{"id":38239,"date":"2026-02-09T19:39:00","date_gmt":"2026-02-10T03:39:00","guid":{"rendered":"https:\/\/www.novogene.com\/us-en\/?post_type=resources&p=38239"},"modified":"2026-02-09T22:07:05","modified_gmt":"2026-02-10T06:07:05","slug":"single-cell-immune-repertoire-sequencing-resolving-tcr-bcr-clonotypes-and-functional-states-at-cellular-resolution","status":"publish","type":"resources","link":"https:\/\/www.novogene.com\/us-en\/resources\/blog\/single-cell-immune-repertoire-sequencing-resolving-tcr-bcr-clonotypes-and-functional-states-at-cellular-resolution\/","title":{"rendered":"Single-Cell Immune Repertoire Sequencing:\u00a0Resolving TCR\/BCR Clonotypes and Functional States at Cellular Resolution"},"content":{"rendered":"
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I.\u00a0Comparison Between Single-Cell TCR Sequencing and Conventional Bulk TCR Sequencing<\/strong>\u00a0<\/p>\n

Bulk TCR sequencing has long served as the standard approach for studying immune repertoire diversity. This method efficiently captures overall clonal composition and CDR3 sequence information, making it particularly\u00a0advantageous\u00a0for estimating clonal frequencies and calculating diversity indices. However, as a population-level technique, bulk sequencing cannot link TCR sequences to the functional states of individual cells, limiting its ability to resolve cellular heterogeneity within clonal populations.\u00a0<\/p>\n

Single-cell TCR sequencing overcomes this limitation by integrating antigen receptor sequences with transcriptomic profiles at single-cell resolution. This advancement fundamentally expands the analytical scope from merely characterizing "clonal composition" to elucidating "clonal function," enabling researchers to dissect the functional heterogeneity within clonally related T cells and to trace the differentiation trajectories of individual clones during immune responses1<\/sup>\u00a0(Table 1<\/strong>).\u00a0<\/p>\n

Table 1<\/strong>\u00a0Analytical capabilities of bulk vs.\u00a0Single-cell\u00a0TCR sequencing\u00a0<\/p>\n\n\n\n\n\n\n\n\n\n\n
Dimension<\/strong>\u00a0\n<\/td>\nBulk TCR Sequencing<\/strong>\u00a0\n<\/td>\nSingle-Cell TCR Sequencing<\/strong>\u00a0\n<\/td>\n<\/tr>\n
Resolution\u00a0\n<\/td>\nPopulation level\u00a0\n<\/td>\nSingle-cell level\u00a0\n<\/td>\n<\/tr>\n
Chain pairing\u00a0\n<\/td>\nTypically\u00a0unavailable\u00a0\n<\/td>\nAccurate paired \u03b1\u03b2 chain reconstruction\u00a0\n<\/td>\n<\/tr>\n
Clonal frequency analysis\u00a0\n<\/td>\n\u2714\u00a0\n<\/td>\n\u2714\u00a0\n<\/td>\n<\/tr>\n
Cell type assignment\u00a0\n<\/td>\n\u2716\u00a0\n<\/td>\n\u2714\u00a0\n<\/td>\n<\/tr>\n
Functional state analysis\u00a0\n<\/td>\n\u2716\u00a0\n<\/td>\n\u2714 (activation, memory, exhaustion, etc.)\u00a0\n<\/td>\n<\/tr>\n
Differentiation trajectory integration\u00a0\n<\/td>\n\u2716\u00a0\n<\/td>\n\u2714\u00a0\n<\/td>\n<\/tr>\n
Tissue\/state transition tracking\u00a0\n<\/td>\nLimited\u00a0\n<\/td>\nEnabled\u00a0\n<\/td>\n<\/tr>\n
Primary application\u00a0\n<\/td>\nGlobal diversity assessment\u00a0\n<\/td>\nClonal fate and functional mechanism studies\u00a0\n<\/td>\n<\/tr>\n<\/table>\n

\u00a0<\/p>\n

II. Structural Basis of TCR\/BCR and Immune Repertoire Diversity<\/strong>\u00a0<\/p>\n

TCRs are composed of either paired \u03b1\u03b2 chains (TRA\/TRB) or\u00a0\u03b3\u03b4\u00a0chains (TRG\/TRD), while BCRs consist of paired heavy and light chains. The extraordinary diversity of these receptors arises from V(D)J gene recombination, a process that generates highly variable complementarity-determining region 3 (CDR3) sequences. The CDR3,\u00a0located\u00a0at the antigen-binding interface,\u00a0determines\u00a0antigen specificity and\u00a0represents\u00a0the defining feature for immune repertoire profiling and clonal tracking2<\/sup>\u00a0(Figure 1<\/strong>).\u00a0<\/p>\n

