Biology subjects

Vuong, H.

Publications and source records attributed to Vuong, H..

10 recordsLinked to original sources

bioRxiv2026

Milk-derived extracellular vesicles are functional mediators of postnatal neuroimmune rescue

Early parental experiences can be translated into biological signals that are transmitted to offspring. These signals influence developmental trajectories to both increase offspring vulnerability following adverse experiences or promote resilience when environments are supported and enriched. Among the potential routes of parental-offspring communication, milk-derived extracellular vesicles (EVs) transferred through nursing are particularly interesting because they carry bioactive molecular cargos capable of influencing recipient cells. Previously, we found that milk-derived EV cargo is remodeled by both adverse and positive experiences and that environmental enrichment (EE) can rescue adversity-associated effects, including EV-miRNA cargo changes. Environmental programming of EVs during lactation may therefore have functional consequences on offspring development and adaptability. Here, we tested whether milk-derived EVs from our rat EE model are sufficient to rescue offspring outcomes following maternal immune activation (MIA). Milk-derived EVs isolated from donor EE dams were administered directly to MIA-exposed pups, mitigating MIA-associated reductions in social behavior. EE-EV administration also promoted social behavior in non-MIA exposed male offspring. To investigate microglia as a cellular target, EE-EVs were administered to LPS-activated BV2 microglial cells. EE-EV exposure attenuated LPS-associated transcriptional alterations across inflammatory pathways, including cytokine, chemokine, and NF-B signaling, while modulating genes associated with cellular homeostasis and metabolic responses. Together, these results 1) support milk-derived EVs as a biologically active interface through which environmental experience may be communicated to offspring, and 2) identify microglial transcriptional responses as a potential cellular pathway underlying these effects.

neuroscience↗

bioRxiv2024

Yanomami skin microbiome complexity challenges prevailing concepts of healthy skin.

The adult skin microbiome contributes to skin homeostasis and generally comprises relatively low microbial complexity, especially sebaceous sites, where lipophilic Cutibacterium and Malassezia spp. predominate. Current understanding of the healthy skin microbiome derives predominantly from studies of western, industrialized populations, with limited representation of diverse cultures and lifestyles. In this study, the skin microbiome of a remote indigenous Yanomami community was investigated, revealing a complex microbial community comprised of 115 novel bacterial genomes. The bacterial community composition included genera common to western skin microbiota and additional diverse taxa, which formed multiplex interactions with a dominant eukaryote, Malassezia globosa. Metatranscriptome-derived functional attributes of the microbial communities contributed to skin homeostasis, fortifying barrier integrity via lipid metabolism and acid production and protecting against oxidative stress. The Yanomami skin microbiome comprised a suite of microbial taxa co-detected within their various surroundings. Longitudinal monitoring of Western expeditioner microbiome revealed acquisition of the Yanomami skin microbiome following immersion into the Amazon and subsequent loss upon return to an industrialized setting. These findings challenge the prevailing Western-centric view of what comprises a healthy adult skin microbiome, suggesting a diverse community that includes bacteria of environmental origin confers benefits not recognized in the current model of healthy skin. Importantly, we highlight the malleability of adult skin microbiome composition, which includes changes sustained by lifestyle modification.

microbiology↗

bioRxiv2024

The maternal microbiome modifies adverse effects of protein undernutrition on offspring neurobehavioral impairment in mice

Protein undernutrition is a global risk factor for impaired growth and neurobehavioral development in children. However, the critical periods, environmental interactions, and maternal versus neonatal influences on programming lasting behavioral abnormalities are poorly understood. In a mouse model of fetal growth restriction, limiting maternal protein intake particularly during pregnancy leads to cognitive and anxiety-like behavioral abnormalities in adult offspring, indicating a critical role for the gestational period. By cross-fostering newborn mice to dams previously exposed to either low protein or standard diet, we find that the adult behavioral impairments require diet-induced conditioning of both fetal development and maternal peripartum physiology, rather than either alone. This suggests that protein undernutrition during pregnancy directly disrupts fetal neurodevelopment and indirectly alters maternal state in ways that interact postnatally to precipitate behavioral deficits. Consistent with this, maternal protein restriction during pregnancy reduces the diversity of the maternal gut microbiome, modulates maternal serum metabolomic profiles, and yields widespread alterations in fetal brain transcriptomic and metabolomic profiles, including subsets of microbiome-dependent metabolites. Depletion of the maternal microbiome in protein-restricted dams further alters fetal brain gene expression and exacerbates neurocognitive behavior in adult offspring, suggesting that the maternal microbiome modifies the impact of gestational protein undernutrition on risk for neurobehavioral impairment in the offspring. To explore the potential for microbiome-targeted interventions, we find that maternal treatment with short chain fatty acids or a cocktail of 10 diet- and microbiome-dependent metabolites each yield differential effects on fetal development and/or postnatal behavior. Results from this study highlight impactful prenatal influences of maternal protein undernutrition on fetal neurodevelopment and adverse neurobehavioral trajectories in offspring, which are mitigated by microbiome-targeted interventions during pregnancy.

animal behavior and cognition↗

bioRxiv2023

Benchmarking AlphaSC: A Leap in Single-Cell Data Processing

AO_SCPLOWBSTRACTC_SCPLOWWe benchmarked AlphaSC, BioTurings GPU-accelerated single-cell analytics package, against other popular tools including Scanpy, Seurat, and RAPIDS. The results demonstrate that AlphaSC operates thousands of times faster than Seurat and Scanpy. Additionally, it surpasses RAPIDS, another GPU-accelerated package from NVIDIA, by an order of magnitude in terms of speed while also consuming considerably less RAM and GPU memory. Importantly, this significant increase in AlphaSCs performance does not compromise its quality. 1

bioinformatics↗

bioRxiv2023

IC-VAE: A Novel Deep Learning Framework for Interpreting Multiplexed Tissue Imaging Data.

