01. What Is Single-Cell RNA Sequencing?
Single-cell RNA sequencing (scRNA-seq) is an experimental method that measures RNA from cells in a mixed tissue while preserving each cell’s identity. It takes a suspension of cells or nuclei and produces a cell × gene matrix containing the detected RNA molecule counts for each cell.
Question for this lesson: How do mixed cells become separate rows in a table?
Start with the output: one cell becomes one row
Section titled “Start with the output: one cell becomes one row”Suppose four cells were captured from a tumour. The values below are illustrative RNA molecule counts.
| cell | EPCAM | CD3D | COL1A1 |
|---|---|---|---|
| cell_1 | 18 | 0 | 0 |
| cell_2 | 15 | 0 | 1 |
| cell_3 | 0 | 21 | 0 |
| cell_4 | 1 | 0 | 16 |
cell_1 and cell_2 have high EPCAM, cell_3 has high CD3D, and cell_4 has high COL1A1. Researchers use combinations like these to identify epithelial cells, T cells, fibroblasts, and other populations.
A row is not a complete copy of the original cell. It contains RNA that survived capture, was converted into a DNA copy called cDNA, and was sampled as short sequence fragments called reads. A value of 0 can mean that an RNA molecule was present but not detected.
A typical microscopic-droplet experiment has four stages
Section titled “A typical microscopic-droplet experiment has four stages”1. Separate the tissue into cells or nuclei
Section titled “1. Separate the tissue into cells or nuclei”Solid tissue is processed enzymatically and mechanically into a cell suspension. Single-nucleus RNA-seq can be used when intact cells are difficult to recover, as is often the case with frozen tissue.
2. Attach a unique label to each cell
Section titled “2. Attach a unique label to each cell”In a droplet workflow, one cell and one bead carrying sequence labels are typically enclosed in a microscopic droplet. When cellular mRNA is copied into cDNA, it receives a cell barcode and a unique molecular identifier (UMI).
- Cell barcode: records which cell produced the cDNA
- UMI: identifies an original RNA molecule so copies created during polymerase chain reaction (PCR) amplification can be collapsed
3. Pool the cDNA and sequence it
Section titled “3. Pool the cDNA and sequence it”After barcoding, cDNA from many cells can be pooled and read on the same sequencer. Each read is connected to a barcode that records its cell of origin and a cDNA sequence that can be assigned to a gene.
4. Aggregate reads back into cell-level counts
Section titled “4. Aggregate reads back into cell-level counts”Primary analysis software assigns cDNA sequences to genes, groups reads by cell barcode, and collapses PCR duplicates with UMIs. The main output is a matrix of gene counts for each barcode. Most values are 0, so the matrix is sparse.
Read the three words in the name separately
Section titled “Read the three words in the name separately”| term | meaning | common misconception |
|---|---|---|
| Single-cell | Barcodes preserve cell identity | Each cell needs a separate sequencing run |
| RNA | Detects transcripts present at capture | Directly measures DNA variants or proteins |
| Sequencing | Samples cDNA fragments as reads | Exhaustively reads every RNA molecule in a cell |
scRNA-seq is therefore not a complete photograph of a cell. It samples part of the RNA present at capture to infer which cell populations exist and which gene programs each population is using.
What questions can it answer?
Section titled “What questions can it answer?”- Which cell populations are mixed in a tissue?
- Is a rare cell population present?
- Do cells of the same type differ in activation, proliferation, or stress state?
- Does a particular cell type change in abundance or expression between conditions?
The count matrix alone does not reveal where a cell was located in the tissue, whether an RNA change became a protein change, or whether an observed difference is a cause or a consequence. Those questions require additional spatial, protein, temporal, or functional measurements.
Its place in the full workflow
Section titled “Its place in the full workflow”A collection of cDNA fragments prepared for sequencing is called a library. The sequencer stores each read sequence and its quality scores in a FASTQ file.
| stage | input | output |
|---|---|---|
| Experiment | tissue, cell suspension, or nuclei | barcoded cDNA library |
| Sequencing | cDNA library | FASTQ reads |
| Primary analysis | FASTQ and reference genome | cell by gene count matrix |
| Secondary analysis | count matrix and sample information | quality control, cell groups, cell types, and condition comparisons |
Summary
Section titled “Summary”Single-cell RNA sequencing labels RNA by cell, sequences the pooled cDNA, and then aggregates reads back into gene counts for each cell. Its core output is a sparse matrix with cells as rows and genes as columns, which supports analysis of cell composition and cell-level states.
Next, From Tissue Averages to Cell-Level Expression compares what remains visible when the same tissue is measured by bulk and single-cell RNA-seq.
Sources
Section titled “Sources”- Early single-cell transcriptome measurement: Tang et al., Nature Methods (2009)
- Droplet-based high-throughput single-cell profiling: Zheng et al., Nature Communications (2017)
- Best practices for single-cell transcriptomics analysis: Heumos et al., Nature Reviews Genetics (2023)