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Introduction

Noise-induced hearing loss (NIHL) is an increasing problem in society and accounts for a third of all cases of acquired hearing loss. NIHL is caused by formation of reactive oxygen species (ROS) in the cochlea causing oxidative stress. Hydrogen gas (H-2) can alleviate the damage caused by oxidative stress and can be easily administered through inhalation.

Objectives

To present a protocol for untargeted metabolomics of guinea pig perilymph and investigate the effect of H-2 administration on the perilymph metabolome of noise exposed guinea pigs.

Methods

The left ear of guinea pigs were exposed to hazardous impulse noise only (Noise, n = 10), noise and H-2 (Noise + H2, n = 10), only H-2 (H2, n = 4), or untreated (Control, n = 2). Scala tympani perilymph was sampled from the cochlea of both ears. The polar component of the perilymph metabolome was analyzed using a HILIC-UHPLC-Q-TOF-MS-based untargeted metabolomics protocol. Multivariate data analysis (MVDA) was performed separately for the exposed- and unexposed ear.

Results

MVDA allowed separation of groups Noise and Noise + H2 in both the exposed and unexposed ear and yielded 15 metabolites with differentiating relative abundances. Seven were found in both exposed and unexposed ear data and included two osmoprotectants. Eight metabolites were unique to the unexposed ear and included a number of short-chain acylcarnitines.

Conclusions

A HILIC-UHPLC-Q-TOF-MS-based protocol for untargeted metabolomics of perilymph is presented and shown to be fit-for-purpose. We found a clear difference in the perilymph metabolome of noise exposed guinea pigs with and without H-2 treatment.

In order to increase metabolite coverage in LC-MS-based untargeted metabolomics, HILIC- and RPLC-mode separations are often combined. Unfortunately, these two techniques pose opposite requirements on sample composition, necessitating either dual sample preparations, increasing needed sample volume, or manipulation of the samples after the first analysis, potentially leading to loss of analytes. When sample material is precious, the number of analyses that can be performed is limited. To that end, an automated single-injection LC-MS method for sequential analysis of both the hydrophilic and lipophilic fractions of biological samples is described. Early eluting compounds in a HILIC separation are collected on a trap column and subsequently analyzed in the RPLC mode. The instrument configuration, composed of commercially available components, allows easy modulation of the dilution ratio of the collected effluent, with sufficient dilution to obtain peak compression in the RPLC column. Furthermore, the method is validated and shown to be fit for purpose for application in untargeted metabolomics. Repeatability in both retention times and peak areas was excellent across over 140 injections of protein-precipitated blood plasma. Finally, the method has been applied to the analysis of real perilymph samples collected in a guinea pig model. The QC sample injections clustered tightly in the PCA scores plot and showed a high repeatability in both retention times and peak areas for selected compounds.

LC-MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow.

Noise-induced hearing loss (NIHL) is believed to be caused by oxidative stress and accounts for up to a third of all cases of acquired hearing loss worldwide. The use of hydrogen gas as a mitigating treatment for NIHL has been successfully demonstrated in the past. We here present the application of a novel LC/LC-HRMS method for sequential analysis of hydrophilic and lipophilic compounds on an untargeted metabolomics study of the attenuating effect of hydrogen gas on NIHL in guinea pigs. The study was conducted using perilymph taken from the basal turn of the cochlea. Samples were taken in the acute stage, immediately following noise exposure along with follow-up samples after 1 and 2 weeks. Data analysis using volcano plots and random forest discriminant models revealed differences in the concentration of potassium within the scala tympani indicating effects on the potassium recycling system of the inner ear. Additionally, discriminant levels of glycerophosphorylcholine, a common osmolyte and antioxidant were discovered. In addition to these two compounds, four additional unknown metabolites are described. The findings are in line with previous reports indicating that the protective effect of hydrogen gas is sustained for at least 2 weeks.