The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
(2008) · Journal of Machine Learning Research · Cited by 33,051
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
RSeQC: quality control of RNA-seq experiments
Wang, L., Wang, S., & Li, W. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts356 · Cited by 2,753
Topographic organization of sensory projections to the olfactory bulb
Vassar, R. J., Chao, S. K., Sitcheran, R., ̃ez, J. M. N., Vosshall, L. B., & Axel, R. (1994) · Cell · doi:10.1016/0092-8674(94)90029-9 · Cited by 1,281
Amplified RNA synthesized from limited quantities of heterogeneous cDNA.
Gelder, R. N. V., von Zastrow, M., Yool, A., Dement, W. C., Barchas, J. D., & Eberwine, J. H. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.5.1663 · Cited by 1,262
CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231
Schaeren‐Wiemers, N., & Gerfin-Moser, A. (1993) · Histochemistry and Cell Biology · doi:10.1007/bf00267823 · Cited by 1,151
RefSeq: an update on mammalian reference sequences
Pruitt, K. D., Brown, G., Hiatt, S. M., Thibaud‐Nissen, F., Astashyn, A., Ermolaeva, O. D., Farrell, C. M., Hart, J. A., Landrum, M., McGarvey, K. M., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1114 · Cited by 936
Gjerdrum, C., Tiron, C., Høiby, T., Stefansson, I., Haugen, H., Sandal, T., Collett, K., Li, S., McCormack, E., Gjertsen, B. T., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909333107 · Cited by 594
Design and Analysis of Single-Cell Sequencing Experiments
Grün, D., & van Oudenaarden, A. (2015) · Cell · doi:10.1016/j.cell.2015.10.039 · Cited by 536
Spatially resolved transcriptomics and beyond
Crosetto, N., Bienko, M., & van Oudenaarden, A. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3832 · Cited by 509
High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin
Achim, K., Pettit, J.-B., Saraiva, L. R., Gavriouchkina, D., Larsson, T., Arendt, D., & Marioni, J. C. (2015) · Nature Biotechnology · doi:10.1038/nbt.3209 · Cited by 395
Molecular Profiling for Breast Cancer: A Comprehensive Review
Kittaneh, M., Montero, A. J., & Glück, S. (2013) · Biomarkers in Cancer · doi:10.4137/bic.s9455 · Cited by 202
Single-molecule mRNA detection and counting in mammalian tissue
Lyubimova, A., Itzkovitz, S., Junker, J. P., Fan, Z. P., Wu, X., & van Oudenaarden, A. (2013) · Nature Protocols · doi:10.1038/nprot.2013.109 · Cited by 198
Increased Throughput by Parallelization of Library Preparation for Massive Sequencing
Lundin, S., Stranneheim, H., Pettersson, E., Klevebring, D., & Lundeberg, J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010029 · Cited by 128
Self-citationCombs, P. A., & Eisen, M. B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071820 · Cited by 65
Mahfouz, A. E., van de Giessen, M., van der Maaten, L. J. P., Huisman, S. M. H., Reinders, M. J. T., Hawrylycz, M., & Lelieveldt, B. P. F. (2014) · Methods · doi:10.1016/j.ymeth.2014.10.004 · Cited by 57
TagGD: Fast and Accurate Software for DNA Tag Generation and Demultiplexing
Costea, P. I., Lundeberg, J., & Akan, P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057521 · Cited by 44
Self-citationZechel, S., Zając, P., Lönnerberg, P., Ibáñez, C. F., & Linnarsson, S. (2014) · Genome biology · doi:10.1186/s13059-014-0486-z · Cited by 34
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Cellpose: a generalist algorithm for cellular segmentation
Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021) · Nature Methods · doi:10.1038/s41592-020-01018-x · Cited by 4,369
Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
