Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and...

Area of Science:

• Molecular Biology
• Systems Biology
• Computational Biology

Background:

• Single-cell analysis techniques like single-cell qPCR, RNA-Seq, and CyTOF are crucial for understanding cellular heterogeneity.
• These methods generate high-dimensional data, revealing diverse cell states within seemingly uniform populations.
• Complementary microscopy techniques provide time-resolved, low-dimensional insights.

Purpose of the Study:

• To bridge the gap between collecting single-cell data and understanding the underlying biological mechanisms.
• To interpret complex single-cell data patterns in the context of molecular regulatory networks.
• To formalize the control of cell state dynamics using dynamical systems theory.

Main Methods:

• Summarizing key data acquisition methods for single-cell molecular profiling.
• Detailing computational analysis techniques for data visualization, dimensionality reduction, and subpopulation identification.
• Explaining the integration of single-cell measurements with dynamical systems theory.

Main Results:

• Demonstrating the power of single-cell analyses to uncover significant cell state heterogeneity.
• Highlighting the utility of computational tools in identifying and visualizing cell subpopulations.
• Establishing a framework for interpreting cell state dynamics as a quasi-potential landscape.

Conclusions:

• Single-cell resolution molecular profiling generates rich datasets revealing cellular heterogeneity.
• Computational methods are essential for analyzing and interpreting these complex datasets.
• Connecting single-cell data to dynamical systems theory provides a powerful approach to understanding cell state control.