Biology subjects

Salem, D.

Publications and source records attributed to Salem, D..

3 recordsLinked to original sources

bioRxiv2026

Molecular adaptations of activated T-cells in an inflammation-associated schizophrenia sub-group

Thirty-five percent of people with schizophrenia-related disorders (SRD) form a high-inflammation subgroup defined by elevated anti-gliadin antibodies (AGA+) and inflammatory proteins and associated with an increased severity of negative symptoms. However, the immune mechanisms mediating these effects remain poorly defined. Here, we characterized transcriptional signatures of peripheral immune cells in AGA+ SRD (n=7) compared to AGA-negative (AGA-) SRD (n=3) and healthy controls (HC; n=5), using single-cell RNA-sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs). AGA+ SRD was associated with increased abundance of T-helper-17 cells (Th17), T-follicular helper-1 (Tfh1), CD5+ B cells, plasmacytoid dendritic cells (pDCs), and several CD8+ T cell subsets, including memory and Natural Killer-T-like activated subsets. In parallel, AGA-SRD exhibited a higher abundance of several monocyte subsets compared to either AGA+ SRD or HC. Pathway analysis revealed upregulation in AGA+ SRD of JAK/STAT, type I Interferon, and IL-6 signaling pathways in distinct subset of activated T-cells. Collectively, these results define a unique T cell predominant inflammatory signature in AGA+ SRD, as well as potential targets for therapeutic intervention.

genomics↗

bioRxiv2024

Projection Statistics ProST Online statistical assessment of group separation in data projection analysis

MotivationUnsupervised data projection for the determination of trends in the data, visualization of multidimensional data in a reduced dimension space or feature space reduction through combination of data is a major step in data mining. Methods such as Principal Component Analysis or t-Distribution Stochastic Neighbor Embedding are regularly used as one of the first steps in computational biology or omics investigation. However, the significance of the separation of sample groups by these methods generally relies on visual assessment. User-friendly application for different projection methods, each focusing on distinct data properties, are needed as well as a rigorous method for statistical determination of the significance of separation of groups of interest in each dataset. ResultsWe present Projection STatistics (ProST), a user-friendly solution for data projection analysis providing three unsupervised (PCA, t-SNE and UMAP) and one supervised (LDA) approach. For each method we are including a novel statistical investigation of the significance of group separation with Mann-Whitney U-rank or t-test analysis as well as necessary preprocessing steps. ProST provides an unbiased, objective application of the determination of the significance of the separation of measurement groups through either linear or manifold projection analysis with methods ranging from a focus on the separation of points based on major variances or on point proximities based on distance. AvailabilityThe ProST software application is freely available at https://complimet.ca/shiny/ProST/ with source code provided on https://github.com/complimet/prost. Contactdanny.salem@nrc-cnrc.gc.ca or Miroslava.cuperlovic-culf@nrc-cnrc.gc.ca Supplementary informationSupplementary help pages are provided at https://complimet.ca/shiny/ProST/.

bioinformatics↗

bioRxiv2020

YeastNet: Deep Learning Enabled Accurate Segmentation of Budding Yeast Cells in Bright-field Microscopy

Accurate and efficient segmentation of live-cell images is critical in maximising data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labelling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub. https://github.com/kaernlab/YeastNet

bioinformatics↗