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. Issue 2 (February 2018)

• Record Type: Journal Article
• Authors: Balbekova, Anna; Lohninger, Hans; van Tilborg, Geralda A.F.; Dijkhuizen, Rick M.; Bonta, Maximilian; Limbeck, Andreas; Lendl, Bernhard; Al-Saad, Khalid A.; Ali, Mohamed; Celikic, Minja; Ofner, Johannes
• Is Part Of: Applied spectroscopy. Volume 72:Issue 2(2018)
• Page Start: 241
• Page End: 250
• Publication Date: 2018-02
• Subjects: Multisensor hyperspectral image analysis -- Fourier transform infrared -- FT-IR -- laser ablation inductively coupled plasma mass spectrometry -- LA-ICP-MS -- random decision forest -- RDF -- partial least squares discriminant analysis -- PLS-DA -- brain ischemia -- photothrombotic stroke; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/0003702817734618
• Languages: English
• ISSNs: 0003-7028

Abstract

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.