Browsing by Author "Acri, Dominic J."

Now showing 1 - 7 of 7
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    A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer’s disease risk and rates of disease progression
    (Wiley, 2023) Bae, Jinhyeong; Logan, Paige E.; Acri, Dominic J.; Bharthur, Apoorva; Nho, Kwangsik; Saykin, Andrew J.; Risacher, Shannon L.; Nudelman, Kelly; Polsinelli, Angelina J.; Pentchev, Valentin; Kim, Jungsu; Hammers, Dustin B.; Apostolova, Liana G.; Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of Medicine
    Background: Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies. Methods: We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. Results: Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression. Discussion: The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.
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    Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing
    (bioRxiv, 2025-03-26) Kersey, Holly N.; Acri, Dominic J.; Dabin, Luke C.; Hartigan, Kelly; Mustaklem, Richard; Park, Jung Hyun; Kim, Jungsu; Medical and Molecular Genetics, School of Medicine
    Single-nucleus RNA sequencing (snRNA-seq) enables resolving cellular heterogeneity in complex tissues. snRNA-seq overcomes limitations of traditional single-cell RNA-seq by using nuclei instead of cells, allowing to utilize frozen tissues and difficult-to-isolate cell types. Although various nuclei isolation methods have been developed, systematic evaluations of their effects on nuclear integrity and subsequent data quality remain lacking, a critical gap with profound implications for the rigor and reproducibility. To address this, we compared three mechanistically distinct nuclei isolation strategies with brain tissues: a sucrose gradient centrifugation-based method, a spin column-based method, and a machine-assisted platform. All methods successfully captured diverse cell types but revealed considerable protocol-dependent differences in cell type proportions, transcriptional homogeneity, and the preservation of cell-type-specific and cell-state-specific markers. Moreover, isolation workflows differentially influenced contamination levels from ambient, mitochondrial, and ribosomal RNAs. Our findings establish nuclei isolation methodology as a critical experimental variable shaping snRNA-seq data quality and biological interpretation. Motivation: Single-nucleus RNA sequencing (snRNA-seq) has become an essential tool for transcriptomic analysis of complex tissues. However, the quality and efficiency of data generation depend heavily on the method used for nuclear isolation. The existing isolation techniques vary in their ability to preserve nuclear integrity, minimize ambient RNA contamination, and optimize recovery rates. Despite these differences in quality, a systematic comparison of these methods, specifically for brain tissue, is lacking. This gap poses a challenge for researchers in choosing the most suitable approach for their particular experimental requirements. To address this critical issue, our study directly compared three nuclei isolation methods and evaluated their performance in terms of yield, purity, and downstream sequencing quality. By providing a comprehensive assessment, we aim to guide researchers in selecting the most appropriate isolation protocol for their snRNA-seq experiments, ensuring optimal results and advancing the study of complex brain tissues at the single-nucleus level.
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    Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis
    (Springer Nature, 2024-05-11) Kim, Byungwook; Dabin, Luke Child; Tate, Mason Douglas; Karahan, Hande; Sharify, Ahmad Daniel; Acri, Dominic J.; Al-Amin, Md Mamun; Philtjens, Stéphanie; Smith, Daniel Curtis; Wijeratne, H. R. Sagara; Park, Jung Hyun; Jucker, Mathias; Kim, Jungsu; Medical and Molecular Genetics, School of Medicine
    SPI1 was recently reported as a genetic risk factor for Alzheimer's disease (AD) in large-scale genome-wide association studies. However, it is unknown whether SPI1 should be downregulated or increased to have therapeutic benefits. To investigate the effect of modulating SPI1 levels on AD pathogenesis, we performed extensive biochemical, histological, and transcriptomic analyses using both Spi1-knockdown and Spi1-overexpression mouse models. Here, we show that the knockdown of Spi1 expression significantly exacerbates insoluble amyloid-β (Aβ) levels, amyloid plaque deposition, and gliosis. Conversely, overexpression of Spi1 significantly ameliorates these phenotypes and dystrophic neurites. Further mechanistic studies using targeted and single-cell transcriptomics approaches demonstrate that altered Spi1 expression modulates several pathways, such as immune response pathways and complement system. Our data suggest that transcriptional reprogramming by targeting transcription factors, like Spi1, might hold promise as a therapeutic strategy. This approach could potentially expand the current landscape of druggable targets for AD.
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    Loss of Inpp5d has disease‐relevant and sex‐specific effects on glial transcriptomes
    (Wiley, 2024) Dabin, Luke C.; Kersey, Holly; Kim, Byungwook; Acri, Dominic J.; Sharify, Daniel; Lee-Gosselin, Audrey; Lasagna-Reeves, Cristian A.; Oblak, Adrian L.; Lamb, Bruce T.; Kim, Jungsu; Medical and Molecular Genetics, School of Medicine
    Introduction: Inpp5d is genetically associated with Alzheimer's disease risk. Loss of Inpp5d alters amyloid pathology in models of amyloidosis. Inpp5d is expressed predominantly in microglia but its function in brain is poorly understood. Methods: We performed single-cell RNA sequencing to study the effect of Inpp5d loss on wild-type mouse brain transcriptomes. Results: Loss of Inpp5d has sex-specific effects on the brain transcriptome. Affected genes are enriched for multiple neurodegeneration terms. Network analyses reveal a gene co-expression module centered around Inpp5d in female mice. Inpp5d loss alters Pleotrophin (PTN), Prosaposin (PSAP), and Vascular Endothelial Growth Factor A (VEGFA) signaling probability between cell types. Discussion: Our data suggest that the normal function of Inpp5d is entangled with mechanisms involved in neurodegeneration. We report the effect of Inpp5d loss without pathology and show that this has dramatic effects on gene expression. Our study provides a critical reference for researchers of neurodegeneration, allowing separation of disease-specific changes mediated by Inpp5d in disease from baseline effects of Inpp5d loss. Highlights: Loss of Inpp5d has different effects in male and female mice. Genes dysregulated by Inpp5d loss relate to neurodegeneration. Total loss of Inpp5d in female mice collapses a conserved gene co-expression module. Loss of microglial Inpp5d affects the transcriptome of other cell types.
