CN117202991A

CN117202991A - Device for biomolecule determination

Info

Publication number: CN117202991A
Application number: CN202280029507.2A
Authority: CN
Inventors: 丹尼尔·霍恩博格; 克雷格·斯托拉尔奇克; 贝扎德·唐盖什; 阿西姆·西迪基; 特里斯坦·布朗; 沙迪·罗什迪费尔多西; 马丁·戈德伯格; 莫拉吉·哈桑
Original assignee: Xier Co
Current assignee: Xier Co
Priority date: 2021-02-26
Filing date: 2022-02-25
Publication date: 2023-12-08

Abstract

Disclosed herein are methods for identifying physicochemical properties associated with protein crown formation at the protein and NP functionalization levels. Further disclosed herein are compositions comprising particle combinations configured for low abundance protein collection and deep proteomic analysis.

Description

Device for biomolecule determination

Cross reference

The present application claims the benefit of U.S. provisional application No. 63/154,660 filed on 26 months 2 of 2021 and U.S. provisional application No. 63/306,951 filed on 4 months 2 of 2022, each of which is incorporated herein by reference in its entirety.

Background

Analysis of low abundance biomolecules is a significant challenge in proteomics. While recent advances have improved the capture of individual biomolecules from samples, diagnostic methods generally require the detection of large numbers of biomolecules to accurately identify biological states, groups, and organisms. Thus, there is a need for compositions and methods for enriching a large portion of low abundance biomolecules from a sample.

Disclosure of Invention

In one aspect, provided herein is a method of selecting a surface for use in a biomolecular assay, comprising: (a) Providing one or more biological samples comprising a plurality of biological molecules; (b) Contacting one or more biological samples with a plurality of surfaces such that each surface of the plurality of surfaces adsorbs a subset of biomolecules of the plurality of biomolecules; (c) Determining, for each of the plurality of surfaces, an abundance of a subset of biomolecules adsorbed thereon; and (d) selecting a subset of surfaces of the plurality of surfaces based at least in part on the abundance when the subset of surfaces adsorbs biomolecules or groupings of biomolecules comprising different abundance patterns as compared to another subset of surfaces of the plurality of surfaces.

In some embodiments, a first surface of the subset of surfaces is selected when it binds to a first set of functionally and/or structurally related biomolecules. In some embodiments, a second surface of the subset of surfaces is selected when it binds to a second set of functionally and/or structurally related biomolecules.

In some embodiments, the first set of functionally related biomolecules, the second set of functionally related biomolecules, or both comprise at least one of the following: hormone proteins, cytolytic proteins, innate immunity proteins, membrane attack complexes, complement pathway proteins, amyloid fibers, proteins involved in cholesterol metabolism, proteins involved in steroid metabolism, proteins having a gamma carboxyglutamic acid domain, proteins associated with amyloidosis, sulfated proteins, proteoglycan proteins, immunoglobulins, adaptive immunity proteins, mitochondrial proteins, membrane proteins, cytoplasts, muscle proteins, proteins associated with genetic material, proteins associated with gene expression and/or regulation, proteins associated with intracellular and/or extracellular space, and any combination thereof.

In some embodiments, the method may further comprise contacting a new biological sample that is not among the one or more biological samples with the subset of surfaces, thereby determining a first or second set of functionally and/or structurally related biomolecules in the new biological sample.

In some embodiments, the first surface and the second surface each adsorb a given biomolecule of the plurality of biomolecules at different relative abundances. In some embodiments, the first surface adsorbs at least one biomolecule that is not adsorbed on the second surface.

In some embodiments, the one or more biological samples are samples obtained from a subject having a given disease such that a selected subset of surfaces is optimized for assaying the new biological samples for the given disease.

In some embodiments, the method further comprises contacting a new biological sample that is not among the one or more biological samples with the subset of surfaces, thereby detecting biomolecules in the new biological sample to determine a disease state of the new biological sample associated with the given disease.

In some embodiments, one or more biological samples are obtained from an individual such that a selected subset of surfaces is optimized for assaying the biological sample from the individual. In some embodiments, one or more biological samples are obtained from a group of individuals having at least one attribute such that a selected subset of surfaces is optimized for determining biological samples from individuals having at least one attribute. In some embodiments, the at least one attribute comprises a genetic factor, a non-genetic factor, or both. In some embodiments, the genetic factors include one or more genetic mutations, the presence or absence of one or more alleles, the presence or absence of one or more genes, the presence or absence of one or more chromosomes, or any combination thereof. In some embodiments, the non-genetic factors include physical activity level, sleep quality and pattern, consumption of drugs and/or alcohol, biometrics, or any combination thereof. In some embodiments, the one or more biological samples are samples obtained from one or more species such that a selected subset of surfaces is optimized for assaying at least one species of the one or more species.

In some embodiments, the first surface and the second surface are selected when the Jaccard index between the identities of the different subsets of biomolecules adsorbed on the first surface and the second surface is at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9. In some embodiments, the first surface and the second surface are selected when the pearson correlation coefficient between the measured intensities of the first set of functionally related biomolecules and the second set of functionally related biomolecules is at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.

In some embodiments, the subset of surfaces is selected when the subset of surfaces adsorbs biomolecules or groups of biomolecules with a greater dynamic range than another subset of surfaces of the plurality of surfaces. In some embodiments, the greater dynamic range is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 orders of magnitude greater.

In some embodiments, one or more biological samples comprise derivatives or portions of the same given biological sample. In some embodiments, the one or more biological samples comprise a human plasma sample. In some embodiments, the one or more biological samples comprise a biological sample standard. In some embodiments, the biological sample standard is a HeLa cell extract.

In some embodiments, the plurality of biomolecules comprises polyamino acids. In some embodiments, the polyamino acid comprises a peptide, a protein, or a combination thereof.

In some embodiments, the subset of different biomolecules adsorbed on at least one of the plurality of surfaces comprises at least two biomolecules that do not share a common binding motif.

In some embodiments, the identity determination in (c) is made by: (i) desorbing a different subset of biomolecules adsorbed on each of the plurality of surfaces to produce desorbed biomolecules, (ii) mass spectrometry of the desorbed biomolecules to produce mass spectrometry signals, and (iii) quantifying the mass spectrometry signals to determine the identity of the different subset of biomolecules. In some embodiments, (i) further comprises digesting at least a portion of the subset of different biomolecules to produce desorbed biomolecules. In some embodiments, the digestion comprises contacting a subset of the different biomolecules with a protease.

In some embodiments, each of the plurality of surfaces adsorbs a different subset of the plurality of biomolecules. In some embodiments, the first subset of different biomolecules adsorbed on a first surface of the plurality of surfaces and the second subset of biomolecules adsorbed on a second surface of the plurality of surfaces comprise at least one common biomolecule. In some embodiments, the first subset of different biomolecules and the second subset of biomolecules comprise at least one non-common biomolecule.

In some embodiments, the different abundance patterns include enrichment of low abundance biomolecules relative to a plurality of biomolecules in one or more biological samples.

In another aspect, described herein is a method of producing an enriched biological sample comprising: (a) providing a sample comprising a plurality of biomolecules; (b) Contacting the sample with a particle or resin to specifically bind at least one biomolecule or biomolecule class target in the sample to the particle or resin; (c) Separating the particles or resin and the at least one biomolecule from the sample, thereby producing a depleted sample; (d) Contacting a depleted sample with a surface, wherein the surface is configured to adsorb a collection of biomolecules in the depleted sample onto the surface; (e) Separating a collection of biomolecules from the depleted sample and the surface; and (f) releasing the collection of biomolecules from the surface to produce an enriched sample comprising the collection of biomolecules.

In some embodiments, the at least one biomolecule or class of biomolecules target comprises: albumin, igG, igA, igM, igD, igE, igG (light chain), alpha-1-acid glycoprotein, fibrinogen, haptoglobin, alpha-1-antitrypsin, alpha-2-macroglobulin, transferrin, apolipoprotein A-1, or any combination thereof.

In some embodiments, the at least one biomolecule or class of biomolecules target comprises a predetermined subset of the plurality of biomolecules, the predetermined subset having a high relative abundance.

In some embodiments, in step (c), the isolating reduces the abundance of at least one biomolecule or biomolecule class target by at least 2, 5, 10, or 100-fold. In some embodiments, in step (c), the generating a depleted sample generates at least about 30% more unique protein, protein packet, or peptide in the enriched sample of step (f). In some embodiments, in step (c), the generation of the depleted sample produces a greater dynamic range of at least about 1 order of magnitude of the unique protein or protein groupings in the enriched sample of step (f).

In some embodiments, the method may further comprise drying the depleted sample to a predetermined concentration or volume after step (c) or before step (d).

In some embodiments, the method may further comprise drying the enriched sample and reconstructing it to a predetermined concentration or volume after step (e).

In some embodiments, the process is performed in less than about 72 hours.

In some embodiments, the biomolecules comprise proteins or protein groupings.

In some embodiments, the surface is a nanoparticle surface.

In some embodiments, the method may further comprise contacting the depleted sample with a second surface, wherein the second surface is configured to adsorb a second set of biomolecules in the depleted sample onto the second surface.

In some embodiments, the releasing in (f) further comprises digesting the collection of biomolecules.

In some embodiments, the particles or resin are disposed in a column.

In another aspect, described herein is a kit for enriching a biological sample, comprising: (a) A first substance configured to specifically bind to a first set of biomolecular targets; (b) A second substance configured to adsorb a second set of biomolecular targets; and (c) a third substance configured to adsorb a third set of biomolecular targets.

In some embodiments, the first substance is a resin or a particle. In some embodiments, the first substance comprises a specific binding moiety configured to bind to a first set of biomolecular targets. In some embodiments, the first substance is configured to specifically bind to at least one of albumin, igG, igA, igM, igD, igE, igG (light chain), alpha-1-acid glycoprotein, fibrinogen, haptoglobin, alpha-1-antitrypsin, alpha-2-macroglobulin, transferrin, and apolipoprotein a-1.

In some embodiments, the kit may further comprise a fourth substance configured to non-specifically bind to a fourth set of biomolecular targets.

In some embodiments, the kit may further comprise a fifth substance configured to non-specifically bind to a fifth set of biomolecular targets.

In some embodiments, the second agent comprises a plurality of domains, wherein each domain of the plurality of domains is configured to non-specifically bind to a different subset of the second set of biomolecular targets. In some embodiments, the second substance comprises a particle surface, and the plurality of domains comprises a plurality of surface regions on the particle surface. In some embodiments, the second substance comprises a plurality of particle surfaces, and the plurality of particle surfaces are disposed on the plurality of particles.

In some embodiments, the kit comprises a chamber or well in which the first substance, the second substance, and the third substance are disposed. In some embodiments, the chamber comprises a column. In some embodiments, the chamber comprises a microfluidic channel.

In some embodiments, the surface area of the well comprises a first substance.

Various aspects of the present disclosure provide methods for determining particle characteristics and further for identifying combinations of particle characteristics that enable deep histology analysis of biological samples. The present disclosure also provides compositions containing particles having a combination of physicochemical properties optimized for the defined capture of proteins from a biological sample (e.g., tailored for a subset of proteins, classes of proteins, or for the broad capture of proteins across multiple classes).

Other aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

Incorporation by reference

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent that publications and patents or patent applications incorporated by reference conflict with the disclosures contained in the specification, the specification intends to supersede and/or take precedence over any such conflicting material.

Drawings

The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description and drawings that set forth illustrative embodiments in which the principles of the disclosure are utilized, and in which (herein also referred to as "the drawings"):

the patent or application document contains at least one drawing in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the office upon request and payment of the necessary fee.

FIG. 1 shows a schematic workflow diagram for particle-based protein collection from biological samples for mass spectrometry analysis.

Fig. 2 depicts an example type of surface functionalization of particles according to some embodiments.

Figure 3A shows a mass spectrum intensity plot of different proteins collected from plasma on 37 different particles according to some embodiments.

Fig. 3B illustrates hierarchical clustering of 37 particles with different combinations of physicochemical properties performed based on protein corona compositions of proteins related to the function and/or structure formed by each particle, according to some embodiments.

Fig. 3C illustrates hierarchical clustering of particles with different combinations of physicochemical properties performed based on protein corona compositions of proteins associated with the structure formed by each particle, according to some embodiments.

Fig. 4A shows graphs of variance decomposition performed on the protein corona data shown in fig. 3A-3B, illustrating the extent of contribution of different factors to the observed protein intensity, according to some embodiments.

Fig. 4B shows a plot of the degree of variance of the relationship of protein intensity to the different particle characteristics of the 37 particles shown in fig. 3A-B and fig. 4A, according to some embodiments.

Figure 5A shows a schematic diagram of four different workflows of plasma protein purification and analysis, according to some embodiments. Some workflows may utilize particles, plasma depletion methods, pure plasma, denaturation, reduction/alkylation, protein digestion, magnetic separation, peptide fractionation, high pH fractionation, or any combination thereof.

FIG. 5B illustrates the number of protein groups identified by the workflow illustrated in FIG. 5A, according to some embodiments.

Fig. 5C illustrates changes in peptide intensity over multiple LC-MS/MS parameters (including LC gradient length and MS instrumentation) according to some embodiments.

Figure 5D shows human plasma protein abundance identified by each workflow shown in figure 5A, according to some embodiments.

Fig. 5E shows proteomic data including plasma proteome (proteome) coverage from each of the workflows shown in fig. 5A, according to some embodiments.

FIG. 5F illustrates the overlap between identified protein groupings between each workflow illustrated in FIG. 5A, according to some embodiments.

Figure 5G shows the number of protein grouping identifications of the five particle set (part set) and high pH depletion workflow shown in figure 5A for 7 different protein annotations, according to some embodiments.

FIG. 6A illustrates the median number of protein packets identified for each workflow illustrated in FIG. 5A using data dependent mass spectrometry acquisition, according to some embodiments.

Fig. 6B shows the Coefficient of Variation (CV) of median normalized peptide intensities filtered for data dependent mass spectrometry identification in the five particle set, high pH fractionation ("depth fractionation") and pure plasma workflow shown in fig. 5A, according to some embodiments.

FIG. 6C illustrates the dynamic range of proteins identified by each of the workflows illustrated in FIGS. 6A-6B, according to some embodiments.

Fig. 6D shows the percentage coverage of human proteomes in some workflows (upper panel), and a comparison of the relative coverage of human proteomes by the five particle group workflow of fig. 6A-C and the first high pH fractionation sample (lower panel), according to some embodiments.

Figure 7 shows the median, jaccard index, correlation coefficient, and coefficient of variation of protein groupings identified from 10 particles and pure and depleted plasma, according to some embodiments.

FIG. 8 illustrates a cluster analysis of the protein intensities and overlapping results shown in FIG. 7 as a distance tree of particles, depleted plasma, and pure plasma, according to some embodiments.

Fig. 9 shows TEM images of each particle, along with their zeta potential, hydrodynamic radius and polydispersity index (PDI, bar graph under image), according to some embodiments.

Fig. 10A shows a volcanic plot depicting coefficients derived from a protein binding model based on properties of three particular particles, according to some embodiments.

Fig. 10B shows results from 500 random samples, which selected 2x12 non-overlapping subjects assayed with 10 particles, according to some embodiments.

Fig. 10C shows the correlation coefficient between the measured coefficient for each protein and the zeta potential of the particles, according to some embodiments.

FIG. 11 illustrates a computer system programmed or otherwise configured to implement the methods provided herein, according to some embodiments.

Figure 12 shows the amount of protein collected by mass spectrometry and subsequently identified after collection on a particle set comprising 1 to 12 particles, according to some embodiments.

FIG. 13A provides an example workflow for enriching a subset of biomolecules from a biological sample according to some embodiments.

FIG. 13B provides an example workflow for determining biomolecules from a biological sample according to some embodiments.

Figure 14 shows the time taken for each plasma protein analysis step for a particle-based method and two high pH depletion-based methods, according to some embodiments.

Fig. 15 illustrates an apparatus according to some embodiments.

Fig. 16 illustrates a transmission unit according to some embodiments.

Fig. 17 illustrates an apparatus and its components according to some embodiments.

Fig. 18 illustrates an apparatus and its components according to some embodiments.

Fig. 19 illustrates a transmission unit according to some embodiments.

Fig. 20 illustrates a transmission unit according to some embodiments.

FIG. 21 shows a diagram of multiple partitions, according to some embodiments.

Fig. 22 illustrates a transmission unit according to some embodiments.

Fig. 23 shows a diagram of an apparatus and its components according to some embodiments.

Fig. 24 illustrates a schematic diagram of a workflow for determining biomolecules from a biological sample using a capture column according to some embodiments.

Fig. 25 illustrates a schematic diagram of a workflow for determining biomolecules from a biological sample using a capture column, according to some embodiments.

Fig. 26 shows a schematic diagram of a workflow for determining biomolecules from a biological sample using nanoparticles and a resin or beads, according to some embodiments.

Fig. 27 shows a schematic diagram of a workflow for assaying biomolecules from a biological sample using a depletion step, according to some embodiments.

Figure 28 illustrates removal of biomolecules by a depletion column according to some embodiments.

29A-29B illustrate depletion columns and depletion wells, respectively, according to some embodiments.

Figure 30 shows protein yields from depletion experiments and control experiments, according to some embodiments.

Fig. 31 shows coefficients of variation from depletion experiments and control experiments, according to some embodiments.

Figure 32 shows protein grouping identifications from depletion experiments and control experiments, according to some embodiments.

Detailed Description

While various embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Many changes, modifications and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed. Furthermore, the headings provided herein are for convenience only and do not interpret the scope or meaning of the claimed disclosure. The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. It should also be noted that the term "or" is generally employed in its sense including "and/or" unless the content clearly dictates otherwise.

The term "optional" or "optionally" means that the subsequently described event or circumstance may but need not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.

The term "about" means within + -1 of the last significant digit of a given value. For example, if it is stated that "depleted sample produces at least about 30% more unique protein, protein packet or peptide in enriched sample" it means a yield between 20% and 40%. In another example, if it is stated that "depleted sample produces at least about 35% more unique protein, protein group or peptide in enriched sample" it means a yield between 34% and 36%.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Suitable methods and materials are described below, but methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure. All references cited herein are incorporated by reference as if fully set forth herein.

The biological sample may be a complex mixture that may contain a large number of arrays of biomolecules with different properties. The presence or absence and concentration of various biomolecules, as well as the correlation between various sub-populations of biomolecules (e.g., proteins and nucleic acids), may be indicative of a biological state (e.g., a health or disease state) of a sample. For example, the pattern of dilution of the abundance of plasma proteins may strongly indicate the health status of a human subject. However, some enrichment methods may not be able to capture more than a narrow subset of biomolecules.

Disclosed herein are systems, methods, and compositions for assaying biomolecules in a biological sample using one or more surfaces configured to capture a broad subset of biomolecules. In some cases, this may be achieved by using one or more surfaces configured to adsorb a plurality of different types of biomolecules thereon. Biomolecules assayed in this manner can provide finer resolution of the composition of the biological sample, which in turn can lead to clearer and in-depth knowledge of the biology or content of the biological sample.

Methods of selecting one or more surfaces for a predetermined function are also described herein. While there may be a variety of surfaces with a variety of physicochemical properties that can be used to determine biomolecules, one or a specific combination of these surfaces can provide an advantageous determination of the intended function. For example, one or more surfaces may be selected to detect a particular disease, which is useful for both diagnosis and prognosis. In some cases, one or more surfaces may be selected to obtain or monitor a biological state of an individual over time. In still other cases, one or more surfaces may be selected to detect a particular grouping of biomolecules (e.g., hormones) associated with a given biochemical system. One or more surfaces may be selected for various predetermined functions, which will be described in further detail herein.

Method for selecting particles and/or surfaces

In some aspects, the disclosure describes a method of selecting a surface for a biomolecular assay. In some cases, the method includes providing one or more biological samples comprising a plurality of biological molecules. In some cases, the method includes contacting one or more biological samples with a plurality of surfaces. In some cases, each of the plurality of surfaces adsorbs a subset of biomolecules of the plurality of biomolecules. In some cases, the method includes determining, for each of the plurality of surfaces, an abundance of a subset of biomolecules adsorbed thereon. In some cases, the method includes determining, for at least a subset of the plurality of surfaces, an abundance of a subset of biomolecules adsorbed thereon. In some cases, the method includes selecting a subset of surfaces of the plurality of surfaces based at least in part on the abundance when the subset of surfaces adsorbs biomolecules or groupings of biomolecules comprising different abundance patterns as compared to another subset of surfaces of the plurality of surfaces.

In some cases, a first surface of the subset of surfaces is selected when it binds to a first set of functionally related biomolecules. In some cases, a subset of surfaces is selected when a second surface of the subset of surfaces binds to a second set of functionally related biomolecules of the same sample. Fig. 3A shows an example of hierarchical clustering of particles based on protein intensities measured using particles. Each particle exhibits a characteristic pattern of biomolecules that are adsorbed and subsequently measured. Fig. 3B shows an example of one-dimensional annotated enrichment scores for Uniprot Keywords-based biomolecules, showing that some particles adsorb some functionally related collections of biomolecules.

In some cases, a first surface subset is selected when the first surface of the subset binds to a first set of structurally related biomolecules. In some cases, a second surface of the subset of surfaces is selected when the second surface binds to a second set of structurally related biomolecules of the same sample. Fig. 3C shows an example of one-dimensional annotated enrichment scores for Uniprot Keywords based biomolecules, showing that some particles adsorb some structurally related collections of biomolecules based on class, structure, topology, homology (CATH) annotations. For example, some CATH structures representing protein secondary structures are enriched with changes in the charge of NP surface functionalization. Enriched notes are represented in red, while depleted notes are represented in blue.

