructed using the selected feature.

Preferably, in the present invention, it is also possible to construct a single database from the analysis results of the samples analyzed in the present invention, and perform a method of selecting features capable of subdividing the database into unsupervised machine learning from the analysis results. It can also give new biological meanings to selected features.

Molecules of the M-SSN-FSS complex harvested in the capture step in the last step of the repetition step and the molecules of the M-SSN-FSS complex formed from the selected binding SSN and molecules constituting the sample may be identified by mass spectrometry. (sympathy phase).

By the above analysis method, it is possible to select a binding SSN group that specifically binds to various molecular groups constituting the sample and has a significantly different binding amount and to identify a corresponding molecular group.

II. Multiple Analysis of AptaSSN

In the present invention, the "binding SSN" developed by the method of item <I> and the aptamer developed by the existing SELEX method and having a structure capable of amplification were collectively referred to as "AptaSSN".

Various methods have been described with respect to AptaSSN and corresponding molecule identification, development of an aptamer for a specific molecule, and single molecule and single AptaSSN having a significant binding difference in the molecular group constituting the sample. Multiplex capable of providing simultaneous detection and/or analysis of a plurality of molecules using such multiple AptaSSNs that can be detected and/or analyzed by contacting the AptaSSN population with a plurality of molecules in a sample composed of different types of molecular populations format, where each AptaSSN (ie, in multiple formats) has a specific affinity for a particular molecule, allowing for multiple assays.

The present invention provides an improved method for performing the AptaSSN multiplex assay for the analysis of multiple molecules that may be present, and the molecules and methods described herein can be used to improve overall assay performance.

may involve the use of 2, 10, 100, 1000 or more aptamers or binding AptaSSNs to simultaneously analyze a plurality of molecules in a sample to perform a single analyte test or multiplex analysis of a sample . A plurality of AptaSSNs are introduced into the sample and any of the assays described can be performed.

Referring to FIG. 3, the present invention analyzes the AptaSSN group separated from the M-AptaSSN complex group formed by contacting the sample composed of the molecular group with the AptaSSN group by nucleic acid analysis technology, and performs an experimental sample, a comparative sample and a control sample. AptaSSN multiplex analysis can be performed to determine the biological significance between two samples by comparing the analysis results of the two samples.

1. Formation of the M-AptaSSN complex

In the AptaSSN multiplex assay of the present invention, AptaSSN is provided to have high affinity and specificity for a molecule and a first tag. The AptaSSN may be an aptamer composed of natural or modified nucleotides. The first tag is added at any time in the assay prior to capture. This first tag is biotin.

AptaSSN having a specific affinity for the molecule and comprising the first tag contacts the first capture component of the first solid support (FSS) suspended in the reaction solution prior to equilibration with the sample to form an AptaSSN-FSS complex do. Formation of the suspension AptaSSN-FSS complex is achieved by contacting AptaSSN with FSS, and directly or indirectly binding a first tag included in the AptaSSN to a first capture component of the suspension FSS.

The FSS is a magnetic bead such as DynaBeads MyOne Streptavidin C1, and the first capture component is streptavidin.

Washing with a solution buffered at pH 11 of the AptaSSN-FSS complex in the reaction solution removes AptaSSN/AptaSSN aggregates, thereby removing the assay background.

Thereafter, the prepared samples have specific affinity for each molecule and are in contact with the suspended AptaSSN-FSS complex. is formed

In another embodiment of the present invention, a sample is contacted with an AptaSSN having a first tag having a specific binding affinity for a target and having an affinity for a first capture component to form an M-AptaSSN complex if the target is present in the sample. preparing a reaction mixture comprising; and a first solid support in suspension (FSS) in a suspended state and having a first capture component to the prepared reaction mixture, whereby the first tag in the AptaSSN of the M-AptaSSN complex and the first capture in the FSS are added. The components may be combined to prepare a first reaction solution in which a suspended M-AptaSSN-FSS complex is formed.

The suspended M-AptaSSN-FSS complex and the free AptaSSN-FSS complex are separated from other mixtures in the first reaction solution, and components of the mixture that are not bound in the first reaction solution are removed. After isolation, the M-AptaSSN-FSS complexes along with any free AptaSSN-FSS complexes in suspension are harvested.

The first separation of the present invention is completed by separation of the suspended M-AptaSSN-FSS complex and the free AptaSSN-FSS complex from the first reaction solution. The first separation is achieved by centrifugation of the suspended M-AptaSSN-FSS complex and the free AptaSSN-FSS complex or a magnetic bead harvesting method using a magnet. The M-AptaSSN-FSS complex may be eluted or collected for later use in an assay, or may be contacted with a stationary SSS to perform the remaining steps of the assay.

The first reaction solution may optionally be subjected to a kinetic challenge. The stringent wash solution helps to reduce any non-specific binding between AptaSSN and non-target. 10 mM dextran sulfate is added to the first reaction solution and treated for about 15 minutes. Other competitors include, but are not limited to, competing nucleic acids. The stringent wash solution was started in the presence of 10 mM dextran sulfate. The stringent washing solution may be additionally performed prior to the second separation and is performed by dilution.

In the present invention, stringent washing solutions can be used to increase the specificity of the assay and reduce non-specific binding. Further reduction of non-specific binding can be achieved by pre-incubation of the sample with the competitor or by adding competitor to the mixture during equilibrium binding.

The molecular component of the suspension M-AptaSSN-FSS complex of the present invention is treated with a reagent that introduces a second tag. The target is a protein or peptide, and the molecule is added with biotin by treatment with NHS-PEO4-biotin.

The second tag introduced into the molecule may be the same as or different from the first tag. If the second tag is identical to the first tag, the free first capture component in the suspended FSS may be blocked prior to initiation of this tagging step. The AptaSSN-FSS complex is washed with free biotin prior to initiation of the molecular tag. Tagging of the target may be performed at any other point in the assay prior to the start of the second separation.

A second separation is performed to remove the suspension free AptaSSN-FSS complex. As described above, the second tag used in the second separation may be added to the molecule, while the second tag may be added to the molecule at another point in the assay prior to the initiation of the second separation.

A second reaction solution is prepared by adding the first reaction solution having the suspended M-AptaSSN-FSS complex, etc. to the wells of the plate, which is SSS having the surface of the reaction sphere to which the second capture component is fixed, The two capture components are capable of binding with high affinity and specificity to the second tag in the molecule of the suspended M-AptaSSN-FSS complex.

The stationary SSS of the present invention is a well surface coated with magnetic beads such as DynaBeads MyOne Streptavidin C1 in a microtiter plate, and the second capture component is streptavidin. The coated magnetic beads provide a convenient method for separation of the separated components of the second reaction solution.

A second tag in a molecule of the suspended state M-AptaSSN-FSS complex in a second reaction solution interacts with a second capture component of the immobilized state SSS to form a fixed state SSS-M-AptaSSN-FSS complex .

The immobilized state SSS-M-AptaSSN-FSS complex is the residue of the mixture by washing the fixed state SSS-M-AptaSSN-FSS complex with a buffer solution containing a buffer containing an organic solvent including but not limited to glycerol. separated from

AptaSSN in the immobilized state SSS-M-AptaSSN-FSS complex was prepared by using a buffer containing a chaotropic salt from the group including, but not limited to, sodium perchlorate and lithium chloride, high temperature, pH. -eluted selectively from the AptaSSN-FSS complex.

The present invention comprises the steps of (a) forming an M-AptaSSN complex between the AptaSign population and the molecular (M) population in the sample; (b) washing the formed M-AptaSSN complex with at least one selected from the group consisting of a buffer solution with the organic solvent and the stringent washing solution (Kinetic challenge); and (c) analyzing the single-stranded nucleic acid population in the washed M-AptaSSN complex.

Preferably, the method may further include binding the molecular population in the sample to a solid support before step (a).

In the present invention, there are two problems in the identification of AptaSSN and corresponding molecules that are significantly different in the amount of binding in the samples, development of an aptamer for a specific molecule, and a multiplex AptaSSN-based assay.

First, the interaction of AptaSSN/AptaSSN is a major source of analytical background and potential limitations on multiplex capacity.

Second, sample matrices (mainly serum and plasma) inhibit immobilization of biotinylated aptamers on streptavidin-substituted matrices.

The present invention provides key techniques to reduce and/or eliminate all of these problems.

In the first method, the AptaSSN-FSS complex is formed in a suspended state between the tag of AptaSSN and the capture component of FSS. In the reaction mixture, the biotin moiety in AptaSSN and streptavidin beads, which are suspended FSS, contact to form a suspended AptaSSN-FSS complex. This complex formation allows the formation of conditions that reduce the interaction of AptaSSN, but also requires stringent requirements for interference with interacting AptaSSN, very robust biotin-streptavidin interaction and isolation of all unbound AptaSSN. Washing (with chaotropic salts) makes this possible. This reduces the number of AptaSSN aggregates present during analysis - aggregates with a certain detectable frequency may contain biotin moieties or be biotinylated.

The AptaSSN can be biotinylated using an enzyme or linker to become a “tag”. Complex formation appears to support a very high multi-potency since biotin can be bound to AptaSSN in a bead-bound form after addition, bypassing the conditions under which AptaSSN can interact and aggregate.

By eliminating the need for AptaSSN adsorption in the first reaction solution for preparing the M-AptaSSN-FSS complex, quantitative complex formation can be performed even in the first reaction solution having a concentration higher than a certain concentration. Allows the use of plasma or serum concentrations up to or including at least 40% v/v than the 10% concentration course or the 5% concentration course used in more recent studies, thereby allowing the use of higher concentrations of the assay. Not only does the overall robustness increase, but the sensitivity also increases approximately 4 to 8 times.

The second method includes any one of an organic solvent including glycerol and the like in the washing buffer of the separation to reduce the dielectric constant of the medium. The addition of organic solvent to the wash buffer effectively enhances the like-charge repulsion of the phosphodiester backbone of AptaSSN, thus promoting the dissociation of the background-causing interaction of AptaSSN. The organic solvent is contrary to the trend of AptaSSN, thus reducing background and increasing multiplexing capacity. This addition is readily detectable even in 5% v/v plasma or serum, and improves the assay sensitivity to allow analysis at concentrations of 5-10% plasma or serum.

A third method involves the use of a chaotropic salt at neutral pH for elution during the second separation. Prior methods involving the use of sodium chloride at high pH (10) disrupt protein/AptaSSN interactions as well as hybridization and AptaSSN/AptaSSN interactions. As mentioned above, the AptaSSN/AptaSSN interaction in hybridization is responsible for the background of the assay.

Chaotropic salts, including, but not limited to, sodium perchlorate, lithium chloride, sodium chloride and magnesium chloride at neutral pH support AptaSSN/AptaSSN interaction in hybridization, but interfere with aptamer/protein interaction. The final conclusion is a significantly reduced background (approximately 10-fold) with an increase in assay sensitivity.

In the present invention, AptaSSN binding to the molecular population constituting the sample and the corresponding molecule identification, the development of an aptamer for a specific molecule, and "separation" in the AptaSSN multiplex assay means separation and concentration of one or more other molecules from the sample. or removal. Separation can be used to increase sensitivity and/or decrease background.

Said separation may be introduced after any step or after every step when an "M-AptaSSN-FSS complex" is formed. Separation also depends on differences in size or other specific properties that exist separately between the "M-AptaSSN-FSS complex" and other components of the sample. Separation can be achieved through specific interactions with AptaSSN or molecules. Separation can also be achieved based on the physical or biochemical properties of AptaSSN, molecular or M-AptaSSN-FSS complexes.

The AptaSSN/AptaSSN interaction is the cause of the interaction with the molecular population constituting the sample, the development of an aptamer, and the background in the AptaSSN multiplex assay. In the present invention, AptaSSN and corresponding molecule identification with a significant difference in the amount of binding to a plurality of molecules constituting a sample, development of an aptamer for a specific molecule, and DNA-DNA and RNA-RNA interaction in AptaSSN multiplex analysis Reagents that reduce Because the interaction of AptaSSN's own structure determines the secondary and tertiary structures of AptaSSN, a reagent that affects the sequence-based structure does not affect the aptamer folding, and thus its corresponding molecule. It cannot be expected to substantially reduce the background signal in a binding multiplex assay.

In the present invention, in the present invention, AptaSSN and corresponding molecular identification, which differ in the amount significantly bound to the molecular population constituting the sample, development of an aptamer for a specific molecule, and in the AptaSSN multiplex assay, many steps can be mixed with background signals. It is designed to isolate a specific M-AptaSSN complex from a sample or assay reagent. Despite carrying out these steps, background is still a problem in these types of assays.

In the present invention, the background in the AptaSSN and corresponding molecular identification, development of an aptamer for a specific molecule, and multiple AptaSSN-based assays that differ in the amount significantly bound to the molecular population constituting the sample in the present invention can be treated empirically. Or, a better approach is to identify the source of the background and eliminate the interactions that cause it.

2. Analysis of AptaSSN Population Eluted from M-AptaSSN Complex

In the present invention, the AptaSSN population eluted from the stationary SSS-M-AptaSSN-FSS complex can be obtained by various methods in the step of separating the AptaSSN from the formed M-AptaSSN complex. By the method of analyzing the eluted AptaSSN, it is possible to identify a population of AptaSSN that specifically binds to a molecule or has a significant difference in binding amount and a molecule corresponding thereto. As a method of analyzing the eluted AptaSSN population, a single analyte test or multiple analysis of a sample may be performed on a sample containing a molecule. Any suitable multiplex nucleic acid detection method can be used to determine the eluted AptaSSN population.

The eluted AptaSSN population may be analyzed by any suitable nucleic acid detection method such as next-generation sequencing, DNA microarray hybridization, Q-PCR, mass spectroscopy, Invader assay, and the like. detected and selectively analyzed. These detection methods are described in more detail below.

After performing the above steps for various types of molecules or samples composed of the same molecule, the eluted AptaSSN obtained in the final round is analyzed with NGS equipment to analyze the produced nucleotide sequence to determine the type and frequency of occurrence of a read for a specific sample .

A database is constructed based on the biological state (biological meaning) of the samples, for example, a disease group and a non-disease group, and a feature selection step representing the two groups is performed using various statistical methods.

The correlation between the selected feature and the biological state may be evaluated by a database evaluation method constructed using the selected feature.

In the present invention, it is also possible to implement a method of constructing a single database from the analysis results of the analyzed sample, and selecting features capable of subdividing the database from the analysis results. It can also give new biological meanings to selected features.

The nucleotide sequence and frequency of appearance of the AptaSSN group separated from the complex formed by reacting the AptaSSN library with the sample comprising the molecular group can be determined by the next-generation sequencing method.

11 is a diagram illustrating an FXRP primer for preparing an NGS library of an AptaSSN group prepared by combining an AptaSSN-specific primer and a primer for preparing an NGS library.

The FXRP primer can be used to amplify the AptaSSN isolated from the formed complex population, as well as to analyze the AptaSSN by sequencing using NGS.

The library for NGS was prepared by preparing an FXRP primer including both a nucleotide sequence specifically binding to the fixed sequence of AptaSSN used and an index primer and a sequencing primer essential for preparing an NGS library. As a result, it was confirmed that a library for NGS could be prepared through one PCR and purification process.

That is, in the present invention, as a result of preparing a library for NGS using the FXRP primer, it was confirmed that a library for NGS could be prepared much simpler and faster than the conventional method.

The AptaSSN population eluted from the complex formed by reacting the sample consisting of the AptaSSN library and the molecular group was subjected to an addition reaction using P5 (Ion A) and P7 (Ion P1) sequencing adapters using the PCR-method to perform Ion Torrent next-generation sequencing (NGS). prepared for

A unique 8-sequence base barcode was included per sample via forward primers to allow samples to be multiplexed into a single sequencing run. The forward primer (5'-P5 Adapter-basic barcode-SELEX primer-3') was designed as follows.

5'-ATAT-CCATCTCATCCCTGCGTGTCTCCGACTCAG-XXXXXXXX-CCACGCTGGGTGGGTC-3'

The reverse primer (5'-P7 Adapter-SELEX primer-3') was designed as follows.

5'-TTTTTTTT-CCTCTCTATGGGCAGTCGGTGAT-CTCTTTCTCTTCTCTCTTTCTCC-3'

Samples were amplified by PCR for 30 cycles, and products were quantified by Pico Green Assay (Invitrogen) and combined into a single mixture at equimolar concentrations. The prepared NGS library was further concentrated on an Amicon Ultra 0.5 column (Millipore, USA). Final samples were subjected to an Ion Torrent PGM 318 chip sequencing run (Thermo Fisher Scientific, USA).

Referring to FIG. 11 , AptaSSN comprising a forward (F) FXRP primer consisting of a first sequencing sequence (P5), a basic barcode and a forward primer, and a reverse (R) FXRP primer consisting of a second sequencing sequence (P7) and a reverse primer It relates to a primer set for NGS library construction.

Preferably, the forward primer corresponds to the 5' fixed sequence of the single-stranded nucleic acid and the reverse primer corresponds to the complementary sequence of the 3' fixed sequence.

The method of selecting a differentially bound AptaSSN group of unconfirmed base sequence using the AptaSSN library is a statistical method by analyzing the base sequence and frequency of occurrence of the AptaSSN group constituting the library to construct a database for the test sample and control sample group. As a result, it is possible to implement a method of selecting a binding AptaSSN group that exists differentially between two groups.

12 shows the steps (a) of forming an M-AptaSSN complex from the molecular group and AptaSSN group constituting the sample for selection of the differential binding AptaSSN group and determining the nucleotide sequence of the eluted AptaSSN as NGS (a) and the eluted AptaSSN molecule It is a diagram of a method (b) for producing and analyzing data, a binding profile.

Referring to FIG. 12 , the sequencing library for the purpose of the nucleotide sequence, appearance frequency, and production of the molecular group constituting the specific sample in the library and the AptaSSN group isolated from the complex formed is (a) the molecular group constituting the analysis sample and selection Reacting the AptaSSN population to form an AptaSSN complex population with molecules, (b) isolating the AptaSSN population from the formed complex population, and (c) constructing a sequencing library from the isolated AptaSSN population. It can be configured as

The method and process for preparing the molecule-AptaSSN complex population can be designed in consideration of the binding specificity and affinity of the molecule and AptaSSN, and the AptaSSN library can be referred to the manufacturing process.

In another embodiment, the separation of the molecules constituting the sample formed in the various reaction solutions and conditions and the AptaSSN complex population may be performed by various methods ( FIG. 13 ).

(i) A method for separating molecules and AptaSSN complex populations by harvesting the solid support and the molecules attached to the solid support and the AptaSSN complex population formed by fixing the molecular population constituting the sample to the solid support and bead structure and contacting the AptaSSN population (FIG. 13 a), (ii) The molecular population constituting the sample is fixed to a solid support, a structure of nitrocellulose and nylon, and the molecule attached to the solid support and the AptaSSN complex population formed by contacting the AptaSSN population are harvested from the solid support. A method of separating the molecule and the AptaSSN complex population by doing (Fig. 13 b), (iii) contacting the molecules constituting the sample with the AptaSSN library, performing capillary electrophoresis, a capillary electrophoresis method to separate It can be performed by the M-AptaSSN complex population analysis method in which the AptaSSN and the graph complex are removed from the sample, the AptaSSN library, and the graphene reaction mixture.

As described above, the AptaSSN population can be isolated from the complex, and a sequencing library can be prepared by a known method.

In the sequencing library, if all AptaSSNs used for molecular population analysis have the same fixed sequence, a sequencing library can be prepared using the same.

In the present invention, the preparation of the sequencing library can be achieved by first preparing the NGS library with the FXRP primers for reverse transcription performed on the eluted RNA AptaSSN population. This step is a process of amplifying the isolated binding AptaSSN using the combination of the FXRP primer set for preparing the binding AptaSSN NGS library, purifying the amplified product, and performing library pooling.

After the sequencing library is constructed, the obtained sequencing library is applied to the sequencer. The method and apparatus for sequencing include, but are not particularly limited to, a chain termination method (Sanger method), and a high-throughput sequencing method is preferred. Therefore, by using the deep sequencing and high-throughput features of these devices, efficiency can be further improved, and subsequent analysis using sequencing data such as statistical tests can be further improved precisely and accurately. High-throughput sequencing methods include, but are not limited to, next-generation sequencing technologies or single sequencing technologies.

By using the prepared library, the binding behavior of the molecules of the test and control sample groups and the AptaSSN constituting the library is tested, and a database is built for this to select an AptaSSN population with biological significance from the test sample and control sample group. Also, it is possible to suggest a method for isolating and identifying molecules using AptaSSN, which has been selected and sequenced.

The method for analyzing the nucleotide sequence and frequency of the AptaSSN population according to the present invention includes a Next Generation Sequencing (NGS) process for the AptaSSN population constituting the prepared AptaSSN library. The NGS refers to a technology for fragmenting the whole genome in a chip-based and PCR-based paired-end format, and sequencing the fragments at high speed based on a chemical reaction (hybridzation).

Next-generation sequencing (Metzker ML, Sequencing technologies-the next generation. Nat Rev Genet. 2010 Jan; 11(1):31-46) was performed by Illumina-Solexa (GATM, Hiseq 2000TM, etc.), ABISolid and Roche-454 ( pyrosequencing) sequencing platforms, including, but not limited to, single sequencing platforms (technology) including True Single Molecule DNA sequencing from Helicos Company, SMRTTM from Pacific Biosciences Company, and nonapore sequencing technology from Oxford Nanopore Technologies, and the like. including, but not limited to, these examples. As sequencing technology is gradually developed, there may be other sequencing methods that can be used for sequencing all nucleic acids by those skilled in the art.

Signal intensity refers to a value at which color and intensity are measured according to the characteristics of binding and base when the form in which a DNA fragment such as an oligo is bound to a DNA fragment attached to a chip is read by a scanner. The number of sequencing folds refers to the number of times sequencing is performed. It is common to perform sequencing at 10x, but if you do 20x, average results are obtained, and sequencing can be performed at multiples of 40x.

The Next Generation sequencer replaces the DNA library production and bacterial cloning process, which took several weeks, with polymerase chain reaction amplification of several hours, compared to the existing Sanger method of sequencing, and provides several capillary arrays ( Parallel sequencing used in capillary arrays can simultaneously sequence hundreds of thousands of clones. Also, it is called high-throughput sequencing because it is possible to generate a large number of sequencing results, and this high-density parallel sequencing is effective for simultaneously detecting many target molecules. . However, due to the bias that occurs during the polymerase chain reaction for library production, a small amount of the target molecule is buried by the large amount of the target molecule. Since the target molecule is often present in a very small amount in an actual clinical sample, there is a problem in that it is difficult to detect a very small amount of the target molecule by the conventional method. In order to solve the above problem, a method of increasing the limit value by increasing the depth (number of reads) of NGS sequencing may be used.

The evaluation of the selected and sequenced AptaSSN population is performed by Q-PCR, MS and hybridization, and Next Generation sequencing (NGS). It can be detected and selectively quantified using a method selected from the group consisting of.

Based on the selected AptaSSN nucleotide sequence, the Q-PCR process may be performed using TaqMan PCR, intercalating fluorescent dyes during PCR, or molecular beacons during PCR, In the hybridization step, the binding AptaSSN or the amplification product of the binding AptaSSN binds to the capture component.

The nucleic acid analysis method includes NGS, biochip technology, PCR-based analysis technology, and the like, and it is the content of analyzing one selected among them. Each of which recognizes a different analyte and optionally multiple binding AptaSSNs are introduced into the sample, and any of the assays described above can be performed. After isolating AptaSSNs, any suitable multiplex nucleic acid detection method may be applied to measure the isolated different AptaSSNs. This can be achieved by hybridization to complementary capture components arranged individually on a solid surface and each of the different AptaSSNs can be detected on the basis of molecular weight using mass spectrometry. Each of the different AptaSSNs can be detected based on electrophoretic mobility, such as in capillary electrophoresis, in gel or in liquid chromatography. A unique PCR capture component can be used to quantify each different AptaSSN using QPCR. In another embodiment, each of the different binding AptaSSNs can be detected based on molecular weight using mass spectrometry.

The method using the NGS is useful, but it is also possible to prepare and analyze two different capture components that bind to the samples by additionally processing the AptaSSN population in the test sample and the control sample. One capture component may have a Cy3 dye and the other is a Cy5 dye. For example, reacting the same AptaSSN population to test and control samples. Each sample is treated individually in the same way. The samples can be mixed homogeneously, and the remaining steps of the assay can be performed. Direct comparison of any other expression (ie, different amounts or concentrations of molecules in the sample) between the test sample and the control sample is possible by measuring the signal from each labeling reagent individually. This method can be incorporated into any other assay. Furthermore, in addition to the use of different dyes, including the use of fluorescent dyes, different labels can be used to differentiate the signal from different individual capture components. AptaSSNs used in each sample may have different staining reagents. This method is useful when the read is a QPCR or hybridization array.

Also described herein are M-AptaSSN complexes that facilitate separation of assay components from AptaSSN complexes and allow separation of AptaSSN for detection and/or quantitation.

The sample group may be classified based on AptaSSN by analyzing the information constituting the AptaSSN profile generated from the molecular complex group of the prepared AptaSSN and the molecular group constituting the sample by the above-described method.

All profile data for the analysis sample can be collected by collecting the profile of the sample produced by analyzing the AptaSSN group separated from the complex group formed by reacting the prepared AptaSSN library with each sample constituting the analysis sample group.

A database is constructed based on the biological meaning of all the collected sample information, and a feature selection is performed, and the constructed database is related to biological meaning through supervised learning among machine learning languages using the selected features. AptaSSN groups that exist can be selected.

It is also possible to select a feature from the collected sample information and classify the sample group as nonsupervised learning among machine learning languages using the selected feature, AptaSSN.

Ⅲ. Target molecule analysis technology using AptaSSN

The AptaSSN population may be an AptaSSN library prepared from a starting library or an AptaSSN population including an aptamer and a SOMAmer developed by an AptaSSN library prepared from the AptaSSN library, and an aptamer and a SOMAmer developed by an existing SELEX method. The AptaSSN population may first be added with biotin for capture and purification.

14 is a diagram illustrating a method for identifying target molecules binding to a single-stranded nucleic acid library and an AptaSSN group having a significant quantitative difference in binding to the molecular group in the sample.

As shown in FIG. 14, specific binding molecules are isolated using the AptaSSN population of the present invention, and the complex is subjected to electrophoresis on a PAGE-SDS gel to confirm protein 　 bands. After extracting the protein by cutting the identified protein band, it is possible to identify a molecule that specifically binds to the AptaSSN prepared according to the present invention through the step of identifying the protein with MALTI TOF-TOF.

15 is a diagram of top-down (a) and bottom-up (b) proteomic studies using AptaSSN populations for molecular populations in a sample to be analyzed.

As shown in FIG. 15 a, the AptaSSN population whose nucleotide sequence is unidentified or confirmed is an ideal ligand for comparative analysis of a sample composed of an unknown and a known protein group. can form. The presence of the protein population may be determined by analyzing the AptaSSN population or the protein population isolated from the formed complex.

'Top-down' 'discovary' or 'non-targeted (non-targeted) proteomics of proteins by Mass Spectrometry (MS) comparative analysis of measurement results for unknown or known intact proteins in limited samples As a representative technique, matrix-assisted laser desorption ionization (MALDI) and surface enhanced laser desorption-time of flight (SELDI-TOF) provide protein profiles but not protein identification. Solid phase extraction (SPE), used in these top-down analyzes, is a fast and efficient method for purifying intact protein from a sample, in which a relatively small subset of the protein is extracted and subsequently detected.

A protein group obtained by collecting and purifying a complex group formed by reacting the AptaSSN group with the molecular group constituting the sample can be analyzed using the discovery proteomics technique.

In contrast to intact protein analysis, analysis of peptide mixtures obtained after proteolytic treatment of protein mixtures is referred to as 'bottom-up', 'shot-gun' or 'targeted' proteomics . 'Bottom-up' proteomics studies are typically implemented for search-based experiments that provide protein identification, and can also provide relative absolute protein quantitation measurements with appropriate experimental design.

15B, solution-based proteolysis and first-round (1D) SDS-PAGE separation of the protein extract separated from the molecular population in the sample to be analyzed into the AptaSSN population, excision of continuous gel regions, and each gel region GeLC (one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by liquid chromatography) analysis involving protein degradation in Configurations include serial MS, *?* peptide identification, and database searches for *?* protein assemblies.

In the present invention, a complex is prepared by mixing a sample containing at least an unknown and a known protein population with the selected AptaSSN population. After the protein population is extracted and separated from the prepared complex, protease treatment of the bottom-up proteomics may be performed. An AptaSSN-bead conjugate can be easily prepared by a known method by modifying one end of the AptaSSN with a functional group and connecting it to a bead having a corresponding functional group using a linker. In a process of separating and purifying the complex formed by a known method by reacting the protein mixture with the prepared AptaSSN-bead conjugate, the protein is extracted and the proteolytic enzyme treatment is performed.

The present invention may extract analytes using AptaSSN affinity in combination with LC-MS from biological samples because of the need for improved sensitivity and specificity as well as sample throughput.

Protein extraction using AptaSSN affinity can be used for sample preparation in various formats. AptaSSN capture refers to extraction based on molecular recognition of AptaSSN for the binding site of a selected protein or protein group. AptaSSN is not degraded by trypsin peptides. Alternatively, the most abundant protein may be removed using the AptaSSN affinity strategy for use in isolating target molecules. AptaSSN-based approaches as a means of sample preparation are available in different overlapping formats such as off-line, on-bead, column, on-line and in-line bead traps.

The peptide mixture obtained by performing the above proteolytic enzyme treatment can be isolated as follows. In general, before MS protein identification and measurement, a protein-like peptide mixture in the excised gel band can be separated by I-D liquid chromatography (LC). 2D-LC is used for the fractionation of complex peptide mixtures such as tissue or cellular proteins. The first dimension typically separates peptides based on pi or hydrophobicity in high pH solvents. The second dimension separation is usually based on peptide hydrophobicity in low pH solvents and is performed 'inline' with the MS-ESI interface between the column tip and the MS orifice. In a less general approach, the LC eluent in the second dimension is directed to a metal plate or target for LC MALDI-TOF analysis.

The mass spectrum of the isolated peptide by tandem MS on the isolated peptide mixture can be used for protein identification in a bottom-up experiment. A program-specific algorithm compares the theoretically derived pattern of peptide fragments with experimental peptide data. Potential peptide database matching proceeds through the ranking, scoring and reporting phases. The highest scoring peptide is used to generate a list of inferred proteins present in a complex mixture (protein assembly). Using parsimonious protein assembly, even the smallest number of proteins is possible with the identification of detected peptides. A variant of the database search algorithm is a number of commercial and open source search programs for database search, each with its own unique peptide candidate scoring schemes and protein inference methods. A match of one or more peptides per protein is sufficient evidence for detection of the protein in the sample.

