Sample Submission Guidelines
Resolve CAR-T Cell States, Clonal Dynamics, and Immune Context at Single-Cell Resolution
CAR-T research often depends on questions that bulk assays cannot fully answer. A bulk RNA-seq profile can show an average expression trend. A bulk TCR-seq profile can show repertoire-level clone abundance. But neither can directly show which clone belongs to which cell state, which population is driving a signal, or how product heterogeneity changes across samples.
Our CAR-T Single-Cell Multiomics Solution is built for those questions. We help R&D teams connect CAR-T or T-cell clonotype identity with transcriptomic state, surface phenotype, chromatin accessibility, and sample context when the study design supports it.
Why Bulk Assays Can Miss CAR-T Biology
Bulk assays are useful when the main goal is a broad signal. They become limiting when the research question depends on cell-level context.
A single CAR-T product may include cytotoxic-like cells, exhausted cells, proliferating cells, memory-like cells, bystander immune cells, and low-frequency clones that may be important for the project. When these signals are averaged together, it can be hard to know what changed and why.
This matters when your team needs to compare:
- Pre-infusion product and post-infusion samples
- Different manufacturing conditions
- Responding and non-responding research cohorts
- Baseline and relapse samples
- CAR-T cells and host immune populations
- Product composition and tumor microenvironment context
What Single-Cell Multiomics Adds to CAR-T Research
Single-cell multiomics helps put each signal back into context. Instead of looking only at average expression or repertoire-level clone frequency, your team can ask which cells carry the signal, which clones are involved, and how that pattern changes across samples.
Depending on the assay design, we can help examine:
- CAR-T or T-cell cell-state composition
- Cytotoxicity, exhaustion, memory-like, and proliferation signatures
- TCR clonotype distribution and V(D)J gene usage
- Clonotype-state relationships
- Surface protein marker patterns through CITE-seq
- Regulatory programs through scATAC-seq or single-cell multiome
- Longitudinal changes across product, blood, marrow, or tumor samples
- Tumor microenvironment and immune interaction patterns
For projects focused on expression-state profiling, our Single-cell RNA Sequencing service can serve as the core assay. When clonotype-state linkage is needed, we can integrate single-cell immune repertoire analysis into the study design.
What We Profile Across CAR-T Product, Blood, Bone Marrow, and Tumor Samples
CAR-T research samples are not interchangeable. A product sample asks one kind of question. A peripheral immune sample asks another. Bone marrow and tumor-derived samples add microenvironment complexity. We design the profiling plan around the sample source, timepoint, and biological question.
Pre-Infusion Product Heterogeneity
A CAR-T product can contain functionally different cell populations even when the cells share the same target or construct. Single-cell profiling helps reveal that heterogeneity before the product is compared with later research samples.
- T-cell subset composition
- Memory-like and effector-like states
- Exhaustion-associated signatures
- Proliferation-associated states
- Cytotoxic gene programs
- Expanded clonotypes and their transcriptional states
- Product-to-product or batch-to-batch differences
This is useful when your team wants to understand whether a product contains the cell states and clone profiles expected for downstream research.
Post-Infusion Expansion and Persistence
Longitudinal sample comparison can help track how CAR-T or T-cell populations change after infusion in research studies. Single-cell multiomics connects clone abundance with expression state and immune context across timepoints.
- Product and post-infusion blood-derived immune cells
- Early and later timepoints
- Expanded and contracted clones
- Persistent and transient cell states
- CAR-T-like signatures and host immune populations
The goal is not to make outcome claims. The goal is to give your team a clearer research view of cellular dynamics and clone-state changes.
Bone Marrow, Tumor Biopsy, and Relapse Research Samples
Bone marrow and tumor-derived samples can reveal immune and tumor microenvironment features that product-only profiling cannot show. These samples may be useful when the research question involves persistence, resistance, relapse biology, or immune suppression.
- Bone marrow immune microenvironment profiling
- Tumor-infiltrating immune cell state mapping
- Relapse sample comparison
- Antigen-expression context when compatible data are available
- Myeloid, stromal, or suppressive immune populations
- CAR-T and host immune population relationships
Tumor Microenvironment and Host Immune Response
CAR-T behavior is shaped not only by the product, but also by the host immune context and tissue environment. Single-cell multiomics can help your team examine how immune populations, tumor cells, and suppressive microenvironment signals appear across samples.
