Abstract
Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.
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References
[No authors listed] Method of the Year 2020: spatially resolved transcriptomics. Nat. Methods 18, 1 (2021).
Stegle, O., Teichmann, S. & Marioni, J. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).
Tang, F. et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Chen, G., Ning, B. & Shi, T. Single-cell RNA-seq technologies and related computational data analysis. Front. Genet. 10, 317 (2019).
van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).
Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).
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 (2020).
Levsky, J. M., Shenoy, S. M., Pezo, R. C. & Singer, R. H. Single-cell gene expression profiling. Science 297, 836–840 (2002).
Lyubimova, A. et al. Single-molecule mRNA detection and counting in mammalian tissue. Nat. Protoc. 8, 1743–1758 (2013).
Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, 1360–1363 (2015).
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).
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).
Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019).
Ståhl, P. L. P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
10× Genomics. Inside Visium spatial capture technology https://pages.10xgenomics.com/rs/446-PBO-704/images/10x_BR060_Inside_Visium_Spatial_Technology.pdf (2019)
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
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).
Cho, C.-S. et al. Seq-Scope: submicrometer-resolution spatial transcriptomics for single cell and subcellular studies. bioRxiv https://doi.org/10.1101/2021.01.25.427807 (2021).
Chen, A. et al. Large field of view-spatially resolved transcriptomics at nanoscale resolution. bioRxiv https://doi.org/10.1101/2021.01.17.427004 (2021).
Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 353–356 (2017).
Ben-Moshe, S. et al. Spatial sorting enables comprehensive characterization of liver zonation. Nat. Metab. 1, 899–907 (2019).
Brosch, M. et al. Epigenomic map of human liver reveals principles of zonated morphogenic and metabolic control. Nat. Commun. 9, 4150 (2018).
Saviano, A., Henderson, N. C. & Baumert, T. F. Review 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).
Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167.e15 (2018).
Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826.e23 (2021).
Baccin, C. et al. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat. Cell Biol. 22, 38–48 (2020).
Peng, G. et al. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 36, 681–697 (2016).
Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018). This article is an exemplary application of integrating multiplexed ISH with scRNA-seq to reveal the spatial organization and circuitry of neuronal subpopulations pertinent to social behaviours at a single-cell resolution.
Chen, H. et al. Dissecting mammalian spermatogenesis using spatial transcriptomics. bioRxiv https://doi.org/10.1101/2020.10.17.343335 (2020).
Asp, M. et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647–1660 (2019). This analysis generates one of the first organ-wide, human developmental transcriptional atlases with single-cell spatial resolution by integrating scRNA-seq and spatial barcoding to yield optimized in situ sequencing.
Burkhard, S. B. & Bakkers, J. Spatially resolved RNA-sequencing of the embryonic heart identifies a role for Wnt/β-catenin signaling in autonomic control of heart rate. eLife 7, e31515 (2018).
Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 497–514 (2020). This exemplary application of multimodal spatial analysis integrates scRNA-seq with spatial barcoding and multiplexed ion beam imaging (akin to spatial proteomics) to inform an in vivo CRISPR screen that identifies gene networks essential to the function of tumorigenic subpopulations.
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).
Thrane, K., Eriksson, H., Maaskola, J., Hansson, J. & Lundeberg, J. Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma. Cancer Res. 78, 5970–5979 (2018).
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).
Chen, W. et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182, 976–991 (2020). This exemplary study characterizes Alzheimer disease-revelant cell types based on biofeature proximity by integrating spatial barcoding and in situ sequencing with histological stainings of disease mouse brain tissue cross-sections, effectively demonstrating how these integrated approaches can help map the spatio-temporal transcriptome at key disease stages.
Maniatis, S. et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364, 89–93 (2019).
Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. bioRxiv https://doi.org/10.1101/2020.12.08.411686 (2020).
Wu, C.-C. et al. Spatially resolved genome-wide transcriptional profiling identifies BMP signaling as essential regulator of zebrafish cardiomyocyte regeneration. Dev. Cell 36, 36–49 (2016).
Boyd, D. F. et al. Exuberant fibroblast activity compromises lung function via ADAMTS4. Nature 587, 466–471 (2020).