A subset of surfaces may be selected such that the subset is specific for determining proteins associated with motor function (e.g., uniprot keywords of muscle proteins, motor proteins, cell shapes, etc.). In some cases, a subset of surfaces may be selected such that the subset is specifically used to determine proteins associated with the metabolism of sterols and related molecules (e.g., uniprot keywords of steroid metabolism, sterol metabolism, cholesterol metabolism, etc.). In some cases, a subset of surfaces may be selected such that the subset is dedicated to assaying as broadly as possible a variety of functionally related biomolecules.

In some cases, the first set of functionally related biomolecules, the second set of functionally related biomolecules, or both may comprise at least one of: hormone proteins, cytolytic proteins, innate immunity proteins, membrane attack complexes, complement pathway proteins, amyloid fibers, proteins involved in cholesterol metabolism, proteins involved in steroid metabolism, proteins having a gamma carboxyglutamic acid domain, proteins associated with amyloidosis, sulfated proteins, proteoglycan proteins, immunoglobulins, adaptive immunity proteins, mitochondrial proteins, membrane proteins, cytoplasts, muscle proteins, proteins associated with genetic material, proteins associated with gene expression and/or regulation, proteins associated with intracellular and/or extracellular space, and any combination thereof. Any one or a combination of functionally related biomolecules may be of interest, and a subset of surfaces may be selected to target those biomolecules.

The selected subset of surfaces may be applied to a new sample. In some cases, the method can include contacting a new (e.g., isolated) biological sample that is not among the one or more biological samples with the subset of surfaces, thereby determining a first or second set of functionally related biomolecules in the new biological sample. In some cases, the method includes contacting a new biological sample that is not among the one or more biological samples with the subset of surfaces, thereby detecting biomolecules in the new biological sample to determine a disease state of the new biological sample associated with the given disease. In some cases, a subset of surfaces or particles may be selected to specifically assay any of the diseases disclosed herein.

In some cases, the one or more biological samples are samples obtained from a subject having a given disease such that a selected subset of surfaces is optimized for assaying the new biological samples for the given disease. Thus, the subset may be used to assay a new sample from a subject to determine whether the subject has a given disease.

In some cases, one or more biological samples are obtained from an individual such that a selected subset of surfaces is optimized for assaying the biological sample from the individual. In some cases, the subset of surfaces may be selected based on the ability to broadly assay (e.g., determine a number of unique proteins or protein groupings) a particular biological sample from an individual (e.g., plasma from a person). The subset of surfaces may then be used to monitor the biological status of the individual over time. In some cases, one or more biological samples include derivatives or portions of the same given biological sample.

In some cases, one or more biological samples are obtained from a group of individuals having at least one attribute such that a selected subset of surfaces is optimized for determining biological samples from individuals having at least one attribute. In some cases, the at least one attribute may include a genetic factor, a non-genetic factor, or both. In some cases, one or more biological samples are obtained from a group of individuals having at least two attributes such that a selected subset of surfaces is optimized for determining biological samples from individuals having at least two attributes. In some cases, the at least two attributes may include at least a genetic factor, a non-genetic factor, or both.

For example, a subset of the surfaces may be optimized for determining biomolecules of individuals with a given genetic disease. In another example, a subset of surfaces may be optimized for determining biomolecules from individuals in a particular geographic region, thereby having a genetic similarity. In some cases, the genetic factors may include one or more genetic mutations, the presence or absence of one or more alleles, the presence or absence of one or more genes, the presence or absence of one or more chromosomes, or any combination thereof.

In some cases, the non-genetic factors include physical activity level, sleep quality and pattern, consumption of drugs and/or alcohol, biometrics, specific lifestyles, socioeconomic status, geographic areas of residence, exposure to certain pollutants, quantitative indicators thereof, or any combination thereof. In some cases, the quantitative indicator may be a self-report of the individual (e.g., a return to a questionnaire or survey), an assessment of a healthcare professional, an assessment of the individual collecting information and/or data, or a combination thereof.

In some cases, the one or more biological samples are samples obtained from one or more species such that the selected subset of surfaces is optimized for assaying at least one of the one or more species.

The subset of surfaces may be selected to be specific for various types of biological samples. In some cases, the biological sample may be a human plasma sample, saliva, stool, tears, cells, tissue, organ, or any other biological sample disclosed herein. In some cases, the biological sample may be part of human plasma, stool, tears, cells, tissues, organs, or any other biological sample disclosed herein.

The performance of a given subset of surfaces can be assessed using biological sample standards. In some examples, the biological sample standard may be a HeLa cell extract. In some examples, the biological sample standard may be a spike protein (e.g., e.coli). In some examples, the biological sample standard may be non-homologous to the species under study.

The differences in biomolecular adsorption between the surfaces in the subset can be characterized in various ways. In some cases, the first surface and the second surface may adsorb similar biomolecules from a biological sample. In some cases, the first surface adsorbs at least one biomolecule that is not adsorbed on the second surface. In some cases, the second surface adsorbs at least one biomolecule that is not adsorbed on the first surface. In some cases, the relative abundance of adsorbed biomolecules may differ between the first surface and the second surface. This may be useful when assaying for adsorbed biomolecules using assays that may have randomness (e.g., some cases of mass spectrometry), where higher abundance of a given biomolecule may provide a higher likelihood of detecting a given biomolecule. Thus, in some cases, the first surface and the second surface may each adsorb a given biomolecule of the plurality of biomolecules at different relative abundances. In some cases, the relative abundance of adsorbed biomolecules may be the same between the first surface and the second surface.

In some cases, the first surface and the second surface are selected when the Jaccard index between identities of different subsets of biomolecules adsorbed on the first surface and the second surface is at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9. In some cases, the first surface and the second surface are selected when the pearson correlation coefficient between the measured intensities of the first set of functionally related biomolecules and the second set of functionally related biomolecules is at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0. In some cases, the identity and/or intensity may be determined by mass spectrometry of biomolecules adsorbed on the first surface and the second surface. For example, determining identity or strength may be performed as follows: (i) desorbing a different subset of biomolecules adsorbed on each of the plurality of surfaces to produce desorbed biomolecules, (ii) mass spectrometry of the desorbed biomolecules to produce mass spectrometry signals, and (iii) quantifying the mass spectrometry signals to determine the identity and/or intensity of the different subset of biomolecules.

In some cases, a subset of surfaces is selected when the subset of surfaces adsorbs biomolecules or groups of biomolecules with a greater dynamic range than another subset of surfaces of the plurality of surfaces. In some cases, the greater dynamic range is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more orders of magnitude greater.

In some cases, the subset of different biomolecules adsorbed on at least one of the plurality of surfaces comprises at least two biomolecules that do not share a common binding motif. In some cases, (i) further comprises digesting at least a portion of the subset of different biomolecules to produce desorbed biomolecules. In some cases, digestion includes contacting a subset of the different biomolecules with a protease. In some cases, each of the plurality of surfaces adsorbs a different subset of the plurality of biomolecules. In some cases, the first subset of different biomolecules adsorbed on a first surface of the plurality of surfaces and the second subset of biomolecules adsorbed on a second surface of the plurality of surfaces comprise at least one common biomolecule. In some cases, the first subset of different biomolecules and the second subset of biomolecules comprise at least one non-common biomolecule. In some cases, the different abundance patterns include enrichment of low abundance biomolecules relative to a plurality of biomolecules in one or more biological samples.

Depletion enhanced proteomics

In some aspects, the disclosure describes a method of producing an enriched biological sample. In some aspects, biomolecules of interest (e.g., low abundance proteins) can be enriched in the biomolecular corona relative to an untreated sample (e.g., a sample that is not assayed using particles). The biomolecule of interest may be a protein. The biomolecular corona may be a protein corona. The level of enrichment can be a percentage or multiple of the increase in the relative abundance of the biomolecule of interest in the biomolecular corona (e.g., the number of copies of the biomolecule of interest relative to the total number of biomolecules) as compared to the biological sample from which the biomolecular corona was collected. By increasing the abundance of the biomolecule of interest in the biomolecular corona, the biomolecule of interest can be enriched in the biomolecular corona compared to a sample that is not contacted with the sensor element. The biomolecules of interest can be enriched by reducing the abundance of the biomolecules in the high abundance biological sample.

In some cases, the biomolecules or biomolecule class targets in the biological sample can be depleted. In some cases, depletion may allow for detection of multiple unique proteins or protein groupings in an assay, e.g., assays comprising contacting a depleted biological sample with a non-specific binding surface and performing mass spectrometry on biomolecules adsorbed on the non-specific binding surface are disclosed herein. In some cases, depletion may refer to a decrease in abundance of a given biomolecule by at least about 2, 5, 10, 100, or more times.

In some cases, the method includes providing a sample comprising a plurality of biomolecules. In some cases, the method includes contacting the sample with a particle or functional resin to specifically bind at least one biomolecule or biomolecule class target in the sample to the particle or resin. In some cases, the method includes separating the particles or resin from the sample, and then separating at least one biomolecule, thereby producing a depleted sample. In some cases, the method includes contacting the depleted sample with a surface, wherein the surface is configured to adsorb a collection of biomolecules remaining in the depleted sample onto the surface. In some cases, the method includes separating the collection of biomolecules and the surface from the depleted sample. In some cases, the method includes releasing the collection of biomolecules from the surface to produce an enriched sample comprising the collection of biomolecules. In some cases, the method may include drying the depleted sample and reconstitution to a predetermined concentration or volume. In some cases, particles or resin may be disposed in the column.

The biomolecule or class of biomolecules target can be any biomolecule in a biological sample. In some cases, a biomolecule or class of biomolecules target may have a high abundance in a biological sample (e.g., relative to other biomolecules or classes of biomolecules). In some cases, the biomolecule or biomolecule class target may comprise: albumin, igG, igA, igM, igD, igE, igG (light chain), alpha-1-acid glycoprotein, fibrinogen, haptoglobin, alpha-1-antitrypsin, alpha-2-macroglobulin, transferrin, apolipoprotein A-1, or any combination thereof. These biomolecules may be high abundance biomolecules in a human plasma sample. However, it should be understood that other biomolecules or classes of biomolecules may be targeted for depletion, as there are many types of biological samples and various biological samples may have different high abundance biomolecules.

In some cases, producing a depleted sample can produce at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 100% more of the unique biomolecules, biomolecule classes, proteins, protein groupings or peptides in the enriched sample that is detected (e.g., using mass spectrometry).

In some cases, generating a depleted sample generates a greater dynamic range of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more orders of magnitude of unique biomolecules or classes of biomolecules, unique proteins, or groupings of proteins in the enriched sample that is detected (e.g., using mass spectrometry).

In some cases, the method is performed in less than one week, 6 days, 5 days, 4 days, 3 days, 2 days, 1 day, 12 hours, 6 hours, 4 hours, 2 hours, 1 hour, or less.

Properties and types of particles and/or surfaces

Particle types and surface types consistent with the methods disclosed herein may be made from a variety of materials. As used herein, "surface" may refer to the surface of a particle. When describing the composition of the particles, physical properties, or uses thereof herein, it is to be understood that in some cases, the surface of the particles may comprise the same composition, the same physical properties, or the same uses thereof. Similarly, when describing a surface composition, physical properties, or use thereof herein, it is understood that the particles may comprise a surface to comprise the same composition, the same physical properties, or the same use thereof.

Materials for particles and surfaces may include metals, glasses, ceramics, metal Organic Frameworks (MOFs), polymers, magnetic materials, and lipids. In some cases, the magnetic particles may be iron oxide particles. Examples of metallic materials include any one or any combination of gold, silver, copper, nickel, cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium, rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten, tantalum, iron, and cadmium, or any other material described in US 7749299. In some cases, the particles disclosed herein can be magnetic particles, such as superparamagnetic iron oxide nanoparticles (SPIONs). In some cases, the magnetic particles may be ferromagnetic particles, ferrimagnetic particles, paramagnetic particles, superparamagnetic particles, or any combination thereof (e.g., the particles may include ferromagnetic and ferrimagnetic materials).

The particles or surfaces may comprise a polymer. The polymer may constitute a core material (e.g., the core of the particle may comprise the particle), a layer (e.g., the particle may comprise a polymer layer disposed between its core and its shell), a shell material (e.g., the surface of the particle may be coated with a polymer that is polymerized in situ or coupled to the particle as a polymer), or any combination thereof. Examples of polymers include any one or any combination of the following: polyethylene, polycarbonate, polyanhydride, polyimide, polyhydroxyacid, polypropylene fumarate, polycaprolactone, polyamide, polyacetal, polyether, polyester, poly (orthoester), polycyanoacrylate, polyvinyl alcohol, polyurethane, polyphosphazene, polyacrylate, polymethacrylate, polycyanoacrylate, polyurea, polystyrene or polyamine, polyalkylene glycol (e.g., polyethylene glycol (PEG)), polyester (e.g., poly (lactide-co-glycolide) (PLGA), polylactic acid or polycaprolactone), polystyrene, or a copolymer of two or more polymers, such as a copolymer of polyalkylene glycol (e.g., PEG) and polyester (e.g., PLGA). The polymer may contain crosslinks. The plurality of polymers in the particles may be phase separated or may contain some degree of phase separation. The polymer may comprise a lipid-terminated polyalkylene glycol and a polyester, or any other material disclosed in US 9549901.

Examples of lipids that can be used to form the particles or surfaces of the present disclosure include cationic, anionic, and neutral charged lipids. For example, the particles and/or surfaces may be made of any one or any combination of the following: di-oleoyl phosphatidyl glycerol (DOPG), di-acyl phosphatidyl choline, di-acyl phosphatidyl ethanolamine, ceramide, sphingomyelin, cephalin, cholesterol, cerebroside and di-acyl glycerol, di-oleoyl phosphatidyl choline (DOPC), dimyristoyl phosphatidyl choline (DMPC), and di-oleoyl phosphatidyl serine (DOPS), phosphatidyl glycerol, cardiolipin, di-acyl phosphatidyl serine, di-acyl phosphatidic acid, N-dodecanoyl phosphatidyl ethanolamine, N-succinyl phosphatidyl ethanolamine, N-glutaryl phosphatidyl ethanolamine, lysyl phosphatidyl glycerol, palmitoyl phosphatidyl glycerol (POPG), lecithin, lysolecithin, phosphatidyl ethanolamine, lysophosphatidyl ethanolamine di-oleoyl phosphatidylethanolamine (DOPE), di-palmitoyl phosphatidylethanolamine (DPPE), di-myristoyl phosphatidylethanolamine (DMPE), di-stearoyl phosphatidylethanolamine (DSPE), palmitoyl Oleoyl Phosphatidylethanolamine (POPE), palmitoyl Oleoyl Phosphatidylcholine (POPC), lecithin (EPC), distearoyl phosphatidylcholine (DSPC), di-oleoyl phosphatidylcholine (DOPC), di-palmitoyl phosphatidylcholine (DPPC), di-oleoyl phosphatidylglycerol (DOPG), di-palmitoyl phosphatidylglycerol (DPPG), palmitoyl Oleoyl Phosphatidylglycerol (POPG), 16-O-monomethyl PE, 16-O-dimethyl PE, 18-1-trans PE, palmitoyl oleoyl-phosphatidylethanolamine (POPE), 1-stearoyl-2-oleoyl-phosphatidylethanolamine (SOPE), phosphatidylserine, phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidic acid, cerebroside, dicetyl phosphate and cholesterol, or any of the other materials listed in US9445994 (which is incorporated herein by reference in its entirety).

Examples of particles of the present disclosure are provided in table 2.

Table 2-examples of particles of the present disclosure

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The particles or surfaces of the present disclosure may be synthetic or the particles or surfaces of the present disclosure may be purchased from commercial suppliers. For example, particles consistent with the present disclosure may be purchased from commercial suppliers including Sigma-Aldrich, life Technologies, fisher Biosciences, nanoComposix, nanopartz, spherotech, and other commercial suppliers. In some cases, the particles or surfaces of the present disclosure may be purchased from commercial suppliers and further modified, coated, or functionalized.

Examples of particle types of the present disclosure may be carboxylate (citrate) superparamagnetic iron oxide nanoparticles (SPION), phenol-formaldehyde coated SPION, silica coated SPION, polystyrene coated SPION, carboxylated poly (styrene-co-methacrylic acid) coated SPION, N- (3-trimethoxysilylpropyl) diethylenetriamine coated SPION, poly (N- (3- (dimethylamino) propyl) methacrylamide) (PDMAPMA) coated SPION, 1,2,4, 5-benzene tetra-carboxylic acid coated SPION, poly (vinylbenzyl trimethylammonium chloride) (PVBTMAC) coated SPION, carboxylate, PAA coated SPION, poly (oligo (ethylene glycol) methyl ether methacrylate) (poe) coated SPION, carboxylate particles, carboxyl functionalized polystyrene particles, carboxylic acid coated particles, silica particles, carboxylic acid particles having a diameter of about 150nm, amino surface particles having a diameter of about 0.4-0.6 μm, silica particles having a diameter of about 0.39.39 μm, and silica particles having a diameter of about 0.39.9.50 μm, or a particle diameter of a carboxylated polystyrene having a diameter of about 0.39.9 μm.

Particles consistent with the present disclosure may comprise a wide range of sizes. In some cases, the particles of the present disclosure may be nanoparticles. In some cases, the nanoparticles of the present disclosure may be about 10nm to about 1000nm in diameter. For example, the nanoparticles disclosed herein can be at least 10nm, at least 100nm, at least 200nm, at least 300nm, at least 400nm, at least 500nm, at least 600nm, at least 700nm, at least 800nm, at least 900nm, 10nm to 50nm, 50nm to 100nm, 100nm to 150nm, 150nm to 200nm, 200nm to 250nm, 250nm to 300nm, 300nm to 350nm, 350nm to 400nm, 400nm to 450nm, 450nm to 500nm, 500nm to 550nm, 550nm to 600nm, 600nm to 650nm, 650nm to 700nm, 700nm to 750nm, 750nm to 800nm, 800nm to 850nm, 850nm to 900nm, 100nm to 300nm, 150nm to 350nm, 200nm to 400nm, 250nm to 450nm, 300nm to 500nm, 350nm to 600nm, 450nm, 500nm to 700nm, 550nm to 750nm, 600nm to 850nm, 650nm to 700nm, 900nm, or 700nm to 700 nm. In some cases, the nanoparticle may have a diameter of less than 1000nm.

The particles of the present disclosure may be microparticles. The microparticles may be particles having a diameter of about 1 μm to about 1000 μm. For example, the number of the cells to be processed, the microparticles disclosed herein can be at least 1 μm, at least 10 μm, at least 100 μm, at least 200 μm, at least 300 μm, at least 400 μm, at least 500 μm, at least 600 μm, at least 700 μm, at least 800 μm, at least 900 μm, 10 μm to 50 μm, 50 μm to 100 μm, 100 μm to 150 μm, 150 μm to 200 μm, 200 μm to 250 μm, 250 μm to 300 μm, 300 μm to 350 μm, 350 μm to 400 μm, 400 μm to 450 μm, 500 μm to 550 μm 550 to 600 μm, 600 to 650 μm, 650 to 700 μm, 700 to 750 μm, 750 to 800 μm, 800 to 850 μm, 850 to 900 μm, 100 to 300 μm, 150 to 350 μm, 200 to 400 μm, 250 to 450 μm, 300 to 500 μm, 350 to 550 μm, 400 to 600 μm, 450 to 650 μm, 500 to 700 μm, 550 to 750 μm, 600 to 800 μm, 650 to 850 μm, 700 to 900 μm, or 10 μm to 900 μm. In some cases, the particles may be less than 1000 μm in diameter.

In the methods of the present disclosure, the ratio between surface area and mass may be a determinant of particle properties. For example, the number and type of biomolecules that a particle adsorbs from a solution may vary with the surface area to mass ratio of the particle. The particles disclosed herein may have a length of 3 to 30cm ² Per mg, 5 to 50cm ² Per mg, 10 to 60cm ² Per mg, 15 to 70cm ² Per mg, 20 to 80cm ² Per mg, 30 to 100cm ² Per mg, 35 to 120cm ² Per mg, 40 to 130cm ² Per mg, 45 to 150cm ² Per mg, 50 to 160cm ² Per mg, 60 to 180cm ² Per mg, 70 to 200cm ² Per mg, 80 to 220cm ² Per mg, 90 to 240cm ² Per mg, 100 to 270cm ² Per mg, 120 to 300cm ² Per mg, 200 to 500cm ² Per mg, 10 to 300cm ² Per mg, 1 to 3000cm ² Per mg, 20 to 150cm ² Per mg, 25 to 120cm ² Per mg, or 40 to 85cm ² Surface area to mass ratio per mg. Small particles (e.g., 50nm or less in diameter) may have a higher surface area to mass ratio than large particles (e.g., 200nm or more in diameter). In some cases (e.g., for small particles), the particles may have a length of 200 to 1000cm ² Per mg, 500 to 2000cm ² Per mg, 1000 to 4000cm ² Per mg, 2000 to 8000cm ² /mg, or 4000 to 10000cm ² Surface area to mass ratio per mg. In some cases (e.g., for large particles), the particles may have a length of 1 to 3cm ² Per mg, 0.5 to 2cm ² Mg, 0.25 to 1.5cm ² Per mg, or 0.1 to 1cm ² Surface area to mass ratio per mg.

In some cases, the plurality of particles (e.g., a group of particles) of the compositions and methods described herein can include a range of surface area to mass ratios. In some cases, the surface area to mass ratio of the plurality of particles is in the range of less than 100cm ² /mg、80cm ² /mg、60cm ² /mg、40cm ² /mg、20cm ² /mg、10cm ² /mg、5cm ² /mg, or 2cm ² /mg. In some cases, the surface area to mass ratio of the plurality of particles varies by no more than 40%, 30%, 20%, 10%, 5%, 3%, 2 between the plurality of particles% or 1%.

