ArticleNature machine intelligence2024
A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features.
Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
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- A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.Scientific reports · 2026Article
- Spatial relationships between vinculin tension and actin at cell adhesions define cell colony positioning and identity.Biophysical journal · 2026Article
- Architecture and regulation of nanoscale chromatin domains.Nature communications · 2026Review
- Scaling deep learning for material imaging with a pseudo 3D model for domain transfer.Nature communications · 2025Article
- ClusterNet: Classifying Single-Molecule Localization Microscopy Datasets with Graph-Based Deep Learning of Supracluster Structure.Small science · 2025Article
- Artificial Intelligence in Infectious Disease Diagnostic Technologies.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy.Nature communications · 2025Article
- Advances in Epstein-Barr Virus Detection: From Traditional Methods to Modern Technologies.Viruses · 2025Review
- Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning.Journal of imaging · 2025Article
- Artificial Intelligence in Relation to Accurate Information and Tasks in Gynecologic Oncology and Clinical Medicine-Dunning-Kruger Effects and Ultracrepidarianism.Diagnostics (Basel, Switzerland) · 2025Review
- A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features.Nature machine intelligence · 2024Article
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12 authors.
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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.
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