Automated cell annotation and classification on histopathology for spatial biomarker discovery
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,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61349-1 |
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| author | Zhe Li Seyed Hossein Mirjahanmardi Rasoul Sali Feyisope Eweje Matthew Gopaulchan Leon Kloker Xiaoming Zhang Guoxin Li Yuming Jiang Ruijiang Li |
| author_facet | Zhe Li Seyed Hossein Mirjahanmardi Rasoul Sali Feyisope Eweje Matthew Gopaulchan Leon Kloker Xiaoming Zhang Guoxin Li Yuming Jiang Ruijiang Li |
| author_sort | Zhe Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-06c6632e20dc42d5b3ef99b153f29604 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-06c6632e20dc42d5b3ef99b153f296042025-08-20T03:46:17ZengNature PortfolioNature Communications2041-17232025-07-0116111510.1038/s41467-025-61349-1Automated cell annotation and classification on histopathology for spatial biomarker discoveryZhe Li0Seyed Hossein Mirjahanmardi1Rasoul Sali2Feyisope Eweje3Matthew Gopaulchan4Leon Kloker5Xiaoming Zhang6Guoxin Li7Yuming Jiang8Ruijiang Li9Department of Radiation Oncology, Stanford University School of MedicineDepartment of Radiation Oncology, Stanford University School of MedicineDepartment of Radiation Oncology, Stanford University School of MedicineDepartment of Radiation Oncology, Stanford University School of MedicineDepartment of Radiation Oncology, Stanford University School of MedicineInstitute for Computational & Mathematical EngineeringDepartment of Pathology, Stanford University School of MedicineDepartment of General Surgery, Nanfang Hospital, Southern Medical UniversityDepartment of Radiation Oncology, Stanford University School of MedicineDepartment of Radiation Oncology, Stanford University School of MedicineAbstract 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.https://doi.org/10.1038/s41467-025-61349-1 |
| spellingShingle | Zhe Li Seyed Hossein Mirjahanmardi Rasoul Sali Feyisope Eweje Matthew Gopaulchan Leon Kloker Xiaoming Zhang Guoxin Li Yuming Jiang Ruijiang Li Automated cell annotation and classification on histopathology for spatial biomarker discovery Nature Communications |
| title | Automated cell annotation and classification on histopathology for spatial biomarker discovery |
| title_full | Automated cell annotation and classification on histopathology for spatial biomarker discovery |
| title_fullStr | Automated cell annotation and classification on histopathology for spatial biomarker discovery |
| title_full_unstemmed | Automated cell annotation and classification on histopathology for spatial biomarker discovery |
| title_short | Automated cell annotation and classification on histopathology for spatial biomarker discovery |
| title_sort | automated cell annotation and classification on histopathology for spatial biomarker discovery |
| url | https://doi.org/10.1038/s41467-025-61349-1 |
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