Profiling Dynamic Patterns of Single-Cell Motility.

Area of Science:

• Cell Biology
• Computational Biology
• Biophysics

Background:

• Cell motility is crucial for biological processes, involving cell movement and interaction within microenvironments.
• Current methods often average cell motility data, limiting the assessment of cellular heterogeneity and individual cell behaviors.
• Ensemble approaches struggle to identify generalizable single-cell patterns, especially under varying conditions.

Purpose of the Study:

• Introduce CaMI, a computational framework to analyze single-cell motility data.
• Leverage single-cell data to classify distinct spatio-temporal behaviors and identify patterns.
• Quantify spatial/temporal heterogeneities and reveal insights missed by conventional analyses.

Main Methods:

• Developed CaMI, a computational framework for analyzing single-cell motility data.
• Applied CaMI to a large dataset (n = 74,253 cells) for robust classification.
• Utilized a multivariate framework to classify emergent single-cell motility patterns.

Main Results:

• CaMI successfully classified distinct spatio-temporal behaviors of individual cells.
• The framework enabled quantification of spatial and temporal heterogeneities in cell motility.
• CaMI revealed biological insights often overlooked by traditional, population-averaged analyses.

Conclusions:

• CaMI provides a robust method for classifying single-cell motility patterns in large datasets.
• The framework highlights the critical role of cellular heterogeneity in population-level cell behaviors.
• CaMI offers a valuable tool for interpreting dynamic cell behaviors and uncovering biological insights.