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Museum of spatial transcriptomics

Nature Methods volume 19, pages 534–546 (2022)Cite this article

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A Publisher Correction to this article was published on 19 April 2022

This article has been updated

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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Fig. 1: Timelines of major events.
Fig. 2: Schematics of common current-era technologies.
Fig. 3: Current-era metadata.
Fig. 4: Growth of the current era.

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Data availability

The database of spatial transcriptomics literature can be accessed at https://docs.google.com/spreadsheets/d/1sJDb9B7AtYmfKv4-m8XR7uc3XXw_k4kGSout8cqZ8bY/edit#gid=1363594152. The version used as of writing is in the metadata.xlsx file in the frozen DOI version of the GitHub repository to reproduce the figures in this paper and render the supplementary website: https://doi.org/10.5281/zenodo.5774128

Code availability

All code used to generate figures in this paper and render the supplementary website is in the GitHub repository: https://github.com/pachterlab/LP_2021. The frozen DOI version of the repository as of final submission of this paper is on Zenodo: https://doi.org/10.5281/zenodo.5774129.

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References

  1. Liao, J., Lu, X., Shao, X., Zhu, L. & Fan, X. Uncovering an organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends Biotechnol. 39, 43–58 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Asp, M., Bergenstråhle, J. & Lundeberg, J. Spatially resolved transcriptomes—next generation tools for tissue exploration. Bioessays 42, e1900221 (2020).

    Article  PubMed  Google Scholar 

  3. Smith, E. A. & Hodges, H. C. The spatial and genomic hierarchy of tumor ecosystems revealed by single-cell technologies. Trends Cancer Res. 5, 411–425 (2019).

    Article  Google Scholar 

  4. Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Saviano, A., Henderson, N. C. & Baumert, T. F. Single-cell genomics and spatial transcriptomics: discovery of novel cell states and cellular interactions in liver physiology and disease biology. J. Hepatol. 73, 1219–1230 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gall, J. G. & Pardue, M. L. Formation and detection of RNA–DNA hybrid molecules in cytological preparations. Proc. Natl Acad. Sci. USA 63, 378–383 (1969).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. John, H. A., Birnstiel, M. L. & Jones, K. W. RNA–DNA hybrids at the cytological level. Nature 223, 582–587 (1969).

    Article  CAS  PubMed  Google Scholar 

  8. Harrison, P. R., Conkie, D., Paul, J. & Jones, K. Localisation of cellular globin messenger RNA by in situ hybridisation to complementary DNA. FEBS Lett. 32, 109–112 (1973).

    Article  CAS  PubMed  Google Scholar 

  9. Langer-Safer, P. R., Levine, M. & Ward, D. C. Immunological method for mapping genes on Drosophila polytene chromosomes. Proc. Natl Acad. Sci. USA 79, 4381–4385 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rudkin, G. T. & Stollar, B. D. High resolution detection of DNA–RNA hybrids in situ by indirect immunofluorescence. Nature 265, 472–473 (1977).

    Article  CAS  PubMed  Google Scholar 

  11. Tautz, D. & Pfeifle, C. A non-radioactive in situ hybridization method for the localization of specific RNAs in Drosophila embryos reveals translational control of the segmentation gene hunchback. Chromosoma 98, 81–85 (1989).

    Article  CAS  PubMed  Google Scholar 

  12. Rosen, B. & Beddington, R. S. Whole-mount in situ hybridization in the mouse embryo: gene expression in three dimensions. Trends Genet. 9, 162–167 (1993).

    Article  CAS  PubMed  Google Scholar 

  13. Giani, A. M., Gallo, G. R., Gianfranceschi, L. & Formenti, G. Long walk to genomics: history and current approaches to genome sequencing and assembly. Comput. Struct. Biotechnol. J. 18, 9–19 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. O’Kane, C. J. & Gehring, W. J. Detection in situ of genomic regulatory elements in Drosophila. Proc. Natl Acad. Sci. USA 84, 9123–9127 (1987). This is the oldest entry in our database. It also gives a glimpse into the early motivations behind profiling gene expression in space.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gossler, A., Joyner, A. L., Rossant, J. & Skarnes, W. C. Mouse embryonic stem cells and reporter constructs to detect developmentally regulated genes. Science 244, 463–465 (1989).

    Article  CAS  PubMed  Google Scholar 

  16. Jenett, A. et al. A GAL4-driver line resource for Drosophila neurobiology. Cell Rep. 2, 991–1001 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Meier-Ruge, W. et al. The laser in the Lowry technique for microdissection of freeze-dried tissue slices. Histochem. J. 8, 387–401 (1976).

