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Spatial components of molecular tissue biology

Nature Biotechnology volume 40, pages 308–318 (2022)Cite this article

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Abstract

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.

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Fig. 1: Components of variation in spatial profiling assays.
Fig. 2: Trajectory inference in new dimensions.
Fig. 3: Analytical challenges and opportunities of spatial molecular data.

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References

  1. Okabe, Y. & Medzhitov, R. Tissue biology perspective on macrophages. Nat. Immunol. 17, 9–17 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. 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 

  3. 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 

  4. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  Google Scholar 

  5. Lin, J.-R., Fallahi-Sichani, M., Chen, J.-Y. & Sorger, P. K. Cyclic immunofluorescence (CycIF), a highly multiplexed method for single-cell imaging. Curr. Protoc. Chem. Biol. 8, 251–264 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Alexandrov, T. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annu. Rev. Biomed. Data Sci. 3, 61–87 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. 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 

  9. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0739-1 (2020).

  10. Salmén, F. et al. Barcoded solid-phase RNA capture for spatial transcriptomics profiling in mammalian tissue sections. Nat. Protoc. 13, 2501–2534 (2018).

    Article  PubMed  Google Scholar 

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

  12. 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 

  13. Dey, P. in Basic and Advanced Laboratory Techniques in Histopathology and Cytology (ed. Dey, P.) 213–220 (Springer, 2018).

  14. Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tabula, Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).

    Article  Google Scholar 

  16. Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).

    Article  PubMed  Google Scholar 

  18. Fazal, F. M. et al. Atlas of subcellular RNA localization revealed by APEX-Seq. Cell 178, 473–490.e26 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang, G. et al. Spatial organization of the transcriptome in individual neurons. Preprint at bioRxiv https://doi.org/10.1101/2020.12.07.414060 (2020).

  20. Ramilowski, J. A. et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Balkwill, F. R., Capasso, M. & Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012).

    Article  CAS  PubMed  Google Scholar 

  22. Boisset, J.-C. et al. Mapping the physical network of cellular interactions. Nat. Methods 15, 547–553 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. 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 

  24. 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 

  25. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Jerby-Arnon, L. & Regev, A. Mapping multicellular programs from single-cell profiles. Preprint at bioRxiv https://doi.org/10.1101/2020.08.11.245472 (2020).

  27. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    Article  CAS  PubMed  Google Scholar 

  29. Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Almet, A. A., Cang, Z., Jin, S. & Nie, Q. The landscape of cell–cell communication through single-cell transcriptomics. Curr. Opin. Syst. Biol. https://doi.org/10.1016/j.coisb.2021.03.007 (2021).

  31. Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184, 4734–4752.e20 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Armingol, E. et al. Inferring the spatial code of cell-cell interactions and communication across a whole animal body. Preprint at bioRxiv https://doi.org/10.1101/2020.11.22.392217 (2020).

  33. Tanevski, J., Flores, R. O. R., Gabor, A., Schapiro, D. & Saez-Rodriguez, J. Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data. Preprint at bioRxiv https://doi.org/10.1101/2020.05.08.084145 (2020).

  34. Phillips, D. et al. Immune cell topography predicts response to PD-1blockade in cutaneous T cell lymphoma. Oncology https://doi.org/10.1101/2020.12.06.20244913 (2020).

  35. Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020).

  36. Hartmann, F. J. et al. Single-cell metabolic profiling of human cytotoxicT cells. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0651-8 (2020).

  37. Lopez, R., Gayoso, A. & Yosef, N. Enhancing scientific discoveries in molecular biology with deep generative models. Mol. Syst. Biol. 16, e9198 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S. & Theis, F. J. Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10, 390 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Borgwardt, K., Ghisu, E., Llinares-López, F., O’Bray, L. & Rieck, B. Graph kernels: state-of-the-art and future challenges. Preprint at arXiv (2020).

