{ "id": "ephjxq6ed2", "slug": "immunosequencing", "title": "Immunosequencing", "updated": "2026-10-25", "topic_path": [ { "id": "life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/topics/life" }, { "id": "life.biology", "label": "Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology" }, { "id": "life.biology.immunology", "label": "Immunology and immune-system biology", "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.immunology" } ], "geo": [ { "id": "geo.nongeo.t2001.life.biology", "label": "Non-geographic · 2001 to 2020: Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t2001", "label": "Non-geographic · 2001 to 2020", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001" }, { "id": "geo.nongeo.t2001.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life" }, { "id": "geo.nongeo.t2001.life.biology", "label": "Biological foundations", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology" } ] } ], "excerpt": "Immunosequencing is a high-throughput sequencing method that enumerates and quantifies T-cell and B-cell receptor clones in a sample, used to track immune responses, monitor minimal residual disease, and profile repertoires in oncology, infection, and vaccination studies.", "snippet": "Immunosequencing is a high-throughput sequencing method that enumerates and quantifies T-cell and B-cell receptor clones in a sample, used to track immune responses, monitor minimal residual disease, and profile repertoires in oncology, infection, and vaccination studies.", "node": "life.biology.immunology", "markdown": "# Immunosequencing\n\nImmunosequencing is a high-throughput sequencing method that enumerates and quantifies the [T-cell receptor](https://www.edgepedia.org/t-cell-receptor) (TCR) and B-cell receptor (BCR) clones in a biological sample, combining bias-controlled multiplex PCR or 5′ RACE amplification with deep sequencing of rearranged antigen-receptor genes.[1](https://jitc.biomedcentral.com/counter/pdf/10.1186/s40425-015-0076-y.pdf) Each distinct rearranged receptor sequence, defined by its CDR3 nucleotide sequence, is treated as a clonal tag, so the method reports clonotype frequencies, repertoire diversity, and clonal expansion in the same measurement.[2](https://adaptivebiotech.com/wp-content/uploads/2019/01/Understanding-the-immunoSEQ-Assay-From-Inquiry-to-Insights.pdf) It is used to track immune responses, monitor minimal residual disease in hematological malignancy, and profile repertoires in oncology, infection, autoimmunity, and vaccination studies.[3](https://doi.org/10.1016/j.coi.2013.09.017)\n\n| Key fact | Value |\n|---|---|\n| What is measured | Identity and frequency of every B- and/or T-cell clone in a sample, via bias-controlled amplification and deep sequencing[1](https://jitc.biomedcentral.com/counter/pdf/10.1186/s40425-015-0076-y.pdf) |\n| Detection sensitivity | Approaches 1:1,000,000 when one million cells' worth of DNA is analyzed, about 100-fold better than flow cytometry[1](https://jitc.biomedcentral.com/counter/pdf/10.1186/s40425-015-0076-y.pdf) |\n| Clonotype definition | Unique CDR3 nucleotide sequence; a clone is reported only if its sequence is identified at least twice[2](https://adaptivebiotech.com/wp-content/uploads/2019/01/Understanding-the-immunoSEQ-Assay-From-Inquiry-to-Insights.pdf) |\n| Directly measured repertoire size | At least 1 million distinct TCRB clonotypes in peripheral blood, from 1,061,522 captured sequences in one individual[4](https://doi.org/10.1101/gr.115428.110) |\n| Typical input | Bulk methods work from as few as 1,000 cells to hundreds of thousands; single-cell methods use from about 500 to 40,000 cells[5](https://www.ncbi.nlm.nih.gov/books/NBK586946/) |\n| Headline diversity metric | Clonality, an evenness index equal to 1 minus normalized Shannon entropy, ranging from 0 (polyclonal, even distribution) to 1 (one or a few clonotypes dominating)[2](https://adaptivebiotech.com/wp-content/uploads/2019/01/Understanding-the-immunoSEQ-Assay-From-Inquiry-to-Insights.pdf) |\n| Known systematic bias | A benchmark of nine TCR-seq methods found large, method-specific differences in accuracy and reproducibility[6](https://doi.org/10.1038/s41587-020-0656-3) |\n\n## How it works\n\nThe method exploits [V(D)J recombination](https://www.edgepedia.org/v-d-j-recombination). The TCR-β locus on chromosome 7 spans approximately 620 kb; during recombination, one of two D regions is joined with one of 13 J regions, followed by addition of one of more than 50 V regions, producing a final VDJ region of roughly 500 bp.