{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:24:56Z","timestamp":1767176696444,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010366","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000}}],"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Research Council","award":["682810"],"award-info":[{"award-number":["682810"]}]},{"name":"Swedish Foundation for Strategic Research","award":["BD150008"],"award-info":[{"award-number":["BD150008"]}]},{"DOI":"10.13039\/100014989","name":"Chan Zuckerberg Initiative","doi-asserted-by":"publisher","award":["SVCF 2017-173964"],"award-info":[{"award-number":["SVCF 2017-173964"]}],"id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007436","name":"Familjen Erling-Perssons Stiftelse","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007436","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004063","name":"Knut och Alice Wallenbergs Stiftelse","doi-asserted-by":"publisher","award":["KAW 2018.0172"],"award-info":[{"award-number":["KAW 2018.0172"]}],"id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["2019-01238"],"award-info":[{"award-number":["2019-01238"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a\n                    <jats:italic>de novo<\/jats:italic>\n                    technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010366","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T13:47:49Z","timestamp":1660312069000},"page":"e1010366","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks"],"prefix":"10.1371","volume":"18","author":[{"given":"Sergio","family":"Marco Salas","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9788-7717","authenticated-orcid":true,"given":"Xiao","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Christer","family":"Sylven","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9985-0387","authenticated-orcid":true,"given":"Mats","family":"Nilsson","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4139-7003","authenticated-orcid":true,"given":"Carolina","family":"W\u00e4hlby","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4482-3119","authenticated-orcid":true,"given":"Gabriele","family":"Partel","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"key":"pcbi.1010366.ref001","article-title":"The human body at cellular resolution: the NIH Human Biomolecular Atlas Program","year":"2019","journal-title":"Nature"},{"key":"pcbi.1010366.ref002","article-title":"The Human Cell Atlas: From vision to reality","author":"O Rozenblatt-Rosen","year":"2017","journal-title":"Nature"},{"key":"pcbi.1010366.ref003","article-title":"Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets","author":"EZ Macosko","year":"2015","journal-title":"Cell"},{"key":"pcbi.1010366.ref004","article-title":"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells","author":"AM Klein","year":"2015","journal-title":"Cell"},{"key":"pcbi.1010366.ref005","doi-asserted-by":"crossref","article-title":"Placing RNA in context and space-methods for spatially resolved transcriptomics","author":"C Strell","DOI":"10.1111\/febs.14435"},{"key":"pcbi.1010366.ref006","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.gde.2020.12.002","article-title":"Spatially resolved transcriptomics and its applications in 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Gise","year":"2012"},{"key":"pcbi.1010366.ref018","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1038\/nature06969","article-title":"A myocardial lineage derives from Tbx18 epicardial cells","volume":"454","author":"CL Cai","year":"2008","journal-title":"Nature"},{"key":"pcbi.1010366.ref019","article-title":"LETTERS Epicardial progenitors contribute to the cardiomyocyte lineage in the developing heart","author":"B Zhou"},{"key":"pcbi.1010366.ref020","article-title":"Direct RNA targeted transcriptomic profiling in tissue using Hybridization-based RNA In Situ Sequencing (HybRISS).","author":"H Lee","year":"2020","journal-title":"bioRxiv"},{"key":"pcbi.1010366.ref021","article-title":"HybISS: Hybridization-based In Situ Sequencing.","author":"D Gyllborg","year":"2020","journal-title":"ProtocolsIo"},{"key":"pcbi.1010366.ref022","article-title":"Segmentation-free inference of cell types from in situ transcriptomics data","author":"J 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