Biology subjects

Simmons, S. K.

Publications and source records attributed to Simmons, S. K..

At least 19 recordsLinked to original sources

bioRxiv2026

Single cell eQTL mapping reveals convergent glial-neuronal risk architecture in Parkinson's disease

Synucleinopathies affect [~]15 million people and are classically divided into neuronal (Parkinsons disease (PD), dementia with Lewy bodies) and glial (multiple system atrophy) disorders. Here we challenge this dichotomy. We functionally fine-map 90 PD GWAS signals across nine cell types in cortex and substantia nigra using disease-context, population-scale single-nucleus eQTL meta-analysis (N = 1,197), bulk brain eQTL analysis (N = 1,182), and Mendelian randomization. A stringent causal framework integrates single-nucleus allelic imbalance (snASE) with orthogonal validation. We identify 125 functional risk genes for 50 loci--nearly doubling supported genes--and assign genes and cell types to over half of GWAS signals. Unexpectedly, 51% of risk genes are regulated in glia, particularly oligodendrocytes and their precursors. Across cell types, risk converges on a shared glial-neuronal vesiculopathy network. These findings uncover a convergent glial-neuronal risk architecture and establish a single-cell atlas for context-aware gene discovery and precision therapeutics for PD.

genetics↗

bioRxiv2026

Concordant transcriptional and morphological remodeling revealed by in vivo Perturb-CLEAR

The principle that form follows function has long guided thinking in biology and architecture alike. In the nervous system, however, form does more than reflect function: neuronal morphology actively constrains input patterns, synaptic integration, and circuit wiring. During postnatal neurodevelopment, dendritic architectures are assembled and remodeled through genetically encoded programs and activities, transforming molecular programs into circuit architecture. However, dendritic morphogenesis has been difficult to quantify at scale while systematically testing how genetic variants, including neurodevelopmental disorder (NDD) risk genes, alter these structures in vivo. We developed Perturb-CLEAR, which integrates pooled CRISPR screening and whole-mount imaging to quantify brain-wide cytoarchitecture, and paired it with Perturb-seq to link structural phenotypes to transcriptomic changes. Applying Perturb-CLEAR to the developing mouse cortex revealed morphogenesis trajectories accompanied by transcriptomic dynamics. Moreover, systematic perturbation of NDD risk genes uncovered gene-specific multimodal phenotypes. Adnp perturbation remodels basal dendrites in L4/5 IT (intratelencephalic) neurons but not other dendritic compartments or cell types, alongside consistent transcriptional shifts. Combined morphology and transcriptome analyses link NDD risk genes to concordant multimodal cellular phenotypes in the developing brain, highlighting diverse paths of perturbation effect propagation across modalities.

neuroscience↗

bioRxiv2026

Integrating Long-Read Structural Variant Analysis with single-nucleus RNA-seq to Elucidate Gene Expression Effects in Disease

Structural variants (SVs) are a major source of genetic diversity, yet how they impact cell types in complex brain diseases remains largely unexplored, partially due to limitations of short-read sequencing. Here, we addressed this fundamental question in Parkinsons disease (PD). generating long-read whole-genome sequencing (WGS) data for 100 post-mortem brain samples from a PD cohort, constructing a high-confidence catalog of 74,552 SVs. To resolve their functional impact, we integrated single-nucleus RNA-sequencing data from two brain regions from the same samples and focused functional analyses on SVs proximal to genes previously nominated as cis-regulated, potential causal targets of PD-associated GWAS loci. Using expression quantitative trait locus and allele-specific expression analyses, we uncovered SVs significantly associated with expression in specific cell types as well as effects shared across cell types. This study demonstrates the power of uniting long-read WGS with transcriptomics to uncover SVs underlying complex disease architecture with cell type resolution.

genomics↗

bioRxiv2026

In vivo multiomic Perturb-seq with enhanced nuclear gRNA capture

In vivo CRISPR screening with joint transcriptomic and chromatin readouts has been limited by inefficient recovery of gRNAs from nuclei. Here, we developed in vivo multiomic Perturb-seq, an effective platform combining nuclear transcript anchoring with gRNA-specific capture and amplification to enable high-fidelity, high-recovery gRNA assignment and scalable perturbation-resolved single-nucleus multiomics. Applying this platform to interrogate neurodevelopmental disorder risk genes in the developing cortex reveals cell-type-specific transcriptomic and epigenomic perturbation phenotypes.

