Single-Cell Immune Profiling Strategies for Characterizing Rare Cell Populations in Autoimmune and Oncology Indications

Understanding rare immune cell populations is increasingly central to both autoimmune disease research and oncology drug development. These low-abundance subsets—pathogenic effector cells, tissue-resident memory T cells, exhausted T cells, unconventional T cell lineages, plasmacytoid dendritic cells, and others—often drive disproportionate biological impact despite representing only a fraction of total immune composition. Traditional bulk profiling obscures their distinct transcriptional programs, activation states, and lineage trajectories. In contrast, single-cell immune profiling provides the resolution needed to distinguish these populations and define their mechanistic roles across disease contexts.

Advances in single-cell RNA sequencing (scRNA-seq), immune repertoire sequencing, multiomic capture, and high-parameter cytometry now enable researchers to delineate cell states with unprecedented depth. The challenge is selecting the right strategy—and validating that the workflow preserves rare populations without introducing bias. Below are the most effective single-cell approaches currently used to interrogate these elusive immune subsets.

High-Resolution Transcriptomic Profiling for Distinguishing Pathogenic Subsets

scRNA-seq remains the foundational strategy for rare cell identification. By quantifying gene expression at the single-cell level, researchers can resolve subtle transcriptional programs that define pathogenic states.

Key advantages include:

• Ability to isolate transcriptionally distinct subsets that are invisible in bulk RNA-seq
• Detection of activation signatures and cytokine programs associated with autoimmune flares or tumor immune evasion
• Reconstruction of differentiation trajectories to determine lineage origins and fate decisions

For autoimmune indications such as SLE and RA, this resolution has revealed rare IFN-responsive monocyte states and autoreactive T follicular helper cell subsets central to disease progression. In oncology, scRNA-seq identifies exhausted T cell clusters, regulatory T cell subpopulations, and myeloid suppressor lineages enriched in the tumor microenvironment.

Multiomic Workflows to Uncover Functional–Phenotypic Coupling

Transcriptomics alone is insufficient for many rare cell populations whose functional roles depend on epigenetic or proteomic context. Multiomic strategies—such as paired scRNA-seq + surface proteomics (CITE-seq), chromatin accessibility profiling (scATAC-seq), or combined V(D)J sequencing—enable deeper characterization.

Multiomic strategies provide:

• Linkage of transcriptional state to surface-marker expression for more accurate cell-type boundaries
• Chromatin accessibility data to reveal regulatory programs driving rare-cell phenotypes
• Clonal lineage reconstruction to identify expanded pathogenic or tumor-reactive clones

These combined modalities are particularly valuable in oncology, where T cell exhaustion signatures must be interpreted alongside clonal expansion and antigen specificity.

Rare-Cell Enrichment and Targeted Sequencing Approaches

Because many rare immune subsets fall below 0.1% of total PBMCs, pre-enrichment strategies improve detection and reduce sequencing noise. Magnetic or flow cytometry–based isolation can enhance representation of target populations before single-cell capture.

Common enrichment strategies include:

• Isolation of CD45RA- or CCR7-defined T cell subsets for naïve vs. memory profiling
• Enrichment of dendritic cell or NK cell subsets for autoimmune disease studies
• Tumor tissue dissociation protocols optimized to preserve fragile myeloid or stromal populations

Targeted single-cell panels—such as targeted gene expression or TCR/BCR sequencing—further improve sensitivity while reducing sequencing burden.

Computational Tools to Resolve Extremely Low-Frequency Populations

The ability to characterize rare subsets depends as much on computational strategy as on wet-lab workflow. Advanced clustering algorithms, batch-correction methods, doublet detection, and trajectory inference models now enable more reliable identification of populations that represent fewer than dozens of cells.

Analytical innovations include:

• Deep-learning–based denoising to recover true biological signal
• Pseudotime modeling to place rare cells within developmental hierarchies
• Differential abundance testing tailored to low-frequency populations

These computational frameworks prevent misclassification and ensure biological validity when working at the limits of detection.

Conclusion

Single-cell immune profiling has become an indispensable tool for understanding rare immune cell populations that drive pathology in autoimmune and oncology indications. Combining high-resolution transcriptomics, multiomic integration, rare-cell enrichment, and advanced computational methods provides a holistic strategy to uncover disease-relevant states with precision. As these techniques become more sensitive and scalable, the ability to identify, track, and functionally characterize rare populations will directly accelerate biomarker discovery, patient stratification, and therapeutic development.