LineageOT is a unified framework for lineage tracing and trajectory inference

Area of Science:

• Developmental Biology
• Computational Biology
• Genetics and Epigenetics

Background:

• Understanding genetic and epigenetic control of cell differentiation is crucial for biology and medicine.
• Single-cell RNA-sequencing and CRISPR-based lineage tracing offer new insights into developmental processes.
• Current computational methods for trajectory inference and lineage reconstruction are often siloed.

Purpose of the Study:

• To develop a unified computational framework, LineageOT, for integrating lineage tracing and trajectory inference.
• To improve the accuracy and efficiency of reconstructing developmental trajectories and cell lineages.
• To enable the recovery of complex developmental pathways not detectable by state-based methods alone.

Main Methods:

• Developed LineageOT, a unified framework leveraging graphical models and optimal transport.
• Reconstructed developmental trajectories using time-course data with cell states and lineages.
• Integrated lineage tracing information into trajectory inference computational methods.

Main Results:

• Lineage data significantly improves the accuracy of disentangling complex cell state transitions.
• The framework enables accurate trajectory reconstruction using fewer measured time points.
• LineageOT can recover developmental pathways that are inaccessible to state-based inference methods.

Conclusions:

• LineageOT provides a unified approach to developmental trajectory inference and lineage reconstruction.
• Integrating lineage tracing enhances the power of computational methods for studying cell differentiation.
• This framework has broad implications for advancing developmental biology and regenerative medicine.