Biology subjects

Acri, D. J.

Publications and source records attributed to Acri, D. J..

9 recordsLinked to original sources

bioRxiv2026

Integration of cell-specific gene expression and chromatin accessibility facilitates localization of neurodegenerative risk in microglia

Genome-wide association studies (GWAS) have identified many loci that contribute to the risk of neurodegenerative diseases. However, a persistent challenge in interpretation of GWAS is to break loci down to specific genes, variants, and cell types, and thus nominate disease mechanisms. Here, we used iPSC-derived cells containing population-level variation to examine GWAS loci across NDDs including Alzheimer's disease, Parkinson's disease and Lewy body dementia. We differentiated a set of 135 iPSC donor lines into two cell types relevant to neurodegeneration, neurons and microglia, and completed single cell gene expression and chromatin accessibility profiling. Meta-analysis of these data with published human brain snRNAseq for QTL mapping identified multiple loci associated with NDDs that are restricted to either neurons or microglia. Colocalization of GWAS and these QTL supports microglia as having a strong contribution to disease risk. We tested peaks nominated at the BIN1 locus for enhancer activity using a perturb-seq-based method in microglia. Our results show one of the nominated peaks controls BIN1 expression in microglia but also modifies expression of other genes at the locus. These results support the hypothesis that common variants affecting gene expression specifically in microglia can contribute directly to NDD risk rather than functioning solely as a secondary response to neurodegeneration. These data also show that iPSC-derived cells are a useful model to experimentally dissect GWAS loci that colocalize with QTL.

genomics↗

bioRxiv2026

Semi-automated annotation refinement accelerates cell type identification in brain spatial and single-cell studies

Backgroundsingle-cell and spatial omic techniques have enabled the investigation of cell type specific alterations in biologically complex tissues. In an effort to map cell taxonomies, large atlas-based studies and multi-laboratory consortia have created sets of annotated cell types. However, application of atlas- or database-level knowledge to individual studies is often resource-limited and computational demands scale with the size of both query and reference datasets. ResultsHere, we report a statistical framework for rapid label transfer using summary statistics and user-defined hyperparameters. Semi-Automated Hand Annotation (SAHA)1 allows the user to investigate magnitude, directionality, and statistical significance of matches between unnamed query clusters and reference cell types using either marker-based or marker-free methodologies. By pre-loading the package with summary statistics from the Allen Brain Cell Atlas of the mouse brain, the SAHA R package is capable of rapid cell type comparisons that closely mimic cell typing by integration-based annotation strategies. Furthermore, this flexible package is capable of comparisons across omic modalities, cluster resolutions, and annotations from any study where summary statistics are available. We demonstrate this flexibility by using multiple single-nuclei studies of the mouse cerebellum, mouse cerebral cortex, human cerebral cortex, human peripheral blood mononuclear cells, and one mouse spatial transcriptomic assay. Importantly, this method avoids privacy concerns as it does not require the sharing or deposition of raw data in a web-based tool. ConclusionsAs a result, SAHA offers a non-deterministic annotation reporting structure with automated html reports and summary statistics for transparency in cell typing decisions. Taken together, this scalable framework implemented as a package in R affords increased biological insight into the annotation of single-cell and spatial datasets. SHORT SUMMARYAcri and colleagues present rapid cell type annotation without the need for dataset integration. This paper outlines the utility of the package, SAHA, in annotating neurological datasets.

bioinformatics↗

bioRxiv2025

Single cell RNA sequencing reveals limited effects of Lrrk2 genotype in a mouse model of acute CNS inflammation

