A Review of the Application of Spatial Transcriptomics in Neuroscience
Abstract
Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.
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In spatial omics, agentic workflows could integrate imaging data, spatially resolved transcriptomics, and histological annotations to reconstruct tissue microenvironments, with reflection modules monitoring consistency across spatial resolutions and batch effects.
“In spatial omics, agentic workflows could integrate imaging data, spatially resolved transcriptomics, and histological annotations to reconstruct tissue microenvironments, with reflection modules monitoring consistency across spatial resolutions and batch effects [16, 21].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [16]
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References 156 · Cited by 29
- Authors
- Zhang, L., Xiong, Z., & Xiao, M.
- Affiliations
- Sichuan University (CN)
- Corresponding author
- Xiao, Ming
- ORCID
- Venue
- Interdisciplinary Sciences: Computational Life Sciences
- Published
- 2024-02-20
- Volume
- 16
- Issue
- 2
- Pages
- 243-260
- ISSN
- 1867-1462 (ISSN-L)
- 1913-2751
- OpenAlex
- W4391969777
- Cited by
- 29
- References
- 156
- Citations by year
- 2026: 15 · 2025: 11 · 2024: 3
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Interdisciplinary Sciences Computational Life Sciences (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62372316)
- National Science and Technology Major Project (2021YFF1201200, 2018ZX10201002)
- China Postdoctoral Science Foundation (2020M673221)
- Fundamental Research Funds for the Central Universities (2020SCU12056)
- Sichuan Science and Technology Program (2022YFS0048)
- Chongqing Technology Innovation and Application Development Project (CSTB2022TIAD-KPX0067)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- spatial transcriptomics, brain development, brain disorders, brain structure-function relationship, multi-omics integration
- MeSH terms
- Animals, Brain, Humans, Neurosciences, Computational Biology, Gene Expression Profiling, Transcriptome
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