[7](https://assets.illumina.com/content/illumina-marketing/amr/en/areas-of-interest/immunogenomics/immune-repertoire-sequencing.html) The junction created by this random joining, the CDR3, is essentially unique to each clone, so sequencing it identifies clonotypes. Because the average CDR3 length is 35 ± 3 bp, short reads of 54 bp were designed to cover the full junction in the foundational genomic-DNA approach.[8](https://doi.org/10.1182/blood-2009-04-217604) Where reads do not span the whole junction, directional de novo assembly reconstructs it; in the iSSAKE pipeline, contig depth is proportional to clonotype frequency, with a correlation above 0.999 in simulations.[9](https://doi.org/10.1101/gr.092924.109)\n\nTemplate choice changes what the assay can see. Genomic DNA offers one stable template per cell but requires multiplex PCR with V (or leader) and J gene primers, which introduces primer bias and loses heavily mutated immunoglobulin sequences. RNA offers multiple templates per cell and allows amplification from the constant region, reducing that bias.[5](https://www.ncbi.nlm.nih.gov/books/NBK586946/) For transcript-based profiling, 5′ RACE is preferred precisely because it mitigates the PCR bias incurred when amplification relies on V- and J-segment-specific primers.[4](https://doi.org/10.1101/gr.115428.110)\n\n## How it is done\n\nA bulk workflow runs as follows. [Nucleic acid](https://www.edgepedia.org/nucleic-acid) is extracted from blood, bone marrow, tumor tissue, or another sample; fresh, frozen, and FFPE material are all usable, with FFPE restricted to DNA-based assays because RNA is degraded.[1](https://jitc.biomedcentral.com/counter/pdf/10.1186/s40425-015-0076-y.pdf) • [10](https://doi.org/10.1016/j.crmeth.2023.100459) The rearranged loci are then amplified, either by multiplex PCR from gDNA or by a 5′ RACE-like route from RNA. The Takara SMART-Seq Human TCR kit, for example, uses oligo-dT priming, SMARTScribe reverse transcriptase, 12-nucleotide UMI tagging, and two rounds of semi-nested constant-region PCR, with a minimum of 500,000 reads recommended for TCRα/β libraries from 10 ng PBMC RNA.[11](https://www.takarabio.com/documents/User%20Manual/SMART/SMART-Seq%20Human%20TCR%20%28with%20UMIs%29%20User%20Manual.pdf) Assembling full-length recombined V(D)J genes plus the partial constant region (400–600 bp inserts) requires a platform capable of 2 × 300 paired-end reads to form a single gapless contig.[12](https://emea.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/nextseq-1k-2k-immune-repertoire-sequencing-m-gl-01149/nextseq-1k-2k-immune-repertoire-sequencing-m-gl-01149.pdf)\n\nBioinformatics then builds UMI consensus sequences, calls V(D)J alleles against IMGT germline references, and computes clonotype frequencies. In the AIRR Community bulk RNA protocol, pRESTO's BuildConsensus.py groups sequences sharing a barcode and dismisses UMI groups with average mismatches above 0.1; Change-O's AssignGenes.py runs IgBLAST for allele calls; and Immcantation tools partition clonal lineages by V gene, J gene, and junction length.[13](https://www.ncbi.nlm.nih.gov/books/NBK586949/) Reported metrics include clonality (inverse normalized Shannon entropy), clone size distributions, and overlap between samples. Inferred clone size distributions follow a power law with exponent ≈ −2.1, and estimated species richness falls between \\( 10^{8} \\) and \\( 10^{9} \\) clones.[14](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1007873) Because repertoire noise is over-dispersed relative to a Poisson model, Fisher's exact and chi-squared tests are inappropriate for comparing repertoires.