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Priyadarshani, P.

Publications and source records attributed to Priyadarshani, P..

2 recordsLinked to original sources

Differential chondrogenic differentiation between iPSC-derived from healthy and OA cartilage is associated with changes in epigenetic and metabolic transcriptomic signatures

Induced pluripotent stem cells (iPSCs) are potential cell sources for regenerative medicine. The iPSCs exhibit a preference for lineage differentiation to the donor cell type indicating the existence of memory of origin. Although the intrinsic effect of the donor cell type on differentiation of iPSCs is well recognized, whether disease-specific factors of donor cells influence the differentiation capacity of iPSC remains unknown. Using viral based reprogramming, we demonstrated the generation of iPSCs from chondrocytes isolated from healthy (AC-iPSCs) and osteoarthritis cartilage (OA-iPSCs). These reprogrammed cells acquired markers of pluripotency and differentiated into uncommitted-mesenchymal progenitors. Interestingly, AC-iPSCs exhibited enhanced chondrogenic potential as compared OA-iPSCs and showed increased expression of chondrogenic genes. Pan-transcriptome analysis showed that chondrocytes derived from AC-iPSCs were enriched in molecular pathways related to energy metabolism and epigenetic regulation, together with distinct expression signature that distinguishes them from OA-iPSCs. The molecular tracing data demonstrated that epigenetic and metabolic marks were imprint of original cell sources from healthy and OA-chondrocytes. Our results suggest that the epigenetic and metabolic memory of disease may predispose OA-iPSCs for their reduced chondrogenic differentiation and thus regulation at epigenetic and metabolic level may be an effective strategy for controlling the chondrogenic potential of iPSCs.

cell biology↗

Integration of imaging modalities with lipidomic characterization to investigate MSCs potency metrics

Mesenchymal stem cells (MSCs) are widely used as therapeutics targets for numerous autoimmune diseases. However, MSC therapies have had limited success so far in clinical trials, mainly being heterogenous population it is difficult to determine MSCs efficiencies. It is critical to understand internal signaling of individual MSCs population that directly affect the cell phenotype. Lipid signaling is closely associated with cell shape so, a holistic approach to understand how changes in lipid metabolites trickles all the way to single cell phenotype could reveal deeper understanding of MSCs functional regulation. So, we aim to evaluate lipid metabolic profiles of single cell MSCs with known variability in immune regulation and explore the phenotypic changes that occur because of differences in lipid signaling. We use longitudinal label free phase imaging strategies to obtain cell phenotypic features which are directly correlated with single cell lipid metabolome obtained using advanced MALDI-MSI technique. Correlation maps indicate associations between lipid signaling and phenotypic changes in MSCs. Moreover, a novel machine learning clustering approach detects the heterogeneity in the MSCs subpopulation then methodically see how each heterogenous population is being impacted by the changes in lipid profiles which could be linked to the functional behaviors of the cell.

cell biology↗