2026-09-18T20:53:51Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1560982024-08-15T03:55:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Stultz, Collin M.
Shu, Jian
Comiter, Charles
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-08-14T19:51:04Z
2024-08-14T19:51:04Z
2024-05
2024-07-10T12:59:34.436Z
https://hdl.handle.net/1721.1/156098
https://orcid.org/0000-0003-4201-1739
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.
S.M.
Massachusetts Institute of Technology
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Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF)
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
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<Title>Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF)</Title>
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<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>
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