[14](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1007873)\n\n## Origin\n\nRepertoire profiling began with low-throughput methods. TCR spectratyping had been used to probe the T-cell repertoire before deep sequencing became practical,[9](https://doi.org/10.1101/gr.092924.109) and a 1999 Sanger-based study by T. Petteri Arstila and colleagues provided a direct estimate of human αβ [T cell](https://www.edgepedia.org/t-cell) receptor diversity of approximately \\( 10^{6} \\) beta chains.[15](https://doi.org/10.1126/science.286.5441.958) In 2009, two groups published deep-sequencing approaches. Harlan S. Robins and colleagues described single-molecule Illumina sequencing of TCRβ CDR3 from genomic DNA in Blood, using 45 Vβ forward and 13 Jβ reverse primers, and found total TCRβ diversity at least 4-fold higher than previous estimates, and at least 10-fold higher in CD45RO+ antigen-experienced cells.[8](https://doi.org/10.1182/blood-2009-04-217604) J. Douglas Freeman and colleagues reported a 5′-RACE plus Illumina strategy with the iSSAKE assembler in Genome Research, identifying 33,664 distinct TCRβ clonotypes from 40.5 million reads.[9](https://doi.org/10.1101/gr.092924.109)\n\nDepth rose quickly: René L. Warren and colleagues captured 1,061,522 TCRB sequences from a single individual in 2011 in Genome Research, placing a directly measured lower bound of about 1 million distinct clonotypes on the peripheral repertoire, consistent with the Arstila estimate.[4](https://doi.org/10.1101/gr.115428.110) Christopher S. Carlson and colleagues introduced synthetic template controls to design an unbiased multiplex PCR assay in [Nature Communications](https://www.edgepedia.org/nature-communications) in 2013, the basis of the bias control used in commercial platforms.[16](https://doi.org/10.1038/ncomms3680) Harlan Robins used the term \"immunosequencing\" for the field in a 2013 review in Current Opinion in [Immunology](https://www.edgepedia.org/immunology), noting that a few diagnostic applications for hematological malignancies were already available.[3](https://doi.org/10.1016/j.coi.2013.09.017) By 2015, roughly 100 manuscripts had used the immunoSEQ platform, run in CLIA/CAP-certified laboratories.[1](https://jitc.biomedcentral.com/counter/pdf/10.1186/s40425-015-0076-y.pdf)\n\n## Variants\n\nBulk assays divide into multiplex PCR from gDNA and 5′ RACE from RNA, with or without UMIs. Commercial options include [Adaptive Biotechnologies](https://www.edgepedia.org/adaptive-biotechnologies)' immunoSEQ, the NEBNext Immune Sequencing Kit, QIAGEN QIAseq, Takara SMART-Seq Human TCR/BCR with UMIs, and the BD Rhapsody TCR/BCR Multiomic Assay Kit.[7](https://assets.illumina.com/content/illumina-marketing/amr/en/areas-of-interest/immunogenomics/immune-repertoire-sequencing.html) Academic alternatives include SEQTR, which amplifies mRNA by in vitro transcription and reverse-transcribes with 100 V primers carrying UMIs, performing no amplification at the V-priming step to reduce bias.[10](https://doi.org/10.1016/j.crmeth.2023.100459)\n\nSingle-cell methods add chain pairing. Bulk immunosequencing profiles TCRA and TCRB separately but cannot determine which α and β chains combine to form a specific receptor, information essential for antigen specificity studies.[17](https://www.science.org/doi/10.1126/scitranslmed.aac5624) Paired approaches include scTCRseq, which assembles full-length rearranged V(D)J sequences from paired-end single-cell RNA-seq reads and recovers paired α and β segments;[18](https://link.springer.com/doi/10.1186/s13073-016-0335-7) paired heavy/light chain sequencing reported by Brandon J. DeKosky and colleagues in 2013;[19](https://doi.org/10.1038/nbt.2492) and commercial droplet or microwell platforms: 10x Chromium processes \\( 5 \\times 10^{2} \\) to \\( 1.5 \\times 10^{4} \\) cells and BD Rhapsody VDJ CDR3 processes \\( 1 \\times 10^{3} \\) to \\( 4 \\times 10^{4} \\) cells.