    Article  CAS  PubMed  Google Scholar 

  18. Emmert-Buck, M. R. et al. Laser capture microdissection. Science 274, 998–1001 (1996).

    Article  CAS  PubMed  Google Scholar 

  19. Becker, I. et al. Single-cell mutation analysis of tumors from stained histologic slides. Lab. Invest. 75, 801–807 (1996).

    CAS  PubMed  Google Scholar 

  20. Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014). This is the original publication for non-SRM seqFISH. Some later smFISH-based methods used seqFISH-like barcoding to profile transcripts of more genes than easily distinguishable colors.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nederlof, P. M. et al. Multiple fluorescence in situ hybridization. Cytometry 11, 126–131 (1990).

    Article  CAS  PubMed  Google Scholar 

  22. Levsky, J. M., Shenoy, S. M., Pezo, R. C. & Singer, R. H. Single-cell gene expression profiling. Science 297, 836–840 (2002).

    Article  CAS  PubMed  Google Scholar 

  23. Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).

    Article  CAS  PubMed  Google Scholar 

  24. Tomancak, P. et al. Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 3, RESEARCH0088 (2002).

  25. Bell, G. W., Yatskievych, T. A. & Antin, P. B. GEISHA, a whole-mount in situ hybridization gene expression screen in chicken embryos. Dev. Dyn. 229, 677–687 (2004).

    Article  CAS  PubMed  Google Scholar 

  26. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007). This is the publication for the ABA and the original CCF, which greatly influenced data analysis in the prequel era, and remains influential in the current era.

    Article  CAS  PubMed  Google Scholar 

  27. Harding, S. D. et al. The GUDMAP database—an online resource for genitourinary research. Development 138, 2845–2853 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ardini-Poleske, M. E. et al. LungMAP: The Molecular Atlas of Lung Development Program. Am. J. Physiol. Lung Cell. Mol. Physiol. 313, L733–L740 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Wienholds, E. MicroRNA expression in zebrafish embryonic development. Science 309, 310–311 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Ringwald, M. et al. A database for mouse development. Science 265, 2033–2034 (1994).

    Article  CAS  PubMed  Google Scholar 

  31. Sprague, J. et al. The Zebrafish Information Network (ZFIN): the zebrafish model organism database. Nucleic Acids Res. 31, 241–243 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–190 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. BRAIN Initiative Cell Census Network (BICCN). A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021).

  35. Baker, D. et al. A cellular reference resource for the mouse urinary bladder. Preprint at bioRxiv https://doi.org/10.1101/2021.09.20.461121 (2021).

  36. Brown, V. M. et al. Multiplex three-dimensional brain gene expression mapping in a mouse model of Parkinson’s sisease. Genome Res. 12, 868–884 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014). While this is not the first attempt to profile transcriptomes from samples microdissected with a microtome, later Tomo-seq works adapted the protocol from this paper. Tomo-seq is the most popular current era technique after LCM, Visium/ST, and GeoMX DSP.

    Article  CAS  PubMed  Google Scholar 

  38. Peng, G. et al. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 55, 802–804 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. Schede, H. H. et al. Spatial tissue profiling by imaging-free molecular tomography. Nat. Biotechnol. 39, 968–977 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Hufnagel, B. et al. High-quality genome sequence of white lupin provides insight into soil exploration and seed quality. Nat. Commun. 11, 492 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 358, 1622–1626 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Genshaft, A. S. et al. Live cell tagging tracking and isolation for spatial transcriptomics using photoactivatable cell dyes. Nat. Commun. 12, 4995 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hu, K. H. et al. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat. Methods 17, 833–843 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020). This is the original publication for GeoMX DSP, which is the most popular current era technique after LCM and Visium, and has been used in several COVID studies.

    Article  CAS  PubMed  Google Scholar 

  45. Roberts, K. et al. Transcriptome-wide spatial RNA profiling maps the cellular architecture of the developing human neocortex. Preprint at bioRxiv https://doi.org/10.1101/2021.03.20.436265 (2021).

  46. Lubeck, E. & Cai, L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9, 743–748 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Eng, C.-H. L., Shah, S., Thomassie, J. & Cai, L. Profiling the transcriptome with RNA SPOTs. Nat. Methods 14, 1153–1155 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015). This is the original publication for MERFISH, which has been used to collect data for the BICCN. Some later smFISH-based techniques use MERFISH-like barcoding to profile transcripts of more genes than easily distinguishable colors.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gyllborg, D. et al. Hybridization-based In Situ Sequencing (HybISS): spatial transcriptomic detection in human and mouse brain tissue. Nucleic Acids Res. 48, e112 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Goh, J. J. L. et al. Highly specific multiplexed RNA imaging in tissues with split-FISH. Nat. Methods 17, 689–693 (2020).

