Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic labeling experiment.

Area of Science:

• Molecular Biology
• Computational Biology
• Genomics

Background:

• Metabolic labeling is crucial for RNA turnover rate determination at the transcriptome-wide scale.
• Current computational methods necessitate multiple RNA sequencing samples.
• Single-cell RNA measurements are increasingly adopted.

Purpose of the Study:

• To develop a computational method for estimating RNA synthesis, processing, and degradation rates from a single sample.
• To reduce the experimental and computational burden of RNA turnover rate analysis.

Main Methods:

• Utilized the analytical solution to the Zeisel model of RNA dynamics.
• Validated the method on metabolic labeling experiments in mouse embryonic stem cells.
• Compared estimated degradation rates with published data and a state-of-the-art method.

Main Results:

• The method is computationally efficient and yields rates correlating well with existing datasets.
• Successfully reproduced the link between biological process dynamics and metabolic rates.
• Degradation rates obtained favorably compared to an alternative method.

Conclusions:

• Estimating RNA rates from a single sample saves experimental and computational time.
• Eliminates the need for error-prone cross-sample normalization, enabling better uncertainty estimation.
• The method is adaptable for nucleotide conversion assays and single-cell metabolic labeling.