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
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 1,007
The spatial transcriptomic landscape of the healing mouse intestine following damage
Parigi, S. M., Larsson, L., Das, S., Flores, R. O. R., Frede, A., Tripathi, K. P., Diaz, O. E., Selin, K., Morales, R. A., Luo, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28497-0 · Cited by 139
Self-citationSpatial proteomics in three-dimensional intact specimens
Bhatia, H. S., Brunner, A.-D., Öztürk, F., Kapoor, S., Rong, Z., Mai, H., Thielert, M., Ali, M., Al-Maskari, R., Paetzold, J. C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.021 · Cited by 129
Precancer Atlas to Drive Precision Prevention Trials
Spira, A. E., Yurgelun, M. B. B., Alexandrov, L. B., Rao, A., Bejar, R., Polyák, K., Giannakis, M., Shilatifard, A., Finn, O. J., Dhodapkar, M., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-16-2346 · Cited by 134
Ščupáková, K., Balluff, B., Tressler, C. M., Adelaja, T., Heeren, R. M. A., Glunde, K., & Ertaylan, G. (2019) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2019-0858 · Cited by 132
Transgenic Mouse Models of Alzheimer’s Disease: An Integrative Analysis
Sanchez-Varo, R. M., Mejias‐Ortega, M., Fernandez-Valenzuela, J. J., Nuñez‐Diaz, C., Cáceres-Palomo, L., Vegas‐Gomez, L., Sánchez‐Mejías, E., Trujillo‐Estrada, L., García‐León, J. A., Moreno‐González, I., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23105404 · Cited by 136
SpatialDB: a database for spatially resolved transcriptomes
Fan, Z., Chen, R., & Chen, X. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz934 · Cited by 128
Recent advances in single-cell sequencing technologies
Wen, L., & Tang, F. (2022) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbac002 · Cited by 127
Allgayer, H., Mahapatra, S., Mishra, B., Swain, B., Saha, S., Khanra, S., Kumari, K., Panda, V. K., Malhotra, D., Patil, N. S., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02338-2 · Cited by 127
Resta, C. D., Galbiati, S., Carrera, P., & Ferrari, M. (2018) · Europe PMC (PubMed Central) · Cited by 128
Shim, J., Oh, S. J., Yeo, E., Park, J., Bae, J. H., Kim, S., Lee, D., & Lee, J. H. (2022) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2022.01.017 · Cited by 128
Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics
Ratz, M., von Berlin, L., Larsson, L., Martin, M., Westholm, J. O., La Manno, G., Lundeberg, J., & Frisén, J. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01011-x · Cited by 127
Self-citationNASA GeneLab: interfaces for the exploration of space omics data
Berrios, D. C., Galazka, J. M., Grigorev, K. A., Gebre, S., & Costes, S. V. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa887 · Cited by 126
Spatial omics technologies at multimodal and single cell/subcellular level
Park, J., Kim, J., Lewy, T., Rice, C. M., Elemento, O., Rendeiro, A. F., & Mason, C. E. (2022) · Genome biology · doi:10.1186/s13059-022-02824-6 · Cited by 133
Advancing CAR T cell therapy through the use of multidimensional omics data
Yang, J., Chen, Y., Jing, Y., Green, M. R., & Han, L. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00729-2 · Cited by 130
Immune cell profiling in atherosclerosis: role in research and precision medicine
Fernandez, D. M., & Giannarelli, C. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00589-2 · Cited by 126
Sun, D., Liu, Z., Li, T., Wu, Q., & Wang, C. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac150 · Cited by 122
Single Cell RNA Sequencing of Rare Immune Cell Populations
Nguyen, A., Khoo, W. H., Moran, I., Croucher, P. I., & Phan, T. G. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01553 · Cited by 122
Three-dimensional understanding of the morphological complexity of the human uterine endometrium
Yamaguchi, M., Yoshihara, K., Suda, K., Nakaoka, H., Yachida, N., Ueda, H., Sugino, K., Mori, Y., Yamawaki, K., Tamura, R., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102258 · Cited by 123
Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems
Castillo, R., Sidhu, I., Dolgalev, I., Chu, T., Prystupa, A., Subudhi, I., Yan, D., Konieczny, P., Hsieh, B., Haberman, R. H., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.abq7991 · Cited by 131
A new era for plant science: spatial single-cell transcriptomics
Giacomello, S. (2021) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2021.102041 · Cited by 115
Self-citationIntegrative spatial analysis of cell morphologies and transcriptional states with MUSE
Bao, F., Deng, Y. C., Wan, S., Shen, S. Q., Wang, B., Dai, Q., Altschuler, S. J., & Wu, L. F. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01251-z · Cited by 119
Computational solutions for spatial transcriptomics
Kleino, I., Frolovaitė, P., Suomi, T., & Elo, L. L. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.043 · Cited by 120
Cell segmentation-free inference of cell types from in situ transcriptomics data
Park, J., Choi, W., Tiesmeyer, S., Long, B., Borm, L. E., Garren, E. J., Nguyen, T. N., Tasic, B., Codeluppi, S., Graf, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23807-4 · Cited by 119
Self-citationIdentification of the novel exhausted T cell CD8 + markers in breast cancer
Liu, H., Dong, A., Rasteh, A. M., Wang, P., & Weng, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-70184-1 · Cited by 125
Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology
Wang, Y., Mashock, M. J., Tong, Z., Mu, X., Chen, H., Zhou, X., Zhang, H., Zhao, G., Liu, B., & Li, X. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00447 · Cited by 123
From bench to bedside: Single-cell analysis for cancer immunotherapy
Davis-Marcisak, E. F., Deshpande, A. M., Stein-O’Brien, G., Ho, W. J., Laheru, D. A., Jaffee, E. M., Fertig, E. J., & Kagohara, L. T. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.07.004 · Cited by 126
Zheng, H., Peng, X., Yang, S., Li, X., Huang, M., Wei, S., Zhang, S., He, G., Liu, J., Fan, Q., et al. (2023) · Cell Death Discovery · doi:10.1038/s41420-023-01356-7 · Cited by 126
Hypoxia and the phenomenon of immune exclusion
Pietrobon, V., & Marincola, F. M. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-020-02667-4 · Cited by 119
Weber, L. M., Saha, A., Datta, A., Hansen, K. D., & Hicks, S. C. (2023) · Nature Communications · doi:10.1038/s41467-023-39748-z · Cited by 116
Liu, W., Liao, X., Luo, Z., Yang, Y., Lau, M. C., Jiao, Y., Shi, X., Zhai, W., Ji, H., Yeong, J. P.-S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-35947-w · Cited by 121
Single-cell transcriptomics for the assessment of cardiac disease
Miranda, A. M. A., Janbandhu, V., Maatz, H., Kanemaru, K., Cranley, J., Teichmann, S. A., Hübner, N., Schneider, M. D., Harvey, R. P., & Noseda, M. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00805-7 · Cited by 117
Cheng, M. L., Nakib, D., Perciani, C. T., & MacParland, S. A. (2021) · Clinical Science · doi:10.1042/cs20190654 · Cited by 130
Single-cell technologies: From research to application
Wen, L., Li, G., Huang, T., Geng, W., Pei, H., Yang, J., Zhu, M., Zhang, P., Hou, R., Tian, G., et al. (2022) · The Innovation · doi:10.1016/j.xinn.2022.100342 · Cited by 124
SODB facilitates comprehensive exploration of spatial omics data
Yuan, Z., Pan, W., Zhao, X., Zhao, F., Xu, Z., Li, X., Zhao, Y. C., Zhang, M. Q., & Yao, J. (2023) · Nature Methods · doi:10.1038/s41592-023-01773-7 · Cited by 116
Single-nucleus transcriptomic landscape of primate hippocampal aging
Zhang, H., Li, J., Ren, J., Sun, S., Ma, S., Zhang, W., Yu, Y., Cai, Y., Yan, K., Li, W., et al. (2021) · Protein & Cell · doi:10.1007/s13238-021-00852-9 · Cited by 122
Miyoshi, E., Morabito, S., Henningfield, C. M., Das, S., Rahimzadeh, N., Shabestari, S. K., Michael, N., Emerson, N., Reese, F., Shi, Z., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01961-x · Cited by 115
stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics
Chen, S., Boheng, Z., Chen, X., Xuegong, Z., & Rui, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab298 · Cited by 114
Yousuf, S., Qiu, M., von Voithenberg, L. V., Hulkkonen, J., Mačinković, I., Schulz, A. R., Hartmann, D., Mueller, F., Mijatovic, M., Ibberson, D., et al. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.05.036 · Cited by 124