  41. Yuan, Y. & Bar-Joseph, Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 21, 300 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Feldman, D. et al. Optical pooled screens in human cells. Cell 179, 787–799.e17 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang, C., Lu, T., Emanuel, G., Babcock, H. P. & Zhuang, X. Imaging-based pooled CRISPR screening reveals regulators of lncRNA localization. Proc. Natl Acad. Sci. USA 116, 10842–10851 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Legnini, I. et al. Optogenetic perturbations of RNA expression in tissue space. Preprint at bioRxiv https://doi.org/10.1101/2021.09.26.461850 (2021).

  46. Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00935-2 (2021).

  47. Chen, Z., Soifer, I., Hilton, H., Keren, L. & Jojic, V. Modeling multiplexed images with spatial-LDA reveals novel tissue microenvironments. J. Comput. Biol. https://doi.org/10.1089/cmb.2019.0340 (2020).

  48. Kietzmann, T. Metabolic zonation of the liver: the oxygen gradient revisited. Redox Biol. 11, 622–630 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rogers, K. W. & Schier, A. F. Morphogen gradients: from generation to interpretation. Annu. Rev. Cell Dev. Biol. 27, 377–407 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. 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. https://doi.org/10.1016/j.jhep.2020.06.004 (2020).

  51. Ladoux, B. & Mège, R.-M. Mechanobiology of collective cell behaviours. Nat. Rev. Mol. Cell Biol. 18, 743–757 (2017).

    Article  CAS  PubMed  Google Scholar 

  52. Vining, K. H. & Mooney, D. J. Mechanical forces direct stem cell behaviour in development and regeneration. Nat. Rev. Mol. Cell Biol. 18, 728–742 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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 

  54. Velten, B., Braunger, J. M., Arnol, D., Argelaguet, R. & Stegle, O. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO. Preprint at bioRxiv https://doi.org/10.1101/2020.11.03.366674 (2020).

  55. Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 111 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Walter, F. C., Stegle, O. & Velten, B. FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution. Preprint at bioRxiv https://doi.org/10.1101/2021.11.04.467354 (2021).

  57. William Townes, F. & Engelhardt, B. E. Nonnegative spatial factorization. Preprint at arXiv https://arxiv.org/abs/2110.06122 (2021).

  58. Fischer, D. S., Schaar, A. C. & Theis, F. J. Learning cell communication from spatial graphs of cells. Preprint at bioRxiv https://doi.org/10.1101/2021.07.11.451750 (2021).

  59. 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).

  60. Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).

    Article  CAS  PubMed  Google Scholar 

  61. Cheplygina, V., de Bruijne, M. & Pluim, J. P. W. Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med. Image Anal. 54, 280–296 (2019).

    Article  PubMed  Google Scholar 

  62. Yang, K. D. et al. Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nat. Commun. 12, 31 (2021).

  63. Cheung, M., Shi, J., Jiang, L., Wright, O. & Moura, J. M. F. Pooling in graph convolutional neural networks. 2019 53rd Asilomar Conference on Signals, Systems, and Computers 462–466 (2019); https://doi.org/10.1109/IEEECONF44664.2019.9048796

  64. Hetzel, L., Fischer, D. S., Günnemann, S. & Theis, F. J. Graph representation learning for single-cell biology. Curr. Opin. Syst. Biol. https://doi.org/10.1016/j.coisb.2021.05.008 (2021).

  65. Rood, J. E. et al. Toward a common coordinate framework for the human body. Cell 179, 1455–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Forrow, A. & Schiebinger, G. LineageOT is a unified framework for lineage tracing and trajectory inference. Nat. Commun. https://doi.org/10.1038/s41467-021-25133-1 (2021).

  67. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0591-3 (2020).

  68. Lange, M. et al. CellRank for directed single-cell fate mapping. Preprint at bioRxiv https://doi.org/10.1101/2020.10.19.345983 (2020).

  69. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Fischer, D. S. et al. Inferring population dynamics from single-cell RNA-sequencing time series data. Nat. Biotechnol. 37, 461–468 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  72. van den Brink, S. C. et al. Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids. Nature 582, 405–409 (2020).

