Biology subjects

Ecker, J. R.

Publications and source records attributed to Ecker, J. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Transient cell states encode positional information to direct asymmetric growth

How tissues generate asymmetric growth from spatially and temporally restricted signals remains a fundamental challenge in developmental biology. We show that transient cell states encode positional information that instructs asymmetric growth in plants. Using spatial single-cell transcriptomics and multi-omics, we uncover a surprisingly complex landscape of transient cell states in the apical hook, including a previously unrecognized population in the apical hypocotyl region that integrates developmental and hormonal cues and bifurcates into opposing growth trajectories. Gene regulatory network analysis and functional perturbation establish GATA TRANSCRIPTION FACTOR 2 (GATA2) as a central regulator that promotes cell elongation through gibberellin signaling. These results reveal that diverse, spatially localized transient cell states function as regulatory hubs that convert positional information into divergent growth programs, providing a unifying framework for how dynamic cellular states shape organ morphology.

plant biology↗

bioRxiv2026

Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures

Dynamic transitions between cell states underlie both normal physiology and disease. However, most single-cell technologies capture only static snapshots. To address this gap, we developed a platform that integrates light-guided hydrogel polymerization with computer vision to generate on-demand compartments around live cells, enabling longitudinal imaging of cellular behavior paired with whole-transcriptome profiling of the same cells at scale. These data link dynamic phenotypes with molecular programs, enabling deeper characterization of cellular states. This approach revealed an adaptive, drug-resistant state in lung cancer cells characterized by potassium channel upregulation and p53-dependent quiescence. In models of adipogenesis and microglial phagocytosis, joint analysis of imaging and transcriptomic data identified key drivers of cellular function that were missed by transcriptomic clustering alone. These results establish the value of paired functional and transcriptomic analysis to resolve molecular drivers of complex cellular behaviors.

systems biology↗

bioRxiv2026

A Community Standard Multispecies Cell Atlas of the Basal Ganglia

The NIH BRAIN Initiative Cell Atlas Network (BICAN) aims to generate a standardized, integrated cell atlas of the human, macaque, marmoset, and mouse brain that serves as a foundational community reference for the classification and study of brain cell types. Here we present the first major component of this effort: a cross-species, multimodal atlas of the basal ganglia, a group of subcortical nuclei central to motor control and implicated in a broad range of neurological disorders. Grounded in large-scale single-cell transcriptomic classification and integrated with epigenomic and spatial genomic modalities, this resource is enabled by coordinated cross-species sampling and harmonized analytical frameworks. It provides extensive phenotypic characterization of cell types, incorporates community-informed annotation, and establishes a highly curated, data-driven taxonomy with standardized nomenclature. The atlas is anchored to species-specific anatomical reference frameworks and linked across species through unified structural ontologies, enabling consistent cross-species comparisons. Multiple complementary datasets are mapped to this reference, including multiomic profiles and developmental trajectories aligned to adult cell states. Realization of this resource has required coordinated standards for tissue processing across human and model organisms, harmonization of donor metadata across brain banks, and the development of unified anatomical reference systems. To support these advances, BICAN has established an integrated ecosystem comprising standardized sequencing pipelines, neuroanatomically grounded data infrastructure, scalable visualization and mapping tools, and interoperable metadata standards. Analogous to the standardization achieved in genome science, this ecosystem provides a FAIR (findable, accessible, interoperable, and reusable) framework that enables researchers to map, compare, and interpret diverse datasets against a shared reference and associated knowledge base. The BICAN reference system is now being extended to the whole brain, with principles that are readily generalizable to other organ systems.

