Kumar K Kalyan, Chowdary M V P, Mathew Stanley, Rao Lakshmi, Krishna C Murali, Kurien Jacob
Center for Atomic and Molecular Physics, Manipal University, Manipal, 576104 Karnataka, India.
J Biophotonics. 2009 May;2(5):313-21. doi: 10.1002/jbio.200810046.
Proteomics is a promising approach for molecular understanding of neoplastic processes including response to treatment. Widely used 2D-gel electrophoresis/Liquid chromatography coupled with mass spectrometry (LC-MS) are time consuming and not cost effective. We have developed a high-sensitivity (femto/subfemtomoles of protein/20 mul) High Performance Liquid Chromatography-Laser Induced Fluorescence HPLC-LIF instrument for studying protein profiles of biological samples. In this study, we have explored the feasibility of classifying breast tissues by multivariate analysis of chromatographic data. We have analyzed 13 normal, 17 malignant, 5 benign and 4 post-treatment breast-tissue homogenates. Data was analyzed by Principal Component Analysis PCA in both unsupervised and supervised modes on derivative and baseline-corrected chromatograms. Our findings suggest that PCA of derivative chromatograms gives better classification. Thus, the HPLC-LIF instrument is not only suitable for generation of chromatographic data using femto/subfemto moles of proteins but the data can also be used for objective diagnosis via multivariate analysis. Prospectively, identified fractions can be collected and analyzed by biochemical and/or MS methods.
蛋白质组学是一种很有前景的方法，可用于从分子层面理解包括对治疗的反应在内的肿瘤形成过程。广泛使用的二维凝胶电泳/液相色谱联用质谱法（LC-MS）既耗时又不具成本效益。我们开发了一种高灵敏度（每20微升中蛋白质含量为飞摩尔/亚飞摩尔）的高效液相色谱-激光诱导荧光（HPLC-LIF）仪器，用于研究生物样品的蛋白质谱。在本研究中，我们通过对色谱数据进行多变量分析，探讨了对乳腺组织进行分类的可行性。我们分析了13份正常乳腺组织匀浆、17份恶性乳腺组织匀浆、5份良性乳腺组织匀浆和4份治疗后乳腺组织匀浆。在无监督和有监督模式下，对导数和基线校正后的色谱图进行主成分分析（PCA）。我们的研究结果表明，导数色谱图的PCA能给出更好的分类效果。因此，HPLC-LIF仪器不仅适用于使用飞摩尔/亚飞摩尔蛋白质生成色谱数据，而且这些数据还可通过多变量分析用于客观诊断。未来，可以收集已鉴定的组分，并通过生化和/或质谱方法进行分析。
