ArticleNature communications2025
Automated cell annotation and classification on histopathology for spatial biomarker discovery.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An agentic framework for autonomous scientific discovery in cancer pathology.Nature medicine · 2026Article
- DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images.bioRxiv : the preprint server for biology · 2026Article
- HER2 Score-Aware Virtual Immunohistochemistry via Non-Contrastive Multi-Task Translation.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- An integrated automated deep learning framework for annotating tumor-infiltrating lymphocytes in lung adenocarcinoma pathology.Frontiers in bioinformatics · 2026Article
- Dynamic Metabolic States in TNBC: Orchestrating Spatiotemporal Adaptation and Therapy.Oncology research · 2026Review
- Myeloid-driven immunosuppression in head and neck cancer: single-cell ATAC/RNA and spatial transcriptomic perspectives.Frontiers in oncology · 2025Review
- Laser capture microdissection.Nature reviews. Methods primers · 2025Article
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Authors and funding
10 authors.
Funding
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Abstract
Histopathology with hematoxylin and eosin (H&E) staining is routinely employed for clinical diagnoses. Single-cell analysis of histopathology provides a powerful tool for understanding the intricate cellular interactions underlying disease progression and therapeutic response. However, existing efforts are hampered by inefficient and error-prone human annotations. Here, we present an experimental and computational approach for automated cell annotation and classification on H&E-stained images. Instead of human annotations, we use multiplexed immunofluorescence (mIF) to define cell types based on cell lineage protein markers. By co-registering H&E images with mIF of the same tissue section at the single-cell level, we create a dataset of 1,127,252 cells with high-quality annotations on tissue microarray cores. A deep learning model combining self-supervised learning with domain adaptation is trained to classify four cell types on H&E images with an overall accuracy of 86%-89%, and the cell classification model is applicable to whole slide images. Further, we show that spatial interactions among specific immune cells in the tumor microenvironment are linked to patient survival and response to immune checkpoint inhibitors. Our work provides a scalable approach for single-cell analysis of standard histopathology and may enable discovery of novel spatial biomarkers for precision oncology.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.