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

作者：Charles Comiter, Xingjian Chen, Eeshit Dhaval Vaishnav, Koseki J. Kobayashi-Kirschvink, Metamia Ciampricotti, Ke Zhang, Jason Murray, Francesco Monticolo, Jianhuan Qi, Ryota Tanaka, Sonia E. Brodowska, Bo Li, Yiming Yang, Scott J. Rodig, Angeliki Karatza, Alvaro Quintanal Villalonga, Madison Turner, Kathleen L. Pfaff, Judit Jané‐Valbuena, Michal Slyper, Julia Waldman, Sébastien Vigneau, Jingyi Wu, Timothy R. Blosser, Åsa Segerstolpe, Daniel L. Abravanel, Nikhil Wagle, Shadmehr Demehri, Xiaowei Zhuang, Charles M. Rudin, Johanna Klughammer, Orit Rozenblatt–Rosen, Collin M. Stultz, Jian Shu, Aviv Regev · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2023 · DOI：10.1101/2023.03.21.533680 · 被引用次数：36 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Cancer Genomics and Diagnostics

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 spatial transcriptomics, 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 in the clinic and either lack spatial resolution or high gene throughput. 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 leverage vision transformers and adversarial deep learning to develop the Single Cell omics from Histology Analysis Framework (SCHAF), which generates a tissue sample's spatially-resolved whole transcriptome single cell omics dataset from its H&E histology image. We demonstrate SCHAF on a variety of tissues- including lung cancer, metastatic breast cancer, placentae, and whole mouse pups-training with matched samples analyzed by sc/snRNA-seq, H&E staining, and, when available, spatial transcriptomics. 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, or direct spatial transcriptomic meas...