Measuring (biological) materials mechanics with atomic force microscopy. 5. Traction force microscopy (cell traction forces).
Juan Carlos Gil-RedondoAndreas WeberMaria dM VivancoJose Luis Toca-HerreraPublished in: Microscopy research and technique (2023)
Cells generate traction forces to probe the mechanical properties of the surroundings and maintain a basal equilibrium state of stress. Traction forces are also implicated in cell migration, adhesion and ECM remodeling, and alteration of these forces is often observed in pathologies such as cancer. Thus, analyzing the traction forces is important for studies of cell mechanics in cancer and metastasis. In this primer, the methodology for conducting two-dimensional traction force microscopy (2D-TFM) experiments is reported. As a practical example, we analyzed the traction forces generated by three human breast cancer cell lines of different metastatic potential: MCF10-A, MCF-7 and MDA-MB-231 cells, and studied the effects of actin cytoskeleton disruption on those traction forces. Contrary to what is often reported in literature, lower traction forces were observed in cells with higher metastatic potential (MDA-MB-231). Implications of substrate stiffness and concentration of extracellular matrix proteins in such findings are discussed in the text. RESEARCH HIGHLIGHTS: Traction force microscopy (TFM) is suitable for studying and quantifying cell-substrate and cell-cell forces. TFM is suitable for investigating the relationship between chemical to mechanical signal transduction and vice versa. TFM can be combined with classical indentation studies providing a compact picture of cell mechanics. TFM still needs new physico-chemical (sample preparation) and computational approaches for more accurate data evaluation.
Keyphrases
- single cell
- single molecule
- cell therapy
- induced apoptosis
- atomic force microscopy
- cell migration
- cell cycle arrest
- high resolution
- extracellular matrix
- breast cancer cells
- systematic review
- stem cells
- squamous cell carcinoma
- escherichia coli
- deep learning
- cystic fibrosis
- machine learning
- pseudomonas aeruginosa
- optical coherence tomography
- bone marrow
- electronic health record
- endothelial cells
- label free
- solid state
- molecular dynamics