In some cases, the plurality of particles (e.g., in a group of particles) may have a wider range of surface area to mass ratios. In some cases, the surface area to mass ratio of the plurality of particles is in a range greater than 100cm ² /mg、150cm ² /mg、200cm ² /mg、250cm ² /mg、300cm ² /mg、400cm ² /mg、500cm ² /mg、800cm ² /mg、1000cm ² /mg、1200cm ² /mg、1500cm ² /mg、2000cm ² /mg、3000cm ² /mg、5000cm ² /mg、7500cm ² /mg、10000cm ² /mg, or greater. In some cases, the surface area to mass ratio of the plurality of particles (e.g., within a group) may vary by greater than 100%, 200%, 300%, 400%, 500%, 1000%, 10000% or more. In some cases, the plurality of particles having a wide range of surface area to mass ratios includes at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, or more different types of particles.

The particles may comprise a wide range of physical properties. Physical properties of the particles may include composition, size, surface charge, hydrophobicity, hydrophilicity, surface functionalization, surface topography, surface curvature, porosity, core material, shell material, shape, and any combination thereof.

The surface functionalization can include polymerizable functional groups, positively or negatively charged functional groups, zwitterionic functional groups, acidic or basic functional groups, polar functional groups, or any combination thereof. The surface functionalization may include a carboxyl group, a hydroxyl group, a thiol group, a cyano group, a cyanate group, a nitro group, an ammonium group, an alkyl group, an imidazolium group, a sulfonium group, a pyridinium group, a pyrrolidinium group, a phosphonium group, an aminopropyl group, an amine group, a boronic acid group, an N-succinimidyl ester group, a PEG group, a streptavidin, a methyl ether group, a triethoxypropylaminosilane group, a PCP group, a citric acid group, a lipoic acid group, a BPEI group, or any combination thereof. The particles of the plurality of particles may be selected from: micelles, liposomes, iron oxide particles, silver particles, gold particles, palladium particles, quantum dots, platinum particles, titanium particles, silica particles, metal or inorganic oxide particles, synthetic polymer particles, copolymer particles, terpolymer particles, polymer particles having a metal core, polymer particles having a metal oxide core, polystyrene sulfonate particles, polyethylene oxide particles, polyoxyethylene glycol particles, polyethylene imine particles, polylactic acid particles, polycaprolactone particles, polyglycolic acid particles, poly (lactide-co-glycolide) polymer particles, cellulose ether polymer particles, polyvinylpyrrolidone particles, polyvinyl acetate particles, polyvinylpyrrolidone-vinyl acetate copolymer particles, polyvinyl alcohol particles, acrylate particles, polyacrylic acid particles, crotonic acid copolymer particles polyvinyl phosphonate particles, polyalkylene particles, carboxyvinyl polymer particles, sodium alginate particles, carrageenan particles, xanthan particles, gum arabic particles, guar gum particles, pullulan particles, agar particles, chitin particles, chitosan particles, pectin particles, karaya particles, carob gum particles, maltodextrin particles, amylose particles, corn starch particles, potato starch particles, rice starch particles, tapioca starch particles, pea starch particles, sweet potato starch particles, barley starch particles, wheat starch particles, hydroxypropylated high amylose starch particles, dextrin particles, levan particles, elsinan particles, gluten particles, collagen particles, whey protein isolate particles, casein particles, milk protein particles, soy protein particles, keratin particles, polyethylene particles, polycarbonate particles, polyanhydride particles, polyhydroxyacid particles, polypropylene fumarate particles, polycaprolactone particles, polyamine particles, polyacetal particles, polyether particles, polyester particles, poly (orthoester) particles, polycyanoacrylate particles, polyurethane particles, polyphosphazene particles, polyacrylate particles, polymethacrylate particles, polycyanoacrylate particles, polyurea particles, polyamine particles, polystyrene particles, poly (lysine) particles, chitosan particles, dextran particles, poly (acrylamide) particles, derivatized poly (acrylamide) particles, gelatin particles, starch particles, chitosan particles, dextran particles, gelatin particles, starch particles, poly beta-amino ester particles, poly (amidoamine) particles, polylactic acid co-glycolic acid particles, polyanhydride particles, bioreductive polymer particles, and 2- (3-aminopropylamino) ethanol particles, and any combination thereof.

One or more physicochemical properties of the particles of the present disclosure may be different. The one or more physicochemical properties are selected from: composition, size, surface charge, hydrophobicity, hydrophilicity, roughness, density, surface functionalization, surface topography, surface curvature, porosity, core material, shell material, shape, and any combination thereof. Surface functionalization may include macromolecular functionalization, small molecule functionalization, or any combination thereof. Small molecule functionalization may include aminopropyl functionalization, amine functionalization, boric acid functionalization, carboxylic acid functionalization, alkyl group functionalization, N-succinimidyl ester functionalization, monosaccharide functionalization, phosphate sugar functionalization, sulfonyl sugar functionalization, ethylene glycol functionalization, streptavidin functionalization, methyl ether functionalization, trimethoxysilylpropyl functionalization, silica functionalization, triethoxypropylaminosilane functionalization, thiol functionalization, PCP functionalization, citric acid functionalization, lipoic acid functionalization, ethyleneimine functionalization. The particle set may include a plurality of particles having a plurality of small molecule functionalities selected from the group consisting of silica functionalization, trimethoxysilylpropyl functionalization, dimethylaminopropyl functionalization, phosphoglyco functionalization, amine functionalization, and carboxyl functionalization.

The small molecule functionalization may comprise a polar functional group. Non-limiting examples of polar functional groups include carboxyl groups, hydroxyl groups, thiol groups, cyano groups, nitro groups, ammonium groups, imidazolium groups, sulfonium groups, pyridinium groups, pyrrolidinium groups, phosphonium groups, or any combination thereof. In some embodiments, the functional groups are acidic functional groups (e.g., sulfonic acid groups, carboxyl groups, etc.), basic functional groups (e.g., amino groups, cyclic secondary amino groups (e.g., pyrrolidinyl groups and piperidinyl groups), pyridinyl groups, imidazolyl groups, guanidinyl groups, etc.), carbamoyl groups, hydroxyl groups, aldehyde groups, etc.

The small molecule functionalization can comprise ionic or ionizable functional groups. Non-limiting examples of ionic or ionizable functional groups include ammonium groups, imidazolium groups, sulfonium groups, pyridinium groups, pyrrolidinium groups, phosphonium groups.

The small molecule functionalization may comprise a polymerizable functional group. Non-limiting examples of polymerizable functional groups include vinyl groups and (meth) acrylic groups. In some embodiments, the functional group is a pyrrolidinyl acrylate, acrylic acid, methacrylic acid, acrylamide, 2- (dimethylamino) ethyl methacrylate, hydroxyethyl methacrylate, and the like.

Surface functionalization can include electrical charges. For example, the particles may be functionalized to carry a net neutral surface charge, a net positive surface charge, a net negative surface charge, or an zwitterionic surface. The surface charge may determine the type of biomolecules collected on the particles. Thus, optimizing the particle set may include selecting particles having different surface charges, which may not only increase the number of different proteins collected on the particle set, but may also increase the likelihood of identifying the biological state of the sample. The particle set may include positively charged particles and negatively charged particles. The particle group may include positively charged particles and neutral particles. The particle set may include positively charged particles and zwitterionic particles. The particle set may include neutral particles and negatively charged particles. The particle set may include neutral particles and zwitterionic particles. The particle set may include negatively charged particles and zwitterionic particles. The particle group may include positively charged particles, negatively charged particles, and neutral particles. The particle sets may include positively charged particles, negatively charged particles, and zwitterionic particles. The particle group may include positively charged particles, neutral particles, and zwitterionic particles. The particle set may include negatively charged particles, neutral particles, and zwitterionic particles.

The present disclosure includes compositions (e.g., particle sets) and methods comprising two or more particles that differ in at least one physicochemical property. The compositions or methods of the present disclosure may comprise 3 to 6 particles that differ in at least one physicochemical property. The compositions or methods of the present disclosure may comprise 4 to 8 particles that differ in at least one physicochemical property. The compositions or methods of the present disclosure may comprise 4 to 10 particles that differ in at least one physicochemical property. The compositions or methods of the present disclosure may comprise 5 to 12 particles that differ in at least one physicochemical property. The compositions or methods of the present disclosure may comprise from 6 to 14 particles that differ in at least one physicochemical property. The compositions or methods of the present disclosure may comprise 8 to 15 particles that differ in at least one physicochemical property. The compositions or methods of the present disclosure may comprise 10 to 20 particles that differ in at least one physicochemical property. The compositions or methods of the present disclosure may include at least 2 different particle types, at least 3 different particle types, at least 4 different particle types, at least 5 different particle types, at least 6 different particle types, at least 7 different particle types, at least 8 different particle types, at least 9 different particle types, at least 10 different particle types, at least 11 different particle types, at least 12 different particle types, at least 13 different particle types, at least 14 different particle types, at least 15 different particle types, at least 20 different particle types, at least 25 particle types, or at least 30 different particle types.

Surface functionalization can affect the composition of the particle biomolecular corona. Such surface functionalization may include small molecule functionalization or large molecule functionalization. The surface functionalization may be coupled to a particulate material, such as a polymer, a metal oxide, an inorganic oxide (e.g., silica), or another surface functionalization.

Surface functionalization may include small molecule functionalization, large molecule functionalization, or a combination of two or more such functionalization. Macromolecular functionalization may comprise biological macromolecules such as proteins or polynucleotides (e.g., 100-mer DNA molecules). Macromolecular functionalization may include proteins, polynucleotides, or polysaccharides, or may be comparable in size to any of the foregoing species. For example, macromolecular functionalization may include at least 6nm ³ At least 8nm ³ At least 12nm ³ At least 15nm ³ At least 20nm ³ At least 30nm ³ At least 50nm ³ At least 80nm ³ At least 120nm ³ At least 180nm ³ At least 300nm ³ At least 500nm ³ At least 800nm ³ At least 1200nm ³ At least 1500nm ³ Or at least 2000nm ³ Is a volume of (c). Macromolecular functionalization may include at least 15nm ² At least 20nm ² At least 25nm ² At least 40nm ² At least 80nm ² At least 150nm ² At least 300nm ² At least 500nm ² At least 800nm ² At least 1200nm ² Or at least 1500nm ² Is a surface area of the substrate. Macromolecular functionalization may comprise a decoy molecule.

Macromolecular functionalization may involve a specific form of particle attachment. Macromolecules may be tethered to the particles by linkers. The linker may hold the macromolecule in proximity to the particle, thereby limiting its movement and reorientation relative to the particle, or may allow the macromolecule to extend away from the particle. The linker may be rigid (e.g., a polyolefin linker) or flexible (e.g., a nucleic acid linker). The linker may be no greater than 0.5nm in length, no greater than 1nm in length, no greater than 1.5nm in length, no greater than 2nm in length, no greater than 3nm in length, no greater than 4nm in length, no greater than 5nm in length, no greater than 8nm in length, or no greater than 10nm in length. The linker may be at least 1nm in length, at least 2nm in length, at least 3nm in length, at least 4nm in length, at least 5nm in length, at least 8nm in length, at least 12nm in length, at least 15nm in length, at least 20nm in length, at least 25nm in length, or at least 30nm in length. Thus, surface functionalization on the particle can be projected beyond the primary crown associated with the particle. The surface functionalization may also be located below or within the biomolecular corona formed on the particle surface.

The macromolecule may be tethered at a specific location, such as the C-terminus of the protein, or may be tethered at a number of possible sites. For example, the peptide may be covalently linked to the particle through any surface-exposed lysine residues thereof.

The particles may comprise a single surface functionalization, e.g. a specific small molecule, or a plurality of surface functionalization, e.g. a plurality of different small molecules.

The particles may comprise a high affinity for a particular biomolecule or class of biomolecules. For example, surface functionalization can comprise a nonpolar moiety (e.g., an organosilane) that interacts strongly with nonpolar protein functional groups and alpha helical chains. Similarly, macromolecular surface functionalization can comprise peptides (e.g., antibodies) with high affinity for a particular molecular target.

The particles may comprise small molecule functionalization. Small molecule functionalization can include masses of less than 600 daltons, less than 500 daltons, less than 400 daltons, less than 300 daltons, less than 200 daltons, or less than 100 daltons. Small molecule functionalization may comprise an ionizable moiety, e.g., pK _a Or pK (K) _b Chemical groups less than 6 or 7. Small molecule functionalization may include small organic molecules such as alcohols (e.g., octanol), amines, alkanes, alkenes, alkynes, heterocycles (e.g., piperidinyl groups), heteroaryl, thiols, carboxylates, carbonyl, amides, esters, thioesters, carbonates, thiocarbonates, carbamates, thiocarbamates, urea, thiourea, halogens, sulfates, phosphates, monosaccharides, disaccharides, lipids, or any combination thereof. For example, small molecule functionalization may include a phosphate sugar, a sugar acid, or a sulfonylated sugar.

The particles of the present disclosure can be contacted with a biological sample (e.g., a biological fluid) to form a biomolecular corona. In some cases, the biomolecular cap may comprise at least two biomolecules that do not share a common binding motif. Particles and biomolecular crowns may be separated from biological samples by, for example, centrifugation, magnetic separation, filtration, or gravity separation. Particle types and biomolecular crowns can be separated from biological samples by utilizing a variety of separation techniques. Non-limiting examples of separation techniques include magnetic separation, column-based separation, filtration, centrifugal column-based separation, centrifugation, ultracentrifugation, density or gradient-based centrifugation, gravity separation, or any combination thereof. Protein corona analysis can be performed on isolated particles and biomolecular crowns. Protein crown analysis may include identifying one or more proteins in a biomolecular crown by, for example, mass spectrometry. A single particle type (e.g., the particle types listed in table 2) may be contacted with a biological sample. A plurality of particle types (e.g., a plurality of particle types provided in table 2) may be contacted with the biological sample. Multiple particle types may be combined and contacted with a single sample volume of biological sample. The plurality of particle types may be contacted with the biological sample sequentially and separated from the biological sample before contacting a subsequent particle type with the biological sample. Protein corona analysis of biomolecular crowns can compress the dynamic range of the analysis compared to total protein analysis methods.

The particles of the present disclosure may be used to interrogate a sample serially, by incubating a first particle type with the sample to form a biomolecular corona on the first particle type, isolating the first particle type, incubating a second particle type with the sample to form a biomolecular corona on the second particle type, isolating the second particle type, and repeating the interrogating and isolating for any number of particle types (by incubating with the sample). In some cases, biomolecular crowns on each particle type used to interrogate the sample serially may be analyzed by protein crown analysis. The biomolecular content of the supernatant may be analyzed after serial interrogation with one or more particle types.

Particle group

The present disclosure provides compositions and methods of use thereof for assaying proteins in a sample. The compositions described herein include a particle set comprising one or more different particle types. The particle groups described herein may differ in the number of particle types and the variety of particle types in a single group. For example, the particles in a group may vary depending on size, polydispersity, shape and morphology, surface charge, surface chemistry and functionalization, and substrate. The group may be incubated with the protein to be analyzed and the sample of protein concentration. Proteins in the sample adsorb to the surface of different particle types in the particle group to form protein crowns. The exact proteins and protein concentration in the particle set that adsorb to a particular particle type may depend on the composition, size, and surface charge of the particle type. Thus, each particle type in a group may have a different protein cap due to adsorption of a different protein set, a different concentration of a particular protein, or a combination thereof. Each particle type in a group may have mutually exclusive protein crowns, or may have overlapping protein crowns. Overlapping protein crowns may overlap in protein identity, protein concentration, or both.

The present disclosure also provides methods of selecting the type of particles to be included in a group based on the type of sample. The types of particles contained in the set may be combinations of particles optimized for removal of high abundance proteins. It is also consistent that the types of particles included in the set are those selected for adsorption of the particular protein of interest. The particles may be nanoparticles. The particles may be microparticles. The particles may be a combination of nanoparticles and microparticles.

The particle sets disclosed herein can be used to identify the number of different proteins disclosed herein, and/or any particular protein disclosed herein, over a wide dynamic range. For example, a set of particles disclosed herein comprising different particle types can enrich for proteins in a sample (e.g., a plasma sample) over the entire dynamic range of proteins present in the sample, which can be identified using a proteogram workflow. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 2. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 3. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 4. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 5. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 6. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 7. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 8. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 9. In some cases, a particle set comprising any number of different particle types disclosed herein is enriched and identified for proteins over a dynamic range of at least 10. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 11. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 12. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 13. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 14. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 15. In some cases, the particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins over a dynamic range of at least 20. In some cases, particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins in a dynamic range of 2 to 100. In some cases, particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins in a dynamic range of 2 to 20. In some cases, particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins in a dynamic range of 2 to 10. In some cases, particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins in a dynamic range of 2 to 5. In some cases, particle sets comprising any number of the different particle types disclosed herein are enriched and identified for proteins in a dynamic range of 5 to 10.

Particle sets comprising any number of different particle types disclosed herein are enriched and identified as a single protein or protein group. In some cases, a single protein or group of proteins may comprise proteins with different post-translational modifications. The introduction of Nanoparticles (NPs) into biological fluids (e.g., plasma) may result in the formation of selective, specific, and reproducible protein crowns at the nanobiological interface driven by the relationship between protein-NP affinity, protein abundance, and protein-protein interactions. For example, a first particle type in a particle set may be enriched for a protein or group of proteins having a first post-translational modification, a second particle type in a particle set may be enriched for the same protein or group of proteins having a second post-translational modification, and a third particle type in a particle set may be enriched for the same protein or group of proteins lacking a post-translational modification. In some cases, a particle set comprising any number of different particle types disclosed herein enriches and identifies a single protein or protein group by binding to different domains, sequences, or epitopes of the single protein or protein group. For example, a first particle type in a particle set can enrich a protein or protein group by binding to a first domain of the protein or protein group, and a second particle type in a particle set can enrich the same protein or the same protein group by binding to a second domain of the protein or protein group.

The particle groups may have more than one particle type. Increasing the number of particle types in a group can be a way to increase the number of proteins that can be identified in a given sample. Examples of how increasing the group size may increase the number of proteins identified are shown in fig. 12, where a group size of one particle type identifies 419 different proteins, a group size of two particle types identifies 588 different proteins, a group size of three particle types identifies 727 different proteins, a group size of four particle types identifies 844 proteins, a group size of five particle types identifies 934 different proteins, a group size of six particle types identifies 1008 different proteins, a group size of seven particle types identifies 1075 different proteins, a group size of eight particle types identifies 1133 different proteins, a group size of nine particle types identifies 1184 different proteins, a group size of ten particle types identifies 1230 different proteins, a group size of eleven particle types identifies 1275 different proteins, and a group size of twelve particle types identifies 1318 different proteins.

The particle set may include a combination of particles with silica and a polymer surface. For example, the particle set may include a thin layer of silica coated SPION, a poly (dimethylaminopropyl methacrylamide) (PDMAPMA) coated SPION, and a poly (ethylene glycol) (PEG) coated SPION. The particle sets consistent with the present disclosure may further include two or more particles selected from the group consisting of silica-coated SPION, N- (3-trimethoxysilylpropyl) diethylenetriamine-coated SPION, PDMAPMA-coated SPION, carboxyl-functionalized polyacrylic-coated SPION, amino-surface-functionalized SPION, polystyrene carboxyl-functionalized SPION, silica particles, and dextran-coated SPION. The particle sets consistent with the present disclosure may further include two or more particles selected from the group consisting of surfactant-free carboxylate particles, carboxyl-functionalized polystyrene particles, silica-coated particles, silica particles, dextran-coated particles, oleic acid-coated particles, boronated nano-powder-coated particles, PDMAPMA-coated particles, poly (glycidyl methacrylate-benzylamine) -coated particles and poly (N- [3- (dimethylamino) propyl ] methacrylamide-co- [2- (methacryloyloxy) ethyl ] dimethyl- (3-sulfopropyl) ammonium hydroxide, P (DMAPMA-co-SBMA) -coated particles.

Particle groups consistent with the present disclosure may include silica functionalized particles, amine functionalized particles, silanol functionalized particles, carboxylate functionalized particles, and benzyl or phenyl functionalized particles. Particle groups consistent with the present disclosure may include silica functionalized particles, amine functionalized particles, silanol functionalized particles, polystyrene functionalized particles, and sugar functionalized particles. Particle sets consistent with the present disclosure may include silica functionalized particles, N- (3-trimethoxysilylpropyl) diethylenetriamine functionalized particles, PDMAPMA functionalized particles, dextran functionalized particles, and polystyrene carboxyl functionalized particles. The particle sets consistent with the present disclosure may include 5 particles including silica functionalized particles, amine functionalized particles, silanol functionalized particles.

