WO2023120562A1

WO2023120562A1 - Device for calculating antibody diversity, method for calculating antibody diversity, and program for calculating antibody diversity

Info

Publication number: WO2023120562A1
Application number: PCT/JP2022/047038
Authority: WO
Inventors: 俊樹植田; 雄太清水; 正博紙田; 佑季川▲崎▼; 哲也佐藤
Original assignee: 合同会社Ｈ．Ｕ．グループ中央研究所
Priority date: 2021-12-22
Filing date: 2022-12-21
Publication date: 2023-06-29

Abstract

The present invention provides a technique that makes it possible to precisely monitor the diversity of antibodies. For example, the present invention provides a method for calculating antibody diversity, the method including: (1) clustering information pertaining to an MS/MS spectrum with regard to antibodies belonging to a specific class, and generating information pertaining to both (i) a cluster number and (ii) the MS/MS spectrum associated with the cluster number with regard to the antibodies belonging to the specific class; and (2) calculating a diversity index with regard to the antibodies belonging to the specific class on the basis of both items of information from (i) and (ii).

Description

Antibody Diversity Calculator, Antibody Diversity Calculation Method, and Antibody Diversity Calculation Program

The present invention relates to an antibody diversity calculation device, an antibody diversity calculation method, and an antibody diversity calculation program.

Antibody diversity is known to decline due to factors such as illness, overwork, stress, and aging, which is directly linked to a decline in immunity. Therefore, antibody diversity monitoring is important for healthcare.

There are several reports on methods related to monitoring antibody diversity. For example, de novo peptide sequence analysis of antibody complementarity determining regions (CDRs) by mass spectrometry (Non-Patent Document 1) has been reported as such a method. However, this method may miss antibodies containing multiple mutations in the variable regions, including the CDRs. The method may also miss antibodies containing specific variable regions with poor spectra obtained by mass spectrometry.

Another method related to monitoring antibody diversity is the B-cell receptor (BCR) repertoire analysis (Patent Document 1). However, since this method indirectly monitors antibody diversity by measuring mRNA in B cells, which are antibody-producing cells, it may not reflect the actual antibody diversity. .

In addition, it has been reported that MS/MS clustering of proteins is used to reduce the redundancy of big data regarding proteins and attempt to identify unidentified spectra regarding proteins (Non-Patent Document 2).

Japanese Patent Application Laid-Open No. 2017-212988

The purpose of the present invention is to monitor antibody diversity directly and with high accuracy.

As a result of intensive studies, the present inventors clustered MS/MS spectral information about antibodies belonging to a specific class, and then calculated the antibody diversity index, thereby directly and highly accurately determining antibody diversity. The present inventors have found that monitoring can be performed by using . None of the above prior art teaches or suggests clustering MS/MS spectral information for antibodies and/or calculating a diversity index for antibodies. That is, the present invention is as follows.

[1] A method for calculating antibody diversity,
(1) clustering MS / MS spectrum information for antibodies belonging to a specific class, (i) the number of clusters for antibodies belonging to a specific class, and (ii) the number of MS / MS spectra associated with the cluster number generating information for both;
(2) calculating a diversity index for antibodies belonging to a particular class based on the information in both (i) and (ii) above.
[2] The method of [1], wherein the MS/MS spectrum for the antibody belonging to the specific class is an MS/MS spectrum measured using a concentrated sample of the antibody belonging to the specific class.
[3] The method of [1], wherein the MS/MS spectrum for the antibody belonging to the specific class is an MS/MS spectrum measured after treating a concentrated sample of the antibody belonging to the specific class with a reducing agent and/or protease. .
[4] (a) MS/MS spectrum information about an antibody belonging to a specific class is collated with amino acid sequences corresponding to a plurality of peptide-based substances to obtain MS/MS spectra attributed to the plurality of peptide-based substances. identifying information;
(b) excluding MS/MS spectral information attributed to a plurality of peptide-based substances from the MS/MS spectral information about the antibody belonging to the specific class, and The information generated by excluding MS/MS spectral information belonging to a plurality of peptide-based substances from the MS/MS spectral information is the MS/MS spectral information for the antibody belonging to the specific class in (1) above. The method of [2] or [3] used as information.
[5] The method of [4], wherein the plurality of peptide-based substances are 10 or more peptide-based substances defined by different amino acid sequences.
[6] The method of [5], wherein the ten or more peptide-based substances defined by different amino acid sequences do not include an antibody defined by the germline amino acid sequence of an antibody belonging to a specific class.
[7] The method of any one of [1] to [6], wherein the antibody belonging to the specific class is one or more antibodies selected from the group consisting of IgG, IgM, IgA, IgD and IgE.
[8] The method of any one of [1] to [7], wherein the number of clusters for an antibody belonging to a specific class is the number of clusters of the heavy or light chain variable region of the antibody belonging to the specific class.
[9] The method according to any one of [1] to [8], wherein the MS/MS spectrum for the antibody belonging to the specific class is an MS/MS spectrum measured using a biological sample taken from the subject.
[10] The method of [9], wherein the biological sample is blood, saliva, tissue wash, mucosal tissue swab, urine, feces, ascites, or amniotic fluid.
[11] The method of any one of [1] to [10], further comprising evaluating the diversity of antibodies belonging to a specific class based on the diversity index.
[12] An antibody diversity calculation device comprising a control unit,
The control unit
(1) clustering MS / MS spectrum information for antibodies belonging to a specific class, (i) the number of clusters for antibodies belonging to a specific class, and (ii) the number of MS / MS spectra associated with the cluster number a clustering means for generating both information;
(2) A device comprising: calculation means for calculating a diversity index for antibodies belonging to a specific class based on the information of both (i) and (ii).
[13] The device of [12], further comprising evaluation means for evaluating the diversity of antibodies belonging to a specific class based on the diversity index.
[14] An antibody diversity calculation program to be executed in an information processing device comprising a control unit,
for execution in the control unit,
(1) clustering MS / MS spectrum information for antibodies belonging to a specific class, (i) the number of clusters for antibodies belonging to a specific class, and (ii) the number of MS / MS spectra associated with the cluster number a clustering step to generate both information;
(2) a calculating step of calculating a diversity index for antibodies belonging to a specific class based on the information of both (i) and (ii);
[15] The program of [14], further comprising an evaluation step of evaluating the diversity of antibodies belonging to a specific class based on the diversity index.

