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Self-supervised deep learning for highly efficient spatial immunophenotyping

Zhang, H; AbdulJabbar, K; Grunewald, T; Akarca, AU; Hagos, Y; Sobhani, F; Lecat, CSY; ... Yuan, Y; + view all (2023) Self-supervised deep learning for highly efficient spatial immunophenotyping. eBioMedicine , 95 , Article 104769. 10.1016/j.ebiom.2023.104769. Green open access

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Abstract

Background: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. / Methods: This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. / Findings: With 1% annotations (18–114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828–11,459 annotated cells (−0.002 to −0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. / Interpretation: By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. / Funding: This study was funded by the Royal Marsden/ ICR National Institute of Health Research Biomedical Research Centre.

Type: Article
Title: Self-supervised deep learning for highly efficient spatial immunophenotyping
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ebiom.2023.104769
Publisher version: https://doi.org/10.1016/j.ebiom.2023.104769
Language: English
Additional information: Copyright © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Cell classification, Deep learning, Imaging mass cytometry, Multiplex imaging, Multiplex immunohistochemistry, Self-supervised learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
URI: https://discovery.ucl.ac.uk/id/eprint/10177164
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