Direct studies focusing on the human brain are difficult to plan and conduct due to ethical and practical reasons. The advent of human pluripotent stem cell (hPSC)-derived neurons has revolutionized the research of the human brain and central nervous system, but relevant analytical techniques have been much less explored. Herein, we have designed a novel bioanalytical strategy to discover the characteristics of human neurogenesis using liquid chromatography-mass spectrometry-based quantitation and time-dependent metabolomics in combination with hPSC-derived neural constructs. To examine the growth of neurons in vitro, a quantitative method for the simultaneous measurement of N-acetylaspartic acid (NAA) and N-acetylglutamic acid (NAG) in a culture medium was established. The analysis of endogenous NAA and NAG concentrations over 28 days of neural cell culture not only illustrated the growth and maturation process of neural progenitors, but also confirmed the successful achievement of human neural constructs. Depending on the quantitative results, day 0, 10, 18, and 28 samples representing different growth phases were selected for further investigation of the global metabolic changes in developing human neurons. A versatile non-targeted, time-dependent metabolomics study identified 17 significantly changed metabolites and revealed the altered metabolic pathways including amino acid metabolism (tryptophan, phenylalanine, aspartate and beta-alanine metabolisms), pantothenate and coenzyme A biosynthesis, fatty acid metabolism, and purine and pyrimidine metabolism. The new metabolite profiles and overall metabolic pathways advance our understanding of human neurodevelopment. Additionally, the bioanalytical approach proposed in this study opens an interesting window for the capture and evaluation of the complex metabolic states of human neural cells, which would potentially be utilized in other in vitro models relevant to pathophysiology and treatment of neurological disorders, benefiting biomarker discovery and metabolic mechanism interpretation.
Read full abstract