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

Single-cell RNA-sequencing (scRNA-seq) has revolutionized our understanding of cancer. However, identifying meaningful disease states from single-cell data remains challenging due to the complex continuum of transcriptional alterations, which may obscure clear phenotype boundaries and complicate biological interpretation and clinical relevance. Here, we systematically explored the chronic myeloid leukemia (CML) specific information content encoded in scRNA-seq versus bulk transcriptomics to resolve this paradox and clarify how discrete disease-defining states emerge from inherently noisy single-cell data. While CML single-cell transcriptomes existed along continuous transcriptional micro-states, clinically relevant leukemia phenotypes clearly manifested only at the pseudobulk (macro-state) level. State-transition theory was leveraged to reveal how disease phenotype state-transitions are governed by cell type specific contributions. Together, these results establish a theoretical framework explaining why discrete disease phenotypes remain hidden at the single-cell scale but emerge clearly at the aggregated macro-state level, enabling previously inaccessible biological insights into leukemia evolution. By resolving how single-cell variation aggregates into macroscopic disease states, this framework provides insights into CML progression and offers a broadly applicable strategy for exploring disease dynamics across cancers and other complex conditions.