PPubMed16 Jun 2025Cited 3×

Spatial Transcriptomics to Study Virus-Host Interactions

Chase Holdener, Shaowen Jiang, Danica M. Sutherland, Kira A. Griswold, Terence S. Dermody, John S.L. Parker, Iwijn De Vlaminck

Abstract

The morbidity and mortality associated with viral diseases in plants, animals, and humans are significant concerns. Understanding how viruses cause disease and identifying the viral and host factors that determine the outcome of infection are essential to develop new antiviral therapeutics and strategies to induce protective immunity. In this review, we focus on the transformative potential of spatial transcriptomics for studies of viral pathogenesis and some of the intricacies of corresponding technologies and how to implement them.

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Spatial transcriptomics provides detailed maps of viral infection and host responses in tissues, but requires careful experimental and computational handling.

What’s new

i.Outlined key viral transcript features that affect spatial transcriptomics capture.
ii.Demonstrated that in situ polyadenylation enables detection of viral and host non‑polyadenylated RNAs.
iii.Provided a framework for computational analysis of spatial viral data, including cell‑type mapping and gene‑correlation methods.
iv.Highlighted applications such as detecting viral integration sites and studying coinfections.

How they did it

The authors performed a comprehensive literature review, synthesizing experimental protocols, technical considerations, and computational pipelines for applying spatial transcriptomics to viral pathogenesis.

What they found

In situ polyadenylation successfully polyadenylated viral and host RNAs, allowing capture of lncRNAs, miRNAs, and low‑abundance transcripts.
Spatial transcriptomics can identify viral integration sites in host DNA, exemplified by HIV latency reservoirs.
Studies using spatial transcriptomics revealed distinct SARS‑CoV‑2‑rich proinflammatory niches in lung tissue.
Spatial profiling uncovered Zika virus‑associated placental niches with up‑regulated immune and complement genes.

Caveats

✕RNA diffusion and noise limit true single‑cell resolution, and cell segmentation remains imperfect.
✕Current methods struggle to scale efficiently to the large datasets generated by newer high‑throughput platforms.
✕Non‑polyadenylated viral RNAs require protocol modifications, such as in situ polyadenylation.
✕Detection of viral transcripts does not always indicate infected cells, risking false‑positive identification.

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