Single-cell RNA-sequencing (population)

• Page authors: PRIMO
• Responsible curator: Hagen Buck-Wiese

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Viral infection in individual cells (scRNA-seq, population)
Approach: single-cell RNA sequencing of sorted populations; viral transcript detection per cell
Context: in situ, lab
Spatial scale: single cell
Temporal scale: hours to days
Units: % of cells with viral transcripts; molecule cell-1
Community captured: individual cells of a specific species or population
Co-measurements: host cell abundance

Method Overview

Cells of a specific species or population of interest are isolated by flow cytometric sorting or microfluidic capture. Individual cells are subjected to single-cell RNA sequencing (scRNA-seq): each cell is lysed, its RNA reverse-transcribed, amplified, and sequenced. The transcriptome of each individual cell is scanned for viral transcripts by mapping reads to viral gene databases. Cells positive for viral gene expression are identified as actively infected. This approach reveals the heterogeneity in infection status within a defined host population — what proportion of cells are infected, at what stage, and how viral gene expression changes over the course of an infection.

Scale of measurement

Single-cell resolution; each cell's transcriptome is sequenced independently. Throughput is limited by the cell sorting and library preparation workflow (typically hundreds to thousands of cells per experiment).

Data generated

Per-cell transcriptome profiles; proportion of cells with detectable viral transcripts (% infected); stage-specific viral gene expression profiles revealing infection progression.

Units & currency

Units are % of cells with viral transcripts, or viral transcript molecules cell^-1. The currency is transcript molecules.

Sample size

Typical samples are < 1 L in volume; the number of cells sequenced per sample is hundreds to thousands.

Limitations

Only ~5% of total transcripts in a cell are captured in most scRNA-seq protocols, so low-abundance viral transcripts may be missed. Viral gene expression does not guarantee eventual cell lysis (e.g., in lysogenic or chronic infection). Sorting of population-specific cells requires prior knowledge of their optical properties or surface markers.

Example Applications & Protocols

Classic examples

• Ku et al. (2020) A single-cell view on alga-virus interactions reveals sequential transcriptional programs and infection states
• Hevroni et al. (2023) Daily turnover of active giant virus infection during algal blooms revealed by single-cell transcriptomics

Common calculations/conversions

• % infected cells = (cells with viral transcript reads / total cells sequenced) × 100.