Immune-cell abundance alone cannot show whether a tumor is infiltrated, excluded, compartmentalized by stromal barriers, or organized into myeloid, T-cell, or tertiary-lymphoid-structure-like niches. CD Genomics connects single-cell and immune-repertoire discovery with spatial transcriptomics, pathology, and selected protein validation to resolve immune states, tissue architecture, local interaction hypotheses, and clonotype-linked behavior where repertoire data are available.
Immuno-oncology studies often measure immune abundance, repertoire diversity, phenotype, or tissue architecture separately. Connecting these layers helps determine whether immune states reach tumor regions, remain behind stromal barriers, form local niches, or change across defined treatment and control groups.
A tumor can contain immune cells while still excluding them from malignant regions. Region, distance, boundary, and neighborhood measurements are needed to distinguish infiltration from stromal or myeloid compartmentalization.
Single-cell sequencing resolves immune phenotypes but removes spatial relationships, leaving it unclear whether cytotoxic, dysfunctional, regulatory, myeloid, or B-cell states occupy tumor cores, boundaries, stromal niches, or lymphoid aggregates.
Spatial co-occurrence and ligand–receptor expression can prioritize plausible interactions. They do not establish antigen specificity or functional communication without additional experimental validation.
The core framework connects immune identity and state with tissue architecture, then adds clonotype information or orthogonal validation when the research question requires it. This structure supports both broad immune-microenvironment studies and focused TCR or BCR projects.
Which T-cell, B-cell, myeloid, and other immune populations are present, and which activation, dysfunction, regulation, memory, or differentiation programs do they carry?
Where do immune states occur relative to tumor cells, invasive margins, stromal barriers, myeloid regions, vessels, and TLS-like niches? Spatial evidence separates measured location from reference-based cell-state mapping.
When repertoire data are relevant, which TCR or BCR clonotypes expand or cross compartments? Selected protein, pathology, or functional follow-up can test whether prioritized RNA-defined patterns are supported by additional evidence.
Validation is selected, not assumed: mIHC spatial immune profiling, GeoMx spatial proteomics, or downstream functional experiments can be added for specific markers and hypotheses. RNA–protein agreement strengthens evidence but does not replace functional validation.
There is no single standard package for spatial immuno-oncology. The most useful starting point depends on what material and data already exist, which comparison must be supported, and whether clonotypes need to be discovered, mapped, or validated.
| Available Material or Data | Primary Research Decision | Recommended Starting Approach | Important Boundary |
|---|---|---|---|
| Fresh tumor, with or without matched blood | Which immune clones expand, and what phenotypes do they occupy? | Single-cell RNA sequencing with TCR or BCR repertoire profiling. | This establishes clone–state relationships but does not preserve tissue location. |
| Fresh tumor plus a matched tissue section | How are clonotype-linked states organized within tumor tissue? | Single-cell RNA + TCR or BCR profiling, matched spatial transcriptomics, and integrated mapping. | Reference mapping is not the same as direct recovery of receptor sequences in the spatial assay. |
| Existing scRNA-seq and V(D)J data | Can previously identified immune states or clones be localized in tissue? | New spatial profiling plus single-cell and spatial transcriptome integration. | Compatibility depends on tissue, condition, metadata, reference quality, and biological matching. |
| Archived FFPE cohort | Are candidate immune states, barriers, or markers reproducible across pathology-defined regions? | FFPE spatial transcriptomics with selected spatial protein profiling. | This route supports spatial validation but does not replace viable-cell repertoire discovery. |
| Paired groups, time points, or treatment conditions | Which clonal, cellular, and spatial features differ between defined research groups? | Use the compatible discovery or FFPE route with balanced replication, matched collection, and a prespecified contrast. | Observed differences are research associations, not clinical response predictors. |
| A direct spatial clonotype question | Must the receptor sequence itself be recovered at a tissue coordinate? | Perform a dedicated feasibility assessment for a compatible direct spatial TCR strategy. | Direct spatial clonotype recovery is a specialized assay question and should not be assumed from standard reference mapping. |
Assays are selected after the study route is defined. The table separates discovery, mapping, and validation so that no technology is asked to support a conclusion beyond its measurement scope.
