Introduction to Single-Cell RNA Sequencing

Since the discovery of the cell, several centuries of technological advances have made it possible to isolate individual cells and study their transcriptomes. Analyzing RNA at the single-cell level can reveal critical biological processes and changes that help explain how healthy and diseased tissues function. In this article, we explore the fundamentals of single-cell RNA sequencing (scRNA-seq), key experimental considerations, important applications, and emerging trends.

The fundamentals of scRNA-seq

Now a routine technique in molecular biology labs, scRNA-seq was developed to perform one primary function. “Single-cell RNA-seq profiles gene expression at the resolution of individual cells,” explained Michael Schnall-Levin, Founding Scientist and Chief Technology Officer at 10x Genomics. “In contrast, standard (bulk) RNA-seq measures the average gene expression across thousands or millions of cells pooled together.” In essence, scRNA-seq is like increasing the magnification to focus on the most relevant cells, rather than surveying the whole population. While fewer cells are typically sequenced than in bulk RNA-seq, the level of detail gained is far greater.

One of the main strengths of this approach is uncovering important differences between cells, also known as cellular heterogeneity. Keith Jackson, Director, Product Management at Scale Bioscience (Scale Bio), noted that scRNA-seq allows for individual gene expression patterns to be dissected on a cell-by-cell basis. “This enables different cell types and states to be uncovered in a sample and identifies differentially expressed genes,” Jackson added. Obtaining this level of resolution is particularly important in contexts like tumor microenvironments or immune responses, where distinct cellular subpopulations can drive divergent functional outcomes.

Designing scRNA-seq experiments

Since bulk sequencing still provides important insights, deciding when to use scRNA-seq depends on the research objective. These are often studies that require resolution beyond what bulk RNA-seq can provide. As Jackson noted, these biological research questions involve understanding tissue heterogeneity, uncovering disease mechanisms on a cell-by-cell basis, and cataloging or atlasing all the cell types in a given tissue or disease state. In addition, Schnall-Levin highlighted that scRNA-seq is well-suited for studying cell differentiation during development, assessing how various cells respond to drugs, and conducting trajectory analysis of state transitions, such as those seen in stem cells or immune cells.

One key consideration when planning a scRNA-seq experiment is determining how many cells are needed to achieve meaningful and reliable results. The minimum number of cells needed varies depending on the platform, study goals, and sample quality. Schnall-Levin noted that while some technologies can yield insights from as few as 100–500 cells, “for robust discovery and statistical confidence, particularly when identifying rare populations, researchers typically aim to capture thousands to tens of thousands of cells per experiment.” In large-scale studies like pooled CRISPR screens, millions may be required.

Jackson agreed that the ideal number depends on the method and experimental design, generally recommending a few thousand to tens of thousands of cells per sample for reliable data. He emphasized the importance of using flexible methods, especially when working with limited or variable-quality clinical samples. He also pointed out that only a fraction of input cells, often 50–60%, are typically recovered in high-quality data due to losses during sample preparation and library construction.

Important applications

One of the most impactful applications of scRNA-seq has been its ability to identify rare cell types that are undetectable with bulk methods. “Because scRNA-seq profiles thousands to millions of individual cells, even rare populations representing less than 1% of a sample can be detected and characterized,” stated Schnall-Levin. He further explained that computational clustering techniques group cells based on their gene expression patterns, helping researchers identify rare but important cell types, such as those involved in disease progression, treatment resistance, or tissue regeneration. As a notable example, he pointed to ionocytes, a rare lung cell type (less than 0.5% of the population) that plays a central role in cystic fibrosis.¹

Jackson shared that the discovery of these previously unknown cell types was among the most fascinating early findings using scRNA-seq. “The power of identifying rare cell types and states in scRNA-seq comes from the ability to profile large numbers of cells simultaneously,” Jackson emphasized. For instance, he noted that some transient cell types involved in T cell development account for less than 0.01% of the thymus, and detecting these rare populations requires analyzing data from millions of cells.

Trends in scRNA-seq

As scRNA-seq becomes more widely adopted, several emerging trends are shaping the future of this technology. Schnall-Levin explained that one key development is higher-throughput, lower-cost profiling, enabled by new chemistries and automation that allow researchers to scale experiments to hundreds of thousands or even millions of cells. Single-cell technologies are also increasingly moving beyond basic research into clinical applications, including biomarker discovery and patient stratification. Another important area Schnall-Levin shared is spatially resolved transcriptomics, which adds anatomical context to single-cell data by revealing where specific cells are located within tissues. Finally, Schnall-Levin noted that multiomic approaches are gaining traction, integrating RNA data with additional layers such as protein expression, chromatin accessibility, or spatial information to provide a more comprehensive view of cell identity and regulatory mechanisms.

Jackson expanded on the shift toward multiomics, noting that while scRNA-seq is now broadly accessible and widely used, researchers are increasingly looking to capture more than just gene expression by incorporating additional modalities such as DNA methylation and protein. “DNA methylation also has the advantage of being a much more stable analyte, and studies using single-cell DNA methylation potentially could identify important biomarkers for clinical use,” stated Jackson.

Alongside these innovations, Jackson described the emergence of more highly flexible scRNA-seq workflows and simpler library preparation methods. However, he explained that running large numbers of samples has traditionally been a challenge due to the cost of sequencing and logistical constraints with some sample types. While researchers often multiplex samples to reduce expenses, some technologies require complex tagging steps or computational demultiplexing, which can fail with genetically similar samples and lead to high data loss. “To overcome these challenges, we’ve developed new methods to both streamline the library prep workflow and also make it more accessible to run hundreds to thousands of samples with improved multiplexing strategies and a lower cost per sample,” stated Jackson.

Recent years have seen a rise in large-scale collaborative projects using scRNA-seq that are generating comprehensive datasets that map cell types, states, and functions across tissues and conditions. These efforts are advancing cell atlasing efforts, deepening our understanding of biology and disease, and enabling AI-driven discoveries. Taken together, these developments are extending the utility of single-cell research across diverse areas of biology and medicine.

Reference

1. Montoro, D.T., Haber, A.L., Biton, M. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018).