BACKGROUNDPrevious data implicates neuroinflammation in the pathogenesis of Parkinsons disease (PD). Of the genes associated with PD, Leucine rich repeat kinase 2 (LRRK2) has previously been proposed to play a role in neuroinflammation. However, the extent to which LRRK2 is involved in endogenous inflammatory signaling is unclear. OBJECTIVETo examine whether endogenous mutations in LRRK2 affect responses to neuroinflammation in vivo. METHODSWe injected cohorts of mice with knock-in mutations in Lrrk2, homologous to those causing human PD, with a single intrastriatal injection of lipopolysaccharide (LPS) or control. We used single cell RNA-Sequencing to examine cell type specific responses to treatment and genotype and validated key results with orthogonal approaches. RESULTSWe found that our chosen paradigm of acute LPS exposure evokes robust transcriptional changes consistent with a multicellular neuroinflammatory response. We also found evidence of peripheral immune cell recruitment into the brain and interaction with brain-resident microglia. However, the transcriptional effects of Lrrk2 mutations were limited to small numbers of genes, including down regulation of gene ontogeny terms related to lysosomes, in microglia. CONCLUSIONSOur data clearly demonstrate that many cells in the brain respond to a single inflammatory insult with strong transcriptional responses and that, even in a model focused on CNS injection, there is interaction between peripheral and central immune cells. In contrast, the quantitative effects of Lrrk2 mutations are modest at the transcriptional level, demonstrating that additional studies are needed to clarify whether Lrrk2 mutations affect neuroinflammation in an endogenous context.

neuroscience↗

bioRxiv2025

Integrative analysis reveals generalizable human neurodegenerative disease-associated glial states

Glial cells are known to respond transcriptionally in multiple neurodegenerative diseases (NDDs). In particular, microglial states have been characterized in Alzheimers disease and mouse models of amyloidosis as disease-associated microglia. Although single-cell transcriptomic technologies have increased the dimensionality of information available across cell states, few studies have systematically tested for changes in glial transcription across brain regions and disease states. Here, we report a statistical framework for glial annotation, disease association, and transcriptional profiling, which facilitate identification of generalizable glial states that are present across a spectrum of NDDs (Alzheimers disease, Parkinsons disease, amyotrophic lateral sclerosis, and frontotemporal dementia) by re-analyzing data available in four multi-region atlases. We identify seven astrocyte substates, 14 microglia/myeloid substates, and five oligodendrocyte substates where transcriptional variability is attributable to region, disease, or study-specific effects. Regional heterogeneity of astrocytes masked disease associations, even within cortical subregions. We found only limited oligodendrocyte transcriptional heterogeneity, resulting in few substates for further interrogation. Notably, microglia showed the strongest evidence for disease association. We show, for the first time, that this association exists across different NDDs. Using latent factor analysis, we created a consensus human neurodegenerative disease-associated microglia (hnDAM) signature, which we experimentally validated in 11 independent sample series. We demonstrate that the hnDAM signature is a statistically testable biomarker for conserved microglial activation in NDDs by: i) comparing to murine DAM-like signatures, ii) performing transcription factor analysis, and iii) modeling transcriptional reprogramming perturbations in iPSC-derived microglia. Importantly, we find for the first time a way to make direct comparisons between DAM-like activation profiles in separate studies and propose a novel modeling paradigm via PIKfyve inhibition. Taken together, this work broadens our understanding of glial activation across neuropathologies and reveals hnDAM as a putative therapeutic target that can be utilized in any transcriptomic study of patients suffering from NDDs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/678630v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1863095org.highwire.dtl.DTLVardef@dfa6c8org.highwire.dtl.DTLVardef@13e95f1org.highwire.dtl.DTLVardef@1e6165e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing

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. MOTIVATIONSingle-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.

neuroscience↗

bioRxiv2024

Single-cell spatial transcriptomics reveals molecular patterns of selective neuronal vulnerability to α-synuclein pathology in a transgenic mouse model of Lewy body disease

In Parkinsons disease and dementia with Lewy bodies, aggregated and phosphorylated -synuclein pathology appears in select neurons throughout cortical and subcortical regions, but little is currently known about why certain populations are selectively vulnerable. Here, using imaging spatial transcriptomics (IST) coupled with downstream immunofluorescence for -synuclein phosphorylated at Ser129 (pSyn) in the same tissue sections, we identified neuronal subtypes in the cortex and hippocampus of transgenic human -synuclein-overexpressing mice that preferentially developed pSyn pathology. Additionally, we investigated the transcriptional underpinnings of this vulnerability, pointing to expression of Plk2, which phosphorylates -synuclein at Ser129, and human SNCA (hSNCA), as key to pSyn pathology development. Finally, we performed differential expression analysis, revealing gene expression changes broadly downstream of hSNCA overexpression, as well as pSyn-dependent alterations in mitochondrial and endolysosomal genes. Overall, this study yields new insights into the formation of -synuclein pathology and its downstream effects in a synucleinopathy mouse model.