| Evidence Need | Relevant Methods | What the Method Adds | What It Does Not Establish Alone |
|---|---|---|---|
| Immune identity and state | Single-cell RNA sequencing plus single-cell TCR or BCR repertoire sequencing | Immune-cell phenotypes, paired receptor sequences, clone size, diversity, and clone–state relationships. | Antigen specificity or tissue location. |
| Clone–state integration | Integrated single-cell transcriptome and immune repertoire analysis | Expanded-clone phenotypes, state sharing, candidate transitions, and blood–tumor overlap when designed. | A causal differentiation path or functional tumor reactivity. |
| Whole-tissue spatial context | Spatial transcriptomics and single-cell reference mapping | Spatial domains, regional immune programs, inferred cell-state distributions, and tumor–stroma gradients. | Direct receptor-sequence localization unless the spatial assay is specifically designed for it. |
| Archived or targeted tissue validation | FFPE spatial RNA profiling or targeted in situ RNA | Pathology-aligned evaluation of selected states, programs, and regions in archived material. | Unbiased viable-cell discovery or a complete immune repertoire. |
| Selected phenotype validation | mIHC or GeoMx spatial proteomics | Protein-level phenotype, checkpoint-marker distribution, compartment comparison, and proximity measurements. | Functional communication or clinical response prediction. |
We select the analytical questions that match the included assays, biological replication, and sample pairing. A tissue-architecture project can focus on immune states and spatial niches, while a repertoire-enabled project can add clone–state and cross-compartment analyses.
Determine which TCR or BCR clonotypes expand, which phenotypes contain them, and whether clones are shared across blood, tumor, regions, time points, or experimental groups.
Evaluate where compatible immune states occur and how they relate to tumor cores, invasive margins, stromal barriers, myeloid regions, lymphoid niches, and local cellular neighborhoods.
Prioritize local signaling relationships supported by compatible expression, tissue region, neighborhood membership, and proximity. These results guide follow-up rather than prove communication.
Compare prespecified groups or time points and assess whether selected RNA-defined patterns are supported by protein measurements, morphology, or an independent tissue cohort.
Repertoire profiling is essential for clonal identity. Integration adds phenotype and tissue context while preserving clear boundaries between direct measurements and computational inference.
| Research Dimension | Immune Repertoire Evidence Alone | Evidence Added by Single-Cell and Spatial Integration |
|---|---|---|
| Clonal Expansion | Identifies expanded, shared, and diverse receptor sequences. | Connects expanded clones with immune-cell identity and transcriptional state. |
| Tissue Location | Does not show whether clones occupy tumor, margin, stroma, or lymphoid regions. | Maps clonotype-linked state signatures into tissue; direct receptor localization depends on assay design. |
| Immune Architecture | Cannot resolve infiltration, exclusion, boundaries, or local neighborhoods. | Quantifies spatial domains, distance gradients, niches, and cellular adjacency. |
| Mechanistic Interpretation | Clonal expansion does not establish antigen specificity or local cellular interactions. | Spatial and protein evidence prioritizes plausible mechanisms, which may still require functional validation. |
| Group Comparison | Compares repertoire features between samples or groups. | Connects clonal changes with cell-state and tissue-architecture changes across the same study design. |
The final technology combination is selected according to the research question, specimen preservation, tissue area, required resolution, cohort design, and validation needs.
Sample type alone does not determine the project. Feasibility review should establish whether the planned evidence chain can be supported without exhausting tissue or introducing avoidable mismatch between modalities.
| Planning Question | Why It Matters | What Should Be Confirmed |
|---|---|---|
| Can viable immune cells be recovered? | Paired single-cell RNA and V(D)J profiling generally depends on a suitable viable-cell suspension. | Collection method, transport interval, dissociation plan, expected immune-cell abundance, and backup material. |
| Is a matched tissue section available? | Clone–state discovery and spatial mapping are easier to interpret when specimens represent the same lesion, region, and biological condition. | Block or section identity, adjacency, pathology, tissue area, orientation, and whether serial sections are acceptable. |
| Is blood, lymph node, or adjacent tissue part of the question? | Additional compartments can reveal clonal sharing or enrichment, but they also change the experimental design and interpretation. | Which compartments are required, collection timing, pairing rules, and the planned comparison. |
| Are groups and time points biologically replicated? | One section or one specimen cannot support a general treatment-group conclusion. | Biological replication, balanced batches, matched collection, exclusion criteria, and prespecified contrasts. |
| What must be measured directly? | Reference-based mapping, direct spatial receptor recovery, protein validation, and functional testing support different claims. | The required evidence level for each conclusion and which findings can remain hypotheses. |
| Can existing data serve as a reference? | Existing scRNA-seq or V(D)J data may reduce new wet-lab work, but only if the reference is biologically and technically compatible. | Raw and processed data availability, metadata, annotation quality, tissue match, condition match, and batch structure. |
The workflow is organized around decisions and evidence boundaries, not around running every available assay. A project may stop after discovery, proceed to spatial mapping, or add validation depending on the research objective.
Specify the immune question, comparison, required evidence level, and conclusions the study should not overreach.
Coordinate viable-cell preparation, tissue sections, pathology, matched compartments, replication, and existing datasets.
Run only the single-cell, repertoire, spatial RNA, or protein assays required by the agreed evidence plan, with assay-specific QC.
Separate direct measurements from reference mapping, spatial associations, and interaction hypotheses while assessing uncertainty and batch effects.
Provide reusable data, methods, figures, limitations, and a ranked set of findings for validation or follow-up research.