In the present invention, the analytical ability of the existing bottom-up proteomics technology can be improved by using the prepared differential binding AptaSSN population.

A protein mixture is extracted from a sample containing the proteins to be analyzed and reacted with a selected AptaSSN population to collect and isolate the target protein population and digest with protease to make a peptide mixture. The peptide mixture is then loaded directly onto a microcapillary column and the peptides are separated by hydrophobicity and charge. As the peptide elutes from the column, it is ionized and separated by m/z in the first step of tandem mass spectrometry. The selected ions undergo collision-induced dissociation or other processes to induce fission. The charged fragments are separated in the second stage of the tandem mass spectrometer.

A "fingerprint" of the fragmentation mass spectrum of each peptide is used to identify the derived protein by searching the sequence database with commercially available software (eg, MASCOT). Examples of sequence databases are the Genpept database or the PIR database. After a database search, each peptide-spectral match (PSM) needs to be evaluated for validity. This analysis allows for the profiling of various biological systems.

SRM is a method used in tandem mass spectrometry to select ions of a specific mass in the first stage of tandem mass spectrometry, and ion products of fragmentation reaction of precursor ions are selected and detected in the second mass spectrometer stage.

SRM can be used for molecular quantitative proteomics by mass spectrometry. For example, after ionization in an electrospray source, the peptide precursors are first isolated to obtain substantial ion populations of mostly the intended species. This population is then fragmented to yield product ions whose signal ions represent the peptide abundance in the sample. This experiment can be performed on triple quadrupole mass spectrometers. Using isotopic labeling with heavy-labeled peptides (e.g., D, ¹³ C or ¹⁵ N) to complex matrices as concentration criteria, SRM provides absolute quantitation (i.e., number of copies per cell) of native light peptides. Can be used to construct a calibration curve and extend its parental protein.

SRM has been used as a method to trigger a complete product ion scan of a peptide that either a) confirms the specificity of SRM transitions or b) detects specific post-translational modifications below the detection limits of standard MS assays. SRM has been developed as a highly sensitive and reproducible mass spectrometer-based protein molecule detection platform (SAFE-SRM), and the new pipeline based on SRM has major advantages in clinical proteomics applications. It showed much better results compared to the traditional SRM pipeline and significantly improved diagnostic performance compared to antibody-based protein biomarker diagnostic methods such as ELISA.

The SWATH-MS approach is reproducible, linear dynamic range and sensitivity similar to SRM, the current gold standard approach for protein quantification, making DIA combined with detailed peptide-driven scoring suitable for large-scale, comprehensive and reproducible proteomics.

The protein mixture is extracted from the sample containing the proteins to be analyzed, reacted with the selected AptaSSN population, to collect and isolate the target protein population, and the peptide mixture obtained by treating the proteolytic enzyme is analyzed with high-performance liquid chromatography and mass spectrometry. Thus, peptides can be identified with various search engines and proteins constituting the captured and isolated protein group can be identified, which is called AptaSSN-based Shotgun Proteomics.

As shown in FIG. 15 b , the method including the protein capture using AptaSSN in the SRM of the present invention and the existing SRM analysis method may show different performance profiles. Combining AptaSSN extraction of the present invention with LC-MS detection can increase sensitivity and capacity. The AptaSSN format, which captures the M protein populations of the present invention, allows for concentration and purification, thereby improving the sensitivity limits of MS. Another bottleneck of the LC-MS system is the selectivity of the AptaSSN of the present invention, which can allow higher throughput by reducing the LC cycle time due to capacity limitation and orthogonality to the LC and improving the detection limit by reducing the interference introduced into the MS. am. On the other hand, MS using such AptaSSN may confer superior specificity compared to conventional immunoassays.

The protein capture and SRM binding strategy by the AptaSSN of the present invention can also analyze differences between very similar proteins such as post-translational modification (PTM) or isoforms, which have limitations in various existing analysis techniques. However, if the PTM is on the binding site or otherwise affects the AptaSSN extraction affinity, this of course can also affect the LC-MS method of the effect of AptaSSN capture, highlighting the importance of the specificity of AptaSSN selectivity.

The sample group may be classified based on the molecule by analyzing information constituting the molecular profile generated from each molecule of the sample and the AptaSSN complex in the sample group composed of the molecular group by the above-described method.

It is possible to collect all the profile data for the analyte sample by collecting a molecular profile produced by analyzing a molecular group separated from a complex group formed by reacting each sample constituting the analysis sample group with the prepared AptaSSN library.

A database is constructed based on the biological meaning of all the collected sample information, and a feature selection is performed, and the constructed database is used as a supervised algorithm among machine learning languages using the selected features and molecular groups. Molecular groups that are related to meaning can be selected.

The sample group may be grouped by a nonsupervised algorithm among machine learning languages by selecting a feature from the collected sample information and using the selected feature and molecular group.

IV. kit

This kit is not limited to AptaSSN multiplex analysis, and kits for AptaSSN selection, molecular analysis, and aptamer development of specific molecules with significant binding differences in molecular groups constituting samples composed of various types of molecules can also be provided. have.

The present invention relates to kits useful for suitably performing any of the methods disclosed herein for AptaSSN multiplex analysis of a sample.

To enhance the versatility of the present invention, reagents may be provided in combination packaged in the same or separate containers such that ratios of reagents provide substantial optimization of the methods and assays.

These reagents may be contained in each individual vessel, or several reagents may be combined in one or more vessels depending on the cross-reactivity of the reagents and the stability of the reagents.

A kit is a combination comprising one or more solid supports each comprising at least one tagged AptaSSN and at least one capture agent.

The kit may also include a washing solution, such as a buffered aqueous medium for sample dilution, as well as washing, sample preparation reagents, and the like. The kit may further comprise reagents useful for introducing a second tag, generally through modification or derivatization of the molecule.

In addition, the kit may include reagents suitable for carrying out the stringent wash solutions desired during the assay method.

The relative amounts of the various reagents in the kit can be varied to provide concentrations of reagents that substantially optimize the reactions that need to occur during the assay, and also substantially optimize the sensitivity of the assay.

Under appropriate circumstances, one or more reagents in the kit may be provided as a dry powder, usually lyophilized under reduced pressure, comprising additives, the dissolution of which is carried out to a concentration suitable for carrying out a method or assay according to the present invention. Eggplant will provide a reagent solution.

The kit may further comprise instructions according to any method as described herein.

A kit for detecting and/or quantifying one or more molecules that may be present in a sample comprises at least one aptamer having a specific affinity for the molecule and a first tag capable of binding to a first capture component, and a first capture component. Includes FSS with . Here, the first capture component may bind to the first tag on the aptamer.

In addition, any of the kits described above may include reagents and molecules for the performance of stringent wash solutions during the detection method of the kit.

"First separation" refers to the separation of the AptaSSN-FSS complex together with the M-AptaSSN-FSS complex in a suspended state from the first reaction solution. The purpose of the first separation is to substantially remove all components in the sample that are not bound to the suspended AptaSSN-FSS complex from the first reaction solution.

Removal of a large number of these components will improve molecular tag efficiency by removing non-target molecules from the molecular tag step used for the second separation and lower the analytical background.

The tag step is attached prior to, during or during analysis by first tagging the AptaSSN. The first tag is a first tag capable of binding to the first capture component. The first tagged AptaSSN may be captured by an FSS, wherein the FSS comprises a first capture component suitable for the first tag. The FSS can then be washed as described herein prior to equilibration with the sample to remove any unwanted molecules.

"Second separation" refers to the separation of the immobilized state SSS-M-AptaSSN-FSS complex from the second reaction solution based on the capture of molecules. The purpose of the second separation is to remove the suspension free AptaSSN-FSS complex from the second reaction solution prior to detection and selective quantification. Removal of the suspended free AptaSSN-FSS complex from the second reaction solution enables detection of the AptaSSN contained in the immobilized SSS-M-AptaSSN-FSS complex by any suitable nucleic acid detection technique.

When using Q-PCR for detection and selective quantification, as a second reaction solution Removal of the free AptaSSN-FSS complex from the suspension can also enable accurate detection and quantification of target molecules using AptaSSN contained in the stationary SSS-M-AptaSSN-FSS complex.

The molecule is a protein or peptide, and the free AptaSSN-FSS complex is incorporated into a sample containing the target protein to prepare a first reaction solution using a reagent capable of preparing a first reaction solution. There is an unreacted free AptaSSN-FSS complex, and a suspended M-AptaSSN-FSS complex and an unreacted free AptaSSN-FSS complex can be separated from the rest of the sample.

The second tagged M-AptaSSN-FSS complex may be immobilized on the stationary SSS. Allows the separation of the stationary SSS-M-AptaSSN-FSS complex from the free AptaSSN-FSS complex in suspension. This second tag is processed for a target protein and may include a biotin moiety.

The second tag is washed and attached to the protein (or peptide) prior to analysis, during assay preparation, or during assay by chemically attaching the second tag to a molecule of the suspended M-AptaSSN-FSS complex. The second tag may be biotin.

The SSS includes a second capture component suitable for a second tag. The washed and suspended second tagged M-AptaSSN-FSS complex may be captured by the second capture component of the immobilized SSS.

The second tagged M-AptaSSN-FSS complex and the AptaSSN-FSS complex in the suspended state may be transferred together to form a second reaction solution by transferring the mixture to the mixture containing the stationary SSS. Thereafter, in the second reaction solution, the free AptaSSN-FSS complex was in a developing state, while the second tagged M-AptaSSN-FSS complex was immobilized due to a binding interaction between the second capture component of the SSS and the second tag of the molecule. It forms the SSS-M-AptaSSN-FSS complex. After that, the free AptaSSN-FSS complex in suspension may be removed from the second reaction solution as a washing step.

The immobilized-state SSS-M-AptaSSN-FSS complex is then prepared in a variety of ways comprising a buffer containing an organic solvent and a buffer containing salts and/or detergent containing salts and/or detergents. washed with a buffer solution.

AptaSSN-FSS from the stationary SSS-M-AptaSSN-FSS complex can be released by any method that disrupts the AptaSSN/molecule interaction. This can be achieved by washing the M-AptaSSN complex bound to the support with a high concentration of salt buffer that dissociates the non-covalently bound M-AptaSSN complex. The eluted free AptaSSN is collected and detected. High or low pH can be used to disrupt the M-AptaSSN complex and high temperature can also be used to dissociate the M-AptaSSN complex. Combinations of any of the above methods may be used.

Aptamers of the present invention may further comprise a "tag", referring to a component that provides a means for attaching or immobilizing the AptaSSN (any molecule bound thereto) to the capture component. A “tag” is a moiety capable of binding to a “capture element”.

The tag may be attached to or included in the AptaSSN by any suitable method. The tag allows AptaSSN to bind directly or indirectly to a receptor or a capture component attached to a solid support. The capture component is typically highly specific for its interaction with the tag and is selected (or designed) to retain binding during subsequent processing steps or procedures.

The tag can position the M-AptaSSN complex at a spatially defined location on a solid support. Thus, different tags can locate different M-AptaSSN covalent complexes at different spatially defined locations on a solid support.

A tag is a polynucleotide, a polypeptide, a peptide nucleic acid, a locked nucleic acid, an oligosaccharide, a polysaccharide, an antibody, an affibody, Antibody mimic, cell receptor, ligand, lipid, biotin, polyhistidine, or any fragment or derivative of these structures, as described above It can be any combination of or capture moieties (or linker molecules as described below) that are designed to have specificity or made to bind with specificity or capable of binding with specificity.

The tag is a biotin group, and the capture component is a biotin-binding protein such as avidin, streptavidin, neutravidin, extravidin or streptavidin. am. Since biotin is readily incorporated into the aptamer during synthesis, and streptavidin beads are readily available, this combination can be suitably used.

The tag is polyhistidine, and the capture component is poly-histidine when chelated with a metal ion such as nickel, cobalt, or iron or nitrilotriacetic acid (NTA). nitrilotriacetic acid chelated with any other metal ion capable of forming a coordination compound.

The tag is a polynucleotide designed to hybridize directly with a capture component comprising a complementary polynucleotide sequence. In this case, the tag is sometimes referred to as a "sequence tag" and the capture moiety is generally referred to as a "probe". The tag is generally constructed, and the hybridization reaction is performed under conditions such that the tag does not hybridize with a capture component other than the perfect complement of the capture component. This allows the design of multiple assay formats such that each tag/capture combination has a unique sequence.

The tag is configured such that it does not interact intermolecularly with an aptamer that is itself or attached to the tag or is part of the tag. If SELEX is used to select an aptamer, the tag may be added to the aptamer before or after SELEX.

The tag is included on the 5' end or on the 3' end of the aptamer after SELEX. The tag may be included at both the 3' and 5' ends of the aptamer in the post-SELEX modification process. The tag may be an internal segment of the aptamer.

The tag contains nucleotides that are part of the aptamer itself. If SELEX is used to identify an aptamer, the aptamer contains a nucleotide sequence that varies depending on the aptamer, ie, a 5'-fixed end separated from a 3'-fixed end by a variable region. The tag may comprise any suitable number of nucleotides contained in the fixed end of the aptamer, such as the entire fixed end or any portion of the fixed end including nucleotides embedded in the fixed end.

The tag can bind directly to the capture moiety and is covalently bound to the capture moiety, whereby the aptamer is covalently bound to the surface of the solid support.

The tag is indirectly bound to the capture moiety through a linker molecule. The tag may comprise a polynucleotide sequence complementary to a specific region or component of the linker molecule. The tag is generally constructed, and the hybridization reaction is performed so as not to hybridize with a polynucleotide sequence other than the polynucleotide sequence included in the linker molecule.

If the tag comprises a polynucleotide, the polynucleotide may comprise any suitable number of nucleotides. The tag comprises at least about 10 nucleotides. The tag comprises from about 10 to about 45 nucleotides. The tag comprises at least about 30 nucleotides. Different tags comprising polynucleotides may contain the same number of nucleotides or different numbers of nucleotides.

The tag component has two functions because it includes a function for specific interaction with the capture component on a solid support and a function for dissociating a molecule attached to the capture component binding component of the tag. Means for dissociating the capture component binding component of the tag include chemical means, photochemical means, or other means depending on the particular tag to be used.

"Capture element" refers to a molecule formed to bind directly or indirectly with a tag. The capture component is a set of replicas of one type of molecule or one type of multi-molecular structure capable of immobilizing a portion to which the tag is attached to a solid support by direct or indirect binding to the tag.

A capture moiety refers to a set of one or more such molecules. The capture moiety may be a polynucleotide, polypeptide, peptide nucleic acid, locked nucleic acid, oligosaccharide, polysaccharide, antibody, affibody, antibody mimetic, cellular receptor, ligand, lipid, any fragment or derivative of such a structure, any of those described above. It can be a combination or any other structure with a tag (or linker molecule) that is designed to be specific, or formed to bind with specificity, or otherwise capable of binding with specificity. The capture component may be attached to the solid support, either covalently or non-covalently, by any suitable method.

The capture element generally refers to a polynucleotide sequence. The capture component includes a polynucleotide having a sequence complementary to a polynucleotide tag sequence. The capture element sequence is generally constructed, and the hybridization reaction is carried out under conditions such that the capture element does not hybridize with a nucleotide sequence other than a tag for the capture element comprising a complementary sequence (i.e., the capture element is generally constructed and the hybridization reaction is performed under conditions such that the capture component does not hybridize with different tags or AptaSSN).

The capture moiety indirectly binds to the tag through, for example, a linker molecule. The capture component may include a polynucleotide sequence complementary to a specific region or component of the linker molecule.

The capture component is generally constituted, and the hybridization reaction is performed under conditions such that the capture component does not hybridize with a polynucleotide sequence other than the polynucleotide sequence contained in the linker molecule.

*If the capture component comprises a polynucleotide, the polynucleotide may comprise any suitable number of nucleotides. The capture component comprises at least about 10 nucleotides. The capture component comprises from about 10 to about 45 nucleotides. The capture component comprises at least about 30 nucleotides. Different capture elements comprising polynucleotides may contain the same number of nucleotides or different numbers of nucleotides.

The capture component has two functions because it includes a function for specific interaction with the polynucleotide tag and a function for separating the capture component from the solid support so that the capture component and AptaSSN are simultaneously released. Means for dissociating the capture moiety from the solid support include chemical means and other means depending on the particular capture capture moiety to be used.

Because of the reciprocal nature of the interaction between a particular tag and a pair of capture components, the tag may be used as a capture component in other implementations, and the capture component may be used as a tag in other implementations. AptaSSN using a biotin tag may be captured with streptavidin attached to a solid support in one embodiment, whereas AptaSSN using a streptavidin tag may be captured with biotin attached to a solid support.

A linker is a molecular structure used to link two functional groups or molecular structures. "Spacing linker" or more simply "spacer" refers to a group of benign atoms that provide a separation or spacing between two different functional groups in AptaSSN. Alternatively, it may contain two or more molecules (referred to herein as "hybridization linkers") in which non-covalent interactions can be broken or disrupted.

It is necessary to spatially separate certain functional groups from others in order to avoid interfering with individual functionalities. It is desirable to separate these groups with a non-interfering moiety that provides sufficient spatial separation. In some embodiments, a “spacing linker” is introduced into the AptaSSN that has both label functionality.

Spacing linkers are introduced into AptaSSN during synthesis, aliphatic carbon chains of 3, 6, 9. 12 and 18 carbon atoms in length, and polyethylene glycol chains of 1, 3 and 9 ethylene glycol units in length. glycol chain), or a tetrahydrofuran moiety (referred to as dSpacer (Glenn Research)) or any combination of the foregoing or any other structure or phosphodiester backbone designed or constructed to add length It may be composed of a plurality of phosphoamidite spacers including, but not limited to, chemical components that may be

The spacing linker includes a polynucleotide such as poly dT, dA, dG, or dC or poly U, A, G or C, or any combination of the foregoing. In other embodiments, the spacer comprises one or more abasic ribose or deoxyribose moieties. These sequences are designed so that they do not interfere with the structure or function of AptaSSN.

"Hybridization linker" refers to a linker comprising two or more molecules in which non-covalent interactions can be broken or disrupted through chemical or physical methods. A hybridization linker is used to link the AptaSSN to the tag. A hybridization linker can be used in any of the described assays to make a connection between AptaSSN and biotin (eg, in an affinity assay).

A hybridizing linker comprises two nucleic acids that hybridize to form a non-covalent bond. In one embodiment, one of the nucleic acids forming the hybridization link may be a site of AptaSSN itself, and the other nucleic acid may be a nucleic acid complementary to its site. Release can be achieved by any suitable mechanism for disrupting the nucleic acid duplex (still retaining affinity for the assay). A hybridizing linker molecule may have any suitable structure and may contain one or more polynucleotides, polypeptides, peptide nucleic acids, locked nucleic acids, oligosaccharides, polysaccharides, antibodies, affibodies, antibody mimics or fragments, receptors, any suitable component, including ligand, lipid, any fragment or derivative of such structures, any combination of those described above, or any other structure or chemical component that can be designed or configured to form a releasable structure may include

The tag consists of at least one polynucleotide consisting of an appropriate number of nucleotides. The polynucleotide component of the linker molecule comprises at least about 10 nucleotides. The polynucleotide component of the linker molecule comprises from about 10 to about 45 nucleotides.

The polynucleotide component of the linker molecule comprises at least about 30 nucleotides. Linker molecules may comprise polynucleotide components having the same number of nucleotides or different numbers of nucleotides.

"Solid support" refers to any substrate having a surface to which molecules can be attached, either directly or indirectly through covalent or non-covalent bonds. The form of the solid support of the present invention is a particle or the surface of a reaction sphere, etc. In the present invention, the form of the first solid support (FSS) is a particle or a first solid support that exposes the immobilized first capture component to the reaction solution in a suspended state. In the form of (SSS), there may be a surface of the reaction sphere that exposes the immobilized second capture component to the reaction solution in a fixed state.

The solid support may include a capture component attached to a surface or any substrate material capable of providing a physical support for the capture component.

The molecule is generally capable of withstanding the conditions associated with the capture component or attachment of the capture component to a surface and any subsequent treatment, manipulation or processing encountered during the performance of the assay.

Molecules used for solid supports can take on any of a variety of configurations, from simple to complex. The solid support is a long strip, a plate, a disk, a rod, a particle, a bead, a tube, a well (microtiter) ) and the like.

The molecule may be a naturally occurring molecule, a compound, or a modification of a naturally occurring molecule. Suitable solid support molecules include silicon wafer chips, graphite, mirrored srufaces, laminates, membranes, ceramics, plastics (e.g., Poly(vinyl chloride), cyclo-olefin copolymer, agarose gel or beads, polyacrylamide, polyacrylate , polyethylene, polypropylene, poly(4-methylbutene) (poly(4-methylbutene), polystyrene, polymethacrylate, poly(ethylene terephthalate) (poly(ethlene) terephthalate), polytetrafluoroethylene (PTFE or Teflon®), nylon, poly(vinyl butyrate) (including polymers such as poly(vinyl butyrate)), germanium, gallium may include gallium arsenide, gold, silver, langmuir Blodgett films, flow through chips, etc., used on their own or in conjunction with other molecules.

Additional rigid materials, such as glass, are contemplated, including silica, further including glass usable as bioglass. Other molecules that may be used include controlled pore glass beads, crossslinked beaded Sepharose® or agarose resins, or crosslinked bisacrylamide ) and porous molecules such as copolymers of azalactone. Other beads include nanoparticles, polymer beads, solid core beads, paramagnetic beads or microbeads. Any other molecule well known in the art is also contemplated, which may have one or more functional groups, such as, for example, any amino, carboxyl, thiol or hydroxyl functional group incorporated on the surface.

The solid support may be porous or non-porous, magnetic, paramagnetic or nonmagnetic, polydisperse or monodisperse, hydrophilic or It may be hydrophobic. The solid support may also be in the form of a gel or slurry of closely-packed or loosely-packed particles (such as in a column matrix).

The attached capture component and the solid support are used to capture the tagged M-AptaSSN complex or the M-AptaSSN covalent complex from the sample mixture. When the tag is a biotin moiety, the solid support is a bead coated with streptavidin or Dynabeads M-280 streptavidin, Dynabeads MyOne streptavidin, Dynabeads M-270 streptavidin (Invitrogen), Streptavidin Agarose Resin (Pierce), Streptavidin Ultralink Resin, Magnavind Streptavidin Beads (MagnaBind Streptavidin Beads; Thermo Scientific), BioMag Streptavidin, ProMag Streptavidin, Silica Streptavidin (Bangs Laboratories), Streptavidin Sepharose (Streptavidin Sepharose High Performance; GE Healthcare), Streptavidin Polystyrene Microspheres (Microspheres-Nanospheres), Streptavidin Coated Polystyrene Particles (Spherotech) or capture biotin-tagged molecules It may be any other streptavidin coated beads or resins commonly used by one of ordinary skill in the art.

When a population of molecules in a sample and a population of AptaSSN form a complex, a “competitor molecule” and a “competitor” may be used to prevent non-target molecules from non-specifically recombination with AptaSSN, for example. It is used interchangeably to refer to any molecule capable of forming a non-specific complex with a non-target molecule. "Competing molecule" or "competitor" refers to one or more such sets of molecules. Competing molecules include oligonucleotides, polyanions (eg, heparin, herring sperm DNA, single-stranded salmon sperm DNA, and polydextran (eg, dextran sulfate)), abasic phosphodiester polymers (basic phosphodiesters) polymers), dNTPs and pyrophosphate.

Before the first separation and the second separation, a washing solution with an organic solvent may be used and/or washed by a strict washing solution method. In the case of stringent wash solutions using a competitor, the competitor may be any molecule capable of forming a non-specific complex with free AptaSSN or a protein to prevent non-specific recombination of AptaSSN to non-target molecules. Such competing molecules may be polycations spermine, spermidine, polylysine and polyarginine) and amino acids (arginine and lysine).

When the competitor is used as a stringent wash solution, a fairly high concentration relative to the expected concentration of total protein or total AptaSSN present in the sample is used. 10 mM dextran sulfate is used as a competitor in stringent wash solutions.

The stringent wash solution is performed by diluting the sample with a binding buffer or any other solution that does not significantly increase the dissociation rate of the M-AptaSSN complex. The dilution may be about 2X, about 3X, about 4X, about 5X, or any suitable order of dilution.

When dilution is used as a stringent wash solution, the amount of dilution is chosen to be practicable in view of the desirability of recovering the M-AptaSSN complex from the initial sample volume and the final (diluted) volume that does not result in significant loss of the complex. .

The stringent wash solution is performed in such a way that the effect of sample dilution and the effect of introducing a competitor are simultaneously achieved. The sample can be diluted with many competitors. Combining these two stringent wash solution strategies can provide a more effective stringent wash solution than can be achieved using one strategy. The dilution can be about 2X, about 3X, about 4X, about 5X or any suitable order of dilution, wherein the competitor is 10 mM dextran sulfate.

The stringent washing solution is obtained by diluting the mixture containing the M-AptaSSN complex, adding a competitor to the mixture containing the M-AptaSSN complex, and the ratio of the measured level of the M-AptaSSN complex to the measured level of the non-specific complex. This involves treating the mixture containing the M-AptaSSN complex for such an increasing amount of time.

AptaSSN-FSS from the stationary SSS-M-AptaSSN-FSS complex can be released by any method that disrupts the AptaSSN/molecule interaction. This can be achieved by washing the M-AptaSSN complex bound to the support with a high concentration of salt buffer that dissociates the non-covalently bound M-AptaSSN complex. The eluted free AptaSSN is collected and detected. High or low pH can be used to disrupt the M-AptaSSN complex and high temperature can also be used to dissociate the M-AptaSSN complex. Combinations of any of the above methods may be used.

Ⅴ. AptaSSN Fluid Magnetic Bead Device

The present invention relates to an apparatus useful for appropriately performing any of the methods disclosed in the present invention for AptaSSN selection, AptaSSN multiplex analysis, aptamer development of a specific molecule, and target molecule analysis.

16 is a schematic diagram of the AptaSSN fluid magnetic bead device 1000 of the present invention.

Specifically, this device is an AptaSSN fluid magnetic bead for selection of AptaSSN with significant binding differences in molecular populations constituting a sample composed of various types of molecules, multiple analysis of AptaSSN, development of aptamers of specific molecules, and analysis of target molecules. The device 1000 may be newly provided.

The AptaSSN fluid magnetic bead device 1000 of the present invention includes an injection unit 1100, a flow channel 1200 and a valve unit made of any one or more selected from the group consisting of a first inlet 1110 and a second inlet 1120. (1300).

In the flow channel 1200, a reaction unit 1210, a discharge unit 1220, and a collection unit 1230 made of any one or more selected from the group consisting of a first reaction port 1211 and a second reaction port 1212. Any one or more selected from the group consisting of are sequentially positioned, and the magnetic force applying unit 1213 may be provided in the second reaction sphere 1212 . The flow channel 1200 may be installed to be detachably attached to the magnetic force applying unit 1213 .

The injection unit 1100 may be injected without any particular limitation as long as it is a material necessary for the reaction, but may be composed of any one or more selected from the group consisting of a first injection port 1110 and a second injection port 1120 composed of a pump. . The first inlet may be connected to a first reagent bottle 1130 composed of several reagent bottles, and the second inlet may be connected to a second reagent bottle 1140 composed of multiple reagent bottles.

Preferably, in the first inlet 1110, a sample solution composed of a group of molecules (M), a single-stranded nucleic acid (SSN) library solution with a first tag, a first solid support (FSS) solution with a first capture component, a first 2 Any one or more substances selected from the group consisting of a tag solution, a reaction solution and an elution solution may be injected.

At least one material selected from the group consisting of air, distilled water, a washing solution and an elution solution may be injected into the second inlet 1120 , and the molecules M may be the same material or may be composed of different materials.

The sample injected through the injection unit 1100 may be injected with the sample after the reaction has been completed, but preferably, the reaction unit 1200 may react. The reaction unit 1200 may include any one or more selected from the group consisting of a first reaction sphere 1210 and a second reaction sphere 1220 .

In the reaction unit 1210 constituting the flow channel 1200 , the solutions selected from the first inlet 1110 are injected into the first reaction port 1211 , and molecules (M) constituting the sample, the first An M-SSN-FSS complex is formed by a single-stranded nucleic acid with a tag, a first capture component, and a magnetic first solid support (FSS), washed, transferred back to the second reaction zone, and the molecule of the complex (M) A second tag may be added to .

The mixed solution formed in the first reaction zone 1211 is preferably a molecule (M) constituting the sample, a binding single-stranded nucleic acid with a first tag (SSN), and a solid support (FSS) having the first capture component. ) may include an M-SSN-FSS complex formed from The FSS may preferably include magnetic particles. Preferably, the volume of the first reaction sphere 1211 may be about 50 ml.

A washing solution may be injected into the first reaction port 1211 from the second inlet 1120 and washing may be performed while injecting air pressure in a constant flow.

Preferably, a magnetic force applying unit 1213 may be located in the second reaction port 1212 of the reaction unit 1210 of the flow channel 1200 . Preferably, the volume of the second reaction sphere 1212 may be about 1 ml.

The valve controls the movement and storage of various solutions in the flow channel 1200 , and a first valve 1310 and a second inlet 1120 located between the first inlet 1110 and the first reaction port 1211 . and a second valve 1320 positioned between the first reaction sphere 1211, a second valve 1330 positioned between the first reaction sphere 1211 and the second reaction sphere 1212, and a second reaction sphere 1212 ) and the position between the discharge unit 1220 is any one or more selected from the group consisting of the fourth valve 1340 and the fifth valve 1350 located between the second reaction port 1212 and the collecting unit 1230. can Preferably, the valve may use a pressure valve.

A sample solution containing a molecular population, AptaSSN solution with a first tag, a solution with a first capture component and a magnetic material, a reaction solution, a washing solution, distilled water, a solution with a second tag, and an elution solution Any one or more solutions that have been made are distributed to the reaction unit 1210 suitable for an appropriate use by controlling the valve located in the flow channel 1200 by the injection unit 1100, and again by controlling the valve, the discharge unit 1220 and the collecting unit It can be moved and saved to (1230).

Next, an attractive force is applied to the magnetic particles by applying a magnetic field. This is to prevent the magnetic particles from being removed by applying an attractive force to the magnetic particles. The magnetic particles in the mixed solution component are magnetic FSS and M-SSN-FSS complexes. In order to apply attractive force to the magnetic particles present in the second reaction sphere, for example, a permanent magnet, an electromagnet, or a magnetic field generator capable of generating a magnetic field may be used.