According to the present invention, antibody diversity can be monitored directly, accurately, and easily.

FIG. 1 is a block diagram showing an example of the configuration of an antibody diversity calculator. FIG. 2 shows an example of MS/MS spectra (PSM) assigned to peptides. The MS/MS spectrum depicted in FIG. 2 is hypothetical. FIG. 3 is a diagram showing another example of PSM. FIG. 4 is a diagram showing an example of information on both (i) the number of clusters (n) and (ii) the number of MS/MS spectra associated with the number of clusters for antibodies belonging to a particular class. FIG. 5 is a diagram showing an example of MS/MS spectra before clustering. FIG. 6 is a diagram showing an example of clustering of MS/MS spectra. Cluster 1: MS/

MS spectra

1,5,6; Cluster 2: MS/

MS spectra

2,3,7; Cluster 3: MS/

MS spectra

4,8. FIG. 7 is a diagram showing an example of a flow for calculating antibody diversity.

Embodiments of the antibody diversity calculation device, the antibody diversity calculation method, and the antibody diversity calculation program will be described in detail below based on the drawings, but the present invention is limited by the embodiments. isn't it. Definitions, examples, and preferred examples of expressions (eg, terms, phrases) and related expressions described in any of the above devices, methods and programs are the same for all of the above devices, methods and programs.

[1. composition]
An example of the configuration of the antibody diversity calculation device 100 according to the present embodiment will be described with reference to FIG. FIG. 1 is a block diagram showing an example of the configuration of an antibody diversity calculation device 100. As shown in FIG.

The computing device 100 is a commercially available desktop personal computer. The computing device 100 is not limited to a stationary information processing device such as a desktop personal computer, but may also be a portable information processing device such as commercially available notebook personal computers, PDAs (Personal Digital Assistants), smart phones, tablet personal computers, and the like. It may be a device.

The computing device 100 includes a control unit 102 , a communication interface unit 104 , a storage unit 106 and an input/output interface unit 108 . Each unit included in the computing device 100 is communicably connected via an arbitrary communication path.

The communication interface unit 104 communicably connects the computing device 100 to the network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 has a function of communicating data with another device via a communication line. Here, the network 300 has a function of connecting the computing device 100 and the measuring device 200 so that they can communicate with each other, and is, for example, the Internet or a LAN (Local Area Network).

An input device 112 and an output device 114 are connected to the input/output interface unit 108 . The output device 114 can be a monitor (including a home television), a speaker, or a printer. The input device 112 can be a keyboard, a mouse, a microphone, or a monitor that realizes a pointing device function in cooperation with a mouse. Note that, hereinafter, the output device 114 may be referred to as the monitor 114 and the input device 112 may be referred to as the keyboard 112 or the mouse 112 .

The storage unit 106 stores various databases, tables, files, and the like. The storage unit 106 stores a computer program for cooperating with the OS (Operating System) to give commands to the CPU (Central Processing Unit) to perform various processes. As the storage unit 106, for example, a memory device such as RAM (Random Access Memory) or ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.

The storage unit 106 includes, for example, acquisition result data 106a, identification result data 106b, exclusion result data 106c, clustering result data 106d, calculation result data 106e, and evaluation result data 106f.

Acquisition result data 106a includes information acquired by acquisition unit 102a (for example, m/z values of precursor ions and various m/z values and their intensity values for product ions associated with the precursor ions). information) is stored.

In the identification result data 106b, information generated by the identification unit 102b described later (identified by collating MS/MS spectrum information about antibodies belonging to a specific class with amino acid sequences corresponding to a plurality of peptide substances) , MS/MS spectrum information attributed to a plurality of peptide-based substances).

