Biology subjects

Nguyen, Q.

Publications and source records attributed to Nguyen, Q..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy of myeloid progenitor cells characterized by successive acquisition of genetic alterations. This inherent heterogeneity poses challenges in the development of precise and effective therapies. To gain insights into the genetic influence on drug response and optimize treatment selection, we performed targeted sequencing, ex vivo drug screening, and single-cell genomic profiling on leukemia cell samples derived from AML patients. We detected genetic signatures associated with sensitivity or resistance to specific agents. By integrating large public datasets, we discovered statistical patterns of co-occurring and mutually exclusive mutations in AML. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct sub- clones within AML patients. Machine learning models were built to predict ex vivo drug sensitivity using the genetic variants. Notably, these models demonstrated high accuracy in predicting sensitivity to some drugs, such as MEK inhibitors. Our study provides valuable resources for characterizing AML patients and predicting drug sensitivity, emphasizing the significance of considering subclonal distribution in drug response prediction. These findings provide a foundation for advancing precision medicine in AML. By tailoring treatment based on individual genetic profiles and functional testing, as well as accounting for the presence of subclones, we envision a future of improved therapeutic strategies for AML patients. One Sentence SummaryIntegrative computational and experimental analysis of mutation patterns and drug responses provide biologic insight and therapeutic guidance for patients with adult AML.

cell biology↗

bioRxiv2023

STimage:robust, confident and interpretable models for predicting gene markers from cancer histopathological images

Spatial transcriptomic (ST) imaging and sequencing data enable us to link tissue morphological features with thousands of previously unseen gene expression values, opening a new horizon for understanding tissue biology and achieving breakthroughs in digital pathology. Deep learning models are emerging to predict gene expression or classify cell types using images as the sole input. Such models hold significant potential for clinical applications, but require improvements in interpretability and robustness. We developed STimage as a comprehensive suite of models for both regression (predicting gene expression) and classification (mapping tissue regions and cell types) tasks. STimage is the first to thoroughly address robustness (uncertainty) and interpretability. For robustness, STimage predicts gene expression based on parameter distributions rather than fixed data points, allowing for generalisation at a population scale. STimage estimates uncertainty from the data (aleatoric) and from the model (epistemic) for each of thousands of imaging tiles. STimage achieves interpretability by analysing model attribution at a single-cell level, and in the context of histopathological annotation. While existing models focus on predicting highly variable genes, STimage predicts functional genes and identifies highly predictable genes. Using diverse datasets from three cancers and one chronic disease, we assessed the models performance on in-distribution and out-of-distribution samples. STimage is robust to technical variations across platforms, data types, sample preservation methods, and disease types. Further, we implemented an ensemble approach, incorporating pre-trained foundation models, to improve performance and reliability, especially in cases with small training datasets. With single-cell resolution Xenium data, STimage could classify cell types for millions of individual cells. Applying STimage to proteomics data such as CODEX, we found that STimage can predict gene expression consistent with protein expression patterns. Finally, we showed that using STimage-predicted values based solely on imaging input, we could stratify patient survival groups. Overall, STimage advances spatial transcriptomics by improving the prediction of gene expression from traditional histopathological images, making it more accessible for tissue biology research and digital pathology applications.

bioinformatics↗

bioRxiv2023

A spatially resolved single cell genomic atlas of the adult human breast

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

genomics↗

bioRxiv2023

Single-cell and spatial transcriptomics identify a macrophage population associated with skeletal muscle fibrosis