    Article  PubMed  Google Scholar 

  73. Asp, M. et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647–1660.e19 (2019).

    Article  CAS  PubMed  Google Scholar 

  74. Lohoff, T. et al. Integration of spatial and single-cell transcriptomic dataelucidates mouse organogenesis. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01006-2 (2021).

  75. Tyser, R. C. V. et al. Single-cell transcriptomic characterization of a gastrulating human embryo. Nature 600, 285–289 (2021).

  76. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).

    Article  CAS  PubMed  Google Scholar 

  77. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

  78. Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. He, Z. et al. Lineage recording reveals dynamics of cerebral organoid regionalization. Preprint at bioRxiv https://doi.org/10.1101/2020.06.19.162032 (2020).

  81. Chow, K.-H. K. et al. Imaging cell lineage with a synthetic digital recording system. Science 372, eabb3099 (2021).

  82. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

    Article  CAS  PubMed  Google Scholar 

  84. Smillie, C. S. et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178, 714–730.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    Article  CAS  PubMed  Google Scholar 

  87. Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Andersson, A. et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat. Commun. 12, 6012 (2021).

  89. Lomakin, A. et al. Spatial genomics maps the structure, character and evolution of cancer clones. Preprint at bioRxiv https://doi.org/10.1101/2021.04.16.439912 (2021).

  90. Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Preprint at bioRxiv https://doi.org/10.1101/2020.12.08.411686 (2020).

  91. Chen, W.-T. et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182, 976–991.e19 (2020).

    Article  CAS  PubMed  Google Scholar 

  92. Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).

    Article  CAS  PubMed  Google Scholar 

  93. Liu, Z. et al. Integrating single-cell RNA-seq and imaging with SCOPE-seq2. Sci. Rep. 10, 19482 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  95. McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).

    Article  CAS  PubMed  Google Scholar 

  97. Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Preprint at bioRxiv https://doi.org/10.1101/2021.03.01.431313 (2021).

  98. Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygons. Preprint at arXiv (2018).

  99. Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).

    Article  CAS  PubMed  Google Scholar 

  100. Mandal, S. & Uhlmann, V. SplineDist: automated cell segmentation with spline curves. Preprint at bioRxiv https://doi.org/10.1101/2020.10.27.357640 (2021).

  101. Hörl, D. et al. BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat. Methods 16, 870–874 (2019).

    Article  PubMed  Google Scholar 

  102. Andersson, A., Diego, F., Hamprecht, F. A. & Wählby, C. ISTDECO: in situ transcriptomics decoding by deconvolution. Preprint at bioRxiv https://doi.org/10.1101/2021.03.01.433040 (2021).

  103. Chen, S. et al. Barcode demixing through non-negative spatial regression (BarDensr). PLoS Comput. Biol. 17, e1008256 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Cleary, B. et al. Compressed sensing for highly efficient imaging transcriptomics. Nat. Biotechnol. 39, 936–94 (2021).

  105. Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J. & Hamarneh, G. Deep semantic segmentation of natural and medical images: a review. Artif. Intell. Rev. 54, 137–178 (2021).

    Article  Google Scholar 

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

  107. Axelrod, S. et al. Starfish: open source image based transcriptomics and proteomics tools. J. Open Source Softw. 6, 2440 (2018).

  108. Palla, G. et al. Squidpy: a scalable framework for spatial single cell analysis. Preprint at bioRxiv https://doi.org/10.1101/2021.02.19.431994 (2021).

  109. von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat. Commun. 12, 2276 (2021).

    Article  Google Scholar 

  110. Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Partel, G. & Wählby, C. Spage2vec: unsupervised representation of localized spatial gene expression signatures. FEBS J. 288, 1859–1870 (2021).

    Article  CAS  PubMed  Google Scholar 

  112. Petukhov, V. et al. Cell segmentation in imaging-based spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01044-w (2021).

  113. Park, J. et al. Cell segmentation-free inference of cell types from in situ transcriptomics data. Nat. Commun. 12, 3545 (2021).

  114. Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021).

  115. Daly, A. C., Geras, K. J. & Bonneau, R. A. A convolutional neural network for common coordinate registration of high-resolution histology images. Bioinformatics https://doi.org/10.1093/bioinformatics/btab447 (2021).

