Biology subjects

Stanley, J. S.

Publications and source records attributed to Stanley, J. S..

3 recordsLinked to original sources

bioRxiv2025

Multi-omic Characterization of HIV Effects at Single Cell Level across Human Brain Regions

HIV infection exerts profound and long-lasting neurodegenerative effects on the central nervous system (CNS) that can persist despite antiretroviral therapy (ART). Here, we used single-nucleus multiome sequencing to map the transcriptomic and epigenetic landscapes of postmortem human brains from 13 healthy individuals and 20 individuals with HIV who have a history of treatment with ART. Our study spanned three distinct regions--the prefrontal cortex, insular cortex, and ventral striatum--enabling a comprehensive exploration of region-specific and cross-regional perturbations. We found widespread and persistent HIV-associated transcriptional and epigenetic alterations across multiple cell types. Detailed analyses of microglia revealed state changes marked by immune activation and metabolic dysregulation, while integrative multiomic profiling of astrocytes identified multiple subpopulations, including a reactive subpopulation unique to HIV-infected brains. These findings suggest that cells from people with HIV exhibit molecular shifts that may underlie ongoing neuroinflammation and CNS dysfunction. Furthermore, cell-cell communication analyses uncovered dysregulated and pro-inflammatory interactions among glial populations, underscoring the multifaceted and enduring impact of HIV on the brain milieu. Collectively, our comprehensive atlas of HIV-associated brain changes reveals distinct glial cell states with signatures of pro-inflammatory signaling and metabolic dysregulation, providing a framework for developing targeted therapies for HIV-associated neurological dysfunction.

bioinformatics↗

bioRxiv2025

Principled PCA separates signal from noise in omics count data

Principal component analysis (PCA) is indispensable for processing high-throughput omics datasets, as it can extract meaningful biological variability while minimizing the influence of noise. However, the suitability of PCA is contingent on appropriate normalization and transformation of count data, and accurate selection of the number of principal components; improper choices can result in the loss of biological information or corruption of the signal due to excessive noise. Typical approaches to these challenges rely on heuristics that lack theoretical foundations. In this work, we present Biwhitened PCA (BiPCA), a theoretically grounded framework for rank estimation and data denoising across a wide range of omics modalities. BiPCA overcomes a fundamental difficulty with handling count noise in omics data by adaptively rescaling the rows and columns - a rigorous procedure that standardizes the noise variances across both dimensions. Through simulations and analysis of over 100 datasets spanning seven omics modalities, we demonstrate that BiPCA reliably recovers the data rank and enhances the biological interpretability of count data. In particular, BiPCA enhances marker gene expression, preserves cell neighborhoods, and mitigates batch effects. Our results establish BiPCA as a robust and versatile framework for high-throughput count data analysis.

bioinformatics↗

bioRxiv2019

Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing

Current methods for comparing scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here, we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm to identify clusters of cells that are enriched or depleted in each condition is on average 57% higher than the next best-performing algorithm tested. Gene signatures derived from these clusters are more accurate compared to six alternative algorithms in ground-truth comparisons.

bioinformatics↗