CytoTRACE

CytoTRACE predicts cellular differentiation states and developmental potential from single-cell RNA sequencing (scRNA-seq) data by using transcriptional diversity measured as the number of detectably expressed genes per cell.

Key Features:

• Transcriptional diversity as a determinant: Uses the number of detectably expressed genes per cell as a metric for developmental potential.
• Framework for ordered differentiation states: Predicts and orders differentiation states from scRNA-seq data and is reported to surpass previous methodologies in accuracy and reliability.
• Broad applicability across lineages and species: Evaluated on approximately 150,000 single-cell transcriptomes spanning 53 lineages and five species.
• Identification of tissue-resident stem cells: Facilitates unbiased identification of tissue-resident stem cells, including cells with long-term regenerative potential.
• Application to cancer research: Applied to human breast tumor data to identify candidate genes associated with less-differentiated luminal progenitor cells and to validate GULP1 as a gene involved in tumorigenesis.

Scientific Applications:

• Delineation of cellular hierarchies: Infers order and relationships among cell states to map lineage structures.
• Embryonic development: Predicts developmental potential and trajectories in embryonic single-cell transcriptomes.
• Tissue regeneration: Identifies stem and progenitor populations relevant to regenerative processes.
• Cancer biology: Detects less-differentiated tumor cell populations and candidate genes linked to tumorigenesis.

Methodology:

Computes transcriptional diversity as the number of detectably expressed genes per cell from scRNA-seq data and uses that measure to predict and order cellular differentiation states.