Biology subjects

Alayoubi, Y.

Publications and source records attributed to Alayoubi, Y..

3 recordsLinked to original sources

bioRxiv2026

Reg3β removes aged neutrophils after myocardial infarction

AO_SCPLOWBSTRACTC_SCPLOWRecruitment and resolution of immune cell accumulation after myocardial infarction (MI) is critical for effective wound healing and prevention of maladaptive remodeling. Numerous signals are known to recruit neutrophils, critical drivers of inflammation, but knowledge about signals containing and resolving their accumulation is limited. We discovered that Regenerating-islet derived protein 3 beta (REG3{beta}) limits the persistence of neutrophils after MI, thereby promoting resolution of inflammation. REG3{beta} selectively binds to aged and hyperactive neutrophils and induces rapid cell death, enabling clearance via macrophage-mediated efferocytosis. Selective binding of REG3{beta} is achieved by interaction with paucimannosylated proteins that translocate from azurophile granules to the plasma membrane in an activation- and age-dependent manner. Endocytotic uptake and accumulation of REG3{beta} in lysosomes initiates programmed cell death of neutrophils via lyosomal membrane permeabilization and release of cathepsins. Our work establishes REG3{beta} as a local immune checkpoint essential for neutrophil resolution and cardiac repair.

immunology↗

bioRxiv2025

SC-Framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets

The accelerated development of single-cell technologies has profoundly impacted the field of biological research, facilitating unparalleled insights into cellular heterogeneity. However, this progress has also produced new computational challenges in the field of bioinformatics: single-cell datasets are increasingly high-dimensional, multimodal, and large-scale, while analysis workflows often remain fragmented, data type specific, ad hoc, and difficult to reproduce. The prevailing methodologies are dependent on a combination of public tools, which hinders the reproducibility of results, limits scalability, and complicates the efforts to establish benchmarks. The necessity for a higher-level, unified framework for single-cell data analysis is paramount to address these inherent limitations. Here, we introduce the SC-Framework, providing the integration of standardized data structures, declarative workflows and standardized computational backends in a containerized environment, enabling analysts to focus on biological interpretation rather than technical overhead. SC-Framework is available at GitHub (https://github.com/loosolab/SC-Framework).

bioinformatics↗

bioRxiv2023

Scanpro: robust proportion analysis for single cell resolution data

In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation such as adjustment of gene expression. However, in addition to shifting their transcriptional profile, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.

bioinformatics↗