# Patent

## Title
Measurement and comparison of immune diversity by high-throughput sequencing

## Abstract

A precise measurement of the immunological receptor diversity present in a sample is obtained by sequence analysis. Samples of interest are generally complex, comprising more than 102, 103, 104, 105, 106, 107, 108, 109, 1010, 1011, 1012 or more different sequences for a receptor of interest. Immunological receptors of interest include immunoglobulins, T cell antigen receptors, and major histocompatibility receptors. The specific composition of immunological receptor sequence variations in the sample can be recorded and output. The composition is useful for predictive, diagnostic and therapeutic methods relating to the immune capabilities and history of an individual. Such predictions and diagnoses are used to guide clinical decisions.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of application Ser. No. 13/696,375, filed (Feb. 22, 2013) which was the national stage entry of international application PCT/US2011/035507 (filed May 6, 2011), which claims priority from provisional application No. 61/395,116 (filed May 7, 2010). The contents of the aforementioned applications are incorporated herein by reference.


BACKGROUND OF THE INVENTION
A feature of the adaptive immune response is the ability to generate a wide diversity of binding molecules, e.g. T cell antigen receptors and antibodies. A variety of molecular mechanisms exist to generate initial diversity, including genetic recombination at multiple sites. Armed with this initial repertoire of binding moieties, naïve B and T cells circulate where they can come in contact with antigen. Upon exposure to antigen there can be a positive selection process, where cells expressing immunological receptors having desired binding properties are expanded, and may undergo further sequence modification, for example somatic hypermutation, and additional recombination. There can also be a negative selection process, where cells expressing immunological receptors having undesirable binding properties, such as self-reactivity, are deleted. As a result of these selective processes, the repertoire of binding specificities in an individual sample can provide a history of past antigenic exposures, as well as being informative of inherent repertoire capabilities and limitations.
Adaptive immunological receptors of interest include immunoglobulins, or antibodies. This repertoire is highly plastic and can be directed to create antibodies with broad chemical diversity and high selectivity. There is also a good understanding of the potential diversity available and the mechanistic aspects of how this diversity is generated. Antibodies are composed of two types of chains (heavy and light), each containing a highly diversified antigen-binding domain (variable). The V, D, and J gene segments of the antibody heavy-chain variable genes go through a series of recombination events to generate a new heavy-chain gene. Antibodies are formed by a mixture of recombination among gene segments, sequence diversification at the junctions of these segments, and point mutations throughout the gene. The mechanisms are reviewed, for example in Maizels (2005) Annu. Revu. Genet. 39:23-46; Jones and Gellert (2004) Immunol. Rev. 200:233-248; Winter and Gearhart (1998) Immunol. Rev. 162:89-96.
Another adaptive immunological receptor of interest is the T cell antigen receptor (TCR), which is a heterodimer of two chains, each of which is a member of the immunoglobulin superfamily, possessing an N-terminal variable (V) domain, and a C terminal constant domain. The variable domain of the TCR α-chain and β-chain has three hypervariable or complementarity determining regions (CDRs). The β-chain has an additional area of hypervariability (HV4) that does not normally contact antigen. Processes for generating diversity of the TCR are similar to those described for immunoglobulins. The TCR alpha chain is generated by VJ recombination, while the beta chain is generated by V(D)J recombination. Similarly, generation of the TCR gamma chain involves VJ recombination, while generation of the TCR delta chain occurs by V(D)J recombination. The intersection of these specific regions (V and J for the alpha or gamma chain, V D and J for the beta or delta chain) corresponds to the CDR3 region that is important for antigen-MHC recognition. It is the unique combination of the segments at this region, along with palindromic and random N- and P-nucleotide additions, which accounts for the TCR binding repertoire.
While reference is made to binding specificities, and indeed a good deal of serological analysis is based on the physical interactions between antigen and receptor, the underlying cause of the diversity lies in the genetic sequences expressed by lymphocytes, which sequences reflect the myriad processes of recombination, mutation and selection that have acted on the cell. Estimates of immune diversity for antibodies or the related T cell receptors either have attempted to extrapolate from small samples to entire systems or have been limited by coarse resolution of immune receptor genes. However, certain very elementary questions have remained open more than a half-century after being posed: It is still unclear what fraction of the potential repertoire is expressed in an individual at any point in time and how similar repertoires are between individuals who have lived in similar environments. Moreover, because each individual's immune system is an independent experiment in evolution by natural selection, these questions about repertoire similarity also inform our understanding of evolutionary diversity and convergence.
Methods of precisely determining the immune receptor repertoire of an individual, or a sample of interest from an individual, are of great interest for prognosis, diagnosis, and characterization. The present invention addresses that issue.
SUMMARY OF THE INVENTION
Methods and compositions are provided for using nucleic acid sequence analysis to measure characteristics and function of the immune system. A principal application of the invention is in measuring the immunological diversity present in a biological sample. By determining the underlying genetics of the immune repertoire, one can better characterize immune response, immune history, and immune competency. Those characterizations, in turn, lead to improved diagnostic, prognostic, and therapeutic outcomes. Finally, methods of the invention allow personalized immune profiling.
The samples from which immunological-receptor encoding nucleic acids are obtained are typically complex and include, among others, blood, lymph, and biopsy samples. Such samples typically comprise greater than 103 or more different sequences for a receptor of interest. The biological sample may be chosen based upon a particular organ or system, condition or disease of interest. In some embodiments the sample comprises immune-related cells, such as lymphocytes, e.g. T cells, B cells, natural killer cells, etc. Immunological receptor molecules of interest include immunoglobulins, T cell antigen receptors, and major histocompatibility receptors, or fragments thereof. The nature of sequence variations in the sample can be recorded and displayed in an informative manner, e.g. represented in a tree, represented in a three dimensional plot, etc. The analysis of sequence variation is useful for predictive and diagnostic methods relating to the immune capabilities and history of an individual. Such predictions and diagnoses can be used to guide clinical decisions.
Any appropriate sequencing method may be used in the context of the invention. Common methods include sequencing-by-synthesis, Sanger or gel-based sequencing, sequencing-by-hybridization, sequencing-by-ligation, or any other available method. Particularly preferred are high throughput sequencing methods, preferably without the need for cloning or functional expression of the targeted immune molecules. In some embodiments, all the cells in the sample are treated as a single sample, i.e. without segregation or sorting, and used as a source of nucleic acids for sequencing. In other embodiments, cells of interest, including cells of the adaptive immune system, e.g. B cells expressing a marker of interest, plasmablasts, T cells expressing a marker of interest, and the like, are sorted from the starting sample population and used as a source of nucleic acids for sequencing. In some embodiments the sorting is by positive selection, while in others, the sorting is performed by negative selection.
The sequencing data are statistically analyzed to compute correlations in the repertoire (or sets of immunological receptors) of different samples, where samples may be obtained from different individuals or from a single individual at different times, different sites of the body, synthetic libraries, etc. Time points may be taken, for example, following exposure to an antigenic challenge, such as a vaccine, in response to a candidate therapy, during a transplantation process, and the like.
The information obtained from the immune repertoire analysis may be used to diagnose a condition, to monitor treatment, to select or modify therapeutic regimens, and to optimize therapy. With this approach, therapeutic and/or diagnostic regimens can be individualized and tailored according to the specificity data obtained at different times over the course of treatment, thereby providing a regimen that is individually appropriate. In addition, patient samples can be obtained at any point during the treatment process for analysis.
Methods of statistical analysis include the use of algorithms to correct for bias introduced in sample preparation and sequencing of immune repertoires. An algorithm, for example using clustering and PCR filter, may be used to correct for sequence errors (or amplification bias) introduced during sample preparation and sequencing of immune repertoires. Algorithms are provided for the assignment of immune repertoire sequences into V, D, J, and C classes. Algorithms are provided for the assignment of immune repertoire sequences to individual heavy chains, light chains, CDR3, T-cell receptor alpha, beta, delta or gamma chains, etc.
The total corrected repertoire (or set of immunological receptors) can be used to determine the heterogeneity of an immune repertoire (or set of immunological receptors) by computing the entropy. The total corrected repertoire can be characterized by computing the frequency distributions of VDJC/antibody heavy chains.
The invention includes suitable sets of primers for obtaining high throughput sequence information for immunological molecules of interest, e.g. immunoglobulin sequence information, T cell receptor sequence information, MHC sequence information, etc. Sequencing can be performed on sets of nucleic acids across many individuals or on multiple loci in a sample obtained from one individual. Sequence analysis is performed on nucleic acid obtained from cells present in the sample of interest, which may be genomic DNA or a portion thereof, cDNA, or portion thereof; or may be mRNA or cDNA obtained therefrom. In some embodiments cDNA is preferred. Where cDNA is analyzed, the methods may include the use of gene specific primers for reverse transcription of the immunological receptor sequences of interest.
Analysis may include amplifying cDNA using a set of primers designed to selectively bind immunological receptor gene sequences. For example, primers may be designed to amplify functional V gene segments of immunoglobulin loci, to amplify functional V gene segments of TCR loci, to amplify immunoglobulin or TCR constant region segments, to amplify consensus MHC gene segments, and the like. In some embodiments, an independent primer set is included to test PCR bias.
The present disclosure also provides a method for diagnosis or prognosis of a condition of interest, comprising: obtaining one or more reference samples comprising cells of interest; performing an immune repertoire analysis on the reference sample(s); using clustering analysis on the immune repertoire analysis results to identify features common to the condition of interest; performing immune repertoire analysis on a test sample obtained from an individual in need of diagnosis; comparing the repertoire analysis results obtained from the test sample to reference repertoire analysis results, wherein a pre-determined level of similarity to reference repertoire analysis results are indicative of the absence or presence of the condition.
Conditions of interest for diagnosis and prognosis include numerous aspects of immune competence and antigenic exposure, e.g. including the absence or presence of autoimmune disease or predisposition to autoimmune disease; the status of transplantation; the presence of cancers of the immune system, e.g. leukemias, lymphomas, myelomas, etc.; exposure to antigenic stimulus, e.g. exposure to cancer antigens; exposure to viral, bacterial, parasitic antigens; exposure to vaccines; exposure to allergens; exposure to foodstuffs, e.g. gluten proteins, etc.; the innate repertoire of an individual indicating an inherent ability to respond to an antigen of interest; and the like.
Yet another method provided herein is a method for screening for a therapeutic agent comprising: exposing a first subject to one or more test agents; obtaining a suitable cell sample from the subject, e.g. a blood sample, etc.; performing immune repertoire analysis on said cell sample; and comparing the immune repertoire analysis results to a immune repertoire analysis result derived from either: (i) a second reference sample with a known response profile; or (ii) the first subject prior to said exposing step; and identifying an agent that affects immune repertoire in a desirable manner, e.g. deletion of self-reactive receptors; enhancement of pathogen-specific receptors; etc. The subject may be, for example, suffering or susceptible to an autoimmune disease, a chronic infection, following transplantation of a tissue, suffering from a cancer, etc. A therapeutic agent can be an antibody or antibody fragment, a drug or other small molecule, nucleic acid (for example an siRNA), RNA, DNA, RNA-DNA chimera, protein, peptide, and the like.
Further provided herein is a method of determining likelihood of a response by a subject to an agent, which may include a therapeutic agent, an infectious agent, a vaccine, an autoantigen, and the like, comprising; obtaining a suitable cell sample from the subject, e.g. a blood sample, etc.; performing immune repertoire analysis on said cell sample; and comparing the immune repertoire analysis results to a immune repertoire analysis result derived from a reference sample with a known response profile to said agent; and determining likelihood of a response by a subject based on immune repertoire.
Also provided herein is a method of collecting data regarding an immune repertoire, comprising the steps of: collecting data regarding a immune repertoire using any of the methods described herein and sending said data to a computer. A computer can be connected to a sequencing apparatus. Data corresponding to an immune repertoire can further be stored after sending, for example the data can be stored on a computer-readable medium which can be extracted from the computer. Data can be transmitted from the computer to a remote location, for example, via the internet.
The present disclosure also provides methods of characterizing a set of immunological receptors, or fragments thereof, comprising: a) sequencing a population of nucleic acids encoding at least 103, 104, 105, 106, 107, 108, 109, 1019, 1011, 1012 or more immunological receptors, or fragments thereof, or obtaining at least 103, 104, 105, 106, 107, 108, 109, 1010, 1011, 1012 or more sequencing reads from a cellular sample; and b) using sequencing data from step a) to characterize said set of immunological receptors. Some embodiments also comprise applying a statistical metric that characterizes diversity or a clustering analysis to the sequencing data from step a) in order to characterize said set of immunological receptors or fragments thereof. In some cases, sequence variation is represented as a function of sequence frequency. In some cases, the statistical metric used is an entropy metric, an ecology metric, a variation of abundance metric, a species richness metric, or a species heterogeneity metric.
Also provided herein are methods of comparing a set of immunological receptors from an organism with a set of immunological receptors from another organism or from a reference sample. In some cases, (1) immunological receptors from an organism are compared to a reference sample; (2) immunological receptors from a second organism are compared to a reference sample; and the results of (1) are compare to those from (2).
Further provided herein are methods of selecting a treatment for a person afflicted with a condition comprising: a) sequencing a population of nucleic acids encoding immunological receptors or fragments thereof of said person; b) using sequence data from step a to characterize said person's immunological response; and c) selecting a treatment based on said characterization. In some embodiments, the method comprises a method of diagnosing a person suspected of having a condition comprising: a) sequencing a population of nucleic acids encoding immunological receptors, or fragments thereof, of said person; b) using sequence data from step a to characterize said person's immunological response; and c) selecting a treatment or diagnosis based on said characterization.
Also provided herein are software products tangibly embodied in a machine-readable medium, the software product comprising instructions operable to cause one or more data processing apparatus to perform operations comprising: a) clustering sequence data from a plurality of immunological receptors or fragments thereof; and b) providing a statistical analysis output on said sequence data. Also provided herein are software products tangibly embodied in a machine-readable medium, the software product comprising instructions operable to cause one or more data processing apparatus to perform operations comprising: storing sequence data for more than 103, 104, 105, 106, 107, 108, 109, 1010, 1011, 1012 immunological receptors or more than 103, 104, 105, 106, 107, 108, 109, 1010, 1011, 1012 sequence reads.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1A-1B Schematic drawing of the VDJ recombination of an antibody heavy-chain gene, the cDNA amplicon library construction, and the informatics pipeline. The heavy-chain VDJ segment of an antibody is created by recombination, junctional diversity, and hypermutation. We designed primer sets to amplify the expressed heavy-chain mRNA, which were then sequenced and analyzed as outlined. High-throughput sequencing allows determination of the identity of nearly all heavy-chain sequences. (FIG. 1B) Gender and family information for the 14 sequenced zebrafish.
FIGS. 2G-2H and 2J-2K. The entire expressed VDJ repertoires for individual fish FIGS. 2G, 2H, 2J, and 2K (top to bottom). The three axes enumerate all possible V, D, and J values, so each point in three-space is a unique VDJ combination. Both the size of the sphere at each point and the intensity correspond to the number of reads matching that particular VDJ combination. Gray scale is plotted on a linear scale, and the dot size is plotted on a log scale. The upper limits of the scales are set to the most populated VDJ combination for each fish, with PCR bias factored out.
FIG. 3A-3D. VDJ repertoire analysis for all 14 fish. (FIG. 3A) Abundance distribution for each VDJ combination. A small number of VDJ combinations are highly represented in each fish, and most VDJ combinations are represented only at low abundance. The shape of the distribution is common among all of the fish sampled. This histogram is oriented sideways (from left to right) to emphasize that a small number of VDJ combinations are highly abundant, with a distribution that falls off rapidly. (FIG. 3B) Rarefaction analysis of VDJ diversity demonstrates that as one sequences more deeply into a fish, the number of new VDJ classes discovered saturates. (FIG. 3C) Histogram of correlations between VDJ repertoires. The data are collected as histograms and compared to simulated fish which have random VDJ repertoires. The simulated fish have no significant correlations, whereas some of the real fish have high correlations, representing 5 SD outliers of the random model. The highest correlations are from males in the same family (table S5A). (FIG. 3D) When the largest VDJ class in each fish is eliminated, the correlations are reduced and there is a larger proportion of moderate female correlations.
FIG. 4A-4D. Antibody heavy-chain repertoire diversity estimates of 200 bp reads for IgM in all 14 fish. (FIG. 4A) Rarefaction analysis of heavy-chain diversity demonstrates that as one sequences more deeply into a fish, the number of new antibodies discovered (while applying a PCR filter with fraction-of-reads per VJ class set to 95%) saturates at a few thousand. (FIG. 4B) Antibody abundance distributions for each fish for clusters with >2 reads. This histogram is oriented sideways (from left to right) to emphasize that a small number of antibodies (clusters) are highly abundant, with a distribution that falls off rapidly as a power law. The shape of the distribution is universal among all of the fish sampled. The bend at small abundance is caused by variability in the total reads sampled per fish bias-normalization and is not significant. (FIG. 4C) Total antibody diversity estimates for IgM using different criteria. VDJ diversity is the number of VDJ classes per fish, as described in FIG. 3A. Antibodies observed (PCR filter, fraction-of-reads set to 95%; VDJ classes composed only of antibody clusters with two or fewer reads are counted as one) is the number of unique antibodies per fish described in FIG. 4A. Capture-recapture estimate 1 refers to an estimate based on observed antibody abundances. Capture-recapture estimate 2 refers to an estimate using equal probability of all antibodies. Antibodies observed, undercount corrected refers to the upper bound. (FIG. 4D) Histogram of number of fish with shared IgM sequences (corresponding to all clusters of size >2 reads). Hundreds of sequences are shared between pairs of fish, while a few tens of sequences are shared between three fish. Five sequences are shared between four or more fish, and none are shared among all fourteen fish. Sequence comparisons without mutations incorporate differences at the V/D and D/J junctions alone. Convergence on the amino acid level is also plotted.
FIG. 5A-5B. Error distributions along read length of 200 bp for the control library run. FIG. 5A, the total number of bases in the control run at a given position and quality score. FIG. 5B, the probability that given any pair of position/quality-score values, the base is incorrect. In the weighting scheme, all bases with quality score less than or equal to 8 were assumed incorrect.
FIG. 6A-6B. Effect of Cluster radii on the control library of 35 PCR cycles. We examine the “correct” clusters representation among the “incorrect” clusters formed by orphan reads aligning to the same template sequence as a function of the cluster radii. FIG. 6A, fraction of total reads in the correct clusters, singlets and doublets group and in-between the correct clusters as a function of cluster radii. FIG. 6B, fraction of total clusters in the correct clusters, singlets and doublets group and in between the correct clusters as a function of cluster radii. Mean and standard deviation were calculate based on each of the 38 known templates.
FIG. 7A-7B. Rarefaction of diversity estimates (estimate 2 from FIG. 4C) for both IgM and IgZ in all 14 fish.
FIG. 8. VDzJz combinations captured as a function of reads sampled for IgZ in all 14 fish.
FIG. 9. Universal convergence of optimized bias parameters between different sets of training and test data. Each data point represents a single VDJ combination from one of the test fish.
FIG. 10. VDJ representation obtained from two primer sets across six fish using optimized V-exon bias correction parameters.
FIG. 11: An example where two lineages are compared to reveal convergence in mutated sequences. Here, (1)(1)/3 3 3/(3 100) 0.033 ij λ=n×n m=××=and therefore, by the null hypothesis, an identically mutated sequence is considered highly improbable by random chance.
FIG. 12. Flow chart depicting a data pipeline.
FIG. 13. Signature of a VDJ-specific antigen response. Read-weighted VDJ-vectors are correlated with lineage-weighted VDJ vectors from control sets of zebrafish. Strong age- and stimulation-dependence is observed among these subsets.
FIG. 14. Correlation of antibody isotype representation between multiplexed and simplexed PCR.
FIG. 15A-15F. Comparison of antibody isotype frequency between multiplexed and simplexed PCR for each B cell samples taken from six subjects. FIG. 15A, FIG. 15B and FIG. 15C—multiplexed PCR; FIG. 15D, FIG. 15E and FIG. 15F—simplexed PCR. Three age groups, 8-17 years old, 18-30 years old and 70-100 years old.
FIG. 16. Zebrafish vaccination experiment. WIK zebrafish from a single cross were raised in a quarantine system from birth until 9 months of age. Fish were divided into tanks with shared circulating water and underwent immersion vaccination by 6 different combinations of 3 haptens, DNP(12)-BSA, TNP(11)-BSA, and ABA(10)-BSA. Immersions were performed three times at one-week intervals, after which fish were euthanized, flash-frozen, and processed for sequencing.
FIG. 17A-17D: FIG. 17A. Read-weighted VDJ correlations (color bar in upper-right) of 40,000 read-subsampled and lineage-analyzed dataset in 29 fish. FIG. 17B. Read-weighted VJ correlations of those sequences belonging to lineages with at least 5 unique sequences. FIG. 17C. Read-weighted VJ correlations of the 50% of the data from panel (FIG. 17B) with the fewest mutations. FIG. 17D. Read-weighted VJ correlations of the 50% of the data from panel (FIG. 17B) with the most mutations.
FIG. 18: isotype usage in PBMC, naïve B cells (NB) and plasma blasts (PB) acquired at different time points for two individuals received TIV vaccine.
FIG. 19. Fractional composition of each isotype at visit 3 minus fractional composition at visit 1. Color-coded according to vaccine and age-group.
FIG. 20. VDJ correlations using visit 1 (pre-vaccination), with V's, D's, and J's grouped by gene sub-family. Patients to be vaccinated with LAIV are labeled with “L.” and those to be vaccinated with TIV are labeled with “T.” The final part of the name “XtoY” indicates age-range. Twins are indicated.
FIG. 21. VDJ correlations from the same individuals as in FIG. 3 at visit 3 (4 weeks post-vaccination), with V's, D's, and J's grouped by gene sub-family. Twins are indicated.
FIG. 22 is a bar graph showing average mutations per lineage.



