Biology subjects

Solovey, M.

Publications and source records attributed to Solovey, M..

4 recordsLinked to original sources

bioRxiv2024

Community assesses differential cell communication using large multi-sample case-control scRNAseq datasets.

Cell-cell communication is essential for physiological tissue function. In disease, this communication often gets disbalances by changes in the tissue cell type composition, fraction of cell engaged in communication and the rising or dropping expression levels of ligands, receptors and adhesion molecules. The changes in all these components of communication can be studied using single cell RNA-sequencing (scRNAseq) methods. With dropping sequencing costs, it is now possible to perform scRNAseq studies in larger cohorts of case and control samples to better address the heterogeneity of diseases. Here we present community, an R-based tool that is designed to perform differential communication analysis using scRNAseq between large cohorts of cases and controls. Community is able to reconstruct communication between different cell types both in the case and the control cohort of a dataset, and subsequently analyze which communication channels are affected in disease. Community is the first tool that integrates cell type abundance into the calculation of an interaction strength. Community is also able to disentangle the mechanisms underlying these changes, as well as detect interactions that are kept compensated by a sender or a receiver despite the disbalanced signaling from the counterpart. We tested community on two disease entities, ulcerative colitis and acute myeloid leukemia, using published scRNAseq datasets. We compared the performance of our tool to other differential communication pipelines, which community outperformed in speed and robustness. Overall, community is a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets disentangling the driving mechanisms of communication shifts in disease.

bioinformatics↗

bioRxiv2023

ARID1A Mutations Protect Follicular Lymphoma from FAS-dependent Immune Surveillance by Reducing RUNX3/ETS1-Driven FAS-Expression

The cell death receptor FAS and its ligand (FASLG) play crucial roles in the selection of B cells during the germinal center (GC) reaction. Failure to eliminate potentially harmful B cells via FAS can lead to lymphoproliferation and the development B cell malignancies. The classic form of follicular lymphoma (FL) is a prototypic GC-derived B cell malignancy, characterized by the t(14;18) (q32;q21)IGH::BCL2 translocation and overexpression of antiapoptotic BCL2. Additional alterations were shown to be clinically relevant, including mutations in ARID1A. ARID1A is part of the SWI/SNF nucleosome remodeling complex that regulates DNA accessibility ("openness"). However, the mechanism how ARID1A mutations contribute to FL pathogenesis remains unclear. We analyzed 151 FL biopsies of patients with advanced stage disease at initial diagnosis and found that ARID1A mutations were recurrent and mainly disruptive, with an overall frequency of 18%. Additionally, we observed that ARID1A mutant FL showed significantly lower FAS protein expression in the FL tumor cell population. Functional experiments in BCL2-translocated lymphoma cells demonstrated that ARID1A is directly involved in the regulation of FAS, and ARID1A loss leads to decreased FAS protein and gene expression. However, ARID1A loss did not affect FAS promotor openness. Instead, we identified and experimentally validated a previously unknown co-transcriptional complex consisting of RUNX3 and ETS1 that regulates FAS expression, and ARID1A loss leads to reduced RUNX3 promotor openness and gene expression. The reduced FAS levels induced by ARID1A loss rendered lymphoma cells resistant to both soluble and T cell membrane-anchored FASLG-induced apoptosis. In summary, we have identified a functionally and clinically relevant mechanism how FL cells can escape FAS-dependent immune surveillance, which may also impact the efficacy of T cell-based therapies, including bispecific antibodies and CAR T cells.

cancer biology↗

bioRxiv2021

Characterisation of the transcriptional dynamics underpinning the function, fate, and migration of the mouse Anterior Visceral Endoderm

During early post-implantation development of the mouse embryo, the Anterior Visceral Endoderm (AVE) differs from surrounding visceral endoderm (VE) in its migratory behaviour and ability to restrict primitive streak formation to the opposite side of the egg cylinder. In order to characterise the molecular basis for the unique properties of the AVE, we combined single-cell RNA-sequencing of the VE prior to and during AVE migration, with high-resolution imaging, short-term lineage labelling, phosphoproteomics and pharmacological intervention. This revealed the transient nature of the AVE, the emergence of heterogeneities in AVE transcriptional states relative to position of cells, and its prominence in establishing gene expression asymmetries within the spatial constraints of the embryo. We identified a previously unknown requirement of Ephrin- and Semaphorin-signalling for AVE migration. These findings point to a tight coupling of transcriptional state and position in the AVE and reveal molecular heterogeneities underpinning its migratory behaviour and function.

developmental biology↗

bioRxiv2019

COMUNET: a tool to explore and visualize intercellular communication

Intercellular communication plays an essential role in multicellular organisms and several algorithms to analyse it from single-cell transcriptional data have been recently published, but the results are often hard to visualize and interpret. We developed COMUNET (Cell cOMmunication exploration with MUltiplex NETworks), a tool that streamlines the interpretation of the results from cell-cell communication analyses. COMUNET uses multiplex networks to represent and cluster all potential communication pathways between cell types. The algorithm also enables the search for specific patterns of communication and can perform comparative analysis between two biological conditions. To exemplify its use, here we apply COMUNET to investigate cell communication patterns in single-cell transcriptomic datasets from mouse embryos and from an acute myeloid leukemia patient at diagnosis and after treatment. Our algorithm is available on GitHub, along with all the code to perform the analysis reported.

bioinformatics↗