Deliverables are defined by the measurements and analyses included in the project. Immune-state discovery, clonotype integration, spatial-architecture mapping, and FFPE validation therefore receive different output sets rather than a fixed checklist.
Applicable raw data, processed matrices, receptor annotations, image-derived data, sample metadata, and assay-specific QC documentation.
Where applicable: annotated immune states, clonotype tables, diversity and expansion summaries, clone–state relationships, and cross-compartment sharing.
Where applicable: spatial state maps, region comparisons, infiltration and exclusion metrics, neighborhood analyses, selected protein evidence, and mapping confidence.
Methods, publication-oriented figures, evidence grading, stated limitations, research interpretation, and prioritized candidates for orthogonal or functional follow-up.
Paired transcriptome and V(D)J analysis connects clonal identity with cell phenotype, helping researchers interpret expansion in the context of immune state rather than frequency alone.
Whole-transcriptome mapping, targeted spatial RNA, mIHC, and spatial proteomics are selected according to specimen type, required resolution, and whether the project needs discovery or validation.
Projects can begin with new fresh-tissue discovery, an existing single-cell reference, or pathology-guided archived material rather than being forced into one standard package.
Direct measurements, reference mapping, spatial associations, and candidate mechanisms are reported as different evidence levels, with methods, QC, limitations, and publication-oriented figures.
These independent published studies illustrate the research value of connecting immune identity, transcriptional state, and tissue organization. The visuals are original conceptual summaries rather than reproductions of published figures.
Clone frequency alone cannot show where T cells reside or whether cells from the same clone occupy similar functional states.
The researchers developed Slide-TCR-seq to profile whole transcriptomes and TCR sequences in intact tissue and applied it to lymphoid and human tumor specimens.
T-cell states and infiltration varied within and between clonotypes, while spatially adjacent tumor and immune cells showed distinct expression patterns.
The study demonstrates why clonal identity, transcriptional phenotype, and tissue position provide complementary evidence in immuno-oncology research.
Immune-cell abundance may not explain why some tumors remain accessible to immune cells while others form spatial barriers at tumor–stroma boundaries.
The study integrated Stereo-seq, single-cell RNA sequencing, and multiplex imaging in treatment-naive and immune-checkpoint-blockade-treated colorectal cancer samples.
Distinct boundary architectures and local immune–stromal organizations were associated with treatment response, including contrasting dendritic/T-cell-rich and fibroblast-rich regions.
The work illustrates how single-cell and spatial evidence can connect cellular programs with tissue barriers and treatment-associated immune organization without treating association as clinical prediction.
Expansion, tumor enrichment, and a cytotoxic or dysfunctional phenotype can help prioritize clonotypes, but antigen specificity requires dedicated experimental evidence.
In a common integrated design, single-cell V(D)J data define clonotype-linked transcriptional states, and compatible state signatures are mapped into spatial data from matched or related tissue. This is different from directly recovering the receptor sequence in the spatial assay.
Direct spatial TCR approaches exist in research settings, but compatibility depends on platform, tissue, assay design, and sequencing strategy. Direct clonotype localization should be confirmed during feasibility review rather than assumed.
Matched blood can help distinguish tumor-enriched, circulating, and shared clonotypes. It is most useful when blood–tumor overlap or systemic immune dynamics is an explicit study endpoint.
The choice depends on the immune populations and hypothesis. TCR profiling is central when the study focuses on T-cell clonal expansion and state. BCR profiling is relevant for B-cell lineage, clonal maturation, plasma-cell programs, antibody-producing niches, or tertiary-lymphoid-structure-related questions. Both should not be included automatically when only one receptor system is biologically relevant.
Yes. Probe-based spatial RNA profiling and multiplex spatial protein assays can support pathology-guided analysis of archived tissue. Viable-cell single-cell repertoire discovery generally requires a separate suitable specimen or existing dataset.
These analyses prioritize positionally and molecularly plausible interactions. Functional communication may still require protein, perturbation, co-culture, imaging, receptor-blocking, or other experimental validation.
Yes, when tissue architecture, tumor and stromal regions, immune-state maps, and appropriate spatial statistics are available. The analysis can compare immune density, distance to tumor regions, boundary crossing, and neighborhood composition while accounting for image quality and assay resolution.
Yes, when the existing data have sufficient quality, metadata, relevant immune states, and biological compatibility with the spatial samples. Reference mapping quality and limitations are reported as part of the analysis.
Yes. Clonal expansion, cell states, spatial domains, boundaries, neighborhoods, and selected protein markers can be compared when replication, sample collection, region selection, and batch structure are planned appropriately.
Share your tumor type, specimen preservation, study groups, matched blood or tissue availability, existing single-cell or repertoire data, and the conclusion your study needs to support. Our team can help define the minimum evidence route required for clone–state discovery, spatial localization, comparative analysis, or selected validation.
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