DETAILED DESCRIPTION
Methods and compositions are provided for sequence analysis of the immune repertoire. Analysis of sequence information underlying the immune repertoire provides a significant improvement in understanding the status and function of the immune system. For example, sequence information is useful to diagnose disease, immune status, prognosis, and response to therapy. Sequencing is also useful in therapeutic selection and monitoring and in the evaluation of therapeutic candidates.
The invention involves obtaining nucleic acid from a biological sample and sequencing DNA or RNA relating to immunological receptor molecules. Sequencing information obtained from an individual sample is then compared to known sequences (e.g., in a database), to sequences from other samples, or to sequences from the same source over time.
Before the subject invention is described further, it is to be understood that the invention is not limited to the particular embodiments of the invention described below, as variations of the particular embodiments may be made and still fall within the scope of the appended claims. It is also to be understood that the terminology employed is for the purpose of describing particular embodiments, and is not intended to be limiting. In this specification and the appended claims, the singular forms “a,” “an” and “the” include plural reference unless the context clearly dictates otherwise.
Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range, and any other stated or intervening value in that stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this invention belongs. Although any methods, devices and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, illustrative methods, devices and materials are now described.
All publications mentioned herein are incorporated herein by reference for the purpose of describing and disclosing the subject components of the invention that are described in the publications, which components might be used in connection with the presently described invention.
The present invention has been described in terms of particular embodiments found or proposed by the present inventor to comprise preferred modes for the practice of the invention. It will be appreciated by those of skill in the art that, in light of the present disclosure, numerous modifications and changes can be made in the particular embodiments exemplified without departing from the intended scope of the invention. For example, due to codon redundancy, changes can be made in the underlying DNA sequence without affecting the protein sequence. Moreover, due to biological functional equivalency considerations, changes can be made in protein structure without affecting the biological action in kind or amount. All such modifications are intended to be included within the scope of the appended claims.
Immune Repertoire Analysis or Analysis of Sets of Immunological Receptors
Methods of the invention allow characterization of the immune repertoire by sequencing all or a portion of the molecules that make up the immune system, including, but not limited to immunoglobulins, T cell receptors, and MHC receptors. Samples may represent all or a part of the immune repertoire of the individual from which the sample is obtained. As described above, any biological sample is complex in terms of the number of immune receptor sequences that are present. Methods of the invention contemplate high-throughput sequence of the complex array of immune-encoding nucleic acids present in a biological sample. Samples may also be processed to produce a library of nucleic acids (e.g., DNA, RNA, cDNA, mRNA, cRNA) encoding immunological receptors. The library may comprise genomic DNA or RNA or may be a synthetic library created by any method known in the art, including from in vitro random mutagenesis of nucleic acids.
The cells in a sample for analysis may have been separated or enriched prior to analysis, or a sample, e.g. a clinical sample, may be analyzed in the absence of any enrichment.
To obtain the sequence information, the cells present in the sample are lysed and nucleic acids of interest (e.g., genomic DNA, mRNA, cDNA, cRNA, etc.) are collected. Where mRNA is being analyzed, it will generally be converted to cDNA by reverse transcriptase. Primers for cDNA synthesis, as described above, may be selective for the immunological receptor of interest. The immune receptor sequences are then amplified with a set of primers selective for the immunological receptor of interest.
During PCR amplification there is a possibility of introducing a bias, and thus it may be desirable to include a control amplification, and an analysis step to normalize the data. The degree of PCR bias introduced in the sample preparation and sequencing process can be estimated by comparing the representation of the known clones before and after PCR, and determining the bias that is introduced. In the quantitative analyses that follow, these measured biases are used to normalize the data. The control data may also be used to measure sequencing errors. Other methods of controlling for amplification bias include one or more of the following methods (described in more detail herein and in the examples): PCR filter, clustering analysis, and using two or more primer sets.
The amplified pool (or, in some cases, a pool that has not been amplified) of nucleic acids is then subjected to high throughput sequencing (e.g., massively-parallel sequencing). In some embodiments of the invention, the analysis uses pyrosequencing (e.g., massively parallel pyrosequencing) relying on the detection of pyrophosphate release on nucleotide incorporation, rather than chain termination with dideoxynucleotides, and as described by, for example, Ronaghi et al. (1998) Science 281:363; and Ronaghi et al. (1996) Analytical Biochemistry 242:84, herein specifically incorporated by reference. The pyrosequencing method is based on detecting the activity of DNA polymerase with another chemiluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detected which base was actually added at each step. The template DNA is immobile and solutions of selected nucleotides are sequentially added and removed. Light is produced only when the nucleotide solution complements the first unpaired base of the template.
Sequencing platforms that can be used in the present disclosure include but are not limited to: pyrosequencing, sequencing-by-synthesis, single-molecule sequencing, nanopore sequencing, sequencing-by-ligation, or sequencing-by-hybridization. Preferred sequencing platforms are those commercially available from Illumina (RNA-Seq) and Helicos (Digital Gene Expression or “DGE”). “Next generation” sequencing methods include, but are not limited to those commercialized by: 1) 454/Roche Lifesciences including but not limited to the methods and apparatus described in Margulies et al., Nature (2005) 437:376-380 (2005); and U.S. Pat. Nos. 7,244,559; 7,335,762; 7,211,390; 7,244,567; 7,264,929; 7,323,305; 2) Helicos BioSciences Corporation (Cambridge, Mass.) as described in U.S. application Ser. No. 11/167,046, and U.S. Pat. Nos. 7,501,245; 7,491,498; 7,276,720; and in U.S. Patent Application Publication Nos. US20090061439; US20080087826; US20060286566; US20060024711; US20060024678; US20080213770; and US20080103058; 3) Applied Biosystems (e.g. SOLiD sequencing); 4) Dover Systems (e.g., Polonator G.007 sequencing); 5) IIlumina as described U.S. Pat. Nos. 5,750,341; 6,306,597; and 5,969,119; and 6) Pacific Biosciences as described in U.S. Pat. Nos. 7,462,452; 7,476,504; 7,405,281; 7,170,050; 7,462,468; 7,476,503; 7,315,019; 7,302,146; 7,313,308; and US Application Publication Nos. US20090029385; US20090068655; US20090024331; and US20080206764. All references are herein incorporated by reference. Such methods and apparatuses are provided here by way of example and are not intended to be limiting.
The effects of sequencing error or amplification error can be mitigated by the clustering process that allows one to determine a consensus sequence by grouping several reads together, and thus average out the error. The clustering algorithm may be tested on the control data in order to validate parameter choices.
The high throughput sequencing provides a very large dataset, which is then analyzed in order to establish the repertoire. Non-limiting examples of data analysis steps are summarized in the flow chart of FIG. 12.
Grouping Identical Sequences and Preliminary V/J Determination:
Initially sequences may be matched based on perfect identity, and the number of identical reads stored. Quality scores of identical reads are then averaged. V- and J-reference genome sequences (or synthetic reference sequences) are Smith-Waterman aligned to each sequence. (Other reference sequences that could be used are any combination of V-, D-, J- and C-). To avoid edge effects (due to enzymatic trimming) the reference-genome alignment five base-pairs away from the edges of the alignment are given higher weight. Those sequences failing to match minimally to any reference gene segment are discarded. Those that are ambiguous (matching equally to more than one reference genome segment) are retained but are recorded in an output file for being ambiguous (their provisional V-assignment is given to the first enumerated V-segment in the ambiguous subset).
Sequence Subsets Grouped in V/J Combinations where V-Segments are Sufficiently Similar:
After preliminary V/J assignments, genomic-V sequences are aligned to one another, and genomic clusters are formed based on single-linkage clustering with a threshold (e.g., 6 bp-distance threshold). Sequences grouped under V/J combinations with V's belonging to the same cluster are grouped for pairwise alignment.
Pairwise Alignment:
Pair-wise alignment of sequences can be achieved with a specific algorithm, e.g., a quality-score-weighted Smith-Waterman algorithm. With the start positions of the alignment fixed (due to common reverse primers), the alignment grid is confined to the area less than or equal to a specific number of base pairs (e.g., 9 bp) off the diagonal (effectively limiting the number of admissible gap-errors or deletion-errors to 9 on a single read length).
Pairwise Distance Matrices:
Matrices such as Smith-Waterman distance matrices for each V/J grouping can be outputted to text files for later reference.
Subsampling/Rarefaction:
Pre-determined sampling depths can be used to randomly select reads across all V/J combinations. Using printed distance matrices, sub-matrices are assembled and used for clustering.
Clustering and Consensus Determination:
Seeded quality-threshold clustering is performed by seeding clusters with the sequence i that maximizes the centrality measure ci=Σjexp(−dij) where dij is the alignment distance between i and all sequences j. Clustering then proceeds by adding to the cluster whichever sequence minimally increases the diameter of the cluster (ie the maximum distance between any two members). Once no sequence can be added without increasing the diameter above a defined threshold, cluster-formation terminates. Consensus sequences for each cluster are determined by sequence-vote: if there is a sequence with the most identical reads corresponding to it, that sequence is made the consensus. Otherwise the consensus is assigned the sequence that maximizes the centrality measure, above, relative to all other members of the same cluster.
Lineage Analysis:
After identical sequences have been grouped (with read-number/abundance stored), sequences containing stop-codons, ambiguous bases, or gaps relative to the reference genome are discarded. Junctional regions (the end of the V-encoded region to the beginning of the J-encoded region) are determined by using a moving window, whose size is equal to its distance from the end of the genomic exon, to find the furthest location from the end of each junction at which sequence-identity dropped below 50%. The junctional boundary is then defined as the furthest occurrence of a mismatch/insertion/deletion within the window (see Example 1)
Any two sequences with junction boundaries varying by at most one nucleotide and having greater than or equal to 80% identity at the VDJ junction are allowed to form single-linkage clusters. These clusters allow sequences to “chain”, so that multiple sequences that differ in increments from one another can be traced back to the original un-mutated sequence. Sequences retain their identity, but the clusters they form defined hypothetical lineages. Whichever member sequence has the fewest differences relative to the reference genome (away from the junction as illustrated above) is defined as the naïve sequence of the lineage. Mutations are determined by direct comparison to this sequence. Similar methods can be used to determine V, D, J, C, VJ, VDJ, VJC, VDJC lineage usage or diversity.
Final VDJ Assignment:
For clustered sequences, the consensus is aligned to V and J segments as in the preliminary assignments (or C-, D-segments as appropriate). The junctions derived using the same algorithm as above are then aligned to all possible D-segments, with a high gap-open penalty (to prevent the alignment from being significantly affected by non-templated nucleotides). Similar methods can be applied to determine final V, D, J, C, VJ, VDJ, VJC, VDJC assignments.
Diversity Determination, Rarefaction, PCR Filter:
Control measurements show clustered 250 bp read-length sequences having 90% of their reads correctly clustered, roughly what is expected for PCR error rates of 5e-5 per base pair per cycle for an effective number of cycles numbering between 20 and 30. Rarefaction controls show clustering correctly accounting for all sequences without PCR, suggesting that “orphan” sequences can be treated as PCR errors alone. This is corroborated by the fact that for PCR-amplified controls, applying the PCR filter with a 90%-of-reads criterion is exactly the point at which diversity counts are allowed to saturate as a function of sequencing depth. Clusters are added to the correct-cluster pool, starting with the most abundant, and adding clusters in decreasing abundance until the top 90% of reads are included, at which point the algorithm terminates. This is done for each V/J (or any other V/J/D/C, etc.) combination independently to avoid bias.
A rough estimate for total diversity, T, can be derived from knowing the distribution of unique sequences, Prob(x), over all abundance x




