Sample Submission Guidelines
Single-Cell Immune Repertoire Sequencing for Clonotype-Level Research
scTCR/BCR-seq is designed to show which T-cell or B-cell receptor sequences are present in individual immune cells. Instead of measuring immune receptor diversity only at the bulk population level, this method keeps receptor information connected to single cells. That connection helps researchers study clonotypes, chain pairing, immune-cell clusters, and cell-state signals in the same project.
For T cells, scTCR-seq can help identify TCR clonotypes, CDR3 sequences, V/J gene usage, clonal expansion patterns, and paired alpha/β chain information. For B cells, scBCR-seq can help profile BCR clonotypes, heavy/light chain pairing, CDR3 sequences, V(D)J usage, and B-cell lineage patterns.
When combined with single-cell gene expression, scTCR/BCR-seq can answer a broader set of questions. You can ask which clone expanded, what cell type or state the clone belongs to, which marker genes are expressed, and how clonotype patterns differ between groups.
This service can support immune repertoire questions such as which TCR or BCR clonotypes are expanded, which CDR3 sequences define dominant clones, which V, D, and J genes are used, and whether receptor chains are paired at the single-cell level.
Common research applications include tumor-infiltrating lymphocyte profiling, T-cell clonal expansion studies, B-cell and antibody repertoire research, vaccine-response research, infection-related immune response profiling, autoimmune and inflammatory disease research, cell therapy research, antibody discovery research, and immune repertoire comparison across tissues, groups, or treatments.
How scTCR/BCR-seq Works: From Sample Intake to Repertoire Results
A successful scTCR/BCR-seq project depends on both the biology of the sample and the quality of the workflow. We combine technical sequencing steps with service checkpoints, so your project can move from cell suspension to interpretable clonotype results with clear quality review at each stage.
1. Project Design and Sample Review
We start by reviewing your research question, sample type, species, target immune-cell population, and comparison groups. This helps us decide whether your project is best served by scTCR-seq, scBCR-seq, or an integrated single-cell gene expression plus V(D)J workflow.
QC checkpoint: project fit, sample type, expected cell recovery, target immune population, and selected workflow.
2. Single-Cell Suspension Preparation and Cell Quality Review
scTCR/BCR-seq requires a clean single-cell suspension. Cells should be well dissociated, viable, and free from large aggregates, excessive debris, and inhibitors that may reduce cDNA quality.
QC checkpoint: cell concentration, viability, debris level, clumping, doublet risk, and sample suitability.
3. Single-Cell Capture and Barcoding
During single-cell capture, individual cells are partitioned with barcoded beads. Each cell receives a cell-level barcode that links its transcript data and immune receptor sequences back to the same cell.
QC checkpoint: target cell loading range, suspension quality, capture compatibility, and expected multiplet risk.
4. cDNA Generation and V(D)J Enrichment
After cell capture, reverse transcription creates barcoded cDNA. V(D)J regions are then selectively enriched using TCR or BCR amplification primers, depending on the project design.
QC checkpoint: cDNA quality, amplification performance, library concentration, and fragment distribution.
5. Sequencing and Data Quality Control
Prepared libraries are sequenced to generate reads for V(D)J analysis and, when selected, single-cell gene expression analysis. Important QC signals include read quality, barcode assignment, barcode-supported read grouping, library complexity, and the number of usable cells with productive receptor information.
QC checkpoint: raw read quality, barcode quality, read grouping quality, V(D)J library quality, and usable cell recovery.
6. V(D)J Data Processing and Clonotype Calling
The V(D)J analysis workflow assembles receptor contigs, annotates V(D)J segments, identifies CDR3 regions, filters productive contigs, and groups cells into clonotypes. For integrated projects, clonotype data can be mapped back onto single-cell gene expression clusters.
QC checkpoint: productive contig rate, clonotype assignment, chain pairing, cell barcode consistency, and integration quality.
7. Report Delivery and Data Handoff
We deliver raw and processed data, QC summaries, analysis tables, visual outputs, and a project report. For custom projects, we can also provide additional group comparisons, visualization support, and tailored bioinformatics outputs.
scTCR-seq, scBCR-seq, or Integrated scRNA-seq + V(D)J: Which Fits Your Study?
