Filbertine Grace, Abdullah Genna A, Gill Lucy, Grosman Rudi, Phelan Marie M, Chiewchengchol Direkrit, Hirankarn Nattiya, Wright Helen L
Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
High-Field NMR Centre, University of Liverpool, Liverpool L69 7ZB, UK.
Metabolites. 2025 Sep 15;15(9):612. doi: 10.3390/metabo15090612.
Untargeted H NMR metabolomics is a robust and reproducible approach used to study the metabolism in biological samples, providing unprecedented insight into altered cellular processes associated with human diseases. Metabolomics is increasingly used alongside other techniques to detect an instantaneous altered cellular function, for example, the role of neutrophils in the inflammatory response. However, in some clinical settings, blood samples may be limited, restricting the amount of cellular material available for a metabolomic analysis. In this study, we wanted to establish an optimal 1D H NMR metabolomic pipeline for use with human neutrophil samples with low amounts of input material. : We compared the effect of different neutrophil isolation protocols on metabolite profiles. We also compared the effect of the absolute cell counts (100,000 to 5,000,000) on the identities of metabolites that were detected with an increasing number of scans (NS) from 256 to 2048. : The variance in the neutrophil profile was equivalent between the isolation methods, and the choice of isolation method did not significantly alter the metabolite profile. The minimum number of cells required for the detection of neutrophil metabolites was 400,000 at an NS of 256 for the spectra acquired with a cryoprobe (700 MHz). Increasing the NS to 2048 increased metabolite detection at the very lowest cell counts (<400,000 neutrophils); however, this was associated with a significant increase in the analysis time, which would be rate-limiting for large studies. The application of a correlation-reliability-score-filtering method to the spectral bins preserved the essential discriminatory features of the PLS-DA models whilst improving the dataset robustness and analytical precision.
非靶向氢核磁共振代谢组学是一种用于研究生物样品中新陈代谢的强大且可重复的方法,它能以前所未有的深度洞察与人类疾病相关的细胞过程变化。代谢组学越来越多地与其他技术一起用于检测瞬时变化的细胞功能,例如中性粒细胞在炎症反应中的作用。然而,在某些临床环境中,血液样本可能有限,从而限制了可用于代谢组学分析的细胞材料数量。在本研究中,我们希望建立一种适用于低输入量人中性粒细胞样本的最佳一维氢核磁共振代谢组学流程。我们比较了不同中性粒细胞分离方案对代谢物谱的影响。我们还比较了绝对细胞计数(10万至500万)对随着扫描次数(NS)从256增加到2048所检测到的代谢物种类的影响。中性粒细胞谱的差异在分离方法之间是等同的,并且分离方法的选择并未显著改变代谢物谱。对于使用低温探头(700 MHz)采集的光谱,在NS为256时,检测中性粒细胞代谢物所需的最少细胞数为40万。将NS增加到2048可在极低细胞计数(<40万中性粒细胞)时增加代谢物检测;然而,这与分析时间的显著增加相关,这对于大型研究可能会成为限速因素。将相关可靠性评分过滤方法应用于光谱区间,在提高数据集稳健性和分析精度的同时,保留了PLS-DA模型的基本判别特征。