Department of Chemistry and Chemical Biology, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA.
Lab Invest. 2010 Apr;90(4):589-98. doi: 10.1038/labinvest.2010.1. Epub 2010 Feb 8.
Spectral cytopathology (SCP) is a novel approach for diagnostic differentiation of disease in individual exfoliated cells. SCP is carried out by collecting information on each cell's biochemical composition through an infrared micro-spectral measurement, followed by multivariate data analysis. Deviations from a cell's natural composition produce specific spectral patterns that are exclusive to the cause of the deviation or disease. These unique spectral patterns are reproducible and can be identified and used through multivariate statistical methods to detect cells compromised at the molecular level by dysplasia, neoplasia, or viral infection. In this proof of concept study, a benchmark for the sensitivity of SCP is established by classifying healthy oral squamous cells according to their anatomical origin in the oral cavity. Classification is achieved by spectrally detecting cells with unique protein expressions: for example, the squamous cells of the tongue are the only cell type in the oral cavity that have significant amounts of intracytoplasmic keratin, which allows them to be spectrally differentiated from other oral mucosa cells. Furthermore, thousands of cells from a number of clinical specimens were examined, among them were squamous cell carcinoma, malignancy-associated changes including reactive atypia, and infection by the herpes simplex virus. Owing to its sensitivity to molecular changes, SCP often can detect the onset of disease earlier than is currently possible by cytopathology visualization. As SCP is based on automated instrumentation and unsupervised software, it constitutes a diagnostic workup of medical samples devoid of bias and inconsistency. Therefore, SCP shows potential as a complementary tool in medical cytopathology.
光谱细胞病理学(SCP)是一种用于对单个脱落细胞中的疾病进行诊断区分的新方法。SCP 通过对每个细胞的生化组成进行信息收集来实现,通过红外微光谱测量,然后进行多元数据分析。细胞的生化组成偏离其自然组成会产生特定的光谱模式,这些模式是由偏离或疾病的原因所特有的。这些独特的光谱模式是可重复的,可以通过多元统计方法识别和使用,以检测在分子水平上受到发育异常、肿瘤形成或病毒感染影响的细胞。在这项概念验证研究中,根据口腔内的解剖起源对健康口腔鳞状细胞进行分类,从而建立了 SCP 灵敏度的基准。分类是通过光谱检测具有独特蛋白表达的细胞来实现的:例如,舌鳞状细胞是口腔中唯一具有大量细胞内角蛋白的细胞类型,这使得它们能够与其他口腔黏膜细胞在光谱上区分开来。此外,数千个来自多个临床标本的细胞被检查,其中包括鳞状细胞癌、包括反应性异型性在内的恶性相关变化,以及单纯疱疹病毒感染。由于 SCP 对分子变化敏感,因此它通常可以比目前通过细胞病理学可视化更早地检测到疾病的发生。由于 SCP 基于自动化仪器和无监督软件,因此它构成了一种没有偏见和不一致性的医学样本诊断分析。因此,SCP 显示出作为医学细胞病理学的补充工具的潜力。