Lee, Y., Bogdanoff, D., Wang, Y., Hartoularos, G. C., Woo, J. M., Mowery, C. T., Nisonoff, H. M., Lee, D. S., Sun, Y., Lee, J., et al. (2021) · Science Advances · doi:10.1126/sciadv.abg4755 · Cited by 110
A Comparative Overview of Epigenomic Profiling Methods
Mehrmohamadi, M., Sepehri, M. H., Nazer, N., & Norouzi, M. R. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.714687 · Cited by 119
Bassiouni, R., Idowu, M. O., Gibbs, L. D., Robila, V., Grizzard, P. J., Webb, M. G., Song, J., Noriega, A., Craig, D. W., & Carpten, J. D. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-2682 · Cited by 119
Spatiotemporal modeling of molecular holograms
Qiu, X., Zhu, D. Y., Lu, Y., Yao, J., Jing, Z., Min, K. H., Cheng, M., Pan, H., Zuo, L., King, S. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.011 · Cited by 131
DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction
Weinstein, J. A., Regev, A., & Zhang, F. (2019) · Cell · doi:10.1016/j.cell.2019.05.019 · Cited by 111
Metabolomics and complementary techniques to investigate the plant phytochemical cosmos
Tsugawa, H., Rai, A., Saito, K., & Nakabayashi, R. (2021) · Natural Product Reports · doi:10.1039/d1np00014d · Cited by 122
conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomicsPreprint
Zong, Y., Yu, T., Wang, X., Wang, Y., Hu, Z., & Li, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.14.476408 · Cited by 110
From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
Chen, X., Teichmann, S. A., & Meyer, K. B. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013452 · Cited by 112
Mergaert, P., Kereszt, A., & Kondorosi, É. (2019) · The Plant Cell · doi:10.1105/tpc.19.00494 · Cited by 111
Single cell cancer epigenetics
Casado-Peláez, M., Bueno-Costa, A., & Esteller, M. (2022) · Trends in cancer · doi:10.1016/j.trecan.2022.06.005 · Cited by 115
Whole-brain comparison of rodent and human brains using spatial transcriptomics
Beauchamp, A., Yee, Y., Darwin, B. C., Raznahan, A., Mars, R. B., & Lerch, J. P. (2022) · eLife · doi:10.7554/elife.79418 · Cited by 122
Spatial transcriptomic characterization of pathologic niches in IPF
Mayr, C. H., Santacruz, D., Jarosch, S., Bleck, M., Dalton, J., McNabola, A., Lempp, C., Neubert, L., Rath, B., Kamp, J. C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adl5473 · Cited by 123
Gao, S., Shi, Q., Zhang, Y., Liang, G., Kang, Z., Huang, B., Ma, D., Wang, L., Jiao, J., Fang, X., et al. (2021) · Cell Research · doi:10.1038/s41422-021-00540-7 · Cited by 113
Deciphering tissue structure and function using spatial transcriptomics
Walker, B. L., Cang, Z., Ren, H., Bourgain-Chang, E., & Nie, Q. H. (2022) · Communications Biology · doi:10.1038/s42003-022-03175-5 · Cited by 109
Wang, J., Kang, Z., Liu, Y., Li, Z., Liu, Y., & Liu, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.956078 · Cited by 111
Solid-phase capture and profiling of open chromatin by spatial ATAC
Llorens-Bobadilla, E., Zamboni, M., Marklund, M., Bhalla, N., Chen, X., Hartman, J., Frisén, J., & Ståhl, P. L. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01603-9 · Cited by 108
Self-citationHeiser, C. N., Simmons, A. J., Revetta, F. L., McKinley, E. T., Ramirez-Solano, M. A., Wang, J., Kaur, H., Shao, J., Ayers, G. D., Wang, Y., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.006 · Cited by 112
Droplet-based single cell RNAseq tools: a practical guide
Salomon, R., Kaczorowski, D., Valdes-Mora, F., Nordon, R. E., Neild, A., Farbehi, N., Bartonicek, N., & Gallego-Ortega, D. (2019) · Lab on a Chip · doi:10.1039/c8lc01239c · Cited by 109
Single-cell genomic approaches for developing the next generation of immunotherapies
Yofe, I., Dahan, R., & Amit, I. (2020) · Nature Medicine · doi:10.1038/s41591-019-0736-4 · Cited by 107
The Stress-Like Cancer Cell State Is a Consistent Component of Tumorigenesis
Baron, M., Tagore, M. M., Hunter, M. V., Kim, I. S., Moncada, R. M., Yan, Y., Campbell, N. R., White, R. M., & Yanai, I. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.018 · Cited by 104
Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing
