Single-cell trajectory inference for detecting transient events in biological processes.

Area of Science:

• Single-cell omics
• Computational biology
• Systems biology

Background:

• Transient gene or protein expression changes are critical regulatory checkpoints in cellular transitions.
• These short-lived expression events are often missed in sparse and noisy single-cell omics data.

Purpose of the Study:

• To introduce scTransient, a novel computational method for detecting transient molecular events in single-cell expression data.
• To enhance the analysis of dynamic cellular processes by quantifying fleeting regulatory programs.

Main Methods:

• scTransient transforms single-cell expression profiles into continuous pseudotime signals.
• It employs wavelet-based signal processing to isolate short-lived gene activity bursts.
• A continuous wavelet transform is applied to assign a transient-event score (TES) to each gene.

Main Results:

• Synthetic benchmarks show TES robustly recovers transient events across various parameters.
• scTransient successfully detected transient events in induced neuron development and single-cell cell cycle datasets.
• The method identified key proteins, including stem cell regulators and S-phase DNA replication factors.

Conclusions:

• scTransient effectively detects transient molecular events that are often missed in traditional analyses.
• The method extends trajectory inference beyond descriptive ordering to quantitative detection of regulatory programs.
• scTransient provides a practical approach to uncover transient events driving cell development, differentiation, and disease.