- Immune cell composition
- T-cell exhaustion and activation programs
- Myeloid and suppressive cell populations
- Cytokine-response signatures
- Cell-cell interaction patterns
- Tumor and immune compartment relationships
Our Service Capability Advantage for CAR-T R&D Projects
CAR-T single-cell studies are not just standard single-cell sequencing projects with a different sample label. They require careful assay selection, sample feasibility review, immune repertoire expertise, and interpretation that matches cell therapy research questions.
At CD Genomics, we help you plan before data generation begins. Our goal is to make the output useful for R&D teams, not just technically complete.
Integrated Single-Cell and Immune Repertoire Expertise
Many CAR-T projects need both cell-state profiling and clonotype tracking. We support study designs that connect transcriptomic states with TCR or immune repertoire information when the assay design allows it.
Our Single-cell Immune Repertoire Sequencing capabilities can help connect paired immune receptor information with single-cell expression profiles. For projects that only require repertoire-level tracking, Immune Repertoire Sequencing may be a more focused option.
Study Design Based on CAR-T Research Questions
We start with the question your team needs to answer.
- If you need product-state profiling, scRNA-seq may be the core assay.
- If you need clone-state linkage, scTCR/scVDJ-seq should be considered.
- If surface markers are central to the phenotype, CITE-seq may add value.
- If regulatory programs matter, scATAC-seq or single-cell multiome may be appropriate.
- If you need tissue context, spatial transcriptomics may be considered as a separate or complementary approach.
- If the question can be answered by bulk repertoire tracking, a simpler design may be enough.
This helps avoid two common problems: a study that is too shallow to answer the biology, or a study that is more complex than the decision requires.
Clear Multiomics Deliverables for Cross-Functional Teams
A CAR-T single-cell multiomics report needs to work for several readers. A scientist may want marker genes and cell states. A bioinformatician may need files, parameters, and analysis objects. A program lead may need the main biological patterns summarized clearly.
- QC summaries
- Cell annotation results
- Marker gene tables
- Cell-state composition charts
- TCR or clonotype tables when applicable
- Clonotype-state integration figures
- Differential expression tables
- Pathway enrichment summaries
- Longitudinal comparison visuals
- Interpretation notes for research discussion
Flexible Assay Depth Without Overbuilding the Study
More omics layers are not always better. The right design depends on sample type, sample quality, timepoints, and the decision your team needs to make.
- scRNA-seq alone
- scRNA-seq plus scTCR/scVDJ-seq
- CITE-seq for surface phenotype
- scATAC-seq or single-cell multiome
- Bulk immune repertoire sequencing
- Longitudinal sample comparison
- Reanalysis of existing single-cell datasets
This keeps the project focused while preserving the option to expand when the research question justifies it.
CAR-T Single-Cell Multiomics Workflow with QC Checkpoints
Our workflow follows the sample from study design to final interpretation. Each step includes a technical process and a QC checkpoint, so your team understands how the data are generated, filtered, integrated, and reported.
Step 1 — Study Design and Sample Timepoint Review: We begin by reviewing your research question, CAR-T construct or cell product context, sample sources, and planned comparisons. We may ask for sample type and timepoint, CAR construct or T-cell engineering context, product, blood, marrow, tumor, or relapse sample source, fresh or cryopreserved sample status, matched sample design, target cell population, whether clonotype tracking is required, and whether protein or chromatin-level information is needed. QC checkpoint: We check whether the proposed assay combination fits the sample type, expected cell recovery, and biological question.
Step 2 — Sample Intake, Viability Check, and Cell Preparation: Single-cell data quality depends strongly on sample quality. After intake, samples are reviewed for feasibility before they move into single-cell capture. Key checks may include cell concentration, viability, debris level, doublet risk, red blood cell contamination where relevant, cell recovery after thawing, and suitability for scRNA-seq, scTCR/scVDJ-seq, CITE-seq, or multiome workflows. QC checkpoint: If the sample is limited, fragile, or lower quality than expected, we discuss whether the workflow should be adjusted before continuing.
Step 3 — Single-Cell Capture and Library Construction: Cells are partitioned into single-cell reaction systems, where cellular barcodes and molecular identifiers connect sequencing reads back to individual cells. Depending on the study design, libraries may be prepared for gene expression, V(D)J enrichment, surface protein tags, chromatin accessibility, or multiome profiling. If the project needs clone-state linkage, V(D)J capture must be included. If surface phenotype matters, antibody-derived tag libraries may be included. If regulatory programs matter, chromatin-accessibility profiling may be added. QC checkpoint: We review library quality, expected library type, and sample identity before sequencing.