  116. Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).

  117. Kiemen, A. L. et al. In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution. Preprint at bioRxiv https://doi.org/10.1101/2020.12.08.416909 (2020).

  118. Lin, J.-R. et al. Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer. Preprint at bioRxiv https://doi.org/10.1101/2021.03.31.437984 (2021).

  119. Qiang, Y. et al. A probabilistic approach for registration of multi-modal spatial transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/2021.10.05.463196 (2021).

  120. Ruiz Tejada Segura, M. et al. A 3D transcriptomics atlas of the mouse olfactory mucosa. Preprint at bioRxiv https://doi.org/10.1101/2021.06.16.448475 (2021).

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

  122. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020).

    Article  CAS  PubMed  Google Scholar 

  124. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  125. 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 

  126. 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  Google Scholar 

  127. 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).

  128. Sztanka-Toth, T. R., Jens, M., Karaiskos, N. & Rajewsky, N. Spacemake: processing and analysis of large-scale spatial transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/2021.11.07.467598 (2021).

  129. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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

  131. Maaskola, J. et al. Charting tissue expression anatomy by spatial transcriptome decomposition. Preprint at bioRxiv https://doi.org/10.1101/362624 (2018).

  132. 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).

  133. Aliee, H. & Theis, F. J. AutoGeneS: automatic gene selection using multi-objective optimization for RNA-seq deconvolution. Cell Syst. 12, 706–715.e4 (2021).

  134. Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Elosua-Bayes, 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. https://doi.org/10.1093/nar/gkab043 (2021).

  137. Danaher, P. et al. Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data. Preprint at bioRxiv https://doi.org/10.1101/2020.08.04.235168 (2020).

  138. Erdmann-Pham, D. D., Fischer, J., Hong, J. & Song, Y. S. Likelihood-based deconvolution of bulk gene expression data using single-cell references. Genome Res. 31, 1794–1806 (2021).

  139. Song, Q. & Su, J. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence. Brief. Bioinform. 22, bbaa414 (2021).

  140. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Argelaguet, R., Cuomo, A. S. E., Stegle, O. & Marioni, J. C. Computationalprinciples and challenges in single-cell data integration. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00895-7 (2021).

  143. 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).

  144. Lopez, R. et al. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. Preprint at arXiv (2019).

  145. Abdelaal, T., Mourragui, S., Mahfouz, A. & Reinders, M. J. T. SpaGE: spatial gene enhancement using scRNA-seq. Nucleic Acids Res. 48, e107 (2020).

  146. Li, Z., Song, T., Yong, J. & Kuang, R. Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion. PLoS Comput. Biol. 17, e1008218 (2021).

  147. Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    Article  CAS  PubMed  Google Scholar 

  148. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Fleck, J. S. et al. Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases. Cell Stem Cell https://doi.org/10.1016/j.stem.2021.02.015 (2021).

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

    Article  CAS  PubMed  Google Scholar 

  151. Chlis, N.-K., Rausch, L., Brocker, T., Kranich, J. & Theis, F. J. Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning. Nucleic Acids Res. 48, 11335–11346 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Tan, X., Su, A., Tran, M. & Nguyen, Q. SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. Bioinformatics https://doi.org/10.1093/bioinformatics/btz914 (2019).

  153. He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020).

    Article  CAS  PubMed  Google Scholar 

  154. Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01075-3 (2021).

  155. Qin, Y. et al. A multi-scale map of cell structure fusing protein images and interactions. Nature 600, 536–542 (2021).

  156. Newell, A. & Deng, J. How useful is self-supervised pretraining for visual tasks? Preprint at arXiv (2020).

  157. Lu, A. X., Kraus, O. Z., Cooper, S. & Moses, A. M. Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting. PLoS Comput. Biol. 15, e1007348 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Mund, A. et al. AI-driven deep visual proteomics defines cell identity and heterogeneity. Preprint at bioRxiv https://doi.org/10.1101/2021.01.25.427969 (2021).

