Okabe, Y. & Medzhitov, R. Tissue biology perspective on macrophages. Nat. Immunol. 17, 9–17 (2016).
Google Scholar
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).
Google Scholar
Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).
Google Scholar
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Google Scholar
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).
Google Scholar
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
Google Scholar
Alexandrov, T. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annu. Rev. Biomed. Data Sci. 3, 61–87 (2020).
Google Scholar
Rodriques, S. G. et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Google Scholar
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).
Salmén, F. et al. Barcoded solid-phase RNA capture for spatial transcriptomics profiling in mammalian tissue sections. Nat. Protoc. 13, 2501–2534 (2018).
Google Scholar
Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681 (2020).
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).
Google Scholar
Dey, P. in Basic and Advanced Laboratory Techniques in Histopathology and Cytology (ed. Dey, P.) 213–220 (Springer, 2018).
Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).
Google Scholar
Tabula, Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).
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).
Google Scholar
Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).
Google Scholar
Fazal, F. M. et al. Atlas of subcellular RNA localization revealed by APEX-Seq. Cell 178, 473–490.e26 (2019).
Google Scholar
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).
Ramilowski, J. A. et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).
Google Scholar
Balkwill, F. R., Capasso, M. & Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012).
Google Scholar
Boisset, J.-C. et al. Mapping the physical network of cellular interactions. Nat. Methods 15, 547–553 (2018).
Google Scholar
Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 358, 1622–1626 (2017).
Google Scholar
Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).
Google Scholar
Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).
Google Scholar
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).
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).
Google Scholar
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
Google Scholar
Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).
Google Scholar
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).
Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184, 4734–4752.e20 (2021).
Google Scholar
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).
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).
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).
Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020).
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).
Lopez, R., Gayoso, A. & Yosef, N. Enhancing scientific discoveries in molecular biology with deep generative models. Mol. Syst. Biol. 16, e9198 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Yuan, Y. & Bar-Joseph, Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 21, 300 (2020).
Google Scholar
Feldman, D. et al. Optical pooled screens in human cells. Cell 179, 787–799.e17 (2019).
Google Scholar
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).
Google Scholar
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
Google Scholar
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).
Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00935-2 (2021).
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).
Kietzmann, T. Metabolic zonation of the liver: the oxygen gradient revisited. Redox Biol. 11, 622–630 (2017).
Google Scholar
Rogers, K. W. & Schier, A. F. Morphogen gradients: from generation to interpretation. Annu. Rev. Cell Dev. Biol. 27, 377–407 (2011).
Google Scholar
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).
Ladoux, B. & Mège, R.-M. Mechanobiology of collective cell behaviours. Nat. Rev. Mol. Cell Biol. 18, 743–757 (2017).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 111 (2020).
Google Scholar
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).
William Townes, F. & Engelhardt, B. E. Nonnegative spatial factorization. Preprint at arXiv https://arxiv.org/abs/2110.06122 (2021).
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).
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).
Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).
Google Scholar
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).
Google Scholar
Yang, K. D. et al. Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nat. Commun. 12, 31 (2021).
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
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).
Rood, J. E. et al. Toward a common coordinate framework for the human body. Cell 179, 1455–1467 (2019).
Google Scholar
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).
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).
Lange, M. et al. CellRank for directed single-cell fate mapping. Preprint at bioRxiv https://doi.org/10.1101/2020.10.19.345983 (2020).
Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943.e22 (2019).
Google Scholar
Fischer, D. S. et al. Inferring population dynamics from single-cell RNA-sequencing time series data. Nat. Biotechnol. 37, 461–468 (2019).
Google Scholar
Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).
Google Scholar
van den Brink, S. C. et al. Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids. Nature 582, 405–409 (2020).
Google Scholar
Asp, M. et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647–1660.e19 (2019).
Google Scholar
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).
Tyser, R. C. V. et al. Single-cell transcriptomic characterization of a gastrulating human embryo. Nature 600, 285–289 (2021).
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).
Google Scholar
McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).
Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).
Google Scholar
Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019).
Google Scholar
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).
Chow, K.-H. K. et al. Imaging cell lineage with a synthetic digital recording system. Science 372, eabb3099 (2021).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Google Scholar
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).
Google Scholar
Smillie, C. S. et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178, 714–730.e22 (2019).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).
