Mitigating Cell Cycle Effects in Multi-Omics Data: Solutions and Analytical Frameworks.

Area of Science:

• Genomics
• Cell Biology
• Bioinformatics

Background:

• Cell cycle heterogeneity significantly impacts multi-omics data interpretation due to variations in DNA dosage, chromatin accessibility, methylation, and gene expression.
• The precise influence of cell cycle phase composition on omics data analysis remains largely uncharacterized.

Purpose of the Study:

• To systematically evaluate how distinct cell cycle phase structures affect the interpretation of various omics features in proliferating cells.
• To propose data processing solutions for mitigating cell cycle-related artifacts in omics datasets.

Main Methods:

• Assessed the impact of cell phase structure on copy number variation (CNV) calling, chromatin accessibility, DNA methylation, and transcriptomic data.
• Applied replication timing domain (RTD) correction to mitigate CNV calling errors.
• Utilized cell cycle sorting for DNA methylation and transcriptomic analyses.
• Developed an integrated pipeline for identifying differentially expressed genes (DEGs) after cell cycle phasing.

Main Results:

• Asynchronous replication timing (RT) causes false CNVs in cells with a high S-phase ratio (SPR), which is reduced by RTD correction.
• Similar noise artifacts were observed in chromatin accessibility data.
• Cell cycle-sorted data provided superior biological insights for DNA methylation and transcriptomic analyses compared to direct comparisons.
• An integrated pipeline effectively identified DEGs post-cell cycle phasing.

Conclusions:

• Extensive cell-cycle heterogeneity necessitates its consideration in multi-omics studies, especially in cells with diverse cell cycle compositions.
• Replication timing domain correction or phase-specific comparisons are effective strategies to reduce cell cycle influence on omics data analysis, particularly when comparing stem and differentiated cells.