# 01. What Is Single-Cell RNA Sequencing?

> How single-cell RNA sequencing labels RNA by cell and turns short measured sequence fragments into a cell-by-gene expression matrix, including its inputs, workflow, outputs, and interpretation limits.

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

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

### 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

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

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

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

| 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?

- 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

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

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](/en/lessons/single-cell-resolution/) compares what remains visible when the same tissue is measured by bulk and single-cell RNA-seq.

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### Sources

- Early single-cell transcriptome measurement: [Tang et al., *Nature Methods* (2009)](https://www.nature.com/articles/nmeth.1315)
- Droplet-based high-throughput single-cell profiling: [Zheng et al., *Nature Communications* (2017)](https://www.nature.com/articles/ncomms14049)
- Best practices for single-cell transcriptomics analysis: [Heumos et al., *Nature Reviews Genetics* (2023)](https://www.nature.com/articles/s41576-023-00586-w)