  
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VDJ Lineage Diversity:
VDJ usage is enumerated by the number of observed lineages falling into each VJ, VDJ, VJC, or VDJC (e.g., VDJ) combination at a given read-depth.
VDJ and Unique Sequence Abundance Histograms:
Histograms are plotted by binning VDJ and unique sequence abundances (the latter which is either clustered or has undergone lineage-analysis filtering and grouping) into log-spaced bins.
3D Representation of VJ, VDJ, VJC, or VDJC (e.g., VDJ) Usage:
Repertoires are represented by applying V-, D-, J-, and/or C-segments to different axes on a three-dimensional plot. Using either abundance (generally read number, which can be bias-normalized) or observed lineage diversity, bubbles of varying sizes are used at each V/D/J/C coordinate to represent the total usage of that combination.
Mutation Vs. Sequence Abundance Plots:
After undergoing lineage analysis, unique sequences are binned by read-number (or bias-normalized abundance) into log-spaced bins. For a given abundance-bin, the number of mutations per unique sequence is averaged, giving a mutation vs. abundance curve.
Correlative Measures of V, D, J, C, VJ, VDJ, VJC, VDJC, Antibody Heavy Chain, Antibody Light Chain, CDR3, or T-Cell Receptor) Usage (Pearson, KL Divergence): VJ, VDJ, VJC, or VDJC (e.g., VDJ) combinations are treated as vectors with indexed components vi, weighted by either lineage-diversity or abundance for that VDJ combination. Pearson correlations and KL-divergences between each pair of individuals are then calculated over the indices i.
The results of the analysis may be referred to herein as an immune repertoire analysis result, which may be represented as a dataset that includes sequence information, representation of V, D, J, C, VJ, VDJ, VJC, VDJC, antibody heavy chain, antibody light chain, CDR3, or T-cell receptor usage, representation for abundance of V, D, J, C, VJ, VDJ, VJC, VDJC, antibody heavy chain, antibody light chain, CDR3, or T-cell receptor and unique sequences; representation of mutation frequency, correlative measures of VJ V, D, J, C, VJ, VDJ, VJC, VDJC, antibody heavy chain, antibody light chain, CDR3, or T-cell receptor usage, etc. Such results may then be output or stored, e.g. in a database of repertoire analyses, and may be used in comparisons with test results, reference results, and the like.
After obtaining an immune repertoire analysis result from the sample being assayed, the repertoire can be compared with a reference or control repertoire to make a diagnosis, prognosis, analysis of drug effectiveness, or other desired analysis. A reference or control repertoire may be obtained by the methods of the invention, and will be selected to be relevant for the sample of interest. A test repertoire result can be compared to a single reference/control repertoire result to obtain information regarding the immune capability and/or history of the individual from which the sample was obtained. Alternately, the obtained repertoire result can be compared to two or more different reference/control repertoire results to obtain more in-depth information regarding the characteristics of the test sample. For example, the obtained repertoire result may be compared to a positive and negative reference repertoire result to obtain confirmed information regarding whether the phenotype of interest. In another example, two “test” repertoires can also be compared with each other. In some cases, a test repertoire is compared to a reference sample and the result is then compared with a result derived from a comparison between a second test repertoire and the same reference sample.
Determination or analysis of the difference values, i.e., the difference between two repertoires can be performed using any conventional methodology, where a variety of methodologies are known to those of skill in the array art, e.g., by comparing digital images of the repertoire output, by comparing databases of usage data, etc.
A statistical analysis step can then be performed to obtain the weighted contribution of the sequence prevalence, e.g. V, D, J, C, VJ, VDJ, VJC, VDJC, antibody heavy chain, antibody light chain, CDR3, or T-cell receptor usage, mutation analysis, etc. For example, nearest shrunken centroids analysis may be applied as described in Tibshirani et al. (2002) P.N.A.S. 99:6567-6572 to compute the centroid for each class, then compute the average squared distance between a given repertoire and each centroid, normalized by the within-class standard deviation.
A statistical analysis may comprise use of a statistical metric (e.g., an entropy metric, an ecology metric, a variation of abundance metric, a species richness metric, or a species heterogeneity metric.) in order to characterize diversity of a set of immunological receptors. Methods used to characterize ecological species diversity can also be used in the present invention. See, e.g., Peet, Annu Rev. Ecol. Syst. 5:285 (1974). A statistical metric may also be used to characterize variation of abundance or heterogeneity. An example of an approach to characterize heterogeneity is based on information theory, specifically the Shannon-Weaver entropy, which summarizes the frequency distribution in a single number. See, e.g., Peet, Annu Rev. Ecol. Syst. 5:285 (1974).
The classification can be probabilistically defined, where the cut-off may be empirically derived. In one embodiment of the invention, a probability of about 0.4 can be used to distinguish between individuals exposed and not-exposed to an antigen of interest, more usually a probability of about 0.5, and can utilize a probability of about 0.6 or higher. A “high” probability can be at least about 0.75, at least about 0.7, at least about 0.6, or at least about 0.5. A “low” probability may be not more than about 0.25, not more than 0.3, or not more than 0.4. In many embodiments, the above-obtained information is employed to predict whether a host, subject or patient should be treated with a therapy of interest and to optimize the dose therein.
As described herein, a rarefaction analysis of sequence data obtained by any methods described herein may be employed to estimate the completeness of the measurement of immunological repertoire (or of the set of immunological receptors).
Diagnostics and Prognostics
The invention finds use in the prevention, treatment, detection, diagnosis, prognosis, or research into any condition or symptom of any condition, including cancer, inflammatory diseases, autoimmune diseases, allergies and infections of an organism. The organism is preferably a human subject but can also be derived from non-human subjects, e.g., non-human mammals. Examples of non-human mammals include, but are not limited to, non-human primates (e.g., apes, monkeys, gorillas), rodents (e.g., mice, rats), cows, pigs, sheep, horses, dogs, cats, or rabbits.
Examples of cancer include prostrate, pancreas, colon, brain, lung, breast, bone, and skin cancers. Examples of inflammatory conditions include irritable bowel syndrome, ulcerative colitis, appendicitis, tonsilitis, dermatitis. Examples of atopic conditions include allergy, asthma, etc. Examples of autoimmune diseases include IDDM, RA, MS, SLE, Crohn's disease, Graves' disease, etc. Autoimmune diseases also include Celiac disease, and dermatitis herpetiformis. For example, determination of an immune response to cancer antigens, autoantigens, pathogenic antigens, vaccine antigens, and the like is of interest.
In some cases, nucleic acids (e.g., genomic DNA, mRNA, etc.) are obtained from an organism after the organism has been challenged with an antigen (e.g., vaccinated). In other cases, the nucleic acids are obtained from an organism before the organism has been challenged with an antigen (e.g., vaccinated). Comparing the diversity of the immunological receptors present before and after challenge, may assist the analysis of the organism's response to the challenge.
Methods are also provided for optimizing therapy, by analyzing the immune repertoire in a sample, and based on that information, selecting the appropriate therapy, dose, treatment modality, etc. that is optimal for stimulating or suppressing a targeted immune response, while minimizing undesirable toxicity. The treatment is optimized by selection for a treatment that minimizes undesirable toxicity, while providing for effective activity. For example, a patient may be assessed for the immune repertoire relevant to an autoimmune disease, and a systemic or targeted immunosuppressive regimen may be selected based on that information.
A signature repertoire for a condition can refer to an immune repertoire result that indicates the presence of a condition of interest. For example a history of cancer (or a specific type of allergy) may be reflected in the presence of immune receptor sequences that bind to one or more cancer antigens. The presence of autoimmune disease may be reflected in the presence of immune receptor sequences that bind to autoantigens. A signature can be obtained from all or a part of a dataset, usually a signature will comprise repertoire information from at least about 100 different immune receptor sequences, at least about 102 different immune receptor sequences, at least about 103 different immune receptor sequences, at least about 104 different immune receptor sequences, at least about 105 different immune receptor sequences, or more. Where a subset of the dataset is used, the subset may comprise, for example, alpha TCR, beta TCR, MHC, IgH, IgL, or combinations thereof.
The classification methods described herein are of interest as a means of detecting the earliest changes along a disease pathway (e.g., a carcinogenesis pathway, inflammatory pathway, etc.), and/or to monitor the efficacy of various therapies and preventive interventions.
The methods disclosed herein can also be utilized to analyze the effects of agents on cells of the immune system. For example, analysis of changes in immune repertoire following exposure to one or more test compounds can performed to analyze the effect(s) of the test compounds on an individual. Such analyses can be useful for multiple purposes, for example in the development of immunosuppressive or immune enhancing therapies.
Agents to be analyzed for potential therapeutic value can be any compound, small molecule, protein, lipid, carbohydrate, nucleic acid or other agent appropriate for therapeutic use. Preferably tests are performed in vivo, e.g. using an animal model, to determine effects on the immune repertoire.
Agents of interest for screening include known and unknown compounds that encompass numerous chemical classes, primarily organic molecules, which may include organometallic molecules, genetic sequences, etc. An important aspect of the invention is to evaluate candidate drugs, including toxicity testing; and the like.
In addition to complex biological agents candidate agents include organic molecules comprising functional groups necessary for structural interactions, particularly hydrogen bonding, and typically include at least an amine, carbonyl, hydroxyl or carboxyl group, frequently at least two of the functional chemical groups. The candidate agents can comprise cyclical carbon or heterocyclic structures and/or aromatic or polyaromatic structures substituted with one or more of the above functional groups. Candidate agents can also be found among biomolecules, including peptides, polynucleotides, saccharides, fatty acids, steroids, purines, pyrimidines, derivatives, structural analogs or combinations thereof. In some instances, test compounds may have known functions (e.g., relief of oxidative stress), but may act through an unknown mechanism or act on an unknown target.
Included are pharmacologically active drugs, genetically active molecules, etc. Compounds of interest include chemotherapeutic agents, hormones or hormone antagonists, etc. Exemplary of pharmaceutical agents suitable for this invention are those described in, “The Pharmacological Basis of Therapeutics,” Goodman and Gilman, McGraw-Hill, New York, N.Y., (1996), Ninth edition, under the sections: Water, Salts and Ions; Drugs Affecting Renal Function and Electrolyte Metabolism; Drugs Affecting Gastrointestinal Function; Chemotherapy of Microbial Diseases; Chemotherapy of Neoplastic Diseases; Drugs Acting on Blood-Forming organs; Hormones and Hormone Antagonists; Vitamins, Dermatology; and Toxicology, all incorporated herein by reference. Also included are toxins, and biological and chemical warfare agents, for example see Somani, S. M. (Ed.), “Chemical Warfare Agents,” Academic Press, New York, 1992).
Test compounds include all of the classes of molecules described above, and can further comprise samples of unknown content. Of interest are complex mixtures of naturally occurring compounds derived from natural sources such as plants, fungi, bacteria, protists or animals. While many samples will comprise compounds in solution, solid samples that can be dissolved in a suitable solvent may also be assayed. Samples of interest include environmental samples, e.g., ground water, sea water, mining waste, etc., biological samples, e.g. lysates prepared from crops, tissue samples, etc.; manufacturing samples, e.g. time course during preparation of pharmaceuticals; as well as libraries of compounds prepared for analysis; and the like (e.g., compounds being assessed for potential therapeutic value, i.e., drug candidates).
Samples or compounds can also include additional components, for example components that affect the ionic strength, pH, total protein concentration, etc. In addition, the samples may be treated to achieve at least partial fractionation or concentration. Biological samples may be stored if care is taken to reduce degradation of the compound, e.g. under nitrogen, frozen, or a combination thereof. The volume of sample used is sufficient to allow for measurable detection, for example from about 0.1 ml to 1 ml of a biological sample can be sufficient.
Compounds, including candidate agents, are obtained from a wide variety of sources including libraries of synthetic or natural compounds. For example, numerous means are available for random and directed synthesis of a wide variety of organic compounds, including biomolecules, including expression of randomized oligonucleotides and oligopeptides. Alternatively, libraries of natural compounds in the form of bacterial, fungal, plant and animal extracts are available or readily produced. Additionally, natural or synthetically produced libraries and compounds are readily modified through conventional chemical, physical and biochemical means, and may be used to produce combinatorial libraries. Known pharmacological agents may be subjected to directed or random chemical modifications, such as acylation, alkylation, esterification, amidification, etc. to produce structural analogs.
Some agent formulations do not include additional components, such as preservatives, that may have a significant effect on the overall formulation. Thus, such formulations consist essentially of a biologically active compound and a physiologically acceptable carrier, e.g. water, ethanol, DMSO, etc. However, if a compound is liquid without a solvent, the formulation may consist essentially of the compound itself.
Databases of Expression Repertoires and Data Analysis
Also provided are databases of immune repertoires or of sets of immunological receptors. Such databases can typically comprise repertoires results derived from various individual conditions, such as individuals having exposure to a vaccine, to a cancer, having an autoimmune disease of interest, infection with a pathogen, and the like. Such databases can also include sequences of immunological receptors derived from synthetic libraries, or from other artificial methods. The repertoire results and databases thereof may be provided in a variety of media to facilitate their use. “Media” refers to a manufacture that contains the expression repertoire information of the present invention. The databases of the present invention can be recorded on computer readable media, e.g. any medium that can be read and accessed directly by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; and hybrids of these categories such as magnetic/optical storage media. One of skill in the art can readily appreciate how any of the presently known computer readable mediums can be used to create a manufacture comprising a recording of the present database information. “Recorded” refers to a process for storing information on computer readable medium, using any such methods as known in the art. Any convenient data storage structure may be chosen, based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g. word processing text file, database format, etc.
As used herein, “a computer-based system” refers to the hardware means, software means, and data storage means used to analyze the information of the present invention. The minimum hardware of the computer-based systems of the present invention comprises a central processing unit (CPU), input means, output means, and data storage means. A skilled artisan can readily appreciate that any one of the currently available computer-based system are suitable for use in the present invention. The data storage means may comprise any manufacture comprising a recording of the present information as described above, or a memory access means that can access such a manufacture.
A variety of structural formats for the input and output means can be used to input and output the information in the computer-based systems of the present invention. Such presentation provides a skilled artisan with a ranking of similarities and identifies the degree of similarity contained in the test expression repertoire.
A scaled approach may also be taken to the data analysis. For example, Pearson correlation of the repertoire results can provide a quantitative score reflecting the signature for each sample. The higher the correlation value, the more the sample resembles a reference repertoire. A negative correlation value indicates the opposite behavior. The threshold for the classification can be moved up or down from zero depending on the clinical goal.
To provide significance ordering, the false discovery rate (FDR) may be determined. First, a set of null distributions of dissimilarity values is generated. In one embodiment, the values of observed repertoires are permuted to create a sequence of distributions of correlation coefficients obtained out of chance, thereby creating an appropriate set of null distributions of correlation coefficients (see Tusher et al. (2001) PNAS 98, 5118-21, herein incorporated by reference). The set of null distribution is obtained by: permuting the values of each repertoire for all available repertoires; calculating the pairwise correlation coefficients for all repertoire results; calculating the probability density function of the correlation coefficients for this permutation; and repeating the procedure for N times, where N is a large number, usually 300. Using the N distributions, one calculates an appropriate measure (mean, median, etc.) of the count of correlation coefficient values that their values exceed the value (of similarity) that is obtained from the distribution of experimentally observed similarity values at given significance level.
The FDR is the ratio of the number of the expected falsely significant correlations (estimated from the correlations greater than this selected Pearson correlation in the set of randomized data) to the number of correlations greater than this selected Pearson correlation in the empirical data (significant correlations). This cut-off correlation value may be applied to the correlations between experimental repertoires.
Using the aforementioned distribution, a level of confidence is chosen for significance. This is used to determine the lowest value of the correlation coefficient that exceeds the result that would have obtained by chance. Using this method, one obtains thresholds for positive correlation, negative correlation or both. Using this threshold(s), the user can filter the observed values of the pairwise correlation coefficients and eliminate those that do not exceed the threshold(s). Furthermore, an estimate of the false positive rate can be obtained for a given threshold. For each of the individual “random correlation” distributions, one can find how many observations fall outside the threshold range. This procedure provides a sequence of counts. The mean and the standard deviation of the sequence provide the average number of potential false positives and its standard deviation.
The data can be subjected to non-supervised hierarchical clustering to reveal relationships among repertoires. For example, hierarchical clustering may be performed, where the Pearson correlation is employed as the clustering metric. Clustering of the correlation matrix, e.g. using multidimensional scaling, enhances the visualization of functional homology similarities and dissimilarities. Multidimensional scaling (MDS) can be applied in one, two or three dimensions.
The analysis may be implemented in hardware or software, or a combination of both. In one embodiment of the invention, a machine-readable storage medium is provided, the medium comprising a data storage material encoded with machine readable data which, when using a machine programmed with instructions for using said data, is capable of displaying a any of the datasets and data comparisons of this invention. Such data may be used for a variety of purposes, such as drug discovery, analysis of interactions between cellular components, and the like. In some embodiments, the invention is implemented in computer programs executing on programmable computers, comprising a processor, a data storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Program code is applied to input data to perform the functions described above and generate output information. The output information is applied to one or more output devices, in known fashion. The computer may be, for example, a personal computer, microcomputer, or workstation of conventional design.
Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Each such computer program can be stored on a storage media or device (e.g., ROM or magnetic diskette) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The system may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.
A variety of structural formats for the input and output means can be used to input and output the information in the computer-based systems of the present invention. One format for an output tests datasets possessing varying degrees of similarity to a trusted repertoire. Such presentation provides a skilled artisan with a ranking of similarities and identifies the degree of similarity contained in the test repertoire.
Storing and Transmission of Data
Further provided herein is a method of storing and/or transmitting, via computer, sequence, and other, data collected by the methods disclosed herein. Any computer or computer accessory including, but not limited to software and storage devices, can be utilized to practice the present invention. Sequence or other data (e.g., immune repertoire analysis results), can be input into a computer by a user either directly or indirectly. Additionally, any of the devices which can be used to sequence DNA or analyze DNA or analyze immune repertoire data can be linked to a computer, such that the data is transferred to a computer and/or computer-compatible storage device. Data can be stored on a computer or suitable storage device (e.g., CD). Data can also be sent from a computer to another computer or data collection point via methods well known in the art (e.g., the internet, ground mail, air mail). Thus, data collected by the methods described herein can be collected at any point or geographical location and sent to any other geographical location.
Reagents and Kits
Also provided are reagents and kits thereof for practicing one or more of the above-described methods. The subject reagents and kits thereof may vary greatly. Reagents of interest include reagents specifically designed for use in production of the above described immune repertoire analysis. For example, reagents can include primer sets for cDNA synthesis, for PCR amplification and/or for high throughput sequencing of a class or subtype of immunological receptors. Gene specific primers and methods for using the same are described in U.S. Pat. No. 5,994,076, the disclosure of which is herein incorporated by reference. Of particular interest are collections of gene specific primers that have at least 2, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 primer sets or more. The gene specific primer collections can include only primers for immunological receptors, or they may include primers for additional genes, e.g., housekeeping genes, controls, etc.
The kits of the subject invention can include the above described gene specific primer collections. The kits can further include a software package for statistical analysis, and may include a reference database for calculating the probability of a match between two repertoires. The kit may include reagents employed in the various methods, such as primers for generating target nucleic acids, dNTPs and/or rNTPs, which may be either premixed or separate, one or more uniquely labeled dNTPs and/or rNTPs, such as biotinylated or Cy3 or Cy5 tagged dNTPs, gold or silver particles with different scattering spectra, or other post synthesis labeling reagent, such as chemically active derivatives of fluorescent dyes, enzymes, such as reverse transcriptases, DNA polymerases, RNA polymerases, and the like, various buffer mediums, e.g. hybridization and washing buffers, prefabricated probe arrays, labeled probe purification reagents and components, like spin columns, etc., signal generation and detection reagents, e.g. streptavidin-alkaline phosphatase conjugate, chemifluorescent or chemiluminescent substrate, and the like.
In addition to the above components, the subject kits will further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc. Yet another means would be a computer readable medium, e.g., diskette, CD, etc., on which the information has been recorded. Yet another means that may be present is a website address which may be used via the internet to access the information at a removed, site. Any convenient means may be present in the kits.
The above-described analytical methods may be embodied as a program of instructions executable by computer to perform the different aspects of the invention. Any of the techniques described above may be performed by means of software components loaded into a computer or other information appliance or digital device. When so enabled, the computer, appliance or device may then perform the above-described techniques to assist the analysis of sets of values associated with a plurality of genes in the manner described above, or for comparing such associated values. The software component may be loaded from a fixed media or accessed through a communication medium such as the internet or other type of computer network. The above features are embodied in one or more computer programs may be performed by one or more computers running such programs.
Software products (or components) may be tangibly embodied in a machine-readable medium, and comprise instructions operable to cause one or more data processing apparatus to perform operations comprising: a) clustering sequence data from a plurality of immunological receptors or fragments thereof; and b) providing a statistical analysis output on said sequence data. Also provided herein are software products (or components) tangibly embodied in a machine-readable medium, and that comprise instructions operable to cause one or more data processing apparatus to perform operations comprising: storing sequence data for more than 102, 103, 104, 105, 106, 107, 108, 109, 1010, 1011, or 1012 immunological receptors or more than 102, 103, 104, 105, 106, 107, 108, 109, 1010, 1011, or 1012 sequence reads.
In some examples, a software product (or component) includes instructions for assigning the sequence data into V, D, J, C, VJ, VDJ, VJC, VDJC, or VJ/VDJ lineage usage classes or instructions for displaying an analysis output in a multidimensional plot. In some cases, a multidimensional plot enumerates all possible values for one of the following: V, D, J, or C. (e.g., a three-dimensional plot that includes one axis that enumerates all possible V values, a second axis that enumerates all possible D values, and a third axis that enumerates all possible J values). In some cases, a software product (or component) includes instructions for identifying one or more unique patterns from a single sample correlated to a condition. The software product (or component0 may also include instructions for normalizing for amplification bias. In some examples, the software product (or component) may include instructions for using control data to normalize for sequencing errors or for using a clustering process to reduce sequencing errors. A software product (or component) may also include instructions for using two separate primer sets or a PCR filter to reduce sequencing errors.
EXAMPLES
The following examples are offered by way of illustration and not by way of limitation.
Example 1
High-Throughput Sequencing of the Zebrafish Antibody Repertoire
High-throughput sequencing of the variable domain of the antibody heavy chain from 14 zebrafish was performed in order to analyze VDJ usage and antibody sequence. Zebrafish were found to use between 50 and 86% of all possible VDJ combinations and shared a similar frequency distribution, with some correlation of VDJ patterns between individuals. Zebrafish antibodies retained a few thousand unique heavy chains that also exhibited a shared frequency distribution. There was evidence of convergence, in which different individuals made the same antibody. This approach provides insight into the breadth of the expressed antibody repertoire and immunological diversity at the level of an individual organism.
Zebrafish are an ideal model system for studying the adaptive immune system because in evolutionary terms they have the earliest recognizable adaptive immune system whose features match the essential human elements. Like humans, zebrafish have a recombination activating gene (RAG) and a combinatorial rearrangement of V, D, and J gene segments to create antibodies. They also have junctional diversity during recombination and somatic hypermutation of antibodies to improve specificity, and the organization of their immunoglobulin (Ig) gene loci approximates that of human. In addition, the zebrafish immune system has only  ̃300,000 antibody-producing B cells, making it three orders of magnitude simpler than mouse and five orders simpler than human in this regard.
The antibody repertoire of zebrafish was characterized by analyzing complementarity-determining region 3 (CDR3) of the heavy chain, which contains the vast majority of immunoglobulin diversity and can be captured in a single sequencing read (FIG. 1). The 454 GS FLX high-throughput pyrosequencing technology allowed sequencing of 640 million bases of zebrafish antibody cDNA from 14 zebrafish in four families (FIG. 1B). Zebrafish were raised in separate aquaria for each family and were allowed to have normal interactions with the environment, including the development of natural internal flora. Analysis was performed on the quiescent state of the immune system, a state where the zebrafish had sampled a complex but fairly innocuous environment and had established an equilibrium of normal immune function. mRNA was prepared from whole fish, and we synthesized cDNA using primers designed to capture the entire variable region.
Between 28,000 and 112,000 useful sequencing reads were obtained per fish, and analysis was focused on CDR3 sequences. Each read was assigned V and J by alignment to a reference with a 99.6% success rate (table S3); failures were due to similarity in some of the V gene segments. D was determined for each read by applying a clustering algorithm to all of the reads within a given VJ and then aligning the consensus sequence from each cluster to a reference. D was assigned to 69.6% of reads; many of the unassignable cases had D regions mostly deleted. Both the isotypes that are known to exist in zebrafish (IgM and IgZ) were found, and their relative abundance agrees with previous studies (12). Our analysis focused on IgM, which is the most abundant species; IgZ data are presented in FIGS. S3 and S4 (13).
There are 975 possible VDJ combinations in zebrafish (39 V×5 D×5 J=975 VDJ). In any given fish, the VDJ combination coverage was at least 50% and in some cases at least 86% (FIG. 2). By using subsets of the full data set to perform rarefaction studies, we demonstrated that our sampling of the VDJ repertoire was asymptoting toward saturation (FIG. 3A). Any VDJ classes that may be missing from the data are occurring at frequencies below 10−4 to 10−5. There was a commonality to the frequency distributions of VDJ usage that was independent of the specific VDJ repertoire for individual fish (FIG. 3B). Specifically, the majority of VDJ combinations in each fish were of low abundance, but a similarly small fraction—although different combinations for different fish—were found at high frequencies. This distribution could be used to constrain theoretical models of repertoire development.
The VDJ repertoire is a vector in which each element records the number of reads that map to a particular VDJ class. The dot product between VDJ repertoire vectors measures the degree of correlation between different fish (table 5 and FIG. 3C).
Most fish were uncorrelated in their VDJ repertoires; however, some fish were highly correlated, and three pairs of fish had correlation coefficients in the range 0.62 to 0.75. Some of these correlations appear to derive from the largest VDJ class in the repertoire (table 5A and FIG. 3C). When the fish-fish VDJ correlations were computed in the absence of the largest VDJ class, the largest correlations disappeared, but a new set of correlations appeared between a larger fraction of the fish (table 5B and FIG. 3D). These correlations were mostly weaker than the previous correlations but still well above the statistical noise.
A model for random VDJ repertoire assembly was then created using simulated VDJ distributions that replicated the actual measured distributions and coverage fractions. The correlations in these simulated VDJ repertoires are all near zero, and the probability of two fish having a highly correlated random repertoire is less than 10−6 (FIGS. 3, C and D). Thus, even though the VDJ repertoire is believed to be generated by a series of random molecular events within independent individual cells, in zebrafish the VDJ repertoire appears substantially structured and nonrandom on a global scale. It is possible that the source of this structure is simply convergent evolution, that the fish see a similar enough environment that selection in their quiescent immune systems converges to correlated VDJ usage. It is also possible that this distribution reflects bias in the VDJ recombination mechanisms, which would have important implications for antibody diversity space and would suggest that the number of solutions to a given antigen recognition problem, or at least the number that are readily evolvable, may be much smaller than previously assumed.
Summarizing the VDJ repertoire with a simple count of the number of different VDJ combinations neglects the variation in abundance of different VDJ species. Ecologists have the same problem in characterizing species diversity; they refer to the counting approach as species richness and have developed other methods to characterize variation of abundance, which they term “heterogeneity”. The most popular approach to characterize heterogeneity is based on information theory, specifically the Shannon-Weaver entropy, which summarizes the frequency distribution in a single number. The VDJ repertoire entropies generally varied between 3.1 and 7.7 bits for individual fish. Exponentiating the entropy indicates the effective size of the VDJ repertoire, and this varied between 9 and 200 with an average of 105, or an average effective VDJ repertoire coverage of about 9%. This can be interpreted as the fraction of highly expressed VDJ classes.
Whereas the VDJ repertoire provides a coarse view of immunological diversity, each VDJ class can contain a large number of distinct individual antibodies that differ as a result of hypermutations and junctional changes. The antibody repertoire was characterized by using quality threshold clustering of Smith-Waterman alignments to group similar reads together; each cluster defining an antibody. Performing this analysis on control data with well-defined sequence clones allowed calibration of the clustering algorithm and separation of true hypermutation diversity from sequencing errors. Many VDJ combinations included a large number of distinct antibodies. The overall distribution of the abundances of the antibodies followed an apparent power law with scaling parameter 2.2, and this was consistent among all fish over two decades (FIG. 4B). This behavior may represent an important signature of the underlying dynamics of the adaptive immune system. It was not observed for either the control data or the VDJ distributions, and thus we ruled out the possibility that it is an artifact of polymerase chain reaction (PCR) bias.
There are several ways to use this data to estimate the number of unique antibodies per fish. The first is to perform rarefaction studies and determine whether the number of independent clusters tends to saturate. Results indicated that the saturation occurs at between  ̃1200 and 3500 unique antibodies per fish (FIG. 4A). Another way is by applying approaches used in ecology to estimate population sizes and diversity—sample and resample techniques. This yielded an estimate of between 1200 and 3700 unique antibodies per fish, whether applied blindly or using knowledge of the antibody abundance distributions (FIG. 4C). Both approaches are lower bounds on the true antibody diversity because antibodies that differ by only one or two mutations will be incorporated into the same cluster. This effect was corrected for by reanalyzing the data within each cluster with zero error tolerance, only matching exact reads. The largest clusters each had several subclusters with more than two reads each, and the control sequence data indicated that probably half of those clusters are real while the other half are artifacts due to sequencing error. By combining this stringent method of finding small differences in common sequences with the more permissive method of clustering rare sequences with less similarity together (thereby having tolerance to sequencing errors on rare transcripts), The upper limit of heavy-chain antibody diversity is within 50% of the lower bound estimates, or between 5000 and 6000 antibodies in an individual fish.
In order to determine how often repertoires converged to the same antibody, we searched for sequences that are shared between fish. Although there were no antibodies common to all fish, some antibodies were shared between smaller groups of fish (FIG. 4D). These cases of convergent evolution were more frequent than one would expect from a random usage model, with P values as low as 10−15. Unexpectedly, different individuals shared heavy chains that were identical in the region we sequenced, even up to hypermutation. Specifically, there were 254 unique sequences shared between two fish and 2 unique sequences shared between five fish. These data illustrate the powerful forces of selection and perhaps can be used to estimate evolutionary dynamics in this system.
The abundance distributions of both the VDJ repertoire and antibody heavy-chain diversity were similar between individuals, that VDJ usage is not uniform, that individuals can have highly correlated VDJ repertoires, and that convergent evolution of identical heavy-chain sequences is unexpectedly common.
Similar measurements are made on mice and humans. These organisms use the same molecular mechanisms for repertoire generation as fish, and thus can be similarly profiled.
Methods
Zebrafish.
14 six-month-old wild type WIK zebrafish were collected from 4 different families. Fish were euthanized according to an animal protocol approved by the Stanford University administrative panel on laboratory animal care and snap frozen in liquid nitrogen and stored in −80° C. Fish gender was determined by scoring the morphological traits and confirmed by quantifying the differential expression level of two splice variants of the vasa gene as previously described.
mRNA Preparation.
Each whole fish was homogenized in the presence of TRIzol® Reagent using a TissueLyzer (Qiagen, Valencia, Calif.). Total RNA from each fish was purified using TRIzol® Plus RNA Purification System (Invitrogen, Carlsbad, Calif.). The mRNA was further purified using Oligotex mRNA Kit (Qiagen, Valencia, Calif.). Manufacture's protocols were followed during these processes and the concentrations of the total RNA and the mRNA were determined using a Nanodrop spectrophotometer.
Primer Design.
The zebrafish heavy-chain locus was previously described by Danilova et al (Danilova, Hohman et al. 2000; Danilova, Bussmann et al. 2005). The consensus leader sequences for 39 functional V gene segments of the zebrafish heavy-chain were used to design the 27 forward primers (set number 2). The first 100 bp of the IgM and IgZ constant domain were used to design the reverse primers. A second, independent primer set, based on the consensus leader and frame region 1 sequences, was designed in order to test PCR bias. Gene specific primers were also designed for the reverse transcription step; these were located about 50 bp downstream from the PCR reverse primers.
cDNA Synthesis and PCR.
cDNA was synthesized using SuperScriptTM III reverse transcriptase (Invitrogen, Carlsbad, Calif.). A quarter of the total mRNA purified from each fish was split into 8 cDNA synthesis reactions with both the primers for IgM and IgZ constant regions and SUPERase•InTM (Ambion, Austin, Tex.). RNase H (Invitrogen Carlsbad, Calif.) was added to each reaction to remove RNA at the end of the cDNA synthesis step. All enzyme concentrations, reaction volumes and the incubation temperature were based on the manufacturer's protocol for synthesis of cDNA from up to 500 ng of mRNA using gene specific primers.
Each cDNA synthesis reaction (20 μl) was split into two PCR reactions, and a total of 16 PCR reactions were set up for each fish. Each of the 27 forward primers was added so that each V segment was represented by a final concentration of 200 nM primer. Since some primers covered multiple V gene segments, their concentration was in proportion to the number of V segments. Both reverse primers were added at a concentration of 7.8 μM. The PCR program began with an initial denaturation at 94° C. for 2 minutes, followed by 28 cycles of denaturation at 94° C. for 30 s, annealing of primer to DNA at 60° C. for 30 s, and extension by Platinum® Taq DNA Polymerase High Fidelity (Invitrogen, Carlsbad, Calif.) at 68° C. for 2 minutes. PCR products were cleaned using QIAquick PCR Purification Kit (Qiagen, Valencia, Calif.) and the concentration was measured using the nanodrop spectrophotometer.