Step 4 — Sequencing, Primary QC, and Cell-Level Filtering: Sequencing data are processed to generate count matrices, cell barcodes, gene-expression profiles, V(D)J clonotype information where applicable, and other assay-specific outputs. Primary analysis may include read-level QC, cell calling, doublet review, low-quality cell filtering, gene count and molecular barcodes count review, mitochondrial content review, V(D)J assembly checks where applicable, and initial clustering and visualization. QC checkpoint: We review whether the dataset is suitable for downstream interpretation and whether any sample-specific issues should be noted.
Step 5 — Multiomics Integration and Report Delivery: After QC and filtering, the data are integrated around the study question. This may include cell annotation, marker analysis, clonotype-state mapping, differential expression, pathway enrichment, trajectory analysis, cell-cell interaction analysis, and longitudinal comparison. The final report explains what was analyzed, which patterns were observed, and how the results should be interpreted within the study design. QC checkpoint: Before delivery, we review consistency across sample metadata, QC summaries, figures, tables, and interpretation notes.
Sample Requirements for CAR-T Single-Cell Multiomics Projects
Sample needs vary by assay, tissue source, preservation method, and expected cell recovery. We confirm final requirements after reviewing the study design and sample condition.
| Sample Type | Recommended Input | Preservation | Shipping | QC Checkpoints | Notes |
|---|---|---|---|---|---|
| CAR-T product | Cell input confirmed after project review | Fresh or cryopreserved | Cold chain or dry ice as advised | Viability, concentration, debris, doublet risk | Provide construct information, product stage, and intended comparison |
| PBMC | Cell input confirmed after project review | Fresh or cryopreserved | Cold chain or dry ice as advised | Viability, cell recovery, red blood cell contamination, debris | Provide timepoint, treatment context, and matched sample metadata |
| Bone marrow aspirate-derived cells | Cell input confirmed after project review | Fresh or cryopreserved | Cold chain or dry ice as advised | Viability, cell concentration, debris, doublet risk | Useful for hematologic malignancy and marrow immune microenvironment studies |
| Tumor biopsy-derived cell suspension | Cell input confirmed after tissue dissociation feasibility review | Fresh preferred; cryopreserved if compatible | Cold chain as advised | Viability, debris, dissociation quality, tumor/immune cell recovery | Provide tissue source, dissociation method if available, and paired sample design |
| Existing single-cell data | FASTQ, count matrices, metadata, and sample annotations | Digital files | Secure file transfer | File integrity, metadata completeness, reference compatibility | Useful for reanalysis, integration, or second-opinion bioinformatics review |
For limited or cryopreserved samples, we recommend discussing feasibility before shipment or data transfer. Sample loss, cell stress, low viability, and incomplete metadata can affect the interpretation of single-cell results.
Bioinformatics Analysis and Deliverables
Bioinformatics is where CAR-T single-cell multiomics becomes useful for the research team. The key value is not only the number of cells captured, but whether the analysis connects cell identity, clone identity, phenotype, and sample context.
Minimum Deliverables
- Raw sequencing files
- Per-sample QC report
- Cell filtering and annotation summary
- UMAP or clustering results
- Marker gene tables
- Cell-type and cell-state composition summary
- TCR/BCR clonotype tables where applicable
- V(D)J gene usage summary where applicable
- Clonotype-state integration results
- Differential expression analysis
- Pathway enrichment summary
- Integrated PDF report with figures and interpretation notes
These outputs are organized so your team can review both the underlying data and the biological patterns.
Optional Add-ons for Deeper Mechanism Research
- CITE-seq surface protein integration
- scATAC-seq or single-cell multiome analysis
- Trajectory or pseudotime analysis
- Cell-cell interaction analysis
- Longitudinal persistence analysis
- Product vs post-infusion comparison
- Tumor microenvironment interaction analysis
- Relapse or resistance mechanism exploration
- Custom CAR detection or transgene-expression analysis if technically supported
- Integration with bulk immune repertoire sequencing
- Reanalysis of existing single-cell datasets
- Cross-sample batch-aware integration
How We Connect Clonotypes, Cell States, and Biological Questions
For CAR-T and T-cell studies, clonotype information becomes more useful when it is interpreted together with cell-state information.
- Are expanded clonotypes enriched in cytotoxic or exhausted states?