The exclusion result data 106c stores information generated by the exclusion unit 102c described later (information obtained by excluding MS/MS spectrum information belonging to a plurality of peptide substances from the information acquired by the acquisition unit 102a). there is

The clustering result data 106d includes information generated by the clustering unit 102d described later (for example, both (i) the number of clusters and (ii) the number of MS/MS spectra associated with the number of clusters for antibodies belonging to a specific class. information) is stored.

The calculation result data 106e stores a calculated value (for example, diversity index) calculated by the calculation unit 102e, which will be described later.

The evaluation result data 106f stores the results of evaluation by the evaluation unit 102f, which will be described later (for example, the evaluation result of "whether the antibody diversity is higher or lower than the reference value").

The control unit 102 is a CPU or the like that controls the computing device 100 in an integrated manner. The control unit 102 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, required data, and the like, and performs various information processing based on these stored programs. Execute.

Functionally, the control unit 102 includes, for example, (1) an acquisition unit 102a as acquisition means for acquiring MS/MS spectrum information about an antibody belonging to a specific class measured by the measuring device 200; ) identification of identifying MS/MS spectral information attributed to a plurality of peptide-based substances by collating MS/MS spectral information about antibodies belonging to a specific class with amino acid sequences corresponding to a plurality of peptide-based substances; and (3) an exclusion unit as exclusion means for excluding MS/MS spectrum information belonging to a plurality of peptide-based substances from MS/MS spectrum information about antibodies belonging to a specific class. 102c, and (4) clustering the MS/MS spectrum information in the acquisition unit 102a or the MS/MS spectrum information generated in the exclusion unit 102c to obtain (i) the number of clusters for antibodies belonging to a specific class; , and (ii) a clustering unit 102d as clustering means for generating information on both the number of MS/MS spectra associated with the number of clusters, and (5) based on the information of both (i) and (ii) , a calculation unit 102e as a calculation means for calculating a diversity index for antibodies belonging to a specific class; and (6) an evaluation as an evaluation means for evaluating the diversity of antibodies belonging to a specific class based on the diversity index. and (7) a display unit 102g as display means for displaying the evaluation result. Among these, in order to calculate the antibody diversity according to this embodiment, the control unit 102 only needs to include a clustering unit 102d and a calculation unit 102e, and the components other than the generation unit 102d and the calculation unit 102e are Optional.

The acquisition unit 102a acquires MS/MS spectrum information for antibodies belonging to a specific class. Such information is MS/MS spectral information about an antibody belonging to a specific class measured by the measuring instrument 200 .

　MS/MS spectrum refers to the fragment ion pattern when a specific precursor ion is analyzed by MS/MS. In MS/MS measurement, specific ions are selected in the first mass separator (MS1), and then collided with an inert gas in the subsequent collision cell to cause fragmentation. Next, fragment ions generated by fragmentation are separated by the second mass separator (MS2) and detected (product ion spectrum). Precursor ions can be detected in MS measurements for acquiring MS spectra, and product ions generated from specific precursor ions can be detected in MS/MS measurements for acquiring MS/MS spectra. . MS/MS spectral information is information related to precursor ions and product ions. More specifically, MS/MS spectral information is information that includes the m/z value of a precursor ion and various m/z values and their intensity values for product ions associated with the precursor ion.

Specific classes of antibodies include, for example, IgG (eg, IgG1, IgG2, IgG3, IgG4), IgM, IgA, IgD, and IgE. Such specific class is preferably IgG or IgA, more preferably IgG.

An antibody belonging to a specific class may be an antibody belonging to a specific class present in a biological sample collected from a subject.

Subjects include, for example, mammals (e.g., primates such as humans and monkeys; rodents such as mice, rats, and rabbits; ungulates such as cows, pigs, goats, horses, and sheep; dogs, cats, etc.); meat), birds (eg, chicken). Preferably, the subject is a mammal such as a human. From the point of view of clinical application, the subject is preferably human. A subject can also be a healthy subject or a non-healthy (ie, an abnormal condition) subject.

The subject may be a subject whose antibody diversity may be reduced. Such subjects include, for example, subjects under the influence of factors such as disease, overwork, stress, aging and the like. The subject may preferably be a subject suffering from or potentially suffering from a disease that may be associated with B-cell abnormalities. B-cell abnormalities are qualitative changes in B-cells (eg, canceration of B-cells) or quantitative changes (eg, increase or decrease in B-cell numbers). Diseases that can be associated with B cell abnormalities include, for example, multiple myeloma, chronic lymphocytic leukemia, Burkitt's lymphoma, systemic lupus erythematosus, antiphospholipid antibody syndrome, Sjögren's syndrome, scleroderma, selective IgA deficiency, Wiskott-Aldrich syndrome.

Examples of biological samples include blood (e.g., whole blood, serum, plasma), saliva, washings of biological tissues (e.g., alveolar washings, oral washings), swabs of mucosal tissues (e.g., pharyngeal swabs, nasal cavities). swabs), urine, feces, ascites, and amniotic fluid. The biological sample is preferably blood or saliva, more preferably blood. The biological sample may be pre-processed. Such treatments include, for example, centrifugation, extraction, dilution, filtration, precipitation, heating, freezing, refrigeration, and agitation, as well as treatment with ingredients such as surfactants.

