Browsing by Author "Chung, Dongjun"

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    Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning
    (Elsevier, 2022-08-24) Chang, Yuzhou; He, Fei; Wang, Juexin; Chen, Shuo; Li, Jingyi; Liu, Jixin; Yu, Yang; Su, Li; Ma, Anjun; Allen, Carter; Lin, Yu; Sun, Shaoli; Liu, Bingqiang; Otero, José Javier; Chung, Dongjun; Fu, Hongjun; Li, Zihai; Xu, Dong; Ma, Qin; Medical and Molecular Genetics, School of Medicine
    Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.
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    Gene Expression Differences Between Young Adults Based on Trauma History and Post-traumatic Stress Disorder
    (Frontiers Media, 2021-04-08) Bountress, Kaitlin E.; Vladimirov, Vladimir; McMichael, Gowon; Taylor, Z. Nathan; Hardiman, Gary; Chung, Dongjun; Adams, Zachary W.; Kmett Danielson, Carla; Amstadter, Ananda B.; Psychiatry, School of Medicine
    Background: The purpose of this study was to identify gene expression differences associated with post-traumatic stress disorder (PTSD) and trauma exposure (TE) in a three-group study design comprised of those with and without trauma exposure and PTSD. Methods: We conducted gene expression and gene network analyses in a sample (n = 45) composed of female subjects of European Ancestry (EA) with PTSD, TE without PTSD, and controls. Results: We identified 283 genes differentially expressed between PTSD-TE groups. In an independent sample of Veterans (n = 78) a small minority of these genes were also differentially expressed. We identified 7 gene network modules significantly associated with PTSD and TE (Bonferroni corrected p ≤ 0.05), which at a false discovery rate (FDR) of q ≤ 0.2, were significantly enriched for biological pathways involved in focal adhesion, neuroactive ligand receptor interaction, and immune related processes among others. Conclusions: This study uses gene network analyses to identify significant gene modules associated with PTSD, TE, and controls. On an individual gene level, we identified a large number of differentially expressed genes between PTSD-TE groups, a minority of which were also differentially expressed in the independent sample. We also demonstrate a lack of network module preservation between PTSD and TE, suggesting that the molecular signature of PTSD and trauma are likely independent of each other. Our results provide a basis for the identification of likely disease pathways and biomarkers involved in the etiology of PTSD.
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    IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis
    (Oxford University Press, 2021) Chang, Yuzhou; Allen, Carter; Wan, Changlin; Chung, Dongjun; Zhang, Chi; Li, Zihai; Ma, Qin; Medical and Molecular Genetics, School of Medicine
    Summary: Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline. Availability and implementation: IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM.
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    Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer’s Disease
    (medRxiv, 2025-01-09) Liu, Shiwei; Cho, Min Young; Huang, Yen-Ning; Park, Tamina; Chaudhuri, Soumilee; Jacobson Rosewood, Thea; Bice, Paula J.; Chung, Dongjun; Bennett, David A.; Ertekin-Taner, Nilüfer; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
    Background: Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) is important for identifying potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but they lack a systematic analysis across various Alzheimer's disease (AD) GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type-specific eQTL datasets. In this study, we integrated brain cell type-specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level. Methods: To prioritize disease-causing genes, we used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with cell-type-specific eQTLs. Combining data from five AD GWAS, three single-cell eQTL datasets, and one bulk tissue eQTL meta-analysis, we identified and confirmed both novel and known candidate causal genes. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction and pathway enrichment analyses, and conducted a drug/compound enrichment analysis with the Drug Signatures Database (DSigDB) to support drug repurposing for AD. Results: We identified 27 candidate causal genes for AD using cell type-specific eQTL datasets, with the highest numbers in microglia, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel astrocyte-specific gene. Our analysis revealed protein-protein interaction (PPI) networks for these causal genes in microglia and astrocytes. We found the "regulation of aspartic-type peptidase activity" pathway being the most enriched among all the causal genes. AD-risk variants associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD. Conclusions: We systematically prioritized AD candidate causal genes based on cell type-specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates the interpretation of AD GWAS results.