Introduction to Single-Cell RNA Sequencing

A primer on single-cell RNA sequencing — from library preparation to computational analysis — and why it has transformed our understanding of cellular heterogeneity.

Why Single-Cell?

Bulk RNA sequencing measures gene expression averaged across thousands of cells, masking the diversity within complex tissues. Single-cell RNA sequencing (scRNA-seq) overcomes this limitation by profiling transcriptomes at individual-cell resolution, enabling the discovery of rare cell types, transitional states, and cell-type-specific responses to perturbation.

Key Steps in a Typical scRNA-seq Workflow

1. Tissue dissociation & cell capture — Droplet-based platforms (10x Genomics Chromium) or plate-based methods (Smart-seq2/3).
2. Library preparation & sequencing — cDNA synthesis, amplification, and Illumina sequencing.
3. Raw data processing — Demultiplexing, alignment (STARsolo, Cell Ranger), and count matrix generation.
4. Quality control — Filtering low-quality cells based on mitochondrial content, gene counts, and doublet detection.
5. Downstream analysis — Normalization, dimensionality reduction (PCA, UMAP), clustering, differential expression, and trajectory inference.

Tools of the Trade

The scverse ecosystem — including Scanpy, AnnData, scvi-tools, and Muon — provides a comprehensive Python framework for single-cell analysis. R users often rely on Seurat and Bioconductor packages.

Looking Ahead

Stay tuned for deeper dives into specific applications: microglial profiling in neurodegeneration, spatial transcriptomics in the tumor microenvironment, and multi-modal single-cell approaches.