genomics↗

bioRxiv2026

Disrupted Sleep-Dependent Neural Oscillations and Transcriptomic Changes in the Cacna1g Loss-of-Function Mouse Model Implicated in Schizophrenia

Sleep is an essential biological process for maintaining brain function, and impaired sleep microarchitecture is a well-established feature of schizophrenia (SCZ). Rare loss-of-function variants in CACNA1G, encoding the T-type calcium channel CaV3.1, confer substantial risk for SCZ, implicating sleep-regulatory circuits in disease pathophysiology. Here, we first show that among individuals with SCZ, carriers of rare CACNA1G missense variants exhibit more pronounced alterations in sleep neurophysiology than non-carriers, identifying a human phenotype suggestive of altered channel function. Motivated by this observation, we examined the consequences of Cacna1g loss of function in mice. Cacna1g deficiency produced profound disruptions in sleep microarchitecture, including reduced sleep spindles, altered slow oscillations, impaired spindle-slow oscillation coupling, and reduced cortical synchrony--closely recapitulating neurophysiological signatures observed in individuals with schizophrenia. Beyond sleep structure, Cacna1g loss disrupted the normal coordination of brain rhythms across the 24-hour cycle, most notably abolishing diurnal modulation of theta oscillations. At the molecular level, Cacna1g loss uncoupled sleep-wake state from gene and protein regulation, producing widespread, sleep-phase-biased transcriptional and synaptic alterations across brain regions and cell types, particularly in cortical excitatory neurons. These network and molecular disruptions were accompanied by behavioral hyperactivity during the active phase. Together, these findings establish CACNA1G/CaV3.1 as a key regulator linking sleep-dependent brain dynamics to molecular homeostasis and behavior, and provide a mechanistic framework through which genetic risk for SCZ may drive disease-relevant sleep and circuit dysfunction. One Sentence SummaryLoss of CACNA1G disrupts sleep rhythms and gene regulation, creating a mismatch between brain state and behavior linked to schizophrenia.

neuroscience↗

bioRxiv2025

GPR34 regulation of disease-associated microglial states and responses to physiological stimuli

Expression of the G protein coupled receptor GPR34 is highly enriched in microglia and has been reported to be downregulated in several brain disease contexts, including Alzheimers disease (AD) and multiple sclerosis (MS). GPR34 function is poorly understood, as is its role in regulation of microglial states. Using RNA-sequencing, we find that microglia from Gpr34 knockout (KO) mouse brains exhibited a transcriptomic shift toward disease-associated microglia (DAM) and inflammatory profiles, partially resembling the microglial phenotype seen in 5xFAD AD model mice. Moreover, when Gpr34 KO mice were crossed with 5xFAD mice, the DAM transcriptional profile of microglia and glial pathology were further enhanced beyond the already robust DAM signature driven by 5xFAD alone. This occurred without affecting amyloid plaque burden. Human stem cell-derived microglia (iMGLs) lacking GPR34 showed reduced calcium (Ca{superscript 2}) and phosphorylated ERK (pERK) signaling in response to stimulation with known GPR34 agonists (lyso-phosphatidylserine (lysoPS) and myelin), as well as transcriptomic changes in immune regulation and cell proliferation related pathways. Interestingly, GPR34 KO iMGLs were selectively impaired in phagocytosis of myelin but not amyloid-{beta} (A{beta}) or E. coli, and showed a diminished transcriptional response elicited by myelin. Together, these findings suggest that GPR34 is important for maintaining microglia in a homeostatic state, promotes phagocytosis of and transcriptional response to myelin, and limits microglial activation in neurodegenerative disease conditions.

neuroscience↗

bioRxiv2025

Abnormal Lipid Metabolism and Altered Neuronal Support by Astrocytes Lacking Akap11, a Risk Gene for Schizophrenia and Bipolar Disorder

