Correlating mechanical and gene expression data on the single cell level to investigate metastatic phenotypes.
Association analysis
Biomechanics
Transcriptomics
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
21 Apr 2023
21 Apr 2023
Historique:
received:
01
10
2021
revised:
09
11
2022
accepted:
07
03
2023
medline:
11
4
2023
entrez:
10
4
2023
pubmed:
11
4
2023
Statut:
epublish
Résumé
Stiffness has been observed to decrease for many cancer cell types as their metastatic potential increases. Although cell mechanics and metastatic potential are related, the underlying molecular factors associated with these phenotypes remain unknown. Therefore, we have developed a workflow to measure the mechanical properties and gene expression of single cells that is used to generate large linked-datasets. The process combines atomic force microscopy to measure the mechanics of individual cells with multiplexed RT-qPCR gene expression analysis on the same single cells. Surprisingly, the genes that most strongly correlated with mechanical properties were not cytoskeletal, but rather were markers of extracellular matrix remodeling, epithelial-to-mesenchymal transition, cell adhesion, and cancer stemness. In addition, dimensionality reduction analysis showed that cell clustering was improved by combining mechanical and gene expression data types. The single cell genomechanics method demonstrates how single cell studies can identify molecular drivers that could affect the biophysical processes underpinning metastasis.
Identifiants
pubmed: 37034996
doi: 10.1016/j.isci.2023.106393
pii: S2589-0042(23)00470-4
pmc: PMC10074148
doi:
Types de publication
Journal Article
Langues
eng
Pagination
106393Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interest.
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