{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T16:59:59Z","timestamp":1764349199671,"version":"3.41.2"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T00:00:00Z","timestamp":1596672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35 GM136375","P30 CA142543","P50CA70907"],"award-info":[{"award-number":["R35 GM136375","P30 CA142543","P50CA70907"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","award":["RP190107","RP180805"],"award-info":[{"award-number":["RP190107","RP180805"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.<\/jats:p>","DOI":"10.1093\/bib\/bbaa145","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T11:09:31Z","timestamp":1594292971000},"source":"Crossref","is-referenced-by-count":35,"title":["Spatial molecular profiling: platforms, applications and analysis tools"],"prefix":"10.1093","volume":"22","author":[{"given":"Minzhe","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Population and Data Sciences at University of Texas Southwestern Medical Center"}]},{"given":"Thomas","family":"Sheffield","sequence":"additional","affiliation":[{"name":"Department of Population and Data Sciences at University of Texas Southwestern Medical Center"}]},{"given":"Xiaowei","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Population and Data Sciences at University of Texas Southwestern Medical Center"}]},{"given":"Qiwei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Mathematics Sciences at University of Texas at Dallas"}]},{"given":"Donghan M","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Population and Data Sciences at University of Texas Southwestern Medical Center"}]},{"given":"Yunguan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Population and Data Sciences at University of Texas Southwestern Medical Center"}]},{"given":"Shidan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Population and Data Sciences at University of Texas Southwestern Medical Center"}]},{"given":"Yang","family":"Xie","sequence":"additional","affiliation":[{"name":"Quantitative Biomedical Research Center at the University of Texas Southwestern Medical Center"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Population and Data Sciences at University of Texas Southwestern Medical Center"}]},{"given":"Guanghua","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Population and Data Sciences at University of Texas Southwestern Medical Center"}]}],"member":"286","published-online":{"date-parts":[[2020,8,6]]},"reference":[{"issue":"5363","key":"2021052110183635000_ref1","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1126\/science.280.5363.585","article-title":"Visualization of single RNA transcripts in situ","volume":"280","author":"Femino","year":"1998","journal-title":"Science"},{"issue":"10","key":"2021052110183635000_ref2","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1038\/nmeth.1253","article-title":"Imaging individual mRNA molecules using multiple singly labeled probes","volume":"5","author":"Raj","year":"2008","journal-title":"Nat Methods"},{"issue":"1","key":"2021052110183635000_ref3","doi-asserted-by":"crossref","DOI":"10.1038\/msb.2009.102","article-title":"Systematic image-driven analysis of the spatial Drosophila embryonic expression landscape","volume":"6","author":"Frise","year":"2010","journal-title":"Mol Syst Biol"},{"issue":"3","key":"2021052110183635000_ref4","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.cell.2014.09.038","article-title":"Genome-wide RNA tomography in the zebrafish embryo","volume":"159","author":"Junker","year":"2014","journal-title":"Cell"},{"issue":"2","key":"2021052110183635000_ref5","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1038\/nmeth.2804","article-title":"Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue","volume":"11","author":"Lovatt","year":"2014","journal-title":"Nat Methods"},{"key":"2021052110183635000_ref6","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btz444","article-title":"deconvSeq: deconvolution of cell mixture distribution in sequencing data","volume":"35","author":"Du","year":"2019","journal-title":"Bioinformatics"},{"issue":"1","key":"2021052110183635000_ref7","article-title":"Bulk tissue cell type deconvolution with multi-subject single-cell expression reference","volume":"10","author":"Wang","year":"2019","journal-title":"Nat Commun"},{"issue":"19","key":"2021052110183635000_ref8","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1186\/s12859-019-3211-9","article-title":"scDC: single cell differential composition analysis","volume":"20","author":"Cao","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"9","key":"2021052110183635000_ref9","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1158\/2159-8290.CD-17-1246","article-title":"An empirical approach leveraging tumorgrafts to dissect the tumor microenvironment in renal cell carcinoma identifies missing link to prognostic inflammatory factors","volume":"8","author":"Wang","year":"2018","journal-title":"Cancer Discov"},{"issue":"7","key":"2021052110183635000_ref10","doi-asserted-by":"crossref","first-page":"531","DOI":"10.3390\/genes10070531","article-title":"SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples","volume":"10","author":"Zhang","year":"2019","journal-title":"Genes"},{"issue":"7785","key":"2021052110183635000_ref11","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41586-019-1773-3","article-title":"Gene expression cartography","volume":"576","author":"Nitzan","year":"2019","journal-title":"Nature"},{"issue":"5","key":"2021052110183635000_ref12","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nbt.3192","article-title":"Spatial reconstruction of single-cell gene expression data","volume":"33","author":"Satija","year":"2015","journal-title":"Nat Biotechnol"},{"issue":"5","key":"2021052110183635000_ref13","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/nbt.3209","article-title":"High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin","volume":"33","author":"Achim","year":"2015","journal-title":"Nat