  159. Mahdessian, D. et al. Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature 590, 649–654 (2021).

    Article  CAS  PubMed  Google Scholar 

  160. Viana, M. P. et al. Robust integrated intracellular organization of the human iPS cell: where, how much, and how variable? Preprint at bioRxiv https://doi.org/10.1101/2020.12.08.415562 (2020).

  161. Heinrich, L. et al. Whole-cell organelle segmentation in volume electron microscopy. Nature 599, 141–146 (2021).

  162. 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. https://doi.org/10.1038/nbt.4260 (2018).

  163. 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  Google Scholar 

  164. 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 

  165. 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 

  166. Zhu, J. & Sabatti, C. Integrative spatial single-cell analysis with graph-based feature learning. Preprint at bioRxiv https://doi.org/10.1101/2020.08.12.248971 (2020).

  167. Kueckelhaus, J. et al. Inferring spatially transient gene expression pattern from spatial transcriptomic studies. Preprint at bioRxiv https://doi.org/10.1101/2020.10.20.346544 (2020).

  168. Kats, I., Vento-Tormo, R. & Stegle, O. SpatialDE2: fast and localized variance component analysis of spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2021.10.27.466045 (2021).

  169. BinTayyash, N. et al. Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments. Bioinformatics https://doi.org/10.1093/bioinformatics/btab486 (2021).

  170. Li, Q., Zhang, M., Xie, Y. & Xiao, G. Bayesian modeling of spatial molecular profiling data via Gaussian process. Preprint at arXiv (2020).

  171. Zhao, E. et al. Spatial transcriptomics at subspot resolution withBayesSpace. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00935-2 (2021).

  172. Chidester, B., Zhou, T. & Ma, J. SPICEMIX: integrative single-cell spatial modeling for inferring cell identity. Preprint at bioRxiv https://doi.org/10.1101/2020.11.29.383067 (2020).

  173. Oh, S., Park, H. & Zhang, X. Hybrid clustering of single-cell gene expression and spatial information via integrated NMF and k-means. Front. Genet. 12, 763263 (2021).

  174. Anderson, A. & Lundeberg, J. sepal: identifying transcript profiles withspatial patterns by diffusion-based modeling. Bioinformatics https://doi.org/10.1093/bioinformatics/btab164 (2021).

  175. Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Li, D., Ding, J. & Bar-Joseph, Z. Identifying signaling genes in spatial single-cell expression data. Bioinformatics 37, 968–975 (2021).

  177. Schmid, K. T. et al. scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies. Nat. Commun. 12, 6625 (2021).

  178. Vieth, B., Ziegenhain, C., Parekh, S., Enard, W. & Hellmann, I. powsimR: power analysis for bulk and single cell RNA-seq experiments. Bioinformatics 33, 3486–3488 (2017).

    Article  CAS  PubMed  Google Scholar 

  179. 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 

  180. Prabhakaran, S., Nawy, T. & Pe’er’, D. Sparcle: assigning transcripts to cells in multiplexed images. Preprint at bioRxiv https://doi.org/10.1101/2021.02.13.431099 (2021).

  181. He, Y. et al. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nat. Commun. 12, 5909 (2021).

  182. He, Y. et al. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nat. Commun. 12, 5909 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Caicedo, J. C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Baranski, A. et al. MAUI (MBI analysis user interface)-an image processing pipeline for multiplexed mass based imaging. PLoS Comput. Biol. 17, e1008887 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. 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).

  186. Bahry, E. et al. RS-FISH: precise, interactive, fast, and scalable FISH spot detection. Preprint at bioRxiv https://doi.org/10.1101/2021.03.09.434205 (2021).

  187. Wang, Y. et al. De-noising spatial expression profiling data based on in situ position and image information. Preprint at bioRxiv https://doi.org/10.1101/2021.11.03.467103 (2021).

  188. Andersson, A. et al. A landmark-based common coordinate framework for spatial transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/2021.11.11.468178 (2021).

  189. Schapiro, D. et al. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat. Methods https://doi.org/10.1038/s41592-021-01308-y (2021).