    Article  CAS  PubMed  Google Scholar 

  54. Battich, N., Stoeger, T. & Pelkmans, L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat. Methods 10, 1127–1133 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Kishi, J. Y. et al. SABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues. Nat. Methods 16, 533–544 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

  57. Chen, F., Tillberg, P. W. & Boyden, E. S. Optical imaging. Expansion Microsc. Sci. 347, 543–548 (2015).

    CAS  Google Scholar 

  58. Coskun, A. F. & Cai, L. Dense transcript profiling in single cells by image correlation decoding. Nat. Methods 13, 657–660 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013). This ISS technique, which has been commercialized by Cartana, is the most popular current era technique after LCM, Visium/ST, GeoMX DSP, and Tomo-seq. The RCA in this technique is also used in several later techniques such as STARmap and BOLORAMIS.

    Article  CAS  PubMed  Google Scholar 

  60. Liu, S. et al. Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses. Nucleic Acids Res. 49, e58 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Shendure, J. et al. Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309, 1728–1732 (2005).

    Article  CAS  PubMed  Google Scholar 

  62. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).

  64. Sun, Y.-C. et al. Integrating barcoded neuroanatomy with spatial transcriptional profiling reveals cadherin correlates of projections shared across the cortex. Nat. Neurosci. 24, 873–885 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual. cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  66. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Lebrigand, K. et al. The spatial landscape of gene expression isoforms in tissue sections. Preprint at bioRxiv https://doi.org/10.1101/2020.08.24.252296 (2022).

  70. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays. Preprint at bioRxiv https://doi.org/10.1101/2021.01.17.427004 (2021).

  71. Cho, C.-S. et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184, 3559–3572.e22 (2021).

    Article  CAS  PubMed  Google Scholar 

  72. Fu, X. et al. Continuous polony gels for tissue mapping with high resolution and RNA capture efficiency. Preprint at bioRxiv https://doi.org/10.1101/2021.03.17.435795 (2021).

  73. Lee, Y. et al. XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv. 7, eabg4755 (2021).

  74. Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373, 111–117 (2021).

    Article  CAS  PubMed  Google Scholar 

  75. Weinstein, J. A., Regev, A. & Zhang, F. DNA microscopy: optics-free spatio-genetic imaging by a stand-alone chemical reaction. Cell 178, 229–241.e16 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Hoffecker, I. T., Yang, Y., Bernardinelli, G., Orponen, P. & Högberg, B. A computational framework for DNA sequencing microscopy. Proc. Natl Acad. Sci. USA 116, 19282–19287 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Fazal, F. M. et al. Atlas of subcellular RNA localization revealed by APEX-seq. Cell 178, 473–490 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Vickovic, S. et al. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 13, 795 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Su, J.-H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-scale imaging of the 3D organization and transcriptional activity of chromatin. Cell 182, 1641–1659.e26 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 174, 363–376 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 598, 137–143 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Kim, M.-H. et al. Molecular and genetic approaches for assaying human cell type synaptic connectivity. Preprint at bioRxiv https://doi.org/10.1101/2020.10.16.343343 (2020).

  85. Li, Q. et al. In situ electro-sequencing in three-dimensional tissues. Preprint at bioRxiv https://doi.org/10.1101/2021.04.22.440941 (2021).

  86. Moffitt, J. R. & Zhuang, X. RNA imaging with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Methods Enzymol. 572, 1–49 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016). This is the precursor of Visium, which is the most popular current era method perhaps after LCM.

    Article  PubMed  CAS  Google Scholar 

  88. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-seq. Genome Biol. 17, 77 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

    Article  PubMed  CAS  Google Scholar 

  92. Lee, J. H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442–458 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Lee, H., Salas, S. M., Gyllborg, D. & Nilsson, M. Direct RNA targeted transcriptomic profiling in tissue using hybridization-based RNA in situ sequencing (HybRISS). Preprint at bioRxiv https://doi.org/10.1101/2020.12.02.408781 (2020).

  94. Delorey, T. M. et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 595, 107–113 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. La Manno, G. et al. Molecular architecture of the developing mouse brain. Nature 596, 92–96 (2021).

