{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:12:03Z","timestamp":1775664723541,"version":"3.50.1"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:p>A major challenge in sequencing-based spatial transcriptomics (ST) is resolution limitations. Tissue sections are divided into hundreds of thousands of spots, where each spot invariably contains a mixture of cell types. Methods have been developed to deconvolute the mixed transcriptional signal into its constituents. Although ST is becoming essential for drug discovery, especially in cardiometabolic diseases, to date, no deconvolution benchmark has been performed on these types of tissues and diseases. However, the three methods, Cell2location, RCTD, and spatialDWLS, have previously been shown to perform well in brain tissue and simulated data. Here, we compare these methods to assess the best performance when using human data from cardiovascular disease (CVD) and chronic kidney disease (CKD) from patients in different pathological states, evaluated using expert annotation. In this study, we found that all three methods performed comparably well in deconvoluting verifiable cell types, including smooth muscle cells and macrophages in vascular samples and podocytes in kidney samples. RCTD shows the best performance accuracy scores in CVD samples, while Cell2location, on average, achieved the highest performance across all test experiments. Although all three methods had similar accuracies, Cell2location needed less reference data to converge at the expense of higher computational intensity. Finally, we also report that RCTD has the fastest computational time and the simplest workflow, requiring fewer computational dependencies. In conclusion, we find that each method has particular advantages, and the optimal choice depends on the use case.<\/jats:p>","DOI":"10.3389\/fbinf.2024.1352594","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:28:38Z","timestamp":1711499318000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A systematic evaluation of state-of-the-art deconvolution methods in spatial transcriptomics: insights from cardiovascular disease and chronic kidney disease"],"prefix":"10.3389","volume":"4","author":[{"given":"Alban Obel","family":"Slabowska","sequence":"first","affiliation":[]},{"given":"Charles","family":"Pyke","sequence":"additional","affiliation":[]},{"given":"Henning","family":"Hvid","sequence":"additional","affiliation":[]},{"given":"Leon Eyrich","family":"Jessen","sequence":"additional","affiliation":[]},{"given":"Simon","family":"Baumgart","sequence":"additional","affiliation":[]},{"given":"Vivek","family":"Das","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"B1","unstructured":"Visium spatial protocols \u2013 tissue preparation guide2022"},{"key":"B2","unstructured":"Visium spatial gene expression reagent kits for FFPE - user guide2023"},{"key":"B3","doi-asserted-by":"publisher","first-page":"4307","DOI":"10.1038\/s41467-020-18158-5","article-title":"Single cell transcriptomics comes of age","volume":"11","author":"Aldridge","year":"2020","journal-title":"Nat. Commun."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1038\/s42003-022-04056-7","article-title":"Decoding the transcriptome of calcified atherosclerotic plaque at single-cell resolution","volume":"5","author":"Alsaigh","year":"2022","journal-title":"Commun. Biol."},{"key":"B5","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1038\/s41587-021-00830-w","article-title":"Robust decomposition of cell type mixtures in spatial transcriptomics","volume":"40","author":"Cable","year":"2022","journal-title":"Nat. Biotechnol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"RP88431","DOI":"10.7554\/eLife.88431.1","article-title":"Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics","volume":"12","author":"Chananchida","year":"2023","journal-title":"eLife"},{"key":"B7","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s13059-021-02362-7","article-title":"SpatialDWLS: accurate deconvolution of spatial transcriptomic data","volume":"22","author":"Dong","year":"2021","journal-title":"Genome Biol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"eabn4965","DOI":"10.1126\/sciadv.abn4965","article-title":"A reference tissue atlas for the human kidney","volume":"8","author":"Hansen","year":"2022","journal-title":"Sci. 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Heart J."},{"key":"B16","doi-asserted-by":"publisher","first-page":"2975","DOI":"10.1038\/s41467-019-10802-z","article-title":"Accurate estimation of cell-type composition from gene expression data","volume":"10","author":"Tsoucas","year":"2019","journal-title":"Nat. Commun."},{"key":"B17","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1038\/s41591-019-0512-5","article-title":"Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis","volume":"25","author":"Wirka","year":"2019","journal-title":"Nat. Med."},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac805","article-title":"Benchmarking and integration of methods for deconvoluting spatial transcriptomic data","volume":"39","author":"Yan","year":"2023","journal-title":"Bioinformatics"}],"container-title":["Frontiers in Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2024.1352594\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:28:46Z","timestamp":1711499326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2024.1352594\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":19,"alternative-id":["10.3389\/fbinf.2024.1352594"],"URL":"https:\/\/doi.org\/10.3389\/fbinf.2024.1352594","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.12.04.569888","asserted-by":"object"}]},"ISSN":["2673-7647"],"issn-type":[{"value":"2673-7647","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,27]]},"article-number":"1352594"}}