What is Single-Cell RNA-seq?

Every cell in your body carries the same DNA — yet a root hair cell and a leaf mesophyll cell look and behave completely differently. The answer lies in which genes each cell actually expresses. Single-cell RNA sequencing (scRNA-seq) lets us measure gene expression in thousands of individual cells at once, revealing diversity that was invisible to earlier methods.

01 The Problem with Bulk RNA-seq

Before scRNA-seq, the standard approach to measuring gene expression was bulk RNA-seq. You take a tissue sample — say, a piece of sorghum root — grind it up, extract all the RNA, and sequence it. The resulting expression values are an average across every cell in that sample.

This averaging problem becomes critical in biology because tissues are not uniform. A plant root contains epidermal cells, cortex cells, endodermis cells, pericycle cells, and root hair cells — each with distinct gene expression programmes. If a disease or stress response only affects 5% of cells, bulk RNA-seq may show almost no signal for the responsive genes because they are drowned out by the 95% of unaffected cells.

02 What is Single-Cell RNA-seq?

Single-cell RNA sequencing is a family of techniques that measures the transcriptome — the complete set of RNA molecules — of individual cells. The key word is individual: instead of averaging thousands of cells together, you get a separate gene expression profile for each one.

In a typical scRNA-seq experiment, you might start with a tissue sample containing 50,000 cells. After dissociation (separating them into a suspension) and processing through a microfluidics device, you end up with gene expression measurements for 5,000–20,000 individual cells. Each cell has a count of how many RNA molecules were detected for each of the ~30,000 genes in the genome.

The result is a large matrix: cells as rows, genes as columns (or sometimes transposed). This matrix, called the count matrix, is the starting point for all downstream analysis — we will explore it in depth in Lesson 3.

03 Bulk RNA-seq vs Single-Cell RNA-seq

These two methods answer different questions. Knowing when to use each one is as important as knowing how to run them.

04 Biological Questions scRNA-seq Can Answer

scRNA-seq has transformed biology precisely because it enables questions that were previously unanswerable. Here are the major categories:

1. Cell-type identification and atlas building

Which cell types exist in this tissue? What genes define each type? This led to whole-organism cell atlases — the Human Cell Atlas, the Mouse Cell Atlas, and more recently plant atlases for Arabidopsis roots and leaves. For a new organism or tissue, scRNA-seq lets you discover cell types without any prior knowledge.

2. Developmental trajectories

How does a stem cell become a specialised cell? scRNA-seq captures cells at intermediate stages, allowing computational methods to reconstruct the order of gene expression changes during differentiation — a concept called pseudotime (covered in Module 28).

3. Rare cell populations

Rare cells that make up less than 1% of a tissue — stem cells, tumour-initiating cells, early stress-response cells — are invisible in bulk RNA-seq. A single-cell experiment with 10,000 cells can detect a population of 50 rare cells (0.5%) and profile their transcriptome fully.

4. Cell-to-cell variability

Even genetically identical cells in the same tissue show different expression patterns due to stochastic gene regulation. scRNA-seq reveals this variability, which has implications for disease resistance, drug response, and phenotypic plasticity.

5. Cell communication

By knowing which receptor genes are expressed in which cell types, and which ligand genes in adjacent cells, you can infer which cells are "talking" to each other — a field called ligand-receptor analysis or cell–cell communication.

05 scRNA-seq in Plant Biology

Plant scRNA-seq is a young but fast-growing field. The main challenge is the cell wall — plant cells are embedded in a rigid polysaccharide wall, which makes mechanical dissociation into single cells much harder than for animal cells. Two approaches have emerged:

1. Protoplasting: Enzymatic digestion of the cell wall (cellulase + macerozyme) releases intact protoplasts. This works well but can induce stress responses that alter gene expression.
2. Single-nucleus RNA-seq (snRNA-seq): Instead of whole cells, isolate nuclei only. The cell wall is bypassed entirely. Nuclei contain pre-mRNA and are more stable. Most plant scRNA-seq papers after 2021 use this approach.

For sorghum specifically, scRNA-seq data is still limited as of 2025, which makes this an active research opportunity. The computational skills you build in this module are directly applicable to any plant dataset.

06 The High-Level scRNA-seq Workflow

Before diving into code, it helps to see the full pipeline you will build across this module and the next three:

# ── WET LAB (Module 24, Lesson 2) ───────────────────────────
Tissue → Dissociation → Single cells/nuclei
        → Droplet encapsulation (10x Genomics)
        → Barcoding + UMI tagging
        → cDNA library → Illumina sequencing

# ── RAW DATA PROCESSING (Module 24, Lessons 4–5) ─────────────
FASTQ files (R1=barcode+UMI, R2=cDNA read)
        → Cell Ranger / STARsolo / Alevin
        → Count matrix (MEX or H5 format)

# ── DATA FORMATS (Module 24, Lessons 6–8) ────────────────────
MEX (matrix.mtx + barcodes.tsv + features.tsv)
        → AnnData (.h5ad)   ← Python / Scanpy
        → SCE (.rds)        ← R / Seurat

# ── QUALITY CONTROL (Module 24, Lessons 9–10) ────────────────
Filter low-quality cells (low genes, high MT%)
Remove doublets → Normalise → Save clean object

# ── DOWNSTREAM (Modules 25–29) ───────────────────────────────
Clustering → Cell-type annotation
        → Differential expression
        → Trajectory analysis

Every step in this workflow produces a specific file format or data structure. One of the biggest sources of confusion for newcomers is not understanding which file format is expected at each stage. That is exactly what Lessons 4–8 cover in detail.

07 Exercises