- Do persistent clones share transcriptional features?
- Are memory-like or proliferative states distributed evenly across clones?
- Do relapse or resistance research samples show different immune-state patterns?
- Are product and post-infusion samples compositionally similar or divergent?
- Are specific cell populations driving pathway-level signals?
This is where single-cell multiomics can provide a clearer view than either expression profiling or repertoire sequencing alone.
Choosing the Right Assay Combination: Bulk TCR, scRNA-seq, scTCR-seq, CITE-seq, or Multiome
The best CAR-T multiomics design depends on the question. A simpler method may be enough for clone tracking. A deeper multiomics design may be needed when cell state, protein phenotype, chromatin regulation, or tissue context matters.
| Method | Biological Question Answered | Strengths | Limitations | Best-Fit CAR-T Use Case | Typical Deliverables |
|---|---|---|---|---|---|
| Bulk RNA-seq | What broad expression programs differ between samples? | Cost-effective, broad transcriptome view | Cannot identify which cell population drives the signal | Broad screening of expression trends | Differential expression, pathway enrichment |
| Bulk TCR-seq | Which clonotypes expand or contract at repertoire level? | Deep clone tracking, useful for longitudinal repertoire studies | Does not directly link clone identity to cell state | Repertoire-level clone tracking | CDR3 tables, clonotype frequency, diversity metrics |
| scRNA-seq | Which cell states and populations are present? | Resolves cell heterogeneity and marker programs | Does not capture clonotype identity alone | Product-state profiling and TME analysis | UMAP, marker tables, cell annotation |
| scTCR/scVDJ-seq | Which clonotypes are linked to which phenotypes? | Connects clone identity with expression state | Requires compatible immune-cell capture design | CAR-T clone-state mapping | V(D)J tables, clonotype-state maps |
| CITE-seq | How do RNA states align with surface protein markers? | Adds protein-level phenotype information | Requires validated antibody panel and sample compatibility | Surface phenotype and biomarker studies | RNA + protein marker matrices, dot plots |
| scATAC-seq / single-cell multiome | What regulatory programs may underlie cell states? | Links chromatin accessibility with regulatory state | More complex sample and analysis requirements | Exhaustion, differentiation, persistence mechanism research | Peak matrices, motif analysis, RNA/ATAC integration |
| Spatial transcriptomics | Where are immune and tumor signals located in tissue? | Preserves tissue context | Lower single-cell resolution depending on platform | TME localization and immune-tumor interaction studies | Spatial gene maps, tissue-region analysis |
Selection Rules by Research Question
- Use bulk TCR-seq when the main goal is repertoire-level clone tracking.
- Use scRNA-seq when the main goal is cell-state and heterogeneity analysis.
- Add scTCR/scVDJ-seq when clonotype identity must be linked to cell state.
- Add CITE-seq when surface marker phenotype is central to the research question.
- Add scATAC-seq or single-cell multiome when regulatory programs, exhaustion, differentiation, or persistence mechanisms are key.
- Add longitudinal design when comparing product, post-infusion, relapse, or tissue-derived samples.
- Use spatial transcriptomics when tissue localization and immune-tumor interaction context are central to the study.
- Avoid overbuilding the assay if the question can be answered with a simpler design.
For teams comparing repertoire-level and single-cell approaches, our guide on Immune Repertoire Sequencing: Methodologies and Experimental Design can provide additional background.
Applications in CAR-T Discovery, Product Optimization, and Translational Research
CAR-T single-cell multiomics can support multiple stages of research. We align the assay design with your sample type, study question, and internal decision point.
CAR-T Candidate and Construct Evaluation
Single-cell profiling can help compare CAR-T candidates or construct designs by examining cell-state composition, activation programs, cytotoxic signatures, exhaustion patterns, and clonotype distribution. This can be useful when teams need to understand whether different constructs or culture conditions are associated with different functional profiles.
Product Heterogeneity and Manufacturing Research
CAR-T products are heterogeneous by nature. Single-cell multiomics can help characterize that heterogeneity and compare product composition across batches, process conditions, or sampling points.
Persistence, Exhaustion, and Functional State Monitoring
Persistence and exhaustion are common research questions in CAR-T studies. Single-cell multiomics can help profile signatures related to proliferation, cytotoxicity, memory, exhaustion, activation, and stress response.
