Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF)

Abstract

Tissue biology involves an intricate balance between cell-intrinsic processes and interactions between cells organized in specific spatial patterns, which can be respectively captured by single-cell profiling methods, such as single-cell RNA-seq (scRNA-seq), and histology imaging data, such as Hematoxylin-and-Eosin (H&E) stains. While single-cell profiles provide rich molecular information, they can be challenging to collect routinely and do not have spatial resolution. Conversely, histological H&E assays have been a cornerstone of tissue pathology for decades, but do not directly report on molecular details, although the observed structure they capture arises from molecules and cells. Here, we develop SCHAF (Single-Cell omics from Histology Analysis Framework), a deep learning framework to generate a tissue sample’s spatially-resolved single-cell omics dataset from its H&E histology image. We demonstrate SCHAF on healthy and diseased—primarily metastatic breast cancer—tissue, training with matched samples analyzed by spatial transcriptomics, sc/snRNA-seq and by H&E staining. SCHAF generated appropriate single-cell profiles from histology images in test data, related them spatially, and compared well to ground-truth scRNA-seq, expert pathologist annotations, and direct MERFISH measurements. SCHAF opens the way to next-generation H&E2.0 analyses and an integrated understanding of cell and tissue biology in health and disease.