Novel Application of Desorption Electrospray Ionization Mass Spectrometry Imaging to the Analysis of Human Peripheral Nerves

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Abstract Body : Introduction: Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is a non-destructive imaging technique that can be used to interrogate the molecular composition of tissues and map the spatial relationships of this information using two-dimensional ion images. While DESI-MSI has been used to evaluate the chemical composition of various tissue types, the use of this technique for the characterization of peripheral nerves has yet to be explored to our knowledge. Such untargeted, molecular imaging techniques may be valuable for improving our ability to discriminate the complex anatomy of microscopic patterns of innervation. Further, developing a basic molecular profile of peripheral nerves could have significant clinical value in understanding and discerning associated peripheral nerve pathologies. This study aimed to assess whether DESI-MSI can be used to differentiate peripheral nerves from surrounding connective tissues and establish whether DESI-MSI can discriminate between peripheral nerve subtypes.
Materials and Methods: Samples from four different nerves (sciatic, femoral, sural, and vagus) were dissected from a cadaveric donor. Tissue samples were harvested to include the nerve bundles and surrounding connective tissue. Nerve samples were flash frozen, embedded in cross-section in optimal cutting temperature compound, and sectioned at 14 μm. Following DESI-MSI acquisition, the tissue sections were stained with hematoxylin and eosin. Using MetaboAnalyst, a combination of statistical methods was performed to evaluate molecular differences between the nerve and adjacent tissue, and between nerve subtypes.
Results: The acquired ion images suggested DESI-MSI can adequately discrimination between nerve and the surrounding tissues based on metabolomic differences. As the imaging experiment is not destructive, this allowed for correlation of the chemical and morphological information. DESI-MS ion images spatially corresponded to the associated histology micrographs. Unsupervised principal component analysis (PCA) supported the separation of the samples into two classes (nerve and adjacent tissue). This was further supported by the k-means clustering analysis which also indicated separation of the nerve and adjacent tissue mass spectra. Differences in ion expression between nerve subtypes was confirmed using ANOVA, which identified statistically significant differences in ion expression across the nerve samples. The associated PCA plot indicated some separation of the nerve subtypes into four classes. This was also supported k-means clustering. Some overlap in classes was noted in these two analyses.
Conclusion: This study provides emerging evidence that DESI-MSI is an effective tool for metabolomic profiling in peripheral nerves. These results indicate that peripheral nerves have characteristic molecular profiles which are distinct from surrounding connective tissues, and that DESI-MSI may be able to discriminate between nerve subtypes. Further work is needed to validate these results using a larger sample size. DESI-MSI of peripheral nerves may be a valuable technique that could be used to improve our understanding of peripheral nerve anatomy and physiology, both in normal and pathologic states. Further, the potential ability to use DESI-MSI in real-time, could prove valuable for improving surgical decision making, including during nerve-sparing procedures in the future.