Different immune repertoire questions need different workflows. We help you select a workflow based on your biological question, sample type, immune-cell population, and downstream analysis needs.
| Workflow | Best-Fit Question | Main Sample Focus | Key Outputs | Strengths | Limitations to Consider |
|---|---|---|---|---|---|
| Bulk TCR/BCR sequencing | How diverse is the immune receptor repertoire in a sample? | DNA or RNA from bulk immune populations | CDR3 sequences, V/J usage, repertoire diversity | Useful for broad repertoire profiling across many samples | Does not preserve single-cell receptor-to-cell-state context |
| scTCR-seq | Which T-cell clonotypes are present at single-cell level? | T cells, PBMCs, TILs, sorted T cells | TCR clonotypes, CDR3, V/J usage, alpha/β pairing | Connects TCR identity to individual cells | Requires high-quality single-cell suspension |
| scBCR-seq | Which B-cell or antibody-related clonotypes are present? | B cells, PBMCs, sorted B cells, tissue-derived B cells | BCR clonotypes, heavy/light chain pairing, CDR3, V(D)J usage | Useful for antibody repertoire and B-cell lineage studies | B-cell abundance and sample quality affect recovery |
| Integrated scRNA-seq + V(D)J | What is the cell state of each clonotype? | Mixed immune cells, PBMCs, TILs, tissue immune cells | Clonotypes, gene expression clusters, UMAP overlays, marker genes | Links receptor identity with phenotype and gene expression | More complex analysis and higher data interpretation needs |
| scTCR/BCR-seq with custom bioinformatics | How do clonotypes differ across groups, tissues, or treatments? | Multi-group or longitudinal immune samples | Group comparisons, diversity metrics, expanded clone tracking | Strong fit for complex study designs | Requires clear metadata and study design |
Choose scTCR-seq When
Your project focuses on T-cell clonality, T-cell expansion, tumor-infiltrating lymphocytes, infection response, vaccine response, autoimmune research, or cell therapy research.
Choose scBCR-seq When
Your project focuses on B-cell clonality, antibody repertoire research, B-cell lineage patterns, humoral immune response, or antibody discovery.
Choose Integrated scRNA-seq + V(D)J When
You need to understand which cell clusters contain expanded clonotypes, which genes are expressed by those clones, and how cell states differ across groups.
Demo Results: What You Can Expect from a scTCR/BCR-seq Project
A scTCR/BCR-seq project can generate several types of results. The exact outputs depend on your sample, species, workflow, sequencing design, and analysis plan. The demo structure below shows three common result groups that are often useful for interpretation and reporting.
Clonotype Frequency and CDR3 Distribution
This output helps you see which clones are most abundant in your sample. It can be shown as a clonotype frequency bar chart, a ranked clonotype table, or a CDR3 sequence distribution plot.
V(D)J Gene Usage and Chain Pairing
This output helps you understand which V, D, and J gene segments are used and whether receptor-chain pairing is recovered across single cells.
Clonotype Overlay on Single-Cell Clusters
For integrated workflows, clonotype information can be mapped onto single-cell gene expression clusters to show whether expanded clones are enriched in specific immune-cell states.
Bioinformatics Analysis and Deliverables
scTCR/BCR-seq produces complex immune repertoire data. We process the data into clear outputs that help you move from raw sequencing files to interpretable clonotype results.
Standard V(D)J Analysis
- Raw data quality control
- Cell barcode processing
- Barcode-supported read grouping
- V(D)J sequence assembly
- Contig annotation
- Productive contig filtering
- CDR3 sequence identification
- V, D, and J gene usage analysis
- Clonotype calling
- Clonotype abundance analysis
- Diversity metrics
- Chain pairing summary
- Repertoire visualization
Integrated Single-Cell Analysis
- Single-cell gene expression QC
- Cell clustering and annotation
- Marker gene analysis
- V(D)J and gene expression integration
- Clonotype mapping to UMAP clusters
- Expanded clone marker analysis
- Group comparison across samples or conditions
- Immune-cell state interpretation
For more complex projects, we can tailor analysis to your study design. Optional analysis may include public and private clonotype comparison, longitudinal clonal tracking, tumor versus adjacent tissue comparison, treatment-group comparison, antibody lineage analysis, cell therapy research support, publication-style figure preparation, and custom visualization.
