Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.

Autor: Wang C; Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA., Choi HJ; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.; Vascular Biology Program and Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA., Woodbury L; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.; Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, 72701, USA., Lee K; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.; Vascular Biology Program and Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Jazyk: angličtina
Zdroj: Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2024 Nov; Vol. 11 (41), pp. e2403547. Date of Electronic Publication: 2024 Sep 06.
DOI: 10.1002/advs.202403547
Abstrakt: Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised machine learning, requiring the extraction of features that not only faithfully preserve this heterogeneity but also effectively discriminate between established biological states, all while remaining interpretable. To tackle these challenges, a self-training deep learning framework designed for fine-grained and interpretable phenotyping is presented. This framework incorporates an unsupervised teacher model with interpretable features to facilitate feature learning in a student deep neural network (DNN). Significantly, an autoencoder-based regularizer is designed to encourage the student DNN to maximize the heterogeneity associated with molecular perturbations. This method enables the acquisition of features with enhanced discriminatory power, while simultaneously preserving the heterogeneity associated with molecular perturbations. This study successfully delineated fine-grained phenotypes within the heterogeneous protrusion dynamics of migrating epithelial cells, revealing specific responses to pharmacological perturbations. Remarkably, this framework adeptly captured a concise set of highly interpretable features uniquely linked to these fine-grained phenotypes, each corresponding to specific temporal intervals crucial for their manifestation. This unique capability establishes it as a valuable tool for investigating diverse cellular dynamics and their heterogeneity.
(© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)
Databáze: MEDLINE
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