Microscopic image-based classification of adipocyte differentiation by machine learning.
Adipocyte differentiation
Image analysis
Machine learning
Obesity
Journal
Histochemistry and cell biology
ISSN: 1432-119X
Titre abrégé: Histochem Cell Biol
Pays: Germany
ID NLM: 9506663
Informations de publication
Date de publication:
Apr 2023
Apr 2023
Historique:
accepted:
17
11
2022
medline:
11
4
2023
pubmed:
13
12
2022
entrez:
12
12
2022
Statut:
ppublish
Résumé
Adipocyte differentiation is a sequential process involving increased expression of peroxisome proliferator-activated receptor gamma (PPARγ), adipocyte-specific gene expression, and accumulation of lipid droplets in the cytoplasm. Expression of the transcription factors involved is usually detected using canonical biochemical or biomolecular procedures such as Western blotting or qPCR of pooled cell lysates. While this provides a useful average index for adipogenesis for some populations, the precise stage of adipogenesis cannot be distinguished at the single-cell level, because the heterogenous nature of differentiation among cells limits the utility of averaged data. We have created a classifier to sort cells, and used it to determine the stage of adipocyte differentiation at the single-cell level. We used a machine learning method with microscopic images of cell stained for PPARγ and lipid droplets as input data. Our results show that the classifier can successfully determine the precise stage of differentiation. Stage classification and subsequent model fitting using the sequential reaction model revealed the action of pioglitazone and rosiglitazone to be promotion of transition from the stage of increased PPARγ expression to the next stage. This indicates that these drugs are PPARγ agonists, and that our classifier and model can accurately estimate drug action points and would be suitable for evaluating the stage/state of individual cells during differentiation or disease progression. The incorporation of both biochemical and morphological information derived from immunofluorescence image of cells and so overcomes limitations of current models.
Identifiants
pubmed: 36504003
doi: 10.1007/s00418-022-02168-z
pii: 10.1007/s00418-022-02168-z
doi:
Substances chimiques
PPAR gamma
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
313-327Subventions
Organisme : JST Moonshot R&D Grant
ID : JPMJMS2021
Organisme : AMED-PRIME, AMED
ID : JP21gm6210015
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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