AO_SCPLOWBSTRACTC_SCPLOWInterpreting protein expression in multiplexed tissue imaging data presents a significant challenge due to the high dimensionality of the resulting images, the variety of intracellular structures, cell shapes resulting from 2-D tissue sectioning, and the presence of technological noise and imaging artifacts. Here, we introduce the Information-Controlled Variational Autoencoder (IC-VAE), a deep generative model designed to tackle this challenge. The contribution of IC-VAE to the VAE framework is the ability to control the shared information among latent subspaces. We use IC-VAE to factorize each cells image into its true protein expression, various cellular components, and background noise, while controlling the shared information among some of these components. Compared with other normalization methods, this approach leads to superior results in downstream analysis, such as analyzing the expression of biomarkers, classification for cell types, or visualizing cell clusters using t-SNE/UMAP techniques.

bioinformatics↗

bioRxiv2022

An Entropy Approach for Choosing Gene Expression Cutoff

Annotating cell types using single-cell transcriptome data usually requires binarizing the expression data to distinguish between the background noise vs. real expression or low expression vs. high expression cases. A common approach is choosing a "reasonable" cutoff value, but it remains unclear how to choose it. In this work, we describe a simple yet effective approach for finding this threshold value.

bioinformatics↗

bioRxiv2020

BBrowser: Making single-cell data easily accessible

BioTurings BBrowser is a software solution that helps scientists effectively analyze single-cell omics data. It combines big data with big computation and modern data visualization to create a unique platform where scientists can interact and obtain important biological insights from the massive amounts of single-cell data. BBrowser has three main components: a curated single-cell database, a big-data analytics layer, and a data visualization module. BBrowser is available for download at: https://bioturing.com/bbrowser/download.

bioinformatics↗

bioRxiv2020

Venice: A new algorithm for finding marker genes in single-cell transcriptomic data

AO_SCPLOWBSTRACTC_SCPLOWMost widely used tools for finding marker genes in single cell data (SeuratT/NegBinom/Poisson, CellRanger, EdgeR, limmatrend) use a conventional definition of differentially expressed genes: genes with different mean expression values. However, in single-cell data, a cell population can be a mixture of many cell types/cell states, hence the mean expression of genes cannot represent the whole population. In addition, these tools assume that gene expression of a population belongs to a specific family of distribution. This assumption is often violated in single-cell data. In this work, we define marker genes of a cell population as genes that can be used to distinguish cells in the population from cells in other populations. Besides log-fold change, we devise a new metric to classify genes into up-regulated, down-regulated, and transitional states. In a benchmark for finding up-regulated and down-regulated genes, our tool outperforms all compared methods, including Seurat, ROTS, scDD, edgeR, MAST, limma, normal t-test, Wilcoxon and Kolmogorov-Smirnov test. Our method is much faster than all compared methods, therefore, enables interactive analysis for large single-cell data sets in BioTuring Browser. Venice algorithm is available within Signac package: https://github.com/bioturing/signac 1).

bioinformatics↗

bioRxiv2019

Hera-T: an efficient and accurate approach for quantifying gene abundances from 10X-Chromium data with high rates of non-exonic reads.

An important but rarely discussed phenomenon in single cell data generated by the 10X-Chromium protocol is that the fraction of non-exonic reads is very high. This number usually exceeds 30% of the total reads. Without aligning them to a complete genome reference, non-exonic reads can be erroneously aligned to the transcriptome reference with higher error rates. To tackle this problem, Cell Ranger chooses to firstly align reads against the whole genome, and at a later step, uses a genome annotation to select reads that align to the transcriptome. Despite its high running time and large memory consumption, Cell Ranger remains the most widely used tool to quantify 10XGenomics single cell RNA-Seq data for its accuracy. In this work, we introduce Hera-T, a fast and accurate tool for estimating gene abundances in single cell data generated by the 10X-Chromium protocol. By devising a new strategy for aligning reads to both transcriptome and genome references, Hera-T reduces both running time and memory consumption from 10 to 100 folds while giving similar results compared to Cell Rangers. Hera-T also addresses some difficult splicing alignment scenarios that Cell Ranger fails to address, and therefore, obtains better accuracy compared to Cell Ranger. Excluding the reads in those scenarios, Hera-T and Cell Ranger results have correlation scores > 0.99. For a single-cell data set with 49 million of reads, Cell Ranger took 3 hours (179 minutes) while Hera-T took 1.75 minutes; for another single-cell data set with 784 millions of reads, Cell Ranger took about 25 hours while Hera-T took 32 minutes. For those data sets, Cell Ranger completely used all 32 GB of memory while Hera-T consumed at most 8 GB. Hera-T package is available for download at: https://bioturing.com/product/hera-t

bioinformatics↗

bioRxiv2018

A revisit of RSEM generative model and its EM algorithm for quantifying transcript abundances.

RSEM has been mainly known for its accuracy in transcript abundance quantification. However, its quantification time is extremely high compared to that of recent quantification tools. In this paper, we revised the RSEMs EM algorithm. In particular, we derived accurate M-step updates to eliminate incorrect heuristic updates in RSEM. We also implement some optimizations that reduce the quantification time about a hundred times while still have better accuracy compared to RSEM. In particular, we noticed that different parameters have different convergence rates, therefore we identified and removed early converged parameters to significantly reduce the model complexity in further iterations, and we also use SQUAREM method to further speed up the convergence rate. We implemented these revisions in a packaged named Hera-EM, with source code available at: https://github.com/bioturing/hera/tree/master/hera-EM

bioinformatics↗