Trajectory inference across multiple conditions with condiments.

Area of Science:

• Computational Biology
• Genomics
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNA-Seq) offers high-resolution insights into dynamic biological processes like cell differentiation.
• Representing cellular states as trajectories in reduced dimensions is crucial for understanding developmental continuums.
• Existing trajectory inference methods often struggle with comparing multiple biological conditions simultaneously.

Purpose of the Study:

• To present "condiments," a novel computational framework for inferring and interpreting cell trajectories across multiple experimental conditions.
• To enable the comparison of differentiation processes between different biological groups (e.g., wild-type vs. knock-out).
• To facilitate the detection of both large-scale and subtle differences in cell state progression and gene expression.

Main Methods:

• Integration of scRNA-Seq datasets from multiple conditions into a unified trajectory.
• Comparative analysis of cell conditions along the inferred trajectory path.
• Identification of genes exhibiting condition-specific expression patterns during differentiation.

Main Results:

• The "condiments" framework successfully integrates multi-condition scRNA-Seq data into a single trajectory.
• It allows for the detection of significant differences in progression and fate selection between conditions.
• Subtle gene expression changes along differentiation paths across conditions can be identified.

Conclusions:

• "Condiments" provides a robust method for comparative trajectory inference in scRNA-Seq data.
• This framework enhances the understanding of how different conditions impact cellular development and gene regulation.
• It offers a powerful tool for interpreting complex biological systems and identifying condition-specific molecular mechanisms.