Biology subjects

Epstein, M.

Publications and source records attributed to Epstein, M..

14 recordsLinked to original sources

bioRxiv2026

From unsupervised clustering to atlas-guided annotation in cohort-scale spatial omics with HiCAT

Pathologist-annotated tissue regions provide a fundamental reference for examining spatial omics data, yet such annotations are available for a limited number of samples due to the substantial manual effort required. Moreover, these annotations are derived from morphology within individual histology images, which can overlook molecularly defined regions and obscure intra-sample heterogeneity. To address these limitations, we present HiCAT, a machine-learning framework that automatically generates pathologist-informed region annotations and characterizes regional heterogeneity in spatial omics data. Across seven datasets, HiCAT consistently outperforms state-of-the-art methods, achieving a median relative improvement of 107% in accuracy. Beyond transferring pathologist annotations, HiCAT uncovers molecularly informed regional heterogeneity not captured by original annotations, including tumor subregions associated with clinical outcomes and brain subregions aligned with spatiotemporal disease progression. By generating consistent, highly granular, and biologically informative region annotations across large cohorts, HiCAT enables scalable downstream analysis and provides training labels for foundation models in spatial biology.

genomics↗

bioRxiv2025

On the mechanism of calcium permeability and magnesium block in NMDA receptors - a central molecular paradigm in neuroplasticity

Neuroplasticity is a fundamental cellular mechanism underlying learning and memory formation and is primed by the coincidental detection of neurotransmitter release from the presynapse and the subsequent calcium influx upon voltage change in the postsynaptic membrane (Bliss and Collingridge, 1993). Molecular assemblies that achieve these events are N-methyl-D-aspartate receptors (NMDARs), which bind the neurotransmitter glutamate and a co-agonist, either glycine or D-serine, and allow Ca2+ influx upon relief of the Mg2+ channel blockade by membrane depolarization. However, the molecular basis governing Ca2+ permeability and Mg2+ blockade in NMDAR remains limited. Here, we demonstrate that Ca{superscript 2} permeation through the narrow constriction of the cation selectivity filter involves partial dehydration, as evidenced by multiple Ca{superscript 2} binding sites captured using single-particle cryo-electron microscopy (cryo-EM). In contrast, Mg2+ binds outside of the selectivity filter through the water network by remaining hydrated, thereby serving as a channel blocker. Furthermore, we show that the lipid network around the selectivity filter influences the stability of Mg2+ binding. Our study details the critical transmembrane chemistry of NMDAR for initiating neuroplasticity.

neuroscience↗

bioRxiv2024

CADET: Enhanced transcriptome-wide association analyses in admixed samples using eQTL summary data

A transcriptome-wide association study (TWAS) is a popular statistical method for identifying genes whose genetically-regulated expression (GReX) component is associated with a trait of interest. Most TWAS approaches fundamentally assume that the training dataset (used to fit the gene expression prediction model) and testing GWAS dataset are from the same ancestrally homogenous population. If this assumption is violated, studies have shown a marked negative impact on expression prediction accuracy as well as reduced power of the downstream genetrait association test. These issues pose a particular problem for admixed individuals, whose genomes represent a mosaic of multiple continental ancestral segments. To resolve these issues, we present CADET, which enables powerful TWAS of admixed cohorts leveraging the local-ancestry (LA) information of the cohort along with summary-level eQTL data from reference panels of different ancestral groups. CADET combines multiple polygenic risk score models based on the summary-level eQTL reference data to predict LA-aware GReX components in admixed test samples. Using simulated data, we compare the imputation accuracy, power, and type I error rate of our proposed LA-aware approach to LA-unaware methods for performing TWASs. We show that CADET performs optimally in nearly all settings regardless of whether the genetic architecture of gene expression is dependent or independent of ancestry. We further illustrate CADET by performing a TWAS of 29 common blood biochemistry phenotypes within an admixed cohort from the UK Biobank and identify 18 hits unique to our LA-aware strategy with the majority of hits supported by existing GWAS findings.

genetics↗

bioRxiv2024

MorphLink: Bridging Cell Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics

Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in multi-modal spatial omics analyses. These linkages provide a transparent depiction of cellular behaviors that drive transcriptomic heterogeneity and immune diversity across different regions within diseased tissues, such as cancer. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies.

