Biology subjects

Nguyen, Q.

Publications and source records attributed to Nguyen, Q..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

HOPX governs a molecular and physiological switch between cardiomyocyte progenitor and maturation gene programs

This study establishes the homeodomain only protein, HOPX, as a determinant controlling the molecular switch between cardiomyocyte progenitor and maturation gene programs. Time-course single-cell gene expression with genome-wide footprinting reveal that HOPX interacts with and controls core cardiac networks by regulating the activity of mutually exclusive developmental gene programs. Upstream hypertrophy and proliferation pathways compete to regulate HOPX transcription. Mitogenic signals override hypertrophic growth signals to suppress HOPX and maintain cardiomyocyte progenitor gene programs. Physiological studies show HOPX directly governs genetic control of cardiomyocyte cell stress responses, electro-mechanical coupling, proliferation, and contractility. We use human genome-wide association studies (GWAS) to show that genetic variation in the HOPX-regulome is significantly associated with complex traits affecting cardiac structure and function. Collectively, this study provides a mechanistic link situating HOPX between competing upstream pathways where HOPX acts as a molecular switch controlling gene regulatory programs underpinning metabolic, signaling, and functional maturation of cardiomyocytes.

developmental biology↗

bioRxiv2022

LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays

Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) array is still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. We aim to develop methods to design SNP arrays for thse countries, so the arrays should be cost-effective (small size), yet can still generate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs (imputed tag SNPs) not included in the array can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the measured and imputed SNPs. It is those functional SNPs that are most likely associated to traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole genome sequencing databases. Our results showed that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited.

bioinformatics↗

bioRxiv2021

Genetically engineered microglia-like cells have therapeutic potential for neurodegenerative disease

Hematopoietic stem/progenitor cell gene therapy (HSPC-GT) results in the engraftment of genetically modified microglia-like cells (MLCs) in the brain. While HSPC-GT has shown a clear neurological benefit in the clinic for specific rare diseases, the nature of MLC engraftment in the brain and the functional characteristics of MLCs remain contentious. Here we comprehensively characterized how different routes of administration affect the engraftment and biodistribution of genetically engineered HSPC-derivatives in mice. Using high-throughput single-cell profiling, we show that MLCs bear a transcriptional signature similar to resident microglia rather than invading macrophages. However, MLCs could clearly be distinguished from resident microglia by expression of a specific set of genes. Finally, in murine models of Parkinsons disease and frontotemporal dementia, we demonstrate that MLCs can provide therapeutically relevant levels of protein to the brain, thereby potentially opening avenues of HSPC-GT to address the underlying disease etiology of these and other similar disorders.

neuroscience↗

bioRxiv2021

A Deep Learning Model for Molecular Label Transfer that Enables Cancer Cell Identification from Histopathology Images

Deep learning cancer classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches depends on prior annotation through a pathologist. This initial step relying on a manual, low-resolution, time-consuming process is highly variable and subject to observer variance. To address this issue, we developed a novel method, H&E Molecular neural network (HEMnet). This two-step process utilises immunohistochemistry as an initial molecular label for cancer cells on a H&E image and then we train a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, we show that HEMnet accurately distinguishes colorectal cancer from normal tissue at high resolution without the need for an initial manual histopathologic evaluation. Our validation study using histopathology images from TCGA samples accurately estimates tumour purity. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: https://github.com/BiomedicalMachineLearning/HEMnet

bioinformatics↗

bioRxiv2020

Spatially resolved transcriptome profiles of mammalian kidneys illustrate the molecular complexity of functional nephron segments, cell-to-cell interactions and genetic variants.

Understanding the molecular mechanisms underlying mammalian kidney function requires transcriptome profiling of the interplay between cells comprising nephron segments. Traditional transcriptomics requires cell dissociation, resulting in loss of the spatial context of gene expression within native tissue. To address this problem, we performed spatial transcriptomics (ST) to retain the spatial context of the transcriptome in human and mouse kidneys. The generated ST data allowed spatially resolved differential gene expression analysis, spatial identification of functional nephron segments, cell-to-cell interaction analysis, and chronic kidney disease-associated genetic variant calling. Novel ST thus provides an opportunity to enhance kidney diagnostics and knowledge, by retaining the spatial context of gene expression within intact tissue.

