Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits

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dc.contributor.authorLee, Jiyoungen
dc.contributor.authorGeng, Shuoen
dc.contributor.authorLi, Songen
dc.contributor.authorLi, Liwuen
dc.contributor.departmentSchool of Plant and Environmental Sciencesen
dc.contributor.departmentBiological Sciencesen
dc.date.accessioned2021-05-12T12:27:20Zen
dc.date.available2021-05-12T12:27:20Zen
dc.date.issued2021-02-23en
dc.description.abstractSubclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.en
dc.description.notesThis work was supported by the Jeffress Trust Awards Program in Interdisciplinary Research to SL, as well as NIH R01HL115835 to LL.en
dc.description.sponsorshipJeffress Trust Awards Program in Interdisciplinary Research; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01HL115835]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fimmu.2021.627036en
dc.identifier.issn1664-3224en
dc.identifier.other627036en
dc.identifier.pmid33708217en
dc.identifier.urihttp://hdl.handle.net/10919/103253en
dc.identifier.volume12en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectmonocyteen
dc.subjectMachine learningen
dc.subjectinflammationen
dc.subjectregulatory motifsen
dc.subjectsingle cell analysisen
dc.titleSingle Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuitsen
dc.title.serialFrontiers in Immunologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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