Biology subjects

Herault, L.

Publications and source records attributed to Herault, L..

8 recordsLinked to original sources

bioRxiv2026

SuperCell2.0 enables semi-supervised construction of multimodal metacell atlases

Multimodal single-cell atlases comprising hundreds of thousands of cells provide unique resources for exploring complex biological tissues and generating testable hypotheses. To streamline the analysis of such large datasets, we introduce SuperCell2.0, a robust workflow to build (semi-)supervised multimodal metacells. We demonstrate that multimodal metacells outperform metacells built with a single modality, improve inter-modality consistency, and facilitate integration of multiomic single-cell datasets. SuperCell2.0 can further leverage full or partial cell type annotations to improve metacell quality. This workflow enables us to construct multimodal metacell atlases from blood and tumor samples and identifies interferon-primed monocytes and macrophages in the circulation and in the tumor microenvironment. Markers derived from the metacell analysis enable us to sort and phenotypically characterize this population in healthy donors. Overall, our work demonstrates how SuperCell2.0 facilitates the analysis of large multimodal single-cell atlases.

bioinformatics↗

bioRxiv2024

SuperSpot: Coarse Graining Spatial Transcriptomic Data into Metaspots

SummarySpatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. As of today, thousands of genes can be detected across hundreds of thousands of spots. Akin to standard single-cell RNA-Seq data, spatial transcriptomic data are very sparse due to the limited amount of RNA within each spot. Building upon the metacell concept, we present a workflow, called SuperSpot, to combine adjacent and transcriptionally similar spots into "metaspots". The process involves representing spots as nodes in a graph with edges connecting spots in spatial proximity and edge weights representing transcriptional similarity. Hierarchical clustering is used to aggregate spots into metaspots at a user-defined resolution. We demonstrate that metaspots can be used to reduce the size of spatial transcriptomic data and remove some of the dropout noise. Availability and implementationSuperSpot is an R package available at https://github.com/GfellerLab/SuperSpot.

bioinformatics↗

bioRxiv2024

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze such data is a constant challenge and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information. Here, we review recent studies that capitalize on the concept of metacells - and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to study single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on construction and analysis of metacells from single-cell RNA-seq data (https://github.com/GfellerLab/MetacellAnalysisTutorial) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods (https://github.com/GfellerLab/MetacellAnalysisToolkit).

bioinformatics↗

bioRxiv2023

Semi-supervised integration of single-cell transcriptomics data

Single-cell sequencing technologies offer unprecedented opportunities to characterize the complexity of biological samples with high resolution. At the same time, variations in sample processing and experimental protocols introduce technical variability - or "batch effects" - in the molecular readouts, hindering comparative analyses across samples and individuals. Although batch effect correction methods are routinely applied in single-cell omics analyses, data integration often leads to overcorrection, resulting in the loss of true biological variability. In this study, we present STACAS v2, a semi-supervised scRNA-seq data integration method that leverages prior knowledge in the form of cell type annotations to preserve biological variance. Through an open and reproducible benchmarking pipeline, we show that semi-supervised STACAS outperforms popular unsupervised methods such as Harmony, FastMNN, Seurat v4, scVI, and Scanorama, as well as supervised methods such as scANVI and scGen. Notably, STACAS is robust to incomplete and imprecise cell type annotations, which are commonly encountered in real-life integration tasks. Highlighting its scalability, we successfully applied semi-supervised STACAS to construct a high-resolution map of tumor-infiltrating CD8 T cells encompassing over 500,000 cells from 265 individuals. Based on our findings, we argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction. STACAS seamlessly integrates with Seurat pipelines and can be run with one command: Run.STACAS(seurat.list, cell.labels).

bioinformatics↗

bioRxiv2022

Single-cell multi-omics analyses reveal EZH2 as a main driver of retinoic acid resistance in PLZF-RARA leukemia.

