An immune-focused supplemental alignment pipeline captures information missed from dominant single-cell RNA-seq analyses, including allele-specific MHC-I regulation - Benjamin, Sebastian; McElfresh, GW; Kaza, Maanasa; Boggy, Gregory J.; Varco-Merth, Benjamin; Ojha, Sohita; Feltham, Shana; Goodwin, William; Nkoy, Candice; Duell, Derick; Selseth, Andrea; Bennett, Tyler; et al. (Frontiers in Immunology, 2025)
IntroductionRNA sequencing (RNA-seq) can measure whole transcriptome gene expression from tissues or even individual cells, providing a powerful tool to study the immune response. Analysis of RNA-seq data involves mapping relatively short sequence reads to a reference genome, and quantifying genes based on the position of alignments relative to annotated genes. While this is usually robust, genetic polymorphism or genome/annotation inaccuracies result in genes with systematically missing or inaccurate data. These issues are frequently hidden or ignored, yet are highly relevant to immunologic data, where balancing selection has generated many polygenic gene families not accurately represented in a ‘one-size-fits-all’ reference genome.MethodsHere we present nimble, a tool to supplement standard RNA-seq pipelines. Nimble uses a previously developed pseudoaligner to process either bulk- or single-cell RNA-seq data using custom gene spaces. Importantly, nimble can apply customizable scoring criteria to each gene set, tailored to the biology of those genes.ResultsWe demonstrate that nimble recovers data in diverse contexts, ranging from simple cases (e.g., incorrect gene annotation or viral RNA), to complex immune genotyping (e.g., major histocompatibility or killer-immunoglobulin-like receptors). We use this enhanced capability to identify killer-immunoglobulin-like receptor expression specific to tissue-resident memory T cells and demonstrate allele-specific regulation of MHC alleles after Mycobacterium tuberculosis stimulation.DiscussionCombining nimble data with standard pipelines enhances the fidelity and accuracy of experiments, maximizing the value of expensive datasets, and identifying cellular subsets not possible with standard tools alone.