Longitudinal single cell RNA-sequencing reveals evolution of micro- and macro-states inchronic myeloid leukemia
David E. Frankhouser, Dandan Zhao, Yu-Hsuan Fu, Anupam Dey, Ziang Chen, Jihyun Irizarry, Jennifer Rangel Ambriz, Sergio Branciamore, Denis O’Meally, Lucy Ghoda, Jeffery M. Trent, Stephen Forman, Adam L. MacLean, Ya-Huei Kuo, Bin Zhang, Russell C. Rockne, Guido Marcucci
Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm defined by expression of the fusion gene BCR::ABL and its potential for disease transformation, yet the precise mechanisms and timing of its evolution remain poorly understood. Previously, we used time-series bulk transcriptome of peripheral blood mononuclear cells from a mouse model to construct a potential landscape that predicted CML development and progression via state-transitionmodeling. Here, we report on a longitudinal single cell (sc) RNA-sequencing in chronic phase(CP) and blast crisis (BC) CML mouse models to study state-transitions at the single cell level.Different from the bulk transcriptome, we could not identify a disease state encoded in the sc-transcriptomes, as the cells were observed in a continuum of micro states occupying both health and leukemia states. By combining the single-cell transcriptomes into cell type pseudobulk(PsB) transcriptome samples, we were able to recreate a smooth transition from a healthy state to a CML state, recapitulating the findings of our previous bulk transcriptome study. The PsBstate-space was characterized by a three well potential landscape with three stable critical points defining the state-transition from health to leukemia. Importantly, by computing the PsBtranscriptome for individual cell types, we first showed that the system level dynamics could be modeled as a linear combination of four individual cell type PsB transcriptomes of individual blood cell types. We validated this approach and our findings in a second independent time-series blast crisis mouse experiment. In conclusion, we provide a mathematical explanation for why phenotypes, like health or leukemia, are not encoded in single-cell transcriptomes (which represent micro states) but are instead determined at the macro state level. This analytical approach is likely applicable to other types of leukemia and cancer to predict disease evolution and treatment response.