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Nikitina, A. A.

Publications and source records attributed to Nikitina, A. A..

2 recordsLinked to original sources

Mass spectrometry imaging reveals early metabolic priming of cell lineage in differentiating human induced pluripotent stem cells

Induced pluripotent stem cells (iPSCs) hold great promise in regenerative medicine; however, few algorithms of quality control at the earliest stages of differentiation have been established. Despite lipids having known functions in cell signaling, their role in pluripotency maintenance and lineage specification is underexplored. We investigated changes in iPSC lipid profiles during initial loss of pluripotency over the course of spontaneous differentiation using co-registration of confocal microscopy and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging. We identified lipids that are highly informative of the temporal stage of the differentiation and can reveal lineage bifurcation occurring metabolically. Several phosphatidylinositol species emerged from machine learning analysis as early metabolic markers of pluripotency loss, preceding changes in Oct4. Manipulation of phospholipids via PI 3-kinase inhibition during differentiation manifested in spatial reorganization of the colony and elevated expression of NCAM-1. In addition, continuous inhibition of phosphatidylethanolamine N-methyltransferase during differentiation resulted in increased pluripotency maintenance. Our machine learning analysis highlights the predictive power of metabolic metrics for evaluating lineage specification in the initial stages of spontaneous iPSC differentiation.

bioengineering↗

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↗