neuroscience↗

bioRxiv2026

Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq

Functional genomics studies have provided critical insights into cell type-specific gene regulatory programs, but to date most have been conducted in wild-type tissues or cell cultures. Here, we present a gene expression functional atlas across the mouse brain. We use an enhanced in vivo Perturb-seq platform to analyze transcriptome-wide responses to loss of 1,947 disease-associated genes, profiling over 7.7 million cells spanning major brain regions and neuronal populations. We find striking cell-type-specific essentiality and transcriptional programs and show that closely related disease genes such as two NMDA receptor subunits can drive opposing transcriptional programs. Together, this work reveals insights into the genetics and mechanisms of neurodevelopmental, psychiatric, and neurodegenerative diseases in vivo, paving the way for the design of future genetic medicine.

genomics↗

bioRxiv2026

Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse Brain

Histone modifications underpin the cell-type-specific gene regulatory programs that drive the remarkable cellular diversity of the mammalian brain. Here, we jointly profiled four histone modifications and the transcriptome in 2.5 million nuclei from the adult mouse brain. By integrating these data with chromatin accessibility, DNA methylation, and three-dimensional genome organization, we constructed a unified epigenomic atlas spanning over 100 brain cell subclasses, and assigned active, primed, and repressive chromatin states across 81% of the genome. Active chromatin states marked by combinatorial histone modifications better nominate candidate enhancers with functional support than chromatin accessibility alone, while H3K27me3- and H3K9me3-marked chromatin states delineate distinct modes of cell-type-specific gene repression. Finally, this multimodal resource enables deep learning models that predict cell-type-specific epigenomic features and gene expression from DNA sequence, providing a comprehensive framework for annotating the brain regulatory genome and interpreting non-coding disease risk variants. HIGHLIGHTS- A single-cell epigenome atlas of transcription and four histone modifications across 2.5M mouse brain cells - Multimodal integration maps chromatin states across [~]81% of the adult mouse brain genome - Cell-type-resolved chromatin landscapes reveal regulatory programs mediated by enhancers, Polycomb and H3K9me3 - Deep learning models predict cell-type-specific epigenomic features and gene expression from DNA sequence

genomics↗

bioRxiv2026

Precision-Based Filtering Facilitates Integration of Conventional and Single-Nucleus Transcriptomes to Identify Time- and Temperature-Sensitive Cell Populations

Transcriptome analysis via RNA sequencing (RNAseq) has become a ubiquitous method of molecular characterization from whole organisms, dissected tissues, and single cells. These experiments have provided an extraordinary volume of data describing the molecular states and responses to many conditions. However, standard approaches to RNAseq analysis commonly use expression level filters that eliminate potentially useful data in the service of decreasing noise. Here we describe the implementation of a coefficient of variation-based filter for RNAseq gene expression data. This filter prioritizes consistent data across replicates, allowing lowly-expressed genes with low-variation measurements to be retained for downstream analysis. We show that, in our Arabidopsis RNAseq data set, this filter allows for the inclusion of many more transcription factors than even a low-stringency expression level filter. We find that these lowly-expressed genes mark specific cell clusters in our single-nucleus (sn)RNAseq dataset. We further characterize communities of co-expressed genes, sampled across the day at two growth temperatures, in relation to snRNAseq cell clusters, finding evidence for a highly photosynthetic cell population, and a cell state marked by high cell division and translation. These methods can be expanded to RNAseq analysis in many systems, facilitating the construction of more detailed models of tissue-specific gene regulatory networks.

bioinformatics↗

bioRxiv2026

An Integrated Single-Cell and Epigenomic Resource for Comparative Analysis of the Basal Ganglia

