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Single-Cell Trajectory Inference for Detecting Transient Events in Biological Processes.

Alexandre Hutton¹, Jesse G Meyer¹

¹Department of Computational Biomedicine, Board of Governors Innovation Center, and Smidt Heart Institute Cedars Sinai Medical Center, Los Angeles CA, 90048, USA.

Biorxiv : the Preprint Server for Biology

|June 4, 2025

Summary

scTransient identifies transient gene expression bursts crucial for cell state transitions, overcoming limitations of single-cell omics data. This method reveals hidden regulatory events driving development and disease.

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Transient gene or protein expression surges are critical regulatory checkpoints in cellular state transitions.
Detecting these short-lived expression bursts is challenging in sparse and noisy single-cell omics data.

Purpose of the Study:

To introduce scTransient, a novel pipeline for detecting transient molecular events in single-cell data.
To enhance trajectory inference by quantifying, not just ordering, cellular processes.

Main Methods:

scTransient integrates trajectory inference with wavelet-based signal processing.
It transforms single-cell expression profiles into pseudotime signals.
A continuous wavelet transform is applied to assign a Transient-Event Score (TES) to each gene.

Main Results:

scTransient robustly recovers simulated transient events across various data conditions.
Analysis of hematopoietic differentiation, monocyte-to-macrophage maturation, and cell cycle progression datasets revealed novel expression spikes.
Identified key regulators like Nfe2, membrane-raft proteins, and S-phase factors, with top genes functionally enriched in relevant pathways.

Conclusions:

scTransient provides a practical method for uncovering transient molecular events driving biological processes.
The pipeline extends trajectory inference capabilities for quantitative detection of fleeting regulatory programs.
Accessible via the PSCS web interface, scTransient aids research into development, differentiation, and disease.