Biology subjects

Quang, D.

Publications and source records attributed to Quang, D..

8 recordsLinked to original sources

bioRxiv2026

High-content imaging of live microglia-containing Alzheimer's disease brain organoids

Alzheimer's disease (AD) is the most common progressive neurodegenerative disease for which there is currently no safe and effective treatment. AD is characterized by the pathological aggregation of amyloid-{beta} (A{beta}) in the brain resulting in neuronal dysfunction/death and cognitive decline. Modeling these processes in vitro remains challenging due to the lack of an immune-competent 3D in vitro human model of AD amenable to high-throughput analyses. Conventional cerebral organoids (COs) lack microglia and are optically dense which precludes real-time longitudinal analyses. Here, we employ the fluorescent amyloid probe PyrPeg to selectively monitor A{beta} aggregation, and calcein AM and propidium iodide to assess cellular viability within microglia-containing COs (MCOs). We identify age-dependent DS-AD-associated phenotypes within T21 MCOs including A{beta} neuropathology, neurodegeneration, and glial cell dysregulation. Our platform supports real-time, multiplexed tracking of disease progression and provides a scalable platform for longitudinal analyses studying disease mechanisms and targeted approaches to treatment.

neuroscience↗

bioRxiv2026

Trisomy 21 cerebral organoids exhibit Alzheimer's disease amyloid and apolipoprotein E co-pathologies

Adults with Down syndrome (DS) develop Alzheimers disease (AD) brain pathology by age 40 due to triplication of the Amyloid Precursor Protein (APP) gene on chromosome 21. Inheritance of the apolipoprotein E-e4 (APOE4) allele of the APOE gene on chromosome 19 remains the greatest genetic risk factor for AD in the typical population, yet its role in DS-associated AD (DS-AD) neuropathogenesis in people with DS is unclear. We generated human induced pluripotent stem cell (hiPSC)-derived neurons, astrocytes, and cerebral organoids (COs) using cells from people with DS and from euploid individuals. Aged DS COs were smaller than aged euploid COs and showed robust amyloid-{beta} neuropathology that was positively correlated with the levels of apoE expression. We then captured extracellular vesicles (EVs) from the conditioned media of COs and observed a decrease in the levels of secreted AD-related proteins, including amyloid, contained within the EVs and in the media from which the EVs were isolated. We also identified distinct neuronal and astrocytic gene expression signatures in DS COs relative to euploid COs, including a set of genes known to interact with both APOE and APP at the gene and/or protein levels. Lastly, we determined that, despite differences in the expression levels of the specific genes involved, several common pathways were upregulated in T21 hiPSC-derived neurons, astrocytes, and COs, including apoptosis, the endolysosome, and structural stabilization pathways. Taken together, our findings provide novel insights into molecular mechanisms that may contribute to DS-AD and indicate that apoE plays an important role in the disease process.

neuroscience↗

bioRxiv2023

Altered Metabolism and DAM-signatures in Female Brains and Microglia with Aging

Despite Alzheimers disease (AD) disproportionately affecting women, the mechanisms remain elusive. In AD, microglia undergo metabolic reprogramming, which contributes to microglial dysfunction and AD pathology. However, how sex and age contribute to metabolic reprogramming in microglia is understudied. Here, we use metabolic imaging, transcriptomics, and metabolic assays to probe age-and sex-associated changes in brain and microglial metabolism. Glycolytic and oxidative metabolism in the whole brain was determined using Fluorescence Lifetime Imaging Microscopy (FLIM). Young female brains appeared less glycolytic than male brains, but with aging, the female brain became male-like. Transcriptomic analysis revealed increased expression of disease-associated microglia (DAM) genes (e.g., ApoE, Trem2, LPL), and genes involved in glycolysis and oxidative metabolism in microglia from aged females compared to males. To determine whether estrogen can alter the expression of these genes, BV-2 microglia-like cell lines, which abundantly express DAM genes, were supplemented with 17{beta}-estradiol (E2). E2 supplementation resulted in reduced expression of DAM genes, reduced lipid and cholesterol transport, and substrate-dependent changes in glycolysis and oxidative metabolism. Consistent with the notion that E2 may suppress DAM-associated factors, LPL activity was elevated in the brains of aged female mice. Similarly, DAM gene and protein expression was higher in monocyte-derived microglia-like (MDMi) cells derived from middle-aged females compared to age-matched males and was responsive to E2 supplementation. FLIM analysis of MDMi from young and middle-aged females revealed reduced oxidative metabolism and FAD+ with age. Overall, our findings show that altered metabolism defines age-associated changes in female microglia and suggest that estrogen may inhibit the expression and activity of DAM-associated factors, which may contribute to increased AD risk, especially in post-menopausal women.

neuroscience↗

bioRxiv2023

Cell specificity of adeno-associated virus (AAV) serotypes in human cortical organoids