When a permanent magnet is used to apply a magnetic field, the magnetic field can be applied to the second reaction sphere 1212 by arranging the permanent magnet 1213 in a position close to the second reaction sphere 1212, By increasing the distance between the reaction sphere 1212 and the permanent magnet 1213 , a magnetic field may not be applied to the reaction sphere 1212 .

When the electromagnet 1213 is used to apply a magnetic field, the magnetic field may be applied to the second reaction sphere 1212 by supplying a current to the electromagnet 1213 or by not supplying the current, or the magnetic field may not be applied. have.

When the magnetic field generator 1213 is used to apply a magnetic field, the magnetic field may be applied to the second reaction sphere 1212 through on/off of the magnetic field generator 1213 or the magnetic field may not be applied. .

When a magnetic field is applied into the reaction sphere 1212, an attractive force may act on the magnetic particles under the influence of the magnetic field, and the attractive force may act on at least one of the FSS and the M-SSN-FSS complex in the reaction sphere.

In contrast, attractive force does not act on at least one of the unbound molecule constituting the sample and the SSN having the first tag.

At least one of the unbound molecules constituting the sample and the SSN having the first tag are removed while the attractive force by the magnetic field is applied.

A solution in which unbound molecules constituting the sample and SSN having the first tag in a free state are supplied to the inside of the second reaction sphere in a state in which attraction is stopped by a magnetic field by supplying a solution to the second reaction sphere 1212 and may be discharged from the second reaction port 1212 to the discharge unit 1220 . In this case, in a state in which the attraction is stopped by the magnetic field, the unbound molecules constituting the sample and the SSN having the first tag are discharged together with the solution discharged to the discharge unit 1220 by the air pressure by the pump of the injection unit 1100 . because it becomes

When the removal process is completed, the FSS and the M-SSN-FSS complex may be present in the second reaction sphere 1212 . The magnetic force applying unit 1213 may collect the magnetic FSS and M-SSN-FSS complexes by the applied magnetic force.

Next, a second tag may be added to the mixed solution composed of the FSS and M-SSN-FSS complex in the second reaction zone 1212 . The second tag has a functional group that can be added to the molecule of the M-SSN-FSS complex. After the addition reaction is completed, the washing solution including the excess reactant may be removed by adding the washing solution to the washing solution by applying attractive force by the magnetic field, and then using the air pressure by the pump 1100 .

Next, in the state where the attraction is stopped by the magnetic field, the solution containing the FSS and the M-SSN-FSS complex to which the second tag is added is collected with the second capture component by the pneumatic pressure by the pump of the injection unit 1100 . The M-SSN-FSS complex transferred to the unit 1230 and the second tag is added by a second solid support (SSS) on the second capture component on the surface of the collecting unit is SSS-M-SSN-FSS It can be collected by forming a complex.

A solution having a second tag may be injected from the first inlet 1110 , and a second tag may be added to the molecules of the M-SSN-FSS complex in the second reaction port 1212 . Preferably, the second tag may be biotin (NHS-biotin) having a succinimidyl group. Additionally, it can react with Alexa Fluor 568 NHS Ester, a type of NHS-dye.

The M-SSN-FSS complex to which the second tag is attached in the second reaction sphere 1212 can be washed while the magnetic field is applied, and the FSS and the M to which the second tag is attached when the magnetic field application is stopped The -SSN-FSS complex solution may be transferred to the collecting unit 1230 .

In the flow channel 1200, an unreacted sample discharge unit 1220 capable of discharging the remaining unreacted samples not collected by the magnetic force application unit is located between the magnetic force application unit 1213 and the collection unit 1230. can

In order to control the fluid flow for discharging the unreacted sample, the valve unit 1300 includes a first valve 1310 , a second valve 1320 , a third valve 1330 , a fourth valve 1340 , and a fifth valve. 1350, it is preferable to use a microvalve such as a pneumatic valve.

After the unreacted sample is discharged through the discharge unit 1220, the FSS and the M-SSN-FSS complex collected by the magnetic force application unit 1213 are returned to the flow channel 1200 by stopping the application of the magnetic force. Through this, it is possible to maintain the state collected in the second reaction sphere 1212 .

On the other hand, the collecting unit 1230 is not particularly limited, but preferably any one or more selected from the group consisting of a plate, a tube and a glass slide in which a plurality of separation hole groups are arranged, and the second capture There may be a second solid support (SSS) comprising components.

Preferably, when a group of separation holes is arranged, the collection unit 1230 enables simultaneous separation of the binding single-stranded nucleic acid pools originating from various samples.

The collecting unit 1230 has a second solid support (SSS) having a second capture component, and in the M-SSN-FSS complex solution to which FSS and a second tag are attached, M-SSN-FSS to which a second tag is attached. Only the complex can be captured to form the SSS-M-SSN-FSS complex. By adding a washing solution to the mixed solution, unreacted substances can be removed, so that only the high-purity SSS-M-SSN-FSS complex can be separated and detected. Preferably, the second capture component in the second solid support (SSS) may be avidin.

The collecting unit 1230 may be formed of a concave groove or a glass slide including a second solid support (SSS) including a second trapping component, and the concave groove is formed of the second solid support (SSS). As a second capture component, the M-SSN-FSS complex to which the second tag is added may be captured to form an SSS-M-SSN-FSS complex.

The collecting unit 1230 is preferably a complex formed between the molecules (M) constituting the sample, the bound single-stranded nucleic acid (SSN), the first solid support (FSS) and the second solid support (SSS), that is, SSS-M- The complex containing single-stranded nucleic acid (SSN) can be eluted by the elution solution in the SSN-FSS complex.

The material of the flow channel 1200 can be used without particular limitation as long as it is a polymer material that does not interfere with the flow of the fluid solution, but preferably, the flow channel is selected from the group consisting of microfluidic chips, pouch types, and tubes. It may be either one. Preferably, the flow channel 1200 may be installed to be detachably attached to the magnetic force applying unit 1213 .

The flow channel pouch 1230 of the present invention is a closed single flow channel constituting the reaction part, and is a flexible plastic film or polyester, polyethylene terephthalate (PET), polycarbonate, polypropylene, polymethyl methacrylate, mixtures, and formulations. It may be formed of two layers of different flexible materials such as These layers may be prepared by any process known in the art including extrusion, plasma deposition and lamination. For example, each layer may consist of one or more layers of a single type or two or more types of material laminated together. Metal foil or plastic with a thin aluminum plate can also be used.

Specifically, it may be composed of injection molded polypropylene heat-welded to two polyester/polypropylene films including a copolymer adhesive layer. The film sheets are adhered together using a heated plate to form a pattern of flow channels 1200 including areas including spaces for the first reaction spheres 1211 and the second reaction spheres 1212 .

After a solution in which various components are mixed is injected into the inlet 1100, the sample is transferred to the reaction unit 1210 including the first reaction port 1211 and the second reaction port 1212 by the precision pump, and the discharge unit It begins to flow into the fluid channel 1200 made up of 1220 and the collecting part 1230 . At this time, the flow through the flow channel 1200 may be configured such that a plurality of lines of pressure points are disposed in the flow direction while allowing the flow through the line of pressure nodes to pass vertically or at a constant angle.

The flow channel 1200 may be formed to have at least one or more bent portions. That is, the flow channel 1200 may have a plurality of bent portions so that the flow direction is vertically bent.

In addition, the flow channel 1200 may be formed in a zigzag shape or a serpentine shape, so that a plurality of pressure point lines may be disposed with respect to the flow direction.

When the components constituting the mixed solution move with the fluid flow and pass the pressure point line, the components of the mixed solution stay at the pressure point or receive a force by the air pressure pressurized by the pump generated in the injection unit 1100 to stop do. Since this air pressure is proportional to the size of the components of the mixed solution, the force acting on the magnetic FSS and M-SSN-FSS complex is very small, so there is a limit to stopping the components of the mixed solution, but at least it is possible to overcome the flow resistance on a microscopic scale. have.

Therefore, in the case of flow through the flow channel 1200 arranged so that the mixed solution component passes through a plurality of pressure point lines, the accumulated total flow resistance experienced by one M-SSN-FSS and FSS particle is the total flow resistance of the flow channel. Pneumatic pressure at the inlet may be essential as it may be of a non-negligible or very significant size during passage through the section.

As described above, the time taken to reach the fluid outlet after the sample is input from the inlet is referred to as a retention time, and the sample can be separated using the difference in the retention time for each particle size. The separated particles can be obtained by using a light source and a photospectrometer in the detection unit installed at the front end of the fluid outlet to obtain an optical characteristic curve for each particle, and through this, the size or shape of the particle can be distinguished.

The mixed solution components included in the fluid flowing in the flow channel 1200 have different degrees of concentration at the pressure point depending on the size, deformability, density, etc., and can be used to separate the components of the mixed solution by properties. More specifically, since components of a mixed solution having a relatively large size are more likely to gather at a pressure point than a component of a mixed solution having a small size, a component having a relatively large size at a pressure point where it meets while flowing along the flow of the fluid is It delays a relatively long time compared to a small component.

Depending on the properties of the components of the mixed solution, a difference is induced in the time at which the components of the mixed solution exit the flow channel 1200, and the components of the mixed solution may be separated according to the difference in time at which the components of the mixed solution exit the fluid outlet. A signal can be detected over time using a light source and a photospectrometer, etc. as a detector for detecting the components of the mixed solution flowing toward the fluid outlet after separation by properties of the components of the mixed solution.

Therefore, by using the AptaSSN fluid magnetic bead device 1000 according to the present invention to separate and detect only the high-purity magnetic SSS-M-SSN-FSS complex and analyze the molecules constituting the sample through this, more accurate and sensitive and reliable analysis can be achieved.

The device for eluting the material group containing the SSN from the sample solution composed of the molecular (M) group, the SSN library solution, and the mixed solution composed of the FSS solution may be the AptaSSN fluid magnetic bead device 1000 shown in FIG. 16 . have.

The operation of the AptaSSN fluid magnetic bead device 1000 used for the purpose of eluting the material group including the SSN is (a) the sample solution composed of the molecular (M) group from the first inlet 1110, the SSN The library solution and the FSS solution are injected, and the molecular (M) group constituting the sample, the first tag of the SSN, and the first capture component of the FSS react in the first reaction zone 1211 to react with the M-SSN. - Preparing a first mixed solution in which the FSS complex is formed; (b) the washing solution is injected from the second inlet 1120, and the M-SSN-FSS complex formed in the first mixed solution is washed in the first reaction port 1211 to non-specifically bind or bind to the SSN A first separation step of removing one or more components of the mixture that have not been mixed with the discharge unit 1220; (c) the second tag solution is injected from the first inlet 1110, and the second tag- attaching a second tag to form an M-SSN-FSS complex; (d) the second tag-M-SSN-FSS complex solution is injected from the second reaction port 1212 , and the second capture component of SSS and the second tag-M- in the collecting unit 1230 preparing a second mixed solution in which the second tag of the SSN-FSS complex reacts to form the SSS-M-SSN-FSS complex; (e) the washing solution is injected from the second inlet 1120, and the SSS-M-SSN-FSS complex formed in the second mixed solution in the collecting unit 1230 is washed, and the second capture component and a second separation step of removing unbound one or more components of the mixture; (f) injecting the elution solution from the first inlet, and eluting the material group including the SSN from the second separated SSS-M-SSN-FSS complex in the collecting unit; (g) preparing an amplified SSN population from the material group including the eluted SSN, or additionally repeating steps (a) to (f) in one round or more; and (h) collecting a material group including the SSN eluted from the collecting unit in step (f).

An apparatus for eluting a molecular population from a sample solution composed of the molecular (M) population, a selected AptaSSN population solution with the first tag, and a mixed solution consisting of the FSS solution is the AptaSSN fluid magnetic bead apparatus 1000 shown in FIG. 16 . can be

The operation of the AptaSSN fluid magnetic bead device 1000 used for the purpose of elution of the target molecule is performed by (a) the sample solution composed of the molecular (M) group from the first inlet 1110, and the selected AptaSSN having the first tag. The population solution and the FSS solution are injected, and the group of molecules (M) constituting the sample, the first tag of the AptaSSN, and the first capture component of the FSS react in the first reaction port 1211 to react with M-AptaSSN. - Preparing a third mixed solution in which the FSS complex is formed; (b) the washing solution is injected from the second inlet 1120, and the M-AptaSSN-FSS complex formed in the third mixed solution is washed in the first reaction port 1211 to non-specifically bind or bind to the AptaSSN Separation step of removing one or more components of the mixture that have not been mixed with the discharge unit 1220; and (c) injecting an elution solution from the first inlet 1110 into the M-AptaSSN-FSS complex transferred from the first reaction port 1211 to the collecting unit 1230, and eluting the molecular (M) group. ; may include.

The apparatus for eluting the AptaSSN population that binds to the molecular population constituting the sample from the sample solution composed of the molecular (M) group, the AptaSSN population solution, and the FSS solution is the AptaSSN fluid magnetic bead device shown in FIG. 16 . (1000).

The operation of the AptaSSN fluid magnetic bead device 1000 for the purpose of dissolution of the AptaSSN is (a) the sample solution composed of the molecular (M) group, the AptaSSN group solution, and the FSS solution are injected from the first inlet 1110; A fourth mixed solution in which the molecular (M) group constituting the sample, the first AptaSSN tag, and the first capture component of the FSS react in the first reaction zone 1211 to form an M-AptaSSN-FSS complex preparing a; (b) the washing solution is injected from the second inlet 1120, and the M-AptaSSN-FSS complex formed in the fourth mixed solution is washed in the first reaction port 1211 to non-specifically bind or bind to the AptaSSN a third separation step of removing one or more components of the mixture that have not been made to the discharge unit 1220; (c) injecting the second tag solution from the first inlet 1110 and adding a second tag to the molecule M of the M-AptaSSN-FSS complex in the second reaction port 1212; (d) the second-tagged M-AptaSSN-FSS complex injected from the second reaction zone 1212 and the second capture component of the SSS in the collection part 1230 come into contact with the SSS-M-AptaSSN - Preparing a fifth mixed solution in which the FSS complex is formed; (e) the washing solution is injected from the second inlet 1120, and the SSS-M-AptaSSN-FSS complex formed in the fifth mixed solution in the collecting unit 1230 is washed, and the second capture component and a fourth separation step of removing unbound one or more components of the mixture; and (f) the elution solution is injected from the first inlet 1110, and eluting the material group containing the AptaSSN from the second separated SSS-M-AptaSSN-FSS complex in the collecting unit 1230. ; may include.

The target molecule test fluid magnetic bead device of the present invention can be manufactured by adding an analysis device to the AptaSSN elution fluid magnetic bead device 1000 shown in FIG. 16 .

The analysis device may be any one or more selected from the group consisting of a microscope, a nucleic acid analysis device, a spectroscopic analysis device, a fluorescent material analysis device, and an electrical signal analysis device.

One object of the present invention is to convert a protein signal into an AptaSSN signal. As a result, the amount of AptaSSN collected/detected represents the amount of bound molecules relative to the amount of molecules in the sample, and can be directly proportional to the amount.

A number of detection schemes can be used without eluting the M-AptaSSN complex from the solid support after the second separation.

Many detection methods require an explicit label to be incorporated into AptaSSN prior to detection. Labels such as fluorescent or chemiluminescent dyes can be incorporated into AptaSSN during or after synthesis using standard techniques for nucleic acid synthesis. Radioactive labels can be incorporated during or after synthesis using standard enzymatic reactions using appropriate reagents. Labeling also occurs after Catch-2 separation and elution by using various enzymatic techniques well known to those skilled in the art.

PCR using primers with the above-mentioned label will incorporate the label into the amplification product of the eluted AptaSSN. When using the gel technique for quantification, mass labels of different sizes can also be incorporated using PCR.

These mass labels also contain different fluorescent or chemiluminescent dyes for additional multiplexing capabilities. Labels can be added indirectly to AptaSSN by using specific tags incorporated into AptaSSN during or after synthesis, then adding a capture component that binds the tag and carries the label.

Such labels include those described above as well as enzymes used in standard assays for colorimetric readout. These enzymes work with enzyme substrates and include, for example, enzymes such as horseradish peroxidase (HRP) and alkaline phosphatase (AP). The label may also include a molecule or compound that is an electrochemical functional group for electrochemical detection.

The AptaSSN may be labeled as described above with a radioisotope such as 32P prior to contact with the sample. AptaSSN detection using any one of the four basic assays and their modifications as described above can be achieved simply by quantifying the radioactivity on the solid support at the end of the assay. The radioactivity level will be directly proportional to the amount of M in the original sample.

Labeling AptaSSN with a fluorescent dye prior to contact with the sample allows simple fluorescence readings directly on the solid support. Chemiluminescent labels or quantum dots can similarly be used for direct reading from a solid support that does not require AptaSSN elution.

By eluting AptaSSN from the solid support or releasing the M-AptaSSN covalent complex, additional detection schemes can be used in addition to those described above. The released AptaSSN can be loaded onto a PAGE gel, detected and optionally quantified by nucleic acid staining such as SYBR Gold. Alternatively, the released AptaSSN can be detected and quantified using capillary gel electrophoresis (CGE) using a fluorescent label incorporated into the AptaSSN as described above. Another detection scheme uses quantitative PCR to detect and quantify eluted AptaSSN using SYBR Green. Alternatively, Invader® DNA assay can be used to detect and quantify eluted AptaSSN. Another alternative detection scheme uses next-generation sequencing.

The amount or concentration of the M-AptaSSN complex is detected using a “molecular beacon” during the amplification process. A molecular beacon is a specific nucleic acid capture component folded into a hairpin loop, and contains fluorine at one end of the hairpin structure and the other end so that little or no signal is generated by the fluorophore when the hairpin is formed. contains a quencher. The loop sequence is specific for the target polynucleotide sequence and produces a fluorescent signal as the hairpin unfolds upon hybridization with the AptaSSN sequence.

In order to detect and quantify each of 2 or 3, or 5 or 10 or more AptaSSN, for multiplex detection of a small number of AptaSSN still bound to a solid support, different excitations/emissions ), a fluorescent dye having a spectrum can be used. Similarly, quantum dots of different sizes can be used for multiple readouts.

The quantum dots can be introduced after separating the free AptaSSN from the solid support. By using AptaSSN-specific hybridization sequences attached to quantum dots, multiplexed reads of 2, 3, 5 and 10 or more AptaSSNs can be performed. Labeling different AptaSSNs with different individually detectable radioisotopes such as ³² P, ¹²⁵ I, ³ H, ¹³ C and ^{35 S can also be used for limited multiplex readouts.}

For multiplex detection of AptaSSN released from the second solid support (SSS), a single fluorescent dye incorporated into AptaSSN, respectively, as described above, can be used as a quantitative method that allows the sequence of AptaSSN to be identified according to quantification of AptaSSN level. have. Methods include, but are not limited to, next-generation sequencing, DNA chip hybridization, micro-bead hybridization, and CGE analysis.

AptaSSN solutions can be amplified and optionally tagged prior to quantitation. Standard PCR amplification can be used with the AptaSSN solution eluted from the second solid support (SSS). This amplification can be used prior to DNA array hybridization, micro-bead hybridization and CGE readout.

M-AptaSSN complexes (or M-AptaSSN covalent complexes) are detected and/or quantified using Q-PCR. As used herein, "Q-PCR" refers to a method in which the result of an assay is quantitative, that is, a method in which the assay can quantify the amount or concentration of AptaSSN present in a sample, and PCR performed under limited conditions. say the reaction

The amount or concentration of M-AptaSSN complex in a sample is determined using TaqMan® PCR. This technique generally relies on the 5'-3' exonuclease activity of an oligonucleotide replication enzyme to generate a signal from a targeted sequence.

The TaqMan capture component is selected based on the sequence of AptaSSN to be quantified in order to generate a signal such as the AptaSSN sequence amplified using polymerase chain reaction (PCR), and 6-carboxyfluorescein (6- a 5'-terminal fluorophore such as carboxyfluorescein, and a 3'-terminal quencher such as 6-carboxytetramethylfluorescein.

As the polymerase replicates the AptaSSN sequence, exonuclease activity generates a signal by liberating the fluorophore from the annealed capture component downstream from the PCR primer. An increased signal is produced as a replicative product. The amount of PCR product depends on both the starting concentration of AptaSSN as well as the number of replication cycles performed.

The amount or concentration of the M-AptaSSN complex is measured using an intercalating fluorescent dye during the replication process. For example, an intercalating dye such as SYBR® green generates a large fluorescence signal in the presence of double-stranded DNA compared to the fluorescence signal generated in the presence of single-stranded DNA. As double-stranded DNA products are formed during PCR, the signal generated by the dye also increases. The magnitude of the generated signal depends on both the PCR cycle and the starting concentration of AptaSSN.

M-AptaSSN complexes are detected and/or quantified using mass spectrometry. Unique mass tags can be introduced using the enzymatic techniques described above. A detection label is not required for the reading of the mass spectrometer, rather the mass itself is used to identify using techniques commonly used by those skilled in the art, and the mass peak generated during mass spectrometry analysis It is quantified based on the location and area below. An example of using mass spectrometry is the MassARRAY® system developed by Sequinom.

The analysis technology of AptaSSN of M-AptaSSN complex using the NGS technology of the present invention prepares an NGS library for AptaSSN isolated from the complex and runs it on NGS equipment. This is a method of producing an AptaSSN profile by comparing the generated FASTAQ file with the AptaSSN sequence, determining the type of the same read as the AptaSSN sequence, and calculating its frequency.

A computer program may be used to perform one or more steps of any one of the methods disclosed herein. Another aspect of the present disclosure is a computer program product comprising a computer readable storage medium having a computer program that, when loaded into a computer, carries out or aids in the execution of any of the methods described herein.

Ⅵ. Molecular data production from various types of molecules

The present invention provides a method for simultaneously analyzing nucleic acids and proteins in a biological sample composed of various types of molecular populations using NGS technology.

17 is a view providing a process for producing nucleic acid data and protein data by simultaneously analyzing nucleic acids and proteins constituting a sample.

The nucleic acid data includes RNA information including mRNA and miRNA, and DNA information including structural variation, methylation, and SNP of genes.

The present invention provides methods, apparatus, kits and reagents for the multiplex analysis of a sample composed of different types of molecular populations, wherein different types of molecular populations in the sample can be simultaneously detected and/or quantified. Ultimately, the methods and reagents are capable of converting different types of molecular concentrations (eg, protein molecule concentrations in a sample) into nucleic acid concentrations that can be detected and quantified by any of a variety of nucleic acid detection and quantitation methods. Once effectively converted to the nucleic acid concentration corresponding to the protein molecule, standard nucleic acid amplification and detection steps can then be used to increase the signal. The present specification provides a method for multiplex analysis of a sample. The method according to the present specification can be carried out in vitro.

A method for analyzing a protein using NGS (Next Generation Sequencing) in the present invention is well described herein. In general, NGS fragments the entire genome in a chip-based and PCR-based paired-end format, and performs sequencing at high speed based on hybridization of the fragment. Methods for producing are known.

With NGS technology, SNP (Single Nucleotide Polymorphism), which is genetic information that can have 2 to 5% different alleles in the population, and a mutation in which wild type amino acids are modified as a result of nucleotide polymorphism (amino acid mutation) can be quickly analyzed. WGS (Whole Genome Sequencing) is a method of reading the human genome in multiples of 10X, 20X, and 40X format through whole genome sequencing by NGS. TS (Target seqencing) is a technology that sequencing only the gene regions involved in target protein production among the above WGS. Accordingly, a data size of WGS > WES > TS is generated. However, since it is a small site, it has the advantage of sequencing many samples. MET (Methylation) read is a sequencing technology for measuring DNA methylation of a gene, and RNA read is a sequencing technology for gene expression, that is, a DNA transcriptome. SV (structural variation) is a mutation that occurs in a large unit (DNA segment) of a chromosome caused by insertion, inversion, and translocation among mutations, and information about this can also be produced with NGS.

Preferably, the process of simultaneously producing protein and nucleic acid data using NGS technology is as follows.

First, reference sequences are constructed with the nucleotide sequence of the target nucleic acids and the nucleotide sequence of AptaSSN for the target protein. In this embodiment, a reference sequence can be constructed using the nucleotide sequence of 1149 AptaSSN and the nucleotide sequence of the target nucleic acid.

Second, a sample for analysis is prepared from protein molecules (M) in a sample composed of different types of molecular populations. <II. Multiple analysis of AptaSSN> Referring to the section, the protein molecular population in the sample is brought into contact with the AptaSSN population, and the formed M-AptaSSN complex population is separated. Alternatively, the protein molecules in the prepared biological sample are attached to the NC disk, contacted with the AptaSSN population in the reaction solution, and the formed M-AptaSSN complex population is washed with a washing solution to remove unbound or non-specific binding and AptaSSN and separate the disk. It is possible to prepare an AptaSSN population that binds to the molecular population constituting the sample attached to the disk.

Third, it is an oligonucleotide design used in the process of amplifying a target gene as a prerequisite for producing nucleic acid data by analyzing nucleic acids in a sample composed of various types of molecular populations. In order to construct an mTAS (multiple target loci assembly sequencing) oligonucleotide having an optimal length capable of annealing at a specific temperature, the present inventors used a computer program, Perl-mTAS. An oligonucleotide capture component is prepared from a target-specific sequence (target hybridization nucleotide sequence) and a 5'-flanking assembly spacer sequence (overlapping sequence). All capture components were approximately 25 bp and annealed at Tm 60°C.

For each target genomic locus, there is a gap of 7 bp including the SNP position (i.e., to the left of the SNP position, 3 bp; the SNP position, 1 bp; to the right of the SNP position, 3 bp) design. For added ease of design, the gap spacing can be adjusted to 0-3 bp. Although the assembly spacer sequence is arbitrarily prepared, the annealing sites present on the assembly sequence can be determined based on nearest neighbor methods to calculate the temperature for overlapping regions between oligonucleotides. have.

A nucleic acid sample can be prepared by a known method from a sample to be separated by dividing a biological sample and stored at -20°C. An NGS library can be prepared using gDNA stored at -20°C.

The NGS library preparation for the total RNA isolated from the sample can be prepared using Ion Ampliseq Library Kit 2.0 (Life technologies, USA). Amplification products bound to sequencing specific sequences and barcodes were washed by adding 45 μl of AMPure XP solution and quantified using an Agilent DNA 1000 chip to prepare a squashing library.

Samples 1.5 ml each from the serum sample and centrifuged for 5 minutes at 10,000 g at room temperature to recover the cells. Total RNA was extracted from the recovered cells using RNeasy Plus Mini Kit (Qiagen, Germany). The concentration and quality of the extracted RNA were determined by measuring at 260 nm and 280 nm using a ^{NanoDrop TM 1000 spectrophotometer (NanoDrop Technologies, USA).}

Sequencing can be performed using bulk simultaneous techniques on cDNA libraries prepared from RNA in a sample. The cDNA library can be synthesized according to the manufacturer's instructions or with slight modifications, for example, using the mRNA-seq Sample preparation Kit (Illumina, USA).

Small RNA sequences obtained from human serum samples and genome-wide miRNAs can be analyzed and compared. Using the mirVAna miRNA isolation kit (Ambion®, USA), small RNA from serum samples was first extracted from total RNA, and a miRNA library can also be prepared according to the Illumina library preparation protocol (Illumina, USA). The purified miRNA library was quantified using an Agilent DNA 1000 chip to prepare an NGS library.

In order to check whether the prepared NGS library can be used for sequencing, the length and amount of the library prepared using a High Sensitivity chip were measured using an Agilent Bioanalyzer 2100 (Agilent, USA) instrument, and the length was 100 to 300 bp, and the amount was Libraries satisfying the condition of 100 pmol/l or more were used for sequencing. Library quality control using High　Sensitivity chip.

The PCR product integrated for each sample and to which the barcode sequence of the sample is attached can be quantified with a Qubit　fluorometer (Invitrogen, USA). The PCR product may be processed in an NGS sequencer to perform nucleotide sequencing.

In the present invention, the NGS library is prepared by mixing the NGS library for the nucleic acid sample isolated from the fractionated biological sample and the NGS library for the isolated M-AptaSSN complex population, or the nucleotide sequence of the separately prepared NGS library can be determined. have. The frequency of appearance was determined by comparing and analyzing the nucleotide sequence of the nucleic acid constituting the prepared NGS library and the reference sequence.

The frequency of appearance of nucleic acids constituting the determined sequence library reflects information on nucleic acids and proteins, which are target molecules, so that the nucleic acid of the nucleic acid sample and the target molecule constituting the biological sample can be analyzed using the analysis results.

VII. Internal quality control and biological meaning decision support system

The present invention provides methods, apparatus, kits and reagents for the multiplex analysis of samples composed of different types of molecules, wherein populations of molecules in samples composed of different types of molecules can be simultaneously detected and/or analyzed.

Ultimately, these methods and reagents are capable of analyzing multiple target molecules by detecting and analyzing the concentration of multiple molecules constituting the sample (eg, the concentration of multiple proteins in the sample) by any detection and analysis method using multiple ligands. have. When the multiple target molecules are effectively converted into multiple ligand concentrations each binding to the multiple molecules by nucleic acid analysis techniques including analysis techniques using ligands and PCR techniques and hybridization techniques, amplification and detection steps are then used to increase the signal. can The present specification provides a method for multiplex analysis of a sample. The method according to the present specification can be carried out in vitro.

The molecules include proteins, polypeptides, nucleic acids, polynucleotides, carbohydrates, polysaccharides, glycoproteins, hormones, receptors, Antigens, Antibodies, Viruses, Substrates, Metabolites, Transition State Analogs, Cofoactors, Inhibitors, Drugs , dyes, nutrients, growth factors, tissues and controlled substances.

The present invention provides a structural diagram and operational steps of an internal quality control and biological meaning determination support system.

18 is a diagram of the device of the internal quality control and biological meaning determination support system (1).

Referring to FIG. 18 , the device of the internal quality control and biological meaning determination support system 1 includes (a) a molecular data production module 100 for producing molecular data for a plurality of molecules constituting a sample; (b) an internal quality control module 200 for internal quality control of the molecular information of the sample using the quality control molecular data selected from the molecular database constructed based on the biological meaning of the samples having the produced molecular data; and (c) a biological meaning determination support module ( 300); may include.

The operating method of the internal quality control and biological meaning determination support system (1) of the present invention comprises the steps of (a) the molecular data production module 10 producing molecular data from the sample; (b) the internal quality control module 20 performs internal quality control on the molecular data of the sample produced in the molecular data production module 10 with the quality control molecular data selected from the database to select effective molecular data to do; and (c) the biological meaning determination support module 300 analyzes the effective molecular data input from the internal quality control module 200 with the classification molecular data selected from the database and a predictive model obtained by learning the database. It may include; implementing biological meaning determination support.