TABLE 1







Sequencing primer set 2















V gene





V gene
segment


SEQ ID
Name
Sequence
segment
amplicon (bp)





SEQ ID NO: 1
ZVH 4-1
TGGTCTCCTCTGCCTTTTGT
 5.3
362


 


SEQ ID NO: 2
ZVH 4-2
AACCATGATCGCCTCATCTC
 5.4, 5.8
362, 359


 


SEQ ID NO: 3
ZVH 4-3
GATGGCAACAACATCCTGTG
 7.1
314


 


SEQ ID NO: 4
ZVH 4-4
TGCATTTCAGTTCTGCTGCT
 8.2, 8.3,
346, 343, 343





 8.4



 


SEQ ID NO: 5
ZVH 4-5
ACGAATGCAGGAGTCAGACA
 4.1
307


 


SEQ ID NO: 6
ZVH 4-6
TGTTTCAACTGTTCGTGGTCA
 1.1, 1.2
310, 311


 


SEQ ID NO: 7
ZVH 4-7
TGGAGTTGTGTTGATGATGATT
 1.3
326


 


SEQ ID NO: 8
ZVH 4-8
TTCATATGCACATGGTCAGTCA
 1.4
302


 


SEQ ID NO: 9
ZVH 4-9
TGTGGTGATTGTCTTTCAAGG
 2.1, 2.2
349, 332


 


SEQ ID NO: 10
ZVH 4-10
TGGAAAAGGAGTCAAAAAGCAT
 2.3
386


 


SEQ ID NO: 11
ZVH 4-11
GCTTTTGTCATGTTTGCTCTCA
 3.2
331


 


SEQ ID NO: 12
ZVH 4-12
GCTTACTGCTGCTCTCATTCAG
 4.3, 4.8,
339, 339, 336





 4.9



 


SEQ ID NO: 13
ZVH 4-13
TTTCTGCTGCTGTGCTTTAC
 4.5, 4.7
343, 334


 


SEQ ID NO: 14
ZVH 4-14
CTGCTGTTTTCATTGGCCTTA
 4.1
337


 


SEQ ID NO: 15
ZVH 4-15
GGTTTATACTGTCAAGGCATGG
 4.2
307


 


SEQ ID NO: 16
ZVH 4-16
CAGCCTCAAGATGAAGAATGC
 4.6
350


 


SEQ ID NO: 17
ZVH 4-17
CTAGTGCTGTTTCTGGCAGT
 5.1, 5.7
328, 328


 


SEQ ID NO: 18
ZVH 4-18
CATGATCACCTCATCTCTCTGC
 5.2, 5.5
356, 359


 


SEQ ID NO: 19
ZVH 4-19
CATGATTCTGAGCATTTTATCATGT
 6.1
329


 


SEQ ID NO: 20
ZVH 4-20
CAATAATCAACTCACTCCTGCTG
 6.2
345


 


SEQ ID NO: 21
ZVH 4-21
CTGCGTCCAGTGTATATTCCA
 8.1
315


 


SEQ ID NO: 22
ZVH 4-22
TGTATTGACTGTCAGGTTGTGC
 9.2, 9.4
304, 304


 


SEQ ID NO: 23
ZVH 4-23
TCTTTCTGCAGTTGGCAG
 9.1, 9.3
330, 334


 


SEQ ID NO: 24
ZVH 4-24
TCTCAAAGTTGTTGGTGTCAGA
10.1
313


 


SEQ ID NO: 25
ZVH 4-25
CTCTCTAAACAAGTGCAAAGGTC
11.1
321


 


SEQ ID NO: 26
ZVH 4-26
TGGACCTTAAACTTAACTGTCTG
11.2
360


 


SEQ ID NO: 27
ZVH 4-27
CCATATGTTTCTGGCATCTCCC
13.2
308


 


SEQ ID NO: 28
C5
TGCACTGAGACAAACCGAAG
C-μ
N/A


 


SEQ ID NO: 29
C6
TCAGAGGCCAGACATCCAAT
C-ζ
N/A


 


SEQ ID NO: 30
VhuCc5
TGATTGACCCATCAGAACCA
C-μ
N/A


 


SEQ ID NO: 31
VhzCc6
GAATGCTGGGTGACGTTTTT
C-ζ
N/A
















TABLE 2







sequencing primer set 1















V gene segment


SEQ ID
Index
Sequence
V gene segment
amplicon (bp)














SEQ ID NO: 1
1
TGGTCTCCTCTGCCTTTTG
 5.3
362




T




 


SEQ ID NO: 32
2
GTCTGGAGTCCATCCCTTC
 4.5, 4.7
297, 297




A




 


SEQ ID NO: 3
3
GATGGCAACAACATCCTGT
 7.1
314




G




 


SEQ ID NO: 4
4
TGCATTTCAGTTCTGCTGC
 8.2, 8.3, 8.4
346, 343, 343




T




 


SEQ ID NO: 33
5
ATTGTCTTGAGGCCTGGTC
 5.2, 5.4, 5.5, 5.8
271, 274, 274, 271




A




 


SEQ ID NO: 34
6
ACCTGTGCATGCAGTGGAT
 9.1, 9.3
229, 233




T




 


SEQ ID NO: 35
7
CTGTGCCTGCAGTGGATTT
 9.2, 9.4
227, 227




A




 


SEQ ID NO: 36
8
TCATCAAACCAGGCAATGA
11.1, 11.2
271, 270




A




 


SEQ ID NO: 37
9
TGGAGTCGATTGAAAGCTC
 4.3, 4.8, 4.9
291, 291, 288




A




 


SEQ ID NO: 38
10
CGTCCTGATGACTCTCTCA
 5.1, 5.7
256, 256




CTATC




 


SEQ ID NO: 39
11
TGTGACAAAGAGACCTGGA
 2.1, 2.2, 2.3
289, 272, 286




GAA




 


SEQ ID NO: 40
12
GTTTCTGGACTCTCCCTTG
 1.4
220




C




 


SEQ ID NO: 41
13
CAATCTCTGGATTCGGTGG
 3.2
228




T




 


SEQ ID NO: 42
14
TTTCCTGTAGGGGATCAGG
 4.1
240




A




 


SEQ ID NO: 43
15
GCCTGGAGAAACTCTGACT
 4.2, 4.3, 4.6, 4.8
257, 260, 245, 260




CTG




 


SEQ ID NO: 2
16
AACCATGATCGCCTCATCT
 5.4, 5.8
362, 359




C




 


SEQ ID NO: 44
17
AACCAGGAGAGTCCCATTC
 6.1
261




A




 


SEQ ID NO: 45
18
CTGGTATGTACCGCCTCTG
 6.2
241




G




 


SEQ ID NO: 46
19
CTGTGGTGAAAAGACCAG
 1.1, 1.2, 1.3
269, 270, 267




GAG




 


SEQ ID NO: 47
20
CTGAGGCAGTGACCATCAA
10.1
279,




A




 


SEQ ID NO: 48
21
TCCCCTGCACAGATTAAAG
13.2
278




C




 


SEQ ID NO: 49
22
TGCAGGAGTCAGACACTG
14.1
302




GT









454 Library Preparation and Sequencing.
About 2 μg of QIAquick cleaned PCR product for each fish was used to start the 454 library preparation process. AMPure SPRI beads (Agencourt, Beverly, Mass.) were used to concentrate PCR product and remove the remaining primers. 454 FLX DNA library construction protocol was followed for all samples. Briefly, double stranded DNA was end polished and ligated to sequencing adaptors which contained a molecular identifier (MID, a nucleotide based barcode system). This allowed us to multiplex the sequencing plate and also served as an internal control. The rest of the Roche 454 protocol was followed which includes library immobilization, fill-in reaction and single stranded template DNA (sstDNA) library isolation. The sstDNA was quantified using a digital-PCR method developed in our lab (White R. A. 2009), which gave the absolute count of DNA molecules in the library. This allowed us to eliminate the manufacturer's suggested titration run. 16 emulsion PCR reactions were prepared for each fish with a ratio of 0.3 molecules per DNA capture bead. Two-region masks were used on the sequencing plate.







TABLE 3







Fraction of reverse reads assigned for each V, D and J gene segment and VDJ


combination coverage measured for both IgM and IgZ.















Control Library Construction.
We performed control experiments using a mixture of cloned immunoglobulin genes that covered all possible V gene segments. The degree of PCR bias introduced in the sample preparation and sequencing process was estimated by comparing the representation of the known clones before and after PCR, and the bias introduced is about 3-fold up or down from the mean, depending on the specific primer. In the quantitative analyses that follow, these measured biases were used to normalize the data. We also used the control data to measure sequencing errors; the overall per base error rate is about 0.25% without PCR and 0.4% after 35 rounds of PCR. (FIG. 5) The effects of sequencing error are mitigated by the clustering process which allows one to determine a consensus sequence by grouping several reads together, and thus average out the error. The clustering algorithm was tested on the control data in order to validate parameter choices, and we found parameters that resulted in 97% of the reads being put into the correct clusters while allowing at most 3 deviations from the consensus sequence per read (FIG. 6).
PCR product from a zebrafish was cloned using TOPO® TA Cloning Kit for Sequencing (Invitrogen, Carlsbad, Calif.). 38 clones for IgM and 35 clones for IgZ containing different V gene segments were picked and plasmids were purified and sequenced using the Applied Biosystems 3730xl DNA Analyzer (Sequetech, Mountain View, Calif.). 73 plasmids were pooled in equal amount to generate a master mix. This master mix was used as the template to generate PCR product, with samples taken at 0, 15, 25, and 35 cycles. For 0 cycle (unamplified) product, a restriction endonuclease (EcoRI, New England Biolabs, Ipswich, Mass.), was used to digest the plasmid. EcoRI sites only exist on the vector, which is 11 bp away from both sides of the insertion, and do not exist in any of the templates. The insertion was separated from the rest of the vector by running on a 2% agarose gel and excising a band corresponding to 200 to 600 bp, and purified using QIAquick Gel Extraction Kit (Qiagen, Valencia, Calif.). These four libraries went through the same 454 library preparation procedures described above using MIDs and were pooled and sequenced.
Informatics Pipeline.
For rapid analysis of sequenced reads, we developed a core algorithm to align, cluster, find consensus sequences, and measure distributions of important parameters. For tasks such as visual representation, the core algorithm's output worked together with short MATLAB subroutines (both the compiled core algorithm, and all subroutines used in this paper are available upon request). Sequenced reads were filtered for those encompassing the CDR3 and truncated by size to 200 bp. The first 10 bp, corresponding the 454 barcode, were removed. Reads were then aligned to V- and J-exons using the Smith-Waterman algorithm (Smith and Waterman 1981). After partitioning each V/J combinatorial match into its own subset, weighted pairwise Hamming distances (see Control run and its analysis) were assigned again by the Smith-Waterman algorithm. Identical sequences were grouped, and clusters of nonzero radius were formed using an implementation of the quality threshold (QT) method described by Heyer et al (Heyer, Kruglyak et al. 1999). Our implementation, applied to control data in FIG. 6, chose read i as a cluster seed if it held the maximum “adjacency” (defined by the sum Σj exp(−dij), with dij being the distances between all sequences j≠i) among all as-yet unclustered sequences. On each iteration of cluster growth, a read entered the cluster if and only if it minimally increased the cluster's diameter (ie the maximum distance between any two cluster members). Once a further read addition required increasing the cluster diameter beyond the pre-set diameter threshold (twice the radius plotted in FIG. 6), cluster formation terminated.
Consensus sequences were assigned to a cluster using the most represented sequence within that cluster. D-segments and junctions were finally assigned and somatic mutations were counted (see VDJ and somatic mutation determination, below). In order to account for cases in which D-segments had insertions, deletions, or mutations beyond recognition, a sixth class of “ambiguous-D” segment was added to the VDJ diversity (see FIG. 2) for an “extended” repertoire of 39×6×5=1170 combinations.
Control Run and its Analysis.
Sequencing error and sequence-specific bias constituted the largest obstacles in the way of characterizing the system accurately. To quantify these, we constructed two control libraries. We used each of the two primer sets to generate amplicons using different numbers of PCR cycles (0, 15, 25 and 35) and sequenced the products of each using the 454 FLX. We generated 4,500 to 5,000 reads from each PCR cycle sample. By aligning with Sanger-sequenced template sequences, we gauged error rates as functions of both quality scores and position relative to our sequencing primers (FIG. 5). We weighted sequence alignments accordingly for clustering.
We also used our control libraries to calibrate our measurements of VDJ abundance to what we could expect from PCR bias alone. By counting the number of occurrences of a given template at 0 cycles, and the same template at 35 cycles, we achieved a set of normalization coefficients that we used to renormalize the abundances of over- and under-represented VDJ combinations (see Bias parameter optimization and technical replicates). The vast majority of PCR bias occurred in the first 15 cycles, and the bias remained stable up to 35 cycles.
Various thresholds were applied to test the sensitivity of the clustering algorithm. We examined the fraction of reads being correctly assigned to a template (correct clusters), fraction of reads forming clusters that had only one or two reads (singlets and doublets), and fraction of reads in between clusters correctly assigned to a template (in-between clusters) as function of cluster radii. We found at cluster radius 3 that 96.6% of the reads were correctly assigned with 2.8% in singlet and doublet clusters and a further 0.6% in incorrectly assigned larger clusters. Thus, we consistently used three as the radius for other analyses and we required that each cluster have at least three reads to be included as an antibody; VDJ assignments are less ambiguous and we allowed individual reads for those analyses.
The power law observed in zebrafish antibody abundance data was not evident for either the control data or for the VDJ distributions, and thus we ruled out the possibility that it was an artifact of PCR bias.
Bias Parameter Optimization and Technical Replicates.
We designed a second, largely independent PCR primer set that allowed us to perform technical replicates on the same fish samples; in other words the same fish sample was amplified and sequenced with two different primer sets, and then the correlation between the two measurements was calculated. These technical replicates show a high degree of correlation for a given fish (average R2=0.91), and very low correlation between fish (table 4), validating the quantitative analysis.







TABLE 4





Correlations and R2 values on VDJ families for the two primer


sets after the PCR bias normalization. Samples were prepared


independently using two primer sets on the same 6 fish mRNA.


