Mass Spectrometry Imaging Reveals Early Metabolic Priming of Cell Lineage in Differentiating Human-Induced Pluripotent Stem Cells.
Arina A NikitinaAlexandria Van GrouwTanya RoysamDanning HuangFacundo M FernándezMelissa L KempPublished in: Analytical chemistry (2023)
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 the changes in iPSC lipid profiles during the initial loss of pluripotency over the course of spontaneous differentiation using the co-registration of confocal microscopy and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging. We identified phosphatidylethanolamine (PE) and phosphatidylinositol (PI) species that are highly informative of the temporal stage of differentiation and can reveal iPS cell lineage bifurcation occurring metabolically. Several PI species emerged from the machine learning analysis of MS data as the early metabolic markers of pluripotency loss, preceding changes in the pluripotency transcription factor Oct4. The manipulation of phospholipids via PI 3-kinase inhibition during differentiation manifested in the spatial reorganization of the iPS cell colony and elevated expression of NCAM-1. In addition, the continuous inhibition of phosphatidylethanolamine N -methyltransferase during differentiation resulted in the enhanced maintenance of pluripotency. Our machine learning analysis highlights the predictive power of lipidomic metrics for evaluating the early lineage specification in the initial stages of spontaneous iPSC differentiation.
Keyphrases
- induced pluripotent stem cells
- single cell
- mass spectrometry
- cell fate
- machine learning
- cell therapy
- high resolution
- transcription factor
- quality control
- big data
- liquid chromatography
- endothelial cells
- magnetic resonance imaging
- deep learning
- dna methylation
- tyrosine kinase
- ms ms
- computed tomography
- long non coding rna
- poor prognosis
- optical coherence tomography
- electronic health record
- bone marrow
- genetic diversity
- data analysis
- pluripotent stem cells