Sci-fate characterizes the dynamics of gene expression in single cells.

Area of Science:

• Molecular Biology
• Genomics
• Cell Biology

Background:

• Gene expression dynamics are crucial for understanding cellular processes but are difficult to measure directly in single cells.
• Existing single-cell profiling methods often capture static snapshots, missing the temporal information of transcriptional changes.
• Understanding dynamic gene expression is key to deciphering cell differentiation, development, and responses to stimuli.

Purpose of the Study:

• To develop a novel method for simultaneously profiling the whole and newly synthesized transcriptome in individual cells.
• To enable the quantitative analysis of transcriptional dynamics and cell-state transitions.
• To investigate the intersection of cell cycle and glucocorticoid receptor activation dynamics.

Main Methods:

• Combined single-cell combinatorial indexing and messenger RNA labeling (sci-fate).
• Utilizes 4-thiouridine labeling to distinguish newly synthesized mRNA from existing transcripts.
• Applies combinatorial indexing to process thousands of single cells concurrently.

Main Results:

• Successfully profiled the transcriptome of over 6,000 single cultured cells in response to cortisol.
• Quantified the dynamics of cell cycle progression and glucocorticoid receptor activation.
• Identified and analyzed cell-state transitions based on transcriptional dynamics.

Conclusions:

• sci-fate provides a powerful tool for dissecting transcriptional dynamics at the single-cell level.
• The method enables quantitative analysis of complex cellular responses, such as the cortisol response.
• This approach is expected to be widely applicable for studying dynamic biological processes across various systems.