1 - sequencing primer set 1, 2 - sequencing primer set 2. Counting


error for the ith VDJ combination with fractional representation


pi is given by the binomial error






















a2
b2
c2
d2
e2
f2











Un-normalized, 38 V-exons













a1
0.9361
0.2116
0.0209
0.3017
0.0447
0.0144


b1
0.0498
0.699
0.0012
0.0549
0.0091
0.0047


c1
0.1195
0.1114
0.8405
0.1262
0.0373
0.013


d1
0.2422
0.2416
0.0108
0.8223
0.0335
0.3091


e1
0.0353
0.0537
0.0014
0.0411
0.8167
0.0012


f1
0.0123
0.0247
−2E−04
0.3409
4E−05
0.984







Normalized, 38 V-exons













a1
0.9932
0.2037
0.1123
0.3952
0.0769
0.0185


b1
0.1556
0.9403
0.0741
0.2816
0.0646
0.0247


c1
0.1457
0.1382
0.9745
0.2216
0.1922
0.0204


d1
0.3084
0.2953
0.1208
0.8808
0.099
0.3536


e1
0.0924
0.1405
0.1195
0.1821
0.926
0.0104


f1
0.0177
0.034
0.0163
0.3995
0.007
0.9981

















a
b
c
d
e
f











Regression residuals for linear fits on all normalized VDJ data













R2
0.9864
0.8842
0.9497
0.7759
0.8574
0.9961







Replicate correlations of VDJ combinations with <25%


counting error













Un-normalized
0.9419
0.7151
0.9484
0.8258
0.9155
0.9881


Normalized
0.9938
0.941
0.9807
0.9532
0.9333
0.9989







Regression residuals for linear fits on normalized VDJ with <25%


counting error













R2
0.9876
0.8855
0.9618
0.9086
0.871
0.9978





σ1/(Npi) ≡ {square root over ((1 − pi)/(Npi))}{square root over ((1 − pi)/(Npi))}, where N is the total sample size.






Bias parameters were optimized using fish a through f, sequenced separately with two primer sets and control libraries generated from both of these primer sets. Since these six pairs of VDJ representations were independent trials with only the original cDNA library in common, bias normalization coefficients took account of all sequence-specific effects in sample preparation, from amplification and elsewhere (equal loading of the 0 cycle library showed this latter effect to cause no ore than 2-fold differences, well within what might be expected from either pipetting error or spectroscopic measurement of sample concentration).
Data was weighted by read count to minimize the effects of counting error. To illustrate the convergence of bias parameters, we took subsets of the fish data being used to constrain these estimates. As illustrated in FIG. 9, two independent fish subsets of three fish each provide sufficient information about the other subset's bias. Taking all six fish together, optimized bias parameters give the VDJ distribution illustrated in Supplementary FIG. 6, with strong statistical correlations and R2 values (averaging 0.91) provided in Table 4.
VDJ and Somatic Mutation Determination.
Take the following raw sequence from fish c:







SEQ ID NO: 50:


agagactcttcaagcagcagcgtgactctgagtggacagaatatgcagac


 


tgaggacacagctgtgtattattgcgccagagagaatagcgggggccagt


 


actttgactactgggggaaaggaaccaaagtgacagtttcctcagctcaa


 


ccatctgcgccccagtcagtcttcggtttgtctcagtgca






It is aligned first to each V-exon to determine the optimal alignment. In this case Vh5.8 has nearly perfect alignment











(SEQ ID NO: 50)
Observed
--------------agagactcttcaagcagcagcgtgactctga





..............|||||||||||||||||||||||||||||||


(SEQ ID NO: 51)
Vh5.8
gattcacagttagcagagactcttcaagcagcagcgtgactctga


 



Observed
gtggacagaatatgcagactgaggacacagctgtgtattattgcg




|||||||||||||||||||||||||||||||||||||||||||||



Vh5.8
gtggacagaatatgcagactgaggacacagctgtgtattattgcg


 



Observed
ccagagagaatagcgggggccagtactttgactactgggggaaag




||||||||~....................................



Vh5.8
ccagagagt------------------------------------


 



Observed
gaaccaaagtgacagtttcctcagctcaaccatctgcgccccagt




.............................................



Vh5.8
---------------------------------------------


 



Observed
cagtcttcggtttgtctcagtgca




........................



Vh5.8
------------------------






where the character ‘.’ indicates a gap, the character ‘ ̃’ indicates a mismatch, and the ‘I’ character indicates a match.

The segment starting at the first tail-mismatch is then aligned to all J segments and this gives:








(SEQ ID NO: 52)



Observed



aatagcgggggccagtactttgactactgggggaaaggaaccaaa


..........~|~|~||||||||||||||||||||||||||||||


 


(SEQ ID NO: 53)



Jh1



----------actactactttgactactgggggaaaggaaccaaa


 









Observed
gtgacagtttcctcagctcaaccatctgcgccccagtcagtcttc




||||||||||||||||.............................


Jh1
gtgacagtttcctcag-----------------------------


 


Observed
ggtttgtctcagtgca



................


Jh1
----------------






The final tail is assigned identity as the constant region, the last 20 bp being the Cm primer. The program then uses the J segment mismatches to determine the full D-segment. Given the options, this gives us the best alignment of:









(SEQ ID NO: 54)



Observed



aatagcgggggccag



~||||||||||....



 



(SEQ ID NO: 55)



Dh4



tatagcggggg----






with exon Dh4. The code then predicts the junctional regions such that none of the mismatches and gaps count as genuine mutations:










(SEQ ID NO: 54)



Adaptive



aatagcgggggccag



x||||||||||xxxx



 



(SEQ ID NO: 55)



Naive



aatagcgggggccag






where the ‘x’ character represents a junction. The predicted adaptive and naive sequences are then concatenated to the V's and J's to give a final “biological” alignment of









(SEQ ID NO: 56)



Adaptive



agagactcttcaagcagcagcgtgactctgagtggacagaatatgcagact


|||||||||||||||||||||||||||||||||||||||||||||||||||


 


(SEQ ID NO: 57)



Naive



agagactcttcaagcagcagcgtgactctgagtggacagaatatgcagact


 









Adaptive
gaggacacagctgtgtattattgcgccagagagaatagcgggggccagtac




|||||||||||||||||||||||||||||||||x||||||||||xxxx|||


Naive
gaggacacagctgtgtattattgcgccagagagaatagcgggggccagtac


 


Adaptive
tttgactactgggggaaaggaaccaaagtgacagtttcctcagctcaacca



|||||||||||||||||||||||||||||||||||||||||||||||||||


Naive
tttgactactgggggaaaggaaccaaagtgacagtttcctcagctcaacca


 


Adaptive
tctgcgccccagtcagtcttcggtttgtctcagtgca



|||||||||||||||||||||||||||||||||||||


Naive
tctgcgccccagtcagtcttcggtttgtctcagtgca






which has all transient mismatches and gaps removed.

The above sequence is taken to therefore be in its “naive” form with no mutations. Similar analysis applied to another such sequence from the same fish:







(SEQ ID NO: 50)


agagattcttccagcagcagcgtgactctgagtggacagaatatgcagag


 


tgaggacacagctgtgtattattgcgccagagagagcatggagtggcgag


 


cctttgattactggggaaagggaacaatggtcactgtcacatcagctcaa


 


ccatctgcgccccagtcagtcttcggtttgtctcagtgca






which we find corresponds well to genomic sequences Vh5.5, Dh3, and Jh2:









(SEQ ID NO: 56)



Adaptive



agagattcttccagcagcagcgtgactctgagtggacagaatatgcagagtg


|||||~|||||||||||||||||||||||||||||||||||||||||||~||


 


(SEQ ID NO: 57)



Naive



agagactcttccagcagcagcgtgactctgagtggacagaatatgcagactg


 









Adaptive
aggacacagctgtgtattattgcgccagagagagcatggagtggcgagcctt




||||||||||||||||||||||||||||||||xx|||||||||||xx|||||


Naive
aggacacagctgtgtattattgcgccagagagagcatggagtggcgagcctt


 


Adaptive
tgattactggggaaagggaacaatggtcactgtcacatcagctcaaccatct



|||~||||||||||||||||||||||||||||||||||||||||||||||||


Naive
tgactactggggaaagggaacaatggtcactgtcacatcagctcaaccatct


 


Adaptive
gcgccccagtcagtcttcggtttgtctcagtgca



||||||||||||||||||||||||||||||||||


Naive
gcgccccagtcagtcttcggtttgtctcagtgca






where a total of 3 mutations have been counted.

Capture-Recapture.
The task of estimating total populations given limited sample sizes finds its origins in ecology. In a closed ecosystem, each animal “occurs” only once. Therefore, one can make use of the assumption that capturing M individuals out of a population T, setting them free, and then recapturing m occurs at the same rate at which that original M were captured out of the total T. Formally, m/M=MIT, and therefore T=M2/m. The problem becomes more challenging when the abundances themselves have a distribution of which we have only partial knowledge.
In each re-sampling of a fish's antibody repertoire, we are positioned to compare individual subsets of antibodies, and ask how much they overlap. In FIG. 4, we plot several estimates of the total IgM antibody repertoire. In the first case, we plot the total number of unique VDJ combinations we observe (excluding the ambiguous-D case). In the second case, we plot a diversity estimate using the equal-representation assumption above. In the third case, we plot the diversity estimate using the observed distribution of abundances.
This last estimate requires some calculation. Let Prob(i in S2|i in S1) be the probability that we find sequence i in sample S2 given we found that same sequence i in sample S1. This is our definition of a recapture rate, which we write ri. Let Prob(xi) be the probability that sequence I (without any prior information) occurs with an abundance proportional to xi. Then, we can write the recapture rate in terms of this distribution of abundances:




  
    
      
        
          r
          i
        
        =
        
        ⁢
        
          Prob
          ⁡
          
            (
            
              
                i
                ∈
                
                  S
                  2
                
              
              |
              
                i
                ∈
                
                  S
                  1
                
              
            
            )
          
        
      
    
  
  
    
      
        =
        
        ⁢
        
          
            ∑
            
              
                x
                i
              
              =
              x
            
          
          ⁢
          
            
          
          ⁢
          
            
              Prob
              ⁡
              
                (
                
                  
                    i
                    ∈
                    
                      S
                      2
                    
                  
                  |
                  
                    x
                    i
                  
                
                )
              
            
            ⁢
            
              Prob
              ⁡
              
                (
                
                  
                    x
                    i
                  
                  |
                  
                    i
                    ∈
                    
                      S
                      1
                    
                  
                
                )
              
            
          
        
      
    
  
  
    
      
        =
        
        ⁢
        
          
            ∑
            
              
                x
                i
              
              =
              x
            
          
          ⁢
          
            
          
          ⁢
          
            
              Prob
              ⁡
              
                (
                
                  
                    i
                    ∈
                    
                      S
                      2
                    
                  
                  |
                  
                    x
                    i
                  
                
                )
              
            
            ⁢
            
              (
              
                
                  
                    Prob
                    ⁡
                    
                      (
                      
                        
                          i
                          ∈
                          
                            S
                            1
                          
                        
                        |
                        
                          x
                          i
                        
                      
                      )
                    
                  
                  ⁢
                  
                    Prob
                    ⁡
                    
                      (
                      
                        x
                        i
                      
                      )
                    
                  
                
                
                  Prob
                  ⁡
                  
                    (
                    
                      i
                      ∈
                      
                        S
                        1
                      
                    
                    )
                  
                
              
              )
            
          
        
      
    
  
  
    
      
        =
        
        ⁢
        
          
            
              ∑
              
                
                  x
                  i
                
                =
                x
              
            
            ⁢
            
              
            
            ⁢
            
              
                Prob
                ⁡
                
                  (
                  
                    
                      i
                      ∈
                      
                        S
                        2
                      
                    
                    |
                    
                      x
                      i
                    
                  
                  )
                
              
              ⁢
              
                Prob
                ⁡
                
                  (
                  
                    
                      i
                      ∈
                      
                        S
                        1
                      
                    
                    |
                    
                      x
                      i
                    
                  
                  )
                
              
              ⁢
              
                Prob
                ⁡
                
                  (
                  
                    x
                    i
                  
                  )
                
              
            
          
          
            
              ∑
              
                
                  x
                  i
                
                =
                x
              
            
            ⁢
            
              
            
            ⁢
            
              
                Prob
                ⁡
                
                  (
                  
                    
                      i
                      ∈
                      
                        S
                        1
                      
                    
                    |
                    
                      x
                      i
                    
                  
                  )
                
              
              ⁢
              
                Prob
                ⁡
                
                  (
                  
                    x
                    i
                  
                  )
                
              
            
          
        
      
    
  




Let M, as before, be the size of a single sample. Since the probabilities Prob(i in S2|xi) and Prob(i in S1|xi) are themselves functions of both the distribution of abundances and the total diversity of sequences, T:




  
    r
    i
  
  =
  
    
      
        ∑
        
          
            x
            i
          
          =
          x
        
      
      ⁢
      
        
      
      ⁢
      
        
          
            (
            
              1
              -
              
                
                  (
                  
                    1
                    -
                    
                      
                        x
                        ⁡
                        
                          (
                          
                            T
                            ⁢
                            
                              
                                ∑
                                j
                              
                              ⁢
                              
                                
                              
                              ⁢
                              
                                
                                  x
                                  j
                                
                                ⁢
                                
                                  Prob
                                  ⁡
                                  
                                    (
                                    
                                      x
                                      j
                                    
                                    )
                                  
                                
                              
                            
                          
                          )
                        
                      
                      
                        -
                        1
                      
                    
                  
                  )
                
                M
              
            
            )
          
          2
        
        ⁢
        
          Prob
          ⁡
          
            (
            x
            )
          
        
      
    
    
      
        ∑
        
          
            x
            i
          
          =
          x
        
      
      ⁢
      
        
      
      ⁢
      
        
          (
          
            1
            -
            
              
                (
                
                  1
                  -
                  
                    
                      x
                      ⁡
                      
                        (
                        
                          T
                          ⁢
                          
                            
                              ∑
                              j
                            
                            ⁢
                            
                              
                            
                            ⁢
                            
                              
                                x
                                j
                              
                              ⁢
                              
                                Prob
                                ⁡
                                
                                  (
                                  
                                    x
                                    j
                                  
                                  )
                                
                              
                            
                          
                        
                        )
                      
                    
                    
                      -
                      1
                    
                  
                
                )
              
              M
            
          
          )
        
        ⁢
        
          Prob
          ⁡
          
            (
            x
            )
          
        
      
    
  




where the quantity T ΣjxjProb(xj) is proportional to the total number of reads in the pool from which the samples are being taken. Therefore, the recapture rate ri and the distribution over abundances Prob(xi) can be used to uniquely determine the total diversity, T.

Convergence of Mutated Sequences in Different Fish.
Here we construct a simple null hypothesis allowing us to quantify the amount of antibody sequence convergence among our population of fish. Consider a particular naive sequence A shared by fish i and fish j. Looking at the repertoire of mutated sequences derived from this common ancestral sequence, there is a probability




  
    p
    ⁡
    
      (
      r
      )
    
  
  =
  
    
      (
      
        
          3
          r
        
        ⁢
        
          (
          
            m
            r
          
          )
        
      
      )
    
    
      -
      1
    
  




that given m mutable base-pairs of which r are mutated on both, a unique sequence drawn randomly from both fish having A as a common ancestor will be identical.

Threshold analysis in FIG. 6 illustrates that of clusters of size greater than two reads, on average 10% are incorrect. We will conservatively place this at its upper bound of 20%. This allows us to make a simple estimate of the fraction of clusters originating in a given naïve sequence we expect to be truly mutated. Let the observed fraction of unmutated sequences be g0. We then expect the fraction of truly mutated sequences, f≧1, to be

f≧1≈(1−g0)−g0(20/80)=1−5g0/4

which is made zero should it wind up being negative.

There is an additional technical caveat to making hypermutation estimates due both to genomic differences between the zebrafish in this study and the zebrafish reference genome, and the phenomenon of allelic exclusion, whereby the chromosome expressing the antibody heavy chain is determined independently during the development of each progenitor B-cell. This introduces up to two polymorphisms per V/D/J combination per fish.
In order to compensate for the potential overestimate in somatic mutation we developed a way to conservatively estimate of the number of mutated antibodies with minimal knowledge of fish genotypes. We did this by applying two “genomic baselines”, one for each chromosome, to find the minimum possible number of differences between the sequence and the reference genome that could be due to somatic mutation.
For every V/J combination, the minimum detected somatic mutations was set as the first genomic baseline, a. The V/J combination's second genomic baseline, b, was then set to the number of polymorphic differences that would minimize the estimate 1 f≧ above (it can be shown easily that this is equal to the most frequent number of mutations greater than a). The number of sequences with mutations was then set to the total number of sequences with mutation count equal to neither a nor b. The fraction g0, meanwhile, included all sequences with mutation count equal to either a or b.
FIG. 11 illustrates the process by which instances of identically mutated sequences may be evaluated in relation to the null hypothesis wherein all convergence is due to random chance. Pairs of mutated sequences from different fish with the same naive sequence are compared to one another. If we conservatively assume that the dominating effect for convergence in mutated sequences from the same naive ancestor is among those with at most one mutation, then on average the ni×nj mutated pairs of sequences from fish i and j will give us approximately

λ≡ninj/3m=(ninj+cov(ninj))/3m 

unique convergent events (if we assume the number of unique mutated sequences on the two fish are independent variables, the covariance goes to zero).