[5](https://www.ncbi.nlm.nih.gov/books/NBK586946/) 10x Chromium captures partial V(D)J sequence from short reads, whereas BD Rhapsody supports full-length TCR sequencing of V, D, J, and constant regions.[20](https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1641491/full)\n\n## Applications\n\nThe clearest clinical use is in hematological malignancy, where a few diagnostic applications were available by 2013 and many more were under study.[3](https://doi.org/10.1016/j.coi.2013.09.017) Because the assay works on blood, bone marrow, tumor, and cell-free DNA and detects clones roughly 100-fold better than flow cytometry, it is used for immune monitoring in oncology, including minimal residual disease tracking.[1](https://jitc.biomedcentral.com/counter/pdf/10.1186/s40425-015-0076-y.pdf) During an immune response, the circulating receptor repertoire shifts from a diverse pool to one dominated by one or a few expanded clones, making repertoire composition informative about disease processes.[12](https://emea.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/nextseq-1k-2k-immune-repertoire-sequencing-m-gl-01149/nextseq-1k-2k-immune-repertoire-sequencing-m-gl-01149.pdf) Early papers proposed tracking responses in infections, autoimmunity, transplantation, and vaccination.[9](https://doi.org/10.1101/gr.092924.109) Related capture-based methods extend the approach to antigen specificity: TCR gene capture, reported by [Carsten Linnemann](https://www.edgepedia.org/carsten-linnemann) and colleagues in 2013, identifies antigen-specific TCRs at high throughput,[21](https://doi.org/10.1038/nm.3359) and MATE-Seq couples microfluidic antigen-TCR engagement with sequencing.[22](https://doi.org/10.1039/c9lc00538b)\n\n## Limitations and alternatives\n\nAmplification bias is the central failure mode. Without correction, 29.3% of 454 and 25.0% of [Ion Torrent](https://www.edgepedia.org/ion-torrent) sequences were out-of-frame versus 1% of Illumina reads, and particular CDR3 clonotypes differed by more than 100-fold between Ion Torrent and Illumina, attributed to multiplex PCR with a complex Vβ primer mix; with advanced correction, quantification accuracy of 50% (95% CI C/1.5 to C × 1.5) was achieved for clonotypes constituting at least 0.1% of T cells.[23](https://onlinelibrary.wiley.com/doi/10.1002/eji.201242517) Uncorrected errors are substantial: nearly 5% erroneous reads were observed in a polyclonal cell-line pool, producing about 100 false-positive variants per clone.[24](https://doi.org/10.1038/nmeth.2960) UMI-based counting addresses this by tagging each starting molecule before amplification, a principle established for absolute molecule counting,[25](https://doi.org/10.1038/nmeth.1778) and molecular amplification fingerprinting improved the correlation of multiplex-PCR antibody libraries with standards from R² = 0.84 to 0.89.[26](https://doi.org/10.1126/sciadv.1501371) However, some UMI protocols use 5′ RACE while others incorporate UMIs through alternative primer or library-preparation designs, including multiplex PCR, so not all commercial protocols include them, and over-sequencing small samples generated 20 times more clonotypes than expected from sample size.[27](https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2018.01038/full)\n\nRNA-input assays have their own limits: about 30% of low-frequency TCRs, especially singletons, can be affected by RNA expression variation, and FFPE samples require DNA-based methods.[10](https://doi.org/10.1016/j.crmeth.2023.100459) A benchmark of nine methods applied to the same T cell sample found marked differences in accuracy and reproducibility, with most methods capturing TRA diversity less well than TRB; one gDNA-based and two non-UMI RNA-based methods were more sensitive than UMI methods for rare clonotypes, despite the better quantification accuracy of UMI methods.[6](https://doi.org/10.1038/s41587-020-0656-3) Platform comparisons also cut against vendor claims: SEQTR triplicates of PBMC overlapped 96% versus 54% for ImmunoSEQ, detecting more clonotypes (97,248 versus 42,582).