Protein analysis method

The use of the particles and methods disclosed herein can bind a variety of unique biomolecules (e.g., proteins) in a biological sample (e.g., biological fluid). For example, the particles disclosed herein may be incubated with a biological sample to form a protein corona, the protein corona comprises at least 100 unique proteins, at least 120 unique proteins, at least 140 unique proteins, at least 160 unique proteins, at least 180 unique proteins, at least 200 unique proteins, at least 220 unique proteins, at least 240 unique proteins, at least 260 unique proteins, at least 280 unique proteins, at least 300 unique proteins, at least 320 unique proteins, at least 340 unique proteins, at least 360 unique proteins, at least 380 unique proteins, at least 400 unique proteins, at least 420 unique proteins, at least 440 unique proteins, at least 460 unique proteins, at least 480 unique proteins, at least 500 unique proteins, at least 520 unique proteins, at least 540 unique proteins, at least 560 unique proteins at least 580 unique proteins, at least 600 unique proteins, at least 620 unique proteins, at least 640 unique proteins, at least 660 unique proteins, at least 680 unique proteins, at least 700 unique proteins, at least 720 unique proteins, at least 740 unique proteins, at least 760 unique proteins, at least 780 unique proteins, at least 800 unique proteins, at least 820 unique proteins, at least 840 unique proteins, at least 860 unique proteins, at least 880 unique proteins, at least 900 unique proteins, at least 920 unique proteins, at least 940 unique proteins, at least 960 unique proteins, at least 980 unique proteins, at least 1000 unique proteins, 100 to 1000 unique proteins, 150 to 950 unique proteins, 200 to 900 unique proteins, 250 to 850 unique proteins, 300 to 800 unique proteins, 350 to 750 unique proteins, 400 to 700 unique proteins, 450 to 650 unique proteins, 500 to 600 unique proteins, 200 to 250 unique proteins, 250 to 300 unique proteins, 300 to 350 unique proteins, 350 to 400 unique proteins, 400 to 450 unique proteins, 450 to 500 unique proteins, 500 to 550 unique proteins, 550 to 600 unique proteins, 600 to 650 unique proteins, 650 to 700 unique proteins, 700 to 750 unique proteins, 750 to 800 unique proteins, 800 to 850 unique proteins, 850 to 900 unique proteins, 900 to 950 unique proteins, 950 to 1000 unique proteins. In some cases, the surfaces disclosed herein can be incubated with biological samples to adsorb at least 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 unique biomolecules. In some cases, the surfaces disclosed herein can be incubated with biological samples to adsorb up to 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 unique biomolecules. In some cases, several different types of particles may be used, alone or in combination, to identify multiple proteins in a particular biological sample. In other words, the particles can be multiplexed to bind and identify multiple proteins in the biological sample. Protein crown analysis can compress the dynamic range of the analysis compared to the protein analysis of the original sample.

The proteins collected on the particles can be further analyzed. A method may include collecting a biomolecular corona or a subset of biomolecules from a biomolecular corona. The collected biomolecular crowns or a subset of the collected biomolecules from the biomolecular crowns may be subjected to further particle-based analysis (e.g., particle adsorption). The collected biomolecular crowns or a subset of the collected biomolecules from the biomolecular crowns may be purified or fractionated (e.g., by chromatography). The collected biomolecular crowns or a subset of the collected biomolecules from the biomolecular crowns may be analyzed (e.g., by mass spectrometry).

Fig. 13A-13B provide examples of methods consistent with the present disclosure. Fig. 13A shows a schematic of protein corona formation, wherein a plurality of particles 1321, 1322, and 1323 particles are in contact with a biological sample 1310 comprising biological molecules 1311, and wherein each particle adsorbs a plurality of biological molecules from the biological sample to its surface 1330. The different particles may be of different particle types (as shown in the center of the figure, where the top, middle and bottom spheres represent three different particle types) such that each particle differs from the other particles by at least one physicochemical property. This difference in physicochemical properties may lead to the formation of different protein corona compositions on the particle surface. Fig. 13B shows a biomolecular corona (e.g., protein corona) analysis workflow, which includes: (1341) particle plasma incubation and protein corona formation; (1342) particle collection (e.g., using a magnet); (1343) Washing off the solution and analytes not adsorbed on the particles; (1434) resuspension of the particles; (1435) digestion of coronatine; and (1346) liquid chromatography-mass spectrometry (LC-MS). In this example, each plasma-NP well is one sample, for a total of 96 samples per plate.

Protein corona analysis can include automated components. For example, an automated instrument can contact a sample with a particle or group of particles, identify proteins on the particle or group of particles (e.g., digest proteins on the particle or group of particles and perform mass spectrometry), and generate data for identifying a particular biomolecule or biological state of the sample. The automated instrument may divide the sample into multiple volumes and analyze each volume. The automated instrument may analyze multiple individual samples, for example, by processing multiple samples within multiple wells of a well plate, and performing parallel analysis on each sample.

The particle sets disclosed herein can be used to identify a plurality of proteins, peptides, protein groupings, or protein classes using the protein analysis workflow described herein (e.g., protein crown analysis workflow). Protein corona analysis can include contacting a sample with different particle types (e.g., particle sets), forming biomolecular crowns on the different particle types, and identifying biomolecules in the biomolecular crowns (e.g., by mass spectrometry). The characteristic intensities as disclosed herein refer to the intensities of discrete peaks ("features") seen on a plot of mass-to-charge ratio of a sample versus operating intensity of a mass spectrum. These features may correspond to variable electric fragments of peptides and/or proteins. Using the data analysis methods described herein, the characteristic intensities can be classified as protein groupings. Protein grouping refers to two or more proteins identified by a shared peptide sequence. Alternatively, a protein group may refer to a protein identified using a unique identification sequence. For example, if in a sample, a peptide sequence shared between two proteins (protein 1: xyzzx and protein 2: xyzyz) is determined, the protein group may be an "XYZ protein group" having two members (protein 1 and protein 2). Alternatively, if the peptide sequence is unique to a single protein (protein 1), the protein group may be a "ZZX" protein group having one member (protein 1). Each protein packet may be supported by more than one peptide sequence. Proteins detected or identified according to the present disclosure may refer to different proteins detected in a sample (e.g., different relative to other proteins detected using mass spectrometry). Thus, analyzing proteins present in different crowns corresponding to different particle types in a particle set can yield a large number of characteristic intensities. This number is reduced when the characteristic intensities are treated as different peptides; this number is further reduced when different peptides are processed into different proteins, and when peptides are grouped into protein groupings (two or more proteins sharing different peptide sequences).

The methods disclosed herein include separating one or more particle types from a sample or more than one sample (e.g., a biological sample or a serially interrogated sample). The particle type can be rapidly separated or separated from the sample by magnetism. Furthermore, a plurality of samples that are spatially separated may be processed in parallel. Thus, the methods disclosed herein allow for separation or isolation of particle types from unbound proteins in a sample. The particle type may be separated by various methods including, but not limited to, magnetic separation, centrifugation, filtration, or gravity separation. The particle sets can be incubated with a plurality of spatially separated samples, wherein each spatially separated sample is in a well of a well plate (e.g., 96-well plate). After incubation, the particle type in each well of the well plate can be separated from unbound protein present in the spatially separated sample by placing the entire plate on a magnet. This simultaneously pulls down the superparamagnetic particles in the particle group. The supernatant in each well can be removed to remove unbound protein. These steps (incubation, pull down) can be repeated to effectively wash the particles to remove residual background unbound proteins that may be present in the sample. This is an example, but many other scenarios can be envisaged by the person skilled in the art, in which superparamagnetic particles are rapidly separated from one or more spatially separated samples simultaneously.

Methods and compositions of the present disclosure provide for the identification and measurement of specific proteins in biological samples by processing proteomic data by digesting crowns formed on the surface of particles. Examples of proteins that can be identified and measured include high abundance proteins, medium abundance proteins, and low abundance proteins. The low abundance protein may be present in the sample at a concentration equal to or below 10 ng/mL. The high abundance protein may be present in the sample at a concentration equal to or higher than 10 μg/mL. The moderately abundant protein may be present in the sample at a concentration of about 10ng/mL to about 10 μg/mL. Examples of high abundance proteins include albumin, igG, and the first 14 abundance proteins that account for 95% of the mass of the analyte in the plasma. In addition, any protein that can be purified using a conventional exhausted column can be detected directly in a sample using the particle sets disclosed herein. Examples of proteins may be any of the proteins listed in published databases such as Keshishian et al (Mol Cell proteins.20150ep; 14 (9): 2375-93.doi:10.1074/mcp.M114.046813.Epub 20150eb 27.), farr et al (J proteins Res.2014Jan 3;13 (1): 60-75.doi:10.1021/pr4010037.Epub 2013Dec 6.) or Pernemalm et al (Expert Rev proteins.4Aug; 11 (4): 431-48.201i: 10.1586/14789450.2014.901157.Epub 2014Mar 24.).

Examples of proteins that can be measured and identified using the methods and compositions disclosed herein include albumin, igG, lysozyme, CEA, HER-2/neu, bladder tumor antigen, thyroglobulin, alpha fetoprotein, PSA, CA125, CA19.9, CA 15.3, leptin, prolactin, osteopontin, IGF-II, CD98, fascicin, sPigR, 14-3-3eta, troponin I, B type natriuretic peptide, BRCA1, c-Myc, IL-6, fibrinogen, EGFR, gastrin, PH, G-CSF, myotonin, NSE, FSH, VEGF, P21, PCNA, calcitonin, PR, CA125, LH, somatostatin, S100, insulin alpha-prolactin, ACTH, bcl-2, ERalpha, ki-67, p53, cathepsin D, beta catenin, VWF, CD15, k-ras, caspase 3, EPN, CD10, FAS, BRCA2, CD30L, CD, CGA, CRP, prothrombin, CD44, APEX, transferrin, GM-CSF, E-cadherin, IL-2, bax, IFN-gamma, beta-2-MG, TNF alpha, c-erbB-2, pancreatic protease, cyclin D1, MG B, XBP-1, HG-1, YKL-40, S-gamma, NESP-55, spindle protein-1 (netrin-1), conn, GADD45A, CDK-6, CCL21, brMS1, 17β HDI, PDGFRA, pcaf, CCL5, MMP3, seal protein-4 and seal protein-3. In some instances, other examples of proteins that can be measured and identified using the particle sets disclosed herein are any protein or protein grouping listed in an open target database for a particular disease indication of interest (e.g., prostate cancer, lung cancer, or alzheimer's disease).

The methods and compositions disclosed herein may also elucidate protein classes, or interactions of protein classes. The protein class may include a group of proteins that share a common function (e.g., amine oxidase or proteins involved in angiogenesis); proteins sharing common physiological, cellular or subcellular localization (e.g., peroxisome proteins or membrane proteins); proteins sharing a common cofactor (e.g., heme or flavin proteins); proteins corresponding to a particular biological state (e.g., hypoxia-related proteins); proteins containing specific structural motifs (e.g., cupin folding); or a protein with post-translational modifications (e.g., ubiquitinated or citrullinated proteins). The protein class may comprise at least 2 proteins, 5 proteins, 10 proteins, 20 proteins, 40 proteins, 60 proteins, 80 proteins, 100 proteins, 150 proteins, 200 proteins, or more.

Proteomic data of biological samples can be identified, measured, and quantified using a variety of different analytical techniques. For example, the proteomic data may be generated using SDS-PAGE or any gel-based separation technique. Peptides and proteins can also be identified, measured and quantified using immunoassays (e.g., ELISA). Alternatively, the proteomic data may be identified, measured and quantified using mass spectrometry, high performance liquid chromatography, LC-MS/MS, edman degradation, immunoaffinity techniques, methods disclosed in EP3548652, WO2019083856, WO2019133892 (each of which is incorporated herein by reference in its entirety), and other protein separation techniques.

Assays may include protein collection, protein digestion, and mass spectrometry (e.g., MS, LC-MS/MS) of the particles. Digestion may include chemical digestion, for example by cyanogen bromide or 2-nitro-5-thiocyanic acid (NTCB). Digestion may include enzymatic digestion, such as by trypsin or pepsin. Digestion may include enzymatic digestion by a variety of proteases. Digestion may include a protease selected from the group consisting of: trypsin, chymotrypsin, glu C, lys C, elastase, subtilisin, proteinase K, thrombin, factor X, arg C, papain, asp N, thermolysin, pepsin, aspartyl protease, cathepsin D, zinc metalloprotease, glycoprotein endopeptidase, proline, aminopeptidase, prenyl protease, caspase, kex2 endoprotease, or any combination thereof. Digestion may cleave peptides at random locations. Digestion may cleave peptides at specific positions (e.g., methionine) or sequences (e.g., glutamate-histidine-glutamate). Digestion allows similar proteins to be distinguished. For example, the assay may be performed using a first digestion method to break down 8 different proteins into individual protein groups and a second digestion method to break down 8 separate proteins with different signals. Digestion may produce average peptide stretches of 8 to 15 amino acids in length. Digestion may produce an average peptide stretch of 12 to 18 amino acids in length. Digestion may produce average peptide stretches of 15 to 25 amino acids in length. Digestion may produce average peptide stretches of 20 to 30 amino acids in length. Digestion may produce average peptide stretches of 30 to 50 amino acids in length.

The assay allows for rapid generation and analysis of proteomic data. Starting from the input of a biological sample (e.g., a buccal or nasal smear, plasma or tissue), the assays of the present disclosure can generate and analyze proteomic data in less than 7 hours. From the input of the biological sample, the assays of the present disclosure can generate and analyze proteomic data within 5-7 hours. From the input of the biological sample, the assays of the present disclosure can generate and analyze proteomic data in less than 5 hours. From the input of the biological sample, the assays of the present disclosure can generate and analyze proteomic data within 3-5 hours. From the input of the biological sample, the assays of the present disclosure can generate and analyze proteomic data in 2-4 hours. From the input of the biological sample, the assays of the present disclosure can generate and analyze proteomic data in 2-3 hours. From the input of the biological sample, the assays of the present disclosure can generate and analyze proteomic data in less than 3 hours. From the input of the biological sample, the assays of the present disclosure can generate and analyze proteomic data in less than 2 hours. Analysis may include identifying protein groupings. Analysis may include identifying a class of proteins. Analysis may include quantifying the abundance of a biomolecule, peptide, protein grouping, or protein class. Analysis may include identifying the ratio of the abundances of two biomolecules, peptides, proteins, protein groupings, or protein categories. Analysis may include identifying a biological state.

Kit for detecting a substance in a sample

Provided herein are kits comprising compositions of the present disclosure useful for practicing methods of the present disclosure. The kit may include one or more particle types to interrogate the sample to identify the biological state of the sample. In some cases, the kit may include the types of particles provided in table 2. The kit may include reagents for functionalizing the particles (e.g., reagents for tethering small molecule functionalization to the surface of the particles). The kit may be pre-packaged in separate samples. In some cases, the kit may include a plurality of different particle types that may be used to interrogate the sample. The plurality of particle types may be pre-packaged, wherein each particle type of the plurality of particle types is individually packaged. Alternatively, multiple particle types may be packaged together to house a combination of particle types in a single package. The particles may be provided in dry (e.g., lyophilized) form, or may be provided in suspension or solution form. Particles may be provided in an orifice plate. For example, the kit may comprise a 24-384 well plate, wherein the particles are sealed within the well. Two of such well plates may contain different particles or particle concentrations. The two wells may contain different buffers or chemical conditions. For example, the well plate may be provided with different particles in the wells of each row and different buffers in each column of the row. The aperture may be sealed with a removable cover. For example, the kit may comprise an well plate comprising a plastic sliding cover covering a plurality of wells. The aperture may be sealed with a penetrable cover. For example, the well may be covered by a septum that the needle may pierce in order for the sample to move into and out of the well.

In some aspects, the disclosure describes a kit for enriching a biological sample. In some cases, the kit includes a first substance configured to specifically bind to a first set of biomolecular targets. In some cases, the kit includes a second substance configured to adsorb a second set of biomolecular targets. In some cases, the kit includes a third substance configured to adsorb a third set of biomolecular targets.

In some cases, the first substance is a resin or a particle. In some cases, the first substance includes a specific binding member configured to bind to a first set of biomolecular targets. In some cases, the first substance is configured to specifically bind to at least one of albumin, igG, igA, igM, igD, igE, igG (light chain), alpha-1-acid glycoprotein, fibrinogen, haptoglobin, alpha-1-antitrypsin, alpha-2-macroglobulin, transferrin, and apolipoprotein a-1.

In some cases, the kit may further include a fourth substance configured to non-specifically bind to a fourth set of biomolecular targets. In some cases, the kit may further comprise a fifth substance configured to non-specifically bind to a fifth set of biomolecular targets. The kit may include various amounts of substances configured to non-specifically bind to a collection of biomolecular targets.

In some cases, the kit may further comprise a fifth substance configured to specifically bind to a fifth set of biomolecular targets. In some cases, the kit may further comprise a sixth substance configured to specifically bind to a sixth set of biomolecular targets. The kit may include various amounts of substances configured to specifically bind to the collection of biomolecular targets.

In some cases, the second agent comprises a plurality of domains. In some cases, each domain of the plurality of domains is configured to non-specifically bind to a different subset of the second set of biomolecular targets. In some cases, the second substance comprises a particle surface. In some cases, the plurality of domains comprises a plurality of surface regions on the surface of the particle. For example, the second substance may comprise particles comprising two or more different regions having different physicochemical properties. In some cases, the second substance includes a plurality of particle surfaces, and the plurality of particle surfaces are disposed on the plurality of particles. For example, the second substance may be a mixture of two or more particles as disclosed herein.

In some cases, the kit includes a chamber or well in which the first substance, the second substance, and the third substance are disposed. The kit may include chambers or wells of various shapes and forms configured to receive biological samples. For example, in some cases, the chamber includes a column. In some cases, the chamber includes a microfluidic channel. In some cases, the surface area of the aperture includes a first substance.

Sample of

The present disclosure provides a range of samples that can be assayed using the particles and methods provided herein. The sample may be a biological sample (e.g., a sample derived from a living organism). The sample may comprise cells or be cell-free. The sample may comprise a biological fluid, such as blood, serum, plasma, urine, or cerebrospinal fluid (CSF). Samples consistent with the present disclosure include biological samples from subjects. The subject may be a human or non-human animal. The biological sample may contain a plurality of proteins or proteomic data that can be analyzed after adsorption of the proteins to the surface of various sensor element (e.g., particle) types in the set and subsequent digestion of the protein corona. The proteomic data may include nucleic acids, peptides or proteins. The biological fluid may be a fluidized solid, such as a tissue homogenate, or a fluid extracted from a biological sample. For example, the biological sample may be a tissue sample or a fine needle penetration (FNA) sample. The biological sample may be a cell culture sample. For example, the biological fluid may be a fluidized cell culture extract.

A wide variety of samples may be compatible for use in the methods and compositions of the present disclosure. The biological sample may include plasma, serum, urine, cerebrospinal fluid, synovial fluid, tear fluid, saliva, whole blood, milk, nipple aspirate fluid, catheter lavage fluid, vaginal fluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid, trabecular fluid, lung lavage fluid, sweat, gingival crevicular fluid, semen, prostatic fluid, sputum, stool, bronchial lavage fluid, swab fluid, bronchial aspirate, fluidized solids, fine needle aspirate sample, tissue homogenate, lymph fluid, cell culture sample, or any combination thereof. The biological sample may include multiple biological samples (e.g., pooled plasma from multiple subjects, or multiple tissue samples from a single subject). The biological sample may comprise a single type of biological fluid or biological material from a single source.

The biological sample may be diluted or pretreated. The biological sample may undergo depletion (e.g., the biological sample comprises serum) before or after contact with the one or more particles. The biological sample may also be subjected to physical (e.g., homogenization or ultrasound) or chemical treatment before or after contact with the one or more particles. The biological sample may be diluted before or after contact with the one or more particles. The dilution medium may comprise a buffer or salt, or be purified water (e.g., distilled water). Different partitions of the biological sample may experience different degrees of dilution. The biological sample or portion thereof may undergo 1.1-fold, 1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 8-fold, 10-fold, 12-fold, 15-fold, 20-fold, 30-fold, 40-fold, 50-fold, 75-fold, 100-fold, 200-fold, 500-fold, or 1000-fold dilution.

In some cases, the biological sample may comprise a plurality of biomolecules. In some cases, the plurality of biomolecules may comprise polyamino acids. In some cases, the polyamino acid comprises a peptide, a protein, or a combination thereof. In some cases, the plurality of biomolecules may comprise nucleic acids, carbohydrates, polyamino acids, or any combination thereof.

Biological state

The compositions and methods disclosed herein can be used to identify various biological states in a particular biological sample. For example, a biological state may refer to an elevated or low level of a particular protein or protein collection. In other examples, biological status may refer to identifying a disease such as cancer. The particles and methods of use thereof can be used to distinguish between two biological states. The two biological states may be related disease states (e.g., two HRA mutant colon cancers or different stages of one type of cancer). The two biological states may be different stages of the disease, such as pre-and mild alzheimer's disease. The two biological states can be distinguished with high accuracy (e.g., a percentage of accurately identified biological states in a sample population). For example, the compositions and methods of the present disclosure can distinguish between two biological states with at least 60% accuracy, at least 70% accuracy, at least 75% accuracy, at least 80% accuracy, at least 85% accuracy, at least 90% accuracy, at least 95% accuracy, at least 98% accuracy, or at least 99% accuracy. The two biological states can be distinguished with a high degree of specificity (e.g., the ratio at which negative results are correctly identified in a sample population). For example, the compositions and methods of the present disclosure can distinguish between two biological states with at least 60% specificity, at least 70% specificity, at least 75% specificity, at least 80% specificity, at least 85% specificity, at least 90% specificity, at least 95% specificity, at least 98% specificity, or at least 99% specificity.

The methods, compositions, and systems described herein can be used to determine a disease state, and/or to prognose or diagnose a disease or disorder. Contemplated diseases or conditions include, but are not limited to, for example, cancer, cardiovascular disease, endocrine disease, inflammatory disease, neurological disease, and the like.

The methods, compositions and systems described herein can be used to determine, prognose and/or diagnose cancer disease states. The term "cancer" refers to any cancer, neoplasm, and preneoplastic disease characterized by abnormal growth of cells, including tumors and benign growths. For example, the cancer may be lung cancer, pancreatic cancer, or skin cancer. In many cases, the methods, compositions, and systems described herein are not only capable of diagnosing cancer (e.g., determining whether a subject is (a) not having cancer, (b) in a pre-cancerous stage, (c) in an early stage of cancer, and (d) in a late stage of cancer), but are also capable of determining the type of cancer.

