Mapping Gene Impact on Single-cell Transcriptomic Networks via Perturbation Response Scanning

Area of Science:

• Systems Biology
• Genomics
• Bioinformatics

Background:

• CRISPR technology has advanced gene function studies through targeted gene disruption.
• Single-cell CRISPR screening reveals how gene perturbations alter cellular states.
• Identifying key genes is challenging due to complex gene network interactions and nonlinear dependencies.

Purpose of the Study:

• To develop a novel method for identifying genes whose perturbation has the most significant system-wide impact.
• To quantify gene knockout effects by evaluating system-level responses to perturbations.
• To assess the disruption of global information flow and cellular robustness.

Main Methods:

• Adapted a perturbation-response framework from protein dynamics for gene regulatory networks.
• Introduced the single-cell Perturbation Impact Index (scPII), a data-driven metric.
• Calculated scPII using gene regulatory networks, independent of CRISPR screening data.

Main Results:

• scPII effectively identifies genes with the greatest system-wide impact upon perturbation.
• A strong correlation was observed between scPII scores and CRISPR screen gene effect scores.
• The metric accurately quantifies gene knockout effects in biological systems.

Conclusions:

• scPII offers a robust, data-driven approach to predict gene knockout impact.
• Integrating perturbation response scanning with gene regulatory networks enhances single-cell data analysis.
• This framework advances biomedical research by improving the understanding of gene function and cellular dynamics.