Leukemia is the most frequently diagnosed pediatric cancer, accounting for nearly one third of childhood cancer diagnoses. Although outcomes for pediatric leukemia patients have improved significantly with advents in molecular diagnostics and targeted treatments, a number of patients nonetheless have poor outcomes including treatment-resistant recurrence of their disease. In this dataset, we utilized 10X v2 single-cell RNA sequencing to profile the transcriptomes of 25 pediatric leukemia samples collected from patients at Children’s Mercy Kansas City at single-cell resolution. Viably preserved blood and bone marrow samples collected at initial diagnosis, remission, or recurrence were banked in the Children’s Mercy Tumor Bank biorepository, then profiled for this study. Represented leukemias include B-cell Acute Lymphoblastic Leukemia (B-ALL), T-cell Acute Lymphoblastic Leukemia (T-ALL), and Acute Myeloid Leukemia (AML). Profiled samples include those with well-characterized subtypes (e.g. ETV6::RUNX1 B-ALL) as well as those where a subtype was not determined by standard molecular and cytogenetic methods. Characterization of these samples at single-cell resolution provides a unique opportunity to study the heterogeneity of these cancers, identify subpopulations of interest (e.g. leukemic stem cells), and explore transcriptional similarities between patients with unknown leukemic subtypes and those with a confident diagnosis. Furthering our understanding of the cellular environment of these leukemias is vital for improved diagnosis and treatment selection for pediatric patients afflicted by these cancers.\n\nSamples were multiplexed across three pools. Demultiplexing to create sample-specific FASTQ files was performed via demuxlet based on patient-specific VCF files generated from whole genome or whole exome sequencing. These demultiplexed sample-specific FASTQ files were treated as individual libraries for the ScPCA-nf workflow.