A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features

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  • dc.contributor.author Carnevali, Davide
  • dc.contributor.author Zhong, Limei
  • dc.contributor.author González-Almela, Esther
  • dc.contributor.author Viana, Carlotta
  • dc.contributor.author Rotkevich, Mikhail
  • dc.contributor.author Wang, Aiping
  • dc.contributor.author Franco-Barranco, Daniel
  • dc.contributor.author González-Marfil, Aitor
  • dc.contributor.author Neguembor, Maria Victoria
  • dc.contributor.author Castells García, Àlvaro, 1991-
  • dc.contributor.author Arganda-Carreras, Ignacio
  • dc.contributor.author Cosma, Maria Pia
  • dc.date.accessioned 2024-12-02T07:17:15Z
  • dc.date.available 2024-12-02T07:17:15Z
  • dc.date.issued 2024
  • dc.description.abstract Cellular phenotypic heterogeneity is an important hallmark of many biological processes and understanding its origins remains a substantial challenge. This heterogeneity often reflects variations in the chromatin structure, influenced by factors such as viral infections and cancer, which dramatically reshape the cellular landscape. To address the challenge of identifying distinct cell states, we developed artificial intelligence of the nucleus (AINU), a deep learning method that can identify specific nuclear signatures at the nanoscale resolution. AINU can distinguish different cell states based on the spatial arrangement of core histone H3, RNA polymerase II or DNA from super-resolution microscopy images. With only a small number of images as the training data, AINU correctly identifies human somatic cells, human-induced pluripotent stem cells, very early stage infected cells transduced with DNA herpes simplex virus type 1 and even cancer cells after appropriate retraining. Finally, using AI interpretability methods, we find that the RNA polymerase II localizations in the nucleoli aid in distinguishing human-induced pluripotent stem cells from their somatic cells. Overall, AINU coupled with super-resolution microscopy of nuclear structures provides a robust tool for the precise detection of cellular heterogeneity, with considerable potential for advancing diagnostics and therapies in regenerative medicine, virology and cancer biology.
  • dc.description.sponsorship We acknowledge support from the European Union’s Horizon 2020 Research and Innovation Programme for two projects (no. 964342 and no. 686637 to M.P.C.); Ministerio de Ciencia e Innovación (grant no. PID2020-114080GB-I00/AEI/10.13039/501100011033 and grant no. BFU2017-86760-P/AEI/FEDER, UE to M.P.C.) and an AGAUR grant from the Departament de Recerca i Universitats de la Generalitat de Catalunya (2021-SGR2021-01300 to M.P.C.); the People Program (Marie Curie Actions) FP7/2007–2013 under REA (grant no. 608959 to M.V.N.); Ministerio de Economia y Competitvidad’s Juan de la Cierva-Incorporación 2017 (IJCI-2017-31831 to M.V.N.); Institucio Catalana de Recerca i Estudis Avançats (ICREA) (to M.P.C.); Ministerio Ciencia e Innovación and ‘ERDF A way of making Europe’ (AEI/10.13039/501100011033 grant PID2021-126701OB-I00 to I.A.-C.); the National Natural Science Foundation of China (31971177 and 32270577 to M.P.C. and 32250410296 to A.C.-G.); and Guangzhou Key Projects of Brain Science and Brain-Like Intelligence Technology (20200730009 to M.P.C.). We acknowledge support of the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI/10.13039/501100011033), and the Generalitat de Catalunya through the CERCA programme. We are grateful to the CRG Core Technologies Programme for their support and assistance in this work. We acknowledge D. Pei (GIBH, China) for kindly sharing the urine epithelial cell line; Z. Li and G. Pan (GIBH, China) for kindly sharing the urine-epithelial-cell-derived iPSC; M. A. Sanz and L. Carrasco (Centro de Biologia Molecular Severo Ochoa, CBMSO, Spain) for kindly sharing HSV-1 (KOS strain); and B. Huang (UCSF, USA) for kindly sharing Insight3 software.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Carnevali D, Zhong L, González-Almela E, Viana C, Rotkevich M, Wang A, et al. A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features. Nat Mach Intell. 2024;6(9):1021-33. DOI: 10.1038/s42256-024-00883-x
  • dc.identifier.doi http://dx.doi.org/10.1038/s42256-024-00883-x
  • dc.identifier.issn 2522-5839
  • dc.identifier.uri http://hdl.handle.net/10230/68872
  • dc.language.iso eng
  • dc.publisher Springer
  • dc.relation.ispartof Nat Mach Intell. 2024;6(9):1021-33
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/964342
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/686637
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-114080GB-I00
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/BFU2017-86760-P
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/608959
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-126701OB-I00
  • dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword Computational biophysics
  • dc.subject.keyword Single-molecule biophysics
  • dc.title A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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