CellRank 2: unified fate mapping in multiview single-cell data.

Area of Science:

• Computational Biology
• Genomics
• Developmental Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables modeling of cellular dynamics and fate decisions.
• Current trajectory inference methods often lack integration of temporal information or multiple data modalities and do not scale well.
• Existing methods for integrating diverse data views are often incompatible or lack scalability.

Purpose of the Study:

• To present CellRank 2, a versatile and scalable framework for studying cellular fate using multiview single-cell data.
• To unify the analysis of diverse single-cell data types, including time-series and multi-modal data, for robust trajectory inference.
• To enable the estimation of cell-specific transcription and degradation rates for a deeper understanding of regulatory mechanisms.

Main Methods:

• Development of CellRank 2, a unified computational framework for analyzing large-scale single-cell data.
• Integration of expression similarity, RNA velocity, and experimental time point information.
• Application of metabolic labeling data to estimate cell-specific kinetic rates.
• Utilizing multiview single-cell data from up to millions of cells.

Main Results:

• CellRank 2 consistently recovers terminal states and fate probabilities across different data modalities.
• Demonstrated successful application in human hematopoiesis and endodermal development.
• Enabled the combination of temporal and cross-timepoint transitions to identify key genes in cell fate determination.
• Successfully delineated differentiation trajectories and identified regulatory strategies in an intestinal organoid system.

Conclusions:

• CellRank 2 provides a scalable and unified approach for comprehensive cellular fate analysis using multiview single-cell data.
• The framework enhances the understanding of developmental processes by integrating temporal and multi-modal information.
• CellRank 2 facilitates the discovery of regulatory mechanisms underlying cell differentiation and fate decisions.