Liu, J., Tran, V., Vemuri, V. N. P., Byrne, A., Borja, M., Kim, Y. J., Agarwal, S., Wang, R., Awayan, K., Murti, A., et al. (2022) · Life Science Alliance · doi:10.26508/lsa.202201701 · Cited by 109
Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling
Robles-Remacho, A., Sanchez-Martin, R. M., & Díaz‐Mochon, J. J. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c02029 · Cited by 118
Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases
Kießling, P., & Kuppe, C. (2024) · Genome Medicine · doi:10.1186/s13073-024-01282-y · Cited by 112
Navarro, J. F., Croteau, D. L., Jurek, A., Andrusivová, Ž., Yang, B., Wang, Y., Ogedegbe, B., Riaz, T., Støen, M., Desler, C., et al. (2020) · iScience · doi:10.1016/j.isci.2020.101556 · Cited by 111
Self-citationSpatial mapping of the total transcriptome by in situ polyadenylation
McKellar, D. W., Mantri, M., Hinchman, M. M., Parker, J. S. L., Sethupathy, P., Cosgrove, B. D., & Vlaminck, I. D. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01517-6 · Cited by 105
The spatial landscape of gene expression isoforms in tissue sections
Lebrigand, K., Bergenstråhle, J., Thrane, K., Mollbrink, A., Meletis, K., Barbry, P., Waldmann, R., & Lundeberg, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad169 · Cited by 113
Self-citationImage-based cell phenotyping with deep learning
Pratapa, A., Doron, M., & Caicedo, J. C. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.001 · Cited by 105
Stem cells and origins of cancer in the upper gastrointestinal tract
Hayakawa, Y., Nakagawa, H., Rustgi, A. K., Que, J., & Wang, T. C. (2021) · Cell stem cell · doi:10.1016/j.stem.2021.05.012 · Cited by 103
SCS: cell segmentation for high-resolution spatial transcriptomics
Chen, H., Li, D., & Bar‐Joseph, Z. (2023) · Nature Methods · doi:10.1038/s41592-023-01939-3 · Cited by 102
Targeting the immune microenvironment for ovarian cancer therapy
Blanc‐Durand, F., Xian, L. C. W., & Tan, D. S. P. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1328651 · Cited by 115
Heinrich, S., Craig, A. J., Ma, L., Heinrich, B., Greten, T. F., & Wang, X. W. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.11.036 · Cited by 105
ST Pipeline: an automated pipeline for spatial mapping of unique transcripts
Navarro, J. F., Sjöstrand, J., Salmén, F., Lundeberg, J., & Ståhl, P. L. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx211 · Cited by 103
Self-citationMonjo, T., Koido, M., Nagasawa, S., Suzuki, Y., & Kamatani, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-07685-4 · Cited by 99
Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues
Ahmed, R., Zaman, T., Chowdhury, F., Mraiche, F., Tariq, M., Ahmad, I. S., & Hasan, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23063042 · Cited by 105
Fan, G., Xie, T., Li, L., Tang, L., Han, X., & Shi, Y. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00564-3 · Cited by 106
SOMDE: a scalable method for identifying spatially variable genes with self-organizing map
Hao, M., Hua, K., & Zhang, X. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab471 · Cited by 96
O’Connor, L. M., O’Connor, B. A., Lim, S. B., Zeng, J., & Lo, C. H. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.06.011 · Cited by 113
Xiao, J., Yu, X., Meng, F., Zhang, Y., Zhou, W., Ren, Y., Li, J., Sun, Y., Sun, H., Chen, G., et al. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-06598-6 · Cited by 119
Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Hu, Y., Li, Y., Xie, M., Rao, M., Shen, W., Luo, C., Qin, H., Baek, J., & Zhou, X. (2024) · Genome biology · doi:10.1186/s13059-024-03361-0 · Cited by 106
Unsupervised Spatially Embedded Deep Representation of Spatial TranscriptomicsPreprint
Fu, H., Xu, H., Chong, K., Li, M., Ang, K. S., Lee, H. K., Ling, J., Chen, A., Shao, L., Liu, L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.15.448542 · Cited by 98
SpiceMix enables integrative single-cell spatial modeling of cell identity
Chidester, B., Zhou, T., Alam, S., & Ma, J. (2023) · Nature Genetics · doi:10.1038/s41588-022-01256-z · Cited by 102
