{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:10:00Z","timestamp":1772136600997,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell\/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION\u2019s scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.<\/jats:p>","DOI":"10.1038\/s43588-024-00597-5","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T06:01:58Z","timestamp":1709532118000},"page":"237-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4424-8422","authenticated-orcid":false,"given":"Daniel","family":"Osorio","sequence":"first","affiliation":[]},{"given":"Anna","family":"Capasso","sequence":"additional","affiliation":[]},{"given":"S. Gail","family":"Eckhardt","sequence":"additional","affiliation":[]},{"given":"Uma","family":"Giri","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Somma","sequence":"additional","affiliation":[]},{"given":"Todd M.","family":"Pitts","sequence":"additional","affiliation":[]},{"given":"Christopher H.","family":"Lieu","sequence":"additional","affiliation":[]},{"given":"Wells A.","family":"Messersmith","sequence":"additional","affiliation":[]},{"given":"Stacey M.","family":"Bagby","sequence":"additional","affiliation":[]},{"given":"Harinder","family":"Singh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5747-064X","authenticated-orcid":false,"given":"Jishnu","family":"Das","sequence":"additional","affiliation":[]},{"given":"Nidhi","family":"Sahni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0047-8103","authenticated-orcid":false,"given":"S. Stephen","family":"Yi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6280-3130","authenticated-orcid":false,"given":"Marieke L.","family":"Kuijjer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"597_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1038\/nature01763","volume":"424","author":"M Levine","year":"2003","unstructured":"Levine, M. & Tjian, R. Transcription regulation and animal diversity. Nature 424, 147\u2013151 (2003).","journal-title":"Nature"},{"key":"597_CR2","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.ceb.2006.04.002","volume":"18","author":"LO Barrera","year":"2006","unstructured":"Barrera, L. O. & Ren, B. The transcriptional regulatory code of eukaryotic cells\u2013insights from genome-wide analysis of chromatin organization and transcription factor binding. Curr. Opin. Cell Biol. 18, 291\u2013298 (2006).","journal-title":"Curr. Opin. Cell Biol."},{"key":"597_CR3","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1038\/nmeth.3799","volume":"13","author":"D Marbach","year":"2016","unstructured":"Marbach, D. et al. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat. Methods 13, 366\u2013370 (2016).","journal-title":"Nat. Methods"},{"key":"597_CR4","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.sbi.2004.05.004","volume":"14","author":"MM Babu","year":"2004","unstructured":"Babu, M. M., Luscombe, N. M., Aravind, L., Gerstein, M. & Teichmann, S. A. Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol. 14, 283\u2013291 (2004).","journal-title":"Curr. Opin. Struct. Biol."},{"key":"597_CR5","doi-asserted-by":"publisher","first-page":"100139","DOI":"10.1016\/j.patter.2020.100139","volume":"1","author":"D Osorio","year":"2020","unstructured":"Osorio, D., Zhong, Y., Li, G., Huang, J. Z. & Cai, J. J. scTenifoldNet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data. Patterns 1, 100139 (2020).","journal-title":"Patterns"},{"key":"597_CR6","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/cells9010014","volume":"9","author":"D Osorio","year":"2019","unstructured":"Osorio, D. et al. Single-cell expression variability implies cell function. Cells 9, 14 (2019).","journal-title":"Cells"},{"key":"597_CR7","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1186\/1471-2105-12-322","volume":"12","author":"JA Miller","year":"2011","unstructured":"Miller, J. A. et al. Strategies for aggregating gene expression data: the collapserows r function. BMC Bioinformatics 12, 322 (2011).","journal-title":"BMC Bioinformatics"},{"key":"597_CR8","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1038\/nrm2503","volume":"9","author":"G Karlebach","year":"2008","unstructured":"Karlebach, G. & Shamir, R. Modelling and analysis of gene regulatory networks. Nature Rev. Mol. Cell Biol. 9, 770\u2013780 (2008).","journal-title":"Nature Rev. Mol. Cell Biol."},{"key":"597_CR9","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.isci.2019.03.021","volume":"14","author":"ML Kuijjer","year":"2019","unstructured":"Kuijjer, M. L., Tung, M. G., Yuan, G., Quackenbush, J. & Glass, K. Estimating sample-specific regulatory networks. iScience 14, 226\u2013240 (2019).","journal-title":"iScience"},{"key":"597_CR10","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1186\/s13059-023-02949-2","volume":"24","author":"Y You","year":"2023","unstructured":"You, Y. et al. Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data. Genome Biol. 24, 107 (2023).","journal-title":"Genome Biol."},{"key":"597_CR11","doi-asserted-by":"publisher","first-page":"64832","DOI":"10.1371\/journal.pone.0064832","volume":"8","author":"K Glass","year":"2013","unstructured":"Glass, K., Huttenhower, C., Quackenbush, J. & Yuan, G.-C. Passing messages between biological networks to refine predicted interactions. PLoS ONE 8, 64832 (2013).","journal-title":"PLoS ONE"},{"key":"597_CR12","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1186\/s12859-022-04861-1","volume":"23","author":"M Bilous","year":"2022","unstructured":"Bilous, M. et al. Metacells untangle large and complex single-cell transcriptome networks. BMC Bioinformatics 23, 336 (2022).","journal-title":"BMC Bioinformatics"},{"key":"597_CR13","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1038\/s41592-019-0690-6","volume":"17","author":"A Pratapa","year":"2020","unstructured":"Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147\u2013154 (2020).","journal-title":"Nat. Methods"},{"key":"597_CR14","doi-asserted-by":"publisher","first-page":"2059","DOI":"10.1053\/j.gastro.2009.12.065","volume":"138","author":"MS Pino","year":"2010","unstructured":"Pino, M. S. & Chung, D. C. The chromosomal instability pathway in colon cancer. Gastroenterology 138, 2059\u20132072 (2010).","journal-title":"Gastroenterology"},{"key":"597_CR15","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1038\/nmeth.2016","volume":"9","author":"D Marbach","year":"2012","unstructured":"Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796\u2013804 (2012).","journal-title":"Nat. Methods"},{"key":"597_CR16","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1038\/s41588-019-0384-0","volume":"51","author":"L Chen","year":"2019","unstructured":"Chen, L. et al. A reinforcing HNF4\u2013SMAD4 feed-forward module stabilizes enterocyte identity. Nat. Genet. 51, 777\u2013785 (2019).","journal-title":"Nat. Genet."},{"key":"597_CR17","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.stem.2022.01.014","volume":"29","author":"J Taubenschmid-Stowers","year":"2022","unstructured":"Taubenschmid-Stowers, J. et al. 8C-like cells capture the human zygotic genome activation program in vitro. Cell Stem Cell 29, 449\u2013459 (2022).","journal-title":"Cell Stem Cell"},{"key":"597_CR18","doi-asserted-by":"publisher","first-page":"104498","DOI":"10.1016\/j.isci.2022.104498","volume":"25","author":"JL Regan","year":"2022","unstructured":"Regan, J. L. et al. Identification of a neural development gene expression signature in colon cancer stem cells reveals a role for EGR2 in tumorigenesis. iScience 25, 104498 (2022).","journal-title":"iScience"},{"key":"597_CR19","first-page":"6510","volume":"8","author":"P He","year":"2015","unstructured":"He, P. et al. HDAC5 promotes colorectal cancer cell proliferation by up-regulating DLL4 expression. Int. J. Clin. Exp. Med. 8, 6510 (2015).","journal-title":"Int. J. Clin. Exp. Med."},{"key":"597_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41419-020-03229-8","volume":"12","author":"X Zhang","year":"2021","unstructured":"Zhang, X. et al. Hsa_circ_0026628 promotes the development of colorectal cancer by targeting SP1 to activate the Wnt\/\u03b2-catenin pathway. Cell Death Dis. 12, 1\u201315 (2021).","journal-title":"Cell Death Dis."},{"key":"597_CR21","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1186\/s13046-019-1405-7","volume":"38","author":"S-Y Park","year":"2019","unstructured":"Park, S.-Y. et al. The JAK2\/STAT3\/CCND2 axis promotes colorectal cancer stem cell persistence and radioresistance. J. Exp. Clin. Cancer Res. 38, 399 (2019).","journal-title":"J. Exp. Clin. Cancer Res."},{"key":"597_CR22","doi-asserted-by":"publisher","first-page":"4397","DOI":"10.1038\/onc.2012.461","volume":"32","author":"J Zhang","year":"2013","unstructured":"Zhang, J. et al. NANOG modulates stemness in human colorectal cancer. Oncogene 32, 4397\u20134405 (2013).","journal-title":"Oncogene"},{"key":"597_CR23","first-page":"1885","volume":"40","author":"B Ji","year":"2018","unstructured":"Ji, B. et al. GPR56 promotes proliferation of colorectal cancer cells and enhances metastasis via epithelial-mesenchymal transition through PI3K\/AKT signaling activation. Oncol. Rep. 40, 1885\u20131896 (2018).","journal-title":"Oncol. Rep."},{"key":"597_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13046-020-01783-9","volume":"39","author":"X-T Hu","year":"2020","unstructured":"Hu, X.-T. et al. HDAC2 inhibits emt-mediated cancer metastasis by downregulating the long noncoding RNA H19 in colorectal cancer. J. Exp. Clin. Cancer Res. 39, 1\u201314 (2020).","journal-title":"J. Exp. Clin. Cancer Res."