ArticleNature methods2025
Automated classification of cellular expression in multiplexed imaging data with Nimbus.
Article in Nature methods, 2025. 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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14 citing papers in PubMed.
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- Spatial analysis of head and neck cancer identifies two ecosystems with distinct modes of epithelial-to-mesenchymal transition.Nature genetics · 2026Article
- CellTune: an integrative software for accurate cell classification in spatial proteomics.Nature methods · 2026Article
- PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.bioRxiv : the preprint server for biology · 2026Article
- Foundation cell segmentation models performance on live microscopy and spatial-omics data.bioRxiv : the preprint server for biology · 2026Article
- Weak-to-strong generalization enables fully automated training of multi-head mask-RCNN model for segmenting densely overlapping cell nuclei in multiplex whole-slice brain images.Frontiers in bioinformatics · 2026Article
- Spatially-smoothed quantification improves cell typing in imaging mass cytometry datasets.bioRxiv : the preprint server for biology · 2025Article
- SPACEc: a streamlined, interactive Python workflow for multiplexed image processing and analysis.Nature communications · 2025Article
- Imaging Mass Cytometry-Based Immune Profiling of Human Peyer's Patches in Patients with Crohn's Disease.European journal of immunology · 2025Article
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- Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.Cell reports methods · 2025Article
- Generalized cell phenotyping for spatial proteomics with language-informed vision models.bioRxiv : the preprint server for biology · 2025Article
- Flexible and robust cell type annotation for highly multiplexed tissue images.bioRxiv : the preprint server for biology · 2024Article
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21 authors.
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Abstract
Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pretrained model that uses the underlying images to classify marker expression of individual cells as positive or negative across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M, and that Nimbus matches or exceeds the accuracy of previous approaches that must be retrained on each dataset. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference .
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