Systematic inference of super-resolution cell spatial profiles from histology images

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly predict the super-resolution cell spatial profiles from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk, which is validated on independent longitudinal cohorts. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts. Overall design: Two gastric samples with both cancer and pre-cancerous lesions were used for this analysis.