molecular biology↗

bioRxiv2020

Spatial analysis of ligand-receptor interactions in skin cancer at genome-wide and single-cell resolution

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed for cancer immunotherapies, to understand molecular mechanisms and to discover potential druggable targets. In this work, we developed a powerful experimental and analytical toolbox to enable genome-wide scale discovery and targeted validation of cellular communication. We assessed the utilities of five sequencing and imaging technologies to study cancer tissue, including single-cell RNA sequencing and Spatial Transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (multiplex 4-12 genes), digital droplet PCR, and Opal multiplex protein staining (4-9 proteins). To spatially integrate multimodal data, we developed a computational method called STRISH that can automatically scan across the whole tissue section for local expression of gene and/or protein markers to recapitulate an interaction landscape across the whole tissue. We evaluated the unique ability of this toolbox to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.

cancer biology↗

bioRxiv2020

Fine-tuning of Epithelial EGFR signals Supports Coordinated Mammary Gland Development

During puberty, robust morphogenesis occurs in the mammary gland; stem- and progenitor-cells develop into mature basal- and luminal-cells to form the ductal tree. The receptor signals that govern this process in mammary epithelial cells (MECs) are incompletely understood. The EGFR has been implicated and here we focused on EGFRs downstream pathway component Rasgrp1. We find that Rasgrp1 dampens EGF-triggered signals in MECs. Biochemically and in vitro, Rasgrp1 perturbation results in increased EGFR-Ras-PI3K-AKT and mTORC1-S6 kinase signals, increased EGF-induced proliferation, and aberrant branching-capacity in 3D cultures. However, in vivo, Rasgrp1 perturbation results in delayed ductal tree maturation with shortened branches and reduced cellularity. Rasgrp1-deficient MEC organoids revealed lower frequencies of basal cells, the compartment that incorporates stem cells. Molecularly, EGF effectively counteracts Wnt signal-driven stem cell gene signature in organoids. Collectively, these studies demonstrate the need for fine-tuning of EGFR signals to properly instruct mammary epithelium during puberty.

cell biology↗

bioRxiv2020

DIFFERENTIAL BIOENERGETICS IN ADULT RAT CARDIOMYOCYTES ISOLATED FROM THE RIGHT VERSUS LEFT VENTRICLE

The right and left ventricle of the heart have distinctly different developmental origins and are affected differently by similar pathological stimuli. Though it is well established that the heart relies almost entirely on mitochondrial function to sustain energy production, it remains unclear whether bioenergetics differ in the two ventricles. Herein, we define a novel methodology to optimize the isolation of intact cardiomyocytes from the right versus the left ventricle. We demonstrate that this segmental Langendorff-free methodology yields viable cardiomyocytes with intact mitochondrial function. Further, we compare bioenergetics in right versus left ventricle cardiomyocytes and show that cardiomyocytes from the right ventricle have a greater maximal capacity for respiration and enhanced glycolytic rate. This increase in respiration was concomitant with increased fatty acid oxidation and levels of fatty acid oxidation proteins, but no change in mitochondrial electron transport complex expression. These data validate a potentially powerful tool to evaluate differences in right and left ventricular function and advance the understanding of cardiac bioenergetic differences. These data will be discussed in the context of differential responses by the right versus ventricle in pathology.

cell biology↗

bioRxiv2019

SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells

MotivationSpatial transcriptomics technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for spatial transcriptomics data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology.\n\nResultsWe developed an user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially-barcoded spots in a tissue. We show the integration approach outperforms the use of gene count alone or imaging data alone to create deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy.\n\nAvailabilityThe SpaCell package is open source under a MIT license and it is available at https://github.com/BiomedicalMachineLearning/SpaCell\n\nContactquan.nguyen@uq.edu.au

bioinformatics↗

bioRxiv2019

Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Our knowledge of transcriptional heterogeneities in epithelial stem/progenitor cell compartments is limited. Epidermal basal cells sustain cutaneous tissue maintenance and drive wound healing. Previous studies have probed basal cell heterogeneity in stem/progenitor potential, but a non-biased dissection of basal cell dynamics during differentiation is lacking. Using single-cell RNA-sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell transcriptional states in homeostatic skin that differ in metabolic preference and become spatially partitioned during wound re-epithelialization. Pseudotemporal trajectory and RNA velocity analyses produce a quasi-linear differentiation hierarchy where basal cells progress from Col17a1high/Trp63high state to early response state, proliferate at the juncture of these two states, or become growth arrested before differentiating into spinous cells. Wound healing induces plasticity manifested by dynamic basal-spinous interconversions at multiple basal states. Our study provides a systematic view of epidermal cellular dynamics supporting a revised \"hierarchical-lineage\" model of homeostasis.