Resistance, Relapse, and Tumor Microenvironment Research
Relapse and resistance research often requires looking beyond the CAR-T product alone. Tumor and immune microenvironment samples can help identify immune suppression, antigen-related context, myeloid populations, T-cell state shifts, or altered cell-cell interaction patterns.
- Product composition analysis
- Memory-like and effector-like state comparison
- Expanded clone distribution
- Exhaustion-associated gene programs
- Manufacturing condition comparison
- Product vs post-infusion comparison
The analysis can support hypothesis generation for follow-up research without making treatment-response claims.
References
- Modified Dendritic cell-based T-cell expansion protocol and single-cell multi-omics allow for the selection of the most expanded and in vitro-effective clonotype via profiling of thousands of MAGE-A3-specific T-cells
- Deciphering and advancing CAR T-cell therapy with single-cell sequencing technologies
- Clonal kinetics and single-cell transcriptional profiling of CAR-T cells in patients undergoing CD19 CAR-T immunotherapy
- Single-cell antigen-specific landscape of CAR T infusion product identifies determinants of CD19-positive relapse in patients with ALL
- Distinct cellular dynamics associated with response to CAR-T therapy for refractory B cell lymphoma
- speedingCARs: accelerating the engineering of CAR T cells by signaling domain shuffling and single-cell sequencing
Demo Results: What Your CAR-T Multiomics Report May Include
The final report should help your team read the data quickly, not just store it. The examples below show the types of outputs we can include depending on assay design and data quality.
CAR-T Cell-State Landscape
A cell-state landscape can show major cell populations and CAR-T-related states in a single visualization. This may include cytotoxic-like, exhausted-like, memory-like, proliferative, regulatory-like, or other annotated populations when supported by marker patterns.
Typical outputs may include UMAP or clustering plots, cell-state annotation, marker gene dot plots, cell-state composition charts, and product-to-product or sample-to-sample comparison.
Clonotype-State Integration Map
When scTCR/scVDJ information is included, clonotypes can be mapped onto single-cell expression states. This helps your team evaluate whether expanded clones are associated with specific phenotypes.
Typical outputs may include dominant clonotype distribution, V(D)J gene usage summary, clonotype overlay on UMAP, clonotype-state bubble plots, clone sharing across samples, and expanded clone phenotype summaries.
Longitudinal Persistence and Sample Comparison View
For studies with multiple timepoints or sample sources, we can organize results into longitudinal views. These may compare product, blood, marrow, tumor-derived, or relapse research samples.
Typical outputs may include alluvial plots, stacked abundance plots, sample-comparison heatmaps, cell-state shift summaries, persistent vs transient clone views, and cross-timepoint marker or pathway comparisons.
These visuals do not replace biological validation. They help your team identify patterns that deserve deeper review.
FAQ: Planning a CAR-T Single-Cell Multiomics Study
1. When is single-cell multiomics more useful than bulk RNA-seq or bulk TCR-seq?
Single-cell multiomics is useful when you need to know which cells or clones are driving a biological pattern. Bulk RNA-seq can show expression trends, and bulk TCR-seq can show repertoire changes. Single-cell multiomics can connect cell state, clone identity, phenotype, and sample context.
2. Should we choose scRNA-seq alone or add scTCR/scVDJ-seq?
Choose scRNA-seq alone when your main question is cell-state composition or gene-expression heterogeneity. Add scTCR/scVDJ-seq when you need to connect clonotype identity with transcriptional phenotype, expanded clones, persistence, or sample-to-sample clone sharing.
3. When is CITE-seq useful in CAR-T research?
CITE-seq is useful when surface protein phenotype is central to the research question. It can help connect transcriptomic states with marker-level information such as activation, exhaustion, memory, or lineage-related protein expression, depending on the antibody panel.
4. When should scATAC-seq or single-cell multiome be considered?
Consider scATAC-seq or single-cell multiome when the research question involves regulatory programs, chromatin accessibility, differentiation, exhaustion, persistence, or state transitions. These assays are more complex and should be selected when they answer a specific mechanism question.
5. What sample types can be used for CAR-T single-cell multiomics?
Common sample types include CAR-T product, PBMC, bone marrow-derived cells, tumor biopsy-derived cell suspensions, sorted immune cells, and existing single-cell datasets. Feasibility depends on cell recovery, viability, preservation method, and the selected assay.
6. Can cryopreserved samples be used?
Cryopreserved samples may be suitable if they meet viability and recovery requirements after thawing. We recommend reviewing sample history, cryopreservation conditions, expected cell number, and study goals before choosing the assay.
