Choi Yong Seok, Lee Kelvin H
Department of Chemical Engineering and Delaware Biotechnology Institute, University of Delaware, Newark, DE, 19711, USA.
College of Pharmacy, Dankook University, 119 Dandae-ro, Dongnam-gu, Cheonan-Si, Chungnam, 330-714, South Korea.
Arch Pharm Res. 2016 Mar;39(3):390-7. doi: 10.1007/s12272-015-0663-y. Epub 2015 Sep 24.
Alzheimer's disease (AD) is the most common type of dementia, but early and accurate diagnosis remains challenging. Previously, a panel of cerebrospinal fluid (CSF) biomarker candidates distinguishing AD and non-AD CSF accurately (>90 %) was reported. Furthermore, a multiple reaction monitoring (MRM) assay based on nano liquid chromatography tandem mass spectrometry (nLC-MS/MS) was developed to help validate putative AD CSF biomarker candidates including proteins from the panel. Despite the good performance of the MRM assay, wide acceptance may be challenging because of limited availability of nLC-MS/MS systems in laboratories. Thus, here, a new MRM assay based on conventional LC-MS/MS is presented. This method monitors 16 peptides representing 16 (of 23) biomarker candidates that belonged to the previous AD CSF panel. A 30-times more concentrated sample than the sample used for the previous study was loaded onto a high capacity trap column, and all 16 MRM transitions showed good linearity (average R(2) = 0.966), intra-day reproducibility (average coefficient of variance (CV) = 4.78 %), and inter-day reproducibility (average CV = 9.85 %). The present method has several advantages such as a shorter analysis time, no possibility of target variability, and no need for an internal standard.
阿尔茨海默病(AD)是最常见的痴呆类型,但早期准确诊断仍具有挑战性。此前,有报道称一组脑脊液(CSF)生物标志物候选物能够准确区分AD和非AD脑脊液(准确率>90%)。此外,还开发了一种基于纳升液相色谱串联质谱(nLC-MS/MS)的多反应监测(MRM)分析方法,以帮助验证包括该组蛋白在内的假定AD脑脊液生物标志物候选物。尽管MRM分析方法性能良好,但由于实验室中nLC-MS/MS系统的可用性有限,其广泛应用可能具有挑战性。因此,本文提出了一种基于传统LC-MS/MS的新型MRM分析方法。该方法监测代表先前AD脑脊液组中23种生物标志物候选物中的16种的16种肽段。将比先前研究中使用的样品浓缩30倍的样品加载到高容量捕集柱上,所有16种MRM跃迁均显示出良好的线性(平均R(2)=0.966)、日内重现性(平均变异系数(CV)=4.78%)和日间重现性(平均CV=9.85%)。本方法具有分析时间短、无目标变异性可能性且无需内标等优点。