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Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Abstract
Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.
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“We specifically focused on the lumbar spinal cord, since this region undergoes multifaceted changes that lead to the irreversible degradation of neuronal function25,26.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [25]
In the citing paper
“It allows gene expression patterns to be mapped onto histologically defined regions, thereby linking molecular signatures to specific pathological features such as amyloid plaques and tau tangles114,121,166,167.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [166]
In the citing paper
“Spatial transcriptomics further enhances transcriptomic profiling by preserving the spatial context of gene expression.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [166]
In the citing paper
“Microarrays and bulk RNA-Seq are scalable and cost-effective but lack single-cell resolution.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [166]
In the citing paper
“Single-cell and single-nucleus methods deliver high granularity but require significant technical and computational resources.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [166]
In the citing paper
“Spatial transcriptomics adds a critical spatial dimension but may trade off resolution or transcriptomic depth114,118,162,166.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [166]
In the citing paper
“These included canonical marker genes highlighted in the primary analysis by Ståhl et al6, such as Penk, Doc2g, and Kctd12, but also additional genes that define the granule cell layer of the bulb.”
From SpatialDE: identification of spatially variable genes (2018) · cites this paper as [6]
In the citing paper
“Critically, such methods can only detect variations that are captured by differences between discrete groups.”
From SpatialDE: identification of spatially variable genes (2018) · cites this paper as [6]
In the citing paper
“Spatial transcriptomics is another newly developed method which allows for transcriptomic detection in small tissue sections by RNA-seq while maintaining the histological information of these sections [30].”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [30]
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Metadata
- Authors
- Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., Mollbrink, A., Linnarsson, S., Codeluppi, S., Borg, Å., Pontén, F., Costea, P. I., Sahlén, P., Mulder, J., Bergmann, O., Lundeberg, J., & Frisén, J.
- Affiliations
- Science for Life Laboratory (SE)
- Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
- Karolinska Institutet (SE)
- Stockholm University (SE)
- Lund University (SE)
- Uppsala University (SE)
- Corresponding author
- Ståhl, Patrik L.
- Salmén, Fredrik
- Lundmark, Anna
- Lundeberg, Joakim
- ORCID
- Ståhl, Patrik L.
- Salmén, Fredrik
- Lundmark, Anna
- Navarro, José Fernández
- Magnusson, Jens
- Giacomello, Stefania
- Asp, Michaela
- Westholm, Jakub O.
- Huss, Mikael
- Mollbrink, Annelie
- Linnarsson, Sten
- Codeluppi, Simone
- Borg, Åke
- Pontén, Fredrik
- Costea, Paul Igor
- Sahlén, Pelin
- Mulder, Jan
- Bergmann, Olaf
- Lundeberg, Joakim
- Frisén, Jonas
- Venue
- Science
- Published
- 2016-07
- Volume
- 353
- Issue
- 6294
- Pages
- 78-82
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2471536144
- Cited by
- 3,834
- References
- 24
- Citations by year
- 2026: 787 · 2025: 640 · 2024: 619 · 2023: 490 · 2022: 418 · 2021: 384 · 2020: 239 · 2019: 118 · 2018: 80 · 2017: 45 · 2016: 11
- FWCI
- 43.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Knut och Alice Wallenberg Foundation
- Swedish Foundation for Strategic Research
- Swedish Cancer Society
- Karolinska Institute
- Tobias Stiftelsen
- Torsten Söderbergs Stiftelse
- Ragnar Söderbergs Stiftelse
- StratRegen
- Åke Wiberg Foundation
- Jeansson Foundations
- Swedish Research Council
- 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 visualization, tissue sections, RNA sequencing, transcriptome, mouse brain, breast cancer, mRNA distribution, histology
- MeSH terms
- Animals, Brain, Breast Neoplasms, Female, Humans, Organ Specificity, RNA, Messenger, Sequence Analysis, RNA, DNA, Complementary, Gene Expression Profiling, Mice, Transcriptome
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