    Article  PubMed  CAS  Google Scholar 

  96. Zimmerman, S. M. et al. Spatially resolved whole transcriptome profiling in human and mouse tissue using digital spatial profiling. Preprint at bioRxiv https://doi.org/10.1101/2021.09.29.462442 (2021).

  97. Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020).

    Article  CAS  PubMed  Google Scholar 

  98. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Villacampa, E. G. et al. Genome-wide spatial expression profiling in formalin-fixed tissues. Cell Genomics 1, 100065 (2021).

    Article  CAS  Google Scholar 

  101. Liu, Y., Enninful, A., Deng, Y. & Fan, R. Spatial transcriptome sequencing of FFPE tissues at cellular level. Preprint at bioRxiv https://doi.org/10.1101/2020.10.13.338475 (2020).

  102. Foley, J. W. et al. Gene expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ. Genome Res. 29, 1816–1825 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Bhaduri, A. et al. An atlas of cortical arealization identifies dynamic molecular signatures. Nature 598, 200–204 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Van Valen, D. A. et al. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol. 12, e1005177 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Petukhov, V., Soldatov, R. A., Khodosevich, K. & Kharchenko, P. V. Bayesian segmentation of spatially resolved transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/2020.10.05.326777 (2020).

  106. Perkel, J. M. Starfish enterprise: finding RNA patterns in single cells. Nature 572, 549–551 (2019).

    Article  CAS  PubMed  Google Scholar 

  107. Karaiskos, N. et al. The embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).

    Article  CAS  PubMed  Google Scholar 

  108. Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).

    Article  CAS  PubMed  Google Scholar 

  109. Lopez, R. et al. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. Preprint at https://arxiv.org/abs/1905.02269 (2019).

  110. Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Kleshchevnikov, V. et al. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2020.11.15.378125 (2020).

  112. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00830-w (2021).

  113. Yang, T. et al. AdRoit is an accurate and robust method to infer complex transcriptome composition. Commun. Biol. 4, 1218 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Elosua, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).

    Article  CAS  Google Scholar 

  115. Sun, D. et al. STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/2021.09.08.459458 (2021).

  116. Miller, B. F., Huang, F., Atta, L., Sahoo, A. & Fan, J. Reference-free cell-type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/2021.06.15.448381 (2021).

  117. Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Righelli, D. et al. SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor. Preprint at bioRxiv https://doi.org/10.1101/2021.01.27.428431 (2021).

  119. Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Zhu, Q., Shah, S., Dries, R., Cai, L. & Yuan, G.-C. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat. Biotechnol. 36, 1183–1190 (2018).

  122. Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. BinTayyash, N. et al. Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments. Bioinformatics 37, 3788–3795 (2021).

    Article  CAS  Google Scholar 

  125. Govek, K. W., Yamajala, V. S. & Camara, P. G. Clustering-independent analysis of genomic data using spectral simplicial theory. PLoS Comput. Biol. 15, e1007509 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  127. Miller, B. F., Bambah-Mukku, D., Dulac, C., Zhuang, X. & Fan, J. Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities. Genome Res. 31, 1843–1855 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Pham, D. et al. stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues. Preprint at bioRxiv https://doi.org/10.1101/2020.05.31.125658 (2020).

  129. Canete, N. P. et al. spicyR: Spatial analysis of in situ cytometry data in R. Preprint at bioRxiv https://doi.org/10.1101/2021.06.07.447307 (2021).

  130. Arnol, D., Schapiro, D., Bodenmiller, B., Saez-Rodriguez, J. & Stegle, O. Modeling cell–cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 29, 202–211.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Lundmark, A. et al. Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics. Sci. Rep. 8, 9370 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Fan, Z., Chen, R. & Chen, X. SpatialDB: a database for spatially resolved transcriptomes. Nucleic Acids Res. 48, D233–D237 (2020).

    CAS  PubMed  Google Scholar 

  134. Armit, C. et al. eMouseAtlas: an atlas-based resource for understanding mammalian embryogenesis. Dev. Biol. 423, 1–11 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Singer, R. H. & Ward, D. C. Actin gene expression visualized in chicken muscle tissue culture by using in situ hybridization with a biotinated nucleotide analog. Proc. Natl Acad. Sci. USA 79, 7331–7335 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Hope, I. A. ‘Promoter trapping’ in Caenorhabditis elegans. Development 113, 399–408 (1991).

    Article  CAS  PubMed  Google Scholar 

  137. Seydoux, G. & Fire, A. Soma-germline asymmetry in the distributions of embryonic RNAs in Caenorhabditis elegans. Development 120, 2823–2834 (1994).

