Biology subjects

Ghannoum, S.

Publications and source records attributed to Ghannoum, S..

4 recordsLinked to original sources

bioRxiv2026

Distinct synovial fluid B-cell differentiation and activation profiles in rheumatoid arthritis

CD11c+CD21- B cells are overrepresented in rheumatoid arthritis (RA). Here we delineate their functional state at site of inflammation and its molecular basis. Phenotypic profiling of paired blood and synovial fluid from patients with anti-citrullinated protein antibody (ACPA) seropositive RA (N=12), seronegative RA (N=10) and spondyloarthritis (N=8), demonstrated a striking enrichment of CD11c+CD21- B cells in synovial fluid in all three diseases (average of CD19+ cells: 67%, 49% and 51%). In ACPA-positive RA we identified increased proportions of CD11c+IgG+CD27low cells and several stages of pre-plasmablasts. The CD11c+CD21- cells had a phenotype indicating BCR activation, proliferation, and antigen presentation. This B-cell profile was mirrored by a LAG3+CTLA4+TIGIT+ peripheral helper T-cell program in ACPA-positive RA, contrasting a CCR6+ Th17-skewing in seronegative RA. Single-cell spatial proteomics revealed a unique CD11chighCD21 low B-cell surface architecture including a BCR co-receptor-tetraspanin signature with CD72-CD81 co-localization, which was elevated in RA. CD11c+ cells also displayed CD44-CD52/CD59 and MHCII-CD40/CD84 immune-modules absent in other B-cell subsets. In summary, CD11c+CD21- B cells are highly diverse, and while an activated antigen-experienced synovial profile was shared between disease groups, ACPA+ RA showed evidence of increased extrafollicular plasmablast differentiation. Surface receptor organization implicated a favoured BCR responsiveness, complement resistance and antigen presentation.

immunology↗

bioRxiv2022

A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression

Few studies so far have investigated the impact of different cell migration traits on tumor progression. To address this, we developed a mathematical model wherein cells migrate in two-dimensional space, divide, die or intravasate into the vasculature. Exploring a wide range of speed and persistence combinations, we find that tumor growth positively correlates with increasing speed and higher persistence. As a biologically relevant example, we focused on Golgi fragmentation induced by depletion of Giantin, a Golgi matrix protein, the downregulation of which correlates with poor patient survival. Applying the migration and invasion traits of Giantin depleted cells to our mathematical model, we predict that loss of Giantin increases the number of intravasating cells. This prediction was validated, by showing that circulating tumor cells express significantly less Giantin than primary tumor cells. Altogether, our computational model identifies cell migration traits that regulate tumor progression and uncovers a role of Giantin in breast cancer progression.

cancer biology↗

bioRxiv2020

CellMAPtracer: A user-friendly tracking tool for long-term migratory and proliferating cells

Background Cell migration is a fundamental cell biological process of key importance in health and disease. Advances in imaging techniques have paved the way to monitor cells motility. An ever-growing collection of computational tools to track cells has improved our ability to analyze moving cells. However, few if any tools let the user supervise and correct cell tracks that are automatically detected. Thus, we developed CellMAPtracer, a tool to track cells in a semi-automated supervised fashion, thereby improving the accuracy and facilitating the long term tracking of migratory and dividing cells. CellMAPtracer is available with a user-friendly graphical user interface and does not require any coding or programming skills.Results We used CellMAPtracer to track fluorescently-labelled BT549 breast cancer cells. It allowed us to track dividing cells and determine the fate of the daughter cells with respect to migration speed or directionality and cell cycle length. Of note, we were able to track multi-daughter divisions, wherein a cell divides and gives rise to more than two cells. We also identified a not previously described speed change in the terminal phase of the cell cycle.Conclusion CellMAPtracer is a software tool for tracking cell migration and proliferation through a user-friendly interface that has a great potential to facilitate new discoveries in cell biology.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2019

DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the trasncriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a computational pipeline using Jupyter notebooks. We also provide a user-friendly, cloud version of the notebook for researchers with very limited programming skills. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation datatset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. These notebooks can be used as tutorials for training purposes and will guide researchers to explore their scRNA-seq data.

bioinformatics↗