bioinformatics↗

bioRxiv2023

STACCato: Supervised Tensor Analysis tool for studying Cell-cell Communication using scRNA-seq data across multiple samples and conditions

Numerous tools have been developed to infer active cell-cell communication (CCC) events, which are essential for understanding biological processes and diseases. However, existing downstream methods for assessing the relationships between CCC events and biological conditions lack clear interpretation, fail to adjust for confounders, and ignore dependencies among CCC events. To address these limitations, we introduce STACCato, a Supervised Tensor Analysis tool designed to identify Condition-related Cell-cell communication events. STACCato employs a tensor-based regression model to enable statistical inference related to the relationships between biological conditions (e.g., disease status, tissue types) and specific CCC events, while adjusting for confounders and CCC dependencies. Through extensive simulations and real-world applications on scRNA-seq datasets of lupus and autism, we demonstrate that STACCato consistently provides improved inference of condition-related CCC events compared to alternative methods. The computational tool implementing the STACCato framework is available on GitHub.

bioinformatics↗

bioRxiv2023

Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWAS) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have only considered regulatory effects of risk associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWAS of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents a first look into the role of trans-eQTLs in the complex molecular mechanisms underlying these diseases.

genetics↗

bioRxiv2023

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This missing heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

genetics↗

bioRxiv2023

Impaired migration and premature differentiation underlie the neurological phenotype associated with PCDH12 loss of function

Protocadherins (PCDHs) are cell adhesion molecules that regulate many essential neurodevelopmental processes related to neuronal maturation, dendritic arbor formation, axon pathfinding, and synaptic plasticity. Bi-allelic loss-of-function variants in PCDH12 are associated with several neurodevelopmental disorders (NDDs) such as diencephalic-mesencephalic dysplasia syndrome, cerebral palsy, cerebellar ataxia, and microcephaly. Despite the highly deleterious outcome resulting from loss of PCDH12, little is known about its role during brain development and disease. Here, we show that PCDH12 loss severely impairs cerebral organoid development with reduced proliferative areas and disrupted laminar organization. 2D models further show that neural progenitor cells lacking PCDH12 prematurely exit cell cycle and differentiate earlier when compared to wildtype. Furthermore, we show that PCDH12 regulates neuronal migration through a mechanism requiring ADAM10-mediated ectodomain shedding and membrane recruitment of cytoskeleton regulators. Our data demonstrate a critical and broad involvement of PCDH12 in cortical development, revealing the pathogenic mechanisms underlying PCDH12-related NDDs.

neuroscience↗

bioRxiv2021

A Kernel Method for Dissecting Genetic Signals in Tests of High-Dimensional Phenotypes

Genomewide association studies increasingly employ multivariate tests of multiple correlated phenotypes to exploit likely pleiotropy to improve power. Typical multivariate methods produce a global p-value of association between a variant (or set of variants) and multiple phenotypes. When the global test is significant, subsequent interest then focuses on dissecting the signal and, in particular, delineating the set of phenotypes where the genetic variant(s) have a direct effect from the remaining phenotypes where the genetic variant(s) possess either indirect or no effect. While existing techniques like mediation models can be utilized for this purpose, they generally cannot handle high-dimensional phenotypic and genotypic data. To assist in filling this important gap, we propose a modification of a kernel distance-covariance framework for gene mapping of multiple variants with multiple phenotypes to test instead whether the association between the variants and a group of phenotypes is driven through a direct association with just a subset of the phenotypes. We use simulated data to show that our new method controls for type I error and is powerful to detect a variety of models demonstrating different patterns of direct and indirect effects. We further illustrate our method using GWAS data from the Grady Trauma Project and show that an existing signal between genetic variants in the ZHX2 gene and 21 items within the Beck Depression Inventory appears to be due to a direct effect of these variants on only 3 of these items. Our approach scales to genomewide analysis, and is applicable to high-dimensional correlated phenotypes.

genomics↗

bioRxiv2021

Efficient Estimation of Indirect Effects in Case-Control Studies Using a Unified Likelihood Framework