  190. Bergenstråhle, J., Bergenstråhle, L. & Lundeberg, J. SpatialCPie: an R/Bioconductor package for spatial transcriptomics cluster evaluation. BMC Bioinforma. 21, 161 (2020).

    Article  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  192. Queen, R., Cheung, K., Lisgo, S., Coxhead, J. & Cockell, S. Spaniel: analysis and interactive sharing of spatial transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/619197 (2019).

  193. Solorzano, L., Partel, G. & Wählby, C. TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data. Bioinformatics 36, 4363–4365 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. Dimitrov, D. et al. Comparison of resources and methods to infer cell-cell communication from single-cell RNA data. Preprint at bioRxiv https://doi.org/10.1101/2021.05.21.445160 (2021).

  195. Gao, C. et al. Iterative single-cell multi-omic integration using online learning. Nat. Biotechnol. 39, 1000–1007 (2021).

  196. Geuenich, M. J. et al. Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data. Cell Syst. 12, 1173–1186.e5 (2021).

  197. Dong, R. & Yuan, G.-C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145 (2021).

  198. Äijö, T. et al. Splotch: robust estimation of aligned spatial temporal gene expression data. Preprint at bioRxiv https://doi.org/10.1101/757096 (2019).

  199. Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).

  200. Lopez, R. et al. Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation. Preprint at bioRxiv https://doi.org/10.1101/2021.05.10.443517 (2021).

  201. Chen, S., Zhang, B., Chen, X., Zhang, X. & Jiang, R. stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics. Bioinformatics https://doi.org/10.1093/bioinformatics/btab298 (2021).

  202. Fleck, J. S. et al. Resolving organoid brain region identities by mappingsingle-cell genomic data to reference atlases. Cell Stem Cell https://doi.org/10.1016/j.stem.2021.02.015 (2021).

  203. Okochi, Y., Sakaguchi, S., Nakae, K., Kondo, T. & Naoki, H. Model-based prediction of spatial gene expression via generative linear mapping. Nat. Commun. 12, 3731 (2021).

  204. Mattiazzi Usaj, M. et al. Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol. Syst. Biol. 16, e9243 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  205. Govek, K. W. et al. Single-cell transcriptomic analysis of mIHC images via antigen mapping. Sci. Adv. 7, eabc5464 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. Gu, C. & Liu, Z. A network regularized linear model to infer spatial expression pattern for single cells. Preprint at bioRxiv https://doi.org/10.1101/2021.03.07.434296 (2021).

  207. Xu, Y. & McCord, R. P. CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis. BMC Bioinformatics 22, 397 (2021).

  208. Hao, M., Hua, K. & Zhang, X. SOMDE: a scalable method for identifying spatially variable genes with self-organizing map. Bioinformatics https://doi.org/10.1093/bioinformatics/btab471 (2021).

  209. Allen, C. et al. A Bayesian multivariate mixture model for spatial transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/2021.06.23.449615 (2021).

  210. Teng, H., Yuan, Y. & Bar-Joseph, Z. Cell type assignments for spatial transcriptomics data. Preprint at bioRxiv https://doi.org/10.1101/2021.02.25.432887 (2021).

  211. Verma, A. & Engelhardt, B. A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experiments. Preprint at bioRxiv https://doi.org/10.1101/2020.01.14.906313 (2020).

  212. Sottosanti, A. & Risso, D. Co-clustering of spatially resolved transcriptomic data. Preprint at arXiv (2021).

  213. Teng, H., Yuan, Y. & Bar-Joseph, Z. Clustering spatial transcriptomics data. Bioinformatics https://doi.org/10.1093/bioinformatics/btab704 (2021).

  214. Stroup, W. W. Power analysis based on spatial effects mixed models: a tool for comparing design and analysis strategies in the presence of spatial variability. J. Agric. Biol. Environ. Stat. 7, 491–511 (2002).

    Article  Google Scholar 

  215. Ellis, M. M., Ivan, J. S., Tucker, J. M. & Schwartz, M. K. rSPACE: spatially based power analysis for conservation and ecology. Methods Ecol. Evol. 6, 621–625 (2015).

