Supporting data for "ASXL1 C-terminal truncation and SRSF2 mutation drive leukemogenesis via immune reprogramming"

This dataset provides a comprehensive molecular resource for a study investigating the mechanisms of leukemia development using a genetically engineered zebrafish model. The research specifically focuses on the synergistic effects of mutations in the epigenetic regulator asxl1 and the RNA splicing factor SRSF2, two genes frequently co-mutated in human myeloid malignancies. To recapitulate the disease, we generated zebrafish models harboring these mutations and performed an integrated analysis combining detailed phenotyping, histopathology, and multi-layered transcriptomic profiling. The raw sequencing data for both bulk and single-cell RNA-seq have been deposited in the NCBI BioProject database under the accession number PRJNA1290872.The data files contained within this specific repository represent the core processed and analytical outputs from this study. The Bulk RNA-seq analyses include spreadsheets identifying differentially expressed genes in double mutant fish compared to wildtype controls, which highlight key transcriptional changes driving leukemia. Furthermore, we provide files detailing alternative spliced genes and corresponding Sashimi plots to visualize the specific splicing events disrupted by the SRSF2 mutation. Complementing the bulk analysis, the Single-cell RNA-seq (scRNA-seq) data allows for the dissection of cellular heterogeneity within the leukemia model. This includes lists of differentially expressed genes across distinct cell populations, as well as the results of Gene Set Variation Analysis (GSVA), which assesses the activity of specific biological pathways in single cells. A summary document is also included, synthesizing the key phenotypic and molecular findings into a consolidated overview.By making this dataset publicly available, we aim to provide the scientific community with a valuable resource to deepen the understanding of the molecular interplay between epigenetic and splicing dysregulation in leukemia, potentially informing future diagnostic and therapeutic strategies. All data is anonymized and free of sensitive information.