| Deliverable | What It Shows | Why It Matters |
|---|---|---|
| Raw sequencing data | FASTQ files | Supports downstream reanalysis and data storage |
| QC summary | Read quality, cell recovery, library performance | Helps evaluate project quality |
| Clonotype table | Clonotype IDs, cell counts, frequency | Shows expanded and shared clones |
| CDR3 table | CDR3 nucleotide and amino acid sequences | Defines receptor specificity-related regions |
| V(D)J usage table | V, D, and J segment usage | Shows repertoire structure |
| Paired-chain summary | TRA/TRB or IGH/light chain pairing | Supports single-cell receptor interpretation |
| Diversity metrics | Repertoire diversity and clonality measures | Helps compare samples or groups |
| UMAP clonotype overlay | Clonotypes mapped to cell clusters | Links immune receptor identity with cell state |
| Final report | Methods, QC, figures, and interpretation notes | Provides a structured project summary |
Sample Requirements for scTCR/BCR-seq
High-quality single-cell input is one of the most important factors in scTCR/BCR-seq. The planning values below follow CD Genomics single-cell sequencing sample guidance and are used to help you prepare a clean, viable cell suspension before project review.
| Sample Type | Recommended Input | Cell Concentration | Viability | Key Preparation Notes | Best-Fit Workflow |
|---|---|---|---|---|---|
| PBMCs | >1×105 high-quality cells | 700–1200 cells/μL | ≥85% preferred | Prepare a single-cell suspension with low debris and minimal clumping. Filter if aggregates are present. | scTCR-seq, scBCR-seq, or integrated profiling |
| Sorted T cells | >1×105 high-quality cells | 700–1200 cells/μL | ≥85% preferred | Confirm cell purity, viability, concentration, and absence of visible cell aggregates before submission. | scTCR-seq |
| Sorted B cells | >1×105 high-quality cells | 700–1200 cells/μL | ≥85% preferred | Confirm enrichment quality and avoid excessive debris, dead cells, or sample carryover from sorting buffer. | scBCR-seq |
| Tumor-infiltrating lymphocytes | >1×105 high-quality recovered immune cells | 700–1200 cells/μL | ≥85% preferred | Dissociate tissue gently and remove debris or cell clumps. A filter with pore size ≤40 μm is recommended when needed. | scTCR-seq or integrated profiling |
| Dissociated tissue immune cells | >1×105 high-quality cells after dissociation | 700–1200 cells/μL | ≥85% preferred | Target cell diameter should remain below 40 μm, and samples should be free of large particles and dissociation debris. | Integrated profiling |
| Mouse immune cells | >1×105 high-quality cells | 700–1200 cells/μL | ≥85% preferred | Prepare a clean suspension from spleen, blood, lymph node, tumor, or tissue-derived immune cells after project review. | scTCR-seq, scBCR-seq, or integrated profiling |
Several factors can affect clonotype recovery and gene-expression quality. Low viability may reduce usable cell recovery. Cell clumps increase multiplet risk. Excess debris can reduce capture quality. Tissue dissociation can alter immune-cell state. Low T-cell or B-cell abundance may reduce receptor recovery. Weak metadata can limit group-level interpretation.
If your sample is limited, fragile, or tissue-derived, contact us before collection or dissociation. We can help review whether enrichment, sorting, viability cleanup, or a different workflow would better fit your study.
Discuss Your ProjectApplications of scTCR/BCR-seq in Immune Research
scTCR/BCR-seq helps connect receptor sequence identity with immune-cell context across oncology, immunology, infection, vaccine, autoimmune, cell therapy, and antibody discovery studies.
Tumor Immunology and Immunotherapy Research
scTCR/BCR-seq can help researchers study immune-cell clonality within tumor microenvironments. For tumor-infiltrating lymphocyte studies, it can show which T-cell clonotypes are expanded and whether those clones are linked to specific immune-cell states.
Vaccine and Infection Research
Adaptive immune responses often involve expansion of specific T-cell or B-cell clones. scTCR/BCR-seq can help compare clonotype patterns before and after immune stimulation, across time points, or between experimental groups.