Biotechnol"},{"issue":"4","key":"2021052110183635000_ref14","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1038\/nmeth.2892","article-title":"Single-cell in situ RNA profiling by sequential hybridization","volume":"11","author":"Lubeck","year":"2014","journal-title":"Nat Methods"},{"issue":"7751","key":"2021052110183635000_ref15","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1038\/s41586-019-1049-y","article-title":"Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+","volume":"568","author":"Eng","year":"2019","journal-title":"Nature"},{"issue":"6233","key":"2021052110183635000_ref16","doi-asserted-by":"crossref","first-page":"aaa6090","DOI":"10.1126\/science.aaa6090","article-title":"Spatially resolved, highly multiplexed RNA profiling in single cells","volume":"348","author":"Chen","year":"2015","journal-title":"Science"},{"issue":"11","key":"2021052110183635000_ref17","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1038\/s41592-018-0175-z","article-title":"Spatial organization of the somatosensory cortex revealed by osmFISH","volume":"15","author":"Codeluppi","year":"2018","journal-title":"Nat Methods"},{"issue":"6400","key":"2021052110183635000_ref18","doi-asserted-by":"crossref","first-page":"eaat5691","DOI":"10.1126\/science.aat5691","article-title":"Three-dimensional intact-tissue sequencing of single-cell transcriptional states","volume":"361","author":"Wang","year":"2018","journal-title":"Science"},{"key":"2021052110183635000_ref19","doi-asserted-by":"crossref","DOI":"10.1038\/s41587-020-0472-9","article-title":"Multiplex digital spatial profiling of proteins and RNA in fixed tissue","volume":"38","author":"Merritt","year":"2020","journal-title":"Nature Biotechnology"},{"issue":"4","key":"2021052110183635000_ref20","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/nmeth.2869","article-title":"Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry","volume":"11","author":"Giesen","year":"2014","journal-title":"Nat Methods"},{"issue":"4","key":"2021052110183635000_ref21","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nm.3488","article-title":"Multiplexed ion beam imaging of human breast tumors","volume":"20","author":"Angelo","year":"2014","journal-title":"Nat Med"},{"key":"2021052110183635000_ref22"},{"issue":"6294","key":"2021052110183635000_ref23","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.aaf2403","article-title":"Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","volume":"353","author":"St\u00e5hl","year":"2016","journal-title":"Science"},{"key":"2021052110183635000_ref24"},{"issue":"6434","key":"2021052110183635000_ref25","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1126\/science.aaw1219","article-title":"Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution","volume":"363","author":"Rodriques","year":"2019","journal-title":"Science"},{"issue":"5","key":"2021052110183635000_ref26","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"issue":"10","key":"2021052110183635000_ref27","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1038\/s41592-019-0548-y","article-title":"High-definition spatial transcriptomics for in situ tissue profiling","volume":"16","author":"Vickovic","year":"2019","journal-title":"Nat Methods"},{"issue":"7635","key":"2021052110183635000_ref28","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1038\/nature20777","article-title":"Synthetic recording and in situ readout of lineage information in single cells","volume":"541","author":"Frieda","year":"2017","journal-title":"Nature"},{"issue":"1","key":"2021052110183635000_ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-01561-w","article-title":"Identification of a neural crest stem cell niche by Spatial Genomic Analysis","volume":"8","author":"Lignell","year":"2017","journal-title":"Nat Commun"},{"issue":"39","key":"2021052110183635000_ref30","doi-asserted-by":"crossref","first-page":"19490","DOI":"10.1073\/pnas.1912459116","article-title":"Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression","volume":"116","author":"Xia","year":"2019","journal-title":"Proc Natl Acad Sci"},{"issue":"9","key":"2021052110183635000_ref31","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1073\/pnas.091062498","article-title":"Significance analysis of microarrays applied to the ionizing radiation response","volume":"98","author":"Tusher","year":"2001","journal-title":"Proc Natl Acad Sci"},{"key":"2021052110183635000_ref32","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"issue":"1","key":"2021052110183635000_ref33","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","article-title":"edgeR: a Bioconductor package for differential expression analysis of digital gene expression data","volume":"26","author":"Robinson","year":"2010","journal-title":"Bioinformatics"},{"issue":"5","key":"2021052110183635000_ref34","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1038\/nmeth.4636","article-title":"SpatialDE: identification of spatially variable genes","volume":"15","author":"Svensson","year":"2018","journal-title":"Nat Methods"},{"issue":"2","key":"2021052110183635000_ref35","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1038\/s41592-019-0701-7","article-title":"Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies","volume":"17","author":"Sun","year":"2020","journal-title":"Nat Methods"},{"issue":"1984","key":"2021052110183635000_ref36","first-page":"20110550","article-title":"Gaussian processes for time-series modelling","volume":"371","author":"Roberts","year":"2013","journal-title":"Philos Trans A Math Phys Eng Sci"},{"issue":"3","key":"2021052110183635000_ref37","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1111\/1467-9876.00113","article-title":"Model-based geostatistics","volume":"47","author":"Diggle","year":"1998","journal-title":"J R Stat Soc Ser C Appl