},{"key":"597_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.biocel.2017.04.011","volume":"88","author":"MA Mansour","year":"2017","unstructured":"Mansour, M. A. & Senga, T. HOXD8 exerts a tumor-suppressing role in colorectal cancer as an apoptotic inducer. Int. J. Biochem. Cell Biol. 88, 1\u201313 (2017).","journal-title":"Int. J. Biochem. Cell Biol."},{"key":"597_CR26","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","volume":"1","author":"A Liberzon","year":"2015","unstructured":"Liberzon, A. et al. The Molecular Signatures Database hallmark gene set collection. Cell Syst. 1, 417\u2013425 (2015).","journal-title":"Cell Syst."},{"key":"597_CR27","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1038\/25292","volume":"396","author":"C Lengauer","year":"1998","unstructured":"Lengauer, C., Kinzler, K. W. & Vogelstein, B. Genetic instabilities in human cancers. Nature 396, 643\u2013649 (1998).","journal-title":"Nature"},{"key":"597_CR28","doi-asserted-by":"publisher","first-page":"10213","DOI":"10.1158\/0008-5472.CAN-10-2720","volume":"70","author":"H Okuyama","year":"2010","unstructured":"Okuyama, H., Endo, H., Akashika, T., Kato, K. & Inoue, M. Downregulation of c-MYC protein levels contributes to cancer cell survival under dual deficiency of oxygen and glucose. Cancer Res. 70, 10213\u201310223 (2010).","journal-title":"Cancer Res."},{"key":"597_CR29","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1038\/nm.3967","volume":"21","author":"J Guinney","year":"2015","unstructured":"Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350\u20131356 (2015).","journal-title":"Nat. Med."},{"key":"597_CR30","doi-asserted-by":"publisher","first-page":"e152616","DOI":"10.1172\/jci.insight.152616","volume":"7","author":"W Guo","year":"2022","unstructured":"Guo, W. et al. Resolving the difference between left-sided and right-sided colorectal cancer by single-cell sequencing. JCI Insight 7, e152616 (2022).","journal-title":"JCI Insight"},{"key":"597_CR31","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s00432-017-2548-6","volume":"144","author":"ML Slattery","year":"2018","unstructured":"Slattery, M. L. et al. The NF-\u03baB signalling pathway in colorectal cancer: associations between dysregulated gene and miRNA expression. J. Cancer Res. Clin. Oncol. 144, 269\u2013283 (2018).","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"597_CR32","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1245\/s10434-008-0015-y","volume":"15","author":"RA Meguid","year":"2008","unstructured":"Meguid, R. A., Slidell, M. B., Wolfgang, C. L., Chang, D. C. & Ahuja, N. Is there a difference in survival between right-versus left-sided colon cancers? Ann. Surg. Oncol. 15, 2388\u20132394 (2008).","journal-title":"Ann. Surg. Oncol."},{"key":"597_CR33","doi-asserted-by":"publisher","first-page":"36750","DOI":"10.18632\/oncotarget.26353","volume":"9","author":"H Tanaka","year":"2018","unstructured":"Tanaka, H., Kuwano, Y., Nishikawa, T., Rokutan, K. & Nishida, K. ZNF350 promoter methylation accelerates colon cancer cell migration. Oncotarget 9, 36750 (2018).","journal-title":"Oncotarget"},{"key":"597_CR34","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1002\/path.2440","volume":"216","author":"F Pont\u00e9n","year":"2008","unstructured":"Pont\u00e9n, F., Jirstr\u00f6m, K. & Uhlen, M. The Human Protein Atlas\u2014a tool for pathology. J. Pathol. 216, 387\u2013393 (2008).","journal-title":"J. Pathol."},{"key":"597_CR35","doi-asserted-by":"crossref","unstructured":"Cancer Genome Atlas Network. et al. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330\u2013337 (2012).","DOI":"10.1038\/nature11252"},{"key":"597_CR36","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1038\/nmeth.4463","volume":"14","author":"S Aibar","year":"2017","unstructured":"Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083\u20131086 (2017).","journal-title":"Nat. Methods"},{"key":"597_CR37","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1038\/s43587-022-00233-9","volume":"2","author":"AK Maity","year":"2022","unstructured":"Maity, A. K., Hu, X., Zhu, T. & Teschendorff, A. E. Inference of age-associated transcription factor regulatory activity changes in single cells. Nat. Aging 2, 548\u2013561 (2022).","journal-title":"Nat. Aging"},{"key":"597_CR38","doi-asserted-by":"publisher","first-page":"100434","DOI":"10.1016\/j.patter.2022.100434","volume":"3","author":"D Osorio","year":"2022","unstructured":"Osorio, D. et al. scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation. Patterns 3, 100434 (2022).","journal-title":"Patterns"},{"key":"597_CR39","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1093\/nar\/gkaa1074","volume":"49","author":"D Szklarczyk","year":"2021","unstructured":"Szklarczyk, D. et al. The STRING database in 2021: customizable protein\u2013protein networks, and functional characterization of user-uploaded gene\/measurement sets. Nucleic Acids Res. 49, 605\u2013612 (2021).","journal-title":"Nucleic Acids Res."