FTIR-spectroscopic and LA-ICP-MS imaging for combined hyperspectral image analysis of tumor models

被引:13
作者
Balbekova, A. [1 ]
Bonta, M. [1 ]
Torok, S. [2 ]
Ofner, J. [1 ]
Dome, B. [2 ,3 ,4 ,5 ]
Limbeck, A. [1 ]
Lendl, B. [1 ]
机构
[1] Tech Univ Wien, Inst Chem Technol & Analyt, Getreidemarkt 9-164, A-1060 Vienna, Austria
[2] Natl Koranyi Inst Pulmonol, Piheno Ut 1, H-1121 Budapest, Hungary
[3] Med Univ Vienna, Dept Surg, Comprehens Canc Ctr, Div Thorac Surg, Wahringer Gurtel 18-20, A-1090 Vienna, Austria
[4] Med Univ Vienna, Div Mol & Gender Imaging, Dept Biomed Imaging & Image Guided Therapy, Wahringer Gurtel 18-20, A-1090 Vienna, Austria
[5] Semmelweis Univ, Natl Inst Oncol, Dept Thorac Surg, Rath Gyorgy Ut 7-9, H-1122 Budapest, Hungary
基金
匈牙利科学研究基金会;
关键词
APOPTOSIS; CELLS; MICROSPECTROSCOPY; NORMALIZATION; CANCER; TISSUE;
D O I
10.1039/c7ay01369h
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Modern chemical imaging techniques provide spatially resolved information on the molecular and elemental composition of samples with both high spatial and spectral resolution. Over the past few decades these techniques, in particular, Fourier transform infrared (FTIR) spectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) have been successfully applied in histopathological research. This work demonstrates that the multivariate analysis of combined FTIR and LA-ICP-MS microscopy hyperspectral images can bring additional knowledge to biomedical research. The concept of such analysis was demonstrated while investigating two different tumor samples subjected to anticancer therapy. Combined analysis has revealed a correlation between the lateral distribution of analytes and sample properties within the different techniques. Correlations between alterations in the average protein secondary structure and platinum distribution were found, as well as between changes in the cell nuclear morphology and a reduction of physiologically relevant trace elements. The results of combined analysis suggested different degrees of tumor viability. Univariate analysis and k-means clustering successfully discriminated dead tumor regions and supported the results of combined analysis.
引用
收藏
页码:5464 / 5471
页数:8
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