developmental biology↗

bioRxiv2019

Integrated single-cell transcriptomics and chromatin accessibility analysis reveals novel regulators of mammary epithelial cell identity

The mammary epithelial cell (MEC) system is a bi-layered ductal epithelial network consisting of luminal and basal cells, which is maintained by a lineage of stem and progenitor cell populations. Here, we used integrated single-cell transcriptomics and chromatin accessibility analysis to reconstruct the cell types of the mouse MEC system and their underlying gene regulatory features in an unbiased manner. We define previously unrealized differentiation states within the secretory type of luminal cells, which can be divided into distinct clusters of progenitor and mature secretory cells. By integrating single-cell transcriptomics and chromatin accessibility landscapes, we identified novel cis- and trans-regulatory elements that are differentially activated in the specific epithelial cell types and our newly defined luminal differentiation states. Our work provides an unprecedented resource to reveal novel cis/trans regulatory elements associated with MEC identity and differentiation that will serve as a valuable reference to determine how the chromatin accessibility landscape changes during breast cancer.

cell biology↗

bioRxiv2019

Defining the emergence of myeloid-derived suppressor cells in breast cancer using single-cell transcriptomics

Myeloid-derived suppressor cells (MDSCs) are innate immune cells that acquire the capacity to suppress adaptive immune responses during cancer. It remains elusive how MDSCs differ from their normal myeloid counterparts, which limits our ability to specifically detect and therapeutically target MDSCs during cancer. Here, we used single-cell RNAseq to compare MDSC-containing splenic myeloid cells from breast tumor-bearing mice to wildtype controls. Our computational analysis of 14,646 single-cell transcriptomes reveals that MDSCs emerge through a previously unrealized aberrant neutrophil maturation trajectory in the spleen giving rise to a unique chemokine-responsive, immunosuppressive cell state that strongly differs from normal myeloid cells. We establish the first MDSC-specific gene signature and identify novel surface markers for improved detection and enrichment of MDSCs in murine and human samples. Our study provides the first single-cell transcriptional map defining the development of MDSCs, which will ultimately enable us to specifically target these cells in cancer patients.\n\nOne Sentence SummaryWe used single cell transcriptomics to identify the unique molecular features distinguishing myeloid-derived suppressor cells (MDSCs) from their normal, myeloid counterparts, which enabled us to reveal distinct transitory gene expression changes during their maturation in the spleen, and to identify novel cell surface markers for improved detection and isolation of MDSCs.

cancer biology↗

bioRxiv2019

Comparative analysis of diverse cell states establishes an epigenetic basis for inferring regulatory genes governing cell identity

Determining genes orchestrating cell differentiation in development and disease remains a fundamental goal of cell biology. This study establishes a genome-wide metric based on the gene-repressive tri-methylation of histone 3 lysine 27 (H3K27me3) across hundreds of diverse cell types to identify genetic regulators of cell differentiation. We introduce a computational method, TRIAGE, that uses discordance between gene-repressive tendency and expression to identify genetic drivers of cell identity. We apply TRIAGE to millions of genome-wide single-cell transcriptomes, diverse omics platforms, and eukaryotic cells and tissue types. Using a wide range of data, we validate TRIAGEs performance for identifying cell-type specific regulatory factors across diverse species including human, mouse, boar, bird, fish, and tunicate. Using CRISPR gene editing, we use TRIAGE to experimentally validate RNF220 as a regulator of Ciona cardiopharyngeal development and SIX3 as required for differentiation of endoderm in human pluripotent stem cells. A record of this papers Transparent Peer Review process is included in the Supplemental Information.

