Profiling extracellular matrix-driven heterogeneity of single cell migration and morphology.

Area of Science:

• Cell Biology
• Biophysics
• Biomaterials Science

Background:

• Cell migration and morphology are crucial for biological processes like development, healing, and cancer.
• Extracellular matrix (ECM) components significantly influence cell behavior.
• Understanding cell-ECM interactions is key to deciphering cellular functions.

Purpose of the Study:

• To investigate the impact of different ECM components (laminin, collagen 1, collagen 3) on HeLa cell migration and morphology.
• To utilize machine learning for quantitative analysis of cell behavior.
• To correlate observed cellular characteristics with underlying biological mechanisms.

Main Methods:

• HeLa cells cultured on laminin (Ln), collagen 1 (Col1), and collagen 3 (Col3).
• Machine learning-based image analysis to quantify migration and morphology.
• Principal Component Analysis (PCA) for classification of cell behavior.
• Biological assays to assess cell-cell interactions and focal adhesions.

Main Results:

• Cells on Ln showed higher motility, increased turning angles, and reduced persistence than on Col1/Col3.
• Ln-cultured cells exhibited larger areas and higher solidity, indicating different attachment.
• PCA successfully classified cells based on motility, with Ln cells forming a distinct cluster.
• Ln promoted increased cell-cell interactions and smaller, more numerous focal adhesions.

Conclusions:

• ECM composition differentially regulates cell migration and morphology.
• Laminin enhances migratory plasticity and intercellular interactions in HeLa cells.
• Machine learning-based quantitative analysis combined with biological interpretation advances understanding of cell-microenvironment interactions.