7. Can you compare pre-infusion and post-infusion samples?
Yes. A longitudinal design can compare product, post-infusion blood-derived samples, marrow-derived samples, tumor-derived samples, or relapse research samples when available. The analysis can examine cell-state shifts, clonotype sharing, persistent populations, and sample-specific immune context.
8. What bioinformatics outputs are included?
Outputs may include QC reports, cell annotation, UMAP plots, marker tables, clonotype tables, V(D)J gene usage summaries, clonotype-state maps, differential expression tables, pathway enrichment, longitudinal comparisons, and an integrated report with interpretation notes.
9. Can the results support internal R&D or translational review?
Yes. The results can support internal research discussions, candidate comparison, assay planning, product heterogeneity studies, and translational sample interpretation. The data should be interpreted within the study design and should not be used as a standalone treatment-decision tool.
10. How should we choose the right assay combination?
Start with the research question. Use scRNA-seq for cell-state profiling, scTCR/scVDJ-seq for clone-state linkage, CITE-seq for surface phenotype, scATAC or multiome for regulatory programs, and bulk repertoire sequencing when deep clone tracking is the main goal.
Literature-Supported Case Example: Single-Cell Multiomics for T-Cell Clonotype Selection
Published Research Highlight
Modified Dendritic cell-based T-cell expansion protocol and single-cell multi-omics allow for the selection of the most expanded and in vitro-effective clonotype via profiling of thousands of MAGE-A3-specific T-cells
Journal: Frontiers in Immunology
Published: 2024
Source: Sennikov et al., Frontiers in Immunology 2024
The case below is based on a published Frontiers in Immunology study. It is included as a literature-supported example of how single-cell multiomics can connect antigen-specific T-cell expansion, clonotype identity, and functional follow-up in adoptive T-cell research.
Background
CAR-T and TCR-T research teams often need to understand more than whether T cells expanded. They need to know which clonotypes expanded, what phenotypes those cells show, and which candidates may deserve deeper functional review.
Sennikov and colleagues studied MAGE-A3-specific T cells using a modified dendritic cell–based expansion protocol and single-cell multi-omic profiling. Although the study focuses on antigen-specific T-cell selection rather than a commercial CAR-T product, it directly supports the core logic of this page: single-cell multiomics can connect T-cell clonotype identity with cellular phenotype and candidate selection.
Methods
The authors used a modified dendritic cell–based protocol to enrich MAGE-A3-specific T cells from HLA-A02-positive donors. They then performed single-cell multi-omic profiling using the BD Rhapsody platform.
The analysis included T-cell receptor clonotype profiling, normalized CD8, CD4, and FOXP3 expression review, predicted binding score review, and downstream in vitro functional testing. The authors used TCRscape for clonotype analysis and connected clonotype-level observations with functional evaluation.
Figure 2 in Sennikov et al., Frontiers in Immunology 2024 shows the single-cell multi-omic T-cell analysis, including TCR clonotype distribution, CD8/CD4/FOXP3 expression, predicted binding scores, and dominant clonotype identification.
Results
The study reported a mean 191-fold increase in MAGE-A3-specific T-cell abundance after enrichment. The frequency increased from 0.02% ± 0.015 to 3.33% ± 2.61 of lymphocytes.
The authors then profiled 5,491 single cells and identified 3,000 T-cell receptor clonotypes. Among these, 191 clonotypes were present in two or more cells. A dominant clonotype represented by 14 cells was highlighted in Figure 2.
These results show how single-cell multiomics can move beyond bulk enrichment measurement. The approach helped connect antigen-specific expansion, TCR clonotype distribution, expression-state information, and candidate selection logic in one research workflow.
Figure 2 from Sennikov et al. shows single-cell multi-omic analysis of T-cell receptor clonotype distribution, CD8/CD4/FOXP3 expression, predicted binding score, and dominant clonotype identification.
Conclusion
This study supports the value of single-cell multiomics for adoptive T-cell research. For CAR-T and TCR-T R&D teams, the same general approach can help connect clone identity with cell phenotype, identify expanded populations, and prioritize candidates for deeper functional review.
We do not use this study to claim direct outcome prediction. We use it as a peer-reviewed example of how single-cell multi-omic profiling can support clonotype selection and phenotype-linked interpretation.
Reference
This service is intended for Research Use Only (RUO). It is not intended for clinical diagnosis, treatment selection, or direct patient-management decisions.