Mediation models are a set of statistical techniques that investigate the mechanisms that produce an observed relationship between an exposure variable and an outcome variable in order to deduce the extent to which the relationship is influenced by intermediate mediator variables. For a case-control study, the most common mediation analysis strategy employs a counterfactual framework that permits estimation of indirect and direct effects on the odds ratio scale for dichotomous outcomes, assuming either binary or continuous mediators. While this framework has become an important tool for mediation analysis, we demonstrate that we can embed this approach in a unified likelihood framework for mediation analysis in case-control studies that leverages more features of the data (in particular, the relationship between exposure and mediator) to improve efficiency of indirect effect estimates. One important feature of our likelihood approach is that it naturally incorporates cases within the exposure-mediator model to improve efficiency. Our approach does not require knowledge of disease prevalence and can model confounders and exposure-mediator interactions, and is straightforward to implement in standard statistical software. We illustrate our approach using both simulated data and real data from a case-control genetic study of lung cancer.

genetics↗

bioRxiv2020

Structural correlates of human muscle nicotinic acetylcholine receptor subunit assembly mediated by δ(+) interface residues

Muscle nicotinic acetylcholine receptors are a class of heteropentameric ligand-gated cation channels with constituent subunits adopting a fixed stoichiometric arrangement. The specific amino acid residues that govern subunit ordering are however, only partially understood. By integrating all-atom molecular dynamics simulations, bioinformatics, two-electrode voltage clamp electrophysiology and 125I--bungarotoxin assays of chimeric nAChR subunits, we identify residues across the extracellular, transmembrane and extended M4 helix of the {delta} subunit that make structural signatures that contribute to intransigent assembly rules. Furthermore, functional differences observed in 2{delta}2{beta} receptors can be rationalized by changes in dynamical behavior that manifest themselves at the agonist binding site.

neuroscience↗

bioRxiv2020

Powerful Variance-Component TWAS method identifies novel and known risk genes for clinical and pathologic Alzheimer's dementia phenotypes

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, existing TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Existing TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTLs estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N=[~]3.4K) and summary-level (N=[~]54K) GWAS data to study Alzheimers dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by existing TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs; these findings also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use. Author SummaryExisting Transcriptome-wide association studies (TWAS) tools make strong assumptions about the relationships among genetic variants, transcriptome, and phenotype that may be violated in practice, thereby substantially reducing the power. Here, we propose a novel variance-component TWAS method (VC-TWAS) that relaxes these assumptions and can be implemented with both individual-level and summary-level GWAS data. Our simulation studies showed that VC-TWAS achieved higher power compared to existing TWAS methods when the underlying assumptions required by existing TWAS tools were violated. We further applied VC-TWAS to both individual-level (N=[~]3.4K) and summary-level (N=[~]54K) GWAS data to study Alzheimers dementia (AD). With individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by existing TWAS methods. Interestingly, 5 of these genes were shown to possess significant pleiotropic effects on AD pathology phenotypes, revealing possible biological mechanisms. With summary-level data of a larger sample size, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. In conclusion, VC-TWAS provides an important analytic tool for identifying risk genes whose effects on phenotypes might be mediated through transcriptomes.

genetics↗

bioRxiv2020

Coding de novo mutations identified by WGS reveal novel orofacial cleft genes

While de novo mutations (DNMs) are known to increase risk of congenital defects, DNMs have not been fully explored regarding orofacial clefts (OFCs), one of the most common human birth defects. Therefore, whole-genome sequencing of 756 case-parent trios of European, Colombian, and Taiwanese ancestry was performed to determine the contributions of coding DNMs to OFC risk. Overall, we identified a significant excess of loss-of-function DNMs in genes highly expressed in craniofacial tissues, as well as genes associated with known autosomal dominant OFC syndromes. This analysis also revealed roles for zinc-finger homeobox domain and SOX2-interacting genes in OFC etiology.

genetics↗

bioRxiv2019

Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform

We present KnowEnG, a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis and expression signature analysis. The system offers knowledge-guided data-mining and machine learning algorithms, where user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge-bases and encoded in a massive Knowledge Network. KnowEnG adheres to FAIR principles: its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution of compute-intensive and data-intensive algorithms, and are interoperable with other computing platforms. They are made available through multiple access modes including a web-portal, and include specialized visualization modules. We present use cases and re-analysis of published cancer data sets using KnowEnG tools and demonstrate its potential value in democratization of advanced tools for the modern genomics era.

cancer biology↗