The biological sample may also be treated with a reducing agent and/or protease to facilitate mass spectrometric analysis of antibodies belonging to a particular class. As a reducing agent, a reagent capable of cleaving disulfide bonds between antibody chains can be used. Examples of such reducing agents include tricarboxylethylphosphine (TCEP), cysteine, dithiothreitol, reduced glutathione, and β-mercaptoethanol. Proteases include, for example, trypsin, chymotrypsin, Lys-C, Asp-N, Glu-C, Arg-C, asparaginyl endopeptidase, arginyl endopeptidase, V8 protease.

The MS/MS spectrum information for antibodies belonging to a specific class may be measured in an enriched sample of antibodies belonging to a specific class. A sample enriched for antibodies belonging to a specific class is, for example, a biological sample collected from a subject that has been enriched so as to increase the concentration of antibodies belonging to a specific class. Any operation capable of purifying antibodies belonging to a specific class can be used as such an enrichment operation. For example, by enriching antibodies belonging to a particular class using affinity peptides or proteins (e.g., protein G, protein A) that have the ability to bind to antibodies belonging to a particular class, An enriched sample can be obtained. A concentrated sample of antibodies belonging to a specific class thus obtained may contain a plurality of peptide-based substances other than antibodies belonging to the specific class as contaminants. Therefore, if the MS/MS spectral information for antibodies belonging to a particular class is measured in an enriched sample of antibodies belonging to the particular class, the device preferably comprises an identifying portion 102b and an excluding portion 102c.

Preferably, the MS/MS spectrum for antibodies belonging to a specific class may be an MS/MS spectrum measured after treating a concentrated sample of antibodies belonging to a specific class with a reducing agent and/or protease.

The identifying unit 102b compares the MS/MS spectral information about the antibody belonging to the specific class with the amino acid sequences corresponding to the plurality of peptide-based substances, and identifies the MS/MS spectral information attributed to the plurality of peptide-based substances. identify. Peptidic substances are substances that can be defined by an amino acid sequence and are typically peptides, polypeptides and proteins. The information on the amino acid sequence corresponding to the peptide-based substance may be information already registered in the storage unit, or may be information acquired for each individual analysis. Preferably, the data is information registered in the storage unit from the viewpoint of simple and short-time implementation. For example, Excel, JMP, python, and R can be used as analysis tools. Alternatively, a specialized protein analysis tool (eg, proteome discoverer) can be used as the analysis tool.

The plurality of peptide-based substances may be 10 or more peptide-based substances defined by different known amino acid sequences. The number of peptide-based substances defined by different known amino acid sequences is preferably 100 or more, more preferably 200 or more, still more preferably 500 or more, and particularly preferably 1,000 or more, 2,000 or more, 3,000. , 4,000 or greater, 5,000 or greater, 6,000 or greater, 7,000 or greater, 8,000 or greater, 9,000 or greater, or 10,000 or greater. The number of peptidic substances defined by different known amino acid sequences is also preferably 10,000,000 or less, more preferably 5,000,000 or less, even more preferably 1,000,000 or less, particularly preferably It may be 500,000 or less, 100,000 or less, 50,000 or less, or 30,000 or less. Therefore, the number of peptidic substances defined by different known amino acid sequences is, for example, 10 to 10,000,000, preferably 100 to 5,000,000, more preferably 200 to 1,000,000, even more It may preferably be from 500 to 500,000, particularly preferably from 1,000 to 100,000. Known peptides defined by known amino acid sequences can be readily identified even with poor MS/MS spectra. For example, in the case of a known peptide consisting of the amino acid sequence of MPCTEDYLSLILNR (SEQ ID NO: 1), the known peptide can be easily identified from the information of the product ion spectrum (MS/MS spectrum) generated by fragmentation (cleavage of amide bond) by MS/MS. can be identified (Figs. 2, 3). For identification, it is preferable to use m/z values of precursor ions and product ions associated therewith as MS/MS spectral information. An MS/MS spectrum annotated (attributed) to a peptide is called PSM (Peptide Spectrum Match). In the present invention, PSM can be used for exclusion. Therefore, the plurality of peptide-based substances may be a plurality of peptide-based substances defined by known amino acid sequences registered in public or commercial databases. By using the known amino acid sequences of such peptide-based substances, MS/MS spectrum (PSM) information attributed to a plurality of peptide-based substances can be excluded comprehensively, simply, and with high accuracy. The clustering accuracy of MS/MS spectra for antibodies belonging to a specific class can be further improved.

The 10 or more peptide-based substances defined by different amino acid sequences may not contain antibodies defined by the germline amino acid sequences of antibodies belonging to a specific class. This makes it possible to calculate the diversity of antibodies including the germline of the antibodies and improve the accuracy of the calculation of the diversity index.