Cancer relapse is caused by a subset of malignant cells that are resistant to treatment. To characterize resistant cells and their vulnerabilities, we studied the retinoic acid (RA)-resistant PLZF-RARA acute promyelocytic leukemia (APL) using single-cell multi-omics. We uncovered transcriptional and chromatin heterogeneity in leukemia cells and identified a subset of cells resistant to RA that depend on a fine-tuned transcriptional network targeting the epigenetic regulator Enhancer of Zeste Homolog 2 (EZH2). Epigenomic and functional analyses validated EZH2 selective dependency of PLZF-RARA leukemia and its driver role in RA resistance. Targeting pan-EZH2 activities (canonical/non-canonical) was necessary to eliminate leukemia relapse initiating cells, which underlies a dependency of resistant cells on an EZH2 non-canonical activity and the necessity to degrade EZH2 to overcome resistance. Our study provides critical insights into the mechanisms of RA resistance that allow us to eliminate treatment-resistant leukemia cells by targeting EZH2, thus highlighting a potential targeted therapy approach. HIGHLIGHTS- sc-RNAseq identifies PLZF-RARA leukemia heterogeneity and retinoic acid resistant cells - sc-ATACseq refines leukemic cell identity and resolves retinoic acid resistant networks - EZH2 is a selective dependency of PLZF-RARA leukemia and drives retinoic acid resistance - Targeting pan-EZH2 activities (canonical/non-canonical) is necessary to overcome leukemia onset

cancer biology↗

bioRxiv2020

Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cell

Hematopoietic stem cells (HSCs) are the guarantor of the proper functioning of hematopoiesis due to their incredible diversity of potential. During aging the heterogeneity of mouse HSCs evolves, which contributes to the deterioration of the immune system. Here we address the transcriptional plasticity of HSC upon aging at the single-cell resolution. Through the analysis of 15,000 young and aged transcriptomes, we reveal 15 clusters of HSCs unveiling rare and specific HSC abilities that change with age. Pseudotime ordering complemented with regulon analysis showed that the consecutive differentiation states of HSC are delayed upon aging. By analysing cell cycle at the single cell level we highlight an imbalance of cell cycle regulators of very immature aged HSC that may contribute to their accumulation in an undifferentiated state. Our results therefore establish a reference map of young and old mouse HSC differentiation and reveal a potential mechanism that delay aged HSC differentiation.

genomics↗

bioRxiv2018

PLZF limits enhancer activity during hematopoietic progenitor aging

PLZF (promyelocytic leukemia zinc finger) is a transcription factor acting as a global regulator of hematopoietic commitment. PLZF displays an epigenetic specificity by recruiting chromatin-modifying factors but little is known about its role in remodeling chromatin of cells committed towards a given specific hematopoietic lineage. In murine myeloid progenitors, we decipher a new role for PLZF in restraining active genes and enhancers by targeting acetylated lysine 27 of Histone H3 (H3K27ac). Functional analyses reveal that active enhancers bound by PLZF are involved in biological processes related to metabolism and associated with hematopoietic aging. Comparing the epigenome of young and old myeloid progenitors, we reveal that H3K27ac variation at active enhancers is a hallmark of hematopoietic aging. Taken together, these data suggest that PLZF, associated with active enhancers, appears to restrain their activity as an epigenetic gatekeeper of hematopoietic aging.

molecular biology↗

bioRxiv2018

Morphofunctional deficits in the cerebral cortex of NeuroD2 mutant mice are associated with autism/schizophrenia-like behaviors

We identified seven families associating NEUROD2 pathogenic mutations with ASD and intellectual disability. To get insight into the pathophysiological mechanisms, we analyzed cortical development in Neurod2 KO mice. Cortical projection neurons (CPNs) over-migrated during embryogenesis, inducing abnormal thickness and laminar positioning of cortical layers. At juvenile ages, dendritic spine turnover and intrinsic excitability were increased in L5 CPNs. Differentially expressed genes in Neurod2 KO mice were enriched for voltage-gated ion channels, and the human orthologs of these genes were strongly associated with ASD. Furthermore, adult Neurod2 KO mice exhibited core ASD-like behavioral abnormalities. Finally, by generating Neurod2 conditional mutant mice we demonstrate that forebrain excitatory neuron-specific Neurod2 deletion recapitulates cellular and behavioral ASD phenotypes found in full KO mice. Our findings demonstrate crucial roles for Neurod2 in cortical development and function, whose alterations likely account for ASD and related symptoms in the newly defined NEUROD2 mutation syndrome.

neuroscience↗