neuroscience↗

bioRxiv2024

Robust single nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity

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 biological processes involved in adipose tissue remodeling during obesity, with broader applicability across diverse biological systems.

molecular biology↗

bioRxiv2023

Network analysis reveals strain-dependent response to misfolded tau aggregates

Mouse genetic backgrounds have been shown to modulate amyloid accumulation and propagation of tau aggregates. Previous research into these effects has highlighted the importance of studying the impact of genetic heterogeneity on modeling Alzheimers disease. However, it is unknown what mechanisms underly these effects of genetic background on modeling Alzheimers disease, specifically tau aggregate-driven pathogenicity. In this study, we induced tau aggregation in wild-derived mice by expressing MAPT (P301L). To investigate the effect of genetic background on the action of tau aggregates, we performed RNA sequencing with brains of 6-month-old C57BL/6J, CAST/EiJ, PWK/PhJ, and WSB/EiJ mice (n=64). We also measured tau seeding activity in the cortex of these mice. We identified three gene signatures: core transcriptional signature, unique signature for each wild-derived genetic background, and tau seeding-associated signature. Our data suggest that microglial response to tau seeds is elevated in CAST/EiJ and PWK/PhJ mice. Together, our study provides the first evidence that mouse genetic context influences the seeding of tau. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/526029v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1c99217org.highwire.dtl.DTLVardef@965522org.highwire.dtl.DTLVardef@1af4105org.highwire.dtl.DTLVardef@d5ea98_HPS_FORMAT_FIGEXP M_FIG C_FIG SUMMARYSeeding of tau predates the phosphorylation and spreading of tau aggregates. Acri and colleagues report transcriptomic responses to tau and elevated tau seeds in wild-derived mice. This paper creates a rich resource by combining genetics, tau biosensor assays, and transcriptomics.

neuroscience↗

bioRxiv2020

Identification of genetic variants regulating the abundance of clinically relevant plasma proteins using the Diversity Outbred mouse model

Although there have been numerous expression quantitative trait loci (eQTL) studies, the effect of genetic variants on the levels of multiple plasma proteins still warrants more systematic investigation. To identify genetic modifiers that influence the levels of clinically relevant plasma proteins, we performed protein quantitative trait locus (pQTL) mapping on 92 proteins using the Diversity Outbred (DO) mouse population and identified 12 significant cis and 6 trans pQTL. Among them, we discovered coding variants in a cis-pQTL in Ahr and a trans-pQTL in Rfx1 for the IL-17A protein. Our study reports an innovative pipeline for the identification of genetic modifiers that may be targeted for drug development. Author SummaryBlood plasma is a body fluid that can be collected in a noninvasive way to detect diseases, such as autoimmune disease. However, it is known that plasma protein levels are affected by both the environment and genetic background. To determine the effect of genetics on plasma protein levels in human, one needs a rather large sample size. To overcome this critical issue, a mouse model, the Diversity Outbred (DO), was established that is genetically as diverse as the human population. In this study, we used N=140 DO mice and genotyped over 140,000 variants. In addition, we measured the levels of 92 proteins in plasma of these DO mice using Olink Proteomics technology. The proteins detected in this panel are known to be detectable in human plasma, making our study translatable to human. We identified 18 significant protein quantitative trait loci. Furthermore, we describe an analysis pipeline that allows for the detection of a single gene in the locus that is responsible for the differences in protein levels. We identified how variants in the Regulatory Factor X1 (Rfx1) gene regulates Interleukin-17A (IL-17A) plasma levels. Our study reports an innovative approach to identify genetic modifiers that may be targeted for drug development.

genetics↗