If we view the generation of instances of convergence as a Poisson process, this gives us a straightforward way to generate p-values, shown in Table 6. Here we take m=100 (which is also conservative, in order to avoid counting the junctional region, where differences from genomic sequence are not counted as somatic mutations).
Taken together, we observe 55 instances of convergent mutated sequences, compared to  ̃8 that would be expected from this model, making the null hypothesis for convergence by random drift completely inadequate to explain the results.
B Cell Counts in Zebrafish.
Although B-cell counts in zebrafish have not been performed directly, in large part due to the difficulty developing seralogical probes for labeling, one can make an estimate based on the existence of lymphocyte counts (of both B-cells and T-cells) and T-cells alone. Zebrafish splenic cell counts range from 4.5×104 to 8×104 and given that T cells comprise approximately 3% (Langenau, Traver et al. 2003) of splenocytes, and lymphocytes comprise 11%, one would predict that B cells comprise approximately 8% of splenocytes, or between 3600 and 6400. Meanwhile, T cells comprise approximately 0.07% of blood cells, lymphocytes comprise 1% and erythrocytes comprise 98%. Since zebrafish have 10 μl of blood and 3×106 erythrocytes per microliter the total blood-borne B cell count should be approximately 0.93% of 3×107, or 3×105. This puts an order of magnitude upper limit on the total possible antibody diversity in a given individual at any point in time.







TABLE 5







VDJome correlations between all 14 fish, across all VDJ combinations (A) and excluding the


most abundant from each fish (B). Families and genders are color coded according to the


legend above. High correlations (>0.5) are shown in red, and moderate (>0.2) are shown in


green




























TABLE 6







Convergence of mutated sequences. The average number of convergent events (A) is


calculated by looking for matching naive sequences in different fish. Every pair of mutated


sequences in different fish with common naive origin is then regarded as an independent


trial, with a probability 1/3m of a match, where m is the number of mutable base-pairs. Of


those sequences that are found to have mutations, the observed number (B) are counted.


The probability that a frequency of convergence at or above the level observed becomes


our p-value. The quantity - log10 p is calculated and shown for every pair of fish in table C.


Any value is considered significant.





















Example 2
Immunization of Zebrafish
In another study using this method, we investigated a simple model of infectious disease by immunizing zebrafish with hapten-conjugated proteins. Antigen-immersion experiments were performed on nine month-old zebrafish over a three-week period. Immersion solutions were prepared using either TNP(11)-BSA (T-5050, Biosearch Technologies), DNP(12)-BSA (D-5050, Biosearch Technologies), and ABA(10)-BSA (A-1200, Biosearch Technologies) dissolved at a total of 210 ug/ml in zebrafish system water. Controls were also set aside without antigen. LPS (lipopolysaccharide, Sigma-Aldrich L2143) was also added to antigen immersion solutions to a total concentration of 70 ug/ml. Zebrafish were first immersed in 4.5% NaCl in system water (see Huising et al. Increased efficacy of immersion vaccination in fish with hyperosmotic pretreatment. Vaccine. 2003 Oct. 1; 21(27-30):4178-93) for 2 minutes, and then placed in antigen/LPS solution for 30 minutes, before being returned to their tanks. This procedure was performed three times over one-week intervals. Fish were terminated one week after final exposure.
RNA was extracted and purified as described previously. cDNA was amplified and underwent 454 sequencing. VDJ profiles were taken from the IgM heavy chain sequences from each fish and fish repertoires were compared within control groups. While correlating lineage diversity-weighted VDJ repertoires produced similar values to what was observed in other zebrafish of the same age-group (see FIG. 13), TNP-stimulated individuals exhibited especially high read-weighted VDJ correlations. These data on VDJ abundance-stereotypy demonstrate diagnosis of an immune stimulus with the methods of the invention.
Example 3
Sequencing Human Antibody Repertoire
We applied the methods of the invention to study the human antibody repertoire in response to influenza vaccination. The first test consisted of B cell samples from subjects that were immunized with either the trivalent inactivated influenza vaccine (TIV) or the live attenuated influenza vaccine (LAIV). Both naïve and plasmablast B cells were sorted from each individual using Fluorescence Activated Cell Sorting (FACS). Naïve cells were sorted based on the surface expression of CD3− CD19+ CD27− CD38−, plasmablasts were sorted based on the surface expression of CD3− CD19+ CD27+ CD38+. The number of cells collected varied from a few thousand to hundreds of thousands. Samples went through RNA purification and cDNAs were synthesized using the reverse transcription primers listed in the table. Two amplification strategies were used to do the PCR, multiplexed and simplexed. In the multiplexed PCR, all 11 forward primers were mixed in the same tube at an equal ratio, along with reverse primers. For the simplexed PCR, cDNA was aliquoted into 11 different tubes with one forward primer, and all reverse primers, in each tube. For each B cell population from each subject, 11 simplexed PCR products were pooled before the 454 library preparation process.
In one 454 run, we sequenced 24 libraries that had been generated from these 6 subjects (2 different cell types from each subject, 2 ways of amplification for each cell type). For most of the antibody isotypes, good correlations existed between multiplexed and simplexed PCR, except for IgE from three samples. These three IgE samples were from plasmablast populations of three subjects. Since the difference appears to stem from different V-primer biases in multiplexed and simplexed PCR, these differences suggest IgE may have a different V gene segment usage from other isotypes. This might be explained by clonal expansion of a small number allergen-specific B cells in these subjects.
We also compared antibody isotype expression in two cell populations for all subjects. It is known that naïve B cells co-express IgM and IgD with IgM dominating the expression. Upon activation, naïve B cell transforms into plasmablast and switch IgM to IgG while down-regulating IgD expression. Using sequencing, we saw the trend of IgM dominating the naïve B cell population with minor IgD expression. However, in plasmablasts, the majority of the reads belong to IgG, except in donor 2 from the 18-30 year-old group. We also noticed surprisingly high amount of IgA expressing B cells (normally localized in mucous membrane) in the plasmablast population.
Reverse Transcription Primers.
Human reverse transcription primers were designed to cover all known antibody heavy chain isotypes, IgA, IgD, IgE, IgG and IgM, where each isotype is covered by one gene specific primer that anneals to the sequence between 35 to 110 bp into constant domain 1 of the heavy chain.
PCR Primers.
V gene leader region sequences were chosen to maximize the full length of the V gene segments when designing the forward PCR primers. Consensus regions were chosen to minimize the number of forward PCR primer while maximizing the coverage for each primer. Forward PCR primers were designed to cover all possible V gene segments listed in IMGT database. Current primer set covers 207 out of 244 (85%) functional V gene segments including polymorphisms. This number increased to 225 (92%) if one allows one mismatch between primers and target sequences.
Reverse PCR primers were designed to cover all known antibody heavy chain isotypes, IgA, IgD, IgE, IgG and IgM. Each isotype is covered by one gene specific primer that anneals upstream of where reverse transcription primer anneals.
PCR products were cleaned and ligated 454 shotgun genomic sequencing adaptor and the rest of the process for Roche 454 shotgun genomic library construction (Roche 454 protocol). The sequencing primer is embedded in the adaptor. Libraries were then quantified and sequenced following Roche 454 shotgun genomic library sequencing protocol.







TABLE 7





Primers for reverse transcription of human anti-


body sequence

















hIgh071609ClgG-RT
SEQ ID NO: 58
GGGAAGTAGTCCTTGACCAG


 


hIgh071609ClgA-RT
SEQ ID NO: 59
GGGGAAGAAGCCCTGGAC


 


hIgh071609ClgM-RT
SEQ ID NO: 60
GGCCACGCTGCTCGTATC


 


hIgh071609ClgE-RT
SEQ ID NO: 61
AGGGAATGTTTTTGCAGCAG


 


hIgh071609ClgD-RT
SEQ ID NO: 62
CCACAGGGCTGTTATCCTTT
















TABLE 8





Forward primers in PCR for human immunoglobulins:

















hIgh062309LR1
SEQ ID NO: 63
cgcagaccctctcactcac


 


hIgh062309LR2
SEQ ID NO: 64
tggagctgaggtgaagaagc


 


hIgh062309LR3
SEQ ID NO: 65
tgcaatctgggtctgagttg


 


hIgh062309LR4
SEQ ID NO: 66
ggctcaggactggtgaagc


 


hIgh062309LR5
SEQ ID NO: 67
tggagcagaggtgaaaaagc


 


hIgh062309LR6
SEQ ID NO: 68
ggtgcagctgttggagtct


 


hIgh062309LR7
SEQ ID NO: 69
actgttgaagccttcggaga


 


hIgh062309LR8
SEQ ID NO: 70
aaacccacacagaccctcac


 


hIgh062309LR9
SEQ ID NO: 71
agtctggggctgaggtgaag


 


hIgh062309LR10
SEQ ID NO: 72
ggcccaggactggtgaag


 


hIgh062309LR11
SEQ ID NO: 73
ggtgcagctggtggagtc
















TABLE 9





Reverse primers in PCR for human immunoglobulins:

















hIgh071609ClgG-PCR
SEQ ID NO: 74
AAGACCGATGGGCCCTTG


 


hIgh071609ClgA-PCR
SEQ ID NO: 75
GAAGACCTTGGGGCTGGT


 


hIgh071609ClgM-PCR
SEQ ID NO: 76
GGGAATTCTCACAGGAGACG


 


hIgh071609ClgE-PCR
SEQ ID NO: 77
GAAGACGGATGGGCTCTGT


 


hIgh071609ClgD-PCR
SEQ ID NO: 78
GGGTGTCTGCACCCTGATA









Example 4
Human T Cell Receptor
T cell receptor is composed of two chains, either α and β or γ and δ chains. Hence, T cells can be categorized into αβ and γδ T cells. All four chains have their own V gene segments and constant genes. The primer set designed here is intended to cover only α and β chains.
Human TCR Reverse Transcription Primers.
The human TCR reverse transcription primers were designed to cover α and β chains. Each chain is covered by one gene specific primer that anneals to the sequence between 400 to 460 bp into constant domain. The long cDNA gives flexibility in the PCR step to tailor the amplicon length according to different sequencing platforms. For example, 400 to 600 bp is preferred for Roche 454 and 200 to 300 bp is preferred for Illumina.
Human TCR PCR Primers.
Consensus region of about 60 bp toward the 3′ of the V gene segments were chosen to design the PCR forward primers. This is constrained by the current read length of Illumina genome sequencer (100 bp). Because TCR does not have somatic hypermutation, therefore the only information that is needed to estimate the diversity is in the VD and DJ junctional region. PCR primers designed here will allow V gene segments identification as well as D and J gene segments to be identified in one Illumina sequencing read. Consensus region were chosen to minimize the number of forward PCR primer while maximize the coverage for each primer. Forward PCR primers were designed to cover all possible V gene segments listed in IMGT database.
For a chain, current primer set covers 94 out of 104 (90%) functional V gene segments as well as a chain V gene segments that have an open reading frame (ORF) with perfect match. Genes with polymorphisms are also included. This number increased to 97 (93%) if one allows one mismatch between primers and target sequences. Part of the 7% uncovered gene segments are due to the fact that the genomic sequences are not documented to their full length.
For β chain, current primer set covers 118 out of 136 (87%) functional V gene segments as well as a chain V gene segments that have an open reading frame (ORF) with perfect match. Genes with polymorphisms are also included. This number increased to 133 (98%) if one allows one mismatch between primers and target sequences. The three uncovered gene segments are due to the fact that those genomic sequences are not documented to their full length.
Reverse PCR primers were designed to cover both α and β chains, one primer for one chain. For Illumina genome sequencer, the gene specific primers anneal to regions that is 120 to 200 bp into constant gene. For Roche 454, the gene specific primers anneal to regions that is 360 to 420 bp into constant gene.
Sequencing Primers.
PCR products will be cleaned and ligated to either Illumina or 454 sequencing adaptor and the rest of the process for either Illumina or Roche 454 library construction (Illumina or Roche 454 protocol). The sequencing primer is embedded in the adaptor. Libraries were then quantified and sequenced following Illumina or Roche 454 sequencing protocol.
To obtain the sequence information, the cells present in the sample are lysed and nucleic acids of interest (e.g., genomic DNA, RNA, etc.) are collected. Where RNA is being analyzed, it will generally be converted to cDNA by reverse transcriptase. Primers for cDNA synthesis, as described above, may be selective for the immune receptor of interest. For example, where the immune receptor is Ig, primer sets of interest may comprise (see human antibody file). Where the immune receptor is the TCR, the primer set may comprise (see separate file for human TCR)
The immune receptor sequences are then amplified with a set of primers selective for the immune receptor of interest. Separate reactions can be performed for a and β chains, or they can be combined in a single tube. Alternatively, each individual cell is used as a PCR reactor, and the α and β chain are ligated within each cell using complementary sequences between α and β chain primers. Then PCR products generated within each cell are pooled and sequenced.)







TABLE 10







PCR forward PCR primer for a chain









name

sequences 5′ to 3′





HuVa4-1
SEQ ID NO: 79
TTCACAACTGGGGGACTCA


 


HuVa4-2
SEQ ID NO: 80
CTCACAGCTGGGGGACACT


 


HuVa4-3
SEQ ID NO: 81
ACTCACAGCTGGGGGATG


 


HuVa4-4
SEQ ID NO: 82
GCCTCACAAGTCGTGGACTC


 


HuVa4-5
SEQ ID NO: 83
CAGCCTGCAGACTCAGCTAC


 


HuVa4-6
SEQ ID NO: 84
GGCAGCAGACACTGCTTCTT


 


HuVa4-7
SEQ ID NO: 85
GACCACAGACTCAGGCGTTT


 


HuVa4-8
SEQ ID NO: 86
GCTCAGTGATTCAGCCACCT


 


HuVa4-9
SEQ ID NO: 87
CCCAGTGATTCAGCCACCTA


 


HuVa4-10
SEQ ID NO: 88
CTCAGCGATTCAGCCTCCTA


 


HuVa4-11
SEQ ID NO: 89
TCCCAGCTCAGTTACTCAGGA


 


HuVa4-12
SEQ ID NO: 90
CAGCCATGCAGGCATCTA


 


HuVa4-13
SEQ ID NO: 91
GCCCAGCCTGGTGATACAG


 


HuVa4-14
SEQ ID NO: 92
CCATACCTAGTGATGTAGGCATCT


 


HuVa4-15
SEQ ID NO: 93
ACATCACAGCCACCCAGAC


 


HuVa4-16
SEQ ID NO: 94
CAACCTGAAGACTCGGCTGT


 


HuVa4-17
SEQ ID NO: 95
TTGCAGCTACTCAACCTGGA


 


HuVa4-18
SEQ ID NO: 96
CCAGACTGGGGACTCAGCTA


 


HuVa4-19
SEQ ID NO: 97
CCCAGCCTGGAGACTCTG


 


HuVa4-20
SEQ ID NO: 98
CCAGCCTGGAGACTCAGC


 


HuVa4-21
SEQ ID NO: 99
AGCCTCCCATCCCAGAGAC


 


HuVa4-22
SEQ ID
CTGCCGTGCATGACCTCT



NO: 100



 


HuVa4-23
SEQ ID
CAAAGGATCCCAGCCTGAA



NO: 101



 


HuVa4-24
SEQ ID
CACAGCCCCTAAACCTGAAG



NO: 102



 


HuVa4-25
SEQ ID
CCGTGCAGCCTGAAGATT



NO: 103



 


HuVa4-26
SEQ ID
GCTTCTCAGCCTGGTGACTC



NO: 104



 


HuVa4-27
SEQ ID
GCTCCAGATGAAAGACTCTGC



NO: 105



 