The monocytic/macrophage system is essential for skeletal muscle homeostasis, but its dysregulation contributes to the pathogenesis of muscle degenerative disorders. Despite our increasing knowledge of the role of macrophages in degenerative disease, it still remains unclear how macrophages contribute to muscle fibrosis. Here, we used single-cell transcriptomics to determine the molecular attributes of dystrophic and healthy muscle macrophages. We identified six novel clusters. Unexpectedly, none corresponded to traditional definitions of M1 or M2 macrophage activation. Rather, the predominant macrophage signature in dystrophic muscle was characterized by high expression of fibrotic factors, galectin-3 and spp1. Spatial transcriptomics and computational inferences of intercellular communication indicated that spp1 regulates stromal progenitor and macrophage interactions during muscular dystrophy. Galectin-3+ macrophages were chronically activated in dystrophic muscle and adoptive transfer assays showed that the galectin-3+ phenotype was the dominant molecular program induced within the dystrophic milieu. Histological examination of human muscle biopsies revealed that galectin-3+ macrophages were also elevated in multiple myopathies. These studies advance our understanding of macrophages in muscular dystrophy by defining the transcriptional programs induced in muscle macrophages, and reveal spp1 as a major regulator of macrophage and stromal progenitor interactions.

immunology↗

bioRxiv2023

i-stLearn: An interactive platform for spatial transcriptomics analysis

SummaryEmerging spatial transcriptomics technologies (e.g. Visium, Slideseq, or MERFISH) have made it possible to keep the spatial information while profiling gene expression of every cell/spatial-spot. Integrating expression values, spatial coordinates, and imaging data type promises to bring more biological insights but is still technically challenging. A user-friendly software tool to enable interactive analysis of spatial transcriptomic data by the broader community is lacking. We present i-stLearn, an all-in-on web application with an analysis pipeline and interactive visualization for studying spatial heterogeneity using spatial transcriptomics data. i-stLearn can be used to gain biological insights from tissue through key analysis types cell-cell interaction analysis, clustering, and trajectory inference. Using functions, users can interactively segment the tissue and identify cellular state transition or cellular communications in a heterogeneous biological sample. Availabilityi-stLearn is freely available at https://github.com/BiomedicalMachineLearning/stlearn_interactive as local web application and we also provide a demo online web service available at https://i-stlearn-demo.web.app. Contactquan.nguyen@imb.uq.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

TMEM106B coding variant is protective and deletion detrimental in a mouse model of tauopathy

TMEM106B is a risk modifier for a growing list of age-associated dementias including Alzheimers and frontotemporal dementia, yet its function remains elusive. Two key questions that emerge from past work are whether the conservative T185S coding variant found in the minor haplotype contributes to protection, and whether the presence of TMEM106B is helpful or harmful in the context of disease. Here we address both issues while extending the testbed for study of TMEM106B from models of TDP to tauopathy. We show that TMEM106B deletion accelerates cognitive decline, hindlimb paralysis, neuropathology, and neurodegeneration. TMEM106B deletion also increases transcriptional overlap with human AD, making it a better model of disease than tau alone. In contrast, the coding variant protects against tau-associated cognitive decline, neurodegeneration, and paralysis without affecting tau pathology. Our findings show that the coding variant contributes to neuroprotection and suggest that TMEM106B is a critical safeguard against tau aggregation.

neuroscience↗

bioRxiv2023

A robust Platform for Integrative Spatial Multi-omics Analysis to Map Immune Responses to SARS-CoV-2 infection in Lung Tissues

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid, and similar, but a robust platform for deep tissue characterisation of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterise and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

bioinformatics↗

bioRxiv2023

Spatial transcriptomic analysis of Sonic Hedgehog Medulloblastoma identifies that the loss of heterogeneity and promotion of differentiation underlies the response to CDK4/6 inhibition

BackgroundMedulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single cell transcriptome studies have defined the cellular states underlying each MB subgroup, however the spatial organisation of these diverse cell states and how this impacts response to therapy remains to be determined. MethodsHere, we used spatially resolved transcriptomics to define the cellular diversity within a sonic hedgehog (SHH) patient-derived model of MB and identify how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We integrated spatial gene expression with histological annotation and single cell gene expression data from MB, developing a analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. ResultsWe distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. ConclusionsOur data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.