[10](https://doi.org/10.1016/j.crmeth.2023.100459) In bulk form, immunosequencing loses chain pairing, which single-cell V(D)J sequencing and scRNA-seq-based reconstruction recover.[17](https://www.science.org/doi/10.1126/scitranslmed.aac5624) • [18](https://link.springer.com/doi/10.1186/s13073-016-0335-7)\n\nRecent tools address these gaps. circVDJ-seq circularizes cDNA via [Gibson assembly](https://www.edgepedia.org/gibson-assembly) so the VDJ region sits next to the UMI and cell barcode, recovering paired full-length TCR sequences from 3′-barcoded single-cell, single-nucleus, ATAC+RNA, and spatial workflows that otherwise lose the VDJ information in the first 500 nucleotides of TCR transcripts; on average 68% and 90% of T cells contained TCRα or TCRβ contigs, yielding 59% paired clonotypes.[28](https://link.springer.com/article/10.1186/s13073-026-01691-1) scRepertoire 2 processes \\( 1 \\times 10^{6} \\) cells in a median of 32.9 seconds, an 85.1% speed increase and 91.9% memory reduction over version 1, and adds deep-learning modules (Trex, Ibex, ImmApex) alongside clonotype tracking and diversity tools.[29](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1012760) TCRscape clusters clonotypes by shared V(D)J gene usage to detect convergent recombination and public clonotypes, and barcode-based MHC-multimer technologies such as dCODE Dextramer and BEAM allow direct inference of antigen specificity when combined with single-cell platforms.[20](https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1641491/full)\n\n## References\n\n1. [Immune monitoring technology primer: immunosequencing (Kirsch, Journal for ImmunoTherapy of Cancer 2015)](https://jitc.biomedcentral.com/counter/pdf/10.1186/s40425-015-0076-y.pdf)\n2. [Understanding the immunoSEQ Assay: From Inquiry to Insights (Adaptive Biotechnologies)](https://adaptivebiotech.com/wp-content/uploads/2019/01/Understanding-the-immunoSEQ-Assay-From-Inquiry-to-Insights.pdf)\n3. [Harlan Robins (2013). Immunosequencing: applications of immune repertoire deep sequencing. Current Opinion in Immunology.](https://doi.org/10.1016/j.coi.2013.09.017)\n4. [René L. Warren and colleagues (2011). Exhaustive T-cell repertoire sequencing of human peripheral blood samples reveals signatures of antigen selection and a directly measured repertoire size of at least 1 million clonotypes. Genome Research.](https://doi.org/10.1101/gr.115428.110)\n5. [Chapter 15 AIRR Community Guide to Planning and Performing AIRR-Seq Experiments](https://www.ncbi.nlm.nih.gov/books/NBK586946/)\n6. [Pierre Barennes and colleagues (2020). Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases. Nature Biotechnology.](https://doi.org/10.1038/s41587-020-0656-3)\n7. [Immune Repertoire Sequencing (IR-Seq) | NGS solutions (Illumina)](https://assets.illumina.com/content/illumina-marketing/amr/en/areas-of-interest/immunogenomics/immune-repertoire-sequencing.html)\n8. [Harlan S. Robins and colleagues (2009). Comprehensive assessment of T-cell receptor β-chain diversity in αβ T cells. Blood.](https://doi.org/10.1182/blood-2009-04-217604)\n9. [J. Douglas Freeman and colleagues (2009). Profiling the T-cell receptor beta-chain repertoire by massively parallel sequencing. Genome Research.](https://doi.org/10.1101/gr.092924.109)\n10. [Raphael Genolet and colleagues (2023). TCR sequencing and cloning methods for repertoire analysis and isolation of tumor-reactive TCRs. Cell Reports Methods.](https://doi.org/10.1016/j.crmeth.2023.100459)\n11. [SMART-Seq Human TCR (with UMIs) User Manual (Takara Bio)](https://www.takarabio.com/documents/User%20Manual/SMART/SMART-Seq%20Human%20TCR%20%28with%20UMIs%29%20User%20Manual.pdf)\n12. [Full-length V(D)J immune repertoire sequencing (IR-Seq) on the NextSeq 1000 and 2000 Systems (Illumina technical note)](https://emea.