The methods, compositions, and systems of the present disclosure may also be used to detect other cancers, such as Acute Lymphoblastic Leukemia (ALL); acute Myeloid Leukemia (AML); teenager cancer; adrenal cortex cancer; childhood adrenocortical carcinoma; childhood unusual cancer; AIDS-related cancers; kaposi's sarcoma (soft tissue sarcoma); AIDS-related lymphomas (lymphomas); primary central nervous system lymphomas (lymphomas); anal cancer; appendiceal cancer-see gastrointestinal carcinoid; astrocytomas, childhood (brain cancer); atypical teratomas/rhabdomyomas, childhood, central nervous system (brain cancer); basal cell carcinoma of the skin-see skin carcinoma; bile duct cancer; bladder cancer; childhood bladder cancer; bone cancer (including ewing's sarcoma and osteosarcoma and malignant fibrous histiocytoma); brain tumor; breast cancer; childhood breast cancer; bronchial tumors, childhood; burkitt lymphoma-see non-hodgkin lymphoma; carcinoid tumor (gastrointestinal tract); childhood carcinoid tumor; unknown primary cancer; primary cancer is unknown in childhood; heart (heart) tumor, childhood; a central nervous system; atypical teratoma/rhabdomyoma tumor, childhood (brain cancer); embryo tumors, childhood (brain cancer); germ cell tumor, childhood (brain cancer); primary central nervous system lymphomas; cervical cancer; cervical cancer in childhood; childhood cancer; childhood infrequent cancers; liver bile duct type liver cancer-see bile duct cancer; chordoma, childhood; chronic Lymphocytic Leukemia (CLL); chronic Myelogenous Leukemia (CML); chronic myeloproliferative neoplasms; colorectal cancer; childhood colorectal cancer; craniopharyngeal pipe tumor, childhood (brain cancer); cutaneous T cell lymphomas-see lymphomas (granuloma mycosis fungoides and saidrime syndrome); ductal Carcinoma In Situ (DCIS) -see breast cancer; embryonic tumors, central nervous system, childhood (brain cancer); endometrial cancer (uterine cancer); ependymoma, childhood (brain cancer); esophageal cancer; esophageal cancer in childhood; nasal glioma (head and neck cancer); ewing's sarcoma (bone cancer); extracranial germ cell tumor, childhood; extragonadal germ cell tumor; eye cancer; childhood intraocular melanoma; intraocular melanoma; retinoblastoma; fallopian tube cancer; fibrohistiocytoma, malignancy, and osteosarcoma; gallbladder cancer; stomach (gastric) cancer; childhood gastric (gastric) cancer; gastrointestinal carcinoid tumor; gastrointestinal stromal tumor (GIST) (soft tissue sarcoma); gastrointestinal stromal tumor in children; germ cell tumor; germ cell tumor of the central nervous system (brain cancer) in childhood; childhood extracranial germ cell tumors; extragonadal germ cell tumors; ovarian germ cell tumor; testicular cancer; gestational trophoblastic disease; hairy cell leukemia; cancer of the head and neck; heart tumor, childhood; hepatocellular (liver) carcinoma; histiocytosis, langerhans cells; hodgkin lymphoma; hypopharynx cancer (head and neck cancer); intraocular melanoma; childhood intraocular melanoma; islet cell tumors, pancreatic neuroendocrine tumors; kaposi's sarcoma (soft tissue sarcoma); kidney (renal cell) carcinoma; langerhans cell tissue cell proliferation; laryngeal cancer (head and neck cancer); leukemia; lip cancer, oral cancer (head and neck cancer); liver cancer; lung cancer (non-small cells and small cells); lung cancer in childhood; lymphomas; male breast cancer; osteomalignant fibrous histiocytoma and osteosarcoma; melanoma; childhood melanoma; melanoma, intraocular (eye); childhood intraocular melanoma; mercker cell carcinoma (skin carcinoma); mesothelioma, malignant; dermatomas during childhood; metastatic cancer; metastatic squamous neck cancer (head and neck cancer) with occult primary; midline cancer with nut gene alterations; oral cancer (head and neck cancer); multiple endocrine tumor syndrome; multiple myeloma/plasma cell tumor; mycosis fungoides (lymphomas); myelodysplastic syndrome, myelodysplastic/myeloproliferative neoplasm; myeloid leukemia, chronic (cml); acute Myeloid Leukemia (AML); chronic myeloproliferative neoplasms; nasal and sinus cancer (head and neck cancer); nasopharyngeal carcinoma (head and neck cancer); neuroblastoma; non-hodgkin's lymphoma; non-small cell lung cancer; oral cancer, lip cavity cancer, oropharyngeal cancer (head and neck cancer); osteosarcoma and osteomalignant fibrous histiocytoma; ovarian cancer; childhood ovarian cancer; pancreatic cancer; childhood pancreatic cancer; pancreatic neuroendocrine tumors (islet cell tumors); papillomatosis (childhood laryngeal carcinoma); paraganglioma; paraganglioma in childhood; paranasal and nasal cancers (head and neck cancers); parathyroid cancer; penile cancer; pharyngeal cancer (head and neck cancer); pheochromocytoma; pheochromocytoma in children; pituitary tumor; plasmacytoma/multiple myeloma; pleural pneumoblastoma; pregnancy and breast cancer; primary Central Nervous System (CNS) lymphomas; primary peritoneal cancer; prostate cancer; rectal cancer; recurrent cancer; renal cell (kidney) carcinoma; retinoblastoma; rhabdomyosarcoma, childhood (soft tissue sarcoma); salivary gland cancer (head and neck cancer); sarcoma; childhood rhabdomyosarcoma (soft tissue sarcoma); childhood vascular tumors (soft tissue sarcomas); ewing's sarcoma (bone cancer); kaposi's sarcoma (soft tissue sarcoma); osteosarcoma (bone cancer); soft tissue sarcoma; uterine sarcoma; saigler syndrome (lymphoma); skin cancer; childhood skin cancer; small cell lung cancer; small intestine cancer; soft tissue sarcoma; squamous cell carcinoma of the skin-see skin carcinoma; with occult primary metastatic squamous neck cancer (head and neck cancer); stomach (gastric) cancer; childhood gastric (gastric) cancer; t cell lymphoma, skin (mycosis fungoides and saieli syndrome); testicular cancer; childhood testicular cancer; laryngeal cancer (head and neck cancer); nasopharyngeal carcinoma; oropharyngeal cancer; hypopharyngeal carcinoma; thymoma and thymus cancer; thyroid cancer; transitional cell carcinoma of the renal pelvis ureter (renal cell) carcinoma); unknown primary cancer; primary cancer is unknown in childhood; childhood unusual cancer; transitional cell carcinoma of the ureter and renal pelvis (renal cell) carcinoma, urinary tract carcinoma, uterine carcinoma, endometrium, uterine sarcoma, vaginal carcinoma, childhood vaginal carcinoma, vascular tumors (soft tissue sarcoma), vulvar carcinoma, wilms' cell tumor and other childhood kidney tumors, or cancer in young adults.

The methods, compositions, and systems of the present disclosure can be used to detect cardiovascular disease states. As used herein, the term "cardiovascular disease" (CVD) or "cardiovascular disorder" is used to classify a variety of diseases affecting the heart, heart valves, and body vasculature (e.g., veins and arteries), and includes diseases and conditions including, but not limited to: atherosclerosis, myocardial infarction, acute coronary syndrome, angina, congestive heart failure, aortic aneurysm, aortic dissection, iliac or femoral aneurysm, pulmonary embolism, atrial fibrillation, stroke, transient ischemic attacks, systolic dysfunction, diastolic dysfunction, myocarditis, atrial tachycardia, ventricular fibrillation, endocarditis, peripheral vascular disease and coronary heart disease (CAD). Furthermore, the term cardiovascular disease refers to a condition in a subject that ultimately develops a cardiovascular event or cardiovascular complication, refers to the manifestation of adverse conditions in a subject caused by a cardiovascular disease, such as sudden cardiac death or acute coronary syndrome, including, but not limited to, myocardial infarction, unstable angina, aneurysms, stroke, heart failure, non-lethal myocardial infarction, stroke, angina, transient ischemic attacks, aortic aneurysms, aortic dissection, cardiomyopathy, cardiac catheter abnormalities, cardiac imaging abnormalities, stent or graft re-vascularization, risk of experiencing abnormal stress testing, risk of experiencing abnormal myocardial perfusion, and death.

As used herein, the ability to detect, diagnose, or predict a cardiovascular disease, such as atherosclerosis, may include determining whether a patient is in the pre-stage of the cardiovascular disease, has developed an early, mid, or severe form of the cardiovascular disease, or has suffered from one or more cardiovascular events or complications associated with the cardiovascular disease.

Atherosclerosis (also known as arteriosclerotic vascular disease or ASVD) is a cardiovascular disease in which arterial walls thicken, resulting in narrowing and stiffening of the artery, due to invasion and accumulation of arterial plaque containing leukocytes and deposition on the innermost layers of the arterial walls. Arterial plaque is an accumulation of macrophages or debris and contains lipids (cholesterol and fatty acids), calcium, and variable amounts of fibrous connective tissue. Diseases associated with atherosclerosis include, but are not limited to, atherosclerosis thrombosis, coronary heart disease, deep vein thrombosis, carotid artery disease, angina pectoris, peripheral arterial disease, chronic kidney disease, acute coronary syndrome, vascular stenosis, myocardial infarction, aneurysm, or stroke. In one embodiment, the automated devices, compositions, and methods of the present disclosure can identify different stages of atherosclerosis in a subject, including but not limited to different degrees of stenosis.

In some cases, the disease or disorder detected by the methods, compositions, or systems of the present disclosure is an endocrine disease. The term "endocrinopathy" refers to a condition associated with a disorder of the subject's endocrinological system. Endocrine disorders may be due to endocrine hormone hypersecretion or hyposecretion by the gland causing hormonal imbalance, or to the development of lesions (such as nodules or tumors) in the endocrine system that may or may not affect hormone levels. Suitable endocrine disorders that can be treated include, but are not limited to, for example, acromegaly, addison's disease, adrenal cancer, adrenal disorders, anaplastic thyroid cancer, cushing's syndrome, de Quervain thyroiditis, diabetes, follicular thyroid cancer, gestational diabetes, goiter's disease, growth disorders, auxin deficiency, hashimoto thyroiditis, hurthle cell thyroid cancer, hyperglycemia, hyperparathyroidism, hyperthyroidism, hypoglycemia, hypoparathyroidism, hypothyroidism, hypotestosterone, medullary thyroid cancer, MEN 1, MEN 2A, MEN B, menopause, metabolic syndrome, obesity, osteoporosis, papillary thyroid cancer, parathyroid disease, pheochromocytoma, pituitary disorders, pituitary tumors, polycystic ovary syndrome, prediabetes, asymptomatic thyroiditis, thyroid cancer, thyroid disease, thyroiditis, sarcoidosis, thyroiditis, type 1 diabetes, type 2 diabetes and the like.

In some cases, the disease or disorder detected by the methods, compositions, or systems of the present disclosure is an inflammatory disease. As used herein, an inflammatory disease refers to a disease caused by uncontrolled inflammation in a subject. Inflammation is a biological response of a subject to an external or internal noxious stimulus, such as pathogens, necrotic cells and tissues, irritants, and the like. However, when the inflammatory response becomes abnormal, it causes damage to the own tissues and may cause various diseases and conditions. Inflammatory diseases include, but are not limited to, asthma, glomerulonephritis, inflammatory bowel disease, rheumatoid arthritis, allergies, pelvic inflammatory disease, autoimmune disease, arthritis; necrotizing Enterocolitis (NEC), gastroenteritis, pelvic Inflammatory Disease (PID), emphysema, pleurisy, pyelonephritis, pharyngitis, angina pectoris, acne vulgaris, urinary tract infection, appendicitis, bursitis, colitis, cystitis, dermatitis, phlebitis, rhinitis, tendinitis, tonsillitis, vasculitis, autoimmune diseases; celiac disease; chronic prostatitis, allergy, reperfusion injury; sarcoidosis, graft rejection, vasculitis, interstitial cystitis, hay fever, periodontitis, atherosclerosis, psoriasis, ankylosing spondylitis, juvenile idiopathic arthritis, behcet's disease, spondyloarthritis, uveitis, systemic lupus erythematosus, and cancer. For example, arthritis includes rheumatoid arthritis, psoriatic arthritis, osteoarthritis, juvenile idiopathic arthritis, and the like.

The methods, compositions, and systems of the present disclosure can detect a neurological disease state. Neurological disorders or neurological diseases may be used interchangeably and refer to diseases of the brain, spine and nerves connecting them. Neurological disorders include, but are not limited to, brain tumors, epilepsy, parkinson's disease, alzheimer's disease, ALS, arteriovenous malformations, cerebrovascular disease, cerebral aneurysms, epilepsy, multiple sclerosis, peripheral neuropathy, post herpetic neuralgia, stroke, frontotemporal dementia, demyelinating diseases (including, but not limited to, multiple sclerosis, deweike's disease (i.e., neuromyelitis), central pontine myelination, progressive multifocal leukoencephalopathy, leukodystrophy, guillain-Barre syndrome, progressive inflammatory neuropathy, charcot-Marie-Tooth disease, chronic inflammatory demyelinating polyneuropathy, and antimmag peripheral neuropathy), and the like. Neurological diseases also include immune-mediated neurological disorders (IMND), which include diseases in which at least one component of the immune system reacts to host proteins present in the central or peripheral nervous system and leads to pathology of the disease. IMND may include, but is not limited to, demyelinating diseases, paraneoplastic neurological syndromes, immune-mediated encephalomyelitis, immune-mediated autonomic neuropathy, myasthenia gravis, autoantibody-related encephalopathy, and acute disseminated encephalomyelitis.

The methods, systems, and/or devices of the present disclosure can accurately distinguish between patients with or without Alzheimer's disease. They also enable detection of pre-symptomatic patients and may develop Alzheimer's disease a few years after screening. This provides the advantage of being able to treat the disease at an early stage prior to its development.

The methods, compositions, and systems of the present disclosure can detect the pre-disease stage of a disease or disorder. The pre-disease stage is the stage in which the patient does not develop any signs or symptoms of the disease. The pre-cancerous stage will be the stage in which the cancer or tumor or cancer cells have not been identified in the subject. The pre-neurological stage refers to a stage in which an individual has not yet developed one or more symptoms of a neurological disorder. The ability to diagnose a disease before one or more signs or symptoms of the disease appear allows for close monitoring of the subject and the ability to treat the disease at a very early stage, thereby increasing the likelihood of preventing disease progression or reducing disease severity.

The methods, compositions, and systems of the present disclosure can detect early stages of a disease or disorder. The early stage of a disease may refer to the time at which the first sign or symptom of the disease appears in the subject. The early stage of the disease may be a stage where there is no external sign or symptom. For example, in alzheimer's disease, the early stage may be the pre-alzheimer's disease stage in which no symptoms are detected, but the patient may develop alzheimer's disease after months or years.

Identification of the disease either prior to onset or early in time can often lead to a higher likelihood of positive outcome for the patient. For example, diagnosing cancer at an early stage (stage 0 or stage 1) may increase the likelihood of survival by more than 80%. Stage 0 cancer may describe cancer before the cancer begins to spread to adjacent tissues. This stage of cancer is often highly curable by surgically removing the entire tumor. Stage 1 cancer may typically be a small cancer or tumor that has not grown deep into adjacent tissue and has not propagated to lymph nodes or other parts of the body.

In some cases, the methods, compositions, and systems of the present disclosure are capable of detecting an intermediate stage of a disease. The intermediate stage of the disease describes the stage of the disease that has passed through the first sign and symptom and the patient is experiencing one or more symptoms of the disease. For example, for cancer, stage II or III cancer is considered an intermediate stage, indicating that a larger cancer or tumor has grown deeper into adjacent tissue. In some cases, stage II or III cancer may also have spread to lymph nodes but not to other parts of the body.

Furthermore, the methods, compositions, and systems of the present disclosure may be capable of detecting the end or late stages of a disease. The later or late stages of a disease may also be referred to as "severe" or "late stage" and generally means that the subject is suffering from various symptoms and effects of the disease. For example, severe cancer includes stage IV, where the cancer has spread to other organs or parts of the body, and is sometimes referred to as advanced or metastatic cancer.

The methods of the present disclosure may include processing the biomolecular corona data of the sample against a collection of biomolecular corona data sets representative of a plurality of diseases and/or a plurality of disease states to determine whether the sample is indicative of a disease and/or disease state. For example, samples may be collected from a population of subjects over time. Once a subject has a disease or disorder, the present disclosure confers the ability to characterize and detect changes in the biomolecule fingerprint in the subject over time by computationally analyzing the biomolecule fingerprint from the same subject's sample prior to its having the disease and the biomolecule fingerprint of the subject already having the disease. Samples may also be taken from patient cohorts all suffering from the same disease, allowing analysis and characterization of biomolecular fingerprints associated with different stages of the disease (e.g., pre-disease state to disease state) of these patients.

In some cases, the methods, compositions, and systems of the present disclosure are capable of distinguishing not only between different types of diseases, but also between different stages of a disease (e.g., early stages of cancer). This may include distinguishing healthy subjects from pre-disease state subjects. The pre-disease state may be stage 0 or stage 1 cancer, neurodegenerative disease, dementia, coronary heart disease, renal disease, cardiovascular disease (e.g., coronary heart disease), diabetes, or liver disease. Distinguishing between different stages of a disease may include distinguishing between two stages of cancer (e.g., stage 0 versus stage 1, or stage 1 versus stage 3).

Device and method for controlling the same

In some aspects, the present disclosure describes a device for assaying a biological sample. Fig. 15 illustrates an apparatus according to some embodiments. In some cases, the apparatus may include a table (1503). In some cases, an assembly of devices may be coupled to a table. In some cases, the table may include one or more brackets (1504) coupled thereto. In some cases, one or more brackets may be configured as a mobile device (1505). In some cases, an assembly of devices may be coupled to one or more brackets. In some cases, the apparatus may include a housing (1501). In some cases, components of the device may be disposed within the housing. In some cases, an apparatus may include a display (1502) coupled thereto. In some cases, the housing may include one or more brackets coupled thereto. In some cases, the apparatus may comprise a track. In some cases, an assembly of the device is movably coupled to the track.

In some cases, an apparatus may include one or more transmission units (1601, 1603). In some cases, the transfer unit may be configured to transport a liquid sample. Fig. 16 shows a transmission unit according to some embodiments. In some cases, the transfer unit may be configured to transport a solid sample. In some cases, the transfer unit may include a pipette (1602). In some cases, the transfer unit may include a plurality of pipettes. In some cases, the transfer unit may include a pump. In some cases, the transmission unit may be movable. In some cases, the transfer unit may include a track. In some cases, the transfer unit may include a motor configured to move the transfer unit on the track. In some cases, the transport unit may include a plurality of tracks such that the transport unit is movable in at least two dimensions. In some cases, the transport unit may include a plurality of tracks such that the transport unit is movable in at least three dimensions. In some cases, the transfer unit may include a robotic arm. In some cases, the transmission unit may include a gripper (1604). In some cases, the grippers may be configured to transport one or more partitions.

Fig. 17 shows a layout of device components according to some embodiments. In some cases, the device may include a sample storage chamber or well (1716) configured to receive and retain a biological sample. In some cases, the sample storage chamber or well may be configured to receive and retain at least 1, 2, 4, 8, 16, 32, 64, 96, 128, or 256 different biological samples. In some cases, the apparatus may include a particle storage chamber or aperture (1717) configured to receive and retain one or more particles. In some cases, the particle storage chamber or well may be configured to receive and retain at least 1, 2, 4, 8, 16, 32, 64, 96, 128, or 256 different particles. In some cases, the device may include a plurality of plates. In some cases, the device may include a cleaning plate (1701), a sample preparation plate (1704), an intermediate plate (1705), a peptide collection plate (1713), or any combination thereof. In some cases, the device may include a plurality of reagent storage chambers or wells. In some cases, the device may include a reagent reservoir or well for a wash solution (1702), a cleaning reagent (1703), a control dilution solution (1706), a denaturing reagent (1707), a reducing reagent (1708), an alkylating reagent (1709), water (1710), a trypsin/lysing reagent (1715), or any combination thereof. In some cases, the apparatus may include a void (1711) for the add-on component. In some cases, the device may include a holder (1714) for the pipette tip. In some cases, the device may include one or more posts (1718).

Fig. 18 shows a schematic representation of the device assembly. In some cases, the apparatus may include a filtration system (1801). In some cases, the filtration system may include a vacuum. In some cases, the filtration system may include a pump. In some cases, the apparatus may include a magnetic separation system (1802). In some cases, the magnetic separation system may include a magnet. In some cases, the magnetic separation system may be configured to couple with one or more partitions. In some cases, the apparatus may include a cooler (1803). In some cases, the apparatus may include a heater, a vibrating screen, or both (1804). In some cases, the apparatus may include a rule (1805). In some cases, the apparatus may include a work surface (1806). In some cases, the apparatus may include a work table (1807).

In some cases, the sample storage unit is operably coupled to one or more transport units. In some cases, one or more transfer units may be temporarily coupled to the sample storage unit to transfer a portion of the biological sample from the sample storage unit. In some cases, one or more transfer units may be moved near the sample storage unit, contacted with the biological sample in the sample storage unit, a portion of the biological sample collected from the sample storage unit, and then moved away from the sample storage unit, for example, as shown in fig. 19.

In some cases, one or more transfer units may be coupled to the sample storage unit by a fluid connection, for example, as shown in fig. 20. In some cases, one or more of the delivery units may activate the pump such that a portion of the biological sample in the sample storage unit is delivered from the sample storage unit to another component in the device.

In some cases, the apparatus may include a partition having particles contained therein. FIG. 21 shows a plurality of partitions according to some embodiments. In some cases, the device may include a single partition (e.g., a microcentrifuge tube) for holding a volume of sample or reagent. In some cases, the device may include multiple partitions (e.g., multiple wells in a 16-well plate, 96-well plate, 384-well plate, microwell plate) for holding a sample or reagent volume. In some cases, a partition may include a well, a channel (e.g., a microfluidic channel in a microfluidic device), or a compartment. In some cases, the partitions may include plastic appliances (e.g., plastic porous plates), metal structures (e.g., metal porous plates), carbon material structures (e.g., carbon composite porous plates), gels, glassappliances, or any combination thereof. In some cases, the fluidic channel or chamber may be a microfluidic or nanofluidic channel or chamber. In some cases, the partition may be sealed (e.g., using an operable plastic fastener or penetrable septum) or sealable (e.g., may include a reusable cap or lid).