The exclusion unit 102c excludes MS/MS spectrum information identified by the identification unit 102b (MS/MS spectrum information belonging to a plurality of peptide-based substances) from the MS/MS spectrum information acquired by the acquisition unit 102a. do. For example, Excel, JMP, python, and R can be used as analysis tools. Alternatively, a specialized protein analysis tool (eg, proteome discoverer) can be used as the analysis tool. The identification and exclusion described above can improve the accuracy of clustering MS/MS spectra for antibodies belonging to a specific class, and thus the accuracy of calculating the diversity index. A concentrated sample of antibodies belonging to a specific class obtained as described above may contain a plurality of peptide-based substances (e.g., albumin) other than antibodies belonging to a specific class as contaminants. In calculating the diversity index of antibodies belonging to a specific class, the influence of multiple peptidic substances can be excluded.

The clustering unit 102d clusters the MS/MS spectrum information about the antibodies belonging to the specific class based on the information obtained by the obtaining unit 102a or the exclusion unit 102c, and calculates (i) the number of clusters for the antibodies belonging to the specific class. , and (ii) the number of MS/MS spectra associated with the number of clusters (FIG. 4). The number of clusters for antibodies belonging to a particular class is the number of clusters of heavy or light chains (λ or κ chains) of antibodies belonging to the particular class. The number of clusters for antibodies belonging to a particular class is preferably the number of variable region clusters.

Clustering of MS/MS spectra is a method of classifying similar MS/MS spectra obtained from precursor ions of the same m/z by hierarchical clustering. For example, eight MS/MS spectra as shown in FIG. 5 can be classified into three clusters as shown in FIG. Clustering preferably utilizes the m/z values of precursor ions and the m/z values and intensities of product ions associated with the precursor ions as information of the MS/MS spectrum. Therefore, the MS/MS spectrum information used for clustering is preferably different from the MS/MS spectrum information used for the identification described above. Clustering can be performed using clustering methods such as MaraCluster, PRIDE Cluster, spectra-cluster, and MSCluster. Also, as analysis tools, for example, Excel, JMP, python, and R can be used.

The calculation unit 102e calculates a diversity index for antibodies belonging to a specific class based on both information (i) and (ii) obtained by the clustering unit 102d. A diversity index is a value calculated from both the number of clusters and the number of MS/MS spectra associated with the number of clusters. For example, Excel, JMP, python, and R can be used as analysis tools.

Diversity index can be calculated using, for example, Diversity Evenness score. The Diversity Evenness score is a score indicating the ratio of clusters occupying a predetermined percentage of MS/MS spectra obtained by summing spectra from clusters having a large number of spectra. Diversity evenness score includes, for example, diversity evenness score (DE50), diversity evenness score (DE30), and diversity evenness score (DE10). Diversity evenness score (DE50) is a score that indicates the ratio of clusters occupying 50% of MS/MS spectra obtained by summing spectra from clusters having a large number of spectra. Diversity evenness score (DE30) is a score indicating the percentage of clusters occupying 30% of MS/MS spectra obtained by summing spectra from clusters having a large number of spectra. Diversity evenness score (DE10) is a score indicating the proportion of clusters occupying 10% of the MS/MS spectra obtained by summing spectra from clusters having a large number of spectra.

A diversity index can also be calculated using the Shannon index. The Shannon index can be defined by the following formula.

pi: proportion of cluster i to the total spectrum b: base. is often e (natural logarithm: ln)

Diversity index calculation can also be performed using Simpson's index. Simpson's index can be defined by the following formula.

pi: proportion of cluster i to the total spectrum

In addition, since the reciprocal of Simpson's index (1/D) is often used to calculate the diversity index related to Simpson's index, such a reciprocal may be used.

Diversity index calculations can also be performed using Pielous's evenness. Pierous's evenness can be defined by the following formula.

H': Shannon index
S: number of clusters

The evaluation unit 102f presents an evaluation result of diversity of antibodies belonging to a specific class based on the diversity index in the calculation unit 102e. The evaluation result is, for example, "whether the antibody diversity is higher or lower than the reference value". Reference values include reference values in healthy subjects (eg, subjects not overworked or overly stressed), subjects of similar age, and subjects suffering from disease. Such a reference value can be appropriately set by those skilled in the art. For example, a reference value can be set by creating an ROC (Receiver Operating Characteristic) curve with reference to cutoff values that are commonly used in clinical practice. The evaluation result may also be “whether antibody diversity is improved or decreased compared to before” when the same subject is evaluated for antibody diversity over a period of time.

The display unit 102g displays the diversity index in the calculation unit 102e and/or the evaluation result in the evaluation unit 102f.

[2. Process flow]
In this section, an example of the antibody diversity calculation flow according to this embodiment will be described with reference to FIG.

[2-1. Step S1: Acquisition process]
As described above, the acquisition unit 102a acquires MS/MS spectrum information about an antibody belonging to a specific class (step S1 in FIG. 7: acquisition processing). Such information includes MS/MS spectrum information for antibodies belonging to a specific class measured by the measuring instrument 200 (for example, various m/z values for precursor ions and product ions and their intensity values). information). Acquisition unit 102a stores information on the acquired MS/MS spectrum in acquisition result data 106a.