Deciphering Tumor Ecosystems at Super-Resolution from Spatial Transcriptomics with Tesla
Hu, J., Coleman, K., Zhang, D., Lee, E. B., Kadara, H. N., Wang, L., & Li, M. (2022) · Cell Systems · doi:10.1016/j.cels.2023.03.008 · Cited by 99
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 104
Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH
Borm, L. E., Albiach, A. M., Mannens, C. C. A., Janusauskas, J., Özgün, C., Fernández-García, D., Hodge, R. D., Castillo, F. A., Hedin, C. R. H., Villablanca, E. J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01455-3 · Cited by 107
Self-citationMapping the transcriptome: Realizing the full potential of spatial data analysis
Zormpas, E., Queen, R. A., Comber, A., & Cockell, S. (2023) · Cell · doi:10.1016/j.cell.2023.11.003 · Cited by 100
Alignment of spatial genomics data using deep Gaussian processes
Jones, A., Townes, F. W., Li, D., & Engelhardt, B. E. (2023) · Nature Methods · doi:10.1038/s41592-023-01972-2 · Cited by 85
Kaufmann, M., Evans, H. G., Schaupp, A.-L., Engler, J. B., Kaur, G., Willing, A., Kursawe, N., Schubert, C., Attfield, K. E., Fugger, L. H., et al. (2021) · Med · doi:10.1016/j.medj.2021.01.006 · Cited by 95
Endothelial cell plasticity at the single-cell level
Pasut, A., Becker, L. M., Cuypers, A., & Carmeliet, P. (2021) · Angiogenesis · doi:10.1007/s10456-021-09797-3 · Cited by 100
Application oriented bioaugmentation processes: Mechanism, performance improvement and scale-up
Ma, H., Zhao, Y., Yang, K., Wang, Y., Zhang, C., & Ji, M. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126192 · Cited by 98
Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer
Liu, S., Gao, Z., Wu, J., Zheng, H., Li, B., Sun, S., Meng, X., & Wu, Q. (2022) · Journal of Hematology & Oncology · doi:10.1186/s13045-022-01236-0 · Cited by 99
Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases
Schäbitz, A., Hillig, C., Mubarak, M., Jargosch, M., Farnoud, A., Scala, E., Kurzen, N., Pilz, A. C., Bhalla, N., Thomas, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35319-w · Cited by 98
Multiomics Research: Principles and Challenges in Integrated Analysis
Luo, Y., Zhao, C., & Chen, F. (2024) · BioDesign Research · doi:10.34133/bdr.0059 · Cited by 108
Park, H., Jo, S. H., Lee, R. H., Macks, C. P., Ku, T., Park, J., Lee, C. W., Hur, J. K., & Sohn, C. H. (2023) · Advanced Science · doi:10.1002/advs.202206939 · Cited by 107
Joint cell segmentation and cell type annotation for spatial transcriptomics
Littman, R., Hemminger, Z., Foreman, R., Arneson, D. V., Zhang, G., Gómez‐Pinilla, F., Yang, X., & Wollman, R. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010108 · Cited by 94
Spatial-linked alignment tool (SLAT) for aligning heterogenous slices
Xia, C., Cao, Z., Tu, X., & Gao, G. (2023) · Nature Communications · doi:10.1038/s41467-023-43105-5 · Cited by 90
Next-Generation Sequencing and Its Application: Empowering in Public Health Beyond Reality
Gupta, N., & Verma, V. K. (2019) · Microorganisms for sustainability · doi:10.1007/978-981-13-8844-6_15 · Cited by 113
Epigenetic and transcriptional regulation of osteoclast differentiation
Kurotaki, D., Yoshida, H., & Tamura, T. (2020) · Bone · doi:10.1016/j.bone.2020.115471 · Cited by 101
Liu, W., Liao, X., Yang, Y., Lin, H., Yeong, J. P.-S., Zhou, X., Shi, X., & Liu, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac219 · Cited by 95
Gong, L., Gu, Y., Han, X., Luan, C., Liu, C., Wang, X., Sun, Y., Zheng, M., Fang, M., Yang, S., et al. (2022) · Neuroscience Bulletin · doi:10.1007/s12264-022-00897-8 · Cited by 103
Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes
Hu, Y., Rong, J., Xu, Y., Xie, R., Peng, J. J., Gao, L., & Tan, K. (2024) · Nature Methods · doi:10.1038/s41592-023-02124-2 · Cited by 95
Imaging of anticancer drug action in single cells
Miller, M. A., & Weissleder, R. (2017) · Nature reviews. Cancer · doi:10.1038/nrc.2017.41 · Cited by 92
Benchmarking and integration of methods for deconvoluting spatial transcriptomic data
Yan, L., & Sun, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac805 · Cited by 92
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