genomics↗

bioRxiv2023

Deep spatial-omics to aid personalization of precision medicine in metastatic recurrent Head & Neck Cancers

Immune checkpoint inhibitor (ICI) modality has had a limited success (<20%) in treating metastatic recurrent Head & Neck Oropharyngeal Squamous cell carcinomas (OPSCCs). To improve response rates to ICIs, tailored approaches capable to capture the tumor complexity and dynamics of each patients disease are needed. Here, we performed advanced analyses of spatial proteogenomic technologies to demonstrate that: (i) compared to standard histopathology, spatial transcriptomics better-identified tumor cells and could specifically classify them into two different metabolic states with therapeutic implications; (ii) our new method (Spatial Proteomics-informed cell deconvolution method or SPiD) improved profiling of local immune cell types relevant to disease progression, (iii) identified clinically relevant alternative treatments and a rational explanation for checkpoint inhibitor therapy failure through comparative analysis of pre- and post-failure tumor data and, (iv) discovered ligand-receptor interactions as potential lead targets for personalized drug treatments. Our work establishes a clear path for incorporating spatial-omics in clinical settings to facilitate treatment personalization.

cancer biology↗

bioRxiv2023

Benchmarking robust spatial transcriptomics approaches to capture the molecular landscape and pathological architecture of archived cancer tissues

AbtractsApplying spatial transcriptomics (ST) to explore a vast amount of formalin-fixed paraffin-embedded (FFPE) archival cancer tissues has been highly challenging due to several critical technical issues. In this work, we optimised ST protocols to generate unprecedented spatial gene expression data for FFPE skin cancer. Skin is among the most challenging tissue types for ST due to its fibrous structure and a high risk of RNAse contamination. We evaluated tissues collected from ten years to two years ago, spanning a range of tissue qualities and complexity. Technical replicates and multiple patient samples were assessed. Further, we integrated gene expression profiles with pathological information, revealing a new layer of molecular information. Such integration is powerful in cancer research and clinical applications. The data allowed us to detect the spatial expression of non-coding RNAs. Together, this work provides important technical perspectives to enable the applications of ST on archived cancer tissues.

pathology↗

bioRxiv2022

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across diverse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

bioinformatics↗

bioRxiv2022

An integrated cell barcoding and computational analysis pipeline for scalable analysis of differentiation at single-cell resolution

Pluripotent stem cells provide a scalable approach to analyse molecular regulation of cell differentiation across multiple developmental lineage trajectories. In this study, we engineered barcoded iPSCs to generate an atlas of multilineage differentiation from pluripotency, encompassing a time-course of WNT-induced differentiation perturbed using modulators of WNT, BMP, and VEGF signalling. Computational mapping of in vitro cell types to in vivo developmental lineages revealed a diversity of iPSC-derived cell types comprising mesendoderm lineage cell types including lateral plate and paraxial mesoderm, neural crest, and primitive gut. Coupling this atlas of in vitro differentiation with Summary data-based Mendelian Randomisation analysis of human complex traits, we identify the WNT-inhibitor protein TMEM88 as a putative regulator of mesendodermal cell types governing development of diverse cardiovascular and anthropometric traits. Using genetic loss of function models, we show that TMEM88 is required for differentiation of diverse endoderm and mesoderm cell lineages in vitro and that TMEM88 knockout in vivo results in a significant dysregulation of arterial blood pressure. This study provides an atlas of multilineage iPSC differentiation coupled with new molecular, computational, and statistical genetic tools to dissect genetic determinants of mammalian developmental physiology.

genomics↗

bioRxiv2022

Dehydration alters transcript levels in the midgut, likely facilitating rapid rehydration