\u00a0<\/p>\n

Figure 1<\/strong>\u00a0Structural organization of T cell receptors (TCRs) and B cell receptors (BCRs).\u00a0<\/p>\n

\u00a0<\/p>\n

III.\u00a0The 10x Genomics 5\u2019 GEM and V(D)J Single-Cell Immune Profiling Strategy<\/strong>\u00a0<\/p>\n

A widely adopted solution for single-cell immune repertoire analysis is the 10\u00d7 Genomics 5′ single-cell transcriptome platform. This system employs microfluidic technology to encapsulate individual cells into Gel Bead-in-Emulsion (GEM) droplets, where cellular mRNA is reverse transcribed and tagged with unique cell-specific barcodes.\u00a0Subsequent\u00a0targeted enrichment of V(D)J regions enables high-fidelity capture and reconstruction of full-length antigen receptor sequences.\u00a0<\/p>\n

This integrated platform\u00a0provides\u00a0three critical capabilities -1) single-cell resolution transcriptome profiling, 2) paired TCR \u03b1\u03b2 or BCR heavy\u2013light chain reconstruction, and 3) direct linkage between antigen receptor sequences and gene expression profiles.\u00a0<\/p>\n

By unifying clonal identity with functional state information, this approach enables researchers to\u00a0identify\u00a0expanded clones within specific cell subsets and to dissect their functional characteristics, differentiation trajectories, and tissue distribution patterns during immune responses.\u00a0<\/p>\n

\u00a0<\/p>\n

IV. Technical Performance and Recent Advancements<\/strong>\u00a0<\/p>\n

The\u00a010\u00d7 Genomics 5′ single-cell immune profiling platform<\/a>\u00a0has\u00a0demonstrated\u00a0stable and reproducible performance across diverse immune samples.\u00a0In 2025, optimizations\u00a0for\u00a0reagents and microfluidic chips further enhanced workflow sensitivity.\u00a0<\/p>\n

Internal validation by the Novogene R&D team using mouse PBMC and spleen samples showed that the\u00a0new 5′ V3 chemistry<\/a>\u00a0achieved higher median gene detection per cell compared to the\u00a0previous\u00a0V2 version, while\u00a0maintaining\u00a0robust cell recovery at scales up to 20,000 cells. Key sequencing quality metrics\u2014including reads in cells, sequencing saturation, and RNA read Q30 scores\u2014remain\u00a0consistently high, ensuring reliable integration of transcriptomic and immune repertoire data (Table 2<\/strong>).\u00a0<\/p>\n

These findings\u00a0establish\u00a0that the 10\u00d7 Genomics 5′ immune profiling system provides a robust framework for clonal expansion analysis, immune cell trajectory mapping, and antigen-specific clonal tracking in single-cell immunology research.\u00a0<\/p>\n

