دورية أكاديمية
أعرض في EDS

MICA: a multi-omics method to predict gene regulatory networks in early human embryos.

التفاصيل البيبلوغرافية
العنوان: MICA: a multi-omics method to predict gene regulatory networks in early human embryos.
المؤلفون: Alanis-Lobato G; Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK g.alanis.lobato@gmail.com., Bartlett TE; Department of Statistical Science, University College, London, UK thomas.bartlett.10@ucl.ac.uk., Huang Q; Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK.; https://ror.org/013meh722 Department of Physiology, Development and Neuroscience, The Centre for Trophoblast Research, University of Cambridge, Cambridge, UK., Simon CS; Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK., McCarthy A; Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK., Elder K; Bourn Hall Clinic, Cambridge, UK., Snell P; Bourn Hall Clinic, Cambridge, UK., Christie L; Bourn Hall Clinic, Cambridge, UK., Niakan KK; Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK kkn21@cam.ac.uk.; https://ror.org/013meh722 Department of Physiology, Development and Neuroscience, The Centre for Trophoblast Research, University of Cambridge, Cambridge, UK.; https://ror.org/013meh722 Wellcome - Medical Research Council Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK.; Epigenetics Programme, Babraham Institute, Cambridge, UK.
المصدر: Life science alliance [Life Sci Alliance] 2023 Oct 25; Vol. 7 (1). Date of Electronic Publication: 2023 Oct 25 (Print Publication: 2024).
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Life Science Alliance, LLC Country of Publication: United States NLM ID: 101728869 Publication Model: Electronic-Print Cited Medium: Internet ISSN: 2575-1077 (Electronic) Linking ISSN: 25751077 NLM ISO Abbreviation: Life Sci Alliance Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [Woodbury, NY] : Life Science Alliance, LLC, [2018]-
مواضيع طبية MeSH: Gene Regulatory Networks*/genetics , Multiomics*, Humans ; Transcription Factors/metabolism ; Transcriptome/genetics ; Embryo, Mammalian
مستخلص: Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.
(© 2023 Alanis-Lobato et al.)
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معلومات مُعتمدة: CC2074 United Kingdom ARC_ Arthritis Research UK; CC2074 United Kingdom WT_ Wellcome Trust; CC2074 United Kingdom MRC_ Medical Research Council; CC2074 United Kingdom CRUK_ Cancer Research UK; 221856/Z/20/Z United Kingdom WT_ Wellcome Trust; United Kingdom WT_ Wellcome Trust; 215116/Z/18/Z United Kingdom WT_ Wellcome Trust; MR/P014070/1 United Kingdom MRC_ Medical Research Council
المشرفين على المادة: 0 (Transcription Factors)
تواريخ الأحداث: Date Created: 20231025 Date Completed: 20231113 Latest Revision: 20240210
رمز التحديث: 20240210
مُعرف محوري في PubMed: PMC10599980
DOI: 10.26508/lsa.202302415
PMID: 37879938
قاعدة البيانات: MEDLINE
الوصف
تدمد:2575-1077
DOI:10.26508/lsa.202302415