    Article  CAS  PubMed  Google Scholar 

  138. Bettenhausen, B. & Gossler, A. Efficient isolation of novel mouse genes differentially expressed in early postimplantation embryos. Genomics 28, 436–441 (1995).

    Article  CAS  PubMed  Google Scholar 

  139. Gawantka, V. et al. Gene expression screening in Xenopus identifies molecular pathways, predicts gene function and provides a global view of embryonic patterning. Mech. Dev. 77, 95–141 (1998).

    Article  CAS  PubMed  Google Scholar 

  140. Ringwald, M., Mangan, M. E., Eppig, J. T., Kadin, J. A. & Richardson, J. E. GXD: a gene expression database for the laboratory mouse. The Gene Expression Database Group. Nucleic Acids Res. 27, 106–112 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Kawashima, T., Kawashima, S., Kanehisa, M., Nishida, H. & Makabe, K. W. MAGEST: MAboya gene expression patterns and sequence tags. Nucleic Acids Res. 28, 133–135 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Maeda, I., Kohara, Y., Yamamoto, M. & Sugimoto, A. Large-scale analysis of gene function in Caenorhabditis elegans by high-throughput RNAi. Curr. Biol. 11, 171–176 (2001).

    Article  CAS  PubMed  Google Scholar 

  143. Satou, Y. et al. Gene expression profiles in Ciona intestinalis tailbud embryos. Development 128, 2893–2904 (2001).

    Article  PubMed  Google Scholar 

  144. Carson, J. P., Thaller, C. & Eichele, G. A transcriptome atlas of the mouse brain at cellular resolution. Curr. Opin. Neurobiol. 12, 562–565 (2002).

    Article  CAS  PubMed  Google Scholar 

  145. Henrich, T. et al. MEPD: a Medaka gene expression pattern database. Nucleic Acids Res. 31, 72–74 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Luengo Hendriks, C. L. et al. Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution I: data acquisition pipeline. Genome Biol. 7, R123 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  147. Lécuyer, E. et al. Global analysis of mRNA localization reveals a prominent role in organizing cellular architecture and function. Cell 131, 174–187 (2007).

    Article  PubMed  CAS  Google Scholar 

  148. Bowes, J. B. et al. Xenbase: a Xenopus biology and genomics resource. Nucleic Acids Res. 36, D761–7 (2008).

    Article  CAS  PubMed  Google Scholar 

  149. Lovell, P. V. et al. ZEBrA: Zebra finch Expression Brain Atlas—a resource for comparative molecular neuroanatomy and brain evolution studies. J. Comp. Neurol. 528, 2099–2131 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  150. Landegren, U., Kaiser, R., Sanders, J. & Hood, L. A ligase-mediated gene detection technique. Science 241, 1077–1080 (1988).

    Article  CAS  PubMed  Google Scholar 

  151. Belyavsky, A., Vinogradova, T. & Rajewsky, K. PCR-based cDNA library construction: general cDNA libraries at the level of a few cells. Nucleic Acids Res. 17, 5883–5883 (1989).

    Article  Google Scholar 

  152. Van Gelder, R. N. et al. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc. Natl Acad. Sci. USA 87, 1663–1667 (1990).

    Article  PubMed  PubMed Central  Google Scholar 

  153. Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    Article  CAS  PubMed  Google Scholar 

  154. Luo, L. et al. Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat. Med. 5, 117–122 (1999).

    Article  CAS  PubMed  Google Scholar 

  155. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Okamura-Oho, Y. et al. Transcriptome tomography for brain analysis in the web-accessible anatomical space. PLoS ONE 7, e45373 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by a grant from the National Institute of Mental Health (NIMH), National Institute of Health (NIH), of the U.S. Department of Health & Human Services (number U19MH114830, L.P.). We thank the following people for providing feedback for earlier versions of this paper and the supplement: D. Furth from the Cold Spring Harbor Laboratories, L. Cai from the California Institute of Technology, and G. Victora from the Rockefeller University.

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  1. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Lambda Moses & Lior Pachter

  2. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Lior Pachter

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  1. Lambda Moses
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  2. Lior Pachter
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L.P. suggested the project. L.M. curated the database, performed the analyses of the metadata, and wrote the manuscript and the supplement, which have been proofread and edited by L.P.

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Correspondence to Lior Pachter.

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Nature Methods thanks Sten Linnarsson, Quan Nguyen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods 19, 534–546 (2022). https://doi.org/10.1038/s41592-022-01409-2

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