In some cases, the partition may be configured to hold a volume of at least 1 to 10 microliters (μl), at least 5 to 25 μl, at least 20 to 50 μl, at least 40 to 200 μl, at least 100 to 500 μl, at least 200 μl to 1ml, at least 2ml, at least 3ml, or more. In some cases, the partition may be configured to hold a volume of less than about 240 μl, 200 μl, 150 μl, 100 μl, 75 μl, 50 μl, 25 μl, 10 μl, 5 μl, 1 μl, or less. In some cases, the zones may be temperature controlled. In some cases, the partitions may be configured to prevent or reduce evaporation. In some cases, the partitions may be designed to minimize the influx of ambient light.

In some cases, the plurality of partitions may be grouped by particle, sample, control, or any combination thereof, as shown in fig. 21. In this example, the plurality of partitions includes 8 rows and 12 columns, which may be used with 5 types of particles (i.e., NP1, NP2, NP3, NP4, and NP 5). In some cases, each nanoparticle may occupy two columns, and up to 16 biological samples may be deposited. In this example, each biological sample is labeled as X1, X2, X3, etc., up to X16. In some cases, there may be two columns for the control experiment, where each control well in a column may receive a control particle composition, a control biological sample, or both. In some cases, each control well may be used between a step or two steps of the experiment so that the following experimental procedure may be troubleshooted. In some cases, particles may be filled into the partition, and then a biological sample is added after the filling. In some cases, the biological sample may be filled into the partitions, and then the particles are added after the filling.

In some cases, subsets of the partitions may be grouped by grain or by sample. In some cases, the plurality of partitions may include rows for the sample and columns for the particles. In some cases, the plurality of partitions may be grouped by a particular granular composition.

In some cases, the partition may comprise a single particle of a single biological sample. In some cases, a partition may include multiple particles of a single biological sample. In some cases, a partition may include a single particle for multiple biological samples. In some cases, the partition may include a plurality of particles for a plurality of biological samples.

In some cases, one or more transfer units may be configured to transfer samples to a single partition, or to divide samples into multiple partitions, or to transfer samples sequentially from one partition to another. For example, 5ml of sample can be evenly distributed between 500 partitions, resulting in a separate 10 μl sample volume. In some cases, the sample may be mixed with the reagent within the partition. In some cases, the sample may be diluted within the partition (e.g., using a buffer).

In some cases, the apparatus may include a magnet configured to apply a magnetic field to the contents of the partition, as shown in fig. 13B. In some cases, the applied magnetic field may separate magnetic from non-magnetic substances within the partition. In some cases, the apparatus may include a vibrating screen. In some cases, the substrate may be instrument rocked, vibrated, or sonicated, as shown in fig. 13B.

In some cases, a partition may be operably coupled to one or more transmission units, for example, as shown in fig. 22. In some cases, one or more transfer units may be temporarily coupled to the partition to transfer a portion of the biological sample from the partition (2201). In some cases, one or more transfer units may be moved about the partition, in contact with the biological sample in the partition, collect a portion of the biological sample from the partition, and then move away from the partition. In some cases, one or more transport units may be temporarily coupled to the partition to transport the partition (2202). In some cases, one or more transmission units may move around, couple to, and transmit a partition.

In some cases, one or more transmission units may be coupled to the partition by a fluid connection. In some cases, one or more delivery units may activate the pump such that a portion of the biological sample in the partition is delivered from the partition to another component in the device.

In some cases, the device may include a plurality of posts. In some cases, the column may include a chamber containing particulate matter (e.g., resin or beads). In some cases, the particulate matter may comprise a substance configured to bind to a protein from the sample. In some cases, the particulate matter may comprise a substance configured to selectively bind one or more proteins. In some cases, the particulate matter may comprise a substance configured to selectively bind to the high abundance protein. In some cases, the particulate matter may comprise a substance configured to bind one or more proteins under one condition and release the one or more proteins under another condition. For example, when one solvent is used, the substance may bind one or more proteins, and when another solvent is used, the substance may release one or more proteins.

In some cases, the column may include one or more openings configured to receive and/or output a sample and/or solvent. In some cases, the column may include sufficient tubing, channels, seals, closures, caps, covers, etc. that are in operative communication with other components in the device. In some cases, a column may be in fluid communication with another column.

In some cases, the device may include a capture column. In some cases, the trapping column can be configured between at least two modes. In the first mode, the capture column may be configured to receive the biological sample under conditions sufficient to capture proteins in the biological sample in the capture column. In the second mode, the capture column may be configured to receive solvent under conditions sufficient to release the protein in the capture column. In some cases, the capture column may include a size sufficient to allow the substance to capture the protein in the biological sample in less than about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 minutes. In some cases, the capture column may include a size sufficient to allow the substance to capture the protein in the biological sample in less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 hours. In some cases, the capture column may include a size sufficient to release the substance from the biological sample in less than about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 minutes. In some cases, the capture column may include a size sufficient to release the substance from the biological sample in less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 hours.

In some cases, the device may include a depletion column. In some cases, the depleted column may comprise a substance configured to selectively bind to a high abundance protein. In some cases, the substance can selectively bind at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 60, 70, 80, 90, or 100 high abundance proteins. In some cases, the substance can selectively bind to one or more high abundance proteins selected from the group consisting of: albumin, igG, igA, igM, igD, igE, igG (light chain), alpha-1-acid glycoprotein, fibrinogen, haptoglobin, alpha-1-antitrypsin, alpha-2-macroglobulin, transferrin and apolipoprotein A-1. In some cases, the depletion column can include a size sufficient to allow the substance to selectively bind high abundance proteins in the biological sample in less than about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 minutes. In some cases, the depletion column may include a size sufficient to allow the substance to selectively bind high abundance proteins in the biological sample in less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 hours.

In some cases, the device may include an analytical column. In some cases, the analytical column may contain a substance configured to chromatographically separate the protein. In some cases, the analytical column may include a size sufficient to allow the substance to chromatographically separate the protein in the biological sample in less than about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 minutes. In some cases, the analytical column may include a size sufficient to allow the substance to chromatographically separate the protein in the biological sample in less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 hours.

In some cases, the plurality of posts may be operably coupled to one or more transmission units. In some cases, one or more transfer units may be temporarily coupled to the plurality of columns to transfer a portion of the biological sample from the plurality of columns. In some cases, one or more transfer units may be moved about the plurality of columns, contacted with the biological sample in the plurality of columns, collected a portion of the biological sample from the plurality of columns, and then moved away from the plurality of columns.

In some cases, one or more transfer units may be coupled to the plurality of columns by a fluid connection. In some cases, one or more delivery units may activate the pump such that a portion of the biological sample in the plurality of columns is delivered from the plurality of columns to another component in the device. In some cases, one or more transport units may transport portions of the biological sample between columns of the plurality of columns.

In some cases, an apparatus may include a control unit including one or more processors. In some cases, the control unit may be in electrical communication with one or more transmission units. In some cases, the control unit may include instructions that, when executed, activate one or more transfer units to transfer the sample between components in the device. In some cases, the one or more transfer units may be configured to transfer the biological sample from the sample storage unit to the depletion column to produce a depleted sample. In some cases, one or more transfer units may be configured to transfer the depleted sample to the partition to adsorb a plurality of biomolecules from the biological sample onto the particles. In some cases, the one or more transfer units may be configured to transfer a plurality of biomolecules to the capture column to produce a purified sample. In some cases, one or more transfer units may be configured to transfer the purified sample to an analytical column to produce a separated sample. In some cases, one or more transmission units may be configured to transmit the separated samples to a mass spectrometer for mass spectrometry of a plurality of biomolecules to generate a plurality of signals. In some cases, the one or more transfer units may be configured to transfer the biological sample from the sample storage unit to adsorb the plurality of biomolecules from the biological sample onto the particles. In some cases, the one or more transfer units may be configured to transfer a plurality of biomolecules to the capture column to produce a purified sample. In some cases, one or more transfer units may be configured to transfer the purified sample to a depletion column to produce a depleted sample. In some cases, one or more transfer units may be configured to transfer the depleted sample to an analytical column to produce a separated sample. In some cases, one or more transmission units may be configured to transmit the separated samples to a mass spectrometer for mass spectrometry of a plurality of biomolecules to generate a plurality of signals.

In some cases, the device may include a plurality of reagent storage units. In some cases, the plurality of reagent storage units may include a reagent storage unit comprising an aqueous solvent therein. In some cases, the plurality of reagent storage units may include a reagent storage unit including an eluting solvent therein. In some cases, the plurality of reagent storage units may include a waste storage unit containing waste therein. In some cases, the plurality of reagent storage units may include a wash solution unit containing a wash solution therein. In some cases, the plurality of reagent storage units may include an enzymatic reagent unit containing an enzyme for processing a biological sample.

In some cases, the apparatus may include a cooler. In some cases, the device may include a heater. In some cases, the apparatus may include a filter plate. In some cases, the apparatus may include an evaporator. In some cases, the device may include a vacuum. In some cases, the device may include a sample preparation unit comprising an HPLC column. In some cases, the device may include a sample preparation unit that includes a filter. In some cases, the device may include a sample preparation unit comprising a centrifuge. In some cases, one or more transfer units may be operably coupled to the sample preparation unit.

Fig. 23 shows some components of a device according to some embodiments. Table 1 provides an illustration of the components in fig. 23.

TABLE 1 modules of an Automation System

In some cases, one or more transfer units are operably coupled to the plurality of reagent storage units. In some cases, one or more transfer units may be temporarily coupled to the plurality of reagent storage units to transfer reagents from the plurality of reagent units to another component in the device. In some cases, one or more transport units may move near the plurality of reagent storage units, contact reagents in the plurality of reagent storage units, collect reagents from the plurality of reagent storage units, and then move away from the plurality of reagent storage units.

In some cases, one or more transfer units may be coupled to a plurality of reagent storage units by fluid connections. In some cases, one or more of the delivery units may activate a pump such that reagents in the plurality of reagent storage units are delivered from the plurality of reagent storage units to another component in the device.

Computer control system

The present disclosure provides a computer control system programmed to implement the methods of the present disclosure. FIG. 11 illustrates a computer system programmed or otherwise configured to implement the methods provided herein. The computer system 1101 can control various aspects of the assays disclosed herein, which can be automated (e.g., moving any of the reagents disclosed herein over a substrate). The computer system 1101 may be the user's electronic device or a computer system remote from the electronic device. The electronic device may be a mobile electronic device.

The computer system 1101 includes a central processing unit (CPU, also referred to herein as a "processor" and a "computer processor") 1105, which may be a single-core or multi-core processor, or a plurality of processors for parallel processing. The computer system 1101 also includes memory or memory location 1110 (e.g., random access memory, read only memory, flash memory), an electronic storage unit 1115 (e.g., a hard disk), a communication interface 1120 for communicating with one or more other systems (e.g., a network adapter), and peripheral devices 1125 such as cache, other memory, data storage, and/or electronic display adapter. The memory 1110, the storage unit 1115, the interface 1120, and the peripheral devices 1125 communicate with the CPU 1105 through a communication bus (solid line) such as a motherboard. The storage unit 1115 may be a data storage unit (or data repository) for storing data. The computer system 1101 may be operatively coupled to a computer network ("network") 1130 by means of a communication interface 1120. The network 1130 may be the Internet, an Internet and/or an extranet, or an intranet and/or an extranet in communication with the Internet. In some cases, network 1130 is a telecommunications and/or data network. Network 1130 may include one or more computer servers, which may implement distributed computing, such as cloud computing. In some cases, network 1130 may implement a peer-to-peer network with the aid of computer system 1101, which may enable devices coupled to computer system 1101 to function as clients or servers.

The CPU 1105 may execute a series of machine readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as memory 1110. Instructions may be directed to the CPU 1105 which may then program or otherwise configure the CPU 1105 to implement the methods of the present disclosure. Examples of operations performed by the CPU 1105 may include fetch, decode, execute, and write back.

The CPU 1105 may be part of a circuit such as an integrated circuit. One or more other components of system 1101 may be included in the circuit. In some cases, the circuit is an Application Specific Integrated Circuit (ASIC).

The storage unit 1115 may store files such as drivers, libraries, and stored programs. The storage unit 1115 may store user data such as user preferences and user programs. In some cases, computer system 1101 may include one or more additional data storage units located external to computer system 1101, such as on a remote server in communication with computer system 1101 via an intranet or the Internet.

The computer system 1101 may communicate with one or more remote computer systems over a network 1130. For example, the computer system 1101 may communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PCs), tablet or tablet PCs (e.g., iPad、/>Galaxy Tab), phone, smart phone (e.g.)>iPhone, android enabled device, +.>) Or a personal digital assistant. A user may access computer system 1101 via network 1130.

The methods described herein may be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 1101, such as the memory 1110 or the electronic storage unit 1115. The machine executable code or machine readable code may be provided in the form of software. During use, this code may be executed by the processor 1105. In some cases, the code may be retrieved from the storage unit 1115 and stored on the memory 1110 for quick access by the processor 1105. In some cases, electronic storage 1115 may be eliminated and machine-executable instructions stored on memory 1110.

The code may be precompiled and configured for use by a machine having a processor adapted to execute the code, or may be compiled during runtime. The code may be provided in a programming language, which may be selected to enable the code to be executed in a precompiled or just-in-time compiled (as-loaded) manner.

Various aspects of the systems and methods provided herein, such as the computer system 1101, may be embodied in programming. Aspects of the technology may be considered an "article of manufacture" or "article of manufacture" in the form of usual machine (or processor) executable code and/or related data carried or embodied in a machine-readable medium. The machine executable code may be stored on an electronic storage unit such as memory (e.g., read only memory, random access memory, flash memory) or a hard disk. A "storage" type medium may include any or all of the tangible memory of a computer, a processor, etc., or related modules thereof, such as various semiconductor memories, tape drives, disk drives, etc., which may provide non-transitory storage for software programming at any time. All or part of the software may sometimes communicate over the internet or various other telecommunications networks. For example, such communication may enable software to be loaded from one computer or processor into another computer or processor, such as from a management server or host into a computer platform of an application server. Accordingly, another type of medium that may carry software elements includes light waves, electric waves, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical landline networks, and various air links. Physical elements carrying such waves, such as wired or wireless links, optical links, etc., may also be considered as media carrying software. As used herein, unless limited to a non-transitory tangible "storage" medium, terms computer or machine "readable medium" and the like refer to any medium that participates in providing instructions to a processor for execution.

Thus, a machine-readable medium, such as a computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium, or a physical transmission medium. Nonvolatile storage media includes, for example, optical or magnetic disks, such as any storage devices in any computer, such as might be used to implement a database as shown in the accompanying drawings. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier wave transmission media can take the form of electrical or electromagnetic signals, or acoustic or light waves, such as those generated during Radio Frequency (RF) and Infrared (IR) data communications. Thus, common forms of computer-readable media include, for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, RAM, ROM, PROM and EPROMs, FLASH-EPROMs, any other memory chip or cartridge, a carrier wave transporting data or instructions, a cable or link transporting such a carrier wave, or any other medium from which a computer can read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

The computer system 1101 may include or be in communication with an electronic display 1135 that includes a User Interface (UI) 1140 for providing a reading of, for example, a protein identified using the methods disclosed herein. Examples of UIs include, but are not limited to, graphical User Interfaces (GUIs) and web-based user interfaces.

The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithm may be implemented in software when executed by the central processing unit 1105.

Determination, analysis, or statistical classification is accomplished by methods known in the art, including, but not limited to, for example, a variety of supervised and unsupervised data analysis and clustering methods, such as Hierarchical Cluster Analysis (HCA), principal Component Analysis (PCA), partial Least Squares Discriminant Analysis (PLSDA), machine learning (also known as random forest), logistic regression, decision trees, support Vector Machines (SVM), K-nearest neighbor, naive bayes, linear regression, polynomial regression, SVM for regression, K-means clustering, and hidden markov models, among others. The computer system may perform analysis of various aspects of the protein aggregates or protein crowns of the present disclosure, e.g., comparing/analyzing biomolecular crowns of multiple samples to determine a common pattern between individual biomolecular crowns with statistical significance to determine a protein aggregate associated with a biological state. The computer system may be used to develop a classifier to detect and identify different protein sets or protein crowns (e.g., characteristics of the composition of protein crowns). The data collected from the sensor arrays disclosed herein may be used to train machine learning algorithms, particularly algorithms that receive array measurements from patients and output specific biomolecular canopy compositions from each patient. The raw data from the array may first be denoised to reduce variability in a single variable before training the algorithm.

Machine learning can be summarized as the ability of a learning machine to accurately perform new, unseen examples/tasks after having undergone a learning dataset. Machine learning may include the following concepts and methods. The supervised learning concepts may include AODE; artificial neural networks, such as back propagation, auto encoders, hopfield networks, boltzmann machines, and spiking neural networks; bayesian statistics, such as bayesian networks, bayesian knowledge bases; based on case reasoning; regression of Gaussian process; programming a gene expression; data packet processing method (GMDH); inductive logic programming; instance-based learning; inert learning; learning automata; quantification of learning vectors; a logic model tree; minimum message length (decision tree, decision graph, etc.), such as nearest neighbor algorithm and analog modeling; possibly approximate correct learning (Probably approximately correct learning, PAC) learning; a chain wave descent rule and a knowledge acquisition method; a symbol machine learning algorithm; a support vector machine; a random forest; sets of classifiers, such as self-service aggregation (bagging method) and lifting method (meta algorithm); sorting in order; information Fuzzy Network (IFN); a conditional random field; ANOVA; linear classifiers, such as Fisher linear discriminant, linear regression, logistic regression, polynomial logistic regression, naive Bayesian classifier, perceptron, support vector machine; a secondary classifier; k neighbor algorithm; a lifting method; decision trees such as C4.5, random forest, ID3, CART, SLIQ SPRINT; bayesian networks, such as naive bayes; and hidden markov models. Unsupervised learning concepts may include; a expectation maximization algorithm; vector quantification; generating a topography map (Generative topographic map); an information bottleneck method; artificial neural networks, such as self-organizing maps; association rule learning, such as Apriori algorithm, eclat algorithm and FPgrowth algorithm; hierarchical clustering, such as single point clustering and concept clustering; cluster analysis, such as K-average algorithm, fuzzy clustering, DBSCAN and OPTICS algorithm; and outlier detection, such as local outlier factors. Semi-supervised learning concepts may include; generating a model; low density separation; a graph-based approach; and co-training. Reinforcement learning concepts may include; time difference learning; q learning; learning automata; and SARSA. Deep learning concepts may include; a deep belief network; a deep boltzmann machine; a deep convolutional neural network; deep circulation neural network; and hierarchical time memory (Hierarchical temporal memory). The computer system may be adapted to implement the methods described herein. The system includes a central computer server programmed to implement the methods described herein. The server includes a central processing unit (CPU, also referred to as a "processor"), which may be a single-core processor, a multi-core processor, or multiple processors for parallel processing. The server also includes memory (e.g., random access memory, read only memory, flash memory); an electronic storage unit (e.g., hard disk); a communication interface (e.g., a network adapter) for communicating with one or more other systems; and peripheral devices, which may include caches, other memory, data storage, and/or electronic display adapters. The memory, storage units, interfaces and peripherals communicate with the processor through a communication bus (solid lines) such as a motherboard. The storage unit may be a data storage unit for storing data. The server is operatively coupled to a computer network ("network") by means of a communication interface. The network may be the internet, an intranet, and/or an intranet and/or extranet in communication with the internet, a telecommunications or data network. In some cases, with the help of a server, the network may implement a peer-to-peer network that may cause devices coupled to the server to appear as clients or servers.

The storage unit may store files (e.g., object reports), and/or communications with any aspect of data about an individual, or data associated with the present disclosure.

The computer server may communicate with one or more remote computer systems over a network. The one or more remote computer systems may be, for example, a personal computer, a notebook computer, a tablet computer, a telephone, a smart phone, or a palmtop computer.

In some applications, the computer system includes a single server. In other cases, the system includes a plurality of servers in communication with each other via an intranet, an extranet, and/or the Internet.

The server may be adapted to store the measurement data or databases provided herein, patient information from the subject, such as medical history, family history, demographic data, and/or other clinical or personal information potentially relevant to a particular application. Such information may be stored on a storage unit or server, and such data may be transmitted over a network.

The methods as described herein may be implemented by machine (or computer processor) executable code (or software) stored on an electronic storage location (e.g., memory or on an electronic storage unit) of a server. During use, code may be executed by a processor. In some cases, the code may be retrieved from a memory unit and stored on the memory for ready access by the processor. In some cases, the electronic storage unit may be eliminated and the machine-executable instructions stored in memory. Alternatively, the code may execute on a second computer system.

Aspects of the systems and methods provided herein, such as a server, may be embodied in programming. Aspects of the technology may be considered an "article of manufacture" or "article of manufacture" in the form of usual machine (or processor) executable code and/or related data carried or embodied in a machine-readable medium. The machine executable code may be stored on an electronic storage unit such as a memory (e.g., read only memory, random access memory, flash memory) or a hard disk. The "storage" media may include any or all of the tangible memory of a computer, processor, etc., or related modules thereof, such as various semiconductor memories, tape drives, disk drives, etc., which may provide non-transitory storage for software programming at any time. All or part of the software may sometimes communicate over the internet or various other telecommunications networks. For example, such communications may cause software to be loaded from one computer or processor to another computer or processor, e.g., from a management server or host computer to a computer platform of an application server. Accordingly, another type of medium that may carry software elements includes light waves, electric waves, and electromagnetic waves, such as those used by wire and optical landline networks, and by various air links, across physical interfaces between local devices. Physical elements carrying such waves, such as wired or wireless links, optical links, etc., may also be considered as media carrying software. As used herein, unless defined as a non-transitory, tangible "storage" medium, terms, such as computer or machine "readable medium," may refer to any medium that participates in providing instructions to a processor for execution.

