Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT).

Area of Science:

• Computational Biology
• Genomics
• Biophysics

Background:

• Single-cell RNA sequencing (scRNA-Seq) enables molecular profiling at an unprecedented resolution.
• Understanding cellular differentiation is key to developmental biology and disease research, particularly in cancer.
• Computational tools are essential for extracting biological insights from complex scRNA-Seq data.

Purpose of the Study:

• To present a detailed protocol for the Single-Cell ENTropy (SCENT) algorithm.
• To enable the estimation of differentiation potency in single cells using scRNA-Seq data.
• To provide a computational method for identifying stem- and progenitor-like cell phenotypes.

Main Methods:

• SCENT algorithm based on an explicit biophysical model.
• Integration of single-cell RNA-Seq profiles with biological interaction networks.
• Approximation of differentiation potency using the entropy of a diffusion process on the network.

Main Results:

• Demonstration of SCENT implementation on a real scRNA-Seq dataset.
• Illustration of SCENT's application to human embryonic stem cells and multipotent progenitors.
• Validation of SCENT's capability to identify cell phenotypes.

Conclusions:

• SCENT offers a novel computational approach for quantifying single-cell differentiation potency.
• The algorithm is valuable for identifying novel stem- or progenitor-like phenotypes in various biological contexts.
• SCENT may be particularly useful for the unbiased identification of cancer stem cells.