The measuring instrument 200 will be explained. The measurement device 200 is a device for measuring MS/MS spectra of antibodies belonging to a specific class. Any mass spectrometry instrument capable of measuring MS/MS spectra can be used as instrument 200, typically LC-MS/MS is used. A biological sample (preferably an enriched sample of antibodies belonging to a particular class) taken from a subject as described above can be provided to meter 200 . Preferably, the biological sample may be treated with reducing agents and/or proteases as described above to facilitate mass spectrometric analysis of antibodies belonging to a particular class.

[2-2. Step S2: Identification processing]
The identification unit 102b collects a plurality of MS/MS spectrum information (preferably, m/z values of precursor ions and product ions associated with the precursor ions) for antibodies belonging to a specific class stored in the acquisition result data 106a. are compared with the amino acid sequences corresponding to the peptidic substances to identify MS/MS spectral information attributed to a plurality of peptidic substances. (Step S2 in FIG. 7: identification processing). The identification unit 102b stores the identification information in the identification result data 106b.

[2-3. Step S3: Exclusion process]
The exclusion unit 102c removes the MS/MS spectrum information identified by the identification unit 102b from the MS/MS spectrum information stored in the acquisition result data 106a (MS/MS spectrum information attributed to a plurality of peptide-based substances). are excluded (step S3 in FIG. 7: exclusion processing). The exclusion unit 102c stores the exclusion information in the exclusion result data 106c.

[2-4. Step S4: Clustering processing]
The clustering unit 102d uses the information of the MS/MS spectrum stored in the acquisition result data 106a or the exclusion result data 106c (preferably, the m/z value of the precursor ion and the m/z value of the product ion associated with the precursor ion). and intensity), the MS/MS spectral information for antibodies belonging to a particular class is clustered to obtain (i) the number of clusters for antibodies belonging to a particular class, and (ii) the MS/MS associated with the number of clusters. Both information on the number of MS spectra is generated (step S4 in FIG. 7: clustering processing). Alternatively, the clustering unit 102d clusters MS/MS spectrum information about antibodies belonging to a specific class based on the exclusion information stored in the exclusion result data 106c to obtain (i) clusters for antibodies belonging to a specific class and (ii) the number of MS/MS spectra associated with the number of clusters (step S4 in FIG. 7: clustering process). The clustering unit 102d stores the clustering information in the clustering result data 106d.

[2-5. Step S5: Calculation processing]
The calculation unit 102e calculates the diversity index for antibodies belonging to a specific class based on both the information (i) and (ii) stored in the clustering result data 106d (step S5 in FIG. 7: calculation processing ). The calculator 102e stores the calculated value (diversity index) in the calculation result data 106e.

[2-6. Step S6: Evaluation processing]
The evaluation unit 102f evaluates the diversity of antibodies belonging to a specific class based on the calculated value (diversity index) stored in the calculation result data 106e (step S6 in FIG. 7: evaluation processing).

The evaluation result may be, for example, "whether the antibody diversity is higher or lower than the reference value" or "whether the antibody diversity is improved or decreased compared to before" as described above. . The evaluation unit 102e stores the evaluation result in the evaluation result data 106e.

[2-7. Step S7: Display processing]
The display unit 102g displays the evaluation results stored in the evaluation result data 106f (step S7 in FIG. 7: display processing). This allows the operator (for example, the person in charge of testing) to know the evaluation result of antibody diversity in the subject. All the processing is completed by the above (end in FIG. 7).

[3. Summary of this embodiment]
As described above, according to the antibody diversity calculator 100 according to the present embodiment, antibody diversity can be easily monitored with high accuracy.

[4. Other embodiments]
The present invention may be implemented in various different embodiments other than the embodiments described above within the scope of the technical idea described in the claims.

For example, among the processes described in the embodiments, all or part of the processes described as being automatically performed can be manually performed, or all of the processes described as being manually performed Alternatively, some can be done automatically by known methods.

In addition, unless otherwise specified, the processing procedures, control procedures, specific names, information including parameters such as registration data and search conditions for each process, screen examples, and database configurations shown in this specification and drawings can be changed arbitrarily.

In addition, regarding the antibody diversity calculation device 100, each component shown in the figure is functionally conceptual, and does not necessarily need to be physically configured as shown in the figure.

For example, all or any part of the processing functions provided in the antibody diversity calculation device 100, particularly each processing function performed by the control unit, is realized by a CPU and a program interpreted and executed by the CPU. Alternatively, it may be implemented as hardware by wired logic. In addition, the program is recorded on a non-temporary computer-readable recording medium containing programmed instructions for causing the information processing apparatus to execute the processing described in this embodiment, and antibody diversity as necessary is mechanically read by the computing device 100 of . That is, a storage unit such as a ROM or a HDD (Hard Disk Drive) stores a computer program for cooperating with the OS to give commands to the CPU to perform various processes. This computer program is executed by being loaded into the RAM and constitutes a control section in cooperation with the CPU.

In addition, this computer program may be stored in an application program server connected to the antibody diversity calculation device 100 via any network, and all or part of it may be downloaded as necessary. is also possible.

