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Baghaei, K.

Publications and source records attributed to Baghaei, K..

2 recordsLinked to original sources

Single-cell fluorescence imaging reveals heterogeneity in senescence biomarkers and identifies rapamycin-responsive sub-populations

Cellular senescence is a state of irreversible cell cycle arrest accompanied by a distinctive inflammatory secretory profile known as the senescence-associated secretory phenotype (SASP). While various biomarkers, such as senescence-associated beta-galactosidase (SA-{beta}gal), EdU incorporation, P21, and P16, are used to identify senescent cells, no single biomarker universally defines cellular senescence, and current methods often fail to address heterogeneity in biomarker expression levels. This study leverages single-cell fluorescence imaging to assess multiple senescence markers including SA-{beta}gal enzymatic activity, P21 and IL-6 expression, and nuclear and cell area, in chemotherapy-induced (mitomycin C) and oxidative stress-induced (D-galactose) senescence models in human fibroblasts. Our findings reveal significant heterogeneity in SA-{beta}gal activity and distinct sub-populations within senescent cells. Nuclear and cell area measurements emerged as robust indicators of cellular senescence, displaying similar variability across individual cells. Importantly, we identified specific nuclear area sub-populations that strongly correlate with IL-6 expression levels, demonstrating a relationship between the heterogeneous expression of senescence biomarkers and the SASP. To address this heterogeneity, we introduced an induction threshold method to more accurately quantify the percentage of cells expressing senescence biomarkers. Furthermore, in both senescence models, we observed that rapamycin, a well-known senomorphic agent, selectively targets specific biomarker-expressing sub-populations. This study underscores the value of assessing cellular heterogeneity in senescence research and provides an improved approach for analysing senescence markers in diverse cellular contexts.

cell biology↗

Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

BackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment. Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < -1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF {beta}, and TNF were identified. ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

bioinformatics↗