    Article  Google Scholar 

  216. Buller, I. D., Brown, D. W., Myers, T. A., Jones, R. R. & Machiela, M. J. sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application. Int. J. Health Geogr. 20, 13 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  217. Martin, K. C. & Ephrussi, A. mRNA localization: gene expression in the spatial dimension. Cell 136, 719–730 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  218. Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).

    Article  CAS  PubMed  Google Scholar 

  219. Burgess, D. J. Spatial transcriptomics coming of age. Nat. Rev. Genet. 20, 317 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

  222. 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 

  223. Wilbrey-Clark, A., Roberts, K. & Teichmann, S. A. Cell Atlas technologies and insights into tissue architecture. Biochem. J. 477, 1427–1442 (2020).

    Article  CAS  PubMed  Google Scholar 

  224. Zhang, M. et al. Spatial molecular profiling: platforms, applications andanalysis tools. Brief. Bioinform. https://doi.org/10.1093/bib/bbaa145 (2020).

  225. Zhuang, X. Spatially resolved single-cell genomics and transcriptomics by imaging. Nat. Methods 18, 18–22 (2021).

    Article  CAS  PubMed  Google Scholar 

  226. Larsson, L., Frisén, J. & Lundeberg, J. Spatially resolved transcriptomics adds a new dimension to genomics. Nat. Methods 18, 15–18 (2021).

    Article  CAS  PubMed  Google Scholar 

  227. Marx, V. Method of the Year: spatially resolved transcriptomics. Nat. Methods 18, 9–14 (2021).

    Article  CAS  PubMed  Google Scholar 

  228. Close, J. L., Long, B. R. & Zeng, H. Spatially resolved transcriptomics in neuroscience. Nat. Methods 18, 23–25 (2021).

    Article  CAS  PubMed  Google Scholar 

  229. Moses, L. & Pachter, L. Museum of spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2021.05.11.443152 (2021).

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Acknowledgements

We thank A. Hupalowska for help with figure graphics and J. Rood for useful comments and suggestions that greatly improved the paper. G.P. is supported by the Helmholtz Association under the joint research school ‘Munich School for Data Science-MUDS’. F.J.T. acknowledges support by the BMBF (grant nos. 01IS18036B and 01IS18053A) and by the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI (grant no. ZT-I-PF-5-01) and sparse2big (grant no. ZT-I-0007). A.R. was an Investigator of the Howard Hughes Medical Institute. D.S.F. acknowledges support from a German Research Foundation (DFG) fellowship through the Graduate School of Quantitative Biosciences Munich (grant no. GSC 1006 to D.S.F.). G.P. and D.S.F. acknowledge support by the Joachim Herz Stiftung.

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  1. Aviv Regev

    Present address: Genentech, South San Francisco, CA, USA

  2. These authors contributed equally: Giovanni Palla, David S. Fischer.

Authors and Affiliations

  1. Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany

    Giovanni Palla, David S. Fischer & Fabian J. Theis

  2. TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany

    Giovanni Palla & David S. Fischer

  3. Klarman Cell Observatory, Broad Institute of MIT and Harvard and Department of Biology, MIT, Cambridge, MA, USA

    Aviv Regev

  4. Department of Mathematics, Technical University of Munich, Munich, Germany

    Fabian J. Theis

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F.J.T. reports receiving consulting fees from Immunai and ownership interest in Dermagnostix GmbH and Cellarity. A.R. is a founder of and equity holder in Celsius Therapeutics, is an equity holder in Immunitas Therapeutics, and was a scientific advisory board member for ThermoFisher Scientific, Syros Pharmaceuticals and Neogene Therapeutics until 1 August, 2020. Since 1 August, 2020, A.R. has been an employee of Genentech. A.R. is a named inventor on several patents and patent applications filed by the Broad Institute in the area of single-cell and spatial genomics.

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Palla, G., Fischer, D.S., Regev, A. et al. Spatial components of molecular tissue biology. Nat Biotechnol 40, 308–318 (2022). https://doi.org/10.1038/s41587-021-01182-1

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