Applications of Spatial Transcriptomics in Veterinary Medicine: A Scoping Review of Research, Diagnostics, and Treatment Strategies
Abstract
1. Introduction
1.1. Rationale
1.2. Objectives
- Identify and characterize published studies that utilize ST to investigate disease processes in human and veterinary contexts;
- Summarize thematic trends across the literature, including types of diseases studied, species represented, spatial platforms and analysis tools employed, and integration with complementary technologies such as single-cell RNA sequencing and imaging;
- Compare applications of ST in human and veterinary research, highlighting patterns, applications, and underexplored areas;
- Identify knowledge gaps, technical challenges, and future opportunities to expand ST within translational and comparative biomedical research.
1.3. Conceptual Framework: Population, Concept, Context (PCC)
- Population: We included studies involving human or animal subjects—such as companion animals, livestock, and experimental disease models—investigated in the context of health or disease. This criterion shaped the inclusion of species-specific research and excluded purely in vitro or plant-based studies;
- Concept: Eligible studies were required to apply ST as a central methodological approach for profiling gene expression in situ. Studies using bulk or single-cell RNA-seq without spatial resolution were excluded. This ensured conceptual focus on ST technologies and their diagnostic, mechanistic, or therapeutic applications;
- Context: Studies had to be conducted within a biomedical or veterinary research context, covering disease-relevant themes such as cancer, infectious diseases, neurological diseases, or immune-mediated conditions. This context criterion informed both inclusion boundaries and the organization of results by disease type.
2. Methods
2.1. Framework
2.2. Eligibility Criteria
2.3. Study Selection and Screening Summary
3. Results
3.1. Overview of Included Studies
3.1.1. Selection of Sources of Evidence
3.1.2. Characteristics of Included Studies
3.1.3. Results of Included Studies
3.1.4. Synthesis of Results
3.2. Descriptive Trends
3.2.1. Publications by Year
3.2.2. Human Versus Animal Model Use
3.2.3. Species Studied
4. Application Areas of ST
4.1. Diagnosis, Therapeutics, and Research Applications
Publications by Application and Central Purpose
4.2. Organ Systems Studied
4.3. Human Disease Applications
4.3.1. Cancer
4.3.2. Metabolic Disease
4.3.3. Infectious Diseases
4.3.4. Gastrointestinal Diseases
4.3.5. Cardiac Diseases
4.3.6. Neurodevelopmental Disorder
4.3.7. Reproductive Diseases
4.3.8. Rare and Orphan Diseases
4.3.9. Psychiatric Disorder
4.4. Veterinary and Zoonotic Applications
5. Technological Platforms and Analytical Tools
5.1. Technologies Used
5.2. Tools and Methods Used for Analysis
5.3. Technology Adoption over Time
6. Discussion
6.1. Summary of Key Findings
6.2. Cross-Species and Comparative Insights
6.3. Technology and Analytical Tools
6.4. Research Gaps and Limitations of the Literature
6.5. Methodological Limitations of This Review
7. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
DOAJ | Directory of Open Access Journals |
PRISMA-ScR | Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews |
PCC | Population, Concept, Context |
FFPE | Formalin-fixed, paraffin-embedded |
scRNA-seq | Single-cell RNA sequencing |
RNA-seq | RNA sequencing |
COVID | Coronavirus disease |
MERFISH | Multiplexed error-robust fluorescence in situ hybridization |
seqFISH | Sequential fluorescence in situ hybridization |
FISH | Fluorescence in situ hybridization |
NAFLD | Non-alcoholic fatty liver disease |
IBD | Inflammatory bowel disease |
CRC | Colorectal cancer |
MI | Myocardial infarction |
ASD | Autism spectrum disorder |
MDD | Major depressive disorder |
Slide-seqV2 | Slide sequencing version 2 |
SpaTIAL-seq | ST integrated with scRNA-seq |
UMAP | Uniform Manifold Approximation and Projection |
PCA | Principal component analysis |
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Inclusion Criteria | Exclusion Criteria |
---|---|
Studies conducted in animal or human health contexts | Non-spatial transcriptomics studies (e.g., bulk or scRNA-seq without spatial data) |
Peer-reviewed journal articles | Studies outside the scope of human or veterinary health and disease, including those focused on method development without new biological/clinical insights |
Original research articles, systematic reviews, or meta-analyses | Non-peer-reviewed sources (e.g., preprints, conference abstracts, and theses) |
Published between 2016 and 7 February 2025 | Case reports and scoping reviews lacking comparative analysis or methodological rigor |
Studies employing animal models, human samples, or publicly available human and animal health datasets | Studies not published in English |