Stat"},{"issue":"5","key":"2021052110183635000_ref38","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1038\/nmeth.4634","article-title":"Identification of spatial expression trends in single-cell gene expression data","volume":"15","author":"Edsg\u00e4rd","year":"2018","journal-title":"Nat Methods"},{"issue":"16","key":"2021052110183635000_ref39","doi-asserted-by":"crossref","first-page":"4290","DOI":"10.1073\/pnas.1521171113","article-title":"Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks","volume":"113","author":"Wu","year":"2016","journal-title":"Proc Natl Acad Sci"},{"issue":"9","key":"2021052110183635000_ref40","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1038\/nmeth.4391","article-title":"histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data","volume":"14","author":"Schapiro","year":"2017","journal-title":"Nat Methods"},{"issue":"1","key":"2021052110183635000_ref41","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40425-018-0488-6","article-title":"Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence","volume":"7","author":"Enfield","year":"2019","journal-title":"J Immunother Cancer"},{"key":"2021052110183635000_ref42","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/978-1-4939-9574-5_17","volume-title":"Stem Cell Mobilization","author":"de Back","year":"2019"},{"volume-title":"Statistical Analysis and Modelling of Spatial Point Patterns","year":"2008","author":"Illian","key":"2021052110183635000_ref43"},{"issue":"i06","key":"2021052110183635000_ref44","first-page":"12","article-title":"spatstat: an R package for analyzing spatial point patterns","author":"Baddeley","year":"2005","journal-title":"J Stat Softw"},{"key":"2021052110183635000_ref45","article-title":"A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images","volume":"20","author":"Li","year":"2018","journal-title":"Biostatistics"},{"issue":"3","key":"2021052110183635000_ref46","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1214\/19-AOAS1254","article-title":"A Bayesian mark interaction model for analysis of tumor pathology images","volume":"13","author":"Li","year":"2019","journal-title":"Ann Appl Statistics"},{"issue":"1","key":"2021052110183635000_ref47","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.celrep.2019.08.077","article-title":"Modeling cell-cell interactions from spatial molecular data with spatial variance component analysis","volume":"29","author":"Arnol","year":"2019","journal-title":"Cell Rep"},{"issue":"9","key":"2021052110183635000_ref48","doi-asserted-by":"crossref","first-page":"e1007324","DOI":"10.1371\/journal.pcbi.1007324","article-title":"Predicting gene regulatory interactions based on spatial gene expression data and deep learning","volume":"15","author":"Yang","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2021052110183635000_ref49","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btz914","article-title":"SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells","volume":"36","author":"Tan","year":"2019","journal-title":"Bioinformatics"},{"key":"2021052110183635000_ref50","first-page":"1","article-title":"The single-cell pathology landscape of breast cancer","author":"Jackson","year":"2020","journal-title":"Nature"},{"issue":"7","key":"2021052110183635000_ref51","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1016\/j.cell.2015.11.018","article-title":"Control of transcript variability in single mammalian cells","volume":"163","author":"Battich","year":"2015","journal-title":"Cell"},{"issue":"4","key":"2021052110183635000_ref52","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.cell.2018.07.010","article-title":"Deep profiling of mouse splenic architecture with CODEX multiplexed imaging","volume":"174","author":"Goltsev","year":"2018","journal-title":"Cell"},{"issue":"22","key":"2021052110183635000_ref53","doi-asserted-by":"crossref","first-page":"4679","DOI":"10.1093\/bioinformatics\/btz288","article-title":"Cell-level somatic mutation detection from single-cell RNA sequencing","volume":"35","author":"Vu","year":"2019","journal-title":"Bioinformatics"},{"issue":"1","key":"2021052110183635000_ref54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-11591-1","article-title":"A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing","volume":"10","author":"Petti","year":"2019","journal-title":"Nat Commun"},{"key":"2021052110183635000_ref55","article-title":"Overcoming genetic drop-outs in variants-based lineage tracing from single-cell RNA sequencing data","author":"Lu","year":"2020","journal-title":"bioRxiv"},{"issue":"1","key":"2021052110183635000_ref56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1922-x","article-title":"DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing","volume":"21","author":"Zhou","year":"2020","journal-title":"Genome Biol"},{"issue":"10","key":"2021052110183635000_ref57","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1038\/s41591-019-0592-2","article-title":"Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer","volume":"25","author":"Joshi","year":"2019","journal-title":"Nat Med"},{"issue":"7","key":"2021052110183635000_ref58","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","article-title":"Comprehensive integration of single-cell data","volume":"177","author":"Stuart","year":"2019","journal-title":"Cell"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/3\/bbaa145\/37963832\/bbaa145.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/3\/bbaa145\/37963832\/bbaa145.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T10:19:29Z","timestamp":1621592369000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaa145\/5881377"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,6]]},"references-count":58,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,5,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaa145","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2021,5]]},"published":{"date-parts":[[2020,8,6]]},"article-number":"bbaa145"}}