Also, the program for executing the processing described in this embodiment may be stored in a non-temporary computer-readable recording medium, or may be configured as a program product. Here, this "recording medium" means memory card, USB (Universal Serial Bus) memory, SD (Secure Digital) card, flexible disk, magneto-optical disk, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (registered Trademark) (Electrically Erasable and Programmable Read Only Memory), CD-ROM (Compact Disk Read Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Disk), and Blu-ray (registered trademark) Disc, etc. shall include any "portable physical medium".

In addition, a "program" is a data processing method written in any language or writing method, regardless of the format such as source code or binary code. In addition, the "program" is not necessarily limited to a single structure, but is distributed as a plurality of modules or libraries, or cooperates with a separate program represented by the OS to achieve its function. Including things. It should be noted that well-known configurations and procedures can be used for the specific configuration and reading procedure for reading the recording medium in each device shown in the embodiments, the installation procedure after reading, and the like.

The various databases stored in the storage unit are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks. It stores programs, tables, databases, files for web pages, and so on.

Further, the antibody diversity calculation device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which any peripheral device is connected. . Further, the antibody diversity calculation device 100 may be implemented by installing software (including programs, data, etc.) that allows the device to implement the processing described in the present embodiment.

Furthermore, the specific form of distribution and integration of devices is not limited to the one shown in the figure, and all or part of them can be functionally or physically arranged in arbitrary units according to various additions or functional loads. It can be distributed and integrated. That is, the embodiments described above may be arbitrarily combined and implemented, or the embodiments may be selectively implemented.

Although the present invention will be described in detail by the following examples, the present invention is not limited to the following examples.

1. Sample Preparation Plasma cell neoplasms, including multiple myeloma, are diseases in which the body overproduces plasma cells. Plasma cells are matured B lymphocytes (B cells), a kind of white blood cells produced in the bone marrow. Plasma cells produce antibodies against various types of bacteria and viruses to prevent infection and disease. Plasma cell neoplasm is a disease in which abnormal plasma cells (myeloma cells) proliferate excessively in the bone marrow. Plasma cell tumors produce an antibody called M protein (monoclonal protein) that lacks an anti-infection function that is unnecessary for living organisms. M protein is found in abnormally large amounts in biological fluids such as blood, urine and bone marrow in plasma cell neoplasms. In this study, we attempted to assess antibody diversity in patients with multiple myeloma.

50 μL of each of 4 multiple myeloma patient sera and 5 healthy human sera was taken, diluted with 950 μL of 0.02% PBT-T, and then added to Protein G-bound magnetic beads. After rotating the bead-containing solution at 37° C. for 60 minutes, the beads were collected and the supernatant was discarded. After washing the beads three times with 0.02% PBS-T, 0.1M glycine buffer (pH 2.0) was added and incubated for 5 minutes. 20 μL of 1 M Tris-HCl buffer (pH 9.0) was added to the solution from which the beads had been removed. The solution was subjected to protein quantification using a Micro BCA Assay Kit (Thermo Fisher Scientific), and 15 μg of the solution was diluted 2-fold with 10% SDS/100 mM triethylammonium bicarbonate (TEAB) (pH 7.55). The diluted solution was subjected to reductive alkylation treatment and trypsin digestion with S-Trap Column (AMR). The resulting eluate was freeze-dried, suspended in 0.1% formic acid water, and then subjected to peptide quantification by Pierce Quantitative Fluorometric Peptide Assay (Thermo Fisher Scientific).

2. NanoLC-MS/MS Measurement Based on the results of peptide quantification, 250 ng of each sample was measured by nanoLC-MS/MS. The nanoLC was measured by Data Dependent Acquisition (DDA) using Ultimate 3000 (Thermo Fisher Scientific) and Q Exactive (Thermo Fisher Scientific) as the mass spectrometer.

3. Analysis-setting of general information (1) Amino acid sequences of all Homo Sapiens proteins (total number of amino acid sequences: 20295) were downloaded from Uniprot (https://www.uniprot.org/) in FASTA format.
(2) Using R, a new FASTA file (total number of amino acid sequences after deletion: 20158) was created by deleting germline amino acid sequences of antibody variable regions from this FASTA file.
(3) The created file was registered in Proteome Discoverer 2.2 (hereinafter referred to as PD, Thermo Fisher Scientific) and used for subsequent analysis.

4. Analysis of Individual Samples (1) Identification Processing Information measured by nanoLC-MS/MS (m/z values of precursor ions and product ions) is input to the PD for each sample, and the pre-registered FASTA file is The search identified MS/MS spectra (PSM) annotated (attributed) to database-registered peptides. This PSM information was output in a text file format.
(2) Exclusion processing Measurement information for each sample was converted to an MGF file by the PD. Based on the information of the obtained MGF file and text file, an MGF file excluding PSM was created using a self-made Python script.
(3) Clustering Processing The obtained MGF files (m/z values of precursor ions and m/z values and intensities of product ions) for each sample were each analyzed by MaraCluster to cluster MS/MS spectra.
(4) Calculation processing Various diversity indices were calculated based on the number of clusters and the number of MS/MS spectra associated therewith, and the diversity of variable region sequences in serum antibodies was evaluated. Diversity evenness score (DE50/DE30/DE10) was used as the diversity index. DE50 is the sum of spectra from a cluster with a large number of spectra, and is the ratio of clusters occupying 50% of the MS / MS spectrum. Diversity Evenness Score (DE10) is the ratio of clusters occupying 10% of MS/MS spectra obtained by summing spectra from clusters with a large number of spectra.