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    Network analysis identifies strain-dependent response to tau and tau seeding-associated genes
    (Rockefeller University Press, 2023) Acri, Dominic J.; You, Yanwen; Tate, Mason D.; Karahan, Hande; Martinez, Pablo; McCord, Brianne; Sharify, A. Daniel; John, Sutha; Kim, Byungwook; Dabin, Luke C.; Philtjens, Stéphanie; Wijeratne, H. R. Sagara; McCray, Tyler J.; Smith, Daniel C.; Bissel, Stephanie J.; Lamb, Bruce T.; Lasagna-Reeves, Cristian A.; Kim, Jungsu; Anatomy, Cell Biology and Physiology, School of Medicine
    Previous research demonstrated that genetic heterogeneity is a critical factor in modeling amyloid accumulation and other Alzheimer's disease phenotypes. However, it is unknown what mechanisms underlie these effects of genetic background on modeling tau aggregate-driven pathogenicity. In this study, we induced tau aggregation in wild-derived mice by expressing MAPT. To investigate the effect of genetic background on the action of tau aggregates, we performed RNA sequencing with brains of C57BL/6J, CAST/EiJ, PWK/PhJ, and WSB/EiJ mice (n = 64) and determined core transcriptional signature conserved in all genetic backgrounds and signature unique to wild-derived backgrounds. By measuring tau seeding activity using the cortex, we identified 19 key genes associated with tau seeding and amyloid response. Interestingly, microglial pathways were strongly associated with tau seeding activity in CAST/EiJ and PWK/PhJ backgrounds. Collectively, our study demonstrates that mouse genetic context affects tau-mediated alteration of transcriptome and tau seeding. The gene modules associated with tau seeding provide an important resource to better model tauopathy.
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    Robust single nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity
    (bioRxiv, 2024-04-08) So, Jisun; Strobel, Olivia; Wann, Jamie; Kim, Kyungchan; Paul, Avishek; Acri, Dominic J.; Dabin, Luke C.; Kim, Jungsu; Roh, Hyun Cheol; Biochemistry and Molecular Biology, School of Medicine
    Single nucleus RNA sequencing (snRNA-seq), an alternative to single cell RNA sequencing (scRNA-seq), encounters technical challenges in obtaining high-quality nuclei and RNA, persistently hindering its applications. Here, we present a robust technique for isolating nuclei across various tissue types, remarkably enhancing snRNA-seq data quality. Employing this approach, we comprehensively characterize the depot-dependent cellular dynamics of various cell types underlying adipose tissue remodeling during obesity. By integrating bulk nuclear RNA-seq from adipocyte nuclei of different sizes, we identify distinct adipocyte subpopulations categorized by size and functionality. These subpopulations follow two divergent trajectories, adaptive and pathological, with their prevalence varying by depot. Specifically, we identify a key molecular feature of dysfunctional hypertrophic adipocytes, a global shutdown in gene expression, along with elevated stress and inflammatory responses. Furthermore, our differential gene expression analysis reveals distinct contributions of adipocyte subpopulations to the overall pathophysiology of adipose tissue. Our study establishes a robust snRNA-seq method, providing novel insights into the mechanisms orchestrating adipose tissue remodeling during obesity, with broader applicability across diverse biological systems.
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    Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity
    (eLife Sciences, 2025-01-13) So, Jisun; Strobel, Olivia; Wann, Jamie; Kim, Kyungchan; Paul, Avishek; Acri, Dominic J.; Dabin, Luke C.; Kim, Jungsu; Peng, Gang; Roh, Hyun Cheol; Biochemistry and Molecular Biology, School of Medicine
    Single-nucleus RNA sequencing (snRNA-seq), an alternative to single-cell RNA sequencing (scRNA-seq), encounters technical challenges in obtaining high-quality nuclei and RNA, persistently hindering its applications. Here, we present a robust technique for isolating nuclei across various tissue types, remarkably enhancing snRNA-seq data quality. Employing this approach, we comprehensively characterize the depot-dependent cellular dynamics of various cell types underlying mouse adipose tissue remodeling during obesity. By integrating bulk nuclear RNA-seq from adipocyte nuclei of different sizes, we identify distinct adipocyte subpopulations categorized by size and functionality. These subpopulations follow two divergent trajectories, adaptive and pathological, with their prevalence varying by depot. Specifically, we identify a key molecular feature of dysfunctional hypertrophic adipocytes, a global shutdown in gene expression, along with elevated stress and inflammatory responses. Furthermore, our differential gene expression analysis reveals distinct contributions of adipocyte subpopulations to the overall pathophysiology of adipose tissue. Our study establishes a robust snRNA-seq method, providing novel insights into the biological processes involved in adipose tissue remodeling during obesity, with broader applicability across diverse biological systems.