Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS One. 2012;7(2):e32406. doi: 10.1371/journal.pone.0032406. Epub 2012 Feb 29.
We present the first demonstration of glycated albumin detection and quantification using Raman spectroscopy without the addition of reagents. Glycated albumin is an important marker for monitoring the long-term glycemic history of diabetics, especially as its concentrations, in contrast to glycated hemoglobin levels, are unaffected by changes in erythrocyte life times. Clinically, glycated albumin concentrations show a strong correlation with the development of serious diabetes complications including nephropathy and retinopathy. In this article, we propose and evaluate the efficacy of Raman spectroscopy for determination of this important analyte. By utilizing the pre-concentration obtained through drop-coating deposition, we show that glycation of albumin leads to subtle, but consistent, changes in vibrational features, which with the help of multivariate classification techniques can be used to discriminate glycated albumin from the unglycated variant with 100% accuracy. Moreover, we demonstrate that the calibration model developed on the glycated albumin spectral dataset shows high predictive power, even at substantially lower concentrations than those typically encountered in clinical practice. In fact, the limit of detection for glycated albumin measurements is calculated to be approximately four times lower than its minimum physiological concentration. Importantly, in relation to the existing detection methods for glycated albumin, the proposed method is also completely reagent-free, requires barely any sample preparation and has the potential for simultaneous determination of glycated hemoglobin levels as well. Given these key advantages, we believe that the proposed approach can provide a uniquely powerful tool for quantification of glycation status of proteins in biopharmaceutical development as well as for glycemic marker determination in routine clinical diagnostics in the future.
我们首次展示了无需添加试剂即可使用拉曼光谱检测和定量糖化白蛋白的方法。糖化白蛋白是监测糖尿病患者长期血糖史的重要标志物,尤其是因为其浓度与糖化血红蛋白水平不同,不受红细胞寿命变化的影响。在临床上,糖化白蛋白浓度与包括肾病和视网膜病变在内的严重糖尿病并发症的发展密切相关。在本文中,我们提出并评估了拉曼光谱法测定该重要分析物的功效。通过利用通过滴涂沉积获得的预浓缩,我们表明白蛋白的糖化会导致振动特征的微妙但一致的变化,借助多元分类技术,可以将糖化白蛋白与未糖化变体准确区分开来,准确率达到 100%。此外,我们证明,即使在远低于临床实践中通常遇到的浓度下,在糖化白蛋白光谱数据集上开发的校准模型也具有很高的预测能力。事实上,糖化白蛋白测量的检测限计算结果约比其最低生理浓度低四倍。重要的是,与现有的糖化白蛋白检测方法相比,该方法还完全无试剂,几乎不需要任何样品制备,并且有可能同时测定糖化血红蛋白水平。鉴于这些关键优势,我们相信该方法可以为生物制药开发中蛋白质糖化状态的定量以及常规临床诊断中的血糖标志物测定提供独特而强大的工具。