Organizations: Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA · Germantown Friends School, Philadelphia, PA, USA · Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
Identifying cell types directly from routine haematoxylin and eosin (H&E) histology would enable single-cell analysis at scale, but training such models has relied on manual pathologist annotations, which are slow, expensive and unreliable for many cell types. We instead supervise morphology with molecules. Imaging-based spatial transcriptomics profiles individual cells in situ on a section that can afterwards be stained with H&E, so that molecular identity and morphology are observed for the same physical cell. We assembled 81 such paired Xenium sections spanning 16 organs, derived per-cell labels by clustering, marker-gene annotation, organ-wise human review and quality control, and mapped them onto the cell types commonly reported in each organ. This yielded 15.4 million cells, each with a paired H&E image patch and one of 23 cell types, on which we trained CytoFormer, a cell foundation model with a multi-task, per-organ classification head. On spatially held-out tissue CytoFormer reached an accuracy of 0.85 and a macro-F1 of 0.78 across all 16 organs, and its predictions reproduced the tissue architecture of an entire held-out section. The representation also transfers: with the encoder frozen, a linear head on CytoFormer features performed better than six pathology foundation models on four expert-annotated benchmarks, including on organs and cell types that were not part of pretraining. Finally, in an interactive active-learning setting, CytoFormer's embeddings are markedly more label-efficient than existing pathology foundation models, detecting normal epithelium amid look-alike tumour with an F1 of 0.82 from only a few annotations and leading the strongest baseline by 0.13 in F1. CytoFormer turns paired H&E and spatial transcriptomics into a reusable, label-efficient representation for cell-level analysis of routine histology.
Figures & tables
Figure 1
Figure 2: CytoFormer classifies cell types on held-out H&E tissue across 16 organs. (a) F1 per cell type and (b) macro-F1 per organ on the spatially held-out test split. (c–e) A breast section held out in its entirety: (c) raw H&E, (d) the cell-type reference derived from the paired spatial transcriptomics and (e) the CytoFormer prediction. (f) Confusion matrix for that section. (g) Crop field-of-view ablation: one fixed set of cells is cropped at each field of view and resized to 224×224 pixels. (h) Macro-F1 and (i) accuracy at each crop field of view. Error bars represent 95 % confidence intervals from bootstrap resampling over test cells. Statistical significance against the 56μ m setting was assessed using a paired two-sided bootstrap test: ∗p<0.05 , ∗∗p<0.01 , ∗∗∗p<0.001 . A bracket is drawn only between the 56μ m setting and the second-best setting and gives their exact p -value; the other settings are marked with asterisks only.
Figure 3Figure 4Figure 5Figure 6
Extended Data Figure 2 : Epithelium detection F1 across active-learning iterations. Epithelium vs all others; 10,627 positive / 151,075 negative cells; same annotations, schedule and 5 seeds for every model. F1 for the Epithelium class, mean of 5 seeds. Bold = best in row.
Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods operate at the spot level, where signals from multiple cells are aggregated and critical cellular heterogeneity is obscured. Extending this paradigm to single-cell resolution is non-trivial. Naively applying pathology foundation models faces a scale mismatch: their patch-level representations mix multiple cells, whereas per-cell cropping or resizing distorts morphology and removes local context. Conversely, segmentation-based models without strong pretrained visual encoders often lack the morphological representation capacity needed for accurate molecular prediction and inherit errors from imperfect cell boundary masks. Here, we present CELLO, an efficient end-to-end framework that performs a single pathology foundation model forward pass per image and uses grid sampling to extract location-specific features for all cells simultaneously. We further introduce a distance-decay cross-attention module that refines each cell representation using spatially biased local morphological context. Using 52 public Xenium-H&E pairs from HEST-1k that span 12 organs and approximately 10 million cells, CELLO improves the average predictive accuracy over the evaluated baselines while reducing the mean whole-slide inference time compared to DeepSpot2Cell, a 14.0x speed-up on average that excludes upstream cell segmentation. Our work establishes a scalable foundation for single-cell gene expression prediction from H&E images.
Zijun Gao, Chunbin Gu, Jinxi Xiang +2
The Chinese University of Hong Kong · Stanford University School of Medicine
Spatial transcriptomics (ST) enables gene expression mapping within anatomical context but remains costly and low-throughput. Hematoxylin and eosin (H&E) staining offers rich morphology yet lacks molecular resolution. We present \ours (Spatial Transcriptomics and histOlogy Representation Model), a foundation model trained on 1.2 million spatially resolved transcriptomic profiles with matched histology across 18 organs. Using a hierarchical architecture integrating morphological features, gene expression, and spatial context, STORM bridges imaging and omics through robust molecular--morphological representations. STORM enhances spatial domain discovery, producing biologically coherent tissue maps, and outperforms existing methods in predicting spatial gene expression from H&E images across 11 tumor types. The model is platform-agnostic, performing consistently across Visium, Xenium, Visium HD, and CosMx. Applied to 23 independent cohorts comprising 7,245 patients, STORM significantly improves immunotherapy response prediction and prognostication over established biomarkers, providing a scalable framework for spatially informed discovery and clinical precision medicine.
Jinxi Xiang, Siyu Hou, Yuchen Li +17
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA · Department of Statistics and Data Science, Yale University, New Haven, CT, USA · Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA +4
Recent advances in pathology foundation models have substantially improved patch and slide level representation learning from whole-slide images (WSIs).However, cell-level representations learning remain underexplored, limiting cell resolved interpretability, biological discovery, and clinical translation. We propose CellDETR, a detection-guided framework built on Deformable DETR for scalable cell representation learning from WSIs. By introducing location feature decoupling and box-constrained attention mechanism, CellDETR enables automated extraction of cell-level embeddings, and outperform existing state-of-the-art methods in supervised cell classification on PanNuke data. In addition, by incorporating contrastive learning design, we build a CellDETR-based pretraining model for scalable cell representation learning from unlabeled WSIs, which improves downstream cell classification performance. Furthermore, we show that after pretraining with Xenium spatial transcriptomics-derived cell annotations, CellDETR achieves accurate cross-dataset cell classification, demonstrating the transferability and biological relevance of the learned cell embeddings. Together, CellDETR provides a scalable route toward general cell-level representation learning framework for interpretable computational patholog
Shikang Zhang, Guojun Li, Yicong Mao +1
Zhejiang University of Technology · Tianjin University · Hangzhou Institute of Medicine, Chinese Academy of Sciences