The basal ganglia regulate motor, cognitive, and affective behaviors, and their dysfunction underlies diverse neurological and psychiatric disorders. Comprehensive, accessible multi-omics resources are needed to understand the regulatory mechanisms governing basal ganglia cell types. Here we present an open, interactive web-based platform for exploring single-cell multi-omics datasets from basal ganglia, generated using 10X Multiome, snm3C-seq, and Paired-Tag technologies from the BICAN (NIH BRAIN Initiative Cell Atlas Network) consortium. The platform is available at https://basalganglia.epigenomes.net/ and enables integrated visualization of gene expression, chromatin accessibility, DNA methylation, histone modifications, and chromatin conformation across cell types and human, macaque, marmoset, and mouse species, with direct genome browser support and comparative epigenomic functionality. Representative analyses demonstrate cell-type-specific regulatory landscapes, conserved and species-specific regulatory elements, and links between epigenomic regulation and transcription. This resource provides a scalable, community-oriented foundation for advancing basal ganglia biology and interpreting regulatory mechanisms relevant to brain function and disease. HighlightsO_LIIntegrated single-cell epigenomic resource for basal ganglia C_LIO_LIInteractive genome browser enables multi-omics and cross-species exploration C_LIO_LIReveals cell-type-specific and species-specific regulatory landscapes C_LIO_LISupports community access to complex brain epigenomic datasets C_LI

genomics↗

bioRxiv2025

Multiscale Spatial Transcriptomic Atlas of Human Basal Ganglia Cell-Type and Cellular Community Organization

We generated a multi-region, subcellular-resolution spatial transcriptomic atlas of the human basal ganglia by integrating MERFISH+ and Stereo-seq across four neurotypical donors. These datasets profiled [~]7 million cells spanning the caudate, putamen, nucleus accumbens, and globus pallidus, resolving 60 transcriptionally distinct cell types. We show region-selective, molecular and spatial diversification of medium-spiny-neuron cell types and multiple non-neuronal populations with distinct molecular identities and spatial localizations. Subcellular RNA localization captures somatic size and projection-inferred signatures that reflect direct and indirect pathway topology. Cellular community analyses reveal the enrichment of sub-clusters of astrocytes and oligodendrocytes at striosome-matrix borders, while primate-expanded interneurons are confined to matrix territories. Cross-species mapping uncovers orthologous striosome-matrix organization and conserved dorsolateral-ventromedial gene expression gradients. This atlas provides a foundational molecular and spatial framework for studying human basal ganglia architecture, offering a multi-centimeter scale resource that links cell types, spatial architecture, and subcellular transcript topography across multiple nuclei. HighlightsO_LIOur multi-centimeter scale spatial taxonomy identifies the precise locations of 60 neuronal and glial cell types of human basal ganglia. C_LIO_LIMERFISH+ and Stereo-seq platforms map consistent spatial modules that align with classical neuroanatomical nuclei. C_LIO_LID1D2 hybrid MSNs and primate-expanded interneurons show regional and domain specific organization C_LIO_LISubcellular RNA localization reports soma morphology and projection-inferred signatures. C_LI

neuroscience↗

bioRxiv2025

Brain Region-Specific Epigenomic Reorganization and Altered Cell States in Alzheimer's Disease

Alzheimers disease (AD) is the most common neurodegenerative disorder, yet the molecular mechanisms underlying its region- and cell-type-specific pathogenesis remain poorly defined. Here, we generated a large-scale, single-cell multi-omic atlas--integrating DNA methylation and 3D genome architecture--from postmortem brain tissue of matched AD patients and cognitively normal controls. Samples were collected from three brain regions with distinct vulnerability to AD pathology: the temporal cortex (TC), primary visual cortex (VC), and prefrontal cortex (PFC). Our dataset comprises over 230,000 individual cells, spanning major neuronal and glial populations, and provides a high-resolution view of multi-layer epigenomic regulation. We identified widespread AD-associated DNA methylation changes and marked reorganization of 3D genome structure, including alterations in A/B compartments, topologically associating domains (TADs), and chromatin loops. These changes are strongly region-specific: TC displays pronounced hypermethylation, transcriptional downregulation, and elevated boundary density, whereas VC shows opposing trends and PFC an intermediate profile. We further uncovered previously unrecognized AD-associated glial and neuronal states defined by coordinated epigenomic dysregulation and recurrent genomic deletions, particularly near telomeric regions. This region-resolved, single-cell multi-omic atlas reveals divergent epigenomic trajectories across brain regions and cell types in AD, offering new mechanistic insights and a framework for targeted therapeutic strategies.