},{"key":"597_CR40","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1038\/s41586-020-2528-x","volume":"583","author":"J Vierstra","year":"2020","unstructured":"Vierstra, J. et al. Global reference mapping of human transcription factor footprints. Nature 583, 729\u2013736 (2020).","journal-title":"Nature"},{"key":"597_CR41","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s12976-015-0023-0","volume":"12","author":"O R\u00edos","year":"2015","unstructured":"R\u00edos, O. et al. A boolean network model of human gonadal sex determination. Theor. Biol. Med. Model. 12, 26 (2015).","journal-title":"Theor. Biol. Med. Model."},{"key":"597_CR42","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","volume":"36","author":"A Butler","year":"2018","unstructured":"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\u2013420 (2018).","journal-title":"Nat. Biotechnol."},{"key":"597_CR43","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","volume":"16","author":"I Korsunsky","year":"2019","unstructured":"Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat. Methods 16, 1289\u20131296 (2019).","journal-title":"Nat. Methods"},{"key":"597_CR44","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1093\/bioinformatics\/btab003","volume":"37","author":"J Alquicira-Hernandez","year":"2021","unstructured":"Alquicira-Hernandez, J. & Powell, J. E. Nebulosa recovers single-cell gene expression signals by kernel density estimation. Bioinformatics 37, 2485\u20132487 (2021).","journal-title":"Bioinformatics"},{"key":"597_CR45","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1093\/bioinformatics\/btaa751","volume":"37","author":"D Osorio","year":"2021","unstructured":"Osorio, D. & Cai, J. J. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics 37, 963\u2013967 (2021).","journal-title":"Bioinformatics"},{"key":"597_CR46","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1038\/s41467-022-29366-6","volume":"13","author":"J Qi","year":"2022","unstructured":"Qi, J. et al. Single-cell and spatial analysis reveal interaction of FAP+ fibroblasts and SPP1+ macrophages in colorectal cancer. Nat. Commun. 13, 1742 (2022).","journal-title":"Nat. Commun."},{"key":"597_CR47","doi-asserted-by":"crossref","unstructured":"Bagby, S. et al. Development and maintenance of a preclinical patient derived tumor xenograft model for the investigation of novel anti-cancer therapies. J. Vis. Exp. 115, 54393 (2016).","DOI":"10.3791\/54393-v"},{"key":"597_CR48","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1038\/s41588-020-0636-z","volume":"52","author":"H-O Lee","year":"2020","unstructured":"Lee, H.-O. et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nat. Genet. 52, 594\u2013603 (2020).","journal-title":"Nat. Genet."},{"key":"597_CR49","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1038\/s41422-020-0355-0","volume":"30","author":"J Qian","year":"2020","unstructured":"Qian, J. et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 30, 745\u2013762 (2020).","journal-title":"Cell Res."},{"key":"597_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41421-021-00312-y","volume":"7","author":"L-H Che","year":"2021","unstructured":"Che, L.-H. et al. A single-cell atlas of liver metastases of colorectal cancer reveals reprogramming of the tumor microenvironment in response to preoperative chemotherapy. Cell Discov. 7, 1\u201321 (2021).","journal-title":"Cell Discov."},{"key":"597_CR51","doi-asserted-by":"publisher","unstructured":"Osorio, D. dosorio\/SCORPION: population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomic data (v.1.0.0). Zenodo https:\/\/doi.org\/10.5281\/zenodo.10515946 (2024).","DOI":"10.5281\/zenodo.10515946"}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-024-00597-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-024-00597-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-024-00597-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T13:03:14Z","timestamp":1711458194000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-024-00597-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["597"],"URL":"https:\/\/doi.org\/10.1038\/s43588-024-00597-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.01.20.524974","asserted-by":"object"}]},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,4]]},"assertion":[{"value":"2 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"D.O. is currently an employee of QIAGEN Digital Insights, QIAGEN, USA. The other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}