1. Molecular data production module

In the present invention, the molecular data production module 100 of the internal quality control and biological meaning determination support system 1 may vary depending on the type of molecule, and there are various methods and devices even for the same type of molecule. It is not limited to the content. When the molecule is a nucleic acid, there are a nucleotide sequencing method, a PCR method, a hybridization method, and the like, and there are various devices based thereon. In the case of proteins, there are various devices using a method using a ligand including an antibody and an aptamer, and an MS method. The method and apparatus may be used as the molecular data module 100 of the present invention.

19 is a diagram illustrating a configuration for generating molecular data from a molecular population in a sample composed of various types of molecules.

Referring to FIG. 19 , the device of the molecular data production module 100 constituting the biological meaning determination support system 1 using the AptaSSN group is

(a) a first reaction section 101 for first separating the M-AptaSSN-FSS complex formed by contacting the molecular group constituting the analysis sample and the AptaSSN group; (b) a second reaction unit 102 for separating the SSS-M-AptaSSN-FSS population from the first separated complex population; (c) an NGS sequencer 103 for preparing an NGS library prepared from the isolated AptaSSN population for the AptaSSN population eluted from the second isolated complex, and generating NGS raw data from the prepared NGS library; and (d) a comparison module 105 for pre-processing the NGS raw data and comparing it with the reference sequence 104 to generate and analyze a binding profile.

The module including the first reaction sphere 101 and the second reaction sphere 102 of the molecular data production module 100 may use the AptaSSN fluid magnetic bead device 1000 shown in FIG. 16 .

The reaction sphere 102 and the NGS sequencer 103 may be replaced with a proteomics analysis sphere.

In the present invention, the method for producing molecular data for a molecular group in a sample from the molecular data production module 100 to the AptaSSN group is (a) the sample in the first reaction section 101 constituting the molecular data production module 100 . first separating the M-AptaSSN-FSS complex formed by contacting the molecular population and the AptaSSN population in a mixed solution; (b) transferring the separated M-AptaSSN-FSS complex to a second reaction zone 102 to isolate a second SSS-M-AptaSSN-FSS population; (c) determining the nucleotide sequence of the AptaSSN-FSS population or the AptaSSN population eluted from the second separated complex using the NGS sequencer 103; (d) generating molecular data and a profile consisting of the type and frequency of AptaSSN by comparing the reference sequence 104 constructed by the comparison module 105 with the determined NGS sequence; and (e) inputting the produced molecular data evaluation sample to the internal quality control module 200 or the learning sample to the biological meaning determination support module 300;

2. Internal quality control of molecular data

The internal quality control module 200 constituting the internal quality control and biological meaning determination support system 1 of the present invention secures the quality of molecular data for a molecular group constituting a specific sample in the molecular data production module 100 To do this, internal quality control can be performed.

19 is a view for explaining the configuration of the statistical internal quality control module 200 of molecular data.

19, the apparatus of the statistical internal quality control module of molecular data includes an input module 201, a control module 202, a display module 204, a storage module 205, and the control module 202 is It may include an interface providing module 203 . The input module 201 implements the data input step described with reference to FIG. 18 , and may be input by an input device that can be input by a user or by a network or data transmission. The control module 202 implements the data analysis step, and logical and mathematical operations are possible by the central processing unit. The display module 204 is a device that displays the data output from the data analysis result or the inquiry output step, and displays the data analysis result including quality control data so that the user can recognize it. The storage module 205 is a place where data analysis results including quality control data are stored, may be implemented as a database, and is a device capable of reading and writing data. The interface providing module 204 outputs data to an output device such as a display and provides a means for inducing a user's input. The interface providing module 204 may be implemented integrally with the control module 202 or may be implemented as a separate means.

The statistical internal quality control result management method of molecular data includes a data input step of receiving internal quality control data, a data analysis step of analyzing internal quality control data according to a preset evaluation standard, outputting the data analysis result on a display, and a user An interface providing step that provides an interface to receive input of unusual or action data according to the data analysis result from the data storage step, a data storage step of storing internal quality control data, data analysis results and action data data in a database, and user input Including any one or more of the mean, standard deviation, and coefficient of variation of the internal quality control data in any one or more of the molecular data production module 100 or the internal quality control data, data analysis result, and action data for molecular data in the inquiry period It includes an inquiry output step of outputting the result.

The operating method of the internal quality control module 200 includes the steps of (a) selecting a quality control molecule (data) from a learning database constructed based on the biological meaning of the learning samples with the molecular data; (b) internal quality control of the molecular data of the evaluation sample input from the molecular data production module 100 as the selected quality control molecule; (c) outputting an internal quality control result of the sample having the internal quality control molecular data; and (d) inputting the sample having the internal quality-controlled effective molecular data into the biological meaning determination support module 300 .

Reliable test results by allowing a large amount of molecular data to be efficiently managed by the statistical internal quality control method and device for molecular data disclosed in the present specification, and to easily identify molecular errors and take action can be obtained

The internal quality control module should select a quality control molecule (data) from a learning database constructed based on the biological meaning of the samples having the molecular data.

The role of the selected quality control molecule (data) is to ensure the quality of analysis by checking whether the data produced by the molecular data production module 10 has reached a target value. The selected quality control molecule data must also have a target value, and in order to obtain such an acceptable range value, performance indicators such as accuracy, sensitivity, specificity, etc. and target values of measured values are calculated through repeated experiments, inter-lab comparisons, and inter-experimental comparisons. have to do it In addition, quality control molecular data should show little change in measured values during analysis.

The internal quality control of the AptaSSN multiple test of the present invention quantifies the size of imprecision among analytical errors occurring in test execution as an objective indicator using repeated measurements and statistical analysis of quality control molecules, and finally, the analysis results It aims to maximize precision. In particular, special consideration should be given to the use of NGS.

For the internal quality control of the present invention, it is very important to first select an appropriate quality control molecule. In the clinical test quality control guidelines and the US CLIA guidelines C24-A3 statistical quality control recommendations for quantitative measurement, an appropriate quality control molecule is selected in consideration of the molecular matrix, stability, concentration setting, analysis range, and cost. can be selected.

In the case of screening tests, the occurrence and type of abnormal results can be detected only when high or low concentrations of internal quality control molecules must be included so that abnormal results can be detected even in most samples within the normal range. In the present invention, it is possible to select an internal quality control molecule containing at least two concentrations or more, a clinically important concentration.

In the internal quality control molecule selection, a molecule capable of securing precision is selected for selection of an internal quality control molecule with little change in the measured value produced by the molecular data production module 10 . In the present invention, a Coefficient of Variation method is used to secure AptaSSN with high precision.

Specifically, a learning database is constructed using an analysis sample as an experimental sample and a control sample based on the biological meaning. For the sample, the molecular data produced by the molecular data production module 100 is converted into an appropriate data format to create a learning database. can be built

Statistical values are calculated based on the constructed learning database. Molecular data for all samples constituting the constructed learning database, the mean, standard deviation, and coefficient of variation (Coefficient of Variation; Equation 1) for each measurement value of the features constituting the profile are calculated.

Coefficient of variation = standard deviation / mean - (Equation 1)

The coefficients of variation are sorted in ascending order, and the top 1% of the sorted coefficients of variation (12 out of 1149 aptamers) are selected. Sort the selected 12 molecules in ascending order based on the mean. Divide the average-ordered molecules into three intervals. (4 aptamers for each section). Molecules selected for each section can be used for quality control.

You should establish your own tolerance for quality control molecules. The acceptable range of the internal quality control molecule can be used by setting its own acceptable range by obtaining the actual mean and standard deviation of the results repeatedly measured under the same optimal conditions for at least 20 days.

The purpose of internal quality control is to detect analytical errors that may occur during inspection in advance, and there are random errors and systematic errors.

Interpretation of results for internal quality control can be used by mixing methods that can reduce false positives and false negatives whenever possible by using appropriate rules. The methods mainly used for internal quality control analysis are the Levey-Jennings Control Chart (Levi Jennings control chart) and Westgard multirules technique (Westgard multirules) as the most representative methods.

There are several principles of quality control used in Westgard multirules. Among these, the most important point is to apply the principle of finding systematic errors and accidental errors.

In order to maintain the reliability of the test results, a single principle may be used if the error detection rate required and a low false rejection rate can be maintained. Since the 1 _2s principle has a high false rejection rate and is difficult to apply in general, _{methods such as 1 2.5s} , 1 _3s , and 1 _3.5s with relatively low false rejection rates can be used. If a medically important error detection rate can be maintained above 90%, a single principle can be used, which can be applied to highly automated and highly precise instruments. However, it should be avoided to use the _{1 2s} principle as a method to minimize the discard rate or reduce costs. If only one principle is to be used, it would be appropriate to set a target with an error detection rate of more than 90% and a false rejection rate of less than 5%, and then apply the appropriate principle.

The present invention suggests principles that can detect systematic errors and random errors in performing statistical quality control. It is appropriate to selectively select and apply principles suitable for each inspection.

The applied principle can be different depending on the number of statistical quality control molecules used. However, as the number of statistical quality control molecules used in clinical chemistry-related tests is generally used in two types with normal and abnormal concentrations, an appropriate principle can be applied with reference to this.

If the test is repeated infinitely, the measured values form a normal distribution with the mean and standard deviation.

The West Guard multiple rule is designed to complement the Leviennings chart so that the false rejection rate is low and the error detection rate is high. This method accumulates the measurement values of quality control molecules and evaluates them semi-quantitatively according to various control rules.

When the number of control molecules used is 2, 4, or 8, the usual rule of 1 _2s /2 _2s /R _4s /4 _s /10 _x can be used, but when the number of control molecules is 3 or 6, it is 3 You can use the same principle as 1 _3s /2 of 3 _2s /R _4s /3 _1s /12 _x , which applies multiples of . These principles are applied not only under the same molecular concentration, but also between molecules and between the number of tests, so that errors can be found once again.

Guidelines for reference include CLSI EP9-A3: Test method comparison and bias estimation using patient samples, CLSI EP26-A: User evaluation based on variation between reagent control numbers, CLSI EP31 Verification of equivalence of patient outcomes within the same medical institution (CLSI C54 -A is changed) and so on.

Internal quality control using quality control molecules must be performed before analysis of samples, and after replacement of all reagents whose test conditions may be changed, major maintenance of equipment or replacement of major parts that may affect test results should be done afterwards.

The statistical internal quality control result management method of molecular data includes a data input step of receiving internal quality control data, a data analysis step of analyzing internal quality control data according to a preset evaluation standard, outputting the data analysis result on a display, and a user An interface providing step that provides an interface to receive input of unusual or action data according to the data analysis result from the data storage step, a data storage step of storing internal quality control data, data analysis results and action data data in a database, and user input Including any one or more of the mean, standard deviation, and coefficient of variation of the internal quality control data in any one or more of the molecular data production module 100 or the internal quality control data, data analysis result, and action data for molecular data in the inquiry period It includes an inquiry output step of outputting the result.

The internal quality control data may be directly input by the user or transmitted through a network from a data storage means or inspection equipment. Quality control data may include overall contents such as equipment, reagents, instruments, and temperature for molecular data production.

The input internal quality control data is analyzed according to the preset evaluation criteria. Here, the preset evaluation criteria for the internal quality control data may include the interpretation of the quality control data by Westgard and a rule related thereto. The preset evaluation criteria may allow the standard value tolerance of the quality control molecule to be changed through the interface providing step.

The data analysis result is output to the user through the display, and the user reviews the data analysis result together with the quality control data and inputs the contents of the specific matters or a series of actions to be taken by the user. Such an input interface may implement a user interface so that it can be output to the display in the form of a pop-up when the user clicks the quality control data displayed according to the preset evaluation criteria.

Quality control data, data analysis results, and action data are stored in the database and can be output in various classification formats and formats according to the user's request.

Inquiry output step) is any one of the mean, standard deviation, and coefficient of variation of the internal quality control data in any one or more of the internal quality control data, data analysis result, and action data for the medical device or control material for the inquiry period entered by the user. Include more than one output. By comparing the actual measured values and their statistics for the inquiry period with the preset reference value allowable range according to the user's result inquiry input, it is possible to easily check the error of the inspection result.

In the inquiry output stage, the lab target data of the own laboratory corresponding to the mean, standard deviation and coefficient of variation of the internal quality control data, the same type of data from other laboratories, internal quality control data measured in the previous month, or data provided by the manufacturer Each mean, standard deviation, and coefficient of variation for any one or more of them can be output together. Here, the homogeneous data may include internal quality control data for the same equipment, the same test method, or the same reagent.

After setting the inquiry period and selecting the equipment name and internal quality control molecule constituting the molecular data production module, the data registered by date is output. If any one or more of the allowable ranges of internal QC molecules is selected here, a result that can compare the measured data with the selected internal QC molecules is output. The tolerance range for internal quality control molecules includes internal quality control data of internal laboratory (Lab Target), homogeneous data of other laboratories (Peer Group), internal quality control data measured in the previous month (comparison with previous month), or internal accuracy provided by the manufacturer. By making it possible to include management data (manufacturer target), it is possible to selectively compare with them. Homogeneous data includes internal quality control data for the same equipment, the same test method, or the same reagent. By making it possible to compare with other data, it is possible to easily check whether the actually measured data is in error.

The actual measured data in the current month can be calculated as the statistical values of the measured data, such as the mean (Mean), standard deviation (SD) and coefficient of variation (CV), and the output result is the internal quality control data ( Lab Target), internal quality control data of other laboratories (Peer Group), internal quality control data measured in the previous month (monthly comparison), or internal quality control data provided by the manufacturer (manufacturer target) By outputting the coefficients together, errors in the actual measured data for the current month can be easily checked through a statistical method, and change trends within the inquiry period can also be checked. In the interface providing step, by providing an interface through which an input can be made of whether or not internal quality control data exceeding a preset evaluation standard among internal quality control data is provided, this can be reflected in statistics.

The inquiry output step outputs internal quality control data according to any one or more of a plurality of medical inspection items, medical equipment, and quality control substances according to the inquiry period in the form of graphs or tables, and the average of internal quality control data for the inquiry period; Any one or more of standard deviation and coefficient of variation can be included in the output.

After selecting an inquiry period, one or more of the feature (target molecule) items constituting a plurality of molecular data are selected, and analysis equipment and quality control molecules are selected. It is shown that the result value for the feature (target molecule) item constituting the molecular data is output.

In particular, you can choose to output the statistics (Mean, SD, CV) for these data together, and through this, you can check the monthly trend through graphs and statistics at the same time, so that the cause of the problem is quality control substances, reagents, medical equipment, and inspectors. It is easy to check whether there is a problem and come up with a countermeasure.

On the other hand, the most useful statistic among statistics is CV, and if the number shows a large change when looking at the monthly trend of this result, you can check the problem by checking the reproducibility of the test result for the month. By outputting the monthly cumulative result graph for CV, it is possible to more easily check the monthly trend. The quality control method of molecular data may be composed of a computer-readable recording medium.

The computer-readable recording medium includes any type of recording medium in which data readable by a computer system is stored. Examples of the computer-readable recording medium include RAM, ROM, CD-ROM, magnetic tape, optical data storage device, floppy disk, and the like, and may be transmitted through the Internet or implemented in the form of a carrier wave.

In addition, the computer-readable recording medium is distributed in a computer system connected through a network, so that the computer-readable code can be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the recording method can be easily inferred by programmers in the technical field to which the present invention pertains.

In addition, the quality control method of molecular data may be implemented as a quality control device of medical examination data. That is, the quality control method of molecular data or the method of quality control of molecular data is configured in a computer-readable recording medium and can be implemented in hardware.

3. Analysis of the biological meaning of molecular data

The internal quality control and biological meaning colon support system includes quality control molecules to secure the quality of molecular data produced by omics technology, and classification molecules to classify specific samples by creating a predictive model based on a learning database composed of molecular data. is needed

21 is a diagram showing the configuration of the biological meaning determination support module 300 .

Referring to FIG. 21 , the biological meaning determination support module 300 includes an input module 302 , an analysis module 303 , and a visualization module in which NGS experimental data is managed by an operating system software 301 in an input device that is an NGS sequencer. Through 304 and the output module 305 , the analysis result is output to the output device 306 and stored in the memory 308 , and overall management is performed by the controller 307 .

22 is a diagram for learning and evaluation performed by the biological meaning determination support module 300 .

Referring to FIG. 22 , the biological meaning determination support module 300 constituting the biological meaning determination support system 1 generates and stores a binding profile that is molecular data in a learning sample, builds a learning database, normalizes, extracts classification features, It consists of six main steps: learning, and classifying the valid molecular data in the final evaluation sample.

The biological meaning determination support module 300 may determine the similarity of a specific sample to the biological meaning-based learning database as a classifier based on a result of analysis of a binding profile that is molecular data of the sample in the biological meaning determination process. The biological meaning of a specific sample (or user) is determined by referring to the binding profile constituting the biological meaning-based learning database. That is, a large amount of biometric information can be produced with an input device implementing the AptaSSN or molecular analysis technology presented in the present invention, and users can be classified using the biological meaning determination support system 1 .

After performing multivariate analysis of the AptaSSN information constituting the binding profile, the characteristics and binding SSNs that can represent the properties of test and control samples well in the learning database are statistically selected, and then the selected AptaSSN patterns are hierarchically clustered ( The degree of contribution to the biological analysis can be used for analysis by methods such as Hierarchical Clustering (HC) and Artificial Neural Network (ANN). In the present invention, a program for performing this AptaCDSS was developed.

The program AptaCDSS may perform AptaSSN, feature selection and evaluation. AptaSSN, which is secured through this process and contributes to the analysis of biological meaning, was selected. The value can be used to classify a specific sample.

1) Molecular data generation and storage

NGS experimental data (read data) produced by the input device, which is an NGS sequencer, is initially accessible only to a specific research group that performed the actual experiment. The comparison module 106 or 205 converts the NGS experimental data, which is the fastaq format produced by the NGS equipment, into the standard format developed in the present invention, converts them into log values to generate a binding profile, and builds and stores these data storage. .

In the present invention, NGS experimental data is collected by individual, platform, and disease by collecting reference sequences (combined SSN data) from existing research results.

In the comparison module, preprocessing means that the NGS experimental data is converted and the raw data collected and stored in the storage is processed before being used as an input. This step is necessary for most data because the input data is noisy, incomplete, or inconsistent. Preprocessing includes data-related operations such as cleaning, transformation, and reduction.

* The NGS experimental data for the binding profile for the molecular population constituting the sample is converted and stored in the storage, and then processed as follows. In the first step, the read data converted from the fastaq format is mapped to the nucleotide sequences of the constant region and the variable region of the structural formula <Structural Formula 1> of the single-stranded nucleic acid constituting the reference database, and then mapped to each of the constant and variable regions. The characteristics of the converted lead data are analyzed in the following way. First, the amount of read data mapped to each variable region for each binding SSN constituting the reference database is measured, and the pattern and amount of read data having a fixed region among the read data mapped to the next variable region are analyzed to determine the binding SSN pattern and amount. to estimate That is, it is possible to determine the type of the bound SSN by pattern analysis of read data having a fixed region and the variable region, and the structure of the bound SSN determined here is the bound SSN structure. The converted read data is classified into binding SSNs constituting a reference database, the amount of each is determined, and a binding binding SSN profile is generated in a sample composed of a specific molecular group.

2) Construction of a learning database

The biological meaning-based learning database construction of the input module 302 builds a learning database according to the biological meaning of a binding profile produced from a sample consisting of a sample group to be analyzed and a molecular group.

In the present invention, as a result of sequencing the binding SSN library for each sample, the binding profile may be classified and divided into the learning database according to the biological meaning of the analyzed sample group.

*The binding profile is a nucleotide sequence group as a result of sequencing of the binding SSN separated from the molecule constituting the sample and the binding SSN complex group, and is expressed by the nucleotide sequence and frequency of occurrence by analyzing it.

Preferably, a statistically significant sample population of samples with biological significance is collected and a learning database is constructed based on the biological meaning, and then a statistically constructed database is compared between the statistically constructed databases, or a significant binding SSN capable of representing each of the constituent databases. may decide.

3) normalization

In the input module 302, measurement and normalization of the existence pattern of the sample constituting the learning database is performed by simultaneously mapping the converted read data to all the combined SSN structures constituting the reference database, and then mapping the variable region region to each of the following combined SSNs. The number of bound SSNs for each star is measured and compared to estimate the expression level of each bound SSN. However, when comparing the number of bound SSNs mapped to variable regions, the number of bound SSNs mapped in the same sample may be normalized. The results can be built as a learning database.

The lead analysis tool, which is a visual analysis for the combined SSN data analysis in the learning database, is largely composed of two modules. The first module is responsible for extracting (calling) and storing coverage data and binding SSN analysis information by aligning the base sequence of FASTAQ (filtered raw data) data generated in NGS to the <Structural Formula 1> structure of single-stranded nucleic acid. . The second module is responsible for providing the analysis result to the user by comparing the analysis data extracted by the first module for each combined SSN or variable region constituting the reference database.

Finally, in the third module, the types and amounts of all expressed binding SSNs are reported by complementing the results of the first and second modules.

The present invention relates to the frequency of appearance and molecular weight of the reference molecule obtained by analyzing a test group in which a reference molecule is added to a sample consisting of a molecular group using a reference molecule and a binding SSN constituting a learning database. Molecules corresponding to known bound SSNs can be quantified, the frequency of occurrence of bound SSNs analyzed in one sample can be corrected, and can be used for normalization between samples constituting the analysis sample population.

Before applying the classification algorithm regardless of the use of reference molecules, it is essential to preprocess the raw data in the same sample to normalize it. This is called feature scaling, and in R, it can be applied as a function called scale. Feature Scaling is as follows.

a) Rescaling converts the range of measured values to 0 to 1 or -1 to +1 (Equation 2).

- (식 2)

- (Equation 2)

b) Standardization Standardization is to divide the error between the data value and the mean by the standard deviation. This process makes it easy to compare and analyze multidimensional values (Equation 3).

- (식 3)

- (Equation 3)

In classifying the data of many samples constituting the learning database, normalization between samples, that is, matching the range of data or making the distribution similar, is an essential task. There is quantile regularization.

Normalization using mean

Normalization in which the average value of the data constituting the learning database is “0” generally assumes that the distribution of the original data values is a 'normal distribution', but it can also be applied when it is not. The basic idea is to assign a value that corresponds to the mean as “0” and a larger value the further away from the mean. The next step is to divide by variance. Since the difference of 1 when the distribution of values is small and the difference of 1 when the distribution of values are very large are clearly different cases, the effect of spreading the original distribution can be offset by dividing by the variance. The operation of subtracting the mean from the data and dividing it by the standard deviation is called z-transformation, and z-transformation does not assume a normal distribution of the data. The value normalized to the average value can be expressed as follows (Equations 4, 5 and 6).

- (식 4)

- (Equation 4)

- (식 5)

- (Equation 5)

- (식 6)

- (Equation 6)

Normalization with Median

Normalization using the median is almost similar to the procedure of normalization using the mean, but when subtracting values from data (ie, in Equation (1) above), the median is used instead of the average of the data. Normalization using the median is a suitable method when there are too many values (outliers) in the data. Unlike the mean, the median of data is not affected by outliers. The reason for using the median rather than the mean is that if there are values that are too bouncing in the data, i.e., outliers, you can get an average value that is overestimated by the outliers. In particular, it often occurs when the value of data is divided by a small number. If the denominator is too small, the result of division increases rapidly, and an average value larger than the original average can be obtained, and the normalized value becomes small. The median of data means the value of the data located at [N/2] when N pieces of data are sorted in ascending order. If N is odd, the k+1th largest value becomes the median for a natural number k expressed as N = 2k + 1, and if N is even, there are several options for determining the median, mainly N = 2k. For k that satisfies , the average of the k-1, k+1-th largest number is used as the median value. After obtaining the median value in this way, in Equation 1 above, if the median value is subtracted instead of E(d), the median value is used for normalization.

Quantile regularization

Quantile regularization is When p quantile data is sorted in ascending order, it refers to data corresponding to p*data number. A useful use of quantile is a QQ plot that judges whether a given distribution is similar to an assumed distribution. A value corresponding to the commonly used 'top few %' has a similar meaning to quantile.

QQ regularization is an operation that makes the distribution of multiple data sets similar. When there are two data sets D1 and D2, and when D1 and D2 are sorted in ascending order, the method to ensure that all data in p% have the same value quantile normalization. When there are N data sets of D1, ..., DN, the operation of correcting the value of the data located in p% of each data set is quantile normalization. If there is a possibility that the overall position of the distribution is changed due to different noises in each data set, it is carried out in the following order.

i) sort each of the N data in ascending order; ii) correct each i-th data of the sorted data with a unit diagonal line on the N dimension; iii) Place the converted data back to its original location.

Step ii) of correcting the data unit diagonally means correcting the data with a straight line that is (1,1) if you think about the second circle, and a straight line that is (1, 1, ..., 1) if you think about it in the N dimension. , and the average value is mainly used for the value to be corrected. In this way, when a new value is assigned, the value is moved from the original data to the original value.

In the sorted data, data with the same sorted rank have the same value, and again only the data are selected separately. The new value is set as the 'average value' of the data corresponding to the sorted rank. The new values set in this way replace the original values. Again, the previous value of the transformed value places the values now transformed into the rank of the original data. This means that the value of the data before the change is replaced with the value of the data after the change. Therefore, it is the final data as a result. You can easily see the effect of quantile normalization by drawing a Q-Q plot that arranges the data in ascending order and plots the data corresponding to the same rank on the coordinates.

4) Feature selection

The analysis module 303 finds important variables in one or more molecules in the molecular data constituting the learning database and many features of the binding profile, and reduces the dimension or finds the main molecules through characteristic transformation (Feature Selection). ) or a feature extraction procedure.

The function of feature selection is to measure each domain with relative information so that the input can be expressed as much as possible. Classification requires extracting most of the relevant features from the raw data. In the present invention, three feature selection algorithms commonly used as a classification problem for input data with many features and a related feature selection method include ANOVA, PCA, and Random Forest.

In the analysis technique using AptaSSN according to the present invention, if the biological meaning of a biomolecule population can be analyzed with the same or similar level of precision, it is preferable in terms of analysis efficiency to reduce the number of AptaSSN to be analyzed by the feature selection method. can do.

The number of AptaSSNs of the present invention by analyzing the contribution of each AptaSSN to the analysis of the biological meaning of the molecule and selecting AptaSSNs by the feature selection method based on the analyzed contribution within the range capable of analyzing the biological meaning of the molecule It may further include the step of reducing

In order to synthesize the AptaSSN population selected by the feature selection method and analyze the biological significance of molecules included in the sample by a method including the AptaSSN population analysis, molecular data of the AptaSSN and molecules and a binding profile will be generated. It is possible to provide a possible AptaSSN group.

Preferably, from the accumulated analysis results obtained through the multi-molecular analysis method using the AptaSSN, specific AptaSSNs contributing to the analysis of the biological meaning of the multi-molecule are determined by a feature selection method. An analysis method is provided.

In addition, a profile of samples constituting a biologically meaningful group is generated using the selected differentially coupled AptaSSN group, a database is built, and the characteristics representing each group are determined by the feature selection method, so that a specific sample is associated with a certain group. For the purpose of discriminating whether or not the sample is similar, the profile generated from the AptaSSN population bound to the molecular population constituting the sample may be used.

In the AptaSSNaCDSS, a characteristic group capable of distinguishing between positive and negative, AptaSSN group, was selected by the T-test technique. The significance probability (p-value) is obtained from the T-test result, and the null hypothesis is generally rejected if the significance level is less than or equal to 5% to 1%.

The T-test technique in AptaSSNaCDSS is as follows. The significance level criterion is a preset value and is used as a reference value when determining the observed significance level. Typically values of 5% to 1% are used.

p-값 ≤ a : 유의수준 a 에서 H0 기각 (통계적으로 유의하다.)

p-value ≤ a : Reject H0 at significance level a (statistically significant)

p-값 > a : 유의수준 a 에서 H0 기각 못함 (통계적으로 유의하지 않다.)

p-value > a : Failed to reject H0 at significance level a (not statistically significant)

The expression of the T-test is as follows (Equations 7, 8 and 9).

T-value = (Difference between group means)/(variability of groups)

- (식 7)

- (Equation 7)

: 샘플 T의 평균,

: 샘플 C의 평균,

: 표준 에러(Standard error) ※

: mean of sample T,

: mean of sample C,

: Standard error

Formula for the standard error of the difference between the means.

- (식 8)

- (Equation 8)

: T 샘플의 값,

: C 샘플의 값,

: T 샘플의 크기,

: C 샘플의 크기 ※

: value of T sample,

: the value of the C sample,

: T sample size,

: C sample size

in other words,

- (식 9)

- (Equation 9)

In AptaSSNaCDSS, the T-test technique used the T-test technique to check the selected characteristics that can distinguish positive and negative, the calculated P-value and statistical values for AptaSSN, and graph the values for the selected characteristics (disease group/non-disease group) can be checked.

The selected feature evaluation was performed primarily with the HC (H technique), and is an algorithm that performs clustering by integrating individual entities with similar entities or groups sequentially and hierarchically. Dendrogram (a tree-shaped structure indicating the order in which entities are combined) Dendrogram), unlike K-means clustering, learning can be performed without pre-determining the number of clusters.After creating a dendrogram, by truncating the tree at an appropriate level, the entire data is divided into several clusters. HC can be calculated in various ways for distance or similarity between all entities.

There is a centroid method (Equation 10) in which the centroid of two clusters is defined, and then the distance between the two centroids is defined as the distance between the clusters.

d(u,v)=|cu*?*cv|2 - (Equation 10)

where cu and cv are the centroids of the two clusters u and v, respectively.

For all combinations of all data i in cluster u and all data j in cluster v, measure the distance to find the minimum value. It is called the Nearest Point method (Equation 11).

d(u,v)=min(dist(u[i],v[j])) - (Equation 11)

After measuring the distance for all combinations of all data i in cluster u and all data j in cluster v, the largest value is obtained. It is called the Farthest Point Algorithm or Voor Hees Algorithm (Equation 12).

d(u,v)=max(dist(u[i],v[j])) - (Equation 12)

For all combinations of all data i in cluster u and all data j in cluster v, there is an average method that measures the distance and calculates the average (Equation 13). |u| and |v| represent the number of elements in the two clusters, respectively.

d(u,v)=∑ijd(u[i],v[j])|u||v| - (Equation 13)

The selected feature evaluation is primarily performed using the HC technique, and the correlation between the disease group and the non-disease group can be checked in the dendrogram. In addition, when the distance is checked, the correlation between each feature is checked to evaluate the selected feature. Additionally, each binding profile of the database may know which group the desired sample belongs to by using the clustered result. You can write a numerical value by checking the input directly, or you can use the clustering function to determine which group is similar to the specific sample that is rotated from among the clustered database by rotating a file (NAP extension) corresponding to a specific sample.