Human-derived cortical organoids (hCOs) recapitulate cell diversity and 3D structure found in the human brain and offer a promising model for discovery of new gene therapies targeting neurological disorders. Adeno-associated viruses (AAVs) are the most promising vehicles for non-invasive gene delivery to the central nervous system (CNS), but reliable and reproducible in vitro models to assess their clinical potential are lacking. hCOs can take on these issues as they are a physiologically relevant model to assess AAV transduction efficiency, cellular tropism, and biodistribution within the tissue parenchyma, all of which could significantly modulate therapeutic efficacy. Here, we examine a variety of naturally occurring AAV serotypes and measure their ability to transduce neurons and glia in hCOs from multiple donors. We demonstrate cell tropism driven by AAV serotype and hCO donor and quantify fractions of neurons and astrocytes transduced with GFP as well as overall hCO health.

neuroscience↗

bioRxiv2020

Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease

Cerebral organoids provide unparalleled access to human brain development in vitro. However, variability induced by current culture methodologies precludes using organoids as robust disease models. To address this, we developed an automated Organoid Culture and Assay (ORCA) system to support longitudinal unbiased phenotyping of organoids at scale across multiple patient lines. We then characterized organoid variability using novel machine learning methods and found that the contribution of donor, clone, and batch is significant and remarkably consistent over gene expression, morphology, and cell-type composition. Next, we performed multi-factorial protocol optimization, producing a directed forebrain protocol compatible with 96-well culture that exhibits low variability while preserving tissue complexity. Finally, we used ORCA to study tuberous sclerosis, a disease with known genetics but poorly representative animal models. For the first time, we report highly reproducible early morphological and molecular signatures of disease in heterozygous TSC+/- forebrain organoids, demonstrating the benefit of a scaled organoid system for phenotype discovery in human disease models.

neuroscience↗

bioRxiv2018

YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU

MotivationMotif discovery in large biopolymer sequence datasets can be computationally demanding, presenting significant challenges for discovery in omics research. MEME, arguably one of the most popular motif discovery software, takes quadratic time with respect to dataset size, leading to excessively long runtimes for large datasets. Therefore, there is a demand for fast programs that can generate results of the same quality as MEME.\n\nResultsHere we describe YAMDA, a highly scalable motif discovery software package. It is built on Pytorch, a tensor computation deep learning library with strong GPU acceleration that is highly optimized for tensor operations that are also useful for motifs. YAMDA takes linear time to find motifs as accurately as MEME, completing in seconds or minutes, which translates to speedups over a thousandfold.\n\nAvailabilityYAMDA is freely available on Github (https://github.com/daquang/YAMDA)\n\nContactdaquang@umich.edu

bioinformatics↗

bioRxiv2017

FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all TF/cell type pairs is not experimentally feasible, owing to constraints in time and resources. To address this issue, we developed a convolutional-recurrent neural network model, called FactorNet, to computationally impute the missing binding data. FactorNet trains on binding data from reference cell types to make accurate predictions on testing cell types by leveraging a variety of features, including genomic sequences, genome annotations, gene expression, and single-nucleotide resolution sequential signals, such as DNase I cleavage. To the best of our knowledge, this is the first deep learning method to study the rules governing TF binding at such a fine resolution. With FactorNet, a researcher can perform a single sequencing assay, such as DNase-seq, on a cell type and computationally impute dozens of TF binding profiles. This is an integral step for reconstructing the complex networks underlying gene regulation. While neural networks can be computationally expensive to train, we introduce several novel strategies to significantly reduce the overhead. By visualizing the neural network models, we can interpret how the model predicts binding which in turn reveals additional insights into regulatory grammar. We also investigate the variables that affect cross-cell type predictive performance to explain why the model performs better on some TF/cell types than others, and offer insights to improve upon this field. Our method ranked among the top four teams in the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge.

genomics↗

bioRxiv2017

Understanding sequence conservation with deep learning

MotivationComparing the human genome to the genomes of closely related mammalian species has been a powerful tool for discovering functional elements in the human genome. Millions of conserved elements have been discovered. However, understanding the functional roles of these elements still remain a challenge, especially in noncoding regions. In particular, it is still unclear why these elements are evolutionarily conserved and what kind of functional elements are encoded within these sequences.\n\nResultsWe present a deep learning framework, called DeepCons, to uncover potential functional elements within conserved sequences. DeepCons is a convolutional neural net (CNN) that receives a short segment of DNA sequence as input and outputs the probability of the sequence of being evolutionary conserved. DeepCons utilizes hundreds of convolution kernels to detect features within DNA sequences, and automatically learns these kernels after training the CNN model using 887,577 conserved elements and a similar number of nonconserved elements in the human genome. On a balanced test dataset, DeepCons can achieve an accuracy of 75% in determining whether a sequence element is conserved or not, and the area under the ROC curve of 0.83, based on information from the human genome alone. We further investigate the properties of the learned kernels. Some kernels are directly related to well-known regulatory motifs corresponding to transcription factors. Many kernels show positional biases relative to transcriptional start sites or transcription end sites. But most of discovered kernels do not correspond to any known functional element, suggesting that they might represent unknown categories of functional elements. We also utilize DeepCons to annotate how changes at each individual nucleotide might impact the conservation properties of the surrounding sequences.\n\nAvailabilityThe source code of DeepCons and all the learned convolution kernels in motif format is publicly available online at https://github.com/uci-cbcl/DeepCons.\n\nContactxhx@ics.uci.edu

bioinformatics↗