Real-Time Cell Analysis Using Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy at Single and Bulk Cellular Levels

质谱法 核磁共振 光谱学 核磁共振波谱 化学 材料科学 分析化学(期刊) 物理 色谱法 量子力学
作者
S.H. Pawar,Deepak Kumar,Rushikesh Biradar,Loganathan Gayathri,Pirangi Srikanth,Khaja Moinuddin Shaik,Sukhendu Nandi
出处
期刊:Critical Reviews in Analytical Chemistry [Informa]
卷期号:: 1-31
标识
DOI:10.1080/10408347.2025.2572754
摘要

Outlining biological processes and disease mechanisms requires a real-time understanding of cellular metabolism. Mass spectrometry (MS) and Nuclear magnetic resonance (NMR) serve as potent analytical methods for metabolomics, leveraging their advantages in the identification and quantification of metabolites. In this review we have discussed MS and NMR based techniques, such as matrix-assisted laser desorption/ionization (MALDI), secondary ionization mass spectrometry (SIMS), desorption electrospray ionization (DESI), direct analysis in real time (DART), and nano electrospray ionization (nano ESI), to develop and implement a MS based technique in its live state for single cell and NMR strategies for thorough, real-time analysis of cell metabolism at bulk cellular level. Single-cell metabolomic investigations using mass spectrometry is a valuable tool for understanding cellular heterogeneity and cell-to-cell variation. However, they can reveal hidden processes and heterogeneity across cells that are often missed by bulk cell analysis. Overcoming the inherently low sensitivity of NMR is crucial for omics studies. This review examines hyperpolarization techniques, including dynamic nuclear polarization (DNP), parahydrogen-induced polarization, sample amplification by reversible exchange (SABER), high-resolution magic angle spinning (HRMAS) NMR, and in vivo magnetic resonance spectroscopy (MRS) for the analysis of live bulk cells. Our discussion encompasses the technological platforms and recent applications of these techniques, utilizing both NMR and MS, along with an overview of metabolomics data analysis tools, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).
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