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UID:DSC-23008
DTSTART;TZID=Europe/Berlin:20260629T110000
SEQUENCE:1782711357
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URL:https://dresden-science-calendar.org/calendar/en/detail/23008
LOCATION:MPI-CBG\, Pfotenhauerstraße 10801307 Dresden
SUMMARY:Nikitina: Quantitative Metabolic Signatures of Stem Cell Fate: Mult
 imodal Imaging and Predictive Modeling of iPSC Differentiation
CLASS:PUBLIC
DESCRIPTION:Speaker: Arina Nikitina\nInstitute of Speaker: Weizmann Institu
 te of Science\, Rehovot\, Israel\nTopics:\n\n Location:\n  Name: MPI-CBG (
 MPI-CBG CBG Galleria)\n  Street: Pfotenhauerstraße 108\n  City: 01307 Dre
 sden\n  Phone: +49 351 210-0\n  Fax: +49 351 210-2000\nDescription: Induce
 d pluripotent stem cells offer a powerful route toward patient-specific di
 sease models and regenerative therapies\, but their clinical translation d
 epends on the ability to monitor\, predict\, and control differentiation o
 utcomes. Current quality-control strategies often rely on endpoint measure
 ments or transcriptional and protein markers that emerge only after major 
 cell fate decisions have already occurred. To address this gap we develope
 d experimental and computational approaches to identify early metabolic fe
 atures associated with pluripotency loss\, lineage specification\, and dif
 ferentiation success.  First\, we developed a multimodal imaging pipeline 
 that co-registers confocal fluorescence microscopy with MALDI mass spectro
 metry imaging\, enabling spatially resolved lipid measurements to be linke
 d with cell-state markers in iPSC colonies. Applying this approach to spon
 taneous differentiation revealed dynamic phospholipid remodeling during th
 e earliest stages of pluripotency loss. A subset of phosphatidylinositol s
 pecies emerged as early markers of cell-state transition\, preceding chang
 es in canonical pluripotency markers such as Oct4 and revealing metabolic 
 evidence of lineage bifurcation. Perturbation experiments further suggeste
 d that phospholipid metabolism is not only a passive marker of differentia
 tion\, but may participate in regulating pluripotency maintenance.  Finall
 y\, I will discuss how real-time oxygen consumption measurements can be us
 ed to predict the outcome of directed cardiomyocyte differentiation. By co
 mbining live metabolic sensing with machine learning analysis of time-seri
 es features\, we found that oxygen consumption dynamics during the first s
 everal days of differentiation were highly predictive of final cardiomyocy
 te yield\, suggesting a scalable route toward early in-process quality con
 trol for cell manufacturing.
DTSTAMP:20260713T003926Z
CREATED:20260625T053836Z
LAST-MODIFIED:20260629T053557Z
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