Self-supervised learning of cell type specificity from immunohistochemical images
Name
btac263.pdf
Description
Published version
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877.02 KB
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Adobe PDF
Checksum (MD5)
5690d819ff60740b93b408b8d007bc9a
Author(s)
Murphy, Michael
Jegelka, Stefanie
Fraenkel, Ernest
Date Issued
June 24, 2022
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Murphy, Michael, Jegelka, Stefanie and Fraenkel, Ernest. 2022. "Self-supervised learning of cell type specificity from immunohistochemical images." Bioinformatics, 38 (Supplement_1).
Version
Final published version
Abstract
Abstract
Motivation Advances in bioimaging now permit in situ proteomic characterization of cell–cell interactions in complex tissues, with important applications across a spectrum of biological problems from development to disease. These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image. Results We develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics. Availability and implementation Code and trained model are available at www.github.com/murphy17/HPA-SimCLR. Supplementary information Supplementary data are available at Bioinformatics online.
Motivation Advances in bioimaging now permit in situ proteomic characterization of cell–cell interactions in complex tissues, with important applications across a spectrum of biological problems from development to disease. These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image. Results We develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics. Availability and implementation Code and trained model are available at www.github.com/murphy17/HPA-SimCLR. Supplementary information Supplementary data are available at Bioinformatics online.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1093/bioinformatics/btac263