Moor, A. E. & Itzkovitz, S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46, 126–133 (2017).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Tarmo, Ä. et al. Splotch: robust estimation of aligned spatial temporal gene expression data. bioRxiv https://doi.org/10.1101/757096 (2019).
Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020).
Maniatis, S., Petrescu, J. & Phatnani, H. Spatially resolved transcriptomics and its applications in cancer. Curr. Opin. Genet. Dev. 66, 70–77 (2021).
Balkwill, F. R., Capasso, M. & Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012).
Whiteside, T. L. The tumor microenvironment and its role in promoting tumor growth. Oncogene 27, 5904–5912 (2008).
Fan, J., Slowikowski, K. & Zhang, F. Single-cell transcriptomics in cancer: computational challenges and opportunities. Exp. Mol. Med. 52, 1452–1465 (2020).
Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral hetereogeneity in primary glioblastoma. Science 344, 1396–1402 (2014).
Xu, L., He, D. & Bai, Y. Microglia-mediated inflammation and neurodegenerative disease. Mol. Neurobiol. 53, 6709–6715 (2016).
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).
Gulati, A. et al. Association of fibrosis with mortality and sudden cardiac death in patients with nonischemic dilated cardiomyopathy. JAMA 309, 896–908 (2013).
Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).
Haque, A., Engel, J., Teichmann, S. A. & Lönnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 9, 75 (2017).
Hwang, B., Lee, J. H. & Bang, D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50, 96 (2018).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).
Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).
Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016). This study exemplifies how scRNA-seq can be wielded to characterize single cells beyond the cell subtype.
Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).
Andrews, T. S. & Hemberg, M. Identifying cell populations with scRNASeq. Mol. Asp. Med. 59, 114–122 (2018).
Duò, A., Robinson, M. D. & Soneson, C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Res. 7, 1141 (2020).
Butler, A. et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).
Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020).
Hu, K. H. et al. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat. Methods 17, 833–843 (2020).
Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat. Methods 11, 190–196 (2014).
Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 1626, 1622–1626 (2017).
Ombrato, L. et al. Metastatic-niche labelling reveals parenchymal cells with stem features. Nature 572, 603–608 (2019).
Bodenmiller, B. Multiplexed epitope-based tissue imaging for discovery and healthcare applications. Cell Syst. 2, 225–238 (2016).
Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285–302 (2019).
Rimm, D. L. Next-gen immunohistochemistry. Nat. Methods 20, 436–442 (2014).
Levenson, R. M., Borowsky, A. D. & Angelo, M. Immunohistochemistry and mass spectrometry for highly multiplexed cellular molecular imaging. Lab. Investig. 95, 397–405 (2015).
Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).
Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalinfixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).
Zrazhevskiy, P. & Gao, X. Quantum dot imaging platform for single-cell molecular profiling. Nat. Commun. 4, 1619 (2013).
Lin, J. R., Fallahi-Sichani, M. & Sorger, P. K. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat. Commun. 6, 8390 (2015).
Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).
Damond, N. et al. A map of human type 1 diabetes progression by imaging mass cytometry. Cell Metab. 29, 755–768 (2019).
Ali, H. R. et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat. Cancer 1, 163–175 (2020).
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–695 (2011).
Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 25–36 (2018).
Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020). This report presents pciSeq for mapping scRNA-seq cell types to multiplexed ISH and in situ sequencing data through probablistic modelling that implements the spatial point process. This is one of very few mapping algorithms specifically tailored towards capture spot deconvolution.
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).
Elosua, M. et al. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021). This article presents SPOTlight, to date the only deconvolution technique published in a peer-reviewed journal that is tailored towards deconvolving spatial barcoding capture spots through regression. The article also presents a comprehensive strategy for benchmarking emerging deconvolution methods.
Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020). This report presents stereoscope, an effective approach for deconvolving spatial barcoding capture spots through probabilistic modelling and one of the few spatial barcoding deconvolution strategies published in a peer-reviewed journal to date.
Avila Cobos, F., Vandesompele, J., Mestdagh, P. & De Preter, K. Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics 34, 1969–1979 (2018).
Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).
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).
