Biology subjects

Pham, S.

Publications and source records attributed to Pham, S..

13 recordsLinked to original sources

bioRxiv2025

Single-Cell Atlas of CD4+ T-Helper 2 Cells Reveals Distinct Cytokine Functions Across Diseases

Despite the prevalence of autoimmune diseases, treatment options for specific conditions remain limited. These diseases are driven by three types of inflammatory pathways, each orchestrated by different T-helper cell subsets; however, a comprehensive atlas capturing their diverse functions across conditions and tissues is lacking. One of these subsets, CD4+ T-helper type 2 (Th2) cells, is a relatively infrequent immune cell population that plays a central role in orchestrating type 2 inflammation. In this study, we developed the first comprehensive CD4+Th2 single-cell atlas by analyzing the BioTuring database, which has data from over 300 million cells. This atlas integrates data from 52 studies, comprising a total of 39,243 cells derived from 9 major tissues, representing over 30 distinct diseases. Leveraging this atlas, we confirmed a rarely recognized CD4+Th2 cell population co-expressing IL22 and IL13 observed exclusively in atopic dermatitis and not in other allergic conditions. By integrating single-cell data with other "omics" technologies, our analysis suggests distinct downstream effects of IL22 and IL13, highlighting the potential for combination therapy to enhance treatment outcomes for AD patients. Additionally, by utilizing data derived from the wide range of diseases, tissues, and time points included in this atlas, we re-confirmed the unique connection between IL9 expression and conditions related to allergen exposure. Pseudotime analysis detailed CD4+Th2 cytokine dynamics upon allergen stimulation, showing a shift from early IL2 and IL4 expression to later IL9 and IL5, with IL13 consistently expressed, and further revealed that IL9 production was transient. Collectively, these findings reveal distinct, context-dependent CD4+Th2 cytokine functions, opening the potential for identifying novel therapeutic strategies for allergic and inflammatory diseases.

immunology↗

bioRxiv2025

The Orai channel inhibitor BTP2 restricts Tulane virus and human norovirus replication independent of store-operated calcium entry

Human norovirus is the leading cause of viral gastroenteritis across all age groups. While there is a need for human norovirus antivirals, therapeutic development has been hindered by a lack of cell culture systems and animal models of infection. Surrogate viruses, such as Tulane virus (TV), have provided tractable systems to screen potential antiviral compounds. Our previous work demonstrated that Tulane virus encodes a viral ion channel, which dysregulates cytosolic calcium signaling. We set out to investigate whether host pathways triggered by viral ion channel activity, including store-operated calcium entry (SOCE), play a role in virus replication. Using pharmacologic inhibitors and genetically engineered cell lines, we establish that the SOCE inhibitor, BTP2, reduces TV replication in an SOCE-independent manner. We observed a significant reduction in TV replication, protein expression, and RNA synthesis in cells with both pre- and post-infection BTP2 treatment. By serial passage and plaque isolation, we demonstrate that TV quasi-species have mixed susceptibility and resistance to BTP2. Sequence comparison of the quasi-species revealed that amino acid changes in the structural proteins were associated with drug resistance. We utilized reverse genetics to generate TV with the resistance-associated VP1 and VP2 amino acid changes and found that a single amino acid change in VP1 (I380M) conferred BTP2 resistance. Further, expression of resistant VP2 alone was sufficient to partially rescue the replication of susceptible virus. Together, this supports that TV structural proteins are the targets of BTP2. Finally, using human intestinal organoids, we demonstrate that BTP2 significantly reduces human norovirus replication. ImportanceOur work identifies BTP2 as a potential human norovirus antiviral pharmacophore and highlights the utility of targeting calicivirus structural proteins to restrict viral replication. Further, we establish a system whereby Tulane virus can be used to screen novel antiviral candidates and establish their mechanism of action. Together, this will facilitate rapid preclinical validation of other novel human norovirus therapeutics.

microbiology↗

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↗

bioRxiv2020

Inhalation: A means to explore and optimize nintedanib's pharmacokinetic/pharmacodynamic relationship

