Neuropeptides (NPs) are the most common signaling molecules in the nervous and endocrine systems, but many of their biological functions remain unknown. Characterization is challenging due to high sequence diversity, a dynamic variety of post-translational modifications, and low abundance in neuronal tissue samples. To decrease the complexities involved with studying NPs, we use a crustacean model system because it is a simple invertebrate model with well characterized neuronal connections and some NPs homologous to those of humans. Glycosylation is a common post-translational modification that changes biomolecule structure and function. Glycoproteins and peptides are of particular interest because of growing implications of their physiological roles in human diseases; even less is known about NPs that are glycosylated. GlycoNPs constitute only a fraction of the already low abundance NPs in tissue samples and require a sensitive and accurate method of analysis, therefore we use mass spectrometry (MS). While already a very powerful technique, MS can only detect and characterize the molecules present in the sample injection. Thus, an optimal extraction method to obtain glycoNPs from neuronal tissue is critical to acquire maximum knowledge about glycoNPs . Our current extraction solvents are optimized for NPs, but glycosylation changes NP properties, such as hydrophilicity, so solvents need to be re-optimized for maximal glycoNP extraction. We aim to reproducibly recover all glycoNPs in a sample, prevent degradation, and remove complexities that can suppress glycoNP signal intensity in MS analysis. To optimize extraction conditions in tissue sample, we extracted in solutions of either acidified methanol (90/9/1 methanol/water/acetic acid v/v/v), acidified ethanol (0.18 M HCl/ 70 % ethanol), or acetone (0.5 M AcOH). Replicated tissue extracts were analyzed using LC-MS to determine the extraction solvent yielding the highest identification of neuropeptides. We additionally expect the three solvents to extract different identities of glycoNPs with varying properties. This research will enable improved studies of glycosylated neuropeptides to be performed, as more will be recovered during sample preparation for MS analysis.

Glycosylated neuropeptide extraction solvent optimization of neuronal tissue for mass spectrometry analysis

ACS Fall 2021

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