Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.
interpretable deep learning
live cell imaging
melanoma metastasis
Journal
Cell systems
ISSN: 2405-4720
Titre abrégé: Cell Syst
Pays: United States
ID NLM: 101656080
Informations de publication
Date de publication:
21 07 2021
21 07 2021
Historique:
received:
13
06
2020
revised:
22
01
2021
accepted:
07
05
2021
pubmed:
3
6
2021
medline:
7
4
2022
entrez:
2
6
2021
Statut:
ppublish
Résumé
Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify patient-derived melanoma xenografts as "efficient" or "inefficient" metastatic, validate predictions regarding melanoma cell lines with unknown metastatic efficiency in mouse xenografts, and use the network to generate in silico cell images that amplify the critical predictive cell properties. These exaggerated images unveiled pseudopodial extensions and increased light scattering as hallmark properties of metastatic cells. We validated this interpretation using live cells spontaneously transitioning between states indicative of low and high metastatic efficiency. This study illustrates how the application of artificial intelligence can support the identification of cellular properties that are predictive of complex phenotypes and integrated cell functions but are too subtle to be identified in the raw imagery by a human expert. A record of this paper's transparent peer review process is included in the supplemental information. VIDEO ABSTRACT.
Identifiants
pubmed: 34077708
pii: S2405-4712(21)00158-7
doi: 10.1016/j.cels.2021.05.003
pmc: PMC8353662
mid: NIHMS1711560
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Video-Audio Media
Langues
eng
Sous-ensembles de citation
IM
Pagination
733-747.e6Subventions
Organisme : NCI NIH HHS
ID : K25 CA204526
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM067230
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM136428
Pays : United States
Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests The authors declare no competing interests.
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