\u00a0<\/p>\n

Table 2<\/strong>\u00a0Performance Comparison of 10\u00d7 Genomics 5\u2019 V2 and V3 Chemistries Based on Internal Validation Using Mouse PBMC and Spleen Samples\u00a0\u00a0<\/p>\n\n\n\n\n\n\n\n\n\n\n
Sample\u00a0<\/strong>\n<\/td>\nMouse\u00a0PBMC\u00a0sample 1\u00a0<\/strong>\n<\/td>\nMouse\u00a0PBMC\u00a0sample 2\u00a0<\/strong>\n<\/td>\nMouse spleen sample 1\u00a0<\/strong>\n<\/td>\nMouse spleen sample 2\u00a0<\/strong>\n<\/td>\n<\/tr>\n
10x kit\u00a0<\/strong>\n<\/td>\n5’V2<\/strong>\u00a0\n<\/td>\n5’V3<\/strong>\u00a0\n<\/td>\n5’V3<\/strong>\u00a0\n<\/td>\n5’V2<\/strong>\u00a0\n<\/td>\n5’V3<\/strong>\u00a0\n<\/td>\n5’V3<\/strong>\u00a0\n<\/td>\n5’V2<\/strong>\u00a0\n<\/td>\n5’V3<\/strong>\u00a0\n<\/td>\n5’V3<\/strong>\u00a0\n<\/td>\n5’V2<\/strong>\u00a0\n<\/td>\n5’V3<\/strong>\u00a0\n<\/td>\n5’V3<\/strong>\u00a0\n<\/td>\n<\/tr>\n
Target\u00a0cell\u00a0number\u00a0<\/strong>\n<\/td>\n10000\u00a0\n<\/td>\n10000\u00a0\n<\/td>\n20000\u00a0\n<\/td>\n10000\u00a0\n<\/td>\n10000\u00a0\n<\/td>\n20000\u00a0\n<\/td>\n10000\u00a0\n<\/td>\n10000\u00a0\n<\/td>\n20000\u00a0\n<\/td>\n10000\u00a0\n<\/td>\n10000\u00a0\n<\/td>\n20000\u00a0\n<\/td>\n<\/tr>\n
Recovered\u00a0cells\u00a0<\/strong>\n<\/td>\n13,263\u00a0\n<\/td>\n12,668\u00a0\n<\/td>\n23,679\u00a0\n<\/td>\n13,808\u00a0\n<\/td>\n12,368\u00a0\n<\/td>\n22,849\u00a0\n<\/td>\n10,832\u00a0\n<\/td>\n10,579\u00a0\n<\/td>\n20,166\u00a0\n<\/td>\n12,182\u00a0\n<\/td>\n10,766\u00a0\n<\/td>\n19,986\u00a0\n<\/td>\n<\/tr>\n
Mean\u00a0reads per\u00a0cell\u00a0<\/strong>\n<\/td>\n25,770\u00a0\n<\/td>\n26,984\u00a0\n<\/td>\n30,530\u00a0\n<\/td>\n24,460\u00a0\n<\/td>\n26,533\u00a0\n<\/td>\n38,727\u00a0\n<\/td>\n34,500\u00a0\n<\/td>\n39,369\u00a0\n<\/td>\n32,768\u00a0\n<\/td>\n27,073\u00a0\n<\/td>\n35,001\u00a0\n<\/td>\n32,722\u00a0\n<\/td>\n<\/tr>\n
Median\u00a0genes per\u00a0cell\u00a0<\/strong>\n<\/td>\n1,605\u00a0\n<\/td>\n2,230\u00a0\n<\/td>\n2,400\u00a0\n<\/td>\n1,516\u00a0\n<\/td>\n2,232\u00a0\n<\/td>\n2,635\u00a0\n<\/td>\n1,486\u00a0\n<\/td>\n2,658\u00a0\n<\/td>\n2,532\u00a0\n<\/td>\n1,650\u00a0\n<\/td>\n2,558\u00a0\n<\/td>\n2,425\u00a0\n<\/td>\n<\/tr>\n
Fraction of reads in cells\u00a0<\/strong>\n<\/td>\n96.70%\u00a0\n<\/td>\n90.00%\u00a0\n<\/td>\n91.90%\u00a0\n<\/td>\n97.30%\u00a0\n<\/td>\n90.40%\u00a0\n<\/td>\n91.60%\u00a0\n<\/td>\n94.90%\u00a0\n<\/td>\n86.60%\u00a0\n<\/td>\n86.60%\u00a0\n<\/td>\n94.10%\u00a0\n<\/td>\n85.00%\u00a0\n<\/td>\n88.90%\u00a0\n<\/td>\n<\/tr>\n
Sequencing Saturation\u00a0<\/strong>\n<\/td>\n74.50%\u00a0\n<\/td>\n50.10%\u00a0\n<\/td>\n52.40%\u00a0\n<\/td>\n73.10%\u00a0\n<\/td>\n47.60%\u00a0\n<\/td>\n53.60%\u00a0\n<\/td>\n83.70%\u00a0\n<\/td>\n55.00%\u00a0\n<\/td>\n47.90%\u00a0\n<\/td>\n76.40%\u00a0\n<\/td>\n51.30%\u00a0\n<\/td>\n55.10%\u00a0\n<\/td>\n<\/tr>\n
RNA Read Q30\u00a0<\/strong>\n<\/td>\n97.50%\u00a0\n<\/td>\n97.30%\u00a0\n<\/td>\n97.40%\u00a0\n<\/td>\n97.60%\u00a0\n<\/td>\n97.30%\u00a0\n<\/td>\n96.80%\u00a0\n<\/td>\n97.70%\u00a0\n<\/td>\n97.30%\u00a0\n<\/td>\n96.60%\u00a0\n<\/td>\n97.60%\u00a0\n<\/td>\n97.40%\u00a0\n<\/td>\n97.00%\u00a0\n<\/td>\n<\/tr>\n<\/table>\n

