Evidence mapPaperPMID 42510771Full record

ReviewGenes2026

Single-Cell RNA Sequencing in Porcine Biology and Production.

Xia Zhang, Yunze Deng, Xiaojing Hu, Hailong Huo, Jinlong Huo

Abstract readReview
In one paragraph

Review in Genes, 2026. 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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.

Xia ZhangDepartment of Biological and Food Engineering, Lyuliang University, Lvliang 033001, China.
Yunze DengDepartment of Biological and Food Engineering, Lyuliang University, Lvliang 033001, China.
Xiaojing HuDepartment of Biological and Food Engineering, Lyuliang University, Lvliang 033001, China.
Hailong HuoYunnan Open University, Kunming 650223, China.
Jinlong HuoCollege of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.ORCID 0000-0002-6350-6784

Funding

Luliang University Fundamental Research Program of Shanxi Province,(202403021222371)
6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology for resolving cellular heterogeneity and deciphering gene regulatory networks in complex tissues. Despite challenges such as incomplete genome annotation, technical variability across platforms, and limitations in robust cell-type annotation, scRNA-seq has substantially advanced our understanding of the developmental processes, physiological regulation, and disease responses in pigs, an economically and biomedically important species, thereby providing insights into traits of agricultural and translational relevance. By profiling transcriptomes at the single-cell resolution, scRNA-seq enables the identification of rare cell populations, dynamic cellular states, and lineage trajectories that are critical for reproduction, growth, immunity, and metabolic homeostasis. Recent porcine scRNA-seq studies have generated high-resolution cellular atlases spanning embryos, reproductive organs, immune tissues, skeletal muscle, and the gastrointestinal tract, revealing cell-type-specific regulatory mechanisms associated with reproductive performance, muscle accretion, adipogenesis, immune competence, and intestinal functionality. This review summarizes the fundamental principles and analytical strategies of scRNA-seq, highlights its major applications in porcine biology and production, and discusses current challenges as well as future perspectives for integrating single-cell technologies into livestock science.

Indexed as

Sequence Analysis, RNASingle-Cell AnalysisAnimalsGene Regulatory NetworksSingle-Cell Gene Expression AnalysisSwineTranscriptomecellular heterogeneitylivestock productionporcinereproductionsingle-cell RNA sequencing

Identifiers

PMID42510771
PMCPMC13409694

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

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 Socratic graph.