While a differentiation hierarchy with leukemia-initiating stem cells (LICs) at the apex is well documented for acute myeloid leukemia, the existence of LICs and their trajectories in B cell acute lymphoblastic leukemia (B-ALL) are debated. B-ALL is a malignant disease displaying considerable phenotypical and functional heterogeneity, yet the underlying cellular organization remains largely elusive. This study aims to investigate the hierarchical landscape of B-ALL by combining barcoding and multiome single cell data to unveil the differentiation architecture and temporal dynamics of leukemic differentiation at clonal and single cell resolution in vivo. Single-cell transcriptome and AbSeq analysis of lentiviral barcoded and transplanted patient-derived B-ALL cells revealed interconnected subpopulations, which could be prospectively isolated via surface markers and exhibited distinct leukemogenicity. Barcode tracking demonstrated the ability of cells to differentiate from a source cluster into differentiated progeny. Using machine learning we identified expression patterns predicting their differentiation potential of each barcoded cell within the immature cell compartment. To determine the dynamical properties of clones, we have designed a mathematical model that simulates the development of these subpopulations from a clonally diverse stem cell compartment. The model confirmed the most likely cluster of origin, supporting our functional data, and revealed the variability in dynamical properties between clones, while largely excluding plasticity. Hence, our work demonstrates a unidirectional differentiation in B-ALL with an immature population exhibiting a high leukemogenic potential.
Geßner et al. (Tue,) studied this question.