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

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.