Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification
Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification
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DOI：
10.1177/0003702817734618
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发表时间：
2018-02-01
影响因子：
3.5
通讯作者：
Ofner, Johannes
中科院分区：
文献类型：
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作者：
Balbekova, Anna;Lohninger, Hans;Ofner, Johannes
显微光谱技术被广泛用于补充组织学研究。由于化学成像领域的最新发展，联合化学分析已经变得很有吸引力。与单一技术或并列分析相比，该技术有助于更深入的分析。在这项研究中，大鼠在光栓性卒中诱导后一周收集大脑进行了研究。用傅立叶变换红外光谱(FT-IR)显微光谱和激光消融电感耦合等离子体质谱(LA-ICP-MS)对相邻脑组织薄片进行成像。使用内标(薄层金层)对LA-ICPMS数据进行归一化。对获取的高光谱数据立方体进行融合，并进行多元分析。使用基于偏最小二乘判别分析(PLS-DA)或随机决策森林(RDF)算法的模型来识别和分类受中风影响的大脑区域以及未受影响的灰质和白质。RDF算法的分类效果最好。与单独的数据集(FT-IR或LA-ICPMS)相比，在融合数据的情况下观察到了更好的分类。变量重要性分析表明，分子和元素含量对改进的RDF分类都有贡献。单变量光谱分析确定了指定组织类型的生化特性。使用RDF算法对多传感器高光谱数据集进行分类，可以获得对不同大脑区域的生化过程和固体化学分配的新的和深入的理解。
Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.