Focus on ST techniques | |
Report biological or clinically relevant findings |
Primary Purpose | Software Tool | Use Case/Description |
---|---|---|
Preprocessing and Primary Analysis | Space Ranger | 10× Genomics pipeline for alignment, barcode filtering, and quantification Link: https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/installation accessed on 23 June 2025 |
STARsolo | Alignment and UMI quantification for single-cell and spatial RNA-seq Link: https://github.com/alexdobin/STAR accessed on 23 June 2025 | |
Slide-seq Tools | Pipeline for decoding bead barcodes and aligning Slide-seq data Link: https://github.com/MacoskoLab/slideseq-tools accessed on 23 June 2025 | |
Stereo-seq Pipeline | BGI’s pipeline for high-resolution Stereo-seq data processing Link: https://github.com/BGIResearch/stereopy accessed on 23 June 2025 | |
Single-cell and Spatial Integration | Seurat (v5.0+) | R-based toolkit for scRNA-seq and spatial integration using anchors Link: https://github.com/satijalab/seurat accessed on 23 June 2025 |
Scanpy | Python-based toolkit for scalable single-cell analysis Link: https://github.com/scverse/scanpy accessed on 23 June 2025 | |
Squidpy | Python >=3.9 library for spatial graph-based analyses and neighborhood enrichment Link: https://github.com/scverse/squidpy accessed on 23 June 2025 | |
Differential Expression and Statistics | edgeR | Statistical analysis of differential expression in count data Link: https://bioconductor.org/packages/release/bioc/html/edgeR.html accessed on 23 June 2025 |
DESeq2 | Model-based differential expression testing for count data Link: https://bioconductor.org/packages/release/bioc/html/DESeq2.html accessed on 23 June 2025 | |
SpatialDE | Detects spatially variable genes from ST data Link: https://github.com/Teichlab/SpatialDE accessed on 23 June 2025 | |
Spatial Patterns and Neighborhood Analysis | SpaGCN | Graph convolutional network to detect spatial domains and gene patterns Link: https://github.com/jianhuupenn/SpaGCN accessed on 23 June 2025 |
BayesSpace | Bayesian clustering for high-resolution spatial domain detection Link: https://github.com/edward130603/BayesSpace accessed on 23 June 2025 | |
Giotto | Visual analytics platform optimized for large tissue sections and 3D data Link: https://github.com/RubD/Giotto accessed on 23 June 2025 | |
stLearn | Integrates histology and spatial gene expression for cell–cell interaction inference Link: https://github.com/BiomedicalMachineLearning/stLearn accessed on 23 June 2025 | |
SpatialExperiment | R/Bioconductor framework for spatial omics data representation and analysis Link: https://bioconductor.org/packages/release/bioc/html/SpatialExperiment.html accessed on 23 June 2025 | |
Cell-Type Deconvolution | CIBERSORT | Deconvolution of immune cell types from bulk or ST data Link: https://cibersort.stanford.edu/ accessed on 23 June 2025 |
Harmony | Batch correction and integration across spatial or single-cell datasets Link: https://github.com/immunogenomics/harmony accessed on 23 June 2025 | |
RCTD | Maps cell types from scRNA-seq onto spatial locations using probabilistic assignment Link: https://github.com/dmcable/RCTD accessed on 23 June 2025 | |
cell2location | Probabilistic deconvolution of ST maps with scRNA-seq reference Link: https://github.com/BayraktarLab/cell2location accessed on 23 June 2025 | |
SPOTlight | NMF-based cell-type deconvolution method for ST Link: https://github.com/MarcElosua/SPOTlight accessed on 23 June 2025 | |
DestVI | Variational inference model for cell-type deconvolution in spatial data Link: https://github.com/YosefLab/scvi-tools accessed on 23 June 2025 |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Weiderman, R.M.; Hasan, M.; Miller, L.C. Applications of Spatial Transcriptomics in Veterinary Medicine: A Scoping Review of Research, Diagnostics, and Treatment Strategies. Int. J. Mol. Sci. 2025, 26, 6163. https://doi.org/10.3390/ijms26136163
Weiderman RM, Hasan M, Miller LC. Applications of Spatial Transcriptomics in Veterinary Medicine: A Scoping Review of Research, Diagnostics, and Treatment Strategies. International Journal of Molecular Sciences. 2025; 26(13):6163. https://doi.org/10.3390/ijms26136163
Chicago/Turabian StyleWeiderman, Rachael M., Mahamudul Hasan, and Laura C. Miller. 2025. "Applications of Spatial Transcriptomics in Veterinary Medicine: A Scoping Review of Research, Diagnostics, and Treatment Strategies" International Journal of Molecular Sciences 26, no. 13: 6163. https://doi.org/10.3390/ijms26136163
APA StyleWeiderman, R. M., Hasan, M., & Miller, L. C. (2025). Applications of Spatial Transcriptomics in Veterinary Medicine: A Scoping Review of Research, Diagnostics, and Treatment Strategies. International Journal of Molecular Sciences, 26(13), 6163. https://doi.org/10.3390/ijms26136163