The computer systems described herein may include computer executable code for performing any of the algorithms or algorithm-based methods described herein. In some applications, the algorithms described herein will utilize memory cells that include at least one database.

Data related to the present disclosure may be transmitted over a network or connection for receipt and/or viewing by a recipient. The recipient may be, but is not limited to, an object to which the report relates; or caregivers thereof, e.g., health care providers, administrators, other health care professionals, or other caregivers; a person or entity performing and/or subscribing to the analysis. The recipient may also be a local or remote system (e.g., a server or other system of a "cloud computing" architecture) for storing such reports. In one embodiment, the computer readable medium comprises a medium adapted to transmit the results of an analysis of a biological sample using the methods described herein.

Aspects of the systems and methods provided herein may be incorporated into programs. Aspects of the technology may be considered an "article of manufacture" or "article of manufacture" in the form of usual machine (or processor) executable code and/or related data carried or embodied in a machine-readable medium. The machine executable code may be stored on an electronic storage unit, such as a memory (e.g., read only memory, random access memory, flash memory) or a hard disk. The "storage" media may include any or all of the tangible memory of a computer, processor, etc., or related modules thereof, such as various semiconductor memories, tape drives, disk drives, etc., which may provide non-transitory storage for software programming at any time. All or part of the software may sometimes communicate over the internet or various other telecommunications networks. For example, such communications may cause software to be loaded from one computer or processor to another computer or processor, e.g., from a management server or host computer to a computer platform of an application server. Accordingly, another type of medium that may carry software elements includes light waves, electric waves, and electromagnetic waves, such as those used by wire and optical landline networks, and by various air links, across physical interfaces between local devices. Physical elements carrying such waves, such as wired or wireless links, optical links, etc., may also be considered as media carrying software. As used herein, unless defined as a non-transitory, tangible "storage" medium, terms, such as computer or machine "readable medium," may refer to any medium that participates in providing instructions to a processor for execution.

Thus, a machine-readable medium, such as computer-executable code, may take many forms, including but not limited to, tangible storage media, carrier wave media, or physical transmission media. Nonvolatile storage media includes, for example, optical or magnetic disks, any storage devices, such as any computers, etc., such as may be used to implement the databases shown in the figures. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and optical fiber, including wires that make up a bus within a computer system. Carrier wave transmission media can take the form of electrical or electromagnetic signals, or acoustic or light waves, such as those generated during Radio Frequency (RF) and Infrared (IR) data communications. Thus, common forms of computer-readable media include, for example: a floppy disk (floppy disk), a flexible disk (flexible disk), hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, or DVD-ROM, any other optical medium, punch paper tape, any other physical storage medium with patterns of holes, RAM, ROM, PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, a cable or link transporting such a carrier wave, or any other medium from which a computer can read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

Protein crown classification based on machine learning

The method of determining a set of proteins associated with the disease or condition and/or disease state comprises analyzing crowns of the at least two samples. The determination, analysis, or statistical classification is accomplished by methods known in the art, including, but not limited to, for example, a variety of supervised and unsupervised data analysis, machine learning, deep learning, and clustering methods, including Hierarchical Cluster Analysis (HCA), principal Component Analysis (PCA), partial least squares discriminant analysis (PLS-DA), random forests, logistic regression, decision trees, support Vector Machines (SVM), K-nearest neighbors, naive Bayes, linear regression, polynomial regression, SVM for regression, K-nearest neighbors, hidden Markov models, and the like. In other words, proteins in the crowns of each sample are compared/analyzed with each other to determine a common pattern between the individual crowns with statistical significance, thereby determining a collection of proteins associated with a disease or disorder or disease state.

Generally, machine learning algorithms are used to construct models that accurately assign class labels to examples based on input features describing the examples. In some cases, it may be advantageous to employ machine learning and/or deep learning methods for the methods described herein. For example, machine learning may be used to associate protein crowns with various disease states (e.g., no disease, precursor to disease, early or late stage of disease, etc.). For example, in some cases, one or more machine learning algorithms are used in conjunction with the methods of the present disclosure to analyze data detected and obtained from protein crowns and protein sets derived therefrom. For example, in one embodiment, machine learning may be coupled with the sensor arrays described herein for determining not only whether a subject has a pre-cancer, has cancer, or has no cancer, or will develop cancer, but also to distinguish the type of cancer.

Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Any reference herein to "or" is intended to encompass "and/or" unless stated otherwise.

Whenever the term "at least", "greater than" or "greater than or equal to" precedes the first value in a series of two or more values, the term "at least", "greater than" or "greater than or equal to" applies to each value in the series of values. For example, 1, 2, or 3 or more is equivalent to 1 or more, 2 or more, or 3 or more.

Whenever the term "no more," "less than or equal to" or "at most" precedes the first value in a series of two or more values, the term "no more," "less than or equal to" or "at most" applies to each value in the series of values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

Where a value is described as a range, it is understood that such disclosure includes all possible subranges within such range as well as the disclosure of a particular value falling within such range, whether or not the particular value or subrange is explicitly stated.

Examples

The following examples are illustrative and not intended to limit the scope of the compositions, devices, systems, kits, and methods described herein.

Example 1

Human plasma sample preparation

The present example provides a method of preparing a plasma sample for analysis. In this example, human plasma samples were depleted using the Agilent 1260Infinity II Bioinert HPLC system. Plasma depletion was performed by the following method: first 20. Mu.L of plasma was diluted to a final volume of 100. Mu.L with Agilent "buffer A" plasma depleted mobile phase. Each diluted sample was filtered through an agilent0.22 μ cellulose acetate spin filter to remove any particulate matter and transferred to a 96-well plate incubated at 4 ℃. Then 80. Mu.L of diluted plasma was injected into an Agilent 4.6X10 mm Human 14 multiplex affinity removal system (MARS 14) depleted column maintained at 20℃and eluted with 100% "buffer A" mobile phase flowing at a rate of 0.125 mL/min. Proteins eluted from the column were detected using an Agilent ultraviolet absorbance detector operating at 209nm with a bandwidth of 4nm. Early elution peaks (representing depleted plasma protein) per sample injection were collected using a triggered, peak intensity based, cooled fraction collector with a maximum peak width of 3 minutes set to a 200mAu threshold. After peak collection, the fractions were kept at 4 ℃. The sample volume was then reduced to about 20. Mu.L using an Amicon centrifugal concentrator (Amicon Ultra-0.5mL,3k MWCO) and the centrifuge was run at 4℃and 14,000Xg. Each depleted sample was subjected to reduction, alkylation, digestion, desalting and analysis according to the sample preparation and MS analysis protocol described below. During each sample depletion cycle, the MARS14 column was regenerated with Agilent "buffer B" mobile phase at a flow rate of 1mL/min for approximately 4 ¹ / ₂ Minute, and equilibrated back to the original protein capture condition by flowing "buffer a" at a flow rate of 1mL/min for about 9 minutes.

Example 2

Identification of particle characteristics associated with protein enrichment

This example encompasses a method of identifying particle characteristics associated with enrichment of different types of proteins. A broad spectrum of particle characteristics was interrogated to identify a set of characteristics responsible for adsorbing specific proteins and protein groupings. A total of 37 different nanoparticles were incubated with human plasma samples, respectively, and the resulting particle-related protein corona content was analyzed by Liquid Chromatography (LC) coupled with tandem mass spectrometry (MS/MS) using data independent acquisition.

Human plasma samples were prepared as described in example 1 above. The particles were provided as a dry powder and reconstituted in DI water to a final concentration of 5mg/mL for all nanoparticles, except for SP-339-008, SP-353-002 at a final concentration of 2.5mg/mL and SP-373-007 at a final concentration of 10mg/mL, followed by sonication for a further 10 minutes and vortexing for 2-3 seconds. To form the protein corona, 100 μl of nanoparticle suspension was mixed with 100 μl of plasma sample in a microtiter plate. The plates were sealed and incubated at 37℃for 1 hour with shaking at 300 rpm. After incubation, the plate was placed on top of the magnetic collection device for 5 minutes to settle the nanoparticles. The supernatant containing the non-cap unbound protein was aspirated through the pipette. The protein corona was washed 3 times with 200. Mu.L of wash buffer (150 mM KCl and 0.05% CHAPS in Tris EDTA buffer pH 7.4).

To digest the protein bound to the nanoparticle, a trypsin digestion kit (iST 96x, preomics, germany) was used according to the protocol provided by the supplier. Briefly, 50. Mu.L of lysis buffer was added to each well and heated at 95℃for 10 minutes with stirring at 1000 rpm. After cooling the plates to room temperature, trypsin digestion buffer was added and the plates were incubated at 37 ℃ for 3 hours with shaking at 500 rpm. After stopping the digestion process by adding the provided stop buffer, the nanoparticles were removed from the reaction by magnetic collection and the remaining reaction supernatant was purified using the provided filter cartridge (styrene divinylbenzene reversed phase sulfonate/SDB-RPS) kit. The peptides were eluted twice with 75 μl of elution buffer and pooled. Peptide concentration was measured by a quantitative colorimetric peptide assay kit from Thermo Fisher Scientific (Waltham, MA).

LC-MS/MS with data independent acquisition was performed on peptides. In order to perform this analysis,peptides were reconstituted in a solution with 5fmol/uL peppalmix from SCIEX (Framingham, MA) added with 0.1% FA and 3% ACN. 5. Mu.g of peptide in 10uL of reconstitution buffer was used for each constant mass MS injection. Each sample was analyzed by an eksig nano lc system coupled to a SCIEX TripleTOF 6600+ mass spectrometer fitted with an OptiFlow source using a capture-elution method. First, peptides were loaded onto a ChromXP C18CL (0.3 mm ID x 10 mm) capture column, then analyzed on a Phenomenex Kinetex analysis column (150mm x 0.3mm,C18,2.6 μm, ) The peptide was isolated at a flow rate of 5 μl over 20 minutes using a gradient of 3-32% solvent B (0.1% FA,100% ACN) mixed into solvent a (0.1% FA,100% water) for a total run time of 33 minutes. Mass spectrometer in SWATH ^TM Operating in mode, 100 variable windows are used in the range 400-1250 m/z.

The measured protein corona composition was interrogated as a function of individual particle characteristics. Each particle is annotated to describe its functionalization, including charge, hydrophobicity, and presence of amine, carboxylate, sugar, phosphate, hydroxyl, or aromatic groups, as shown in fig. 2. To illustrate the likelihood that the reactions that functionalize the particles do not necessarily result in complete coverage of the particles, the particles are further classified according to their "reaction classification" which is the particular type of chemical reaction used in the last step of particle synthesis. FIG. 3A provides protein intensity patterns grouped by particle characteristics.

One-dimensional annotation enrichment analysis was then used to identify clusters of physicochemical properties that have similar effects on protein enrichment. As shown in fig. 3B, hierarchical clustering of 37 nanoparticles based on their one-dimensional enrichment scores resulted in five conceptually different groupings of particles. Fisher's exact test was applied to each cluster to highlight their main distinguishing characteristics. Cluster 1 (C1, fig. 3B) consists of "sponge" or silica-coated SPION particles treated with succinic anhydride that underwent ring opening to form surface-exposed carboxylate groups. Several members of the group (S-182 to S-186) have other groups (e.g., butyl, pyridyl, hydroxyethyl) tethered to their surfaces by amide coupling. Cluster 2 (C2, fig. 3B) includes core sponge particles (S-113) and other particles that are expected to be hydrophilic or amphiphilic. Two particles (P-039, S-179) in this cluster have polystyrene surfaces functionalized with acidic or basic groups (carboxylic acid and sulfonate, respectively). Cluster 3 (C3, fig. 3B) consisted essentially of amine functionalized particles, as well as single hydroxyethyl functionalized particles and isopropylamide particles (with isopropylamide attached via ARGET-ATRP polymerization). Cluster 4 (C4, fig. 3B) and cluster 5 (C5, fig. 3B) consisted essentially of particles with hydrophilic surface functionalization, encompassing most of the hydroxyl-functionalized particles and all of the sugar-functionalized particles. Cluster 4 and cluster 5 also included several carboxylate functionalized particles, methylamine functionalized particles and cationic tetraalkylammonium functionalized particles.

Then, variance decomposition was performed on the protein corona data to determine how much variance was observed in protein abundance (approximated by mass spectral signal intensity), the results of which are shown in fig. 4A. Of all the 37 particle types tested, more than 50% of the variance in most protein intensities can be explained by particle characteristics. FIG. 4B illustrates the extent of the releasable variance of protein intensity as a function of different particle characteristics. Of all functional groups, charge and carboxylate contribute the most to the protein intensity level.

Example 3

Comparing particle set enrichment to plasma depletion for protein identification using data independent mass spectrometry

This example compares the different plasma proteomic workflows in terms of depth of coverage. Mass spectrometry was performed on proteins collected on the 5 particle set, proteins from high pH depleted ("deep fractionation") plasma, proteins from depleted plasma, and proteins from pure plasma. Plasma sample preparation, particle crown preparation and collection and mass spectrometry analysis were performed as outlined in examples 1 and 2. The workflow of each of these four analysis types is summarized in fig. 5A. Each workflow was performed in triplicate. The workflow times and steps for the 5-particle group workflow and the two depth fractionation workflows are provided in fig. 14.

FIG. 5B outlines the number of protein packets identified by each workflow. Each bar indicates the average number of protein groupings obtained per workflow. Error bars represent standard deviation of duplicate determinations. The top dash represents the number of proteins identified in any sample, and the bottom dash represents the number of proteins identified in 3 out of 3 replicates (complete signature). The number of proteins identified by the 5-particle set workflow is greatest, which can produce 2300 more protein packet identifications that are increased by about 2-fold, about 4-fold, and about 6-fold compared to high pH depletion, and pure plasma analysis. Fig. 5C outlines the change in peptide intensity at a number of LC-MS/MS parameters including LC gradient length and MS instrument. Comparing the accuracy of peptide quantification between different workflows (five particle set, plasma depletion and pure plasma) yields an average Coefficient of Variation (CV) of less than 20% (16.9%, about 18% and 7.8%, respectively), whereas depth fractionation yields an average CV of about 2 times, about 34%.

The dynamic range of each workflow is determined by mapping plasma protein data to known human plasma concentrations. The results of this analysis are summarized in fig. 5D, indicating that the 5-particle set workflow covers a greater dynamic range and more low concentration protein than the other three workflows.

Fig. 5E provides proteomic data for each workflow according to plasma proteomic coverage, indicating percent coverage for each intensity range, ordering proteins from high abundance to low abundance. While high pH fractionation covered 18% more abundant protein (defined as the first 50% intensity) than the 5-particle group workflow, in contrast the coverage of the 5-particle group workflow was 10-fold higher at the lowest 2 orders of magnitude, 62% more protein captured at the last 50% intensity level.

Fig. 5F shows the overlap between identified protein packets in four workflows. Of 1706 identified protein groups in three replicates of the 5 particle set workflow, 900 were uniquely identified, 184 were identified together in all four workflows, and 169 were only shared in high pH fractionation. In contrast, the high pH fractionation workflow only provided 172 unique protein groupings.

The protein grouping identification between the 5-particle group workflow and the high pH depletion workflow was compared based on functional annotations (phosphoproteins, signal transduction proteins, protein complexes, transport proteins, immune system process related proteins, secreted proteins and lipoproteins) in fig. 5G. For the seven categories interrogated, the 5-particle set workflow covered 2 to 9 times more protein groupings than high pH fractionation.

Example 4

Comparing particle set enrichment to plasma depletion for protein identification using data dependent mass spectrometry

This example compares the identification of protein packets using data dependent profiling using the 5 particle set, high pH fractionation ("depth fractionation") and pure plasma workflow outlined in example 3. Separate analyses were performed with two separate depth fractionated samples ('depth fractionation-in' and 'depth fractionation-out').

FIG. 6A provides the median number of protein groupings identified for each workflow. Error bars represent standard deviation of duplicate determinations. The top dash describes the number of proteins identified in any sample, the lower dash represents the number of proteins identified in 3 out of 3 replicates (all features). Five particle set workflows identified about 300 more proteins than the first high pH fractionated sample, almost twice the number of proteins identified by the second high pH fractionated sample, and about 7 times more than the pure plasma workflows. Fig. 6B provides the Coefficient of Variation (CV) of the average normalized peptide intensity filtered for identification in duplicate assays, with the average CV depicted on each plot.

FIG. 6C provides the dynamic range of the proteins identified for each workflow, with the average logarithmic intensity of the complete features, outliers removed, shown in each block diagram.

Fig. 6D provides the percentage of coverage of human proteomes in each workflow (upper panel) and a comparison of the relative coverage of human proteomes for five particle group workflows and the first high pH fractionation sample at negative relative protein log10 intensities (lower panel). The 95% interval is shown gray.

Example 5

Interrogation of plasma proteomes by 10 particle sets

This example covers plasma proteome analysis using a set of 10 different particles with different physicochemical properties. Plasma preparation, particle-based protein collection and mass spectrometry analysis were performed as outlined in examples 1 and 2. Proteomic data obtained with these particles, as well as proteomic data obtained from pure and depleted plasma, were analyzed based on quantitative and qualitative differences in the number, accuracy, and composition of the protein corona identified.

Panel A of FIG. 7 provides the median number of protein groupings identified from pure and depleted plasma and 10 particle sets. Error bars represent standard deviation of protein ID in duplicate assays. The lower dashes represent the number of proteins identified (complete signature) throughout the repeated assays. For depleted plasma and pure plasma, the bar graph shows median counts and standard deviation in three replicates. Top dashes describe the amount of protein identified in any sample.

To determine the degree of overlap of the proteins identified on the nanoparticles with those identified by plasma, a Jaccard index ("JI") was calculated for each pair of particles, as well as between the particles and pure and depleted plasma assays. The upper triangle of panel B of fig. 7 provides JI indicating the extent of overlap identification for each pair of particles and pure and depleted plasma. The size of each box is scaled by the size of JI. The pearson correlation coefficient ("r") is provided in the lower triangle of panel B of fig. 7, which represents the correlation of the median normalized log10 intensity as the mean value r of the entire replicate assay (right column) and compares individual nanoparticles to pure plasma. The loop size is scaled by the size of r. Qualitative reproducibility in triplicate determinations at a time is provided in panel C of fig. 7, which provides the average correlation coefficient (near 1) and the coefficient of variation for each particle as well as for pure and depleted plasma.

To further map out the similarity and dissimilarity of all particles and pure and depleted plasma, the golgi distance between each pair of particles and each particle and the pure and depleted plasma samples was calculated based on their proteomic distribution. Fig. 8 summarizes the results as a distance tree of average protein intensities. From this graph, it can be seen that the clusters are mainly consistent with positive and negative zeta potentials. However, some of the clustered particles (e.g., SP-365 and SP-373) do not strictly follow this rule (FIG. 2E), indicating that the protein abundance characteristics on the particles are driven by more complex aspects of their physicochemical properties.

Example 6

Comparison of particle Properties against protein corona composition for the 10 particle group

To explore to what extent the composition of the protein corona can be interpreted based on the physicochemical properties of the particles, the physicochemical properties of 10 different particles and the composition of the protein corona were compared. Plasma protein analysis was performed according to the method outlined in examples 1 and 2. Particle characterization was performed with Transmission Electron Microscopy (TEM) and Dynamic Light Scattering (DLS). The morphology and surface characteristics of the particles were assessed by TEM. DLS assays were performed to determine characteristics of particles in solution, such as hydrodynamic size and polydispersity index (PDI). These measurements indicate that the 10 particle set includes a variety of features ranging in size from about 150nm to 1 μm. TEM indicated that all nanoparticle samples had a spherical or hemispherical morphology, except SP-373-007, which appears to be a mixture of dextran networks with embedded small nanoparticles of about 10-15nm in size (shown in Panel A of FIG. 9). Panel A of FIG. 9 provides TEM images of each particle, as well as their zeta potential, hydrodynamic radius, and PDI (bar chart under image).

Panel A of FIG. 10 provides a volcanic plot depicting coefficients derived from a protein binding model based on the characteristics of three specific particles, each protein plotted against p-value. Panel B of FIG. 10 provides 500 randomly sampled results, with 2x 12 non-overlapping objects measured with 10 particles selected to build a linear mixture effect model for computing the Pearson correlation. Panel C of FIG. 10 provides a correlation coefficient between the coefficient of each protein determined and the zeta potential of the particle. The color represents the predicted isoelectric point for each protein. The gray shaded regression line is described with 95% confidence intervals.

Example 7

Powerful, high throughput and deep plasma proteomics workflow employing engineered nanoparticle sets

Data Independent Acquisition (DIA) proteomics can be used to classify thousands of proteins in complex biological samples (e.g., human plasma) in high throughput LC-MS proteomics methods. For large-scale proteomics studies, powerful LC and MS systems are favored for widespread use by users with different levels of analytical expertise, but do not affect coverage of peptides and proteins. The present disclosure describes label-free high throughput plasmaphotoproteomics workflow using a Orbitrap Exploris mass spectrometer coupled to a UltiMate3000 nano LC system and a micro-column array chromatography column (μPACTM). The results are obtained by a powerful and high throughput analytical setup for deep proteomic analysis of plasma samples.

