Tissue-dependent mechanosensing by cells derived from human tumors

Area of Science:

• Biophysics
• Cancer Biology
• Materials Science

Background:

• Extracellular matrix (ECM) alterations, including mechanical and chemical changes, are implicated in tumor progression.
• Understanding cellular responses to ECM variations is crucial for cancer research.

Purpose of the Study:

• To investigate how cancer and non-tumorigenic cells from various tissue types respond to diverse ECM mechanical and compositional changes.
• To identify patterns and specificities in cellular physical characteristics across different ECM conditions.

Main Methods:

• Measured physical characteristics (adherent area, shape, stiffness, speed) of 25 cancer and 5 non-tumorigenic cell lines.
• Utilized 7 different substrate conditions to simulate ECM variations.
• Employed unsupervised machine learning to classify cellular phenotypic plasticity.

Main Results:

• Observed significant heterogeneity in cell mechanics responses across tissue types and cell lines.
• Demonstrated that hyaluronic acid (HA) presence in soft substrates can induce cell mechanics changes comparable to stiff substrates.
• Highlighted the critical role of tissue-type and cell line specificity in interpreting cancer cell physical properties.

Conclusions:

• Cellular responses to ECM modifications are highly specific to cell type and tissue origin.
• Hyaluronic acid plays a notable role in modulating cell mechanics.
• Machine learning can effectively categorize cell plasticity in response to ECM cues, aiding in understanding cancer progression.