Spatially resolved transcriptomes—next generation tools for tissue exploration
Open accessAbstract
Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method-specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue-type dependence, and limited capacity to obtain detailed single-cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon.
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Spatial transcriptomics has enabled characterization of fine-scale, cell-type-specific transcriptional heterogeneity within intact tissue, revealing molecular gradients not captured by region-averaged approaches.
“More recently, spatial transcriptomics has enabled characterization of fine‐scale, cell‐type–specific transcriptional heterogeneity within intact tissue, revealing molecular gradients not captured by region‐averaged approaches. 62 , 84”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [84]
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- Authors
- Asp, M., Bergenstråhle, J., & Lundeberg, J.
- Affiliations
- Science for Life Laboratory (SE)
- Corresponding author
- Lundeberg, Joakim
- Venue
- BioEssays
- Publisher
- Wiley
- Published
- 2020-05-04
- Volume
- 42
- Issue
- 10
- Pages
- e1900221-e1900221
- ISSN
- 0265-9247 (ISSN-L)
- 1521-1878
- OpenAlex
- W3021920958
- Cited by
- 666
- References
- 64
- Citations by year
- 2026: 101 · 2025: 109 · 2024: 161 · 2023: 109 · 2022: 89 · 2021: 80 · 2020: 17
- FWCI
- 7.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- BioEssays (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- 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, gene expression mapping, single-cell transcriptomics, spatial resolution, sensitivity
- MeSH terms
- Transcriptome
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