Zendehdel Rezvan, H Shirazi Farshad
Department of Occupational Hygiene, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
SBMU Pharmaceutical Research Center, Tehran, Iran. ; Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Science, Tehran, Iran.
Iran J Pharm Res. 2015 Summer;14(3):803-10.
Variations in biochemical features are extensive among cells. Identification of marker that is specific for each cell is essential for following the differentiation of stem cell and metastatic growing. Fourier transform infrared spectroscopy (FTIR) as a biochemical analysis more focused on diagnosis of cancerous cells. In this study, commercially obtained cell lines such as Human ovarian carcinoma (A2780), Human lung adenocarcinoma (A549) and Human hepatocarcinoma (HepG2) cell lines in 20 individual samples for each cell lines were used for FTIR spectral measurements. Data dimension were reduced through principal component analysis (PCA) and then subjected to neural network and linear discrimination analysis to classify FTIR pattern in different cell lines. The results showed dramatic changes of FTIR spectra among different cell types. These appeared to be associated with changes in lipid bands from CH2 symmetric and asymmetric bands, as well as amide I and amid II bands of proteins. The PCA-ANN analysis provided over 90% accuracy for classifying the spectrum of lipid section in different cell lines. This work supports future study to establish the data bank of FTIR feature for different cells and move forward to tissues as more complex systems.
细胞间的生化特征差异很大。识别每种细胞特有的标志物对于追踪干细胞分化和转移生长至关重要。傅里叶变换红外光谱（FTIR）作为一种生化分析方法，更侧重于癌细胞的诊断。在本研究中，使用了商业获取的细胞系，如人卵巢癌（A2780）、人肺腺癌（A549）和人肝癌（HepG2）细胞系，每个细胞系有20个独立样本用于FTIR光谱测量。通过主成分分析（PCA）降低数据维度，然后进行神经网络和线性判别分析，以对不同细胞系的FTIR模式进行分类。结果显示不同细胞类型之间FTIR光谱有显著变化。这些变化似乎与脂质带中CH2对称和不对称带以及蛋白质的酰胺I和酰胺II带的变化有关。PCA-ANN分析对不同细胞系脂质部分光谱分类的准确率超过90%。这项工作支持未来建立不同细胞FTIR特征数据库的研究，并朝着更复杂的组织系统迈进。