Autoimmune and Inflammatory Disease Research
In autoimmune and inflammatory disease research, scTCR/BCR-seq can help study whether certain immune clones are enriched in tissues, disease models, or treatment groups.
Cell Therapy and Antibody Discovery Research
For cell therapy research, scTCR-seq can support T-cell clone tracking, receptor sequence discovery, and candidate prioritization workflows. For antibody discovery research, scBCR-seq can support B-cell clonotype analysis, heavy/light chain pairing, and antibody lineage studies.
Why Choose CD Genomics for scTCR/BCR-seq
We support scTCR/BCR-seq as a complete project workflow, not just a sequencing run. Our team helps you think through sample suitability, workflow choice, sequencing design, bioinformatics needs, and final deliverables.
- End-to-End Project Execution: We support sample and project review, workflow selection, library preparation, sequencing, QC, clonotype analysis, integrated gene-expression analysis, and report delivery.
- Single-Cell and Immune Repertoire Expertise: We help connect single-cell sequencing, immune receptor biology, and bioinformatics so your results are easier to understand and use.
- Custom Bioinformatics Support: We can help build group comparison plots, UMAP overlays, diversity summaries, clonotype tracking tables, and presentation-ready figures.
- Clear Deliverables: We organize raw data, processed tables, QC summaries, visualizations, and report content into practical outputs for research teams.
References
- Irac SE, Soon MSF, Borcherding N, et al. Single-cell immune repertoire analysis. Nature Methods. 2024;21:777–792.
- Perik-Zavodskii R, Perik-Zavodskaia O, Volynets M, Alrhmoun S, Sennikov S. TCRscape: a single-cell multi-omic TCR profiling toolkit. Frontiers in Bioinformatics. 2025;5:1641491.
- Mandel J, Gleason L, Joffe D, Bhatti S, Nikbakht N. Immunosequencing applications in cutaneous T-cell lymphoma. Frontiers in Immunology. 2023;14:1300061.
- Rouet R, Jackson KJL, Langley DB, Christ D. Next-Generation Sequencing of Antibody Display Repertoires. Frontiers in Immunology. 2018;9:118.
Disclaimer
CD Genomics provides this service for Research Use Only. This service is not intended for clinical diagnosis, patient treatment guidance, patient management, or direct-to-consumer genetic testing.
Demo Results
Clonotype frequency and CDR3 sequence outputs help identify dominant T-cell or B-cell receptor clones in a sample.
V(D)J gene usage and paired-chain summaries help describe receptor structure and chain pairing patterns.
UMAP clonotype overlays help connect expanded immune clones with cell clusters and gene-expression states.
Frequently Asked Questions About scTCR/BCR-seq
1. What is scTCR/BCR-seq?
scTCR/BCR-seq is a single-cell sequencing method used to profile T-cell receptor and B-cell receptor sequences from individual immune cells. It helps identify clonotypes, CDR3 sequences, V(D)J gene usage, and receptor-chain pairing.
2. What is the difference between scTCR/BCR-seq and bulk TCR/BCR sequencing?
Bulk TCR/BCR sequencing profiles receptor diversity from a mixed population of cells. scTCR/BCR-seq keeps single-cell context, which means receptor sequences can be linked to individual cells, paired chains, and optional gene-expression profiles.
3. Can scTCR/BCR-seq be combined with single-cell RNA sequencing?
Yes. scTCR/BCR-seq can be combined with single-cell gene expression analysis. This allows clonotypes to be mapped onto immune-cell clusters and marker gene patterns.
4. What samples can be used for scTCR/BCR-seq?
Common sample types include PBMCs, sorted T cells, sorted B cells, tumor-infiltrating lymphocytes, dissociated tissue-derived immune cells, and mouse immune-cell samples. Sample quality, viability, and cell concentration should be reviewed before submission.
5. What outputs are included in a scTCR/BCR-seq project?
Typical outputs include raw sequencing data, QC summaries, clonotype tables, CDR3 sequences, V(D)J gene usage, diversity metrics, paired-chain summaries, and optional UMAP overlays with gene-expression integration.