5) Learning

The machine learning of the analysis module 303 is one of many parts of artificial intelligence, which gives the ability to learn without special programming and constructs a model with an allowable amount of data regardless of the instructions for solving the problem. can yield valuable predictions of Consequently, machine learning can learn a learning database by adapting it using a variety of methods and present a variety of models that improve outcomes from experience. Machine learning includes supervised learning, unsupervised learning, and semi-supervised learning.

Supervised learning is where the input and output variables have already been determined and the learning algorithm learns how to map inputs to outputs. That is, a supervised learning algorithm learns by predicting a given input x and a desired output y and modifying the prediction. Currently, Linear Regression (LR), K-nearest Neighbors (KNN), support vector machine, decision trees, Naive Bayes classifier linear regression analysis (LR), K-Nearest Neighbors (KNN), artificial neural networks, artificial intelligence networks, support vectors There are several supervised learning algorithms, such as machines, decision trees, and Naive Bayes classifiers.

In unsupervised learning, the data constituting the learning database does not include the information of the corresponding output variable. The main goal of unsupervised learning is to increase our understanding of the input data by finding meaningful patterns in distributions or modeling underlying structures. Unlike supervised learning, there is no *?**?* "right answer" or supervisor to control the learning process, so it is called unsupervised. Algorithms can make their own connections to interpret data and discover new structures. The most common use of unsupervised learning is cluster analysis.

Supervised learning is placed between supervised and unsupervised learning and involves function estimation on labeled and unlabeled data. The main goal of unsupervised learning is to make predictions on unlabeled data using unsupervised learning and feed these data to supervised learning to predict new data.

After performing the specific selection process, the learning database is learned by the supervised learning with the selected characteristics to classify the molecular data and the coupling profile group as a construction database, or the analysis sample group is classified by learning by unsupervised learning, and the learning is carried out. It is possible to manufacture a classifier that predicts the degree to which a specific binding profile can be classified into a specific group by storing it.

Learning is a procedure of classifying a learning target sample group for each class using the selected characteristics, after performing feature selection with the built-up learning database, and performing learning to generate a predictive model. It may be a procedure to evaluate the AptaSSN selected as a method, and the evaluation result may evaluate the accuracy by 10 fold cross validation.

In the present invention, the classifier uses an artificial neural network, which has been applied in various fields such as pattern recognition, function approximation, and classification technique because of the characteristic of determining behavior according to input and output. The artificial neural network is composed of several layers, nodes, and connection weights between neural networks. The neural network interlayer connection method used in the present invention is a feed-forward method. The output value was calculated using the neural network connection weight and activation function for each node according to the input pattern. When a task is processed through the generated combined information and the estimated value and the actual result value are different, the process of reducing the error is repeatedly found while comparing the calculated value with the actual result value.

A large amount of combined profile data generated by the NGS sequencer and NAPConverter can be evaluated more accurately by learning the feature group and AptaSSN group selected by the feature selection process using a program based on a case-based machine learning inference system.

The learning of the selected feature group is an AptaSSNaCDSS program implemented with an artificial neural network algorithm, and the structure of the implemented artificial neural network is shown in <Structural Figure 1> below.

<Structural diagram 1>

For the aptamer characteristics of the input layer of the artificial neural network, the value of the aptamer selected through T-test is input. The hidden layer of the artificial neural network uses 10 nodes. The output layer of the artificial neural network uses two nodes. The two nodes represent a positive value and a positive value of ALS, respectively.

Forward propagation implemented in AptaSSNaCDSS is Equation 14 and is as follows.

- (식 14)

- (Equation 14)

The artificial neural network is expressed by the following function.

- (식 15)

- (Equation 15)

- (식 16)

- (Equation 16)

Equation 15 represents the exaggeration going from the input layer to the hidden layer, and it is repeatedly calculated as many as the number of nodes in each hidden layer. Here, sigmoid was used as the activation function of the hidden layer. Equation 16 shows the process from the hidden layer to the output layer, and it is repeatedly calculated as many as the number of nodes in each output layer. Here, sigmoid was used as the activation function of the output layer.

Learning of artificial neural networks must go through a back propagation process, and in this case, a loss function (back propagation rule) is used. The loss function is expressed as (Equation 17).

- (식 17)

- (Equation 17)

Using Equation (15) above, we adjust the weights of the network to make the actual output close to the target output, minimizing the error of each output neuron and the network as a whole. And the artificial neural network learning is as follows (Equation 18).

- (식 18)

- (Equation 18)

AptaCDSS of the present invention is applied with the artificial neural network algorithm, and can be largely divided into two steps. It was carried out by dividing the artificial neural network learning and evaluation stages.

Loading a DB file that calls a learning database (DB) for the disease group and non-disease group composed of the coupling profile is performed. A log conversion operation is performed so that the feature group of the NAP file changes the value of the frequency of occurrence of AptaSSN references to be close to a normal distribution. Normalization is performed to make the range and distribution of the data of the AptaSSN reference, which is a characteristic of the NAP file, similar. Feature selection is performed to select AptaSSN group and feature group that can distinguish DB well from NAP file. The feature group and AptaSSN group selected from the built learning database are learned using an artificial neural network (MLP) algorithm, and 10-fold cross validation (artificial neural network learning evaluation) is performed to check the performance of the configured DB. MOD file saving is performed, which saves the learning result of the artificial neural network of the built learning database as a file in the MOD format (MOD: refers to the file in which the learning result is saved).

A value produced by the learning process of the learning db constructed based on the biological meaning of a specific sample as the developed AptaCDSS program was referred to as the degree of similarity. Criteria and cutoff (threshold) values for classifying samples into the constructed specific DB with similarity of specific samples are ROC curves according to the MOD file generated in the 10-fold cross validation process that evaluates the learning results of the AptaCDSS program. was decided.

10-fold cross validation, which is the evaluation method of the learning, divides the entire training set into 10 subsets, learns by adding 9 subsets, and evaluates one subset remaining as a learning product (MOD file). Conduct. The 10 MOD files obtained by performing this process 10 times can be used to evaluate the ability to distinguish and predict the learning set according to the selected feature. In the present invention, the ROC (receiver operating characteristic) curve is created using the sensitivity and specificity obtained by evaluating one subset of the remaining 10% with the MOD file obtained by learning 90% of the samples constituting the training set Thresholds were determined with a certain degree of similarity. In total, 10 thresholds for 10 MOD files can be obtained, and the MOD file with the best clinical sensitivity and specificity is the AptaDx program.

The Receiver Operating Characteristic (ROC) curve is a graph showing the diagnostic capability of a binary classifier system as its identification threshold changes. In a binary classifier, there are four possible outcomes as a two-class prediction problem (binary classification) where the outcome is classified as positive (p) and negative (n). When the actual value is p and the prediction result is p, it is called TP (True Positive), and when the actual value is p, when the prediction result is n, it is called FN (False Negative). However, when the actual value is n and the prediction result is p, FP (false positive) occurs, and conversely, when both the predicted result and the actual value are n, TN (true negative) occurs.

The ROC curve is built by plotting the true true positive rate (TPR) versus the false positive rate (FPR) at different threshold settings. TPR is the sensitivity or detection probability in machine learning, and the false positive rate (FPR) can be calculated as [1- specificity] as the false alarm probability. In general, if the probability distributions for both sensitivity and specificity are known, the ROC curve can be constructed by plotting the area under the probability distribution from the cumulative distribution function of sensitivity on the y-axis to the threshold versus the cumulative distribution function of [1-specificity] on the x-axis. can create

ROC is also called relative operating characteristic curve because it compares two operating characteristics (TPR and FPR) when the reference is changed. A classification model (classifier or diagnosis) is a mapping between specific classes/groups, and the classifier or diagnosis result may be an actual value (continuous output), in which case the classifier boundary between classes is determined by a threshold value. Or it could be an individual class label that represents one of the classes.

The best possible prediction method generates a point at the upper-left corner or coordinates (0,1) of the ROC space that exhibits 100% sensitivity and 100% specificity. The point (0,1) is also called perfect classification. Random guess (regardless of positive and negative base speed) gives points along a diagonal (non-discriminatory line) from the bottom left to the top right corner. An intuitive example of random guessing is flipping a coin. As the sample size increases, the ROC point of the random classifier is directed diagonally. For balanced coins, it will be points (0.5, 0.5).

The diagonal divides the ROC space. Dots on the diagonal indicate good classification results (better than random). Dots below the line indicate bad results (worse than random). The output of a consistently bad predictor can simply be inverted to get a good predictor.

So, the ROC curve is a curve connecting (0,0) to (1,1) with both X and Y in the range [0,1]. At this time, the closer the area of the ROC curve to 1 (ie, the closer to the upper left vertex), the better the performance. And the area, that is, AUC has a range of 0.5 to 1 (0.5 is no performance at all, 1 is the best performance). AUC (the Area Under a ROC Curve) is a value obtained by obtaining the area under the ROC curve, and the closer this value is to 1, the better the performance. If you easily set the prediction criterion of 1, the sensitivity increases. Instead, all cases are set to 1, thus lowering specificity. Therefore, both of these values must be close to 1 to be meaningful. So, when drawing the ROC curve, 1-specificity is placed on the X-axis and sensitivity is placed on the Y-axis. Then, when y = 1 when x = 0, it is the best performance, and it indicates how quickly the sensitivity increases compared to the rate at which the specificity decreases as it goes down to the right. If AUC = 0.5, sensitivity increases just as the specificity decreases, that is, there is no point at which sensitivity and specificity can be simultaneously increased by any criterion. If AUC is 0.5, the ratio of sensitivity 0 when specificity is 1 and sensitivity 1 when specificity 0 is exactly a trade-off relationship, so the sum of the two values is always 1. Therefore, the AUC value can show the correlation between overall sensitivity and specificity, so it is a very convenient performance measure.

For example, blood protein levels in diseased and healthy people can normally be defined as 2 g/dL and 1 g/dL, respectively. A medical test can measure the level of a specific protein in a blood sample and classify it above a certain threshold as indicative of a disease. The experimenter can adjust the threshold, which in turn changes the FPR. Increasing the threshold reduces the FNR and is consistent with the left movement in the curve. The actual shape of the curve is determined by how much the two distributions overlap.

The sample group to be analyzed may be divided into databases based on biological meaning, and the minimum size of the composition may be 30 or more.

The present invention selects the AptaSSN group and the molecular group by using a database based on the biological meaning of the sample group and the feature selection method for a binding profile produced based on each aspect of a corresponding molecule in a biological sample composed of unknown or known molecules And it can be usefully used for biomarker development by evaluating the AptaSSN group and molecular group selected as a learning process using the machine learning language.

6) Classification

The classification of the analysis module 303 is evaluated with features selected with AptaDx, an optimized predictive model obtained by executing the classifier that performs learning to generate a predictive model after performing feature selection on the constructed learning database. As a procedure for classifying a target specific sample (or user) for each class, it may be a procedure for predicting the user's class with the AptaSSN selected by the feature selection method.

The biometric information of all the collected samples is to build a learning database according to the biological meaning of the sample, perform the feature selection, and evaluate the AptaSSN group that is related to the biological meaning by supervised learning using the selected feature using the built learning database. can

Also, it is possible to select a feature from the biometric information of the collected sample and classify the sample group by unsupervised learning using the selected feature.

The biological meaning determination may be based on the molecular binding profile analysis result of the sample, and the degree of similarity of a specific sample to the biological meaning-based learning database may be determined as a classifier. The biological meaning of a specific sample (or user) is determined by referring to the binding profile constituting the biological meaning-based learning database. That is, by using AptaSSN or molecular analysis technology presented in the present invention, a large amount of biometric information can be produced and users can be classified using the classifier.

More specifically, the present invention can be applied to a sample in a pathological condition and a healthy state to select an AptaSSN group and a molecular group that specifically binds to each sample and contributes to the analysis of biological significance. The learning and classification of AptaSSN groups and molecular groups that contribute to the analysis of biological meanings provide a basis for analyzing two or more physiological states to be compared. Therefore, according to the present invention, it has the potential to be usefully used in the discovery of new therapeutic targets or diagnostic tools.

7) Internal quality control and biological meaning determination support system

The internal quality control and biological meaning colon support system includes quality control molecules to secure the quality of molecular data produced by omics technology, and classification molecules to classify specific samples by creating a predictive model based on a learning database composed of molecular data. is needed

It is an object of the present invention to classify using AptaSSN as an AptaSSN group that contributes to the analysis of biological meaning after establishing a database of combined molecular groups and AptaSSN groups constituting each sample of a learning database classified according to biological meaning By applying the classification process of a specific sample, it is possible to provide a classification method of AptaSSN that improves analysis efficiency for a target test sample and a control sample, and contributes to the analysis of biological significance.

The internal quality control and biological significance colon support system (1) is described in <Ⅱ. Multiple analysis of AptaSSN> Referring to the section , (a) a unit for determining the nucleotide sequence of the AptaSSN group eluted from the complex formed by mixing the molecular group and the AptaSSN group in the sample with the NGS sequencer (FIG. 12a); and (b) a molecular data production module 100 including a unit (b of FIG. 12) for converting the produced NGS nucleotide sequence into molecular data and a binding profile; (c) above <V. 2> with reference to the contents of the item, the internal quality control module 200 for internal quality control of the molecular data in the evaluation target sample with the internal quality control molecular data selected from the built-up learning database; and (d) the <V. 3> With reference to the contents of paragraph 3>, biological comprising six main steps of collection and storage of the produced molecular data, construction of a learning database, pre-processing, classification feature extraction, learning and classification to final internal quality-controlled valid samples. It may be configured to include; a semantic determination support module 300 .

In the present invention, the classifier developed by learning the molecular data of the sample constituting the biological meaning-based learning database produced by using the AptaSSN library in the present invention selects and evaluates the AptaSSN group that classifies the patient who provided the sample or sample with excellent ability and the corresponding molecular groups can be isolated.

An object of the present invention is to provide a method and equipment for predicting various biological information related to cells, tissues or diseases from the data, which can produce a large amount of data having high-dimensional characteristics by simultaneously analyzing the molecular population constituting the sample.

The present invention can provide an internal quality control and biological meaning determination support system (1) for determining the biological meaning of the sample having the molecular data. The internal quality control and biological meaning determination support system 1 may be the same as the system for selecting and evaluating the AptaSSN group and the target molecule group contributing to the determination of the biological meaning.

The biological meaning value provided by the internal quality control and biological meaning decision support system (1) is a process of making a decision about biological meaning for the purpose of diagnosing, treating, alleviating, treating, and preventing a disease or other condition. means “classification”, “score”, and “index” that support Alternatively, it may be a specific result produced so that a disease can be inferred or discriminated from the collected information using a machine learning algorithm.

In the present invention, useful AptaSSN may be determined according to the criterion of biological significance for the binding profile data produced by AptaSSN, and the biological significance is illustratively diagnostic, but not limited thereto.

Biomarkers can be discovered by separating, purifying, and identifying target proteins corresponding to each using the differential binding AptaSSN population determined by a series of processes for determining the AptaSSN contributing to the analysis of the biological significance of the analysis sample population.

In a sample group composed of molecular groups, information constituting a binding profile generated from a complex of a molecular group and an AptaSSN group of each sample may be analyzed to classify the sample group based on the molecule. It can be implemented using non-supervised learning.

AptaSSN selected according to the methods described herein has a dissociation constant (Kd) with a specific molecule of ^{less than 2.0x10 -9} and a ratio of the dissociation constant to other molecular dissociation constants, that is, an aptamer that is a nucleic acid having a specificity of 5 or more. and the AptaSSN is useful within the scope of biomarker development, diagnosis and treatment methods.

The module for generating a biological meaning determination support model by performing learning with the produced binding profile is a procedure for classifying selected features for each class for biological meaning through an appropriate classifier.

The biological meaning means the physiological change of the sample or the person who took the sample, and in accordance with the clinical test value, prevention, diagnosis, treatment, alleviation, and treatment for the purpose of health management. It may be information necessary for the process.

The method for determining biological meaning based on the binding profile for the molecular group of the sample may be the degree of similarity between a specific sample and a biological meaning-based database, and the biological meaning may be determined by referring to clinical information of samples constituting the biological meaning-based database. . More specifically, it is possible to classify whether the test sample is a pathological condition and a healthy condition using AptaSSN, which specifically binds to multiple molecules of a sample of pathological and healthy conditions and contributes to the analysis of the biological meaning determined above. .

The AptaSSN manufacturing method according to the present invention is applied in the fields of biotechnology, medicine, biology and biochemistry. The application of the present invention has an ultimate aim in the management of human health conditions, animals and plants. The present invention develops a selective AptaSSN by comparatively analyzing an AptaSSN library that binds to each of multiple biological biological samples (eg, a test sample and a control sample) in order to identify molecules for biomarker development, therapeutic means and diagnostic purposes, and This is possible by identifying biological samples using molecules that bind.

Also provided are diagnostic or assay devices, such as columns, test strips or biochips having AptaSSN immobilized on the solid surface of one or more of the devices. AptaSSN can be positioned to bind to a target molecule in contact with a solid surface forming an M-AptaSSN complex adhered to the surface of the device, thereby capturing and detecting the target, and selectively quantifying the target. A series of AptaSSNs (which may be the same or different) may be provided in one device.

A diagnostic method in which binding of AptaSSN to a population of molecules in a sample composed of various types of molecules can be used to detect the presence, absence, content or concentration of a prolonged interaction of the target molecule and AptaSSN, and to facilitate the detection of the target. will be useful for Similar advantages may be provided when differential AptaSSN is used in in vitro or in vivo imaging methods. The extended correlation between AptaSSN and M also provides an improved therapeutic effect, for example, by activating or inhibiting a target molecule or a downstream signaling cascade for a longer period of time.

It is a product of any of the methods described herein, i.e., an analysis result that can be evaluated at a test site, or, if desired, it can be transported to another location for evaluation and communication with personnel at a remote location. As used herein, "remote location" refers to a location physically different from where the result was obtained. Thus, the results may be sent to different rooms, different buildings, different parts of a city, different cities, and the like. Data may be delivered by any suitable means, such as, for example, fax, mail, overnight delivery, email, ftp, voice mail, and the like.

"Communicating" information refers to the transfer of data representing information as an electronic signal via an appropriate delivery path (eg, a private or public network). "Forwarding" an item refers to any means of transferring an item from one place to the next, whether physically or (if possible) by some other means, and at least in the case of data: It includes physically transporting the medium that transports or communicates the data.

The scope of the present invention is not limited to the embodiments described above, but is defined by the claims, and a person of ordinary skill in the art may make various modifications and adaptations within the scope of the claims. It is self-evident that it can

* The present invention provides a method, an apparatus, and their use for simultaneously developing a biomarker and a ligand that can analyze a sample consisting of a molecular population more efficiently and contribute to the analysis of biological significance. The method and apparatus according to the present invention are widely useful in the design and development of tools for the discovery and study of biomarkers and the development of AptaSSN-based therapies as well as in diagnostic applications.

1 is a single-stranded nucleic acid (SSN) library prepared from a starting library including an arbitrary nucleotide sequence. The difference in the amount of specific and significant binding to a biomolecule (BM) population in a sample composed of various types of biomolecules is shown. It is a diagram that presents a method for selecting a single-stranded nucleic acid (SSN) group and a molecule corresponding thereto,
2 is a diagram illustrating a method for isolating and identifying a target molecule from a sample consisting of a biomolecule population as a binding SSN population;
3 is an AptaSSN-FSS complex library using a first suspended solid support (FSS) by adding a first tag to the AptaSSN population prepared in the AptaSSN multiplex analysis for samples composed of various types of molecules. A first separation is performed by capturing a molecule with the prepared complex, and a second tag is added to the harvested complex molecule to form a complex with a second immobilized solid support (SSS). It can be carried out by a method comprising an analysis step of isolating the formed complex and eluting the bound AptaSSN and amplifying it.
4 is a diagram showing the structure of a starting single-stranded nucleic acid comprising a fixed region-variable region-fixed region as the structure of the starting single-stranded nucleic acid;
5 is a primer for preparing a DS oligonucleotide library from the starting library as a template;
6 is It is a diagram of the primer pair (T7 primer and DS primer C2') DP essential for the production of DS oligonucleotide, which is a template for RNA SSN library production,
7 is a diagram that provides a binding site of a primer pair necessary for the differential and equivalent process, wherein the binding SSN provides a binding site for the differential and equivalent process when the equivalent or differential process of the binding SSN library is performed by the PCR method;
8 is a diagram of another example in which the binding SSN provides the binding site of a primer pair necessary for the differential and equivalent process when the equivalent or differential process of the binding SSN library is performed by the PCR method;
9 is a diagram of an adapter for preparing Tester 1 and Tester 2 by ligation to the DS DNA-binding SSN of a test sample, and the ligation method for performing the equivalent and differential process of the binding SSN library includes a specific sequence;
10 is a method for preparing an SSN library from the starting nucleic acid library for the purpose of evaluating and selecting a binding SSN, a binding SSN profile for an experimental group and a control group, differential binding SSN evaluation and selection, and identification of a target molecule from the molecular population and SSN library in the sample. It is an exemplary drawing,
11 is a diagram of preparing an FXRP primer for NGS library production of the AptaSSN population prepared by combining the used AptaSSN-specific primer and the primer for NGS library production;
12 shows the molecular data for selection of the differential binding AptaSSN group, that is, the nucleotide sequence and frequency of the AptaSSN group isolated from the molecular group constituting the specific sample in the library, and the complex formed in the library, analysis sample for the purpose of production A diagram of a process for forming a molecule and an AptaSSN complex population by reacting the molecular population constituting the It is a drawing (b) for the process,
13 is a diagram of generating a binding profile for analyte samples using SSN-FSS bead population (a) or nitrocellolose disc (b) and constructing a database based on biological significance,
14 is a method for securing an AptaSSN population with significant quantitative differences in binding to a plurality of groups constituting a sample, characterizing the characteristics, and identifying molecules binding thereto. A method for isolating and identifying molecules binding to binding SSN. has been suggested, and specific binding molecules are isolated using binding SSNs, and protein target bands are identified by electrophoresis of this complex on a PAGE-SDS gel. After extracting the protein by cutting the identified protein band, it is a diagram illustrating the process of identifying a molecule that specifically binds to the binding SSN through the step of identifying the protein with MALTITOF-TOF,
Figure 15 shows solution-based proteolytic digestion of protein extracts obtained with bound SSN binding (a) and AptaSSN population (b) from molecular populations in a sample, and first-source (1D) SDS-PAGE separation of proteins, excision of continuous gel regions. and GeLC (one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by liquid chromatography) analysis accompanying proteolytic digestion of proteins in each gel region.
16 is a diagram of an AptaSSN fluid magnetic bead device;
17 is a diagram of a process for simultaneously producing protein and nucleic acid data using NGS technology;
18 is a diagram of the configuration of the internal quality control and biological meaning determination support system (1),
19 is a diagram of the configuration of the molecular data production module 100,
20 is a diagram of the configuration of the internal quality control module 200,
21 is a diagram of the configuration of the biological meaning determination support module 300,
22 is a diagram of the steps of learning and evaluation carried out in the biological meaning determination support module 300,
23 is a view of the results of confirming the size and completeness of the library using the NGS prepared using the Agilent DNA 1000 Kit and Bioanalyzer,
24 is a diagram comparing the ratio of the nucleotide sequences and the frequency of 5395 binding SSNs constituting the binding SSN library prepared in the test sample and the binding SSN library prepared in the control sample;
25 is a diagram of a result of hierarchical clustering with features selected by the biological meaning determination support module 300;
26 is a diagram of the distribution of features selected by the biological meaning determination support module 300,
27 shows the results of evaluating the binding degree of serial number 768 binding SSN to the serum of a small cell lung cancer patient, a test sample, and a control sample, the serum of a non-small cell lung cancer patient. is a drawing,
28 shows that the input module 302 of the biological meaning determination support module 300 builds the lung cancer presence/absence learning set DB and the stage set DB constructed based on the biological meaning, and first, Min-Max samples for the learning set DB are generated. Normalization, Qauntile Normalization was performed between samples, and one-way ANOVA was performed on the normalized DB to select a classifying molecule (feature) group.
29 is a diagram of the results of analysis by a clustering technique on a learning sample as a primary evaluation of a selected classification feature group;
30 is a diagram showing the results of learning using a machine learning classifier as a secondary evaluation of the selected classification feature group and evaluating it by a 10-fold cross validation method.

The following examples are provided for illustrative purposes only and do not limit the scope of the invention as defined in the appended claims.

The foregoing has set forth the specification with respect to various implementations and examples. No particular implementation, example, or component of a particular implementation or example may constitute any significant, necessary or essential element or feature of a claim.

Various modifications and substitutions may be made to the described embodiments without departing from the scope of the specification as set forth in the following claims. The specification, including drawings and examples, is to be regarded as illustrative rather than restrictive, and all such modifications and substitutions are intended to be included within the scope of the specification. Accordingly, the scope of this specification should be determined by the appended claims and their legal equivalents rather than by the examples. For example, the steps recited in any method or preparation claim may be performed in any practicable order and are not limited to the order in which they appear in any embodiment, example, or claim.

Example

purchased reagents

HEPES, NaCl, KCl, EDTA, EGTA, MgCl2 and Tween-20 were purchased from Fisher Biosciences (USA). Dextran sulfate sodium salt (DxSO4) having a molecular weight of 8000 was purchased from Sigma-Aldrich (USA) and dialyzed against deionized water for at least 20 hours in an exchanger.

KOD EX DNA polymerase was purchased from Merk (USA).

Tetramethylammonium chloride and CAPSO (3-(Cyclohexylamino)-2-hydroxy-1-propanesulfonic acid) were purchased from Sigma-Aldrich, and streptavidin phycoerythrin (SAPE) was purchased from Moss Inc. (USA). 4-(2-Aminoethyl)-benzenesulfonylfluoride hydrochloride and 4-(2-Aminoethyl)-benzenesulfonylfluoride hydrochloride (AEBSF) were purchased from Gold Biotechnology (USA). Streptavidin-coated 96-well plates (Pierce streptavidin coated plates HBC, clear 96-wells, product number 15500 or 15501) were purchased from Thermo Scientific (USA). NHS-PEG4-Biotin (EZ-Link NHS-PEG4-Biotin, product number 21329) was purchased from Thermo Scientific, dissolved in anhydrous DMSO, and stored frozen as single-use aliquots. Yeast tRNA (Life Technologies, USA) was used.

Selection of the binding SSN population from the modified DNA SSN library was performed using 5-( N- benzyl carboxamide)-2-deoxyuridine (Bn-dU) 5-(N-benzylcarboxamide) purchased from Somalogic (USA) instead of dT. -2-deoxyuridine (Bn-dU) or 5-[ N- (1-naphthylmethyl)carboxamide]-2-deoxyuridine (Nap-dU) 5-[N-(1-naphthylmethyl)carboxamide] A modified DNA SSN library consisting of random 40 bases consisting of -2-deoxyuridine (Nap-dU) was used.

All C5 position modified cytidine (dC) and uridine (dU) triphosphates (modified dNTPs) and phosphoramidite were synthesized as previously reported. PCR grade natural nucleotide triphosphates dATP, dTTP, dCTP and dGTP were obtained from Thermo Fisher Scientific.

A starting library and forward and reverse primers were custom synthesized (TriLink BioTechnologies, USA).

buffer

Buffer SB18 consists of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2 and 0.05% (v/v) Tween 20 adjusted to pH 7.5 with NaOH. Buffer SB17 is SB18 supplemented with 1 mM trisodium EDTA.

Buffer PB1 consists of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM trisodium EDTA and 0.05% (v/v) Tween-20 adjusted to pH 7.5 with NaOH.

CAPSO elution buffer consists of 100 mM CAPSO pH 7.5 and 1M NaCl. The neutralization buffer consists of 500 mM HEPES, 500 mM HCl and 0.05% (v/v) Tween-20. Unless otherwise stated, all steps were carried out at room temperature.

Sample preparation

Samples with clear biological definitions were collected from Asan Medical Center, Seoul (IRB approval number: 2015-0607) and used in the present invention. The serum used in the present invention was extracted from blood collected from lung disease patients with lung cancer and non-lung cancer.

For sample preparation, whole blood should be stored in a red top tube. After the whole blood is collected, the test tube is treated intact at room temperature to allow the blood to clot for 30 minutes. Thereafter, the serum and blood clots are separated by centrifugation at 1,000 to 2,000×g for 10 minutes in a refrigerated centrifuge. After centrifugation, the supernatant serum is immediately transferred to a clean tube. Samples are maintained at a temperature of 2 to 8 °C during processing. If the serum is not analyzed in situ, the serum is immediately aliquoted into small samples and stored at -20°C or lower. It is important to avoid cold-thaw cycles as they are lethal to many serum components. Hemolytic, jaundic, or hyperlipidemic samples may invalidate certain tests.

Example 1. Starting single-stranded nucleic acid and primer structure

As shown in FIG. 4, the starting library (base sequence 1) of the structure <Structural Formula 1> (random single-stranded nucleic acids) for preparing an SSN library to be reacted with a biological sample (test sample and control sample) consisting of a molecular population ) was prepared (Korea, 　Bionia).

First, as shown in FIG. 4, the single-stranded nucleic acid (base sequence 1) constituting the starting library of the <Structural Formula 1> structure has a SSN of 5'-5' fixed sequence-variable sequence-3' fixed sequence-3'. was composed of

SSN nucleotide sequence of the <Structural Formula 1> structure:

5'-CCACGCTGGGTGGGTC N ₄₀ GGACAAAGAGAGAAGAGAAAGAG-3' (nucleotide sequence I)

(Here, the underlined nucleotide sequence is a fixed region of the starting library, and N ₄₀ of the 40 base variable sequence means that bases such as A, G, T, and C exist at the same concentration at each position.)

5 and 6 , a DS DNA library can be prepared by PCR using the starting library as a template. As the primers used at this time are shown in Figures 2 and 3, DS primer C1 is the nucleotide sequence (base sequence 2) of the 5' fixed sequence of <Structural formula 1>, and T7 primer T7 (base sequence 3) is <Structural formula It is a fusion sequence (base sequence 3) of the 5' fixed sequence of 1> and the promoter sequence for RNA polymerase of bacteriophage T7, and DS primer C2' is the complementary sequence of the 3' fixed sequence of <Structural formula 1>

DS Primer C1:

5'-CCACGCTGGGTGGGTC-3' (nucleotide sequence 2)

T7 Primer T1:

5´-TAATACGACTCACTATAGGGCCACGCTGGGTGGGTC-3´ (nucleotide sequence 3)

DS Primer C2':

5'-CTCTTTCTCTTCTCTCTTTCTCC-3' (nucleotide sequence 4)

Using the starting library of the <Structural Formula 1> structure prepared as above as a template, the T7 primer T1 and the DS primer C2' through a PCR (Polymerase Chain Reaction) process, DS DNA (nucleotide sequence 5) having a T7 promoter is as follows. And it is possible to prepare an SS RNA library prepared by in vitro transcription with the following PCR product (base sequence 4).

