Biology subjects

Stone, M. R.

Publications and source records attributed to Stone, M. R..

5 recordsLinked to original sources

bioRxiv2023

Switching circuits: Modulating NK cell response to TGF-β using novel receptors

Engineered natural killer (NK) cells offer a promising treatment strategy for multiple therapeutic areas, yet tuning cell function to diverse environmental and inhibitory contexts remains a significant challenge. An attractive strategy for functional rescue in the presence of inhibitory ligands is the use of switch receptors, surface-expressed chimeric receptors that convert an external inhibitory cue into an intracellular activation signal. Here, we discover novel engineered switch receptors in NK cells that are responsive to TGF-{beta}, a soluble inhibitory factor present in many therapeutic contexts. Through a pooled screen of an 11,131 member library, we identified multiple novel signaling endodomains and endodomain combinations that improve both the acute cytotoxicity and persistence of NK cells in the presence of TGF-{beta}, demonstrating a novel and flexible approach to switch receptor discovery.

immunology↗

bioRxiv2023

TRAFfic signals: High-throughput CAR discovery in NK cells reveals novel TRAF-binding endodomains that drive enhanced persistence and cytotoxicity

Natural killer (NK) cells are a promising alternative therapeutic platform to CAR T cells given their favorable safety profile and potent killing ability. However, CAR NK cells suffer from limited persistence in vivo, which is, in part, thought to be the consequence of limited cytokine signaling. To address this challenge, we developed an innovative high-throughput screening strategy to identify CAR endodomains that could drive enhanced persistence while maintaining potent cytotoxicity. We uncovered a family of TRAF-binding endodomains that outperform benchmarks in primary NK cells along dimensions of persistence and cytotoxicity, even in low IL-2 conditions. This work highlights the importance of cell-type-specific cell therapy engineering and unlocks a wide range of high-throughput molecular engineering avenues in NK cells.

immunology↗

bioRxiv2020

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpaces improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data. In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

bioinformatics↗

bioRxiv2019

An open resource of structural variation for medical and population genetics

Structural variants (SVs) rearrange large segments of the genome and can have profound consequences for evolution and human diseases. As national biobanks, disease association studies, and clinical genetic testing grow increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) have become integral for interpreting genetic variation. To date, no large-scale reference maps of SVs exist from high-coverage sequencing comparable to those available for point mutations in protein-coding genes. Here, we constructed a reference atlas of SVs across 14,891 genomes from diverse global populations (54% non-European) as a component of gnomAD. We discovered a rich landscape of 433,371 distinct SVs, including 5,295 multi-breakpoint complex SVs across 11 mutational subclasses, and examples of localized chromosome shattering, as in chromothripsis. The average individual harbored 7,439 SVs, which accounted for 25-29% of all rare protein-truncating events per genome. We found strong correlations between constraint against damaging point mutations and rare SVs that both disrupt and duplicate protein-coding sequence, suggesting intolerance to reciprocal dosage alterations for a subset of tightly regulated genes. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than any effect on noncoding SVs. Finally, we benchmarked carrier rates for medically relevant SVs, finding very large ([≥]1Mb) rare SVs in 3.8% of genomes (~1:26 individuals) and clinically reportable incidental SVs in 0.18% of genomes (~1:556 individuals). These data have been integrated directly into the gnomAD browser (https://gnomad.broadinstitute.org) and will have broad utility for population genetics, disease association, and diagnostic screening.

genomics↗

bioRxiv2017

Limited contribution of rare, noncoding variation to autism spectrum disorder from sequencing of 2,076 genomes in quartet families

Genomic studies to date in autism spectrum disorder (ASD) have largely focused on newly arising mutations that disrupt protein coding sequence and strongly influence risk. We evaluate the contribution of noncoding regulatory variation across the size and frequency spectrum through whole genome sequencing of 519 ASD cases, their unaffected sibling controls, and parents. Cases carry a small excess of de novo (1.02-fold) noncoding variants, which is not significant after correcting for paternal age. Assessing 51,801 regulatory classes, no category is significantly associated with ASD after correction for multiple testing. The strongest signals are observed in coding regions, including structural variation not detected by previous technologies and missense variation. While rare noncoding variation likely contributes to risk in neurodevelopmental disorders, no category of variation has impact equivalent to loss-of-function mutations. Average effect sizes are likely to be smaller than that for coding variation, requiring substantially larger samples to quantify this risk.

genomics↗