6. Can scTCR/BCR-seq recover paired receptor chains?
scTCR/BCR-seq is designed to support paired-chain analysis, such as TCR alpha/β pairing or BCR heavy/light chain pairing. Recovery depends on sample quality, cell type, library quality, and sequencing performance.
7. How should I choose between scTCR-seq and scBCR-seq?
Choose scTCR-seq when your project focuses on T-cell clonotypes, T-cell expansion, or T-cell receptor analysis. Choose scBCR-seq when your project focuses on B-cell clonotypes, antibody repertoire, or heavy/light chain analysis. Choose integrated single-cell gene expression plus V(D)J sequencing when you need receptor identity and cell-state information together.
8. Do I need custom bioinformatics analysis?
Custom bioinformatics is helpful when your project includes multiple tissues, groups, time points, treatments, or integrated gene-expression data. It can also help when you need specific visualizations, group comparisons, or publication-style figures.
Case Study: Single-Cell Multi-Omic TCR Profiling for Clonotype Discovery
Background
Single-cell immune profiling is valuable because TCR sequence alone does not fully describe a T cell. A clonotype may be expanded, but researchers also need to know whether that clone belongs to a CD8-positive population, whether it expresses activation markers, and whether it appears after immune stimulation.
In the published study TCRscape: a single-cell multi-omic TCR profiling toolkit, the authors developed an analysis framework for single-cell multi-omic TCR profiling. The study used CD8-positive T cells collected before and after stimulation with HPV antigens presented by dendritic cells. The dataset included two pre-treatment and two post-treatment samples.
Methods
The study introduced TCRscape, an open-source Python tool for high-resolution TCR clonotype discovery and quantification. The method integrated full-length TCR sequence data with gene expression and surface protein profiles.
The workflow imported expression matrices and an Adaptive Immune Receptor Repertoire matrix, normalized gene expression data, encoded sample tags and clonotype IDs, and used V(D)J gene-segment information to improve clonotype-level resolution. Productive TCR sequences were filtered by read-count support, and paired chains were retained for analysis.
The authors analyzed alpha-β and gamma-delta T-cell populations and projected multimodal features onto UMAP space.
Results
The study showed that CDR3-types and full-length TCR clonotypes could be quantified across single cells. In Figure 2, the authors visualized CDR3-type distribution and clonotype distribution in the post-treatment group. Two highlighted CDR3-type segments included AGYSGNTPLV and SVVAHYTEAF.
In Figure 3, the authors projected TCR and gene-expression features onto UMAP plots. The figure included experimental group, CD8A expression, CD4 expression, TCR type, number of cells per TCR clonotype, IFNG expression, dominant alpha-chain V fragment, dominant β-chain V fragment, and IL2RA expression.
A key observation was that a dominant clonotype appeared in the post-treatment group. The authors identified this dominant clonotype as alpha-β TCR. The same cells showed CD8-positive, IL2RA-positive, and IFNG-positive features, which supported an activated T-cell state after antigen stimulation.
Figure 3 from TCRscape: a single-cell multi-omic TCR profiling toolkit shows how TCR clonotype data are integrated with single-cell gene-expression features to visualize immune-cell identity, receptor type, and dominant clonotype patterns.
Figure 3 shows how TCR clonotype data can be integrated with single-cell gene-expression features to visualize immune-cell identity, receptor type, and dominant clonotype patterns.
Conclusion
This study shows why scTCR-seq becomes more useful when clonotype information is interpreted together with single-cell phenotype and gene-expression context. For immune repertoire projects, receptor sequence recovery is only one part of the question. The stronger value comes from connecting clonotype identity with cell state, group differences, and biological function.
Related Publications
The following publications are related to immune repertoire, TCR, BCR, or immune sequencing research.
Journal: Gut
Year: 2025
Relevance: Related TCR-based immunotherapy research
Immunosequencing applications in cutaneous T-cell lymphoma
Journal: Frontiers in Immunology
Year: 2023
Relevance: Related immune repertoire sequencing application
Next-Generation Sequencing of Antibody Display Repertoires
Journal: Frontiers in Immunology
Year: 2018
Relevance: Related antibody repertoire sequencing research
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