The mosquito midgut is an important site for bloodmeal regulation while also acting as a primary site for pathogen exposure within the mosquito. Recent studies show that exposure to dehydrating conditions alters mosquito bloodfeeding behaviors as well as post-feeding regulation, likely altering how pathogens interact with the mosquito. Unfortunately, few studies have explored the underlying dynamics between dehydration and bloodmeal utilization, and the overall impact on disease transmission dynamics remains veiled. In this study, we find that dehydration-based feeding in the yellow fever mosquito, Aedes aegypti, prompts alterations to midgut gene expression, as well as subsequent physiological factors involving water control and post-bloodfeeding (pbf) regulation. Altered expression of ion transporter genes in the midgut of dehydrated mosquitoes and rapid reequilibration of hemolymph osmolality after a bloodmeal indicate an ability to expedite fluid and ion processing. These alterations ultimately indicate that female A. aegypti employ mechanisms to ameliorate the detriments of dehydration by imbibing a bloodmeal, providing an effective avenue for rehydration. Continued research into bloodmeal utilization and the resulting effects on arthropod-borne transmission dynamics becomes increasingly important as drought prevalence is increased by climate change.

physiology↗

bioRxiv2022

Cellular Heterogeneity of Pluripotent Stem Cell Derived Cardiomyocyte Grafts is Mechanistically Linked to Treatable Arrhythmias

BackgroundExciting pre-clinical data have confirmed that human pluripotent stem cell derived cardiomyocytes (PSC-CMs) can remuscularise the injured or diseased heart, with several clinical trials now in planning or recruitment stages worldwide. However, ventricular arrhythmias are a predictable complication following engraftment of intramyocardially injected PSC-CMs. Therefore, there is an urgent unmet need to gain mechanistic insights and treatment strategies to control or prevent these engraftment arrhythmias (EAs). MethodsWe used a porcine model of myocardial infarction and PSC-CM transplantation to investigate efficacy of pharmacologic and catheter based anti-arrhythmic strategies in mitigating EAs. Furthermore, cell doses were robustly phenotyped using single cell ribonucleic acid sequencing and high parameter flow cytometry to identify cellular characteristics predictive of arrhythmogenesis. ResultsCombination therapy with amiodarone and ivabradine significantly reduced EA rate and burden following PSC-CM transplantation. Catheter ablation was also a feasible and effective treatment strategy which could be considered in the case of pharmacologically refractory arrhythmias. In addition, we show that EAs are mechanistically linked to cellular heterogeneity in the input PSC-CM and resultant graft. Specifically, we identify atrial and pacemaker-like cardiomyocytes as culprit arrhythmogenic subpopulations. We further describe two unique surface marker signatures, SIRPA+/CD90-/CD200+ and SIRPA+/CD90-/CD200-, which identify arrhythmogenic and non-arrhythmogenic cardiomyocytes respectively. ConclusionOur data deepens mechanistic understanding of EAs and suggests that modifications to current PSC-CM production and/or selection protocols could ameliorate this problem. We further show that current clinical pharmacologic and interventional anti-arrhythmic strategies can control and potentially abolish these arrhythmias, an important safety consideration given several impending clinical trials.

cell biology↗

bioRxiv2022

Chorography and conformational dynamism of the Soluble Human Fibrinogen in solution

Fibrinogen is a soluble, multi-subunit and multi-domain dimeric protein, which, upon its proteolytic cleavage by thrombin, is converted to insoluble fibrin initiating polymerization that substantially contributes to clot growth. The consentaneous structural view of the soluble form of fibrinogen is relatively straight and rigid-like. However, fibrinogen contains numerous, transiently-accessible "cryptic" epitopes for hemostatic and immunologic proteins, suggesting that fibrinogen exhibits conformational flexibility, which may play functional roles in its temporal and spatial interactions. Hitherto, there has been limited information on the solution structure and internal flexibility of soluble fibrinogen. Here, utilizing an integrative, biophysical approach involving temperature-dependent hydrogen-deuterium exchange mass spectrometry, small angle X-ray scattering, and negative stain electron microscopy, we present a holistic, conformationally dynamic model of human fibrinogen in solution. Our data reveal a high degree of internal flexibility, accommodated by four major and distinct flexible sites along the central axis of the protein. We propose that the fibrinogen structure in solution consists of a complex, conformational landscape with multiple local minima, rather than a single, rigid topology. This is further supported by the location of numerous point mutations that are linked to dysfibrinogenemia, and post-translational modifications, residing near fibrinogen flexions. This work provides a molecular basis for the structural "dynamism" of fibrinogen that is expected to influence the broad swath of functionally diverse macromolecular interactions and fine-tune the structural and mechanical properties of blood clots.

