Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning
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 featur...
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| Vydáno v: | Advanced science Ročník 11; číslo 41; s. e2403547 - n/a |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Germany
John Wiley & Sons, Inc
01.11.2024
John Wiley and Sons Inc Wiley |
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| ISSN: | 2198-3844, 2198-3844 |
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| Abstract | 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.
An unsupervised deep learning framework is developed to analyze live cell dynamics by combining an unsupervised teacher model with a student deep neural network. This method successfully delineates detailed subcellular protrusion phenotypes and their responses to drugs. This approach preserves cellular heterogeneity while improving feature discrimination and interpretation, making it a valuable tool for studying subcellular dynamics. |
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| AbstractList | 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. 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. An unsupervised deep learning framework is developed to analyze live cell dynamics by combining an unsupervised teacher model with a student deep neural network. This method successfully delineates detailed subcellular protrusion phenotypes and their responses to drugs. This approach preserves cellular heterogeneity while improving feature discrimination and interpretation, making it a valuable tool for studying subcellular dynamics. Abstract 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. 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.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. 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. An unsupervised deep learning framework is developed to analyze live cell dynamics by combining an unsupervised teacher model with a student deep neural network. This method successfully delineates detailed subcellular protrusion phenotypes and their responses to drugs. This approach preserves cellular heterogeneity while improving feature discrimination and interpretation, making it a valuable tool for studying subcellular dynamics. |
| Author | Wang, Chuangqi Choi, Hee June Woodbury, Lucy Lee, Kwonmoo |
| AuthorAffiliation | 3 Vascular Biology Program and Department of Surgery Boston Children's Hospital Harvard Medical School Boston MA 02115 USA 4 Department of Biomedical Engineering University of Arkansas Fayetteville AR 72701 USA 1 Department of Immunology and Microbiology University of Colorado Anschutz Medical Campus Aurora CO 80045 USA 2 Department of Biomedical Engineering Worcester Polytechnic Institute Worcester MA 01609 USA |
| AuthorAffiliation_xml | – name: 2 Department of Biomedical Engineering Worcester Polytechnic Institute Worcester MA 01609 USA – name: 4 Department of Biomedical Engineering University of Arkansas Fayetteville AR 72701 USA – name: 1 Department of Immunology and Microbiology University of Colorado Anschutz Medical Campus Aurora CO 80045 USA – name: 3 Vascular Biology Program and Department of Surgery Boston Children's Hospital Harvard Medical School Boston MA 02115 USA |
| Author_xml | – sequence: 1 givenname: Chuangqi surname: Wang fullname: Wang, Chuangqi organization: Worcester Polytechnic Institute – sequence: 2 givenname: Hee June surname: Choi fullname: Choi, Hee June organization: Harvard Medical School – sequence: 3 givenname: Lucy surname: Woodbury fullname: Woodbury, Lucy organization: University of Arkansas – sequence: 4 givenname: Kwonmoo orcidid: 0000-0001-6838-7094 surname: Lee fullname: Lee, Kwonmoo email: kwonmoo.lee@childrens.harvard.edu organization: Harvard Medical School |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39239705$$D View this record in MEDLINE/PubMed |
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| Keywords | cell migration morphodynamics live cell imaging machine learning phenotyping |
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| Snippet | Uncovering fine‐grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological... Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological... Abstract Uncovering fine‐grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased... |
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| SubjectTerms | Cancer cell migration Cell Movement - physiology Clustering Datasets Deep Learning Humans live cell imaging Machine learning morphodynamics Neural networks Neural Networks, Computer Phenotype phenotyping Time series Unsupervised Machine Learning |
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| Title | Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning |
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