Biology subjects

Armand, E.

Publications and source records attributed to Armand, E..

5 recordsLinked to original sources

bioRxiv2026

Single-Cell Atlas of Transcription and Chromatin States Reveals Regulatory Programs in the Human Brain

Directly measuring chromatin states alongside transcription is essential for understanding how cell-type-specific regulatory programs are established and maintained in the adult human brain. We present a large-scale single-cell multimodal atlas generated by jointly profiling transcriptome with active (H3K27ac) and repressive (H3K27me3) histone modifications across 18 brain regions. We profile >750,000 nuclei spanning 160 cell types and integrate these data with chromatin accessibility, DNA methylation, 3D genome architecture, and spatial transcriptome. This framework annotates >500,000 regulatory elements and resolves cell-type-specific chromatin states. We link enhancers to target genes, infer gene regulatory networks, and classify chromatin interactions, revealing neuron-enriched long-range Polycomb repression of developmental genes. Integrating these maps with GWAS data and sequence-based model prioritizes noncoding variants, effector genes, and vulnerable cell types for neuropsychiatric disorders. Finally, cross-species comparisons show conserved activation but more divergent repression. Together, this study provides a functional reference for interpreting noncoding variants, epigenetic memory, and brain organization. HIGHLIGHTSO_LIJoint single-cell profiling of transcriptomes with active or repressive histone modification in >750,000 nuclei across adult human brain. C_LIO_LIChromatin state annotation of >500,000 candidate cis-regulatory elements distinguishes active enhancers from accessible and Polycomb-repressed regions. C_LIO_LICell-type-resolved regulatory networks and sequence-based deep learning model prioritize functional neuropsychiatric risk variants. C_LIO_LISpatial epigenomic imputation reveals laminar layer-specific Polycomb repression programs. C_LIO_LIIntegration with 3D genome architecture reveals neuron-specific super long-range chromatin loops silencing early developmental genes. C_LIO_LIEvolutionary analysis uncovers conserved active regulatory grammar but divergent repressive landscape. C_LI

genomics↗

bioRxiv2025

GAME: Genomic API for Model Evaluation

The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models on practical applications has been challenging because individual projects evaluate their models in ad hoc ways, and there is substantial heterogeneity of both model architectures and benchmarking tasks. To address this challenge, we have created GAME, a system for large-scale, community-led standardized model benchmarking on user-defined evaluation tasks. We borrow concepts from the Application Programming Interface (API) paradigm to allow for seamless communication between pre-trained models and benchmarking tasks, ensuring consistent evaluation protocols. Because all models and benchmarks are inherently compatible in this framework, the continual addition of new models and new benchmarks is easy. We also developed a Matcher module powered by a large language model (LLM) to automate ambiguous task alignment between benchmarks and models. Containerization of these modules enhances reproducibility and facilitates the deployment of models and benchmarks across computing platforms. By focusing on predicting underlying biochemical phenomena (e.g. gene expression, open chromatin, DNA binding), we ensure that tasks remain technology-independent. We provide examples of benchmarks and models implementing this framework, and anticipate that the community will contribute their own, leading to an ever-expanding and evolving set of models and evaluation tasks. This resource will accelerate genomics research by illuminating the best models for a given task, motivating novel functional genomic benchmarks, and providing a more nuanced understanding of model abilities.

bioinformatics↗

bioRxiv2025

Context-Dependent and Gene-Specific Role of Chromatin Architecture Mediated by Histone Modifiers and Loop-extrusion Machinery

Loop-extrusion machinery, comprising the cohesin complex and CCCTC-binding factor CTCF, organizes the interphase chromosomes into topologically associating domains (TADs) and loops, but acute depletion of components of this machinery results in variable transcriptional changes in different cell types, highlighting the complex relationship between chromatin organization and gene regulation. Here, we systematically investigated the role of 3D genome architecture in gene regulation in mouse embryonic stem cells under various perturbation conditions. We found that acute depletion of cohesin or CTCF disrupts the formation of TADs, but affects gene regulation in a gene-specific and context-dependent manner. Furthermore, the loop extrusion machinery was dispensable for transcription from most genes in steady state, consistent with prior results, but became critical for a large number of genes during transition of cellular states. Through a genome-wide CRISPR screen, we uncovered multiple factors that can modulate the role of loop extrusion machinery in gene regulation in a gene-specific manner. Among them were the MORF acetyltransferase complex members (Kat6b, Ing5, Brpf1), which could antagonize the transcriptional insulation mediated by CTCF and cohesin complex at developmental genes. Interestingly, inhibition of Kat6b partially rescues the insulator defects in cells lacking the cohesin loader Nipbl, mutations of which are responsible for the developmental disorder Cornelia de Lange syndrome. Taken together, our findings uncovered interplays between the loop extrusion machinery and histone modifying complex that underscore the context-dependent and gene-specific role of the 3D genome.

genomics↗

bioRxiv2023

SnapATAC2: a fast, scalable and versatile tool for single-cell omics analysis

Single-cell omics technologies have ushered in a new era for the study of dynamic gene regulation in complex tissues during development and disease pathogenesis. A major computational challenge in analyzing these datasets is to project the large-scale and high dimensional data into low-dimensional space while retaining the relative relationships between cells in order to decompose the cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Conventional dimensionality reduction methods suffer from computational inefficiency, difficulty to capture the full spectrum of cellular heterogeneity, or inability to apply across diverse molecular modalities. Here, we report a fast and nonlinear dimensionality reduction algorithm that not only more accurately captures the heterogeneities of single-cell omics data, but also features runtime and memory usage that is computational efficient and linearly proportional to cell numbers. We implement this algorithm in a Python package named SnapATAC2, and demonstrate its superior performance, remarkable scalability and general adaptability using an array of single-cell omics data types, including single-cell ATAC-seq, single-cell RNA-seq, single-cell Hi-C, and single-cell multiomics datasets.

genomics↗

bioRxiv2020

A multimodal cell census and atlas of the mammalian primary motor cortex

We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

neuroscience↗