School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Anal Chem. 2023 Mar 21;95(11):4880-4888. doi: 10.1021/acs.analchem.2c04416. Epub 2023 Mar 10.
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 -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.
诱导多能干细胞（iPSCs）在再生医学中具有巨大的应用前景；然而，在分化的早期阶段，很少有质量控制算法被建立。尽管脂质在细胞信号传递中具有已知的功能，但它们在维持多能性和谱系特化方面的作用尚未得到充分探索。我们使用共聚焦显微镜和基质辅助激光解吸/电离（MALDI）质谱成像技术，研究了 iPSC 在自发分化过程中最初失去多能性时的脂质谱变化。我们确定了磷脂酰乙醇胺（PE）和磷脂酰肌醇（PI）的物种，这些物种对分化的时间阶段具有高度的信息性，可以揭示 iPS 细胞谱系分叉发生在代谢水平上。在对 MS 数据进行机器学习分析时，几种 PI 物种成为多能性丧失的早期代谢标志物，早于多能性转录因子 Oct4 的变化。在分化过程中通过 PI3-激酶抑制对磷脂的操纵，表现为 iPS 细胞集落的空间重组和 NCAM-1 的表达升高。此外，在分化过程中持续抑制磷脂酰乙醇胺甲基转移酶，导致多能性的维持增强。我们的机器学习分析突出了脂质组学指标在评估自发 iPSC 分化早期谱系特化方面的预测能力。
