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{| class="model-ib" style="float:right; margin-left:1em; margin-bottom:1em;" ! Viral infection across the plankton community (scRNA-seq) |- | '''Approach:''' community-level single-cell RNA sequencing; viral transcripts detected in individually barcoded cells |- | '''Context:..."

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{| class="model-ib" style="float:right; margin-left:1em; margin-bottom:1em;"
! Viral infection across the plankton community (scRNA-seq)
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| '''Approach:''' community-level single-cell RNA sequencing; viral transcripts detected in individually barcoded cells
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| '''Context:''' ''in situ''
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| '''Spatial scale:''' single cell
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| '''Temporal scale:''' hours to days
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| '''Units:''' % of cells with viral transcripts; molecule cell<sup>-1</sup>
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| '''Community captured:''' individual cells across the full plankton community
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| '''Co-measurements:''' host cell abundance
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== Method Overview ==

Community-level single-cell RNA sequencing (scRNA-seq) captures the transcriptomes of individual cells across the entire planktonic community without prior cell type selection. Seawater is collected, cells are fixed or kept live, dissociated (if necessary), and encapsulated in microdroplets or nanowells with barcoded oligonucleotides (e.g., 10x Genomics Chromium, Drop-seq). Each cell's RNA is reverse-transcribed using the barcode, creating a library where each read is associated with a unique cell. After sequencing, reads are demultiplexed by barcode and assembled into per-cell transcriptomes. Viral transcripts detected in individual cell libraries, combined with host taxonomic assignment from eukaryotic or prokaryotic marker genes, reveal which community members are infected and the frequency of infection<ref name="Fromm2024">Fromm, A., Hevroni, G., Vincent, F., Schatz, D., Martinez-Gutierrez, C. A., Aylward, F. O., & Vardi, A. (2024). Single-cell RNA-seq of the rare virosphere reveals the native hosts of giant viruses in the marine environment. ''Nature Microbiology'', 9, 1619–1629. https://doi.org/10.1038/s41564-024-01669-y</ref>.

=== Scale of measurement ===

Single-cell resolution across taxonomically diverse community members. Thousands of cells can be profiled simultaneously, enabling statistically robust estimates of infection frequency.

=== Data generated ===

Per-cell transcriptomes enabling: (1) taxonomic classification of each cell; (2) detection of viral transcripts indicating active infection; (3) identification of which host taxa are infected by which viruses. The proportion of infected cells per taxon is a measure of virus–host specificity.

=== Units & currency ===

Units are % of cells with viral transcripts per taxon; molecule cell<sup>-1</sup>. The currency is transcript molecules.

=== Sample size ===

Typical samples are < 1 L in volume; thousands of individual cells are profiled per experiment.

=== Repositories & databases ===

Sequencing data deposited at NCBI SRA; viral and host reference databases.

== Limitations ==

Only ~5% of cellular transcripts are captured per cell in most droplet-based scRNA-seq protocols, limiting detection of low-abundance viral transcripts. Taxonomic assignment of viral and host transcripts depends on database completeness. Cell encapsulation can cause doublets (two cells captured together), complicating per-cell analyses.

== Example Applications & Protocols ==

=== Classic examples ===
* Fromm et al. (2024) ''Single-cell RNA-seq of the rare virosphere reveals the native hosts of giant viruses in the marine environment'' <ref name="Fromm2024" />

=== Recent applications ===
*

=== Common calculations/conversions ===
* Infection frequency per taxon = (cells of taxon X with viral reads / total cells assigned to taxon X) × 100.

== References ==

[[Category:Main Pages|Model types]]

Single-cell RNA-sequencing (community) - Revision history