Longitudinal Single-Cell RNA-Sequencing Reveals Evolution of Micro- and Macro-states in Chronic Myeloid Leukemia

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) offers deep insights into cancer biology.
• Interpreting complex transcriptional alterations in scRNA-seq data to define distinct disease states remains a significant challenge.
• Chronic myeloid leukemia (CML) presents a model for investigating these challenges due to its continuous transcriptional variations.

Purpose of the Study:

• To systematically compare information content in scRNA-seq versus bulk transcriptomics for chronic myeloid leukemia (CML).
• To resolve the paradox of identifying discrete disease states from noisy single-cell data.
• To establish a theoretical framework explaining phenotype emergence at different data aggregation levels.

Main Methods:

• Comparative analysis of scRNA-seq and bulk transcriptomics data in CML.
• Application of pseudobulk analysis to aggregate single-cell data to a macro-state level.
• Leveraging state-transition theory to model disease phenotype dynamics.

Main Results:

• CML single-cell transcriptomes exhibit continuous transcriptional micro-states.
• Clinically relevant leukemia phenotypes become apparent only at the pseudobulk (macro-state) level.
• State-transition theory elucidated cell type-specific contributions governing phenotype transitions.

Conclusions:

• Discrete disease phenotypes are obscured at the single-cell level but emerge clearly at the aggregated macro-state level.
• This framework provides a theoretical basis for understanding leukemia evolution and CML progression.
• The approach offers a broadly applicable strategy for analyzing disease dynamics in complex conditions.