Center of Genomic Medicine, 'Victor Babes' University of Medicine and Pharmacy, Timisoara 300041, Romania.
Department of Natural Sciences, Middlesex University, London NW4 4BT, UK.
Int J Mol Med. 2017 Oct;40(4):1096-1104. doi: 10.3892/ijmm.2017.3083. Epub 2017 Jul 27.
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.
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)已成为微生物鉴定的有价值平台,它也经常应用于生物学和临床研究中,以鉴定病理条件下表达的新标志物。本研究旨在评估该方法在癌细胞系分类中的潜力,作为细胞细胞器蛋白质组学分析的可量化方法。使用 MALDI-TOF MS 分析来自不同肿瘤细胞系(人源和鼠源)的完整蛋白质提取物,并使用 Bruker Biotyper®软件中的主成分分析(PCA)对获得的质量列表进行处理。此外,为每个细胞系创建了参考光谱,并用于分类。基于完整的蛋白质谱,我们能够区分和分类六种癌细胞系:两种鼠黑色素瘤(B16-F0 和 B164A5)、一种人黑色素瘤(A375)、两种人乳腺癌(MCF7 和 MDA-MB-231)和一种人肝癌(HepG2)。根据癌症类型和起源物种以及转移潜力对细胞系进行分类,有区分非侵袭性和侵袭性细胞的可能性。所获得的结果为开发广泛的癌症细胞识别和分类策略铺平了道路。