biochemistry↗

bioRxiv2022

Imputation and polygenic score performances of human genotyping arrays in diverse populations

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and to stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number, and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public, and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in term of concordance to PGS estimated from whole genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole genome sequencing data in most of cases. When optimal arrays are used, the correlations of key PGS metrics between two types of data can be higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provide interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/

genomics↗

bioRxiv2022

Phosphoproteomic mapping reveals distinct signaling actions and activation of protein synthesis and muscle hypertrophy by Isthmin-1

The secreted protein Isthmin-1 (Ism1) mitigates diabetes by increasing adipocyte and skeletal muscle glucose uptake by activating the PI3K-Akt pathway. However, while both Ism1 and insulin converge on these common targets, Ism1 has distinct cellular actions suggesting divergence in downstream intracellular signaling pathways. To understand the biological complexity of Ism1 signaling, we performed phosphoproteomic analysis after acute exposure, revealing overlapping and distinct pathways of Ism1 and insulin. We identify a 53 % overlap between Ism1 and insulin signaling and Ism1-mediated phosphoproteome-wide alterations in [~] 450 proteins that are not shared with insulin. Interestingly, we find several unknown phosphorylation sites on proteins related to protein translation, mTOR pathway and, unexpectedly, muscle function in the Ism1 signaling network. Physiologically, Ism1 ablation in mice results in altered proteostasis, including lower muscle protein levels under fed and fasted conditions, reduced amino acid incorporation into proteins, and reduced phosphorylation of the key protein synthesis effectors Akt and downstream mTORC1 targets. As metabolic disorders such as diabetes are associated with accelerated loss of skeletal muscle protein content, these studies define a non-canonical mechanism by which this anti-diabetic circulating protein controls muscle biology.

cell biology↗

bioRxiv2022

Functional genomics analysis identifies impairment of HNF1B function as a cause of Mayer-Rokitansky-Küster-Hauser syndrome.

Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome is a congenital condition characterized by aplasia or hypoplasia of the uterus and vagina in women with a typical 46,XX karyotype. This condition can occur as type I when isolated or as type II when associated with extragenital anomalies including kidney and skeletal abnormalities. The genetic basis of MRKH syndrome remains unexplained and several candidate genes have been proposed to play a role in its etiology, including HNF1B, LHX1, and WNT4. Here, we conducted a genomic analysis of 13 women affected by MRKH syndrome, resulting in the identification of candidate genes, including several novel candidates. We focused on HNF1B for further investigation due to its known association with, but unknown etiological role in, MRKH syndrome. We ablated Hnf1b specifically in the epithelium of the Mullerian ducts in mice, and found that this caused hypoplastic development of both the epithelial and stromal compartments of the uterus, as well as kidney anomalies, closely mirroring the MRKH type II phenotype. Using single-cell RNA sequencing of uterine tissue in the Hnf1b-ablated embryos, we analyzed the molecules and pathways downstream of Hnf1b, revealing a dysregulation of processes associated with cell proliferation, migration, and differentiation. Thus, we establish that loss of Hnf1b function leads to an MRKH phenotype, and generate the first mouse model of MRKH syndrome type II. Our results support the diagnostic value of HNF1B in clinical genetic testing for MRKH syndrome, and shed new light on the genetic causes of this poorly understood condition in womens reproductive health.

developmental biology↗