Application of Single-Cell RNA Sequencing in Investigating Virus-Host Interactions.

Weizheng Liang, Peng Wang, Xiran Wang, Dandan Xu, Chunfu Zheng

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Weizheng Liang *Hebei Key Laboratory of Systems Biology and Gene Regulation, Central Laboratory, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China.

Peng Wang *Hebei North University, Zhangjiakou, Hebei, China.

Xiran WangDepartment of Bioinformatics, School of Health Care, Changchun Vocational College of Health, Changchun, Jilin, China.

Dandan XuHebei Key Laboratory of Systems Biology and Gene Regulation, Central Laboratory, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China.

Chunfu ZhengDepartment of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology capable of analyzing host and viral transcripts and host-virus interactions at the single-cell level. Viral infection is a complex process involving interactions between the virus and the host cell. While traditional research methods often have difficulty revealing cellular heterogeneity and dynamics during viral infections, scRNA-seq technology, which enables the sequencing of individual cells, provides a powerful tool for the in-depth study of viral-host interactions. scRNA-seq was able to reveal the mechanism of molecular changes in host cells during viral infection, providing new ideas and methods for the diagnosis and treatment of viral infections. This article describes the application, advantages, and experimental procedures of scRNA-seq in host virology.

Indexed as

Host-Pathogen InteractionsSequence Analysis, RNASingle-Cell AnalysisVirus DiseasesVirusesAnimalsHigh-Throughput Nucleotide SequencingHumansscRNA-seqVirusVirus–host interactions

Identifiers

PMID40515904

DOI10.1016/j.coviro.2018.03.001

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.