Chan James W, Taylor Douglas S, Lane Stephen M, Zwerdling Theodore, Tuscano Joseph, Huser Thomas
Applied Physics and Biophysics Division, Physical Sciences Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-211, Livermore, California 94551, USA.
Anal Chem. 2008 Mar 15;80(6):2180-7. doi: 10.1021/ac7022348. Epub 2008 Feb 9.
Currently, a combination of technologies is typically required to assess the malignancy of cancer cells. These methods often lack the specificity and sensitivity necessary for early, accurate diagnosis. Here we demonstrate using clinical samples the application of laser trapping Raman spectroscopy as a novel approach that provides intrinsic biochemical markers for the noninvasive detection of individual cancer cells. The Raman spectra of live, hematopoietic cells provide reliable molecular fingerprints that reflect their biochemical composition and biology. Populations of normal T and B lymphocytes from four healthy individuals and cells from three leukemia patients were analyzed, and multiple intrinsic Raman markers associated with DNA and protein vibrational modes have been identified that exhibit excellent discriminating power for cancer cell identification. A combination of two multivariate statistical methods, principal component analysis (PCA) and linear discriminant analysis (LDA), was used to confirm the significance of these markers for identifying cancer cells and classifying the data. The results indicate that, on average, 95% of the normal cells and 90% of the patient cells were accurately classified into their respective cell types. We also provide evidence that these markers are unique to cancer cells and not purely a function of differences in their cellular activation.
目前,通常需要多种技术结合来评估癌细胞的恶性程度。这些方法往往缺乏早期准确诊断所需的特异性和敏感性。在此,我们利用临床样本展示了激光捕获拉曼光谱作为一种新方法的应用,该方法可为单个癌细胞的非侵入性检测提供内在生化标记。活造血细胞的拉曼光谱提供了可靠的分子指纹,反映了它们的生化组成和生物学特性。分析了来自四名健康个体的正常T和B淋巴细胞群体以及三名白血病患者的细胞,识别出了多个与DNA和蛋白质振动模式相关的内在拉曼标记,这些标记对癌细胞识别具有出色的区分能力。使用主成分分析(PCA)和线性判别分析(LDA)这两种多元统计方法的组合,来确认这些标记对于识别癌细胞和对数据进行分类的重要性。结果表明,平均而言,95%的正常细胞和90%的患者细胞被准确分类到各自的细胞类型中。我们还提供证据表明,这些标记是癌细胞所特有的,并非仅仅是细胞活化差异的函数。