genomics↗

bioRxiv2025

Dissecting the epigenome dynamics in human immune cells upon viral and chemical exposure by multimodal single-cell profiling

Environmental and pathogen exposure can lead to profound remodeling of the gene-regulatory landscape across human immune cell populations. Here, we present a single-nucleus chromatin accessibility atlas of human immune cells comprising 271,299 cells and 319,420 candidate regulatory elements from individuals exposed to HIV-1, COVID-19, Influenza virus, organophosphates, as well as healthy controls. Our longitudinal HIV cohort reveals epigenetic signatures of T cell exhaustion manifested in changes in the accessibility at binding sites for the FOXP family transcription factors. Upon severe exposure to SARS-Cov2 we identified changes in chromatin accessibility manifesting in a regulatory switch in cytokine networks characterized by the downregulation of the NF-{kappa}B motif family, alongside concordant changes in AP-1 and IRF factor networks in CD14+ classical monocytes. By integrating single-cell profiles of DNA methylation from matched samples, we created a multimodal epigenome atlas of human immune cell states using the accessibility-derived candidate regulatory elements. Both modalities exhibit complementary epigenetic signatures at transcription factor binding sites associated with cell state, as exemplified in the process of memory formation in T-cells, where BATF, AP-1, and ETS motifs exhibit significant epigenetic covariance across both epigenomic layers. Finally, by linking potentially regulatory DNA methylation signatures to changes in chromatin accessibility in monocytes, we observe that severe COVID-19 involves selective, multiomics remodeling of epigenetic profiles at TF binding sites manifested in concordant DNA methylation and accessibility dynamics at inflammation-associated regulatory TFs.

bioinformatics↗

bioRxiv2025

Maternal Immune Activation Disrupts Epigenomic and Functional Maturation of Cortical Excitatory Neurons

Elevated levels of maternal pro-inflammatory cytokines during gestation can disrupt offspring neural development, increasing the risk of neurodevelopmental disorders. We studied the effects of Poly(I:C)-induced maternal immune activation (PIC-MIA) during mid-gestation on developing cortical excitatory neurons DNA methylation and transcriptome. PIC-MIA disrupted the developmental regulation of synapse-related genes and of genes implicated in autism spectrum disorders. Genomic regions that gain or lose DNA methylation during normal development were altered following PIC-MIA, including neurodevelopmental transcription factor binding sites. The DNA methylation and transcriptional changes were consistent with a delay in excitatory neuron maturation. Whole-cell recordings showed that PIC-MIA preferentially altered the physiological development of layer 5 excitatory neurons. Taken together, present results suggest that alterations in the epigenome, through the disruption of circuit formation, may drive the long-term consequences of maternal infection during gestation.

neuroscience↗

bioRxiv2025

Cell-type-specific transposable element demethylation and TAD remodeling in the aging mouse brain

Aging is a major risk factor for neurodegenerative diseases, yet underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types. We observed that age-related methylation changes were more pronounced in non-neuronal cells. Transposable element methylation alone distinguished age groups, showing cell-type-specific genome-wide demethylation. Chromatin conformation analysis demonstrated age-related increases in TAD boundary strength with enhanced accessibility at CTCF binding sites. Spatial transcriptomics across 895,296 cells revealed regional heterogeneity during aging within identical cell types. Finally, we developed novel deep-learning models that accurately predict age-related gene expression changes using multi-modal epigenetic features, providing mechanistic insights into gene regulation. This dataset advances our understanding of brain aging and offers potential translational applications. HighlightsO_LISingle-cell multi-omic profiling maps the epigenetic and spatial transcriptomic landscape of brain aging across multiple regions. C_LIO_LICell-type-specific genome-wide demethylation of retrotransposable elements correlates with increased chromatin accessibility and expression. C_LIO_LIElevated TAD boundary strength emerges as a unique marker of brain aging associated with CTCF gaining accessibility. C_LIO_LIA novel deep-learning model reveals the significance of epigenetic features on age-related transcriptomic changes across genes. C_LI

