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Biology subjects

Harazin, A.

Publications and source records attributed to Harazin, A..

2 recordsLinked to original sources

Dynamic Responses to an Inflammatory Challenge Distinguish Metabolic Health Across Lean and Obese Individuals

Inflammation is a key driver of cardiometabolic disease, yet it remains unclear whether systemic inflammatory markers can distinguish metabolically healthy from unhealthy individuals or capture the temporal dynamics of inflammation. Here, we combined systemic immune profiling with a cantharidin-induced peripheral blister model to investigate dynamic inflammatory regulation across metabolic phenotypes in metabolically healthy lean (MHL), metabolically unhealthy lean (MUL), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) individuals. While systemic inflammation was elevated in obesity, differences between metabolically healthy and unhealthy groups were modest, with limited discrimination by plasma proteomics, circulating leukocyte phenotyping, and whole-blood transcriptomics. In contrast, the dynamic response to inflammatory challenge revealed pronounced differences at proteomic, cellular, and transcriptomic levels. Metabolically unhealthy individuals exhibited exaggerated early innate immune responses, impaired inflammatory resolution and tissue repair, reduced recruitment of reparative immune cells, and sustained T cell presence. Transcriptomic analyses further showed blunted dynamic gene regulation and defective epidermal barrier restoration. These findings indicate that metabolic health is better reflected in tissue-level inflammatory dynamics than in systemic measures.

immunology↗

Analysis of cortical cell polarity by imaging flow cytometry

Metastasis is the main cause of cancer-related death and therapies specifically targeting metastasis are highly needed. Cortical cell polarity (CCP) is a pro-metastatic property of circulating tumor cells (CTCs) affecting their ability to exit blood vessels and form new metastases that constitutes a promising point of attack to prevent metastasis. However, conventional fluorescence microscopy on single cells and manual quantification of CCP are time-consuming and unsuitable for screening of regulators. In this study, we developed an imaging flow cytometry (IFC)-based method for high-throughput screening of factors affecting CCP in melanoma cells. The artificial intelligence (AI)-supported analysis method we developed is highly reproducible, accurate, and orders of magnitude faster than manual quantification. Additionally, this method is flexible and can be adapted to include additional cellular parameters. In a small-scale pilot experiment using polarity-, cytoskeleton-or membrane-affecting drugs, we demonstrate that our workflow provides a straightforward and efficient approach for screening factors affecting CCP in cells in suspension and provide insights into the specific function of these drugs in this cellular system. The method and workflow presented here will facilitate large-scale studies to reveal novel cell-intrinsic as well as systemic factors controlling CCP during metastasis.

cell biology↗