Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data.

Area of Science:

• Computational Biology
• Genomics
• Developmental Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding dynamic biological processes like tissue development.
• Existing trajectory inference methods often neglect valuable time-series information present in scRNA-seq data.
• This limitation hinders accurate cell ordering and understanding of developmental transitions.

Purpose of the Study:

• To introduce Tempora, a novel trajectory inference method specifically designed for time-series scRNA-seq data.
• To leverage temporal information and biological pathways for improved cell ordering and lineage reconstruction.
• To enhance the accuracy and speed of trajectory inference compared to current state-of-the-art approaches.

Main Methods:

• Tempora utilizes time information from time-series scRNA-seq datasets to order cells.
• The method operates at the cell cluster (type) level, integrating biological pathway information.
• Performance was evaluated against existing methods using diverse tissue development time-series datasets.

Main Results:

• Tempora successfully inferred known developmental lineages from three distinct tissue development datasets.
• The method demonstrated superior accuracy and speed in trajectory inference compared to state-of-the-art techniques.
• Integrating time and pathway information improved signal, processing speed, and interpretability.

Conclusions:

• Tempora offers a significant advancement in trajectory inference for time-series scRNA-seq data.
• The combination of temporal and pathway information provides a powerful framework for supervised trajectory inference.
• This approach enhances the analysis of dynamic biological processes using scRNA-seq.