ArticleNeuropathology and applied neurobiology2022
Image-based deep learning reveals the responses of human motor neurons to stress and VCP-related ALS.
Article in Neuropathology and applied neurobiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Scalable quantification of dynamic subcellular spatial organization in single cells across tissues.Research square · 2026Article
- Review
- Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration.Heliyon · 2024Article
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Stress granule assembly in vivo is deficient in the CNS of mutant TDP-43 ALS mice.Human molecular genetics · 2023Article
- Image-based deep learning reveals the responses of human motor neurons to stress and VCP-related ALS.Neuropathology and applied neurobiology · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
aimsAlthough morphological attributes of cells and their substructures are recognised readouts of physiological or pathophysiological states, these have been relatively understudied in amyotrophic lateral sclerosis (ALS) research.
methodsIn this study, we integrate multichannel fluorescence high-content microscopy data with deep learning imaging methods to reveal-directly from unsegmented images-novel neurite-associated morphological perturbations associated with (ALS-causing) VCP-mutant human motor neurons (MNs).
resultsSurprisingly, we reveal that previously unrecognised disease-relevant information is withheld in broadly used and often considered 'generic' biological markers of nuclei (DAPI) and neurons (
conclusionsOur study therefore reveals disease-relevant information contained in a range of both generic and more specific fluorescent markers and establishes the use of image-based deep learning methods for rapid, automated and unbiased identification of biological hypotheses.
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