{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/156098"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/156098","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Comiter, Charles"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF)"]}]}],"canonical_facts":{"dc:contributor.advisor":["Stultz, Collin M.","Shu, Jian"],"dc:contributor.department":["Massachusetts Institute of Technology. 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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. 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