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Single-cell and spatial transcriptomics inform mechanistic physiology in non-model animals.

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Τίτλος: Single-cell and spatial transcriptomics inform mechanistic physiology in non-model animals.
Συγγραφείς: Amin A; Department of Life Sciences, Yeungnam University, Gyeongsan, 38541, Republic of Korea., Zaman W; Department of Life Sciences, Yeungnam University, Gyeongsan, 38541, Republic of Korea. wajidzaman@yu.ac.kr.
Πηγή: Functional & integrative genomics [Funct Integr Genomics] 2026 Sep 23; Vol. 26 (1). Date of Electronic Publication: 2026 Sep 23.
Τύπος έκδοσης: Journal Article; Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Springer Country of Publication: Germany NLM ID: 100939343 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-7948 (Electronic) Linking ISSN: 1438793X NLM ISO Abbreviation: Funct Integr Genomics Subsets: MEDLINE
Imprint Name(s): Original Publication: Berlin : Springer, c2000-
Ιατρικοί όροι (MeSH): Physiology, Comparative*/methods , Invertebrates*/classification , Invertebrates*/cytology , Invertebrates*/genetics , Single-Cell Gene Expression Analysis* , Spatial Transcriptomics*, Skates, Fish/classification ; Skates, Fish/genetics ; Animals ; Genome ; Molecular Sequence Annotation ; Atlases as Topic
Περίληψη: Comparative physiology increasingly requires cellular resolution at the level of cell types, tissue microenvironments, and regulatory programs that associate genotype with function under environmental variation. Bulk transcriptomics and tissue-level assays have revealed pathways associated with stress responses, metabolic shifts, and developmental transitions; however, they average signals across heterogeneous cell populations and frequently obscure which cells contribute to physiological phenotypes. Single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq), and spatial transcriptomics (ST) address complementary aspects of this limitation by resolving transcriptional heterogeneity and, for spatial approaches, preserving tissue context. These technologies are extending beyond classical biomedical models to non-model animals, enabling discovery of cell states, comparison of cell-type evolution, and spatially informed hypotheses concerning ionoregulation, respiration, immune defense, endocrine signaling, regeneration, and symbiosis. This review provides a physiology-centered blueprint for applying these approaches to non-model species and critically evaluates dissociation and preservation bias, genome annotation, seasonal and ecological variation, biological replication, pseudoreplication, cross-species integration, and spatial resolution. We distinguish descriptive mapping, replicate-aware association, mechanistic hypothesis generation, and causal validation because expression patterns, colocalization, trajectories, and ligand-receptor predictions do not by themselves demonstrate mechanism. Practical reporting and validation standards are proposed to improve reproducibility and connect cellular maps with independent physiological measurements and perturbation experiments.
(© 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable. Declaration of generative AI and AI-assisted technologies in the writing process: The authors used ChatGPT and Grammarly for language support and figures initial layout during manuscript preparation and revisions. All scientific content, interpretations, references, and final wording were independently reviewed and edited by the authors, who take full responsibility for the article.
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Contributed Indexing: Keywords: Biological replication; Comparative physiology; Non-model animals; Single-cell RNA sequencing; Single-nucleus RNA sequencing; Spatial transcriptomics
Entry Date(s): Date Created: 20260922 Date Completed: 20260924 Latest Revision: 20260924
Update Code: 20260924
DOI: 10.1007/s10142-026-02054-0
PMID: 42773353
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1438-7948
DOI:10.1007/s10142-026-02054-0