Dong, M. et al. SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references. Brief. Bioinform. 22, 416–427 (2021).
Gong, T. & Szustakowski, J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-seq data. Bioinformatics 29, 1083–1085 (2013).
Du, R., Carey, V. & Weiss, S. T. DeconvSeq: deconvolution of cell mixture distribution in sequencing data. Bioinformatics 35, 5095–5102 (2019).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Aliee, H. & Theis, F. AutoGeneS: automatic gene selection using multi-objective optimization for RNA-seq deconvolution. bioRxiv https://doi.org/10.1101/2020.02.21.940650 (2020).
Dong, R., Yuan, GC. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145 (2021).
Kleshchevnikov, V. et al. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics. bioRxiv https://doi.org/10.1101/2020.11.15.378125 (2020).
Zhang, X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–D728 (2019).
Cao, Y., Wang, X. & Peng, G. SCSA: a cell type annotation tool for single-cell RNA-seq data. Front. Genet. 11, 490 (2020).
McGinnis, C. S. et al. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat. Methods 16, 619–626 (2019).
Andersson, A. et al. Spatial deconvolution of HER2-positive breast tumors reveals novel intercellular relationships. bioRxiv https://doi.org/10.1101/2020.07.14.200600 (2020).
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).
Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).
Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).
Forcato, M., Romano, O. & Bicciato, S. Computational methods for the integrative analysis of single-cell data. Brief. Bioinform. 22, 20–29 (2021).
Thi, H. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019). This report details the inner workings of the Seurat Integration method, which is an exemplary method for mapping scRNA-seq cell types onto single-cell resolution spatial data. Seurat Integation is one part of a widely used R toolkit for analysing single-cell genomics data.
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1295 (2019).
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
Abdelaal, T., Mourragui, S., Mahfouz, A. & Reinders, M. J. T. SpaGE: Spatial Gene Enhancement using scRNA-seq. Nucleic Acids Res. 48, E107–E107 (2020).
Bishop, C. M. Pattern Recognition and Machine Learning. Oxidation Communications Vol. 27 (Springer, 2004).
Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2020).
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (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).
Noël, F. et al. Dissection of intercellular communication using the transcriptome-based framework ICELLNET Floriane. Nat. Commun. 12, 1089 (2021).
Choi, H. et al. Transcriptome analysis of individual stromal cell populations identifies stroma–tumor crosstalk in mouse lung cancer model. Cell Rep. 10, 1187–1201 (2015).
Wang, S., Karikomi, M., Maclean, A. L. & Nie, Q. Cell lineage and communication network inference via optimization for single-cell transcriptomics. Nucleic Acids Res. 47, e66 (2019).
Cillo, A. R. et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity 52, 183–199.e9 (2020).
Cabello-Aguilar, S. et al. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 48, e55 (2020).
Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).
Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).
Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020). This report presents SpaOTsc, one of the few peer-reviewed methods that formally integrate spatial and scRNA-seq data into an algorithm to decode intercellular communication in tissues. SpaOTsc predicts the maximum signalling range for ligand–receptor pairs through a spatial transcriptomic analysis of each ligand–receptor pair’s target genes.
Villani, C. Optimal Transport, Old and New Vol. 338 (Springer, 2009).
Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).
Ren, X. et al. Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand–receptor mediated self-assembly. Cell Res. 30, 763–778 (2020).
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 (2019).
Tanevski, J., Gabor, A., Flores, R. O. R., Schapiro, D. & Saez-Rodriguez, J. Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data. bioRxiv https://doi.org/10.1101/2020.05.08.084145 (2020).
Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020).
Liu, D. S., Loh, K. H., Lam, S. S., White, K. A. & Ting, A. Y. Imaging trans-cellular neurexin–neuroligin interactions by enzymatic probe ligation. PLoS ONE 8, e52823 (2013).
Pasqual, G. et al. Monitoring T cell-dendritic cell interactions in vivo by intercellular enzymatic labelling. Nature 553, 496–500 (2018).
He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020). This pioneering work presents ST-Net, one of the first applications of deep learning models for analysing spatial transcriptomic data, and one of the first methods to formally automate analysis of histological images for characterizing the spatial transcriptome.
Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. bioRxiv https://doi.org/10.1101/2020.02.28.963413 (2020).
Yuan, Y. & Bar-Joseph, Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 21, 300 (2020).
Xu, Y. & McCord, R. P. CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis. bioRxiv https://doi.org/10.1101/2021.01.12.426400 (2021).
Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).
SoRelle, E. D. et al. Spatiotemporal tracking of brain-tumor-associated myeloid cells in vivo through optical coherence tomography with plasmonic labeling and speckle modulation. ACS Nano 13, 7985–7995 (2019).
Jung, K. O. et al. Whole-body tracking of single cells via positron emission tomography. Nat. Biomed. Eng. 4, 835–844 (2020).
Rodriques, S. G. et al. RNA timestamps identify the age of single molecules in RNA sequencing. Nat. Biotechnol. 39, 320–325 (2021).
Crick, F. Central dogma. Nature 227, 561–563 (2008).
Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).
Deng, Y. et al. Spatial epigenome sequencing at tissue scale and cellular level. bioRxiv https://doi.org/10.1101/2021.03.11.434985 (2021).
Payne, A. C. et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371, eaay3446 (2021).
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).
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).
Hendriks, G.-J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138 (2019).
Helmink, B. et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 577, 549–555 (2020).
Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
Regev, A. et al. Science forum: the Human Cell Atlas. eLife 6, e27041 (2017).
Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).
Bakken, T. E. et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367–375 (2016).
Fan, Z., Chen, R. & Chen, X. SpatialDB: a database for spatially resolved transcriptomes. Nucleic Acids Res. 48, D233–D237 (2020). This article is one of the first publications describing a database directly geared towards aggregating spatial transcriptomic data.
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).
Asp, M., Bergenstråhle, J. & Lundeberg, J. Spatially resolved transcriptomes — next generation tools for tissue exploration. BioEssays 42, 1–16 (2020).
Zhuang, X. Spatially resolved single-cell genomics and transcriptomics by imaging. Nat. Methods 18, 18–22 (2021).
Waylen, L. N., Nim, H. T., Martelotto, L. G. & Ramialison, M. From whole-mount to single-cell spatial assessment of gene expression in 3D. Commun. Biol. 3, 602 (2020).
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).
Acknowledgements
The authors thank members of the Khavari, Nolan, Altman, Zou and Lundeberg laboratories for helpful discussions. This work was supported by the US Veterans Affairs Office of Research and Development I01BX00140908, National Institutes of Health, National Cancer Institute (NIH/NCI) CA142635, NIH, National Institute for Arthritis and Musculoskeletal and Skin Diseases (NIH/NIAMS) AR43799 and AR49737 (P.A.K.), and a Physician-Scientist Training Award from the Damon Runyon Cancer Research Foundation (A.L.J.).
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S.K.L. researched the literature and wrote the article. M.G.G., A.L.J. and P.A.K. contributed substantially to discussions of the content. All authors reviewed and/or edited the manuscript.
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Glossary
- Single-cell RNA sequencing
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(scRNA-seq). A method that sequences RNA transcripts (primarily mRNA) isolated from each cell of a tissue, thereby characterizing individual cells’ transcriptomes; aggregated data characterize the gene expression distribution of tissue cell subpopulations.
- Intercellular communication
-
Communication between cells, typically through ligand–receptor interaction. Juxtracrine denotes direct cellular contact for signalling molecules to be passed between cells, whereas paracrine refers to diffusion of signalling molecules from a sender to a receiver cell.
- Spatial transcriptomics
-
A method that localizes mRNA transcripts to precise spatial locations (single cells or capture spots, formerly regions) in a tissue. In this Review, the term refers broadly to all spatial transcriptomics methods, not exclusively spatial barcoding.
- Bulk RNA-seq
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A method that sequences a mixture of RNA transcripts (primarily mRNA) from the whole tissue to generate an average expression level for each gene across all cells sequenced.
- High-plex RNA imaging
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(HPRI). A targeted spatial transcriptomics method that localizes and quantifies RNA transcripts through multiplexed fluorescent microscopy imaging. Can typically target up to ~100–200 genes simultaneously in intact tissue sections.
