Hawkins, A. G. | bioRxiv Science

bioRxiv2024

The Single-cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors

The Single-cell Pediatric Cancer Atlas (ScPCA) Portal (https://scpca.alexslemonade.org/) is a data resource for uniformly processed single-cell and single-nuclei RNA sequencing (RNA-seq) data and de-identified metadata from pediatric tumor samples. Originally comprised of data from 10 projects funded by Alexs Lemonade Stand Foundation (ALSF), the Portal currently contains summarized gene expression data for over 700 samples across 55 cancer types from ALSF-funded and community-contributed datasets. Downloads include gene expression data as SinglecellExperiment or AnnData objects containing raw and normalized counts, PCA and UMAP coordinates, and automated cell type annotations, along with summary reports. Some samples have additional data from bulk RNA-seq, spatial transcriptomics, and/or feature barcoding (e.g., CITE-seq and cell hashing) included in the download. All data on the Portal were uniformly processed using scpca-nf, an efficient and open-source Nextflow workflow that uses alevin-fry to quantify gene expression. Comprehensive documentation, including descriptions of file contents and a guide to getting started, is available at https://scpca.readthedocs.io.

bioinformatics↗

bioRxiv2022

Single-cell multi-omics defines the cell-type specific impact of splicing aberrations in human hematopoietic clonal outgrowths

RNA splicing factors are recurrently affected by alteration-of-function mutations in clonal blood disorders, highlighting the importance of splicing regulation in hematopoiesis. However, our understanding of the impact of dysregulated RNA splicing has been hampered by the inability to distinguish mutant and wildtype cells in primary patient samples, the cell-type complexity of the hematopoietic system, and the sparse and biased coverage of splice junctions by short-read sequencing typically used in single-cell RNA sequencing. To overcome these limitations, we developed GoT-Splice by integrating Genotyping of Transcriptomes (GoT) with enhanced efficiency long-read single-cell transcriptome profiling, as well as proteogenomics (with CITE-seq). This allowed for the simultaneous single-cell profiling of gene expression, cell surface protein markers, somatic mutation status, and RNA splicing. We applied GoT-Splice to bone marrow progenitors from patients with myelodysplastic syndrome (MDS) affected by mutations in the most prevalent mutated RNA splicing factor - the core RNA splicing factor SF3B1. High-resolution mapping of SF3B1mut vs. SF3B1wt hematopoietic progenitors revealed a fitness advantage of SF3B1mut cells in the megakaryocytic-erythroid lineage, resulting in an expansion of SF3B1mut erythroid progenitor (EP) cells. SF3B1mut EP cells exhibited upregulation of genes involved in regulation of cell cycle and mRNA translation. Long-read single-cell transcriptomes revealed the previously reported increase of aberrant 3 splicing site usage in SF3B1mut cells. However, the ability to profile splicing within individual cell populations uncovered distinct cryptic 3 splice site usage across different progenitor populations, as well as stage-specific aberrant splicing during erythroid maturation. Lastly, as splice factor mutations occur in clonal hematopoiesis (CH) with increased risk of neoplastic transformation, we applied GoT-Splice to CH samples. These data revealed that the erythroid lineage bias, as well as cell-type specific cryptic 3 splice site usage in SF3B1mut cells, precede overt MDS. Collectively, we present an expanded multi-omics single-cell toolkit to define the cell-type specific impact of somatic mutations on RNA splicing, from the earliest phases of clonal outgrowths to overt neoplasia, directly in human samples.

cancer biology↗