Biology subjects

Trimbour, R.

Publications and source records attributed to Trimbour, R..

4 recordsLinked to original sources

bioRxiv2026

Modelling multicellular coordination by bridging cell-cell communication and intracellular regulation through multilayer networks

In multicellular organisms, cells with various roles and locations coordinate to provide systemic and cohesive response to perturbations. These complex behaviors emerge from a complex interplay between intracellular regulation and intercellular signals that mediate cell-cell communication. While single-cell technologies opened the possibility of studying both, most methods focus solely on one of these aspects. Thus, they are only able to partially recover in vivo and multicellular behaviors. We here introduce ReCoN (REconstruction of multicellular COordination Networks from single-cell data), a framework combining intracellular gene regulation and cell-cell communication to provide insights into multicellular coordination from single-cell data. First, ReCoN infers from single-cell data a heterogeneous multilayer network containing both cell-type-specific intracellular subnetworks and ligand-receptor interactions. Through random walk with restart explorations, ReCoN then infers the response of each cell type to both intra- and extracellular perturbations, such as a gene knock-out or a cytokine, respectively. ReCoN was evaluated on predicting the in vivo response of immune cell-types to different cytokines and on recovering cardiac cell-type response in heart failure. It highlighted the role of indirect effects, where cells emit secondary messengers in response to the initial perturbation to coordinate multicellular transcriptomic responses. Additionally, ReCoN predicted distinct fibroblast states emerging in different microenvironments reconstructed from spatial data. ReCoN provides an interpretable modeling framework for multicellular systems that allows for the simulation of perturbations, including the assessment of the cellular selectivity of these treatments in vivo. Ultimately, it can help design patient-specific molecular therapies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/700561v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@7cdceforg.highwire.dtl.DTLVardef@18cfa3eorg.highwire.dtl.DTLVardef@812645org.highwire.dtl.DTLVardef@f7bce5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data

Chromatin 3D folding creates numerous DNA interactions, participating in gene expression regulation. Single-cell chromatin-accessibility assays now profile hundreds of thousands of cells, challenging existing methods for mapping cis-regulatory interactions. We present CIRCE, a fast and scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data. CIRCE re-implements the Cicero workflow to analyse single-cell atlases, cutting runtime and memory use by several orders of magnitude. We also provide new options to compute metacells, grouping similar cells to reduce data sparsity. We benchmarked CIRCE against Cicero on two datasets of different sizes and demonstrated the improvement from CIRCEs metacells strategy with promoter capture Hi-C data. We also evaluated how DNA interaction predictions are impacted by different pre-processing. We observed a negative impact of Ciceros count normalization, and the best performance was obtained with the single-cell count matrix directly. Finally, we demonstrated the scalability of CIRCE by processing a dataset of more than 700000 cells and 1 million DNA regions in less than an hour. CIRCE should greatly facilitate the prediction of DNA region interactions for scverse and Python users, while providing new and up-to-date pre-processing insights. Availability and reproducibilityCIRCE is released as an open-source software under the AGPL-3.0 license. The package source code is available on GitHub at https://github.com/cantinilab/CIRCE, and its documentation is accessible at https://circe.readthedocs.io. The code to reproduce the presented results is available as a Snakemake pipeline at https://github.com/cantinilab/circe_reproducibility.

bioinformatics↗

bioRxiv2024

Comparison and evaluation of methods to infer gene regulatory networks from multimodal single-cell data

Cells regulate their functions through gene expression, driven by a complex interplay of transcription factors and other regulatory mechanisms that together can be modeled as gene regulatory networks (GRNs). The emergence of single-cell multi-omics technologies has driven the development of several methods that integrate transcriptomics and chromatin accessibility data to infer GRNs. While these methods provide examples of their utility in discovering new regulatory interactions, a comprehensive benchmark evaluating their mechanistic and predictive properties as well as their ability to recover known interactions is lacking. To address this, we built a comprehensive framework, Gene Regulatory nETwork Analysis (GRETA), available as a Snakemake pipeline, that includes state of the art methods decomposing their different steps in a modular manner. With it, we found that the GRNs were highly sensitive to methods choices, such as changes in random seeds, or replacing steps in the inference pipelines, as well as whether they use paired or unpaired multimodal data. Although the obtained networks performed well in predictive evaluation tasks and partially recovered known interactions, they struggled to capture causal relationships from perturbation assays. Our work brings attention to the challenges of inferring GRNs from single-cell omics, offers guidelines, and presents a flexible framework for developing and testing new approaches. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/629764v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@11a452forg.highwire.dtl.DTLVardef@1b44cb9org.highwire.dtl.DTLVardef@190dbdorg.highwire.dtl.DTLVardef@d4f66b_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS

The molecular identity of a cell results from a complex interplay between heterogeneous molecular layers. Recent advances in single-cell sequencing technologies have opened the possibility to measure such molecular layers of regulation. Here, we present HuMMuS, a new method for inferring regulatory mechanisms from single-cell multi-omics data. Differently from the state-of-the-art, HuMMuS captures cooperation between biological macromolecules and can easily include additional layers of molecular regulation. We benchmarked HuMMuS with respect to the state-of-the-art on both paired and unpaired multi-omics datasets. Our results proved the improvements provided by HuMMus in terms of TF targets, TF binding motifs and regulatory regions prediction. Finally, once applied to snmC-seq, scATAC-seq and scRNA-seq data from mouse brain cortex, HuMMuS enabled to accurately cluster scRNA profiles and to identify potential driver TFs.

bioinformatics↗