\u00a0<\/p>\n

V. General Analytical Framework for Single-Cell TCR Data<\/strong>\u00a0<\/p>\n

Single-cell TCR data are typically analyzed in conjunction with\u00a0transcriptomic profiles generated from the same GEM-based 5′ workflow<\/a>, enabling direct integration of immune receptor sequences with gene expression states at single-cell resolution. The analytical framework encompasses four key dimensions3,4<\/sup>:\u00a0<\/p>\n

1. Receptor Sequence Analysis<\/strong>\u00a0<\/p>\n

Characterization of V(D)J gene usage, CDR3 sequence features, clonotype definition, and clonal frequency estimation.\u00a0<\/p>\n

2. Clonal Expansion and Diversity Analysis<\/strong>\u00a0<\/p>\n

Assessment of clone size distribution, diversity indices (e.g., Shannon entropy, clonality), and clonal overlap across samples or experimental conditions.\u00a0<\/p>\n

3. Integration of Clonotypes with Cellular Phenotypes<\/strong>\u00a0<\/p>\n

Because transcriptome and TCR information originate from the same cells, expanded clones can be mapped to transcriptionally defined T cell subsets (e.g., na\u00efve, effector, memory, exhausted) to\u00a0identify\u00a0subset-specific clonal enrichment patterns.\u00a0<\/p>\n

4. Clonotype-Resolved Trajectory and Functional Analysis<\/strong>\u00a0<\/p>\n

Through integration with\u00a0pseudotime\u00a0analysis, RNA velocity, or pathway enrichment methods, clonotype-associated cells can be positioned along differentiation trajectories to dissect clonal fate decisions and functional state transitions.\u00a0<\/p>\n

References\u00a0<\/p>\n

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  1. Chen, L., Wang, Y., Zhang, R., & Li, M. (2024). Advances and challenges in investigating B-cells via single-cell transcriptomics. Nature Methods, 21(7), 889\u2013898.\u00a0<\/li>\n
  2. Schroeder HW Jr, Cavacini L.\u00a0Structure\u00a0and function of immunoglobulins. J Allergy Clin Immunol. 2010 Feb;125(2 Suppl 2):S41-52.\u00a0doi: 10.1016\/j.jaci.2009.09.046. PMID: 20176268; PMCID: PMC3670108.\u00a0<\/li>\n
  3. Yang, Q, & Safina, K., Nguyen, K., Tuong, Z.K., & Borcherding, N. (2025). \u201cscRepertoire\u00a02: Enhanced and efficient toolkit for single-cell immune profiling.\u201d\u00a0PLoS\u00a0Computational Biology\u00a0https:\/\/doi.org\/10.1371\/journal.pcbi.1012760<\/a>\u00a0<\/li>\n
  4. Borcherding, Nicholas, Nicholas L. Bormann, and Gloria Kraus. \u201cscRepertoire: An R-based toolkit for single-cell immune receptor analysis.\u201d\u00a0F1000Research\u00a0https:\/\/doi.org\/10.12688\/f1000research.22139.2\u00a0<\/li>\n<\/ol>\n

    \u00a0<\/p>\n

    Why Choose Novogene for Single-cell RNA Sequencing (single-cell RNA-Seq or\u00a0scRNA-Seq)?<\/strong>\u00a0<\/p>\n

    Proven Expertise:\u00a0With over 200,000 successfully sequenced samples, Novogene delivers great project results at industry-leading turnaround times. We excel at handling challenging sample types, including nerve and adipose cells.\u00a0<\/p>\n

    Enhanced Sample Processing:\u00a0We offer a diverse range of sample processing capabilities, including nuclei extraction and specialized pipelines for frozen tissues. This ensures high-quality gene expression data in Single-cell RNA Sequencing (single-cell RNA-Seq or\u00a0scRNA-Seq) projects.\u00a0<\/p>\n

    Certified Excellence:\u00a0As a\u00a010x Genomics Certified Service Provider, we\u00a0leverage\u00a0the advanced Chromium X platform combined with GEM-X technology for superior reproducibility and efficiency.\u00a0<\/p>\n

    Cost-Effective Solutions:\u00a0We have\u00a0state-of-the-art\u00a0high-throughput sequencing platforms, coupled with expert support, which ensure exceptional data quality and provide cost-effective solutions for single-cell projects.\u00a0<\/p>\n

    <\/a>\u00a0<\/p>\n<\/div>\n

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