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Classification of cancer cell lines using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and statistical analysis

机译:使用基质辅助激光解吸/电离飞行时间质谱和统计分析对癌细胞系进行分类

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摘要

Over the past decade, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been established as a valuable platform for microbial identification, and it is also frequently applied in biology and clinical studies to identify new markers expressed in pathological conditions. The aim of the present study was to assess the potential of using this approach for the classification of cancer cell lines as a quantifiable method for the proteomic profiling of cellular organelles. Intact protein extracts isolated from different tumor cell lines (human and murine) were analyzed using MALDI-TOF MS and the obtained mass lists were processed using principle component analysis (PCA) within Bruker Biotyper® software. Furthermore, reference spectra were created for each cell line and were used for classification. Based on the intact protein profiles, we were able to differentiate and classify six cancer cell lines: two murine melanoma (B16-F0 and B164A5), one human melanoma (A375), two human breast carcinoma (MCF7 and MDA-MB-231) and one human liver carcinoma (HepG2). The cell lines were classified according to cancer type and the species they originated from, as well as by their metastatic potential, offering the possibility to differentiate non-invasive from invasive cells. The obtained results pave the way for developing a broad-based strategy for the identification and classification of cancer cells.
机译:在过去的十年中,基质辅助激光解吸/电离飞行时间质谱(MALDI-TOF MS)已被确立为微生物鉴定的宝贵平台,并且还经常用于生物学和临床研究中以鉴定新的在病理条件下表达的标记。本研究的目的是评估使用这种方法对癌细胞系进行分类的潜力,将其作为可量化的细胞器蛋白质组学分析方法。使用MALDI-TOF MS分析从不同肿瘤细胞系(人和鼠)分离得到的完整蛋白提取物,并使用Bruker Biotyper ®软件中的主成分分析(PCA)处理获得的质量清单。此外,为每个细胞系创建参考光谱并将其用于分类。根据完整的蛋白质谱,我们能够区分六种癌细胞系:两种鼠类黑色素瘤(B16-F0和B164A5),一种人黑色素瘤(A375),两种人乳腺癌(MCF7和MDA-MB-231)一名人类肝癌(HepG2)。根据癌症类型及其来源物种以及转移潜能对细胞系进行分类,从而提供了区分非侵入性细胞与侵入性细胞的可能性。获得的结果为发展用于癌症细胞鉴定和分类的广泛策略铺平了道路。

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