LC-MS analysis was performed with a 50cm and 110cm C18. Mu. PAC column (Pharmafuidics, belgium) coupled to a Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific). Pure plasma and plasma proteins enriched in 5 nanoparticles (5 NP) were digested and then analyzed in DIA in highest velocity mode with label-free MS data acquisition. Each of the 5 nanoparticles and the pure plasma digests were evaluated using a 30 minute DIA method using a 120 minute single sample injection method or 5 separate samples for comparison of these methods and for assessing flux and improvement in protein coverage depth using a nanoparticle-based method. Using Proteome Discoverer ^TM 3.0 software and MaxQuant software for label-free data analysis, resulting in improved peptide and protein coverage, FDR rate of 1%.

The unlabeled proteomic performance on Orbitrap Exploris MS was evaluated using a data independent collection method using 500ng of pure plasma and 5 nanoparticle-rich protein digests on a 50cm C18 mupac column at a flow rate of 1uL/min and a 30 minute Reverse Phase (RP) gradient, which resulted in the identification of about 1500 protein packets in a single pooled plasma digest as a performance baseline. 110cm C18. Mu. PAC was used at a flow rate of 0.5uL/min and a single sample injection gradient of 120 minutes, 2.5ug of mixed 5 nanoparticle plasma digest was injected. The column support structure allows LC to flow with minimal back pressure and allows steps to be performed including sample loading, column rebalancing, and ending the gradient cleaning process. In some cases, column rebalancing and ending the gradient wash process may affect the throughput of the nano lc process performed at higher flow rates up to 1 μl. Peptides were analyzed and separated after sample injection at a lower nanoflow of 500 nL/min.

Proteome Discoverer 3.0.0 software (with the modified peptide identification workflow using ultra-fast single step MSPepSearch for human NIST Oribitrap HCD library and CHIMERES) provides the depth of coverage required for deep plasma proteomics workflow. Percoloator FDR calculations were used to allow reporting of only those spectra that were within 1% FDR rate. We identified at least 1500 protein groupings from 0.5 μg of 5 nanoparticle-rich protein digests in less than 2.5 hours using this optimized workflow. The workflow may be further optimized to perform the method in a shorter time (e.g., in less than 2 hours).

Example 8

Powerful deep label-free plasma proteomics with engineered nanoparticle sets: evaluation of micro-column array chromatography column and FAIMS peptide separation

LC-MS based proteomic analysis can be used to identify and quantify thousands of proteins in complex biological samples such as human plasma. For large-scale proteomics studies, powerful LC and MS systems are advantageous for widespread use by users with different levels of analytical expertise, but do not affect the coverage of peptides and proteins. The present disclosure describes label-free plasma proteomics workflow on a Orbitrap Fusion Lumos Tribrid mass spectrometer coupled with a high field asymmetric waveform ion mobility spectrometer (FAIMS) interface and a micropillar array chromatography column (mupactm) as a powerful analytical setup for deep plasma proteomics analysis.

LC-MS analysis was performed with 110cm and 200cm C18 μpac columns (PharmaFluidics, belgium) coupled to Orbitrap Fusion LumosTribrid mass spectrometers and FAIMS Pro Interface (Thermo Fisher Scientific). Pure plasma, 5 nanoparticles (5 NP) and digested plasma proteins, and standard HeLa digests (Pierce) were analyzed using label-free MS data collection in DDA high-speed mode with multi-CV FAIMS peptide fractionation, according to peptide mass and charge status. The performance of the different chromatographic columns was assessed using a 300 minute single sample injection method. Proteome Discoverer ^TM 3.0 software and MaxQuant software were used for label-free data analysis, providing improved peptide and protein coverage with FDR rates of 1%.

The unlabeled proteomic performance at Orbitrap Fusion Lumos Tribrid MS was assessed in a 300 min Reverse Phase (RP) gradient using FAIMS Pro Interface in a data dependent collection method using 4 μg standard HeLa digest on a 200cm C18 μpac column. The method results in the identification of at least 10,000 proteins and at least 120,000 peptides. The μpac performance allows loading up to 4 μg of sample on a column with both nanoflow sensitivity and analytical column robustness. The column support structure allows LC to flow with minimal back pressure, which allows steps including sample loading, column rebalancing, and ending the gradient cleaning process. The column rebalancing and ending gradient washing process can sometimes affect the throughput of the nano lc process performed at higher flow rates up to 1 μl and subsequent analytical separation of peptides at lower nano flow rates of 600 nL/min.

Proteome Discoverer 3.0.0 software (with the modified peptide identification workflow using ultra-fast single step MSPepSearch for human NIST Oribitrap HCD library and CHIMERES) provides the depth of coverage required for deep plasma proteomics workflow. Percoloator FDR calculations were used to allow reporting of only those spectra that were within 1% FDR rate. We identified about 9500 proteomes and about 120,000 peptide groups from a large amount of HeLa digest of 4. Mu.g in about 5 hours using this optimized workflow. The workflow may be performed by a single sample injection for deep plasma proteomic analysis.

Example 9

Nanobiological interactions of 37 engineered nanoparticles for deep plasma proteomics studies at unprecedented scale prior to modeling implementation

The introduction of Nanoparticles (NPs) into biological fluids (e.g., plasma) can result in the formation of selective, specific, and reproducible protein crowns at the nanobiological interface driven by the relationship between protein-NP affinity, protein abundance, and protein-protein interactions. There are many possibilities for NP physicochemical design that can be tailored to enhance and differentiate protein selectivity. This example illustrates the relationship between nanoparticle chemical functionalization and crown formation, creating a linear mixed effect model that can enhance NP design.

The pooled plasma samples were interrogated by treating a collection of 37 engineered nanoparticles with specific physicochemical properties with a Protegr. Proteomic data were collected using an Orbitrap Lumos LC run for 30 minutes. MaxQuant raw data processing identified more than 1500 protein groupings at 1% protein and peptide FDR. By developing a machine learning (linear mixed effect) model, a significant relationship identified between physicochemical NP properties (including zeta potential, amine and carboxyl functionalization) and differential abundance of individual proteins and protein classes within the NP corona was identified. For example, 23% of the abundance of C-reactive protein (CRP) in protein crowns is related to NP zeta potential and 22% can be assigned to polymerization and sugar surface functionalization. In contrast, it was observed that the abundance of plasma kallikrein (KLKB 1) was not affected by the NP zeta potential, but more than 50% was driven by glycofunctionalization.

The results indicate that the relationship between NP surface functionalization and a particular protein or class of proteins in a complex biological sample can be modeled. Such information may guide NP design to further increase the usefulness of the proteogram platform in proteomics research and biomarker discovery.

Example 10

Protein depletion and peptide fractionation improve the depth of coverage of proteomes when coupled to nanoparticle crowning

This example shows a significant improvement in the performance of NP-based biomolecular assays in terms of proteomic coverage by depleting the input plasma prior to formation of biomolecular crowns on the particles.

An example of a workflow is shown in fig. 27. A200. Mu.l plasma volume (100. Mu.l per spin column) was depleted in a Top14 resin spin column. The depleted sample was freeze-dried and then reconstituted to 40 μl. The reconstituted sample was then contacted with V1.2 particles to enrich the sample. The enriched sample from the particles was separated from the V1.2 particles and then reconstituted and then injected into a mass spectrometer. For comparison, in the different arms of the study, plasma was incubated with Top14 resin and V1.2 particles (40 ul and 100 ul), or plasma was incubated with V1.2 particles only, or plasma was incubated with spent resin only.

The total amount of protein quantified from the experiment is shown in fig. 30. Proteome coverage from the experiment is shown in figure 30. Depletion of plasma prior to enrichment with V1.2 particles increased proteome coverage by 20-30%.

Numbered embodiments

Embodiments contemplated herein include embodiments 1 through 20.

Embodiment 1. A device for assaying a biological sample comprising: (a) one or more transmission units; (b) A sample storage unit configured to receive and retain the biological sample, wherein the sample storage unit is operably coupled to the one or more transport units; (c) A partition containing particles therein, wherein the partition is operably coupled to the one or more transport units; (d) A plurality of columns, including a capture column, a depletion column, and an analysis column, wherein the plurality of columns are in fluid communication with each other and are operably coupled to the one or more transport units; and (e) a control unit comprising one or more processors, wherein the control unit is in electrical communication with the one or more transmission units.

Embodiment 2. The device of embodiment 1, wherein the one or more transfer units are configured to transfer the biological sample from the sample storage unit to the depletion column to produce a depleted sample.

Embodiment 3. The device of embodiment 2, wherein the one or more transfer units are configured to transfer the depleted sample to the partition to adsorb a plurality of biomolecules from the biological sample onto the particles.

Embodiment 4. The device of embodiment 3, wherein the one or more transfer units are configured to transfer the plurality of biomolecules to the capture column to produce a purified sample.

Embodiment 5. The device of embodiment 4, wherein the one or more transfer units are configured to transfer the purified sample to the analytical column to produce a separation sample.

Embodiment 6 the device of embodiment 5, wherein the one or more transmission units are configured to transmit the separated samples to a mass spectrometer for mass spectrometry of the plurality of biomolecules to generate a plurality of signals.

Embodiment 7 the device of embodiment 1, further comprising a plurality of reagent storage units including a first reagent storage unit containing an aqueous solvent therein and a second reagent storage unit containing an eluting solvent therein, wherein the one or more transfer units are operably coupled to the plurality of reagent storage units.

Embodiment 8 the device of embodiment 1, wherein the one or more transfer units comprise a pipette.

Embodiment 9. The device of embodiment 1, wherein the one or more transfer units comprise a fluidic connection.

Embodiment 10. The device of embodiment 1, wherein the sample storage unit is configured to receive and retain a plurality of biological samples.

Embodiment 11 the device of embodiment 1, further comprising a sample preparation unit comprising at least one of an HPLC column, a filter, and a centrifuge, wherein the one or more transfer units are operably coupled to the sample preparation unit.

Embodiment 12 the device of embodiment 1, further comprising a second partition in which the second particles are contained.

Embodiment 13. The device of embodiment 1, wherein the particles are paramagnetic particles.

Embodiment 14. The device of embodiment 1, wherein the partition houses at least two different particle types therein.

Embodiment 15 the device of embodiment 1, wherein the one or more transfer units are configured to transfer the biological sample from the sample storage unit to adsorb a plurality of biological molecules from the biological sample onto the particles.

Embodiment 16. The device of embodiment 15, wherein the one or more transfer units are configured to transfer the plurality of biomolecules to the capture column to produce a purified sample.

Embodiment 17 the device of embodiment 16, wherein the one or more transfer units are configured to transfer the purified sample to the depletion column to produce a depleted sample.

Embodiment 18 the device of embodiment 17, wherein the one or more transfer units are configured to transfer the depleted sample to the analytical column to produce a separated sample.

Embodiment 19 the device of embodiment 18, wherein the one or more transmission units are configured to transmit the separated samples to a mass spectrometer for mass spectrometry of the plurality of biomolecules to generate a plurality of signals.

Embodiment 20. The device of embodiment 1, wherein the analytical column comprises a plurality of micropillars disposed therein.

While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. The present disclosure is not intended to be limited to the specific examples provided in the specification. While the application has been described with reference to the foregoing specification, the description and illustrations of embodiments herein are not intended to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. Furthermore, it is to be understood that all aspects of the disclosure are not limited to the specific descriptions, configurations, or relative proportions set forth herein, depending on various conditions and variables. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure. It is therefore contemplated that the present disclosure should also cover any such alternatives, modifications, variations, or equivalents. It is intended that the following claims define the scope of the application and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

1. A method of selecting a surface for use in a biomolecular assay, the method comprising:

(a) Providing one or more biological samples comprising a plurality of biological molecules;

(b) Contacting the one or more biological samples with a plurality of surfaces such that each of the plurality of surfaces adsorbs a subset of biomolecules in the plurality of biomolecules;

(c) Determining, for each of the plurality of surfaces, an abundance of the subset of biomolecules adsorbed thereon; and

(d) A subset of surfaces of the plurality of surfaces is selected based at least in part on the abundance when the subset of surfaces adsorbs biomolecules or groupings of biomolecules comprising different patterns of abundance as compared to another subset of surfaces of the plurality of surfaces.

2. The method of claim 1, wherein a first surface subset of the surface subsets is selected when it binds to a first set of functionally and/or structurally related biomolecules.

3. The method of claim 2, wherein the subset of surfaces is selected when a second surface of the subset of surfaces binds to a second set of functionally and/or structurally related biomolecules.

4. The method of claim 2 or 3, wherein the first set of functionally related biomolecules, the second set of functionally related biomolecules, or both comprise at least one of: hormone proteins, cytolytic proteins, innate immunity proteins, membrane attack complexes, complement pathway proteins, amyloid fibers, proteins involved in cholesterol metabolism, proteins involved in steroid metabolism, proteins having a gamma carboxyglutamic acid domain, proteins associated with amyloidosis, sulfated proteins, proteoglycan proteins, immunoglobulins, adaptive immunity proteins, mitochondrial proteins, membrane proteins, cytoplasts, muscle proteins, proteins associated with genetic material, proteins associated with gene expression and/or regulation, proteins associated with intracellular and/or extracellular space, and any combination thereof.

5. The method of any one of claims 2-4, further comprising contacting a new biological sample that is not among the one or more biological samples with the subset of surfaces, thereby determining the first or second set of functionally and/or structurally related biomolecules in the new biological sample.

6. The method of any one of claims 2-5, wherein the first surface and the second surface each adsorb a given biomolecule of the plurality of biomolecules at different relative abundances.

7. The method of any one of claims 2-6, wherein the first surface adsorbs at least one biomolecule that is not adsorbed on the second surface.

8. The method of any one of claims 1-7, wherein the one or more biological samples are samples obtained from a subject having a given disease such that the selected subset of surfaces is optimized for assaying a new biological sample for the given disease.

9. The method of claim 8, further comprising contacting a new biological sample that is not among the one or more biological samples with the subset of surfaces, thereby detecting biomolecules in the new biological sample to determine a disease state of the new biological sample associated with the given disease.

10. The method of any one of claims 1-7, wherein the one or more biological samples are obtained from an individual such that the selected subset of surfaces is optimized for assaying a biological sample from the individual.

11. The method of any one of claims 1-7, wherein the one or more biological samples are obtained from a group of individuals having at least one attribute such that the selected subset of surfaces is optimized for assaying biological samples from individuals having the at least one attribute.

12. The method of claim 11, wherein the at least one attribute comprises a genetic factor, a non-genetic factor, or both.

13. The method of claim 12, wherein the genetic factors comprise one or more genetic mutations, the presence or absence of one or more alleles, the presence or absence of one or more genes, the presence or absence of one or more chromosomes, or any combination thereof.

14. The method of claim 12 or 13, wherein the non-genetic factors comprise physical activity level, sleep quality and pattern, consumption of drugs and/or alcohol, biometrics, or any combination thereof.

15. The method of any one of claims 1-7, wherein the one or more biological samples are samples obtained from one or more species such that the selected subset of surfaces is optimized for assaying at least one of the one or more species.

16. The method of any one of claims 3-15, wherein the first surface and the second surface are selected when a Jaccard index between identities of different subsets of biomolecules adsorbed on the first surface and the second surface is at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9.

17. The method of any one of claims 3-16, wherein the first surface and the second surface are selected when the pearson correlation coefficient between measured intensities of the first set of functionally related biomolecules and the second set of functionally related biomolecules is at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.

18. The method of any one of claims 1-17, wherein the subset of surfaces is selected when the subset of surfaces adsorbs biomolecules or groups of biomolecules with a greater dynamic range than another subset of surfaces of the plurality of surfaces.

19. The method of claim 18, wherein the greater dynamic range is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 orders of magnitude greater.

20. The method of any one of claims 1-19, wherein the one or more biological samples comprise derivatives or portions of the same given biological sample.

21. The method of any one of claims 1-19, wherein the one or more biological samples comprise a human plasma sample.

22. The method of any one of claims 1-19, wherein the one or more biological samples comprise a biological sample standard.

23. The method of claim 22, wherein the biological sample standard is HeLa cell extract.

24. The method of any one of claims 1-23, wherein the plurality of biomolecules comprises polyamino acids.

25. The method of claim 24, wherein the polyamino acid comprises a peptide, a protein, or a combination thereof.

26. The method of any one of claims 1-25, wherein the different subset of biomolecules adsorbed on at least one of the plurality of surfaces comprises at least two biomolecules that do not share a common binding motif.

27. The method of any one of claims 1-26, wherein the determining identity in (c) is by: (i) desorbing the different subset of biomolecules adsorbed on each of the plurality of surfaces to produce desorbed biomolecules, (ii) mass spectrometry of the desorbed biomolecules to produce mass spectrometry signals, and (iii) quantifying the mass spectrometry signals to determine the identity of the different subset of biomolecules.

28. The method of claim 27, wherein (i) further comprises digesting at least a portion of the subset of different biomolecules to produce desorbed biomolecules.

29. The method of claim 28, wherein the digestion comprises contacting the subset of different biomolecules with a protease.

30. The method of any one of claims 1-29, wherein each surface of the plurality of surfaces adsorbs a different subset of biomolecules of the plurality of biomolecules.

31. The method of claim 30, wherein the first subset of different biomolecules adsorbed on a first surface of the plurality of surfaces and the second subset of biomolecules adsorbed on a second surface of the plurality of surfaces comprise at least one common biomolecule.

32. The method of claim 30 or 31, wherein the first subset of different biomolecules and the second subset of biomolecules comprise at least one non-common biomolecule.

33. The method of any one of claims 1-32, wherein the different abundance patterns comprises an enrichment of low abundance biomolecules relative to the plurality of biomolecules in the one or more biological samples.

34. A method of producing an enriched biological sample, the method comprising:

(a) Providing a sample comprising a plurality of biomolecules;

(b) Contacting the sample with a particle or resin to specifically bind at least one biomolecule or biomolecule class target in the sample to the particle or resin;

(c) Separating the particles or resin and the at least one biomolecule from the sample, thereby producing a depleted sample;

(d) Contacting the depleted sample with a surface, wherein the surface is configured to adsorb a collection of biomolecules in the depleted sample onto the surface;

(e) Separating the collection of biomolecules and the surface from the depleted sample; and

(f) Releasing the collection of biomolecules from the surface to produce an enriched sample comprising the collection of biomolecules.

35. The method of claim 34, wherein the at least one biomolecule or biomolecule-class target comprises: albumin, igG, igA, igM, igD, igE, igG (light chain), alpha-1-acid glycoprotein, fibrinogen, haptoglobin, alpha-1-antitrypsin, alpha-2-macroglobulin, transferrin, apolipoprotein A-1, or any combination thereof.

36. The method of claim 34 or 35, wherein the at least one biomolecule or biomolecule class target comprises a predetermined subset of the plurality of biomolecules, the predetermined subset having a high relative abundance.

37. The method of any one of claims 34-36, wherein in step (c), the isolating reduces the abundance of the at least one biomolecule or biomolecule-class target by at least 2, 5, 10, or 100-fold.

38. The method of any one of claims 35-37, wherein in step (c) generating the depleted sample generates at least about 30% more unique proteins, protein groupings, or peptides in the enriched sample of step (f).

39. The method of any one of claims 35-38, wherein in step (c) generating the depleted sample results in a greater dynamic range of at least about 1 order of magnitude of the unique protein or protein groupings in the enriched sample of step (f).

40. The method of any one of claims 35-39, further comprising drying the depleted sample to a predetermined concentration or volume after step (c) or before step (d).

41. The method of claims 35-40, further comprising drying and reconstituting the enriched sample to a predetermined concentration or volume after step (e).

42. The method of any one of claims 35-41, wherein the method is performed in less than about 72 hours.

43. The method of any one of claims 35-42, wherein the biomolecule comprises a protein or a protein grouping.

44. The method of any one of claims 35-43, wherein the surface is a nanoparticle surface.

45. The method of any one of claims 35-44, further comprising contacting the depleted sample with a second surface, wherein the second surface is configured to adsorb a second set of biomolecules in the depleted sample onto the second surface.

46. The method of any one of claims 35-45, wherein the releasing in (f) further comprises digesting the collection of biomolecules.

47. The method of any one of claims 35-46, wherein the particles or resin are disposed in a column.

48. A kit for enriching a biological sample, the kit comprising:

a first substance configured to specifically bind to a first set of biomolecular targets;

a second substance configured to adsorb a second set of biomolecular targets; and

A third substance configured to adsorb a third set of biomolecular targets.

49. The kit of claim 48, wherein the first substance is a resin or a particle.

50. The kit of claim 48 or 49, wherein the first substance comprises a specific binding moiety configured to bind to the first set of biomolecular targets.

51. The kit of any one of claims 48-50, wherein the first substance is configured to specifically bind to at least one of albumin, igG, igA, igM, igD, igE, igG (light chain), alpha-1-acid glycoprotein, fibrinogen, haptoglobin, alpha-1-antitrypsin, alpha-2-macroglobulin, transferrin, and apolipoprotein a-1.

52. The kit of any one of claims 48-51, further comprising a fourth substance configured to non-specifically bind to a fourth set of biomolecular targets.

53. The kit of any one of claims 48-52, further comprising a fifth substance configured to non-specifically bind to a fifth set of biomolecular targets.

54. The kit of any one of claims 48-53, wherein the second substance comprises a plurality of domains, wherein each domain of the plurality of domains is configured to non-specifically bind to a different subset of the second set of biomolecular targets.

55. The kit of any one of claims 48-54, wherein the second substance comprises a particle surface and the plurality of domains comprises a plurality of surface regions on the particle surface.

56. The kit of any one of claims 48-55, wherein the second substance comprises a plurality of particle surfaces and the plurality of particle surfaces are disposed on a plurality of particles.

57. The kit of any one of claims 48-56, wherein the kit comprises a chamber or well in which the first substance, the second substance, and the third substance are disposed.

58. The kit of claim 48, wherein the chamber comprises a column.

59. The kit of claim 48, wherein the chamber comprises a microfluidic channel.

60. The kit of claim 48, wherein the surface area of the well comprises the first substance.