Ó³»­´«Ã½

Charles Comiter
Massachusetts Institute of Technology, Massachusetts General Hospital, 
Harvard Medical School, Ó³»­´«Ã½ of MIT and Harvard

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

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 leverage adversarial machine learning to develop SCHAF (Single-Cell omics from Histology Analysis Framework), to generate a tissue sample’s spatially resolved single-cell omics dataset from its H&E histology image. We demonstrate SCHAF on two types of human tumors—from lung and metastatic breast cancer—training with matched samples analyzed by both 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, or 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.

Jian Shu, PhD
Massachusetts General Hospital, Harvard Medical School
Associate Member Ó³»­´«Ã½ of MIT and Harvard

Primer: Image2Omics: Generating Omics Data from Images

Genomics is expensive and destructive, making it challenging to monitor live cells in tissues and humans over time. Although imaging is non-destructive, low-cost, and scalable, it can be difficult to interpret. We aim to develop novel experimental and computational frameworks (Image2Omics) that use ML to bridge the gap between imaging and genomics and generate Omics data from various novel imaging modalities. This will enable fast and scalable query and prediction of genomics information from imaging, providing a foundation for the development of more generalizable ML methods for translating the language of biology (e.g., DALL-E and AlphaFold for Omics).

For more information visit: /mia.