A-Kinase Anchoring Protein 11 (AKAP11) is a shared genetic risk factor for schizophrenia and bipolar disorder, yet its role in the brain remains poorly understood. Through multi-omic analysis of Akap11 mutant mouse brains and cultured astrocytes, we identified significant transcriptomic, proteomic, and metabolomic alterations. Key findings include the upregulation of cholesterol and fatty acid metabolic pathways, accumulation of lipid species such as cholesteryl esters, triacylglycerols, ceramides, and glycerophospholipids, and elevated levels of 3,5-cyclic AMP and protein kinase A (PKA) signaling. These metabolic perturbations manifested as increased lipid droplet accumulation in Akap11 mutant astrocytes, highlighting AKAP11s critical role in regulating intracellular lipid homeostasis. Mechanistically, AKAP11 functions as an autophagy receptor mediating PKA degradation and interacts with endoplasmic reticulum-resident proteins VAP-A and VAP-B through its FFAT motif, providing possible molecular insight into AKAP11s regulation of lipid metabolism. Co-culture experiments with mouse astrocytes and human induced pluripotent stem cell-derived neurons demonstrated that loss of Akap11 in astrocytes, relative to wild-type, increases excitatory neurotransmission and neuronal activity. Collectively, these findings link AKAP11-mediated lipid and synaptic dysregulation to psychiatric disease risk and highlight the potential role of astrocytes in these disorders.

cell biology↗

bioRxiv2025

Meta-analysis of the brain transcriptomes of multiple genetic mouse models of schizophrenia highlights dysregulation in striatum and thalamus

Schizophrenia is a severe mental illness with high heritability, but its underlying mechanisms are poorly understood. We meta-analyzed large-scale brain transcriptomic data from mice harboring individual loss-of-function mutations in seven schizophrenia risk genes (Akap11, Dagla, Gria3, Grin2a, Sp4, Srrm2, Zmym2). While all studied brain regions were affected, the striatum and the thalamus emerged as key brain regions of convergence. Striatum showed downregulation of synapse-and oxidative phosphorylation-related gene sets in all models. In the thalamus, mutants separated into two groups based on transcriptomic phenotype: synapse-related gene sets were upregulated in mutants with only schizophrenia and bipolar association, and were downregulated in mutants that are associated with developmental delay/intellectual disability in addition to schizophrenia. Overall, our meta-analysis reveals convergence and divergence in brain transcriptomic phenotype in these schizophrenia genetic models, supports the involvement of striatal disturbance and synapse dysfunction in schizophrenia, and points to a key role of the thalamus.

neuroscience↗

bioRxiv2025

Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas.

The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. We generated a benchmarking dataset for the renal cortex by integrating 3 and 5 scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we developed the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assessed integration strategies. "Horizontal" integration of scRNA and snRNA-seq improved cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq had an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration was especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.

genomics↗

bioRxiv2024

Elevated synaptic PKA activity and abnormal striatal dopamine signaling in Akap11 mutant mice, a genetic model of schizophrenia and bipolar disorder

AbstractLoss-of-function mutations in AKAP11 (a protein kinase A (PKA)-binding protein) greatly increase the risk of bipolar disorder and schizophrenia. To determine the neurobiological functions of AKAP11, we conduct multi-omic and neurobiological analyses of Akap11 mutant mouse brains. We find that AKAP11 is a key regulator of PKA proteostasis in the brain whose loss leads to dramatically increased levels of PKA subunits and phosphorylated PKA substrates, especially in synapses. Akap11 mutant mice show extensive transcriptomic changes throughout the brain, including prominent decreases in synapse-related genes sets. Gene expression is highly impacted in spiny projection neurons of the striatum, a brain region implicated in motivation, cognition and psychotic disorders. Real-time measurements of PKA activity reveal elevated basal PKA activity in the striatum of Akap11-/- mice, with exaggerated additional response to dopamine receptor antagonists. Behaviorally, Akap11 mutant mice show abnormally prolonged locomotor response to amphetamine, deficits in associative learning and contextual discrimination, as well as depression-like behaviors. Our study connects molecular changes to circuit dysfunction and behavioral disturbance in a genetically valid animal model of psychotic disorder.

