Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program.

Area of Science:

• Cell Biology
• Systems Biology
• Computational Biology

Background:

• Cell synchronization is crucial for studying cell-cycle dynamics, but cell-to-cell variability causes loss of synchrony over time.
• Imperfect synchronization convolutes time-series measurements, obscuring true biological dynamics.
• Existing methods struggle to accurately capture dynamic cell-cycle processes due to these limitations.

Purpose of the Study:

• To develop a computational method for deconvoluting cell-cycle processes from unsynchronized cell populations.
• To improve the accuracy, dynamic range, and temporal resolution of cell-cycle time-series data.
• To identify cell-cycle-regulated transcription and cell-specific gene expression programs.

Main Methods:

• Developed a branching process deconvolution algorithm.
• Employed wavelet-basis regularization to enhance signal while minimizing noise.
• Applied the algorithm to Saccharomyces cerevisiae cell-cycle transcription data.

Main Results:

• The algorithm accurately reconstructs cell-cycle dynamics, overcoming limitations of imperfect synchronization.
• Enhanced data quality with increased dynamic range and temporal resolution.
• Identified 82 genes with daughter-specific transcription in early G1 phase, revealing distinct transcriptional programs.

Conclusions:

• The developed deconvolution algorithm provides a more accurate view of dynamic cell-cycle processes.
• The method significantly improves the detection of cell-cycle-regulated transcription and subtle timing differences.
• Revealed daughter-specific gene expression programs, offering new insights into cell division and differentiation.