Information‐Distilled Generative Label‐Free Morphological Profiling Encodes Cellular Heterogeneity Article Swipe
Michelle C. K. Lo
,
Dickson M. D. Siu
,
Kelvin C. M. Lee
,
Justin S. J. Wong
,
Maximus C.F. Yeung
,
Michael K. Y. Hsin
,
Jcm Ho
,
Kevin K. Tsia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202307591
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202307591
Image‐based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre‐existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, “Cyto‐Morphology Adversarial Distillation” (CytoMAD), a self‐supervised multi‐task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its “morphology distillation”, symbiotically paired with deep‐learning image‐contrast translation—offering additional interpretable insights into label‐free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label‐free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial‐mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost‐effective diagnosis and screening.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/advs.202307591
- OA Status
- gold
- Cited By
- 8
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399575357
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399575357Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/advs.202307591Digital Object Identifier
- Title
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Information‐Distilled Generative Label‐Free Morphological Profiling Encodes Cellular HeterogeneityWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-12Full publication date if available
- Authors
-
Michelle C. K. Lo, Dickson M. D. Siu, Kelvin C. M. Lee, Justin S. J. Wong, Maximus C.F. Yeung, Michael K. Y. Hsin, Jcm Ho, Kevin K. TsiaList of authors in order
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https://doi.org/10.1002/advs.202307591Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1002/advs.202307591Direct OA link when available
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Computer science, Profiling (computer programming), Artificial intelligence, Cytometry, Flow cytometry, Pattern recognition (psychology), Biological system, Biology, Genetics, Operating systemTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 6, 2024: 2Per-year citation counts (last 5 years)
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65Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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