neuroscience↗

bioRxiv2024

Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinsons disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

genomics↗

bioRxiv2023

Massively parallel in vivo Perturb-seq reveals cell type-specific transcriptional networks in cortical development

Systematic analysis of gene function across diverse cell types in vivo is hindered by two challenges: obtaining sufficient cells from live tissues and accurately identifying each cells perturbation in high-throughput single-cell assays. Leveraging AAVs versatile cell type tropism and high labeling capacity, we expanded the resolution and scale of in vivo CRISPR screens: allowing phenotypic analysis at single-cell resolution across a multitude of cell types in the embryonic brain, adult brain, and peripheral nervous system. We undertook extensive tests of 86 AAV serotypes, combined with a transposon system, to substantially amplify labeling and accelerate in vivo gene delivery from weeks to days. Using this platform, we performed an in utero genetic screen as proof-of-principle and identified pleiotropic regulatory networks of Foxg1 in cortical development, including Layer 6 corticothalamic neurons where it tightly controls distinct networks essential for cell fate specification. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% (mediated by lentivirus), and achieve analysis of over 30,000 cells in one experiment, thus enabling massively parallel in vivo Perturb-seq. Compatible with various perturbation techniques (CRISPRa/i) and phenotypic measurements (single-cell or spatial multi-omics), our platform presents a flexible, modular approach to interrogate gene function across diverse cell types in vivo, connecting gene variants to their causal functions.

neuroscience↗

bioRxiv2022

Brain region-specific changes in neurons and glia and dysregulation of dopamine signaling in Grin2a mutant mice

Schizophrenia disease mechanisms remain poorly understood, in large part due to a lack of valid animal models. Rare heterozygous loss-of-function mutations in GRIN2A, encoding a subunit of the NMDA (N-methyl-d-aspartate) receptor, greatly increase the risk of schizophrenia. By transcriptomic, proteomic, electroencephalogram (EEG) recording and behavioral analysis, we report that heterozygous Grin2a mutant mice show: (i) large-scale gene expression changes across multiple brain regions and in neuronal (excitatory and inhibitory) and non-neuronal cells (astrocytes, oligodendrocytes); (ii) evidence of reduced activity in prefrontal cortex and increased activity in hippocampus and striatum; (iii) elevated dopamine signaling in striatum; (iv) altered cholesterol biosynthesis in astrocytes; (v) reduction of glutamatergic receptor signalin g proteins in the synapse; (iv) heightened gamma oscillation power in EEG; (vi) aberrant locomotor behavioral pattern opposite of that induced by antipsychotic drugs. These findings reveal potential pathophysiologic mechanisms, provide support for both the "hypo-glutamate" and "hyper-dopamine" hypotheses of schizophrenia, and underscore the utility of Grin2a-deficient mice as a new genetic model of schizophrenia.

neuroscience↗

bioRxiv2022

Single cell RNA-seq by mostly-natural sequencing by synthesis

Massively parallel single cell RNA-seq (scRNA-seq) for diverse applications, from cell atlases to functional screens, is increasingly limited by sequencing costs, and large-scale low-cost sequencing can open many additional applications, including patient diagnostics and drug screens. Here, we adapted and systematically benchmarked a newly developed, mostly-natural sequencing by synthesis method for scRNA-seq. We demonstrate successful application in four scRNA-seq case studies of different technical and biological types, including 5 and 3 scRNA-seq, human peripheral blood mononuclear cells from a single individual and in multiplex, as well as Perturb-Seq. Our data show comparable results to existing technology, including compatibility with state-of-the-art scRNA-seq libraries independent of the sequencing technology used - thus providing an enhanced cost-effective path for large scale scRNA-seq.