HuVa4-28
SEQ ID
CTGCCCTTGTGAGCGACT



NO: 106



 


HuVa4-29
SEQ ID
AGCGACGCGGCTGAGTA



NO: 107



 


HuVa4-30
SEQ ID
ACCGACCCGGCTGAGTA



NO: 108



 


HuVa4-31
SEQ ID
TCTGTGCATTGGAGTGATGC



NO: 109



 


HuVa4-32
SEQ ID
GTGCAGTGGAGTGACACAGC



NO: 110



 


HuVa4-33
SEQ ID
TCAGTTCAAGTGTCAGACTCAGC



NO: 111



 


HuVa4-34
SEQ ID
GAAAGACTCAGTTCAAGAGTCAGA



NO: 112



 


HuVa4-35
SEQ ID
CAGTCCAGGTATCAGACTCAGC



NO: 113



 


HuVa4-36
SEQ ID
GGTGCAGCTGTCGGACTC



NO: 114



 


HuVa4-37
SEQ ID
TGCTCAAGAGGAAGACTCAGC



NO: 115



 


HuVa4-38
SEQ ID
GGAGGCAGATGCTGCTGT



NO: 116



 


HuVa4-39
SEQ ID
CCACGCTACGCTGAGAGAC



NO: 117



 


HuVa4-40
SEQ ID
CGTGCTACCTTGAGAGATGCT



NO: 118



 


HuVa4-41
SEQ ID
TCCCTGAGCGACACTGCT



NO: 119



 


HuVa4-42
SEQ ID
caacccatgtgagtgatgct



NO: 120
















TABLE 11







Primers in the reverse transcription for a chain









name

sequences 5′ to 3′





HuCa3′RT-4
SEQ ID NO: 121
cagatctcagctggaccaca
















TABLE 12







PCR reverse primers for a chain










Sequencer
name

sequences 5′ to 3′





Illumina
HuCa3′
SEQ ID
GCACTGTTGCTCTTGAAGTCC



illumina-4
NO: 122



 


454
HuCa3′454-4
SEQ ID
gattaaacccggccactttc




NO: 123
















TABLE 13







PCR forward PCR primer for β chain









name

sequences 5′ to 3′





HuVb4-1
SEQ ID
GGGGACTCGGCCATGTAT



NO: 124



 


HuVb4-2
SEQ ID
GGGGGACTCAGCCGTGTAT



NO: 125



 


HuVb4-3
SEQ ID
GGGGGACACAGCCATGTA



NO: 126



 


HuVb4-4
SEQ ID
GAGGACTCCGCCGTGTATC



NO: 127



 


HuVb4-5
SEQ ID
GCGGGACTCAGCCATGTAT



NO: 128



 


HuVb4-6
SEQ ID
GGACTCGGCCGTGTATCT



NO: 129



 


HuVb4-7
SEQ ID
AGAACCCAGGGACTCAGC



NO: 130



 


HuVb4-8
SEQ ID
CTGGAGGACTCAGCCATGT



NO: 131



 


HuVb4-9
SEQ ID
CTGGAGGATTCTGGAGTTTATTTC



NO: 132



 


HuVb4-10
SEQ ID
AGGAGATTCGGCAGCTTATTT



NO: 133



 


HuVb4-11
SEQ ID
GCTTGAGGATTCAGCAGTGT



NO: 134



 


HuVb4-12
SEQ ID
TTGGTGACTCTGCTGTGTATTTC



NO: 135



 


HuVb4-13
SEQ ID
AGAAGACTCGGCCCTGTATC



NO: 136



 


HuVb4-14
SEQ ID
GGGGACTCAGCCCTGTACT



NO: 137



 


HuVb4-15
SEQ ID
GGGGGACTCAGCTTTGTATTT



NO: 138



 


HuVb4-16
SEQ ID
GGGGACTCGGCCCTTT



NO: 139



 


HuVb4-17
SEQ ID
GACGACTCGGCCCTGTATC



NO: 140



 


HuVb4-18
SEQ ID
GGACTCGGCCCTGTATCTC



NO: 141



 


HuVb4-19
SEQ ID
TCAGTGACTCTGGCTTCTATCTC



NO: 142



 


HuVb4-20
SEQ ID
CCTCCTCCCAGACATCTGTA



NO: 143



 


HuVb4-21
SEQ ID
CGCTCCCAGACATCTGTGTAT



NO: 144



 


HuVb4-22
SEQ ID
GCTACCAGCTCCCAGACATC



NO: 145



 


HuVb4-23
SEQ ID
CCCTCTCAGACATCTGTGTACTT



NO: 146



 


HuVb4-24
SEQ ID
CCCTCCCAAACATCTGTGTA



NO: 147



 


HuVb4-25
SEQ ID
CCTCCCAGACATCTGTGTACTT



NO: 148



 


HuVb4-26
SEQ ID
CCCTCCCAGACATCTGTATACTT



NO: 149



 


HuVb4-27
SEQ ID
CCCAACCAGACCTCTCTGT



NO: 150



 


HuVb4-28
SEQ ID
CCAACCAGACATCTATGTACCTCT



NO: 151



 


HuVb4-29
SEQ ID
CCCTCACATACCTCTCAGTACC



NO: 152



 


HuVb4-30
SEQ ID
CCCAACCAGACAGCTCTTTAC



NO: 153



 


HuVb3-31
SEQ ID
GAACCCGACAGCTTTCTATCTC



NO: 154



 


HuVb4-32
SEQ ID
TGCCCATCCTGAAGACAGC



NO: 155



 


HuVb4-33
SEQ ID
CATGAGCCCTGAAGACAGC



NO: 156



 


HuVb4-34
SEQ ID
CTCGGAACCGGGAGACAC



NO: 157



 


HuVb4-35
SEQ ID
CAGAGCCGAGGGACTCAG



NO: 158



 


HuVb4-36
SEQ ID
GGGGGACTTGGCTGTGTAT



NO: 159



 


HuVb4-37
SEQ ID
CCAGACAGCTTCTAGGTTACTTCAG



NO: 160



 


HuVb4-38
SEQ ID
GCTCCCTCTCAGACTTCTGTTT



NO: 161



 


HuVb4-39
SEQ ID
CAGGAGACCTGAAGACAGCA



NO: 162
















TABLE 14







Primers in the reverse transcription for β chain









name

sequences 5′ to 3′





HuCb3′RT-4
SEQ ID NO: 163
tcatagaggatggtggcaga
















TABLE 15







PCR reverse primers for β chain










Sequencer
name

sequences 5′ to 3′





Illumina
HuCb3′
SEQ ID
cacctccttcccattcacc



illumina-4
NO: 164



 


454
HuCb3′454-4
SEQ ID
agccacagtctgctctaccc




NO: 165









Example 5
Antigenic Stimulation in Zebrafish
Zebrafish were challenged with immersion vaccination using different combinations of antigens. Methods of the invention were used to analyze the effects of external stimulation on the immune repertoire.
Antigen-immersion experiments were performed on nine month-old WIK zebrafish over a three-week period. Immersion solutions were prepared using either TNP(11)-BSA (T-5050, Biosearch Technologies), DNP(12)-BSA (D-5050, Biosearch Technologies), and ABA(10)-BSA (A-1200, Biosearch Technologies) dissolved at a total of 210 ug/ml in zebrafish system water. Controls were also set aside without antigen. Lipopolysaccharide (Sigma-Aldrich L2143) was also added to antigen immersion solutions to a total concentration of 70 ug/ml. Zebrafish were first immersed in 4.5% NaCl in system water (Huising et al, 2003) for 2 minutes, and then placed in antigen/LPS solution for 30 minutes, before being returned to their tanks. This procedure was performed three times over one-week intervals. Fish were terminated one week after final exposure. The experiment is diagrammed in FIG. 16.
Fish were euthanized and snap frozen in liquid nitrogen and stored in −80 degrees C. RNA was extracted and purified and cDNA was amplified as described in Weinstein et al, May 8, 2009: 807-810, incorporated by reference herein. Standard Roche 454 GS Titanium shotgun library protocol was followed. Multiplex Identifier (MID)-containing oligonucleotides were synthesized by Integrated DNA Technologies, Inc. (Coralville, Iowa) and were annealed to form 454 adaptor according to Roche's protocol.
Lineage-analysis was performed as described above, with each individual subsampled (without replacement) to 40,000 reads. Sequences were filtered on having indels outside of their junctional regions as well as lacking any indels relative to the most abundant sequence in their respective lineage. VDJ correlations were initially performed on read-weighted repertoires, as described in Weinstein et al. 2009 and Jiang et al. 2011. By performing hierarchical clustering with the Euclidean distances measured between correlation vectors, the data was observed to partition into groups correlating well and others not correlating well (FIG. 17A). 4 out of the 5 no-antigen samples were found to cluster among the poorly correlated samples, whereas antigen-stimulated samples were found to cluster among the better-correlated samples.
To better probe this partition and to reduce the noise in the analysis, only those sequences existing in lineages of diversity 5 or greater were considered. A similar examination on the hierarchically-clustered VJ-correlation matrix of this data showed a far better partition, with no-antigen samples clustering entirely among the poorly-correlated group (FIG. 17B). Furthermore, the specificity of the immune response to specific antigens began emerging, as zebrafish stimulated with single antigens clustered together (eg TNP-alone), and those stimulated with double antigens clustered together (DNP-TNP).
In order to probe the way in which sequence characteristics like somatic mutations played a role in the partition of groups of antigen-stimulated individuals, we sorted the sequences analyzed in FIG. 17 by the number of mutations found outside of their VDJ junctions. By dividing equal numbers of reads in each sample into “sub-repertoires” containing more than or less than the median number of mutations, each repertoire was effectively decomposed into lower- and higher-mutated halves. Low-mutation VJ correlations (FIG. 17C) showed a breakdown in the partition observed in FIGS. 17A and 17B. Meanwhile, the VJ correlations of the highly-mutated half (FIG. 17D) retained a very strong partition, with all 5 no-antigen samples clustering tightly into the uncorrelated group of individuals, and antigen-stimulated individuals clustering almost entirely outside of this region.
The data demonstrate the ability for VDJ and VJ correlations to provide information about the stimuli experienced by the immune system. By using measurements of known biological significance, such as somatic mutations, to decompose the repertoire into both minimally and maximally informative partitions, we at once validate our measurement as containing biological signal and uncover potentially powerful ways to filter out noise.
Example 6
Human subjects were immunized with seasonal influenza virus vaccine and methods of the invention were used to monitor dynamic changes in the subject's immune repertoire on the day of immunization (visit 1) and 7 days (visit 2) and 28 days (visit 3) subsequent to immunization.
Peripheral blood mononuclear cells were purified from blood drawn on visit 1 and visit 3. Naïve and plasmablast B cells were further sorted for samples acquired from visit 2 using fluorescence activated cell sorting (FACS). Naïve cells were sorted based on the surface expression of CD3− CD19+ CD27− CD38−. Plasmablasts were sorted based on the surface expression of CD3− CD19+ CD27+ CD38+. Samples went through RNA purification, cDNAs were synthesized using the reverse transcription primers listed in the tables of Example 4, and simplex PCR was performed. In this scheme, cDNA was aliquoted into 11 different tubes with one forward primer, and all reverse primers, in each tube. For each B cell population from each subject, 11 simplexed PCR products were pooled before the 454 library preparation process. The PCR condition was optimized to be initial denaturing at 94° C. for 2 min followed by 23 cycles of denaturing at 94° C. for 30 s, annealing at 60° C. for 30 s and extension at 68° C. for 2 min. This was followed by a final extension at 68° C. for 7 min.
Isotype usage is visualized in the form of a pie chart (FIG. 18). There are isotype switchings for samples acquired at different time points, e.g. an increase of IgG content and decrease of IgM content in visit 3 compared with visit 1. The overall changes of isotype switch for the most important 3 isotypes (IgA, IgG and IgM) in all 12 subjects sequenced are summarized in FIG. 19, wherein the fractional composition of IgA, IgG, and IgM at visit 3 was subtracted from that at visit 1 (making each line into a “trajectory”). Subjects receiving a LAIV nasal flu vaccine showed an increase in IgA fraction. This is consistent with the fact that attenuated live viruses in LAIV vaccine proliferate in the nasal mucosal membrane, possibly causing strong IgA mediated mucosal immunity.
Twins consistently had higher correlations in VDJ usage in all isotypes (0.54 for IgA, 0.83 for IgD, 0.35 for IgG and 0.97 for IgM) compared with non-twins for visit 1 sample (0.33 for IgA, 0.69 for IgD, 0.27 for IgG and 0.88 for IgM). IgM had the highest correlation in VDJ usage compared with other isotypes.
This correlation analysis can also be used in conjunction with hierarchical clustering (using a Euclidean distance metric, as in the zebrafish data) to distinguish similar groups of patient antibody repertoires. For example, FIG. 20 shows that twins have higher VDJ correlations than non-twins for visit 1 samples and they are closer to each other in terms of hierarchical distance than non-twins. However, for visit 3, individuals received the same vaccines are clustered together in the analysis (FIG. 21) indicating that different vaccines may induce a difference signature in terms of VDJ usage. This may serve as a biomarker in disease diagnosis and vaccine efficacy evaluation.

## Claims

1. A method for obtaining immune repertoire data for an individual, comprising:
a) obtaining immunological receptor encoding mRNA from lymphocytes of the individual, wherein the lymphocytes comprise B cells or T cells;
b) producing cDNA from the mRNA obtained in step (a);
c) producing first amplicons from the cDNA produced in Step (b), wherein the amplicons are produced using a set of primers that amplify a plurality of immunoglobulin heavy chain VDJ exon sequences or that amplify a plurality of T cell receptor beta chain VDJ exon sequences, wherein said amplicons comprise both the VD junction and the DJ junction;
d) sequencing first amplicons produced in Step (c) using massively-parallel sequencing to obtain at least 104 sequence reads of immunoglobulin heavy chain sequences or at least 104 sequence reads of T cell receptor beta chain sequences, comprising sequences from a plurality of different genomic V segments, a plurality of different genomic D segments, and a plurality of different genomic J segments, thereby producing immune repertoire sequence data;
e) producing second amplicons from a control library comprising DNA with known immunological receptor sequences, wherein the second amplicons are produced using the primers used in step (c);
f) sequencing second amplicons produced in Step (e) using massively-parallel sequencing to obtain at least 104 sequence reads of immunoglobulin heavy chain sequences or at least 104 sequence reads of T cell receptor beta chain sequences, comprising sequences from a plurality of different genomic V segments, a plurality of different genomic D segments, and a plurality of different genomic J segments, thereby producing control sequence data;
g) determining amplification bias and/or sequencing error by comparing the control sequence data with the known sequences, wherein said comparing step comprises compiling sequence data on a computer-readable medium and processing said data on a computer to identify regions of similarity and difference between said sequences;
h) normalizing the immune repertoire sequence data based on the amplification bias and/or sequencing error determined in (g), thereby producing corrected immune repertoire sequence data, wherein the corrected immune repertoire sequence data set has reduced amplification bias and/or sequencing error compared to the immune repertoire sequence data of step (d);
thereby obtaining immune repertoire data for an individual.

2. The method of claim 1 wherein the lymphocytes are B lymphocytes and the immunological receptors are immunoglobulins.

3. The method of claim 1 wherein the lymphocytes are T lymphocytes and the immunological receptors are T cell receptors.

4. The method of claim 1 wherein the lymphocytes are from a blood or tissue sample obtained from the individual.

5. The method of claim 2 wherein the lymphocytes are from a blood or tissue sample obtained from the individual.

6. The method of claim 3 wherein the lymphocytes are from a blood or tissue sample obtained from the individual.

7. The method of claim 1 wherein the massively-parallel sequencing comprises sequencing-by-synthesis.

8. A method of characterizing an organism's immune repertoire, comprising: characterizing the immune repertoire of the organism at a first point in time using the method of claim 1;
characterizing the immune repertoire of the organism at a second point in time using the method of claim 1; and
identifying differences in the characterization at the first point in time with the characterization at the second point in time.

9. A method of comparing the immune repertoires of two or more organisms, the method comprising the steps of:
characterizing a first organism's immune repertoire using the method of claim 1;
characterizing a second organism's immune repertoire using the method of claim 1; and
comparing the characterization the first organism's immune repertoire with the characterization of the second organism's immune repertoire.

10. The method of claim 1, further comprising storing sequence information obtained in said sequencing step on a computer-readable medium.