Automated Bio-AFM Generation of Large Mechanome Data Set and Their Analysis by Machine Learning to Classify Cancerous Cell Lines.
ACS Appl Mater Interfaces
; 16(34): 44504-44517, 2024 Aug 28.
Article
en En
| MEDLINE
| ID: mdl-39162348
ABSTRACT
Mechanobiological measurements have the potential to discriminate healthy cells from pathological cells. However, a technology frequently used to measure these properties, i.e., atomic force microscopy (AFM), suffers from its low output and lack of standardization. In this work, we have optimized AFM mechanical measurement on cell populations and developed a technology combining cell patterning and AFM automation that has the potential to record data on hundreds of cells (956 cells measured for publication). On each cell, 16 force curves (FCs) and seven features/FC, constituting the mechanome, were calculated. All of the FCs were then classified using machine learning tools with a statistical approach based on a fuzzy logic algorithm, trained to discriminate between nonmalignant and cancerous cells (training base, up to 120 cells/cell line). The proof of concept was first made on prostate nonmalignant (RWPE-1) and cancerous cell lines (PC3-GFP), then on nonmalignant (Hs 895.Sk) and cancerous (Hs 895.T) skin fibroblast cell lines, and demonstrated the ability of our method to classify correctly 73% of the cells (194 cells in the database/cell line) despite the very high degree of similarity of the whole set of measurements (79-100% similarity).
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Microscopía de Fuerza Atómica
/
Aprendizaje Automático
Límite:
Humans
Idioma:
En
Revista:
ACS Appl Mater Interfaces
Asunto de la revista:
BIOTECNOLOGIA
/
ENGENHARIA BIOMEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Francia
Pais de publicación:
Estados Unidos