- Spatial barcoding
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Spatial transcriptomic methods that use a microarray of poly-T oligonucleotides to ‘capture’ mRNA transcripts of tissue cross-sections, typically followed by next-generation sequencing.
- Coverage
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In the context of single-cell or spatial transcrtipomics assays, the number of distinct genes that are represented from captured RNA molecules.
- Capture spots
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Individual coordinates or capture locations on the microarray used to ‘capture’ mRNA transcripts for spatial barcoding and identified by a DNA barcode; each capture spot generally captures mRNA from multiple cells.
- Resolution
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In spatial data, the distance between spatial coordinates that identify the source of molecules. For spatial barcoding, higher resolution refers to a smaller distance between capture spot coordinates with smaller capture spot diameter.
- Depth
-
In the context of single-cell or spatial transcriptomics assays, the number of unique RNA molecules captured for a particular gene.
- Fluorescent-activated cell sorting
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(FACS). A cell sorting technique based on flow cytometry for isolating and identifying different cell types using fluorescent antibodies targeting known cell type-specific cell surface proteins.
- Microdissection
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A method, such as laser-capture microdissection (LCM), that finely isolates different tissue parts from cryo-sections. LCM-seq is a spatial transcriptomics method that couples LCM with RNA sequencing.
- Deconvolution
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The process of predicting cell-type proportions at a given spatial barcoding capture spot based on its mRNA mixture. Cell types are typically derived from single-cell RNA sequencing profiles of the tissue.
- Pseudo-time analysis
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Computational ordering of single cells, namely of single-cell RNA sequencing, based on a gradual evolution of their transcriptomes to measure progress through a given biological process (for example, differentiation or proliferation)
- Mapping
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The process of assigning a single-cell RNA sequencing (scRNA-seq)-based cell type to each cell spatially resolved by high-plex RNA imaging assays; secondarily, predicting the spatial location of each scRNA-seq cell based on its transcriptome
- Statistical regression
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In the context of deconvolution, linear regression models are fit to determine to what extent each cell type explains the gene expression values for each capture spot.
- Bayesian statistical framework
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In the deconvolution context, statistical models that rely on inferences about the distribution of transcripts for each cell type to yield a probability that a mixture of transcripts can be explained by a specific single-cell RNA sequencing cell type.
- Maximum a posteriori
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A Bayesian estimate of an unknown conditional. In the context of spatial transcriptomics integration methods, the maximum a posteriori estimate most often refers to the cell-type distribution given the gene distribution at a capture spot.
- Enrichment
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Refers to a particular class of genes that is over-represented in a large set of genes. In the deconvolution and mapping contexts, this class is often the cell type.
- Mismatch
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Incongruity between cell types detected as present in single-cell RNA sequencing and spatial transcriptomics data that can complicate spatial barcoding deconvolution, but less so high-plex RNA imaging mapping.
- Mutual nearest neighbour
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A robust algorithm for clustering single cells between two different single-cell transcriptomic data sets (that is, single-cell RNA sequencing with high-plex RNA imaging) based on the cell subtype.
- Dimensionality reduction
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A mathematical technique to represent high-dimensional data in lower dimensions. Mostly used in transcriptomic analysis for visualization of cell subtype clustering in 2D, sometimes 3D, space to establish subtypes.
- Non-negative matrix factorization
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A method commonly used in bioinformatics for dimensionality reduction of gene expression data as the non-negativity constraint reflects that genes are either expressed or not and cannot be negatively expressed.
- Factor loading
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In the context of transcriptomics, correlation coefficients between known gene transcript levels and latent cell subtype information. Can be plotted in low-dimensional space prior to joint clustering of single-cell RNA sequencing and high-plex RNA imaging data.
- Impute
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In the context of spatial transcriptomics, the computational process of determining unknown gene expression values of spatially resolved single cells (high-plex RNA imaging) with corresponding values from mapped single-cell RNA sequencing data.
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Longo, S.K., Guo, M.G., Ji, A.L. et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 22, 627–644 (2021). https://doi.org/10.1038/s41576-021-00370-8
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DOI: https://doi.org/10.1038/s41576-021-00370-8
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