Carnevali, DavideZhong, LimeiGonzález-Almela, EstherViana, CarlottaRotkevich, MikhailWang, AipingFranco-Barranco, DanielGonzález-Marfil, AitorNeguembor, Maria VictoriaCastells García, Àlvaro, 1991-Arganda-Carreras, IgnacioCosma, Maria Pia2024-12-022024-12-022024Carnevali 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-x2522-5839http://hdl.handle.net/10230/68872Cellular 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.application/pdfeng© 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/.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s42256-024-00883-xComputational biophysicsSingle-molecule biophysicsinfo:eu-repo/semantics/openAccess