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/nextseq-1k-2k-immune-repertoire-sequencing-m-gl-01149/nextseq-1k-2k-immune-repertoire-sequencing-m-gl-01149.pdf)\n13. [Chapter 19 Bulk Sequencing from mRNA with UMI for Evaluation of B-Cell Isotype and Clonal Evolution: A Method by the AIRR Community](https://www.ncbi.nlm.nih.gov/books/NBK586949/)\n14. [Inferring the immune response from repertoire sequencing (PLOS Computational Biology 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1007873)\n15. [T. Petteri Arstila and colleagues (1999). A Direct Estimate of the Human αβ T Cell Receptor Diversity. Science.](https://doi.org/10.1126/science.286.5441.958)\n16. [Christopher S. Carlson and colleagues (2013). Using synthetic templates to design an unbiased multiplex PCR assay. Nature Communications.](https://doi.org/10.1038/ncomms3680)\n17. [High-throughput pairing of T cell receptor α and β sequences (Science Translational Medicine)](https://www.science.org/doi/10.1126/scitranslmed.aac5624)\n18. [Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq (Genome Medicine 2016)](https://link.springer.com/doi/10.1186/s13073-016-0335-7)\n19. [Brandon J DeKosky and colleagues (2013). High-throughput sequencing of the paired human immunoglobulin heavy and light chain repertoire. Nature Biotechnology.](https://doi.org/10.1038/nbt.2492)\n20. [TCRscape: a single-cell multi-omic TCR profiling toolkit (Frontiers in Bioinformatics 2025)](https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1641491/full)\n21. [Carsten Linnemann and colleagues (2013). High-throughput identification of antigen-specific TCRs by TCR gene capture. Nature Medicine.](https://doi.org/10.1038/nm.3359)\n22. [Alphonsus H. C. Ng and colleagues (2019). MATE-Seq: microfluidic antigen-TCR engagement sequencing. Lab on a Chip.](https://doi.org/10.1039/c9lc00538b)\n23. [Next generation sequencing for TCR repertoire profiling: Platform-specific features and correction algorithms (Eur J Immunol 2012/2013, Bolotin et al.)](https://onlinelibrary.wiley.com/doi/10.1002/eji.201242517)\n24. [Mikhail Shugay and colleagues (2014). Towards error-free profiling of immune repertoires. Nature Methods.](https://doi.org/10.1038/nmeth.2960)\n25. [Teemu Kivioja and colleagues (2011). Counting absolute numbers of molecules using unique molecular identifiers. Nature Methods.](https://doi.org/10.1038/nmeth.1778)\n26. [Tarik A. Khan and colleagues (2016). Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting. Science Advances.](https://doi.org/10.1126/sciadv.1501371)\n27. [RepSeq Data Representativeness and Robustness Assessment by Shannon Entropy (Frontiers in Immunology 2018)](https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2018.01038/full)\n28. [circVDJ-seq for T cell clonotype detection in single-cell and spatial multi-omics (Genome Medicine 2026)](https://link.springer.com/article/10.1186/s13073-026-01691-1)\n29. [scRepertoire 2: Enhanced and efficient toolkit for single-cell immune profiling (PLOS Computational Biology 2024/2025)](https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1012760)\n\n---\n*Topic: Encyclopedia › Life and health › Biological foundations › Immunology and immune-system biology*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026; Oct 25, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/immunosequencing", "markdown_url": "https://www.edgepedia.org/immunosequencing.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Immunosequencing\", Edgepedia (EdgeChat), https://www.edgepedia.org/immunosequencing. Edgepedia Community License 1.0.", "credit_md": "\"[Immunosequencing](https://www.edgepedia.org/immunosequencing)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/immunosequencing](https://www.edgepedia.org/immunosequencing). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Immunosequencing\", Edgepedia (EdgeChat), https://www.edgepedia.org/immunosequencing. Edgepedia Community License 1.0.", "speakable": "Immunosequencing is a high-throughput sequencing method that enumerates and quantifies T-cell and B-cell receptor clones in a sample, used to track immune responses, monitor minimal residual disease, and profile repertoires in oncology, infection, and vaccination studies." }