Biology subjects

Ttoouli, D.

Publications and source records attributed to Ttoouli, D..

4 recordsLinked to original sources

bioRxiv2026

CITE-seq of murine bone marrow plasmacytoid dendritic cells and haematopoietic progenitor cells in sepsis

This dataset contains single-cell transcriptomic and surface protein profiles of bone marrow cells enriched for plasmacytoid dendritic cells (pDCs) and haematopoietic progenitor populations from septic and sham-operated mice. Sepsis was induced in BALB/c wild-type mice using cecal ligation and puncture (CLP) of moderate severity, with sham surgery as control. Bone marrow was collected 36 hours after surgery, depleted for selected lineage-positive cells by fluorescence-activated cell sorting, and processed using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq). Sample demultiplexing was performed using hashtag oligonucleotides. The dataset includes gene expression matrices, antibody-derived tag counts, and barcode assignments for 13,725 cells, with a comparable distribution of sham and CLP samples across the total cell number. It may be used to study transcriptional states, surface marker expression, and phenotypic diversity of bone marrow cells in sepsis, to compare with other immunological and haematological single-cell datasets, or to train and benchmark computational tools for multimodal single-cell data analysis.

immunology↗

bioRxiv2025

Multiplets in scRNA-seq data: extent of the problem and efficacy of methods for removal

Multiplets--droplets that capture more than one cell--are a known artefact in droplet-based single-cell RNA sequencing (scRNA-seq), yet their prevalence and impact remain underestimated. In this study, we assess the frequency of multiplets across diverse publicly available datasets and evaluate how well commonly used detection tools are able to identify them. Using cell hashing data to determine a lower bound of the true multiplet rate, we demonstrate that commonly used heuristic estimations systematically underestimate multiplet rates, and that existing tools--despite optimized parameters--detect only a small subset of cell-hashing multiplets. We further refine a Poisson-based model to estimate the true multiplet rate, revealing that actual rates can exceed heuristic predictions by more than twofold. Downstream analyses are significantly affected by multiplets: they are not confined to isolated clusters but are distributed throughout the transcriptional landscape, where they distort clustering and cell type annotation. Using both quantitative and qualitative approaches, we visualize these effects and show that cell-hashing-informed multiplet removal eliminates artefactual clusters and improves annotation clarity, whereas computationally detected multiplets fail to fully remove artefacts in the most common experimental contexts. Our findings confirm that multiplet contamination remains a pervasive and under-addressed issue in scRNA-seq analysis. Since most datasets lack multiplexing, researchers must often rely on heuristics and limited tools, leaving many multiplets unidentified. We advocate for more robust multiplet-detection strategies, including multimodal validation, to ensure more accurate and interpretable scRNA-seq results.

bioinformatics↗

bioRxiv2023

scBubbletree: quantitative visualization of single cell RNA-seq data

MotivationVisualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. ResultsWe present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as "bubbles" at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. Availability and Implementationthe R package scBubbletree is freely available at: https://bioconductor.org/packages/scBubbletree/ Contactsimo.kitanovski@uni-due.de, daniel.hoffmann@uni-due.de

bioinformatics↗

bioRxiv2022

Peyers Patch B cells undergo cell death via neutrophil-released toxic DNA following sterile tissue injury

Lymphocyte contraction (LC) in central immune organs is a concomitant of sterile tissue injury, for example after stroke. Intestinal Peyers patches (PP) harbor large numbers of B cells, but how sterile tissue injury leads to LC in PP has not been explored. We observed rapid and macroscopically evident shrinkage of PP after stroke and myocardial infarction. Light-sheet fluorescence microscopy and flow cytometry revealed a strong reduction in the number of PP-resident B cells. Mechanistically, tissue injury triggered the activation of neutrophils that released B cell-toxic neutrophil extracellular traps (NETs) decorated with citrullinated histone-H3. Antibody-mediated or genetically induced neutrophil-loss, NETs-degradation or blockade of their generation completely reversed B cell loss and preserved the tissue architecture of PP. We also found NET-like elements in human post-stroke plasma. Hence, we propose that targeting NET-generation or -function counteracts post-injury B cell contraction in PP and thereby maintains immune homeostasis at mucosal barriers. In briefHigh numbers of B cells reside in the intestinal Peyers patches. Tuz et al. revealed that in response to sterile tissue injury, activated neutrophils release histone-decorated DNA into the circulation which induces B cell death. The loss of B cells results in the shrinkage of Peyers patches and reduced amounts of secretory IgA. HighlightsO_LIStroke and myocardial infarction induce the melting of Peyers patch C_LIO_LILight-sheet microscopy and cytometry revealed B cell loss in Peyers patch C_LIO_LIPost-injury activated neutrophils release NETs and trigger B cell death C_LIO_LIInhibition of NETs rescues B cell loss and degeneration of Peyers patch C_LI

immunology↗