Cell identity revealed by precise cell cycle state mapping links data modalities.

Area of Science:

• Single-cell biology
• Genomics
• Cellular imaging

Background:

• Cell cycle inference methods are common for sequencing data but scarce for imaging data.
• Accurate cell cycle state classification is crucial for understanding cellular dynamics.

Purpose of the Study:

• To develop and validate a method for classifying cell cycle states using integrated imaging and sequencing data.
• To enable genotype-phenotype mapping at single-cell resolution by linking cell states forward in time.

Main Methods:

• Integrated cell cycle pseudo-times derived from both sequencing and imaging data.
• Assigned 449 imaged cells to 693 sequenced cells with high resolution.
• Analyzed correlations between thousands of pathways and organelle features.

Main Results:

• Successfully integrated multi-modal data for cell cycle state classification.
• Identified numerous correlated pathways and organelle features, including novel associations.
• Demonstrated the potential for linking cell transcriptome states to their living counterparts.

Conclusions:

• This integrated approach advances cell cycle analysis by combining imaging and sequencing.
• The method facilitates genotype-phenotype mapping at single-cell resolution.
• Opens new avenues for studying cellular dynamics and gene function over time.