Base sequence of PCR product with the primers:

5´- TAATACGACTCACTATAGGG CCACGCTGGGTGGGTC N ₄₀ GGACAAAGAGAGAAGAGAAAGAG-3' (nucleotide sequence 5)

The prepared SS RNA library or the prepared SS RNA library is subjected to reverse transcription of a single-stranded RNA group separated from the M-linked SSN complex group formed by contacting the molecules constituting the sample to prepare cDNA and PCR as a template. A double-stranded DNA library can be constructed. In the above process, DS primer C2', which serves as a priming for reverse transcription, and the DS primer C1 and DS primer C2' used for PCR for the reverse transcription product cDNA.

Example 2. Preparation of modified RNA SSN library

In this example, the SSN library to react with the molecular population constituting the biological sample (test sample and control sample) is an RNA library containing 2'-F-substituted pyrimidines, which are modified nucleotides. <Structural Formula 　I> It was prepared by PCR and in vitro transcription methods using the starting library of the structure and primers.

PCR was performed using a 1,000 pmoles starting library, 2,500 pmoles DS primer pair (T7 primer T1, DS primer C2'), 50 mM KCl, 10 mM Tris-Cl (pH 8.3), 3 mM MgCl2, 0.5 mM dNTP (dATP, dCTP, dGTP, and dTTP). ), 0.1U Taq DNA Polymerase (Perkin-Elmer, USA), followed by purification with a QIAquick-spin PCR purification column (QIAGEN Inc., Chatsworth Calif.). A DS DNA having a T7 promoter was prepared.

Using the DS DNA prepared above, a randomly modified SS RNA library containing 2'-F-substituted pyrimidines was synthesized and purified by in vitro transcription using the Durascribe T7 Transcription Kit (EPICENTER, USA). 200 pmoles DS DNA transcript, 40 mM Tris-Cl (pH 8.0), 12 mM MgCl2, 5 mM DTT, 1 mM spermidine, 0.002% Triton X-100, 4% PEG 8000, 5U T7 RNA Polymerase and 1 mM (ATP, GTP) and 3 mM ( 2'F-CTP, 2'F-UTP) was reacted at 37° C. for 6 to 12 hours, and then purified by a Bio-Spin 6 chromatography column (Bio-Rad Laboratories, USA), and then using a UV spectrometer. to analyze the amount of purified nucleic acid and its purity.

Example 3. Selection of modified RNA-binding SSN population

3-1. SSN-FSS library preparation

1) Preparation of SSN library to which the first tag is added

Thaw all kit components except 30% PEG and DMSO on ice. Thaw DMSO at room temperature and heat 30% PEG at 37° C. for 5-10 minutes to warm to a liquid in volume. The heating block was adjusted to 85°C.

5 μL of the SSN library was transferred to a microcentrifuge tube. After heating the SSN library at 85° C. for 3-5 minutes, immediately place the SSN library on ice. The SSN library may need to be heated to relax the secondary structure. In addition, heating the SSN library in the presence of ~25% DMSO may increase the efficiency of the SSN library with a significant second structure.

Labeling reactions were prepared for control systems or test SSN libraries by adding the components in the order listed in Table 1.

First tag addition reaction to SSN library component Volume ( μ L) Final Concentration (Amount) Nuclease-free Water 3 --- 10X RNA Ligase Reaction Buffer 3 1X RNase Inhibitors One 1.33U/μL (40U) SSN Library 5 1.67 μM (50 pmol) Biotinylated Cytidine (Bis)phosphate One 33.3 μM (1 nmol) T4 RNA Ligase 2 1.33U/μL (40U) 30% PEG 15 15% Total 30 ---

30% PEG is the last reagent added and after carefully pipetting 30% PEG to the reaction mixture, a new pipette tip was used to mix the ligation reaction.

The SSN library, which is the nucleotide sequence construct (1), was reacted at 16° C. for 2 hours. Ligation may require overnight treatment to increase efficiency. 70 μl of nuclease-free water was added to the ligation reaction.

100 μl of chloroform:isoamyl alcohol was added to each reaction to extract RNA ligase. The mixture was gently mixed and then the phases were separated by centrifugation in a microcentrifuge at high speed for 2-3 minutes. Carefully take the top (aqueous layer) and transfer it to a new tube. By adding biotin to the SSN library, a Biotin-SSN library, which is a nucleotide sequence structure (2), was prepared.

The stock concentration of each Biotin-SSN library was 4 nM. The Biotin-SSN library stock mix was diluted 4-fold with SB17 buffer and heated at 95° C. for 5 minutes and then cooled to 37° C. for 15 minutes before use. Streptavidin beads were washed twice with 150 mL buffer PB1 before use.

2) SSN-FSS library preparation

133 μL of Suspension First Solid Support (FSS) in 1xSB17, Tw (40 mM HEPES, 102 mM NaCl, 1 mM EDTA, 5 mM MgCl2, 5 mM KCl, 0.05% Tween-20), Streptavidin-Coupled Dynabeads (Thermo Fisher Scientific) , USA) into the tube. Approximately 1.1x Bio-SSN libraries (all AptaSSNs contain a biotin moiety at the 3' end) were vortexed and thawed.

The 1.1x Biotin-SSN library was then boiled for 10 minutes, vortexed for 30 seconds and cooled to 20° C. in a water bath for 20 minutes, 100 μL of the 1.1x Bio-SSN library was prepared with the Streptavidin-Coupled Dynabeads. added to the tube. The mixture was protected from light and treated at 25°C for 20 minutes on a shaker set at 850 rpm. A state SSN-FSS library was prepared.

The solution was removed by centrifugation of the tube with the suspension SSN-FSS library. 190 μL of 1x CAPS prewash buffer (50 mM CAPS, 1 mM EDTA, 0.05% Tw-20, pH 11.0) was added, and the suspension SSN-FSS library was incubated for 1 minute with shaking. The CAPS wash solution was then removed via centrifugation. The CAPS wash was then repeated once more.

190 μL of 1xSX17, Tw was added to the tube with the suspension SSN-FSS library, and the suspension was treated for 1 minute while shaking the SSN-FSS library. 1xSB17, Tw was then removed by centrifugation. An additional 190 μL of 1xSX17, Tw was added, and the suspension SSN-FSS library was shaken for 1 minute. 1xSB17, Tw was then removed by centrifugation (1 min at 1000x g).

150 μL of storage buffer (150 mM NaCl, 40 mM HEPES, 1 mM EDTA, 0.02% sodium azide, 0.05% Tween-20) was added to the tube containing the suspension SSN-FSS library to The FSS bead library was purified. The tube was carefully sealed and stored in the dark at 4°C until use.

These solutions were stored for a long time at -20 °C. On the day of analysis, each suspension SSN-FSS library was thawed at 37 °C for 10 min, placed in a boiling water bath for 10 min, and cooled to 25 °C for 20 min, with vigorous mixing between each step.

After heat-cooling, 55ul of each 2x suspension SSN-FSS library was manually pipetted into a 96-well PCR plate and the tube sealed with foil.

3-2. Analytical sample preparation

Frozen 100% serum samples stored at -80°C were placed in a 25°C water bath for 5 min. The thawed sample was placed on ice to create a slow vortex, and then placed on ice again.

Serum (stored at -80°C in 100 μl aliquots) was thawed in a 25°C water bath for 10 min and then stored on ice prior to sample dilution. The samples were mixed by vortexing lightly for 8 seconds. A 6% serum sample solution was prepared by diluting in 0.94x SB17 supplemented with 0.6 mM MgCl2, 1 mM Trisodium EDTA, 0.8 mM AEBSF, and yeast tRNA at 5 times the SSN concentration used. A portion of the 6% serum stock solution in SB17 was diluted 10-fold to make a 0.6% serum stock. Stocks of 6% and 0.6% were used to detect high and low concentrations of analyte, respectively.

After putting 3ul of a sample into a 96-well PCR tube using a 20ul pipette, 72ul (1xSB17, 0.02% Tween-20) of an appropriate sample dilution was added to each tube to prepare a 4% sample solution (final 2x). After mixing the samples by pipetting several times, 4 ul of a 4% sample solution was moved and mixed with 76 ul of a titrated sample dilution solution to obtain a 0.2% sample solution (final 2x). Finally, 6ul of the 0.2% sample solution was moved and mixed with 54ul of the sample dilution, to obtain a 0.02% sample solution (final 2x). After preparing the sample dilution solution, 55ul of each sample was put into a new PCR tube for equilibrium binding.

2%, 0.1% and the suspension SSN-FSS bead library 1xSB17 (40mM HEPES, 102mM NaCl, 5mM KCl, 5mM MgCl2, 1mM EDTA), 0.05% Tween-20 0.01% for serum was prepared with 2x concentration.

3-3. SSN-FSS library and sample reaction

55 ul of the suspension heat-cooled SSN-FSS library solution was added to a tube with a volume of 55 ul of titrated sample dilution solution. The sample composed of the molecular population and the suspended SSN-FSS library were mixed by pipetting and sealed with a foil cover. After that, the tube was placed in a 37 °C processor for 3 hours and 30 minutes to conduct a complex formation reaction to form a suspension M-SSN-FSS complex in which the first tag of SSN and the first capture component in the FSS bead were combined. carried out.

After the complex formation reaction, the tube with 100 ul of the reaction mixture was placed on a stand with a magnet or centrifuged to carefully remove the solution layer with a pipette. The tube was washed 4 times with 300ul of buffer PB1 supplemented with 1mM dextran sulfate and 500uM biotin. Then, the tube was washed 3 more times with 300ul of buffer PB1 to prepare the M-SSN-FSS complex population.

3-4. Combined SSN Population Selection

To the tube containing the M-SSN-FSS complex population, 150 uL of a freshly prepared 1 mM NHS-PEG4-biotin solution in buffer PB1 was added and treated with shaking for 5 minutes to bind the second tag to the molecule of the M-SSN-FSS complex in suspension. Biotin was added to it.

The tube was centrifuged to remove the liquid by suction, and the tube was washed 8 times with 300ul of buffer PB1 supplemented with 10 mM glycerin. 100ul of buffer PB1 supplemented with 1 mM dextran sulfate was added.

The buffer solution containing the second-tagged M-SSN-FSS complex population in suspension in the tube was transferred to the wells of a plate coated with a second solid support (SSS), streptavidin. The second separation in which the second tag of the suspended M-SSN-FSS and the second capture component immobilized on the plate is combined was carried out in a thermomixer at 800 rpm and room temperature for 10 minutes, and the stationary SSS-M-SSN-FSS A complex was prepared.

After performing the second separation, the liquid was carefully removed from the wells of the plate with a pipette, and washed 8 times with 300ul of buffer PB1 supplemented with 25% Propylene Glycol or 30% glycerol. Wells were washed 5 times with 300ul of SB17T buffer, and the final wash was aspirated.

The tube was centrifuged to remove the liquid by aspiration, and the tube was washed 8 times with 300ul of SB17T buffer supplemented with 10mM glycerin. 100ul of SB17T buffer supplemented with 1mM dextran sulfate was added.

In order to preferentially select sequences having a slow dissociation rate of the complex, the following stringent washing solution method was performed. This is in the immobilized state SSS-M-SSN-FSS complex solution, 20-fold SB17T 15 minutes before capture in rounds 2-5, 400-fold SB17T 60 minutes before capture in rounds 6-7, and 10 mM in round 8 60 minutes before capture. This was achieved by adding 400 times SB17T containing dextran sulfate.

100ul of CAPSO elution buffer was added, and the fixed-state SSS-M-SSN-FSS complex was eluted with shaking for 5 minutes. The SSN-FSS population was obtained by manually transferring 90 ul of the solution from the wells of the plate to the wells of the PCR plate containing 10 uL of neutralization buffer. Alternatively, the wells of the plate with the immobilized SSS-M-SSN-FSS complex population were heated for 5 minutes to elute the bound SSN.

As shown in Figure 1, Based on the <Example 1>, an amplification step of performing RT-PCR on the binding SSN population was performed. The SSN in the immobilized SSS-M-SSN-FSS complex population was primed with DS primer C2' and reverse transcribed (RT) to prepare cDNA, and PCR was performed using a pair of T7 primer T1 and DS primer C2'. .

Based on the <Example 2> above, the binding SSN library was prepared by performing in vitro transcription on the DNA template, which is the amplification product having the T7 promoter.

From the addition of the first tag in the preparation of the SSN-FSS library, which is the nucleotide sequence structure (1) of <Example 3-1>, to the preparation of the binding SSN group of <Example 3-4> for the prepared binding SSN library The selection process up to

The binding SSN library may be prepared by repeating the above selection and amplification process multiple times.

Ideally, the binding SSN library that binds to a biological sample should reflect the diversity of molecules contained in the biological sample as it is. A well-reflecting binding SSN library was prepared. The libraries prepared from the test sample and the control sample were referred to as the former M-SSN of the test sample and the M-SSN library of the control sample.

Reverse transcription (RT) was performed by priming the SSN in the washed complex with DS primer C2' to prepare cDNA, and performing PCR using a pair of DS primer C1 and DS primer C2' to obtain the SSN population binding to serum proteins. The DNA population for Korea was amplified. The obtained PCR product is a nucleic acid sample for the purpose of preparing a sequence library.

Example 4. Determination of base sequence and frequency of modified RNA-binding SSN

A fastaq file is generated with the NGS sequencer 104 of the molecular data production module 100 of FIG. 17 with the combined SSNs constituting the combined SSN library prepared above or the combined SSN library prepared by a differential and equivalent process, and a comparison module 106 ), the nucleotide sequence, appearance frequency, and binding profile of the binding SSN were determined with the NAPConverter program constituting the .

The modified RNA and binding SSN population secured in the first reaction zone 101 and the separation zone 102 of the molecular data production module 100 of FIG. 19 as a template are presented in FIG. 11 in the second reaction zone 103 RT-PCR with FXRP primer to prepare an NGS library. The obtained DNA group was analyzed using Ion Torrent (104, ThermoFisher).

Referring to FIG. 11 , the binding SSN population eluted from the complex formed by reacting the single-stranded nucleic acid library and the sample composed of the molecular population or the AptaSSN population eluted from the complex formed by reacting the sample composed of the AptaSSN library and the molecular population is P5 (Ion A) and P7 (Ion P1) sequencing adapters were prepared for Ion Torrent next-generation sequencing (NGS) by performing an addition reaction using the PCR method.

A unique 8-sequence base barcode was included per sample via forward primers to allow samples to be multiplexed into a single sequencing run. The FX (forward) primer (5'-P5 Adapter-basic barcode-SELEX primer-3') was designed as follows.

5'-ATAT-CCATCTCATCCCTGCGTGTCTCCGACTCAG-XXXXXXXX-CCACGCTGGGTGGGTC-3'

The RP (reverse) primer (5'-P7 Adapter-SELEX primer-3') was designed as follows.

5'-TTTTTTTT-CCTCTCTATGGGCAGTCGGTGAT-CTCTTTCTCTTCTCTCTTTCTCC-3'

A detailed look at the reaction in the second reaction sphere 103 is as follows. Take 36 µl of the binding SSN population solution, which is the modified RNA obtained in the above example, and transfer it to a 0.2 ml PCR tube. To 36 μl of the reaction solution transferred to a 0.2 ml PCR tube, 10 μl of 5X RT buffer and 1 μl of reverse primer were added. After reacting at 65° C. for 5 minutes, the reaction was performed at room temperature for 10 minutes. Additionally, 2 μl of 10mM dNTP and 1 μl of M-MLV RTase (200 u/μl) were added and reacted at 37° C. for 30 minutes and 95° C. for 5 minutes.

Reagents in Table 2 were added to 4 μl of the 50 μl of the product after the RT reaction as a template.

PCR mixture ingredient Volume (μl) RT product 4 10X Pfu reaction buffer 5 10 mM each dNTP One Primer mixture(Forward+Reverse) 4 D.W. 35.5 Pfu enzyme 0.5 Total 50

PCR was performed according to the PCR cycle program in Table 3. If the following process is not carried out immediately, store it in the refrigerator (5±5℃) or at -20℃ or lower for long-term storage.

PCR cycle program order step Temperature hour cycle One Pre-denaturation 95℃ 2 minutes - 2 Denaturation 95℃ 20 seconds 12 cycles 3 Annealing 56℃ 30 seconds 4 Extension 72℃ 1 minute 5 Post-extension 72℃ 5 minutes -

The PCR product was quantified with a Qubit 　 fluorometer (Invitrogen). Transfer the PCR product after the reaction to a 1.5 ml tube. Put HiAccu beads 1.8 times (90 μl) of the sample volume into the sample, pipetting 3-5 times to mix the DNA and beads well, spin-down, and then react at room temperature for 5 minutes. After spin-down, place the tube containing the sample on the magnetic rack and let it sit for 3 minutes. Remove the remaining supernatant except for the bead pellet. Put 300 μl of 70% ethanol into the sample tube mounted on the magnetic rack. After reacting at room temperature for 30 seconds, rotate the tube slowly and repeat twice to wash evenly, and remove the supernatant while taking care not to disperse the pellet. The washing process is repeated once more. After spin-down to completely remove the remaining ethanol, place it back on the magnetic rack and carefully remove the remaining ethanol. Dry the beads on a magnetic rack with the lid of the sample tube open. Do not leave the lid open for more than 3 to 5 minutes at this time. After removing the tube from the magnetic rack, put 21 μl of Nuclease Free Water, pipette 3-5 times, and mix for 10 seconds to separate DNA well. After spin-down, the tube was mounted on a magnetic rack, waited for about 1 minute, and when the supernatant became transparent, 18 μl of the supernatant was transferred to a newly prepared 1.5 ml tube and transferred to prepare an NGS library.

The concentration of the prepared library product was measured with Qubit dsDNA HS Reagent according to the manual of Qubit (Invitrogen). A concentration of at least 100 pmole is required for the NGS reaction, and if this concentration is not obtained, the results will not be reliable. In addition, the size and completeness of the library were confirmed using the Agilent DNA 1000 Kit (Agilent) according to the manual of the Bioanalyzer (Agilent), and the results were recorded (FIG. 23).

The prepared NGS library was further concentrated on an Amicon Ultra 0.5 column (Millipore). Final samples were subjected to an Ion Torrent PGM 318 chip sequencing run (Thermo Fisher Scientific).

The execution of the Ion S5 XL Sequencer 104 constituting the molecular data production module 100 of FIG. 19 was performed according to the manufacturer's manual, first, an operation plan was established and variables were set, and the Ion Chef system was set and operated. 25 μl of each diluted library was injected into the Ion Chef library sample tube. The lids of the two library sample tubes were opened, and each library diluted by 25 μl was placed in the A and B positions of the reagent cartridge. After washing the equipment, it was reacted. When the reaction is complete, the chip is separated from the Ion Chef equipment and the sequencing process is performed immediately. Ion S5 Sequencer or Ion S5 XL Sequencer was initialized about 40 minutes before the Ion Chef system chip rotation process was finished.

Ion S5 initialization was performed and nucleotide sequence determination was started. When the sequencing is completed, the purification procedure automatically proceeds, and when the purification is completed, the main screen is returned. The nucleotide sequence analysis results of the NGS library were confirmed in the Torrent Browser.

The sequencing results were analyzed by software quality of Ion Torrent (104, ThermoFisher). The binding profile of the sample using the binding SSN was generated by performing downstream analysis using NAPConverter (106), which is an additionally developed internally software.

The NAPConverter constituting the comparison module 106 of the molecular data production module 100 of FIG. 19 analyzes according to the following five processes to generate a NAP file. First, the FASTAQ file is analyzed to perform a Qaulity filter process that removes low-quality sequences.

Referring to FIG. 11, the matching step of the NAPConverter 106 is a 5' fixed sequence (16 bp), a variable sequence (40 bp) and 3' in the <Structural Formula 1> structure of the single-stranded nucleic acid in the reference sequence 105. In the process of comparison based on the pattern of 79 base pairs (bp) of the sequence including the fixed sequence (23 bp), any sequence that did not match the pattern was excluded. One mismatch in each stationary state sequence was allowed during pattern matching. The fixed-state sequence sequence was excluded after filtering, leaving only the 40 bp variable sequence sequence for downstream analysis. The reverse supplemental tag was associated with the forward sequence tag. In addition, the reference sequence 105 may be configured as a binding SSN group with a known base sequence.

By analyzing the FASTAQ file to which the above steps are applied, the operation of checking so that only the reads in which the nucleotide sequence of the fixed state sequence is maintained remain. Analyzes the FASTAQ file to which the above steps are applied, and performs an operation to check the length of the nucleotide sequence of the read. The FASTAQ file to which the above steps are applied is compared with the reference sequence DB 105 for the analyzed nucleotide sequence, and if there is no identical sequence, it is classified as a new binding SSN sequence. A NAP file is generated by performing the round enrichment analysis method to increase the frequency of each binding SSN while classifying all the nucleotide sequences as described above.

The NAP file and binding profile were completed by determining the nucleotide sequence and frequency of occurrence of a specific binding SSN constituting the library as a result of the matching step and count round of the NAPConverter of the comparison module 106. 15,000,000 to 18,000,000 sequences were analyzed per selected binding SSN library.

Finally, the ratio of the nucleotide sequence and frequency of 5395 binding SSNs constituting the binding SSN library prepared from the test sample and the binding SSN library prepared from the control sample was compared (FIG. 23).

The binding SSN sequence analysis constituting the binding SSN library produced by differential and equivalent processes is performed by comparing the reference sequences and sequences constructed from the binding SSN sequence determined in the binding SSN library to the EBI website (http://www. Clustering analysis was performed with Omega Cluster located in .ebi.ac.uk/Tools/msa/clustalo/).

As a result of the above results, a total of 1,149 binding SSNs that can be used for comparative analysis or to represent each test sample and control sample were determined.

Example 5. Determination of base sequence and frequency of modified DNA-binding SSN

For the starting library, the ratio of each nucleotide (dA/dG/dC/dT) was 1:1:1:1 (25% each) to amplify the PCR by PCR (Bio-Rad) using the primer extension reaction. used as a template.

The structure of the modified DNA SSN library has random 40 bases (N40) in the center and PCR priming regions at both ends (nucleotide SEQ ID NO: 1).

The primer extension reaction was carried out by adding 10 µM template, 12 µM 5' biotin-primer, 0.5 mM native or modified dNTPs and 0.25 U/mL KOD polymerase (exo-) to 1X SQ20 buffer. The mixture was heat-cooled before addition of DNA polymerase, the reaction was carried out at 68 °C for 6-8 h, then cooled at 10 °C and the duplexes were subjected to high-capacity MyOne- as primary solid support (FSS) for 2 h at room temperature. A library of double DNA-FSS complexes, either modified or unmodified, was randomly prepared by capture from streptavidin paramagnetic beads (Invitrogen, catalog number 65001).

The prepared double DNA-FSS complex library was washed several times with a selection buffer (SBT: 40 mM HEPES, pH 7.4; 102 mM NaCl; 5 mM KCl, 5 mM MgCl2 0.05% Tween 20). The modified sense strand was then eluted by suspending the beads in 20 mM NaOH for 5 min. The eluent was neutralized to pH 7.4 with the required volume of 700 mM HCl, 180 mM HEPES, pH 7.4, 0.45 % Tween 20 and then concentrated with an Amicon Ultra-15 (Millipore) centrifugal filter to a residual volume of approximately 250 μL. A modified random DNA SSN-FSS complex library was prepared as described above, which was referred to as “SSN-FSS complex”.

The sample prepared in <Example 3-2>, serum composed of a plurality of proteins (M) and ≥1000 pmol (~ 10 ¹⁵ unique sequences) The prepared SSN-FSS complex was mixed with SB17T buffer (40 mM HEPES, pH 7.5, M-SSN-FSS complex was formed by mixing in 102 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM EDTA, 0.05% Tween 20), and washed with SB17T buffer to remove unbound serum protein and SSN-Biotin complex.

To the tube containing the M-SSN-FSS complex population, 150 uL of a freshly prepared 1 mM NHS-PEG4-biotin solution in buffer PB1 was added and treated with shaking for 5 minutes to bind the second tag to the molecule of the M-SSN-FSS complex in suspension. Biotin was added to it.

The formed M-SSN-FSS complex population can be biotinylated by covalent bonding with NHS-PEG4-biotin (Thermo Scientific, Pittsburgh, PA, catalog no. 21329) and lysine residues according to the manufacturer's protocol.

The formed M-SSN-FSS complex population (300 pmol in 50 μl) was transferred to a Sephadex G-25 MicroSpin column with SB17T buffer (40 mM HEPES, pH 7.5, 102 mM NaCl, 5 mM KCl, 5 mM MgCl 2 , 1 mM EDTA, 0.05 % Tween 20). was exchanged for NHS-PEG4-biotin was added to 30 μM, and the reaction was incubated at 4° C. for 16 hours. Unreacted NHS-PEG4-biotin was removed using a Sephadex G-25 MicroSpin column.

The buffer solution containing the second-tagged M-SSN-FSS complex population in suspension in the tube was transferred to the wells of a plate coated with a second solid support (SSS), streptavidin. The second separation in which the second tag of the suspended M-SSN-FSS and the second capture component immobilized on the plate is combined was carried out in a thermomixer at 800 rpm and room temperature for 10 minutes, and the stationary SSS-M-SSN-FSS A complex was prepared.

After performing the second separation, the liquid was carefully removed from the wells of the plate with a pipette, and washed 8 times with 300ul of buffer PB1 supplemented with 25% Propylene Glycol or 30% glycerol. Wells were washed 5 times with 300ul of SB17T buffer, and the final wash was aspirated.

The tube was centrifuged to remove the liquid by aspiration, and the tube was washed 8 times with 300ul of SB17T buffer supplemented with 10mM glycerin. 100ul of SB17T buffer supplemented with 1mM dextran sulfate was added.

In order to preferentially select sequences having a slow dissociation rate of the complex, the following stringent washing solution (Kinetic challenge) was performed. This is in the immobilized state SSS-M-SSN-FSS complex solution 20-fold with SB17T 15 minutes before capture in rounds 2-5, 400-fold with SB17T 60 minutes before capture in rounds 6-7, and 10 mM in round 8 60 minutes before capture. A 400-fold addition was achieved with SB17T containing dextran sulfate.

100ul of CAPSO elution buffer was added, and the fixed-state SSS-M-SSN-FSS complex was eluted with shaking for 5 minutes. The SSN-FSS population was obtained by manually transferring 90 ul of the solution from the wells of the plate to the wells of the PCR plate containing 10 uL of neutralization buffer.

For the SSN-FSS population obtained above, the ratio of each native nucleotide (dA/dG/dC/dT) was 1:1:1:1 (25% each) and PCR amplified by PCR (Bio-Rad). did it

The primer extension reaction for the amplified PCR product was performed in 1X SQ20 buffer (120 mM Tris-HCl, pH 7.8; 10 mM KCl; 6 mM (NH4)2SO4, 7 mM MgSO4, 0.1% Triton X-100 and 0.1 mg/mL BSA). ), 10 μM template, 12 μM 5′ biotin-primer, 0.5 mM native or modified dNTPs and 0.25 U/mL KOD polymerase (exo-) were added. Before adding DNA polymerase, the mixture was heated and cooled, and primer extension reaction was performed at 68° C. for 6-8 hours.

Then, it was cooled at 10 °C and the primer extension reaction product, the duplex, was subjected to high-capacity MyOne-streptavidin paramagnetic beads (Invitrogen, catalog number 65001) or paramagnetic Dynabeads® MyOne™ Streptavidin C1 (Thermo) as primary solid supports (FSS) for 2 h at room temperature. Fisher Scientific) captured and prepared a modified or unmodified random DNA SSN-FSS complex library, which was referred to as “SSN-FSS complex”.

A modified enriched SSN-FSS complex library was prepared for use in the next round by preparing SSN-FSS complexes containing modified nucleotides by primer extension reaction.

After the 6th round, the binding SSN group and initial starting library eluted from the complex formed by reacting the selected modified DNA SSN library with the sample consisting of the molecular group were subjected to an addition reaction using Ion A and Ion P1 sequencing adapters using the PCR method. It can be prepared for Ion Torrent next-generation sequencing (NGS), and the binding SSN population was analyzed by carrying out according to the above <Example 4>.

A fastaq file is generated with the NGS sequencer 104 of the molecular data production module 100 of FIG. 18 with the combined SSNs constituting the combined SSN library prepared above or the combined SSN library prepared by a differential and equivalent process, and a comparison module 106 ), the nucleotide sequence, appearance frequency, and binding profile of the binding SSN were determined with the NAPConverter program constituting the .

The modified RNA and binding SSN population secured in the first reaction zone 101 and the separation zone 102 of the molecular data production module 100 of FIG. 19 as a template are presented in FIG. 11 in the second reaction zone 103 RT-PCR with FXRP primer to prepare an NGS library. The obtained DNA group was analyzed using Ion Torrent (104, ThermoFisher).

Example 6. Selection of differential binding SSNs and identification of corresponding target molecules

As a result of the binding profile analysis using the NGS of the binding SSN library that binds to the molecules constituting a specific human serum test sample, it was found that there is a correlation between the specific binding profile and the number of specific molecules constituting the serum sample in the specific binding SSN library. can

The profile reflecting the frequency of occurrence of the binding SSN constituting the binding SSN library that binds to the molecules constituting the specific serum sample of the present invention is the molecular profile (information synthesizing the quantitative state) of a specific serum sample composed of a molecular population.

Referring to FIG. 12 , a detailed implementation process is as follows. First, in the first reaction section 101 of the molecular data production module 100 of FIG. 19 , the molecular group and the binding SSN group react according to the term <Example 3>. In the isolator 102, the bound SSN population was separated from the formed complex population. In the second reaction section 103, an NGS library was prepared for the isolated oligonucleotide population. The NGS sequencer 104 stores the nucleotide sequence analysis information generated in the prepared NGS library as FASTAQ. FASTA has header and read unit analyzed base sequence information as payload. FASTAQ refers to a file format in which quality score information is added to FASTA information. The nucleotide sequence information analyzed in the NGS sequencer was stored as FASTAQ, compared with the reference sequence 105 using the NAPConvertor of the comparison module 106, and the reads were counted cumulatively and converted into a NAP file format. The NAP file refers to a file format in which the nucleotide sequence of the binding SSN (read) and the frequency of occurrence of the read, ie, the binding profile, are stored in the FASTAQ information. NAPConverter generates a NAP file that is a combined profile by calculating the frequency of a series of data conversion processes and reads to generate a frequency measurement value.