Oral nintedanib is marketed for the treatment of idiopathic pulmonary fibrosis (IPF). While effective slowing fibrosis progression, as an oral medicine nintedanib is limited. To reduce side effects and maximize efficacy, nintedanib was reformulated as a solution for nebulization and inhaled administration. To predict effectiveness treating IPF, the nintedanib pharmacokinetic/pharmacodynamic relationship was dissected. Pharmacokinetic analysis indicated oral-delivered nintedanib plasma exposure and lung tissue partitioning were not dose-proportional and resulting lung levels were substantially higher than blood. Although initial-oral absorbed nintedanib efficiently partitioned into the lung, only a quickly eliminated fraction appeared available to epithelial lining fluid (ELF). Because IPF disease appears to initiate and progress near the epithelial surface, this observation suggests short duration nintedanib exposure (oral portion efficiently partitioned to ELF) is sufficient for IPF efficacy. To test this hypothesis, exposure duration required for nintedanib activity was explored. In vitro, IPF-cellular matrix (IPF-CM) increased primary normal human fibroblast (nHLF) aggregate size and reduced nHLF cell count. IPF-CM also increased nHLF ACTA2 and COL1A expression. Whether short duration (inhalation mimic) or continuous exposure (oral mimic), nintedanib (1-100 nM) reversed these effects. In vivo, intubated silica produced a strong pulmonary fibrotic response. Once-daily (QD) 0.021, 0.21 and 2.1 mg/kg intranasal (IN; short duration inhaled exposure) and twice daily (BID) 30 mg/kg oral (PO; long duration oral exposure) showed that at equivalent-delivered lung concentrations, QD short duration inhaled nintedanib exposure (0.21 mg/kg IN vs. 30 mg/kg PO) exhibited equivalent-to-superior activity as BID oral (reduced silica-induced elastance, alpha-smooth muscle actin, interleukin-1 beta (IL-1{beta}) and soluble collagen, and lung macrophage and neutrophils). An increased lung dose (2.1 mg/kg IN vs. 30 mg/kg PO) exhibited increased effect by further reducing silica-induced elastance, IL-1{beta} and soluble collagen. Neither oral nor inhaled nintedanib reduced silica-induced parenchymal collagen. Both QD inhaled and BID oral nintedanib reduced silica-induced inflammatory index with oral achieving significance. In summary, nintedanib pulmonary anti-fibrotic activity can be achieved using small, infrequent inhaled doses to deliver oral equivalent-to-superior therapeutic effect.

pharmacology and toxicology↗

bioRxiv2019

Ultra-low input single tube linked-read library method enables short-read NGS systems to generate highly accurate and economical long-range sequencing information for de novo genome assembly and haplotype phasing

Long-range sequencing information is required for haplotype phasing, de novo assembly and structural variation detection. Current long-read sequencing technologies can provide valuable long-range information but at a high cost with low accuracy and high DNA input requirement. We have developed a single-tube Transposase Enzyme Linked Long-read Sequencing (TELL-Seq) technology, which enables a low-cost, high-accuracy and high-throughput short-read next generation sequencer to routinely generate over 100 Kb long-range sequencing information with as little as 0.1 ng input material. In a PCR tube, millions of clonally barcoded beads are used to uniquely barcode long DNA molecules in an open bulk reaction without dilution and compartmentation. The barcode linked reads are used to successfully assemble genomes ranging from microbes to human. These linked-reads also generate mega-base-long phased blocks and provide a cost-effective tool for detecting structural variants in a genome, which are important to identify compound heterozygosity in recessive Mendelian diseases and discover genetic drivers and diagnostic biomarkers in cancers.

genomics↗

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↗

bioRxiv2017

Repeat associated mechanisms of genome evolution and function revealed by the Mus caroli and Mus pahari genomes

Understanding the mechanisms driving lineage-specific evolution in both primates and rodents has been hindered by the lack of sister clades with a similar phylogenetic structure having high-quality genome assemblies. Here, we have created chromosome-level assemblies of the Mus caroli and Mus pahari genomes. Together with the Mus musculus and Rattus norvegicus genomes, this set of rodent genomes is similar in divergence times to the Hominidae (human-chimpanzee-gorilla-orangutan). By comparing the evolutionary dynamics between the Muridae and Hominidae, we identified punctate events of chromosome reshuffling that shaped the ancestral karyotype of Mus musculus and Mus caroli between 3 to 6 MYA, but that are absent in the Hominidae. In fact, Hominidae show between four-and seven-fold lower rates of nucleotide change and feature turnover in both neutral and functional sequences suggesting an underlying coherence to the Muridae acceleration. Our system of matched, high-quality genome assemblies revealed how specific classes of repeats can play lineage-specific roles in related species. For example, recent LINE activity has remodeled protein-coding loci to a greater extent across the Muridae than the Hominidae, with functional consequences at the species level such as reproductive isolation. Furthermore, we charted a Muridae-specific retrotransposon expansion at unprecedented resolution, revealing how a single nucleotide mutation transformed a specific SINE element into an active CTCF binding site carrier specifically in Mus caroli. This process resulted in thousands of novel, species-specific CTCF binding sites. Our results demonstrate that the comparison of matched phylogenetic sets of genomes will be an increasingly powerful strategy for understanding mammalian biology.

genomics↗

bioRxiv2016

Chromosome assembly of large and complex genomes using multiple references

Despite the rapid development of sequencing technologies, assembly of mammalian-scale genomes into complete chromosomes remains one of the most challenging problems in bioinformatics. To help address this difficulty, we developed Ragout, a reference-assisted assembly tool that now works for large and complex genomes. Taking one or more target assemblies (generated from an NGS assembler) and one or multiple related reference genomes, Ragout infers the evolutionary relationships between the genomes and builds the final assemblies using a genome rearrangement approach. Using Ragout, we transformed NGS assemblies of 15 different Mus musculus and one Mus spretus genomes into sets of complete chromosomes, leaving less than 5% of sequence unlocalized per set. Various benchmarks, including PCR testing and realigning of long PacBio reads, suggest only a small number of structural errors in the final assemblies, comparable with direct assembly approaches. Additionally, we applied Ragout to Mus caroli and Mus pahari genomes, which exhibit karyotype-scale variations compared to other genomes from the Muridae family. Chromosome color maps confirmed most large-scale rearrangements that Ragout detected.

bioinformatics↗