genomics↗

bioRxiv2022

Cell Type Composition Analysis: Comparison of statistical methods

Measuring changes in cell type composition between conditions (disease vs not, knockout vs wild type, treated vs not, etc) is fast becoming a standard step in single cell RNA-Seq analysis. Despite that, there is no agreement on the best approach for this type of analysis. As such, we decided to test numerous methods for cell type composition analysis, seeing how they performed in terms of false positive rate and power. Though there is not one clear winner, we do find two method (the propeller method with asin normalization and Dirichlet regression with the alternative parameterization) perform well in most situations. Most importantly, consistent with results in differential expression analysis, we see that it is important to take into account sample to sample (mouse to mouse, person to person, etc) variability to avoid high false positive rates. We also see evidence that aggregation (aka pseudobulk) based method slightly outperform the mixed model methods we tested.

bioinformatics↗

bioRxiv2022

Heterochronic parabiosis reprograms the mouse brain transcriptome by shifting aging signatures in multiple cell types

Aging is a complex process involving transcriptomic changes associated with deterioration across multiple tissues and organs, including the brain. Recent studies using heterochronic parabiosis have shown that various aspects of aging-associated decline are modifiable or even reversible. To better understand how this occurs, we performed single-cell transcriptomic profiling of young and old mouse brains following parabiosis. For each cell type, we catalogued alterations in gene expression, molecular pathways, transcriptional networks, ligand-receptor interactions, and senescence status. Our analyses identified gene signatures demonstrating that heterochronic parabiosis regulates several hallmarks of aging in a cell-type-specific manner. Brain endothelial cells were found to be especially malleable to this intervention, exhibiting dynamic transcriptional changes that affect vascular structure and function. These findings suggest novel strategies for slowing deterioration and driving regeneration in the aging brain through approaches that do not rely on disease-specific mechanisms or actions of individual circulating factors.

neuroscience↗

bioRxiv2020

Pluripotent stem cell derived models of neurological diseases reveal early transcriptional heterogeneity

BackgroundMany neurodegenerative diseases (NDs) develop only later in life, when cells in the nervous system lose their structure or function. In genetic forms of NDs, this late onset phenomenon remains largely unexplained. ResultsAnalyzing single cell RNA sequencing (scRNA-seq) from Alzheimers disease (AD) patients, we find increased transcriptional heterogeneity in AD excitatory neurons. We hypothesized that transcriptional heterogeneity precedes ND pathologies. To test this idea experimentally, we used juvenile forms (72Q; 180Q) of Huntingtons disease (HD) iPSCs, differentiated them into committed neuronal progenitors, and obtained single cell expression profiles. We show a global increase in gene expression variability in HD. Autophagy genes become more stable, while energy and actin-related genes become more variable in the mutant cells. Knocking-down several differentially-variable genes resulted in increased aggregate formation, a pathology associated with HD. We further validated the increased transcriptional heterogeneity in CHD8+/- cells, a model for autism spectrum disorder. ConclusionsOverall, our results suggest that although NDs develop over time, transcriptional regulation imbalance is present already at very early developmental stages. Therefore, an intervention aimed at this early phenotype may be of high diagnostic value.

molecular biology↗

bioRxiv2020

Human brain organoids reveal accelerated development of cortical neuron classes as a shared feature of autism risk genes

Genetic risk for autism spectrum disorder (ASD) has been associated with hundreds of genes spanning a wide range of biological functions. The phenotypic alterations in the human brain resulting from mutations in ASD risk genes remain unclear, and the level at which these alterations converge on shared disease pathology is poorly understood. Here, we leveraged reproducible organoid models of the human cerebral cortex to identify cell type-specific developmental abnormalities associated with haploinsufficiency in three ASD risk genes, SUV420H1 (KMT5B), PTEN, and CHD8. We performed comprehensive single-cell RNA-sequencing (scRNA-seq) of over 400,000 cells, and proteomic analysis on individual organoids sampled at different developmental stages to investigate phenotypic convergence among these genes. We find that within a defined period of early cortical development, each of the three mutations demonstrates accelerated development of cortical neurons. Notably, they do so by affecting different neuronal populations: excitatory deep layer (SUV420H1) and callosal (PTEN) neurons, and inhibitory interneurons (CHD8). This work shows that haploinsufficiency in ASD risk genes converge on early developmental defects in the generation of neurons of the cortical microcircuit.

developmental biology↗