The binding profile for the sample group to be analyzed was created, a binding profile DB was constructed based on the biological meaning of the sample group to be analyzed, and a characteristic group and a binding SSN group that can distinguish the constructed DB well were selected and evaluated.

Binding SSNs binding to serum proteins harvested after reacting a population of binding SSNs binding to various serum proteins (N=1,149) to the lung cancer serum (N=10) and non-lung cancer lung disease patient serum (N=22) shown in Table 4 The nucleotide sequence of the binding SSN was determined for the population by NGS. A database (N= 32) is built.

Clinical information for lung and non-lung cancer patients lung cancer Non-Lung Cancer Patients with Lung Disease gender male 10 21 woman 0 One age 40-49 One 0 50-59 5 9 60~69 4 11 70-79 0 One Sum 10 22

Figure 25 shows the distribution of the characteristics and binding SSNs selected by the AptaCDSS program of the biological meaning determination support system 300 for lung cancer and non-lung cancer lung disease patients with the produced binding profile.

Referring to <Example 3>, the binding SSN population composed of the 1149 binding SSNs determined above was reacted with the analyte sample, and the binding SSN population separated from the complex population formed was analyzed with an NGS sequencer, and the molecular data of FIG. 19 A binding profile was produced with NAPConverter in the comparison module 106 of the production module 100 .

The result of hierarchical clustering according to the selected characteristics is shown in FIG. 26, and it can be seen that lung cancer and non-lung cancer lung disease patients form independent clusters, respectively, so that the patients can be distinguished by the binding profile for the analysis sample. is showing

Contribute to the analysis of the biological significance of specifically binding to the test sample, the serum protein of lung and non-lung cancer lung disease patients, by exploring and analyzing the molecular data profile obtained by performing NGS on the binding SSN group that binds to the molecular group constituting the biological sample. Differential combining SSNs can be selected.

Among them, as a result of evaluating the degree of binding to the serum of a lung cancer patient, a test sample of the serial number 768 binding SSN, and a control sample, the serum of a non-lung cancer lung disease patient, it was found that it reacted specifically only to the serum of a lung cancer patient (Fig. 27).

By comparative analysis of the database composed of each experimental group, useful spots were determined, binding SSNs contributing to the analysis of corresponding biological meanings were prepared, and corresponding proteins were isolated and identified with reference to FIG. 13 .

Biotin is attached to one side of the selected binding SSN, and the complex obtained by reacting with streptavidin is reacted with a serum sample to separate the serum protein-binding SSN complex, and then the band formed by electrophoresis is separated to separate the serum Proteins were identified. By separating the protein that binds to the selected SS oligonucleotide, the amino acid sequence of the protein can be determined with the MALDI-TOF-TOF instrument, and the dissociation constant (Kd) of the serum protein that binds to the binding SSN is ^{less than 20x10 -9 as a} "target molecule" said.

Example 7. Molecular data production

The internal quality control and biological meaning determination support system 1 presented in FIG. 18 of the present invention may include a molecular data production module 100 , an internal quality control module 200 and a biological meaning determination support module 300 . . The molecular data production module 100 may measure the expression of a molecular group or gene in a sample composed of various types of molecules by various techniques well known in the art.

7-1. AptaSSN multiplex method

Figure 3 illustrates the AptaSSN multiplex assay for analyzing a sample for biomarker identification. In the present invention, AptaSSN includes an AptaSSN selected as a target molecule by the orthodox SELEX method and an SSN that binds to a molecular group constituting the sample. In the present invention, AptaSSN and binding SSN are used interchangeably.

1) AptaSSN-FSS population preparation

AptaSSN population solutions for 2%, 0.1% and 0.01% serum were prepared at 2x concentrations in 1xSB17, 0.05% Tween-20. As the AptaSSN population solution, 1,149 binding SSNs and AptaSSN populations determined in Example 4 were used.

These solutions were stored at -20 °C until use. Immediately prior to analysis, the AptaSSN population solution was thawed at 37 °C for 10 min, placed in a boiling water bath for 10 min, and cooled to 25 °C for 20 min, with vigorous mixing between each step. After heat-cooling, 55ul of each 2x AptaSSN population solution was manually pipetted into 96-well PCR tubes and the tubes sealed with foil.

<Adding first tag to AptaSSN group>

Thaw all kit components except 30% PEG and DMSO on ice. Thaw DMSO at room temperature and heat 30% PEG at 37° C. for 5-10 minutes to warm to a liquid in volume. The heating block was adjusted to 85°C.

5 μL of AptaSSN population solution was transferred to a microcentrifuge tube. After heating the AptaSSN population solution at 85° C. for 3-5 minutes, place the AptaSSN population solution immediately on ice. AptaSSN stock solution may be heated to relax the secondary structure. In addition, heating the AptaSSN population solution in the presence of ~25% DMSO may increase the efficiency of the AptaSSN population solution with an important second structure.

Label reactions were prepared for either control systems or test AptaSSN population solutions by adding the components in the order listed in Table 5.

First tag addition reaction of AptaSSN solution component Volume ( μ L) Final Concentration (Amount) Nuclease-free Water 3 --- 10X RNA Ligase Reaction Buffer 3 1X RNase Inhibitors One 1.33U/μL (40U) AptaSSN solution 5 1.67 μM (50 pmol) Biotinylated Cytidine (Bis)phosphate One 33.3 μM (1 nmol) T4 RNA Ligase 2 1.33U/μL (40U) 30% PEG 15 15% Total 30 ---

30% PEG is the last reagent added and after carefully pipetting 30% PEG to the reaction mixture, a new pipette tip was used to mix the ligation reaction.

A Bio-SSN library was prepared by adding a first tag to the AptaSSN solution by treating the reaction at 16°C for 2 hours. The treatment can be done overnight to increase the efficiency of ligation. 70 μl of nuclease-free water was added to the ligation reaction.

100 μl of chloroform:isoamyl alcohol was added to each reaction to extract RNA ligase. The mixture was gently mixed and then the phases were separated by centrifugation in a microcentrifuge at high speed for 2-3 minutes. Carefully take the top (aqueous layer) and transfer it to a new tube. The Bio-SSN library was purified by adding biotin, a first tag to the SSN library.

The concentration of each Bio-AptaSSN solution was 4 nM. The Bio-AptaSSN solution was diluted 4-fold with SB17 buffer and heated at 95°C for 5 minutes and then cooled to 37°C for 15 minutes before use. Suspended primary solid support (FSS), streptavidin beads were washed twice with 150 mL buffer PB1 before use.

<AptaSSN-FSS collective manufacturing>

133 μL of Suspension First Solid Support (FSS) in 1xSB17, Tw (40 mM HEPES, 102 mM NaCl, 1 mM EDTA, 5 mM MgCl2, 5 mM KCl, 0.05% Tween-20), Streptavidin-Coupled Dynabeads (Thermo Fisher Scientific) , USA) into the tube. Thaw by vortexing approximately 1.1x Bio-AptaSSN solution (all AptaSSNs contain a biotin moiety at the 3' end).

The 1.1x Bio-AptaSSN solution was then boiled for 10 minutes, vortexed for 30 seconds, and cooled to 20° C. in a water bath for 20 minutes. 100 μL of 1.1x Bio-AptaSSN solution was added to the tube with Streptavidin-Coupled Dynabeads. The mixture was protected from light and treated at 25° C. for 20 minutes on a shaker set at 850 rpm. The first tag biotin portion of the Bio-SSN library and the first capture component avidin portion of Streptavidin-Coupled Dynabeads reacted to form a suspension AptaSSN -FSS solution was prepared.

The suspended AptaSSN-FSS solution was removed by centrifugation. 190 μL of 1x CAPS prewash buffer (50 mM CAPS, 1 mM EDTA, 0.05% Tw-20, pH 11.0) was added, and the mixture was treated with shaking for 1 minute. The CAPS wash solution was then removed via centrifugation. Washed with the CAPS and repeated once more.

190 μL of 1xSX17, Tw was added and the mixture was treated with shaking for 1 minute. 1xSB17, Tw was removed by centrifugation. An additional 190 μL of 1xSX17, Tw was added, and the mixture was treated with shaking for 1 minute. 1xSB17, Tw was removed by centrifugation (1 min at 1000x g). After removing 1xSB17 and Tw, 150 μL of storage buffer (150 mM NaCl, 40 mM HEPES, 1 mM EDTA, 0.02% sodium azide, 0.05% Tween-20) was added to prepare the suspended AptaSSN-FSS bead solution. Purified.

The tube was carefully sealed and stored in the dark at 4°C until use. Suspension AptaSSN-FSS solutions for 2%, 0.1% and 0.01% serum were prepared at 2x concentrations in 1xSB17 (40mM HEPES, 102mM NaCl, 5mM KCl, 5mM MgCl2, 1mM EDTA), 0.05% Tween-20.

These solutions were stored at -20 °C until use. On the day of analysis, each suspension AptaSSN-FSS solution was thawed at 37 °C for 10 min, placed in a boiling water bath for 10 min, and cooled to 25 °C for 20 min, with vigorous mixing between each step. After heat-cooling, 55ul of each suspension 2x AptaSSN-FSS solution was manually pipetted into a 96-well PCR tube and the tube sealed with foil.

2) The reaction between the AptaSSN population and the molecular population in the sample

Frozen 100% serum samples stored at -80°C were placed in a 25°C water bath for 5 min. The thawed sample was placed on ice to create a slow vortex, and then placed on ice again.

By using a 20ul pipette to transfer 3ul of the sample to a 96-well PCR tube, 72ul of an appropriate sample dilution (1xSB17, 0.02% Tween-20) was put into each tube to prepare a 4% sample solution (2x final). After mixing the samples by pipetting several times, 4 ul of a 4% sample solution was taken and mixed with 76 ul of a titrated sample dilution to obtain a 0.2% sample solution (final 2x). Finally, 6ul of the 0.2% sample solution was moved and mixed with 54ul of the sample dilution, to obtain a 0.02% sample solution (final 2x). After preparing the sample dilution solution, 55ul of each sample was transferred to a new PCR tube for equilibrium binding.

55ul of the above hot-cooled suspension AptaSSN-FSS solution was added to a tube with a volume of 55ul of titrated sample dilution solution. The sample consisting of the molecular population and the AptaSSN-FSS solution were mixed by pipetting and sealed with a foil cover. Thereafter, the plate was subjected to equilibrium binding in a treatment process at 37 °C for 3 hours and 30 minutes so that each molecule in the sample and each AptaSSN were bound to form a suspended M-AptaSSN-FSS complex.

Place the tube on a stand with a magnet or centrifuge to carefully pipet the solution layer away, and remove the tube with the harvested M-AptaSSN-FSS complex with 300ul of buffer PB1 supplemented with 1 mM dextran sulfate and 500 uM biotin. Washed 4 times. Then the tube was washed 3 more times with 300ul of buffer PB1.

3) Analysis of the eluted AptaSSN population

A second tag was added to the protein of the M-AptaSSN-FSS bead complex by adding 150 uL of freshly prepared second-tagged 1 mM NHS-PEG4-biotin solution in buffer PB1 to the washed tube and shaking for 5 minutes.

After centrifuging the tube containing the M-AptaSSN-FSS bead complex with the second tag in the suspension state to remove the liquid by suction, the tube was washed 8 times with 300ul of buffer PB1 supplemented with 10 mM glycerin. 100ul of buffer PB1 supplemented with 1 mM dextran sulfate was added.

The buffer solution containing the M-AptaSSN-FSS bead complex with the second tag in the tube was pipetted into the wells of the second solid support (SSS), streptavidin-coated plate with the second capture component. The second tag of the suspended M-AptaSSN-FSS bead complex and the second capture component of the plate, which is the immobilized state SSS, bind to the immobilized state SSS-M-AptaSSN-FSS bead complex by performing in a thermomixer at 800 rpm and room temperature for 10 minutes. A second separation was performed to form

After performing the second separation, the liquid was carefully removed from the wells of the plate with a pipette, and washed 8 times with 300ul of buffer PB1 supplemented with 25% propylene glycol or 30% glycerol. Wells were washed 5 times with 300ul of buffer PB1, and the final wash was aspirated. 100ul of CAPSO elution buffer was added and the AptaSSN population was eluted from the stationary SSS-M-AptaSSN-FSS bead complex population by shaking for 5 minutes. The bound AptaSSN population was harvested by manually transferring 90ul of the sample from the wells of the plate to the wells of the PCR plate containing 10uL Neutralization buffer. Alternatively, the bound AptaSSN may be eluted by heating the wells of the plate with the immobilized state SSS-M-AptaSSN-FSS bead complex population for 5 minutes.

According to <Example 4>, the AptaSSN population eluted from the fixed-state SSS-M-AptaSSN-FSS bead complex population was primed with DS primer C2' to prepare cDNA by reverse transcription (RT), followed by DS primer C1 and PCR was performed using DS 　 primer 　 C2' to amplify the DNA group for the AptaSSN group that binds to the serum protein. The obtained PCR product was used as a nucleic acid sample for the purpose of preparing a sequence library. The harvested PCR product was subjected to NGS analysis in the same manner as in <Example 4>.

A document converted from the fastaq data produced by the molecular data production module 100 to the NAP conveter may be input to the internal quality control module 200 .

The FASTAQ data produced by the NGS equipment was compared and analyzed with the database constructed with the base sequence of 1,149 AptaSSN established through the NAF converter to determine the type and frequency of appearance of the bound SSG and AptaSSN.

The read of the FASTAQ has an agreement rate of 10 to 90% and preferably 40 to 60% with a reference database consisting of a reference nucleotide sequence.

The molecular group constituting the sample can be analyzed by the type and frequency of AptaSSN produced by the AptaSSN multiple test method. The amount of molecule in the sample is closely related to the amount of target molecule to which AptaSSN is used. After eluting AptaSSN bound to the target molecule and sequencing the eluted AptaSSN with NGS equipment, the frequency of AptaSSN is determined. The amount of molecular population constituting the sample was determined from

7-2. Molecular data of mRNA and miRNA

EDTA blood samples were collected from test and control samples and processed for plasma within 2 hours of collection. Blood was centrifuged at 1,300 g for 20 minutes at 10°C. The supernatant (plasma) was transferred to a centrifuge tube, followed by a second high-speed centrifugation step at 15,500 g for 10 minutes at 10°C to remove cell debris and fragments. Approximately 1 ml of plasma was immediately aliquoted into three 300-400 μl aliquots into frozen vials, quenched in liquid nitrogen, stored at -80 °C until use, and subsequently mRNA and miRNA profiles as a molecular data production model (10). Stored at -70°C for analysis.

1) RNA isolation for mRNA and miRNA analysis

Total RNA from analysis samples was isolated from 400 μl of plasma using MagMAx mirVana total RNA isolation kit and King Fisher Flex purification system (Thermo Scientific, USA). The final total RNA recovered ranged from 5 to 25 μg and was stored at -70 °C. 1.5 volume of absolute ethanol was added to the tube with the recovered total RNA, and the mixture was applied to a miRNeasy Mini kit column (Qiagen, Germany). After washing, miRNAs were dissolved in 30 μl of RNase-free water. RNA quantity and purity were assessed using a Nanodrop ND-8000 instrument (Thermo Scientific, USA) with 2-4 μl of each aliquot set aside for purity analysis using an Agilent 2200 TapeStation system (Agilent Technologies, USA). Samples eligible for sequencing by testing for purity, concentration and integrity of RNA (RIN, RNA Integrity Number) values ≥8.0, 28S/18S ≥1.5; no standard improvement; 5S peak normal) was used.

2) RNAseq method for mRNA analysis.

A cDNA library was prepared using the TruSeq Stranded mRNA Sample Prep Kit (Illumina, USA). 50 bp paired-end and strand-specific sequencing was performed on the Illumina platform generating 25 to 35 million reads per sample. A document converted from the fastaq data produced by the molecular data production module 10 to the NAP conveter may be input to the internal quality control module 20 .

3) RNAseq method for miRNA analysis.

1.5 μg of total RNA was taken and a small RNA library was constructed using the method described in the NEBNext® Ultra™ Small RNA Sample Library Prep Kit for Illumina® (NEB, USA). After building the library, the library concentration was tested using Qubit 2.0 and adjusted to 1 ng/μl. Insert size was measured with an Agilent 2100 bioanalyzer. The prepared NGS library was subjected to high-throughput sequencing using Illumina HiSeq2500. The library generated 10 million reads per sample. The developed method is designed to greatly enrich the microRNA (miRNA) portion of the RNA fraction.

Like the internal quality control and biological meaning determination support system 1 shown in FIG. 18 , the molecular data production module 100 can produce molecular data for nucleic acids or proteins in a sample composed of various types of molecules. The evaluation sample having the produced molecular data is input to the internal quality control module 200, and only valid molecular data determined by quality control with the internal quality control rule can be input to the biological meaning determination support module 300.

Example 8. Internal quality control and biological meaning determination support system

The internal quality control and biological meaning determination support system 1 of FIG. 18 may include a molecular data production module 100 , an internal quality control module 200 and a biological meaning determination support module 300 . The molecular data production module 100 may measure the expression of a molecular group or gene in a sample composed of various types of molecules by various techniques well known in the art.

The protein group of serum has a dynamic range of 12 orders of magnitude, so high-abundance proteins can inhibit the analysis of low-abundance proteins. For sample analysis, three characteristic QC samples were measured with the sample to minimize assay variation. 3, the analysis sample was prepared by dilution method by dividing the high level, medium level and low level of protein concentration in the detection range of the analysis method.

The role of the internal quality control molecule in the present invention is to ensure the quality of the analysis by checking whether the data measured after the analysis reaches the target value. Quality control molecules must also have target values, and in order to obtain these target values, performance indicators such as accuracy, sensitivity, and specificity and target values of measurement values must be calculated through repeated experiments, interlaboratory comparisons, and inter-experimental comparisons. In addition, the internal quality control molecule should have little change in the measured value during analysis.

1) Securing the database

In the case of AptaSign lung cancer products, it is necessary to obtain data converted from the biometric information generated after NGS analysis into its own data format according to the AptaSgin analysis protocol, using the experimental group as lung cancer and the control group as non-lung cancer lung disease patients.

2) Selection of internal quality control molecules

The average and standard deviation of each 1149 AptaSSN constituting the molecular data of the sample are calculated. The coefficient of variation for each 1149 AptaSSNs is calculated.

Sort the coefficients of variation in ascending order. Select the top 1% (12 out of 1149 AptaSSNs) sorted by coefficient of variation. The selected 12 AptaSSNs are sorted in ascending order based on the average. The average-sorted AptaSSN is divided into 3 intervals (4 aptamers in each interval). AptaSSN selected for each section is used for quality control testing.

3) Internal quality control method

The purpose of internal quality control is to detect analytical errors that may occur during inspection in advance, and there are random errors and systematic errors.

If the test is repeated indefinitely, the measured values form a normal distribution with the mean and standard deviation. Random error is the error with the same mean but increased standard deviation, and systematic error can be defined as the error with the same standard deviation but shifted mean. .

The Westgard Multirules Control Chart is designed to complement the Levey-Jennings Control Chart to have a low false rejection rate and high error detection rate. This method accumulates the measurement values of quality control substances and evaluates them semi-quantitatively according to various control rules.

When applying the West Guard multiple rule using Levey Chart, there should be as many Levey Charts as the number of quality control molecules. West Guard Multi Rule Check the criteria required in each step on the Levey Chart and judge whether the sample is rejected.

The mean and standard deviation are calculated using the database built for the selection of 12 internal quality control molecules. %CV (Coefficient of variation) is calculated using the mean and standard deviation. Based on the %CV value, 12 small internal quality control molecules are selected (1% of the total aptamer is used).

After selecting 3 sections of internal QC molecules, the 12 selected internal QC molecules are divided into 3 sections based on the average. There are three sections, high, medium, and low, and 4 internal quality control molecules are selected for each section.

<Structural diagram 2>

Referring to <Structure 2>, first, by applying the 1:3S, 2of3:2S, R4S and 3:1S multiple rules, using the selected internal QC molecules for each of the three internal QC molecules (high, medium, low) Quality was controlled, and then, the 12X rule was applied to control the quality using 12 internal quality control molecules.

① 1:3S

Rejection rule applied (random error) when one reference molecule measurement is outside the limit of ±3SD from the mean.

② 2of3:2S

Rejection rule is applied when two of the three reference molecule measurements show +2SD or -2SD in the same direction.

③ R4:1S

Rejection rule applied (random error) when one of the two reference molecule measurements is outside the limit of ±2SD from the mean and the other is outside the limit of -2SD.

④ 3:1S

Rejection rule is applied when three reference molecule measurements are continuously outside the limit of +1SD or -1SD, which is the same direction of the average value.

⑤ 12x

Rejection rule is applied when 12 reference molecule measurements in succession fall in one direction of the average value.

The internal quality control module 200 presented in FIG. 20 includes software implementing the West Guard multiple rule <Structural Figure 2>.

4) Determination of biological meaning

Determination of the biological meaning of the evaluation sample with molecular data is the internal quality control and biological meaning determination support system 1 shown in FIG. 18, the molecular data production module 100 shown in FIG. 19, and the internal quality control shown in FIG. The module 200 and the biological meaning determination support 300 presented in FIG. 21 were utilized.

Preferably, in the learning step, the molecular data production module 100 compares the experimental data produced by the NGS sequencer 104 for the nucleic acid or protein molecular population in the sample to the comparison module 106 to produce molecular data and a binding profile. , stored and input to the biological meaning determination support system 300 to build a learning database. An internal quality control molecule, a classification molecule, and AptaDx are selected as far as necessary for the evaluation of the evaluation sample using the learning database.

In the classification step, the molecular data converted by the NAPConverter built in the analysis module 306 to the experimental data produced by the NGS sequencer 104 obtained by the molecular data production module 100 analyzing the evaluation sample, the internal quality control input to module 200 . The internal quality control module 200 inputs the molecular data of the sample determined to be valid by internally controlling the input molecular data to the biological meaning determination support module 300 . The AptaDx program built in the biological meaning determination support module 300 analyzes the input valid molecular data to predict and classify the sample or the biological state of the organism providing the sample.

Samples with clear biological definitions were collected from Asan Medical Center (IRB approval number: 2015-0607) in Seoul and used in this example. Table 6 shows clinical information of lung cancer-related diagnostic samples used in this Example.

Clinical information of lung cancer-related samples
learning set evaluation set lung cancer control lung cancer control sample water 150 150 50 50 average age 62.9 56.1 64.3 56.6 median mean age 63 56 65 56 Castle man 116 92 40 34 Woman 34 58 10 16 Histopathology of NSCLC Squamous cell carcinoma 62 18 Adenocarcinoma 61 20 NSCLC Stage I 32 10 Stage II 15 4 Stage III 30 11 Stage IV 46 13 small cell lung cancer 27 12 SCLC LD 8 3 ED 19 9 Pneumonia 40 21 Tuberculosis 33 10 Benign mass 3 2 Etc 74 17

<Learning stage>

First, the binding profile for the sample to be analyzed in Table 6 was constructed as a standard db for lung cancer and non-lung cancer. In addition, it is possible to construct a detailed db that divides lung cancer samples into small cell lung cancer and non-small cell lung cancer, and for non-small cell lung cancer, a db that divides adeno type and sqamous type according to the cell type can be constructed, and depending on the stage 1 and 2 It is possible to build a db composed of the initial type of period and the 3rd and 4th stage of the terminal type.

Referring to <Example 7> and FIG. 13 in the first reaction section 101 of the molecular data production module 100 shown in FIG. 19, AptaSSN-FSS beads were added to the serum samples, respectively, and protein-AptaSSN complexes Populations were formed ( FIG. 13 a ). Alternatively, each of the above serum samples was attached to a nitrocellulose disc to form a protein-AptaSSN complex population (FIG. 13b). A FASTAQ file was generated by preparing an NGS library according to <Example 4> in the second reaction zone 103 of the AptaSSN population separated in the separation zone 102 from the formed complex and processing it in the NGS sequencer 104. After the comparison module 106 compares and analyzes the reference sequence 105 with the reference sequence 105 to generate molecular data and binding profiles for the sample, various standard dbs were constructed according to the biological meaning in the biological meaning determination support module 300 .

The input module 302 of the biological meaning determination support module 300 of FIG. 21 builds the lung cancer presence/absence learning set DB and the stage set DB constructed according to the biological meaning, and first, Min-Max Normalization in the sample for the learning set DB, Qauntile Normalization was performed between samples, and one-way Anova was performed on the normalized DB to select features to select a classifying molecular group. The distribution of the selected feature group is shown in FIG. 28 .

The primary evaluation of the selected feature group was performed using a clustering technique, and the results are shown in FIG. 29 .

In the analysis module 304 of the biological meaning determination support module 300 of FIG. 21, the combined profile of the learning set is learned with a classifier composed of an artificial neural network, and the learning result can be evaluated by storing the MOD file. . The evaluation was performed by 10-flod cross validation, and the ROC curve, clinical sensitivity and specificity according to the threshold were calculated for each section. This threshold value was based on clinical evaluation (determining to maintain optimal sensitivity and specificity) without considering the technical performance characteristics of this test method. did. The MOD file, an educational product that can maintain ideal clinical sensitivity and specificity, was selected as the AptaSSNaDx program and used as a program to classify the sample binding profile constituting the evaluation set.

The secondary evaluation of the selected feature group is learned using a machine learning classifier, and the result of evaluation by a 10-fold cross validation method is shown in FIG. 30 .

As shown in FIG. 29 , the results of evaluating the learning results with the AptaCDSS program are threshold values, clinical specificity, sensitivity, and the like for 10 sections. The ideal educational product, MOD file for the lung cancer presence/absence learning set db is a 1-9 fold educational product, MOD file showing a threshold value of 0.50, sensitivity 1.0, and specificity 1.0, and this was used as the AptaDx program. The result of analyzing the binding profile of the training set analysis sample with the AptaCDSS program is expressed as a degree of similarity.

<Classification stage>

The internal quality control program built in the internal quality control module 200 shown in FIG. 20, and the NAPConverter and AptaCDSS program programs built in the analysis module 303 of the biological meaning determination support module 300 shown in FIG. Classification molecules, internal quality control molecules, and AptaDx programs were determined for the constructed lung cancer learning set DB.

The internal quality control module 200 presented in FIG. 20 selects 12 quality control molecules using the mean, mean deviation, and coefficient of variation for molecular data in the sample constituting the learning database, and selects the molecular data to be analyzed. The internal quality control module 200 including software for applying the Westgard Multirules Control Chart using the selected 12 molecular data uses the molecular data production module 100 to internally control the molecular data in the sample to be effective molecules data was determined.

The Westgard Multirules Control Chart implementation program included in the internal quality control module 200 presented in FIG. 20 analyzed molecular data in the samples constituting the evaluation set DB to determine the effective sample, and the result was that the effective rate was 100%.

The diagnosis procedure of the AptaDx is as follows. The artificial neural network is initialized by reading the MOD format file in which the learning results of the artificial neural network are stored in the learning set DB. That is, the MOD file that sets the artificial neural network to the previously learned state was driven. The value of the frequency of appearance of the combined SSN, which is a characteristic of the NAP file, was converted into a log value. Min-max normalization was performed on the data range and distribution of the binding SSN, which is a characteristic of the converted NAP file. In addition, quantile normalization was divided into non-implemented cases and non-implemented cases, and in the case of implementation, the DB was performed on lung cancer DB, non-lung cancer lung disease DB, and all DBs, that is, three DBs that combined the two DBs. The binding profile for the evaluation set db sample desired for diagnosis was analyzed by retrieving the value of the characteristic and binding SSN group specified in the MOD file from 4 types of normalized NAP files. Therefore, four types of normalization process were performed on the binding profile for a specific sample, and four analysis results were calculated, and the final result was determined to be three or more results.

Two result values indicating the possibility of positive or negative test results are output. The output value is called similarity, and the positive threshold value is 0.5. If it is 0.5 or more, it is judged as positive, or if it is 0.5 or less, it is judged as negative.

positive ≥ threshold

negative < threshold

Table 7 shows the results of classifying the evaluation set samples with the predictive model AptaDx program, and the clinical sensitivity is 98.0% and the specificity is 100.0%.

