Biology subjects

Wu, S. H.

Publications and source records attributed to Wu, S. H..

3 recordsLinked to original sources

bioRxiv2026

A Rare Nestin-Expressing Granule Cell Precursor Subpopulation Underlies SHH Medulloblastoma Formation

Granule cell precursors (GCPs) drive the major postnatal expansion of the cerebellum and are the cells of origin of sonic hedgehog medulloblastoma (SHH MB). Although GCPs are often treated as a uniform population, increasing evidence suggests they are heterogeneous, and whether specific subpopulations show distinct tumorigenic competence remains unclear. Here, we identified a rare Nestin-expressing GCP subpopulation in the normal early postnatal cerebellum and showed that it is spatially restricted, molecularly distinct and highly competent to form tumors. These cells are enriched in the posterior external granule layer and co-express Atoh1. Using different SHH MB mouse models, we showed that when this rare subpopulation of GCPs is targeted they can give rise to SHH MB with an efficiency comparable to targeting a larger number of Atoh1-expressing GCPs and that tumors derived from Nestin-expressing GCPs arise in the posterior-lateral cerebellum. Single cell RNA sequencing revealed that Nestin-expressing GCP have a transcriptome indicating reduced neuronal differentiation and enrichment for stem cell genes compared to bulk GCPs and more closely align with SHH MB cells. Together, our findings reveal functionally important heterogeneity within the GCP lineage and suggest that SHH MB arises preferentially from a small subpopulation of GCPs that express Nestin.

cancer biology↗

bioRxiv2023

Tumor-wide RNA splicing aberrations generate immunogenic public neoantigens

T-cell-mediated immunotherapies are limited by the extent to which cancer-specific antigens are homogenously expressed throughout a tumor. We reasoned that recurrent splicing aberrations in cancer represent a potential source of tumor-wide and public neoantigens, and to test this possibility, we developed a novel pipeline for identifying neojunctions expressed uniformly within a tumor across diverse cancer types. Our analyses revealed multiple neojunctions that recur across patients and either exhibited intratumor heterogeneity or, in some cases, were tumor-wide. We identified CD8+ T-cell clones specific for neoantigens derived from tumor-wide and conserved neojunctions in GNAS and RPL22, respectively. TCR-engineered CD8+ T-cells targeting these mutations conferred neoantigen-specific tumor cell eradication. Furthermore, we revealed that cancer-specific dysregulation in splicing factor expression leads to recurrent neojunction expression. Together, these data reveal that a subset of neojunctions are both intratumorally conserved and public, providing the molecular basis for novel T-cell-based immunotherapies that address intratumoral heterogeneity.

cancer biology↗

bioRxiv2017

accuMUlate: A mutation caller designed for mutation accumulation experiments

MotivationMutation accumulation (MA) is the most widely used method for directly studying the effects of mutation. Modern sequencing technologies have led to an increased interest in MA experiments. By sequencing whole genomes from MA lines, researchers can directly study the rate and molecular spectra of spontaneous mutations and use these results to understand how mutation contributes to biological processes. At present there is no software designed specifically for identifying mutations from MA lines. Studies that combine MA with whole genome sequencing use custom bioinformatic pipelines that implement heuristic rules to identify putative mutations.\n\nResultsHere we describe O_SCPLOWACCUC_SCPLOWMUO_SCPLOWLATEC_SCPLOW, a program that is designed to detect mutations from MA experiments. O_SCPLOWACCUC_SCPLOWMUO_SCPLOWLATEC_SCPLOW implements a probabilistic model that reflects the design of a typical MA experiments while being flexible enough to accommodate properties unique to any particular experiment. For each putative mutation identified from this model O_SCPLOWACCUC_SCPLOWMUO_SCPLOWLATEC_SCPLOW calculates a set of summary statistics that can be used to filter sites that may be false positives. A companion tool, O_SCPLOWDENOMINATEC_SCPLOW, can be used to apply filtering rules based on these statistics to simulated mutations and thus identify the number of callable sites per sample.\n\nAvailabilitySource code and releases available from https://github.com/dwinter/accuMUlate.

bioinformatics↗