The nervous system is complex and requires a network of neurons to transmit signals throughout an organism. The nervous system coordinates sensory information and biological response. The intricate and convoluted interactions involved in signal transmission require many different signaling molecules such as neurotransmitters, steroids, and neuropeptides. As neuropeptides are the most diverse and potent signaling molecules that play key roles in activating physiological responses to stress and other stimuli, it is of interest to determine their specific role in these physiological processes. Neuropeptide function is related to their amino acid structure, which determines mobility, receptor interaction, and affinity1. Techniques such as histochemistry, which is important because neuropeptides can be synthesized, stored, and released in different regions of the tissue, and electrophysiology have been employed to investigate neuropeptide structure and function2,3,4, but these methods are limited by throughput and specificity to resolve the vast sequence diversity of neuropeptides.
Mass spectrometry (MS) enables the high throughput analysis of neuropeptide structure and abundance. This can be performed through different MS techniques, most commonly liquid chromatography-electrospray ionization MS (LC-ESI-MS)5 and matrix-assisted laser desorption/ionization MS (MALDI-MS)6. Utilizing high accuracy mass measurements and MS fragmentation, MS provides the ability to assign amino acid sequence and post-translational modification (PTM) status to neuropeptides from complex mixtures without a priori knowledge to aid in ascertaining their function7,8. In addition to qualitative information, MS enables quantitative information of neuropeptides through label-free quantitation (LFQ) or label-based methods such as isotopic or isobaric labeling9. The main advantages of LFQ include its simplicity, low cost of analysis, and decreased sample preparation steps which can minimize sample loss. However, the disadvantages of LFQ include increased instrument time costs as it requires multiple technical replicates to address quantitative error from run-to-run variability. This also leads to a decreased ability to accurately quantify small variations. Label-based methods are subjected to less systematic variation as multiple samples can be differentially labeled using a variety of stable isotopes, combined into one sample, and analyzed through mass spectrometry simultaneously. This also increases throughput, although isotopic labels can be time consuming and costly to synthesize or purchase. Full scan mass spectra (MS1) spectral complexity also increases as multiplexing increases, which decreases the number of unique neuropeptides able to be fragmented and therefore, identified. Conversely, isobaric labeling does not increase spectral complexity at the MS1 level, although it introduces challenges for low abundance analytes such as neuropeptides. As isobaric quantitation is performed at the fragment ion mass spectra (MS2) level, low-abundance neuropeptides may be unable to be quantified as more abundant matrix components may be selected for fragmentation and those selected may not have high enough abundance to be quantified. With isotopic labeling, quantitation can be performed on every identified peptide.
In addition to identification and quantification, localization information can be obtained by MS through MALDI-MS imaging (MALDI-MSI)10. By rastering a laser across a sample surface, MS spectra can be compiled into a heat map image for each m/z value. Mapping transient neuropeptide signal intensity in different regions across conditions can provide valuable information for function determination11. Localization of neuropeptides is especially important because neuropeptide function may differ depending on location12.
Neuropeptides are found in lower abundance in vivo than other signaling molecules, such as neurotransmitters, and thus require sensitive methods for detection13. This can be achieved through the removal of higher abundance matrix components, such as lipids11,14. Additional considerations for the analysis of neuropeptides need to be made when compared to common proteomics workflows, mainly because most neuropeptidomic analyses omit enzymatic digestion. This limits software options for neuropeptide data analysis as most were built with algorithms based on proteomics data and protein matches informed by peptide detection. However, many software such as PEAKS15 is more suited to neuropeptide analysis due to their de novo sequencing capabilities. Several factors need to be considered for the analysis of neuropeptides starting from extraction method to MS data analysis.
The protocols described here include methods for sample preparation and dimethyl isotopic labeling, data acquisition, and data analysis of neuropeptides by LC-ESI-MS, MALDI-MS, and MALDI-MSI. Through representative results from several experiments, the utility and ability of these methods to identify, quantify, and localize neuropeptides from blue crabs, Callinectes sapidus, is demonstrated. To better understand the nervous system, model systems are commonly used. Many organisms do not have a fully sequenced genome available, which prevents comprehensive neuropeptide discovery at the peptide level. In order to mitigate this challenge, a protocol for identifying novel neuropeptides and transcriptome mining to generate databases for organisms without complete genome information is included. All protocols presented here can be optimized for neuropeptide samples from any species, as well as applied for the analysis of any endogenous peptides.