J Appl Biomed 12:119-125, 2014 | DOI: 10.1016/j.jab.2013.12.001

Tissue profiling by nanogold-mediated mass spectrometry and artificial neural networks in the mouse model of human primary hyperoxaluria 1

Jan Houška^a, Eladia María Peña-Méndez^b, Juan R. Hernandez-Fernaud^c, Eduardo Salido^c, Aleš Hampl^d,e, Josef Havel^a, Petr Vaňhara^d,*

^a Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic

^b Department of Analytical Chemistry, Nutrition and Food Science, Faculty of Chemistry, University of La Laguna, La Laguna, Tenerife, Spain

^c Rare Disease Center (CIBERER) and Biomedical Institute (CIBICAN), University La Laguna, Tenerife, Spain

^d Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic

^e International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic

Correct assessment of tissue histopathology is a necessary prerequisite for any clinical diagnosis. Nowadays, classical methods of histochemistry and immunohistochemistry are complemented by various techniques adopted from molecular biology and bioanalytical chemistry. Mass spectrometry profiling or imaging offered a new level of tissue visualization in the last decade, revealing hidden patterns of tissue molecular organization. It can be adapted to diagnostic purposes to improve decisions on complex and morphologically not apparent diagnoses. In this work, we successfully combined tissue profiling by mass spectrometry with analysis by artificial neural networks to classify normal and diseased liver and kidney tissues in a mouse model of primary hyperoxaluria type 1. Lack of the liver l-alanine:glyoxylate aminotransferase catalyzing conversion of l-alanine and glyoxylate to pyruvate and glycine causes accumulation of oxalate salts in various tissues, especially urinary system, resulting in compromised renal function and finally end stage renal disease. As the accumulation of oxalate salts alters chemical composition of affected tissues, it makes it available for examination by bioanalytical methods. We demonstrated that the direct tissue MALDI-TOF MS combined with neural computing offers an efficient tool for diagnosis of primary hyperoxaluria type I and potentially for other metabolic disorders altering chemical composition of tissues.

Keywords: MALDI-TOF mass spectrometry; Primary hyperoxaluria I; Artificial neural networks; Diagnostics; Tissue profiling

Received: October 10, 2013; Revised: December 2, 2013; Accepted: December 12, 2013; Published: April 1, 2014  Show citation