Museum of spatial transcriptomics
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Abstract
The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .
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Spatially resolved transcriptomics links cellular position to sequencing data and offers an in-depth view of gene expression within tissues.
“Spatially resolved transcriptomics, which links cellular position to sequencing data, offers an in-depth view of gene expression within tissues.”
From Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging (2024) · cites this paper as [19]
Spatial transcriptomics methods enable creation of detailed molecular maps with location information that is lost in scRNAseq.
“Spatial transcriptomics methods enable the creation of detailed molecular maps with location information that is lost in scRNAseq.19”
From Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging (2024) · cites this paper as [19]
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- Authors
- Moses, L., & Pachter, L.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Pachter, Lior
- Venue
- Nature Methods
- Published
- 2022-03-10
- Volume
- 19
- Issue
- 5
- Pages
- 534-546
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W4220765701
- Cited by
- 1,255
- References
- 13
- Citations by year
- 2026: 319 · 2025: 330 · 2024: 304 · 2023: 217 · 2022: 79 · 2021: 5 · 2020: 1
- FWCI
- 24.26
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-04-19)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (U19MH114830)
- 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 › Genomics and Chromatin Dynamics
- Keywords
- spatial transcriptomics, spatial gene expression, research trends
- MeSH terms
- Liver, Museums, Transcriptome
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