Google Scholar
Andersson, A. et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat. Commun. 12, 6012 (2021).
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).
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).
Chen, W.-T. et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182, 976–991.e19 (2020).
Google Scholar
Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).
Google Scholar
Liu, Z. et al. Integrating single-cell RNA-seq and imaging with SCOPE-seq2. Sci. Rep. 10, 19482 (2020).
Google Scholar
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Google Scholar
McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).
Google Scholar
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
Google Scholar
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).
Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygons. Preprint at arXiv (2018).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Google Scholar
Mandal, S. & Uhlmann, V. SplineDist: automated cell segmentation with spline curves. Preprint at bioRxiv https://doi.org/10.1101/2020.10.27.357640 (2021).
Hörl, D. et al. BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat. Methods 16, 870–874 (2019).
Google Scholar
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).
Chen, S. et al. Barcode demixing through non-negative spatial regression (BarDensr). PLoS Comput. Biol. 17, e1008256 (2021).
Google Scholar
Cleary, B. et al. Compressed sensing for highly efficient imaging transcriptomics. Nat. Biotechnol. 39, 936–94 (2021).
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).
Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).
Axelrod, S. et al. Starfish: open source image based transcriptomics and proteomics tools. J. Open Source Softw. 6, 2440 (2018).
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).
von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat. Commun. 12, 2276 (2021).
Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).
Google Scholar
Partel, G. & Wählby, C. Spage2vec: unsupervised representation of localized spatial gene expression signatures. FEBS J. 288, 1859–1870 (2021).
Google Scholar
Petukhov, V. et al. Cell segmentation in imaging-based spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01044-w (2021).
Park, J. et al. Cell segmentation-free inference of cell types from in situ transcriptomics data. Nat. Commun. 12, 3545 (2021).
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).
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).
Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).
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).
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).
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).
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).
Schede, H. H. et al. Spatial tissue profiling by imaging-free molecular tomography. Nat. Biotechnol. 39, 968–977 (2021).
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).
Google Scholar
Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020).
Google Scholar
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Google Scholar
Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).
Google Scholar
Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020).
Google Scholar
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).
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).
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).
Google Scholar
Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).
Maaskola, J. et al. Charting tissue expression anatomy by spatial transcriptome decomposition. Preprint at bioRxiv https://doi.org/10.1101/362624 (2018).
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).
Aliee, H. & Theis, F. J. AutoGeneS: automatic gene selection using multi-objective optimization for RNA-seq deconvolution. Cell Syst. 12, 706–715.e4 (2021).
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).
Google Scholar
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Google Scholar
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).
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).
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).
Song, Q. & Su, J. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence. Brief. Bioinform. 22, bbaa414 (2021).
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).
Google Scholar
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Google Scholar
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).
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).
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).
Abdelaal, T., Mourragui, S., Mahfouz, A. & Reinders, M. J. T. SpaGE: spatial gene enhancement using scRNA-seq. Nucleic Acids Res. 48, e107 (2020).
Li, Z., Song, T., Yong, J. & Kuang, R. Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion. PLoS Comput. Biol. 17, e1008218 (2021).
Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).
Google Scholar
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).
Google Scholar
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).
Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).
Google Scholar
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).
Google Scholar
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).
He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020).
Google Scholar
Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01075-3 (2021).
Qin, Y. et al. A multi-scale map of cell structure fusing protein images and interactions. Nature 600, 536–542 (2021).
Newell, A. & Deng, J. How useful is self-supervised pretraining for visual tasks? Preprint at arXiv (2020).
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).
Google Scholar
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).
Mahdessian, D. et al. Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature 590, 649–654 (2021).
Google Scholar
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).
Heinrich, L. et al. Whole-cell organelle segmentation in volume electron microscopy. Nature 599, 141–146 (2021).
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).
Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).
Google Scholar
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
Google Scholar
Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).
Google Scholar
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).
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).
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).
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).
Li, Q., Zhang, M., Xie, Y. & Xiao, G. Bayesian modeling of spatial molecular profiling data via Gaussian process. Preprint at arXiv (2020).
Zhao, E. et al. Spatial transcriptomics at subspot resolution withBayesSpace. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00935-2 (2021).
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).
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).
Anderson, A. & Lundeberg, J. sepal: identifying transcript profiles withspatial patterns by diffusion-based modeling. Bioinformatics https://doi.org/10.1093/bioinformatics/btab164 (2021).
Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).
Google Scholar
Li, D., Ding, J. & Bar-Joseph, Z. Identifying signaling genes in spatial single-cell expression data. Bioinformatics 37, 968–975 (2021).
Schmid, K. T. et al. scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies. Nat. Commun. 12, 6625 (2021).
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).
Google Scholar
Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020).
Google Scholar
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).
He, Y. et al. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nat. Commun. 12, 5909 (2021).
He, Y. et al. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nat. Commun. 12, 5909 (2021).
Google Scholar
Caicedo, J. C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017).
Google Scholar
Baranski, A. et al. MAUI (MBI analysis user interface)-an image processing pipeline for multiplexed mass based imaging. PLoS Comput. Biol. 17, e1008887 (2021).
Google Scholar
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).
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).
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).
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).
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).
Bergenstråhle, J., Bergenstråhle, L. & Lundeberg, J. SpatialCPie: an R/Bioconductor package for spatial transcriptomics cluster evaluation. BMC Bioinforma. 21, 161 (2020).
Fan, Z., Chen, R. & Chen, X. SpatialDB: a database for spatially resolved transcriptomes. Nucleic Acids Res. 48, D233–D237 (2020).
Google Scholar
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).
Solorzano, L., Partel, G. & Wählby, C. TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data. Bioinformatics 36, 4363–4365 (2020).
Google Scholar
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).
Gao, C. et al. Iterative single-cell multi-omic integration using online learning. Nat. Biotechnol. 39, 1000–1007 (2021).
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).
Dong, R. & Yuan, G.-C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145 (2021).
Äijö, T. et al. Splotch: robust estimation of aligned spatial temporal gene expression data. Preprint at bioRxiv https://doi.org/10.1101/757096 (2019).
Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).
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).
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).
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).
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).
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).
Google Scholar
Govek, K. W. et al. Single-cell transcriptomic analysis of mIHC images via antigen mapping. Sci. Adv. 7, eabc5464 (2021).
Google Scholar
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).
Xu, Y. & McCord, R. P. CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis. BMC Bioinformatics 22, 397 (2021).
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).
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).
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).
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).
Sottosanti, A. & Risso, D. Co-clustering of spatially resolved transcriptomic data. Preprint at arXiv (2021).
Teng, H., Yuan, Y. & Bar-Joseph, Z. Clustering spatial transcriptomics data. Bioinformatics https://doi.org/10.1093/bioinformatics/btab704 (2021).
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).
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).
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).
Google Scholar
Martin, K. C. & Ephrussi, A. mRNA localization: gene expression in the spatial dimension. Cell 136, 719–730 (2009).
Google Scholar
Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).
Google Scholar
Burgess, D. J. Spatial transcriptomics coming of age. Nat. Rev. Genet. 20, 317 (2019).
Google Scholar
Perkel, J. M. Starfish enterprise: finding RNA patterns in single cells. Nature 572, 549–551 (2019).
Google Scholar
Asp, M., Bergenstråhle, J. & Lundeberg, J. Spatially resolved transcriptomes—next generation tools for tissue exploration. Bioessays 42, e1900221 (2020).
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).
Wilbrey-Clark, A., Roberts, K. & Teichmann, S. A. Cell Atlas technologies and insights into tissue architecture. Biochem. J. 477, 1427–1442 (2020).
Google Scholar
Zhang, M. et al. Spatial molecular profiling: platforms, applications andanalysis tools. Brief. Bioinform. https://doi.org/10.1093/bib/bbaa145 (2020).
Zhuang, X. Spatially resolved single-cell genomics and transcriptomics by imaging. Nat. Methods 18, 18–22 (2021).
Google Scholar
Larsson, L., Frisén, J. & Lundeberg, J. Spatially resolved transcriptomics adds a new dimension to genomics. Nat. Methods 18, 15–18 (2021).
Google Scholar
Marx, V. Method of the Year: spatially resolved transcriptomics. Nat. Methods 18, 9–14 (2021).
Google Scholar
Close, J. L., Long, B. R. & Zeng, H. Spatially resolved transcriptomics in neuroscience. Nat. Methods 18, 23–25 (2021).
Google Scholar
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2021.05.11.443152 (2021).
