Stenlund Hans, Gorzsás András, Persson Per, Sundberg Björn, Trygg Johan
Computational Life Science Cluster (CLIC), KBC, Umeå University, SE-901 87 Umeå, Sweden.
Anal Chem. 2008 Sep 15;80(18):6898-906. doi: 10.1021/ac8005318. Epub 2008 Aug 20.
In this study, the orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to assess the in situ chemical composition of two different cell types in mouse liver samples, hepatocytes and erythrocytes. High spatial resolution FT-IR microspectroscopy equipped with a focal plan array (FPA) detector is capable of simultaneously recording over 4000 spectra from 64 x 64 pixels with a maximum spatial resolution of about 5 microm x 5 microm, which allows for the differentiation of individual cells. The main benefit with OPLS-DA lies in the ability to separate predictive variation (between cell type) from variation that is uncorrelated to cell type in order to facilitate understanding of different sources of variation. OPLS-DA was able to differentiate between chemical properties and physical properties (e.g., edge effects). OPLS-DA model interpretation of the chemical features that separated the two cell types clearly highlighted proteins and lipids/bile acids. The modeled variation that was uncorrelated to cell type made up a larger portion of the total variation and displayed strong variability in the amide I region. This could be traced back to a gradient in the high intensity (high-density) areas vs the low intensity areas (close to empty areas) that as a result of normalization had an adverse effect on FT-IR spectral profiles. This highlights that OPLS-DA provides an effective solution to identify different sources of variability, both predictive and uncorrelated, and also facilitates understanding of any sampling, experimental, or preprocessing issues.
在本研究中,采用潜在结构判别分析的正交投影法(OPLS-DA)来评估小鼠肝脏样本中两种不同细胞类型,即肝细胞和红细胞的原位化学成分。配备焦平面阵列(FPA)探测器的高空间分辨率傅里叶变换红外(FT-IR)显微光谱仪能够同时记录来自64×64像素的4000多个光谱,最大空间分辨率约为5微米×5微米,这使得能够区分单个细胞。OPLS-DA的主要优势在于能够将预测性变异(细胞类型之间)与与细胞类型不相关的变异分开,以便于理解不同的变异来源。OPLS-DA能够区分化学性质和物理性质(例如边缘效应)。对区分两种细胞类型的化学特征的OPLS-DA模型解释清楚地突出了蛋白质和脂质/胆汁酸。与细胞类型不相关的建模变异在总变异中占较大比例,并且在酰胺I区域表现出强烈的变异性。这可以追溯到高强度(高密度)区域与低强度区域(接近空白区域)之间的梯度,由于归一化,这对FT-IR光谱轮廓产生了不利影响。这突出表明,OPLS-DA为识别预测性和不相关的不同变异来源提供了一种有效的解决方案,并且还便于理解任何采样、实验或预处理问题。