The results are shown in Tables 1 and 2.

Reference percentage (%) is 100 (%) × concentration of M protein (g/L)/[concentration of M protein (g/L) + standard amount of antibody in serum (assumed to be 12 g/L) .

As a result, DE, an index of clonality, was shown to be lower in multiple myeloma patients than in healthy subjects. DE shows a low value when the number of clusters is small or when the MS/MS spectrum is biased toward a specific cluster, and conversely shows a high value when the number of clusters is large and the spectrum is dispersed over the entire cluster. Samples derived from multiple myeloma patients are thought to have lower DE values than healthy subjects as a result of the production of large clusters derived from the M protein, a monoclonal antibody.
Therefore, samples derived from multiple myeloma patients have a DE of less than 50:32, a DE of less than 30:10, and a DE of less than 10:1, and therefore have lower antibody (M protein) diversity than samples from healthy individuals. It has been shown.

100 antibody diversity calculator 102 control unit 102a acquisition unit

102b identification unit

102c exclusion unit

102d clustering unit 102e calculation unit

102f evaluation unit

102g display unit 104 communication interface unit 106 storage unit 106a acquisition result data 106b identification result data 106c exclusion result data 106d clustering result data 106e calculation result data 106f evaluation result data 108 input/output interface section 112 input device 114 output device 200 measuring device 300 network

Claims

A method for calculating antibody diversity, comprising:
(1) clustering MS / MS spectrum information for antibodies belonging to a specific class, (i) the number of clusters for antibodies belonging to a specific class, and (ii) the number of MS / MS spectra associated with the cluster number generating information for both;
(2) calculating a diversity index for antibodies belonging to a particular class based on the information in both (i) and (ii) above.
　The method according to claim 1, wherein the MS/MS spectrum for the antibody belonging to the specific class is an MS/MS spectrum measured using an enriched sample of the antibody belonging to the specific class.
　The method according to claim 1, wherein the MS/MS spectrum for the antibody belonging to the specific class is an MS/MS spectrum measured after treating a concentrated sample of the antibody belonging to the specific class with a reducing agent and/or protease.
(a) MS/MS spectral information about an antibody belonging to a specific class is collated with amino acid sequences corresponding to a plurality of peptide-based substances to identify MS/MS spectral information attributed to a plurality of peptide-based substances. and
(b) excluding MS/MS spectral information attributed to a plurality of peptide-based substances from the MS/MS spectral information about the antibody belonging to the specific class, and The information generated by excluding MS/MS spectral information belonging to a plurality of peptide-based substances from the MS/MS spectral information is the MS/MS spectral information for the antibody belonging to the specific class in (1) above. 3. The method of claim 2, used as information.
The method according to claim 4, wherein the plurality of peptide-based substances are 10 or more peptide-based substances defined by different amino acid sequences.
The method according to claim 5, wherein the ten or more peptide-based substances defined by different amino acid sequences do not include antibodies defined by germline amino acid sequences of antibodies belonging to a particular class.
The method according to claim 1, wherein the antibody belonging to the specific class is one or more antibodies selected from the group consisting of IgG, IgM, IgA, IgD and IgE.
The method according to claim 1, wherein the number of clusters for antibodies belonging to a particular class is the number of clusters of variable regions of heavy or light chains of antibodies belonging to the particular class.
　The method according to claim 1, wherein the MS/MS spectrum for an antibody belonging to a specific class is an MS/MS spectrum measured using a biological sample taken from a subject.
　The method according to claim 9, wherein the biological sample is blood, saliva, tissue wash, mucosal tissue swab, urine, feces, ascites, or amniotic fluid.
The method according to any one of claims 1 to 10, further comprising evaluating the diversity of antibodies belonging to a specific class based on the diversity index.
An antibody diversity calculation device comprising a control unit,
The control unit
(1) clustering MS / MS spectrum information for antibodies belonging to a specific class, (i) the number of clusters for antibodies belonging to a specific class, and (ii) the number of MS / MS spectra associated with the cluster number a clustering means for generating both information;
(2) A device comprising: calculation means for calculating a diversity index for antibodies belonging to a specific class based on the information of both (i) and (ii).
The apparatus according to claim 12, further comprising evaluation means for evaluating diversity of antibodies belonging to a specific class based on said diversity index.
An antibody diversity calculation program for execution in an information processing device comprising a control unit,
for execution in the control unit,
(1) clustering MS / MS spectrum information for antibodies belonging to a specific class, (i) the number of clusters for antibodies belonging to a specific class, and (ii) the number of MS / MS spectra associated with the cluster number a clustering step to generate both information;
(2) a calculating step of calculating a diversity index for antibodies belonging to a specific class based on the information of both (i) and (ii);
The program according to claim 14, further comprising an evaluation step of evaluating diversity of antibodies belonging to a specific class based on said diversity index.