Clinical evaluation of samples from the evaluation set disease or condition Confirmation result Sum lung cancer Lung disease other than lung cancer Technology developed in this study lung cancer 49 0 49 People with lung disease, not lung cancer One 50 51 Sum 50 50 100

1: internal quality control and biological meaning determination support system, 100: molecular data production module, 101: first reaction zone, 102: second reaction zone, 103: sequencer, 104: reference sequence, 105: comparison module, 200: internal Quality control module, 201: input module, 202: control module, 203: interface providing module, 204: display module, 205: storage module, 300: biological meaning determination support module, 301: operating system software, 302: input module, 303 : analysis module, 304: visualization module, 305: output module, 306: output device, 307: controller, 308: memory,
1000: AptaSSN fluid magnetic bead device, 1100: injection unit, 1110: first inlet, 1120: second inlet, 1130: first reagent bottle, 1140: second reagent bottle, 1200: flow channel, 1210: reaction port, 1211: second 1 reaction sphere, 1212: second reaction sphere, 1213: magnetic field applying unit, 1220: discharge unit, 1230; Collection unit, 1310: first valve, 1320: second valve, 1330: third valve, 1340: fourth valve, 1350: fifth valve

Claims (71)

(a) comprising a central region consisting of an arbitrary nucleotide sequence and both terminal regions consisting of a fixed sequence,
(b) comprising a first tag at either end selected from both ends,
(c) A single-stranded nucleic acid library comprising a plurality of single-stranded nucleic acids (SSNs) binding to a sample composed of one or more types of molecular (M) populations. The method according to claim 1, wherein the fixed sequences of both terminal regions are subjected to PCR using the single-stranded nucleic acid as a template;
(a) the 5' end fixed sequence with the same nucleotide sequence as the nucleotide sequence of the forward primer; and
(b) a single-stranded nucleic acid library comprising a 3' end fixed sequence for a nucleotide sequence complementary to the nucleotide sequence of the reverse primer. The method of claim 1, wherein the single-stranded nucleic acid is
(a) a starting library consisting of a DNA oligonucleotide comprising an arbitrary base sequence; and
(b) a starting library consisting of a DNA oligonucleotide comprising a fixed sequence at both ends of the DNA oligonucleotide library of step (a); Nucleic Acid Library. The method of claim 1, wherein the nucleotides constituting the single-stranded nucleic acid are
A single-stranded nucleic acid library comprising at least one selected from the group consisting of natural and modified nucleotides. According to claim 1, wherein the sample is
A single-stranded nucleic acid library, characterized in that any one selected from the group comprising a biological sample, an environmental sample, a chemical sample, a pharmaceutical sample, a food sample, an agricultural sample, and a veterinary sample. The method of claim 5, wherein the biological sample is
blood, whole blood, leukocyte, peripheral blood mononuclear cells, plasma, serum, sputum, breath, urine ), semen, saliva, meningeal fluid, amniotic fluid, glandular fluid, lymph fluid, nipple aspirate, bronchial aspirate aspirate, synovial fluid, joint aspirate, virus, bacteria, cell, cellular extract, stool, tissue, tissue A single-stranded nucleic acid library characterized by a biological sample selected from the group comprising a tissue extract, tissue biopsy, and cerebrospinal fluid. According to claim 1, wherein the molecule (M) is
Proteins, polypeptides, carbohydrates, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, viruses, Substrates, metabolites, transition state analogs, cofoactors, inhibitors, drugs, dyes, nutrients, growth factors factors), bacteria, tissues, and a single-stranded nucleic acid library characterized by any one selected from the group comprising a controlled substance. According to claim 1, wherein the first tag is a material that binds to the first capture component.
Polynucleotide, polypeptide, peptide nucleic acid, locked nucleic acid, oligosaccharide, polysaccharide, antibody, affibody, antibody imitation Antibody mimic, cell receptor, ligand, lipid, biotin, avidin, streptavidin, extravidin, neutravidin ), streptavidin (streptavidin), metal (metal), histidine (histidine), and single-stranded nucleic acid library, characterized in that any one selected from the group containing any part of any one of these structures. The method of claim 1, wherein the single-stranded nucleic acid (SSN) is
Forming an M-SSN complex group by contacting the sample consisting of the plurality of kinds of molecular (M) groups,
To provide an M-SSN complex population that simultaneously provides one or more types of AptaSSN that allows the sample to be classified according to the biological significance of the sample by using the quantitative difference between the samples of the SSN constituting the M-SSN complex group Single-stranded nucleic acid library, characterized in that. The method of claim 1, wherein the biological meaning is
(a) The process of making decisions for the purpose of health management of prevention, diagnosis, treatment, alleviation and treatment in accordance with the clinical test value information necessary for, or
(b) means “classification”, “score” or “index” to support the process of making decisions about biological significance for the purpose of diagnosing, treating, mitigating, treating, or preventing a disease or other condition; , after collecting the sample for which the biological meaning is determined or the clinical information of the person who took the sample and the produced multiple molecular values, a disease from the collected information using a statistical technique or a machine learning algorithm A single-stranded nucleic acid library comprising; a specific result produced to be able to infer or discriminate. 11. The method of claim 1 to 10,
(a) a single-stranded nucleic acid (SSN) library with a first tag prepared in the starting library and a first solid support (FSS) having a first capture component are brought into contact so that the first tag and the first capture component are A first mixed solution in which a bound SSN-FSS complex is prepared, and the SSN-FSS complex and a sample composed of a molecular (M) group are brought into contact to form an SSN-FSS complex (M-SSN-FSS complex) bound to the molecule or, by contacting the SSN library with a sample composed of a molecular population to prepare a mixed solution containing the SSN complex (M-SSN complex) bound to the molecule, the prepared mixed solution contains a first solid support (FSS) is added, and the first tag in the SSN of the M-SSN complex is combined with the first capture component in the FSS to prepare a first mixed solution in which the M-SSN-FSS complex is formed;
(b) washing the M-SSN-FSS complex with one or more washing solutions to dissociate components of the first mixed solution that do not bind to the SSN or have non-specific binding in the first mixed solution, A first separation step of removing one or more components of the mixture that are non-specifically bound to the SSN or not;
(c) addition of a second tag having an affinity for the second capture component and a second tag forming a second tag-M-SSN-FSS complex from the molecule (M) of the first isolated M-SSN-FSS complex step;
(d) the M-SSN-FSS complex to which the second tag is added and a second solid support (SSS) having the second capture component come into contact with each other, and the second tag and the second capture component bind to the SSS - Preparing a second mixed solution in which the M-SSN-FSS complex is formed;
(e) a second separation step of removing one or more components of the mixture not bound to the second capture component from the second mixed solution;
(f) eluting a material population comprising SSN from the second isolated SSS-M-SSN-FSS complex;
(g) preparing an amplified SSN population from the material group containing the eluted SSN, or additionally repeating steps (a) to (f) in one round or more; and
(h) analyzing the substance group including the SSN eluted in step (f); 12. The method of claim 11,
The apparatus for eluting the material group including the SSN from the second separated SSS-M-SSN-FSS complex is a fluid structure comprising an injection unit 1100, a flow channel 1200 and a valve unit 1300,
The injection unit 1100 is composed of one or more injection ports and a reagent bottle, a sample solution composed of a molecular (M) group, a single-stranded nucleic acid (SSN) library solution with a first tag, and a first solid support containing a first capture component. (FSS) solution, second tag solution, reaction solution, washing solution, distilled water, air, and any one or more selected from the group consisting of elution solution is injected from each reagent bottle,
The flow channel 1200 is sequentially positioned at least one selected from the group consisting of a reaction unit 1210, a discharge unit 1220, and a collection unit 1230,
The reaction unit 1210 is composed of one or more reaction spheres, forms and washes the M-SSN-FSS complex, and adds a second tag to the molecules (M) of the washed complex,
The collection unit 1230 includes a second solid support (SSS) having a second capture component, forms the SSS-M-SSN-FSS complex, and uses the elution solution to form the SSS-M-SSN-FSS eluting a material population comprising SSN from the complex,
The valve unit 1300 is composed of one or more valves that enable movement and storage of the solution from the injection unit 1100 to the flow channel 1200,
AptaSSN fluid magnetic bead apparatus (1000), characterized in that the reaction unit (1210) is provided with a magnetic force applying unit (1213). 13. The method of claim 12,
The flow channel 1200 is an AptaSSN fluid magnetic bead device 1000, characterized in that at least one selected from the group consisting of a microfluidic chip, a pouch, and a tube. 14. The method of claim 13, wherein the pouch (1200)
Made of any one flexible plastic film or other flexible material selected from the group consisting of polyester, polyethylene terephthalate (PET), polycarbonate, polypropylene, polymethyl methacrylate, and mixtures thereof,
AptaSSN fluid magnetic bead apparatus 1000, characterized in that it is manufactured by any one process selected from processes including extrusion, plasma deposition and lamination. 15. The method according to claim 12 to 14,
AptaSSN fluid magnetic bead device (1000), characterized in that the flow channel is installed to be detachably attached to the magnetic force applying unit. 16. The method according to claim 12 to 15,
The AptaSSN fluid is composed of the injection unit 1100 composed of a first inlet 1110 and a second inlet 1120 and the reaction unit 1200 composed of a first reaction port 1210 and a second reaction port 1220 . The operation of the magnetic bead device 1000 is
(a) the sample solution composed of the molecular (M) group, the SSN library solution, and the FSS solution are injected from the first inlet 1110, the molecular (M) group constituting the sample in the reaction unit; preparing a first mixed solution in which an M-SSN-FSS complex is formed by reacting the first tag of the SSN and the first capture component of the FSS;
(b) the washing solution is injected from the second inlet 1120, and the M-SSN-FSS complex formed in the first mixed solution is washed in the first reaction port 1211 to non-specifically bind or bind to the SSN A first separation step of removing one or more components of the mixture that have not been mixed with the discharge unit 1220;
(c) the second tag solution is injected from the first inlet 1110, and the second tag- adding a second tag to form an M-SSN-FSS complex;
(d) the second tag-M-SSN-FSS complex solution is injected from the second reaction port 1212 , and the second capture component of SSS and the second tag-M- in the collecting unit 1230 preparing a second mixed solution in which the second tag of the SSN-FSS complex reacts to form the SSS-M-SSN-FSS complex;
(e) the washing solution is injected from the second inlet 1120, and the SSS-M-SSN-FSS complex formed in the second mixed solution in the collecting unit 1230 is washed, and the second capture component and a second separation step of removing unbound one or more components of the mixture;
(f) the dissolution solution is injected from the first inlet 1110, and eluting the material group containing the SSN from the second separated SSS-M-SSN-FSS complex in the collecting unit 1230 ;
(g) preparing an amplified SSN population from the material group containing the eluted SSN, or additionally repeating steps (a) to (f) in one round or more; and
(h) collecting the material group including the SSN eluted from the collecting unit 1230 in the step (f); 13. The method of claim 11 to 12, wherein the second tag is a material that binds to the second capture component,
Polynucleotide, polypeptide, peptide nucleic acid, locked nucleic acid, oligosaccharide, polysaccharide, antibody, affibody, antibody imitation Antibody mimic, cell receptor, ligand, lipid, biotin, avidin, streptavidin, extravidin, neutravidin ), streptavidin (streptavidin), metal (metal), histidine (histidine) and AptaSSN selection method, characterized in that any one selected from the group comprising any part of any one of these structures. The method of claim 11 , wherein the first capture component is a material that binds to a first tag or the second capture component is a material that binds to a second tag.
Polynucleotide, polypeptide, peptide nucleic acid, locked nucleic acid, oligosaccharide, polysaccharide, antibody, affibody, antibody imitation Antibody mimic, cell receptor, ligand, lipid, biotin, avidin, streptavidin, extravidin, neutravidin ), streptavidin (streptavidin), metal (metal), histidine (histidine) and AptaSSN selection method, characterized in that any one selected from the group comprising any part of any one of these structures. According to claim 11 to 13, The first solid support (FSS) or the second solid support (SSS) is
Cyclo-olefin copolymer substrate, membrane, plastic substrate, nylon, langmuir Blodgett films, glass, germanium substrate (germanium substrate), silicon substrate (silicon substrate), polytetrafluoroethylene substrate (polytetrafluoroethylene substrate), polystyrene substrate (polystyrene substrate), gallium arsenide substrate (gallium arsenide substrate), gold substrate (gold substrate), silver substrate ( silver substrate), polymer beads, agarose beads, polystyrene beads, acrylamide beads, solid core beads, paramagnetic beads, Glass bead, microbead, nanoparticle, glass, germanium substrate, silicon substrate, microtiter well, silicon wafer chip ( Silicon wafer chip) or a flow-through chip (flow through chip) and controlled pore beads (controlled pore beads) AptaSSN selection method, characterized in that any one selected from the group. 14. The method according to claim 11 to 13,
(a) a first solid support (FSS) comprising the first capture component in the first mixed solution; and
(b) a second solid support (SSS) having a magnetism capable of being fixed or on the surface of the reaction sphere containing the second capture component in the second mixed solution; AptaSSN selection method comprising a. 12. The method of claim 11,
In any one or more steps selected from the group comprising steps (b) and (e), the washing solution is
AptaSSN selection method, characterized by at least one solution selected from the group comprising a washing solution with an organic solvent and a strict washing solution. 22. The method of claim 21,
The organic solvent is AptaSSN selection method, characterized in that any one selected from the group comprising Propylene Glycol and glycerol. 22. The method of claim 21, wherein the method using the stringent washing solution comprises:
(a) diluting the M-SSN-FSS complex or a mixed solution containing the SSS-M-SSN-FSS complex;
(b) adding a competitor to the M-SSN-FSS complex or a mixed solution containing the SSS-M-SSN-FSS complex; and
(c) AptaSSN selection comprising at least one selected from the group consisting of diluting the M-SSN-FSS complex or a mixed solution containing the SSS-M-SSN-FSS complex and adding a competitor Way. 24. The method of claim 23,
The competitor is oligonucleotide, heparin, herring sperm DNA, salmon sperm DNA, dextran sulfate, polyanion, abasic phosphodi AptaSSN selection method, characterized in that at least one selected from the group comprising an ester polymer (basic phosphodiester polymer), dNTP and pyrophophate. 12. The method of claim 11, wherein the washing
(a) washing the SSS-M-SSN-FSS complex solution by adding a buffer solution several tens of times before separation from the first additional round to the 50% round of the previous round;
(b) washing by adding a buffer solution hundreds of times before separation from step (a) to the remaining 50% rounds; and
(c) in the final round, washing by adding a buffer solution containing dextran sulfate several hundred times before separation; 12. The method of claim 11, wherein in the step of eluting the material group containing SSN from the second separated SSS-M-SSN-FSS complex in step (f), the elution solution is
A method for selecting AptaSSN, characterized in that the solution is subjected to any one method selected from the group comprising a pH method, a temperature method and a chaotropic salts method. 27. The method of claim 26, wherein the pH method is
AptaSSN selection method, characterized in that the complex is treated with a solution maintaining pH>9 or <5. 27. The method of claim 26, wherein the temperature method
AptaSSN selection method, characterized in that the method of treating the complex with a solution maintaining 40 ~ 100 ℃. 27. The method of claim 26, wherein the chaotropic salt method is
The complex was prepared using any one selected from chaotrophic salts including sodium perchlorate, lithium chloride, magnesium chloride, sodium chloride and guanidine-HCl. AptaSSN selection method, characterized in that the method of treatment with a solution containing. The method according to claim 11, wherein the method for analyzing a substance containing SSN eluted in step (f) comprises:
(a) preparing an NGS library from the SSN-FSS complex population or the binding SSN population eluted from the complex formed from the sample composed of the molecular population and the SSN library;
(b) determining the nucleotide sequence of the prepared NGS library with NGS equipment;
(c) analyzing the type and frequency of occurrence of the read from the determined nucleotide sequence;
(d) constructing a database using the analyzed sample having the type and frequency of appearance of the analyzed read;
(e) selecting a biologically meaningful feature, SSN from the constructed database; and
(f) evaluating the database constructed using the selected feature and SSN; AptaSSN selection method comprising the. The method of claim 30, wherein the database configuration in step (d) and feature selection in step (e) are
(a) feature selection using two or more databases for analysis samples with molecular data based on the biological meaning of the sample and using a supervised machine learning language; and
(b) AptaSSN selection method, characterized in that any one or more selected from the group comprising; . 32. The method of claim 1-31, wherein the selected and evaluated AptaSSN is
AptaSSN selection method, characterized in that it is used as a ligand for a biomarker for diagnostic and new drug purposes. 33. The method according to any one of claims 11 to 32,
(a) a normalization process for the AptaSSN population with respect to the molecular population constituting the specific sample;
(b) a differential screening process for the AptaSSN population with respect to the molecular population constituting the experimental sample and the control sample; and
(c) AptaSSN selection method, characterized in that at least one process selected from the group comprising; (a) the M-SSN-FSS complex according to claim 11, which is first separated in step (b);
(b) the SSS-M-SSN-FSS complex isolated in step (e) according to claim 11; and
(c) The complex of any one of claims 11 to 32, wherein the AptaSSN population and the molecular population constituting the sample form a complex; Target molecule analysis method, characterized in that the analysis. Any one or more single-stranded nucleic acids selected from the group comprising the binding SSN (AptaSSN) developed in claims 1 to 33 and aptamers having fixed sequences at both ends; and
AptaSSN, characterized in that a single-stranded nucleic acid capable of being amplified simultaneously by the fixed sequence. (a) adding a first tag to the AptaSSN group;
(b) the AptaSSN-FSS in which the first tag and the first capture component are bound by contacting the one or more types of the AptaSSN population with the first tag and the first solid support (FSS) having the first capture component preparing a complex, and preparing a third mixed solution in which the AptaSSN-FSS complex and a sample composed of a molecular (M) group are brought into contact to form an AptaSSN-FSS complex (M-AptaSSN-FSS complex) bound to the molecule; , or the sample composed of the AptaSSN population and the molecular population is brought into contact to prepare a reaction mixture comprising an AptaSSN complex (M-AptaSSN complex) bound to the molecule, and a first solid support (FSS) is added to the prepared reaction mixture preparing a third mixed solution in which the M-AptaSSN-FSS complex is formed by combining the first tag in the AptaSSN of the M-AptaSSN complex and the first capture component in the FSS;
(c) washing and harvesting the M-AptaSSNFSS complex;
(d) eluting the molecular population from the harvested M-AptaSSN-FSS complex; and
(e) analyzing the harvested M-AptaSSN-FSS complexes or the eluted molecular population by mass spectrometry; 37. The method of claim 36, wherein the apparatus for eluting the molecular population by performing steps (b) to (d)
16. A method for analyzing a target molecule using the AptaSSN fluid magnetic bead device 1000 according to any one of claims 12 to 15. 38. The method of claim 37,
The AptaSSN fluid is composed of the injection unit 1100 composed of a first inlet 1110 and a second inlet 1120 and the reaction unit 1200 composed of a first reaction port 1210 and a second reaction port 1220 . The operation of the magnetic bead device 1000 is
(a) the sample solution composed of the molecular (M) group, the selected AptaSSN group solution with the first tag, and the FSS solution are injected from the first inlet 1110, and in the first reaction port 1211 preparing a third mixed solution in which an M-AptaSSN-FSS complex is formed by reacting the molecular (M) group constituting the sample, the first tag of the AptaSSN, and the first capture component of the FSS;
(b) the washing solution is injected from the second inlet 1120, and the M-AptaSSN-FSS complex formed in the third mixed solution is washed in the second reaction port 1212 to non-specifically bind or bind to the AptaSSN a third separation step of removing one or more components of the mixture that have not been made to the discharge unit 1220; and
(c) injecting an elution solution from the first inlet 1110 into the M-AptaSSN-FSS complex transferred from the second reaction port 1212 to the collecting unit 1220, and eluting the molecule (M) group. ; AptaSSN fluid magnetic bead device comprising a. 39. The method of claim 38,
adding a second tag to the molecule (M) of the M-AptaSSN-FSS complex in the second reaction zone 1212;
The collecting unit 1230 forms an SSS-M-AptaSSN-FSS complex by collecting the M-AptaSSN-FSS complex as a second capturing component of SSS on the surface,
AptaSSN fluid magnetic bead apparatus 1000, characterized in that additionally performing eluting of the molecular (M) population from the formed SSS-M-AptaSSN-FSS complex. 37. The method of claim 34 and 36, wherein the mass spectrometer is
Any one selected from the group including Timeof-Flight (TOF), Quadrupole, Quadrupolar Ion Trap (QIT), Fourier Transform Ion Cyclotron Resonance (FT-ICR), Orbitrap ionized with Matrix Assisted Laser Desorption Ionization (MALDI) A target molecule analysis method characterized in one. (a) a first solid support (FSS) having a first capture component and a population of AptaSSN having a binding affinity specific to the target molecule (M) and including a first tag having an affinity for the first capture component, contacting the An AptaSSN-FSS complex in which a first tag and the first capture component are bound is prepared, and when the AptaSSN-FSS complex and a sample composed of a molecular population come into contact with each other and the target molecule is present in the sample, the M-AptaSSN-FSS complex is formed A fourth mixed solution is prepared or the sample is contacted with the AptaSSN population including the first tag having a specific binding affinity for the target molecule (M) and having an affinity for the first capture component, so that the target molecule is If present in the sample, a reaction mixture containing the M-AptaSSN complex is prepared, a first solid support (FSS) having a first capture component is added to the prepared reaction mixture, and the second agent in AptaSSN of the M-AptaSSN complex is added. preparing a fourth mixed solution in which the first tag and the first capture component of the FSS are combined to form an M-AptaSSN-FSS complex;
(b) In the fourth mixed solution, in order to dissociate the components of the fourth mixed solution that do not bind or have non-specific binding to the AptaSSN, the M-AptaSSN-FSS complex is washed with one or more solutions, and the AptaSSN and a first separation step of removing one or more components of the mixture that have non-specific binding or are not bound;
(c) adding a second tag having an affinity for a second capture component to the molecule (M) of the M-AptaSSN-FSS complex;
(d) the M-AptaSSN-FSS complex including the second tag and a second solid support (SSS) having the second capture component come into contact, so that the second tag binds to the second capture component, resulting in SSS- preparing a fifth mixed solution in which the M-AptaSSN-FSS complex is formed;
(e) a second separation step of removing one or more components of the mixture that are not bound to the second capture component from the fifth mixed solution; and
(f) eluting a material population comprising the AptaSSN from the SSS-M-AptaSSN-FSS complex; and
(g) analyzing the AptaSSN of the substance group including the eluted AptaSSN; 42. The method of claim 41, wherein the apparatus for eluting a substance group containing AptaSSN by performing steps (a) to (f)
A molecular analysis method using AptaSSN, characterized in that it uses the AptaSSN fluid magnetic bead device 1000 according to claims 12 to 15. 43. The method of claim 42,
The AptaSSN fluid is composed of the injection unit 1100 composed of a first inlet 1110 and a second inlet 1120 and the reaction unit 1200 composed of a first reaction port 1210 and a second reaction port 1220 . The operation of the magnetic bead device 1000 is
(a) the sample solution composed of the molecular (M) group, the AptaSSN group solution, and the FSS solution are injected from the first inlet 1110, and molecules constituting the sample in the first reaction port 1211 ( M) preparing a fourth mixed solution in which the group, the first tag of AptaSSN, and the first capture component of the FSS react to form an M-AptaSSN-FSS complex;
(b) the washing solution is injected from the second inlet 1120, and the M-AptaSSN-FSS complex formed in the fourth mixed solution is washed in the first reaction unit 1211 to non-specifically bind or bind to the AptaSSN a fourth separation step of removing one or more components of the mixture that have not been made to the discharge unit 1220;
(c) injecting the second tag solution from the first inlet 1110 and adding a second tag to the molecule M of the M-AptaSSN-FSS complex in the second reaction port 1212;
(d) the second-tagged M-AptaSSN-FSS complex injected from the second reaction zone 1212 and the second capture component of the SSS in the collection part 1230 come into contact with the SSS-M-AptaSSN - Preparing a fifth mixed solution in which the FSS complex is formed;
(e) the washing solution is injected from the second inlet 1120, and the SSS-M-AptaSSN-FSS complex formed in the fifth mixed solution in the collecting unit 1230 is washed, and the second capture component and a fifth separation step of removing unbound one or more components of the mixture; and
(f) the elution solution is injected from the first inlet 1110, and eluting the material group containing the AptaSSN from the second separated SSS-M-AptaSSN-FSS complex in the collecting unit 1230;
AptaSSN fluid magnetic bead device (1000) comprising a. 44. The method of claim 34 to 43, wherein the AptaSSN is
A molecular analysis method using AptaSSN, comprising a detectable moiety. 45. The method of claim 44, wherein the detectable moiety is
oligonucleotides, dyes, quantum dots, radiolabels, electrochemical functional groups and enzymes and detectable enzyme substrates. Molecular analysis method using AptaSSN characterized in any one. 46. The method of claim 45, wherein the dye is
A molecular analysis method using AptaSSN, characterized in that it is a fluorescent dye. 42. The method of claim 41, wherein at least one step selected in steps (b) and (e) is
A molecular analysis method using AptaSSN, characterized in that it is carried out by at least one method selected from the group including a method for washing with a buffer solution with an organic solvent and a method for a strict washing solution. The method according to claim 41, wherein the elution solution used in the elution step of the AptaSSN-FSS complex population or the AptaSSN population in step (f) is
A molecular analysis method using AptaSSN, characterized in that the solution can be performed by any one method selected from the group including pH method, temperature method and chaotropic salts method. (a) forming an M-AptaSSN complex between the AptaSSN population and the molecular (M) population in the sample;
(b) washing the formed M-AptaSSN complex by one or more methods selected from the group consisting of a method for washing the M-AptaSSN complex with an organic solvent and a method for a strict washing solution; and
(c) analyzing the AptaSSN population in the washed M-AptaSSN complex; 50. The method of claim 49,
binding the molecular population in the sample to any one solid support selected from the group comprising nitrocellulose, polyvinylidene difluoride (PVDF) and nylon before step (a); or
Molecular analysis method using AptaSSN, characterized in that it further comprises the step of capillary electrophoresis after step (b). 49. The method of claim 39, wherein the analysis of AptaSSN of the substance population comprising the eluted AptaSSN of step (g) or step (c) of claim 48 comprises:
AptaSSN comprising a method for detecting and selectively quantifying using a method selected from the group consisting of Q-PCR, mass spectroscopy, next-generation sequencing and hybridization Molecular analysis method used. The method of claim 51, wherein the Q-PCR method comprises:
A method for molecular analysis using AptaSSN, comprising a method using TaqMan®PCR, intercalating fluorescent dye, or molecular beacon during PCR during PCR. 52. The method of claim 51, wherein the mass spectrometer is
Any one selected from the group including Timeof-Flight (TOF), Quadrupole, Quadrupolar Ion Trap (QIT), Fourier Transform Ion Cyclotron Resonance (FT-ICR), Orbitrap and ionized with Matrix Assisted Laser Desorption Ionization (MALDI) A molecular analysis method using AptaSSN, characterized in that one. The method of claim 51, wherein the next-generation sequencing method comprises:
(a) constructing a database with the base sequence of AptaSSN to be analyzed;
(b) preparing an NGS library from the AptaSSN-FSS complex population eluted from the sample composed of the molecular population and the complex formed from the AptaSSN population or from the eluted AptaSSN population;
(c) determining the nucleotide sequence of the prepared NGS library with NGS equipment;
(d) determining the ALC appearance frequency of the eluted AptaSSN type by comparing and analyzing the determined base sequence data with the AptaSSN base sequence of the constructed database; and
(e) analyzing the molecular population constituting the sample using the type and frequency of occurrence of the analyzed AptaSSN; 55. The method of claim 54,
and a method for determining the profile of eluted AptaSSNs from the frequency of occurrence of the AptaSSN. The method of claim 51 , wherein the hybridization method comprises:
and hybridizing the AptaSSN-FSS complex population eluted in step (f) of claim 2 to the third solid support, AptaSSN, or an amplification product of the AptaSSN, wherein the third solid support has a plurality of positionable features ( addressable features),
A method for molecular analysis using AptaSSN, wherein at least one of the above features comprises a minimum capture element disposed therein complementary to any sequence included in the AptaSSN. 40. The method of claim 39,
AptaSSN comprising the step of detecting the molecule by detecting the M-AptaSSN-FSS complex of step (b) or the AptaSSN portion of the SSS-M-AptaSSN-FSS complex of step (f) Molecular analysis method used. (a) an AptaSSN population specific for the target molecule population in the sample;
(b) one or more of said solid supports;
(c) one or more of said separation reagents and washing solutions; and
(d) an elution solution for eluting AptaSSN from the AptaSSN complex; Molecular analysis and test kit using AptaSSN, comprising a. The magnetic fluid magnetic bead device for testing a target molecule according to claim 12, wherein an analysis device is added to the AptaSSN fluid magnetic bead device (1000). 60. The method of claim 59,
The analysis device is a target molecule test fluid magnetic bead device, characterized in that at least one selected from the group consisting of a microscope, a nucleic acid analysis device, a spectroscopic analysis device, a fluorescent material analysis device, and an electrical signal analysis device. (a) a molecular data production module 100 for producing molecular data on molecular populations in a sample composed of various types of molecular populations;
(b) internal quality control for the molecular data of the sample to be evaluated with internal quality control molecules selected from the learning database constructed based on the biological meaning of the samples with molecular data produced in the molecular data production module 100 internal quality control module 200; and
(c) a biological meaning determination support for a specific sample having valid molecular data input from the internal quality control module 200 with a predictive model obtained by learning the classification molecule and the learning database selected from the built learning database The biological meaning determination support module 300; Internal quality control and biological meaning determination support system (1), characterized in that it includes. 62. The method of claim 61,
(a) generating, by the molecular data production module 100, molecular data on the molecular population in the sample;
(b) the internal quality control module 200 performs internal quality control on the molecular data of the sample to be evaluated input from the molecular data production module 100 as internal quality control molecular data selected from the built-up learning database to select effective molecular data; and
(c) the biological meaning determination support module 300 analyzes the effective molecular data input from the internal quality control module 200 with a predictive model obtained by learning the selected classification molecular data and the building learning database Implementing semantic decision support; Internal quality control and biological semantic decision support system (1), characterized in that it includes. 63. The method according to claim 61 to 62, wherein the molecule capable of producing molecular data in the molecular data production module 100 is
Proteins, polypeptides, nucleic acids, polynucleotides, carbohydrates, polysaccharides, glycoproteins, hormones, receptors, antigens ), antibodies, viruses, substrates, metabolites, transition state analogs, cofoactors, inhibitors, drugs, dyes ( dyes), nutrients, growth factors, cells, unicellular organisms, tissues and controlled substances Semantic decision support system (1). The method according to claim 61 to 63, wherein the configuration of the internal quality control module 200 is
(a) a module for selecting one or more of the internal quality control molecules by analyzing the built-up database; or
(b) one or more internal quality control molecules selected above; and
(c) software for confirming whether the selected internal quality control molecule is within an acceptable range of the molecular data of a specific sample input from the molecular data production module 100; Support system (1). 65. The method of claim 61-64, wherein the selection of the internal quality control molecule is
(a) calculating the average and standard deviation of the molecular data of the samples constituting the constructed database;
(b) selecting one or more internal quality control molecules by calculating a coefficient of variation (CV) with the calculated mean and standard deviation;
(c) determining a group based on the average value of the selected internal quality control molecules; and
(d) selecting an internal quality control molecule representing the determined group; internal quality control and biological meaning determination support system (1) comprising a. 66. The method of claim 61 to 65, wherein the operation of the internal quality control module 200 is
(a) determining effective molecular data by performing internal quality control with the selected internal quality control molecules on the molecular data of a specific sample input from the molecular data module 100; and
(b) inputting the specific sample having the determined effective molecular data into the biological meaning determination support module 300; Internal quality control and biological meaning determination support system (1) comprising a. 67. The method of claim 61 to 66, wherein the internal quality control is
Internal quality control and biological meaning determination support system ( One). 68. The method according to claim 61 to 67, wherein the configuration of the biological meaning determination support module 300 is
(a) a module for selecting one or more classification molecules by analyzing the constructed database; or
(b) one or more classification molecules selected above; and
(c) the database and the selected classifying molecular data, the software learned using any one or more selected from the machine learning language group; internal quality control and biological meaning determination support system (1) comprising a. 69. The method according to claim 61 to 68, wherein the selection of the classification molecule is
Internal quality control and biological meaning determination support system, characterized in that any one or more selected from the group including ANOVA, PCA, and Random Forest is performed on the samples with the molecular data based on the biological meaning (One). 70. The method according to claim 61 to 69, wherein the operation of the biological meaning determination support module 300 is
(a) selecting one or more classified molecules from molecular data in a sample constituting the database;
(b) learning a predictive model for the database and the selected classification molecule data; and
(c) analyzing the classification molecular data for the effective molecular data of a specific sample input from the module 200 with the learned predictive model to perform biological meaning determination support; Internal quality control comprising: and biological meaning determination support system (1). 71. The method of claim 61-70,
The internal quality control and biological meaning determination support system (1), characterized in that it comprises a molecular data production module (100) for simultaneously analyzing the nucleic acid and protein populations in the sample composed of the various types of molecular groups.

Images