Next-Gen Immune Monitoring: The Value of Single-Cell Sequencing

What is single-cell sequencing?

Single-cell sequencing is a set of technologies used to measure molecular information by next-generation sequencing in individual cells, rather than pooled cell populations as in bulk sequencing. While bulk sequencing methods report an average across mixed cell populations, single-cell sequencing captures subtle variations between cells that would otherwise go undetected, giving deeper insights that can drive therapeutic development.

Applying it effectively in clinical studies relies on understanding where it brings the most value, and what it can offer beyond bulk sequencing. So, what insights can single-cell sequencing offer, and where can this valuable technique fit into a clinical strategy?

Moving beyond averages: the value of single-cell sequencing

Analyzing gene expression at single-cell resolution allows researchers to uncover patterns that bulk methods often obscure. With this approach, it becomes possible to:

• Trace biological signals back to specific cell types
• Capture subtle differences between cells within the same sample
• Investigate how heterogeneous cell populations respond to therapy or evolve over time
• Define new predictive biomarkers based on cellular features

Single-cell sequencing offers multi-omics insight into each individual cell by combining, as needed, single-cell transcriptomics (RNA expression), single-cell proteomics (protein expression profiling through the use of DNA-barcoded antibodies), and single-cell immune repertoire analysis (TCR and BCR clonotyping), to give a broad window into cellular identity, state, and function. Researchers can also add spatial resolution, with single-cell spatial sequencing, tailoring workflows to suit a wide range of applications and sample types.

Single-cell applications: where does it fit in the clinical development pipeline?

Single-cell sequencing can support development across multiple therapeutics development stages:

• Discovery: utilize single-cell transcriptomics to profile complex cell populations, identify novel biomarkers, and explore transcriptomic changes across conditions, cell types, or timepoints
• Preclinical: measure pharmacodynamic effects, validate candidate biomarkers, or evaluate target engagement and off-target activity in complex tissues
• Clinical: understand variability in response across individuals or cohorts and identify a narrow set of biomarkers for later clinical phase

These capabilities can support development across numerous different therapeutic modalities, for example:

Find out more about single-cell sequencing applications

Flexible Single-Cell Sequencing with 10X Genomics Workflows

To support a range of study goals, sample types, practical requirements, and research questions, CellCarta implements two widely used 10x Genomics workflows.

Chromium GEM-X Single Cell Workflow

The 5’ Single Cell Immune Profiling workflow captures gene expression across the full transcriptome. It is well suited to early-stage research or exploratory single-cell applications where researchers are aiming to characterize complex immune populations, identify novel biomarkers or  investigate the immune repertoire composition.

• Offers broad coverage of ~28,000 genes
• Supports multiomic readouts (RNA, protein, TCR/BCR)
• Uses fresh or cryopreserved peripheral blood mononuclear cells (PBMCs), whole blood, and cell lines

Chromium GEM-X Flex Workflow

Rather than sequencing the full transcriptome, this method uses pre-designed probe panels to focus on a curated set of protein-coding genes. It provides a more focused dataset and can be more tolerant of fixed or partially degraded samples, making it a practical option for studies using archival material or working within specific biomarker frameworks.

• Coverage of ~18,000 genes
• Supports protein and RNA readouts
• Compatible with fresh or cryopreserved PBMCs, whole blood, and cell lines, as well as fixed samples

Single-Cell Data Analysis Tool

Generating high-quality single-cell data is only the first step; how that data is analyzed determines its usefulness for informing study decisions. At CellCarta, we take an immunology-first approach to data analysis, using our cloud-based CellEngine® software to mirror a traditional flow cytometry analysis.

Through CellEngine, immunologists can explore single-cell transcriptomic data using familiar, interactive tools like gating, clustering (tSNE, UMAP), and visualizations such as dot plots, contour plots, and histograms. The software also supports secondary analysis across patients, timepoints, and treatment conditions using heatmaps, bar charts, and other comparative formats.

This approach helps researchers generate highly relevant, actionable insights with confidence. Explore how this approach supports deep immunophenotyping in clinical trials in our CITE-seq case study.

Supporting more informed research at every stage

Single-cell sequencing gives researchers a clearer view of how therapies interact with complex biological systems. By analyzing cells individually, it becomes possible to uncover novel biomarkers, track immune dynamics, and explore differences in response that bulk sequencing methods miss, enabling more informed decisions throughout development.

Interested in finding out more on how single-cell sequencing could support your study? Get in touch with the CellCarta team!

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