cell biology↗

bioRxiv2019

Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing

The libraries generated by high-throughput single cell RNA-sequencing platforms such as the Chromium from 10X Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use this data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3 libraries consisting of over 70,000 cells. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and cell, UMI, and gene detection. However, compared with the NextSeq 500, the MGISEQ-2000 platform performs consistently better, identifying more cells, genes, and UMIs at equalised read depth. We were able to call an additional 1,065,659 SNPs from sequence data generated by the BGI platform, enabling an additional 14% of cells to be assigned to the correct donor from a multiplexed library. However, both the NextSeq 500 and MGISEQ-2000 detected similar frequencies of gRNAs from a pooled CRISPR single cell screen. Our study provides a benchmark for high capacity sequencing platforms applied to high-throughput single cell RNA-seq libraries.

genomics↗

bioRxiv2018

Super-resolution Molecular Map of Basal Foot Reveals Novel Cilium in Airway Multiciliated Cells

Motile cilia are beating machines that play a critical role in airway defense. During airway cell differentiation, hundreds of motile cilia are templated from basal bodies that extend a basal foot, an appendage that links motile cilia together to ensure beating coordination. This assembly has thus far escaped structural analysis because its size is below the resolution limit. Here, we determine the molecular architecture and identify basal foot proteins using a super-resolution-driven approach. Quantitative super-resolution image analysis shows that the basal foot is organized in three main regions linked by elongated coiled-coil proteins. FIB-SEM tomography and comparative super-resolution mapping of basal feet reveal that, among hundreds of motile cilia of an airway cell, a hybrid cilium with features of primary and motile cilia is harbored. The hybrid cilium is conserved in mammalian multiciliated cells and originates from parental centrioles. We further demonstrate that this novel cilium is a signalling centre whose cellular position is dependent on flow.

cell biology↗

bioRxiv2018

Generation of human neural retina transcriptome atlas by single cell RNA sequencing

The retina is a highly specialized neural tissue that senses light and initiates image processing. Although the functional organisation of specific cells within the retina has been well-studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile cell types in the human retina, we performed single cell RNA-sequencing on 20,009 cells obtained post-mortem from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct clusters representing all known retinal cells: rod photoreceptors, cone photoreceptors, Muller glia cells, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, retinal astrocytes and microglia. Notably, our data captured molecular profiles for healthy and early degenerating rod photoreceptors, and revealed a novel role of MALAT1 in putative rod degeneration. We also demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Muller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to our understanding of retinal biology and disease.

systems biology↗

bioRxiv2018

scPred: Single cell prediction using singular value decomposition and machine learning classification

Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many human tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an unknown cell based on its transcriptional profile; and clearly, the ability to accurately predict a cell type and any pathologic-related state will play a critical role in the early diagnosis of disease and decisions around the personalized treatment for patients. Here we present a new generalizable method (scPred) for prediction of cell type(s), using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning classification. scPred solves several problems associated with the identification of individual gene feature selection, and is able to capture subtle effects of many genes, increasing the overall variance explained by the model, and correspondingly improving the prediction accuracy. We validate the performance of scPred by performing experiments to classify tumor versus non-tumor epithelial cells in gastric cancer, then using independent molecular techniques (cyclic immunohistochemistry) to confirm our prediction, achieving an accuracy of classifying the disease state of individual cells of 99%. Moreover, we apply scPred to scRNA-seq data from pancreatic tissue, colorectal tumor biopsies, and circulating dendritic cells, and show that scPred is able to classify cell subtypes with an accuracy of 96.1-99.2%. Collectively, our results demonstrate the utility of scPred as a single cell prediction method that can be used for a wide variety of applications. The generalized method is implemented in software available here: https://github.com/IMB-Computational-Genomics-Lab/scPred/

genomics↗

bioRxiv2018

Detection of HPV E7 transcription at single-cell resolution in epidermis

Persistent human papillomavirus (HPV) infection is responsible for at least 5% of human malignancies. Most HPV-associated cancers are initiated by the HPV16 genotype, as confirmed by detection of integrated HPV DNA in cells of oral and anogenital epithelial cancers. However, single-cell RNA-sequencing (scRNA-seq) may enable prediction of HPV involvement in carcinogenesis at other sites. We conducted scRNA-seq on keratinocytes from a mouse transgenic for the E7 gene of HPV16, and showed sensitive and specific detection of HPV16-E7 mRNA, predominantly in basal keratinocytes. We showed that increased E7 mRNA copy number per cell was associated with increased expression of E7 induced genes. This technique enhances detection of viral transcripts in solid tissue and may clarify possible linkage of HPV infection to development of squamous cell carcinoma.

cell biology↗