genomics↗

bioRxiv2025

Human Body Single-Cell Atlas of 3D Genome Organization and DNA Methylation

Higher-order chromatin structure and DNA methylation are critical for gene regulation, but how these vary across the human body remains unclear. We performed multi-omic profiling of 3D genome structure and DNA methylation for 86,689 single nuclei across 16 human tissues, identifying 35 major and 206 cell subtypes. We revealed extensive changes in CG and non-CG methylation across almost all cell types and characterized 3D chromatin structure at an unprecedented cellular resolution. Intriguingly, extensive discrepancies exist between cell types delineated by DNA methylation and genome structure, indicating that the role of distinct epigenomic features in maintaining cell identity may vary by lineage. This study expands our understanding of the diversity of DNA methylation and chromatin structure and offers an extensive reference for exploring gene regulation in human health and disease.

genomics↗

bioRxiv2025

Stress drives plasticity in leaf maturation transcriptional dynamics

Leaf development is dynamic, adapting to environmental stress to optimize resource use. For example, in response to drought, Arabidopsis restricts leaf growth to enhance water use efficiency. While this plastic response is well described at the physiological level, the underlying transcriptional changes that facilitate adjustments in leaf development in response to stress remain poorly described. By constructing a [~]1 million single-nuclei transcriptome atlas, we demonstrate that drought stress limits leaf growth by advancing transcriptional responses related to maturation and aging. Notably, we find that these transcriptional changes scale with environmental stress, and help explain how shoot size can decline proportionally with stress intensity. Leveraging these insights, we increased leaf growth under stress by cell-type targeted upregulation of FERRIC REDUCTION OXIDASE 6 in the mesophyll.

plant biology↗

bioRxiv2024

Innate immune epigenomic landscape following controlled human influenza virus infection

Viral infections can induce changes in innate immunity that persist after virus clearance. Here, we used blood samples from a human influenza H3N2 challenge study to perform comprehensive multi-omic analyses. We detected remodeling of immune programs in innate immune cells after resolution of the infection that was proportional in magnitude to the level of prior viral load. We found changes associated with suppressed inflammation including decreased cytokine and AP-1 gene expression as well as decreased accessibility at AP-1 targets and interleukin-related gene promoter regions. We also found decreased histone deacetylase gene expression, increased MAP kinase gene expression, and increased accessibility at interferon-related gene promoter regions. Genes involved in inflammation and epigenetic-remodeling showed modulation of gene-chromatin site regulatory circuit activity. These results reveal a coordinated rewiring of the epigenetic landscape in innate immune cells induced by mild influenza virus infection.

immunology↗

bioRxiv2024

Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for identifying functional enhancers in mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian cortex and to designing more efficient genetic tools to target cortical cell types.

genomics↗

bioRxiv2024

scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia

DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.

genomics↗

bioRxiv2023

Cell type specific enrichment of somatic aneuploidy in the mammalian brain

Somatic mutations alter the genomes of a subset of an individuals brain cells1-3, impacting gene regulation and contributing to disease processes4,5. Mosaic single nucleotide variants have been characterized with single-cell resolution in the brain2,3, but we have limited information about large-scale structural variation, including whole-chromosome duplication or loss1,6,7. We used a dataset of over 415,000 single-cell DNA methylation and chromatin conformation profiles across the adult mouse brain to identify aneuploid cells comprehensively. Whole-chromosome loss or duplication occurred in <1% of cells, with rates up to 1.8% in non-neuronal cell types, including oligodendrocyte precursors and pericytes. Among all aneuploidies, we observed a strong enrichment of trisomy on chromosome 16, which is syntenic with human chromosome 21 and constitutively trisomic in Down syndrome. Chromosome 16 trisomy occurred in multiple cell types and across brain regions, suggesting that nondisjunction is a recurrent feature of somatic variation in the brain.

genomics↗