Quantitative neuropeptide analysis by mass spectrometry: advancing methodologies for biological discovery

Abstract

Neuropeptides are critical endogenous signaling molecules involved in a wide range of biological processes, including neurotransmission, hormonal regulation, immune responses, and stress management. Despite their importance, the field of neuropeptide research has been historically hampered by significant technical challenges. These include their low abundance in biological systems, diverse and complex post-translational modifications, dynamic expression patterns, and susceptibility to degradation. As such, traditional proteomics approaches often fall short of accurately characterizing neuropeptides, underscoring the need for specialized methodologies to unlock their biological and translational potential. This review evaluates state-of-the-art quantitative mass spectrometry (MS)-based peptidomics, emphasizing their impact on neuropeptide analysis. We highlight how strategies in label-free and label-based quantitation, tandem MS acquisition, and mass spectrometry imaging provide unprecedented sensitivity and throughput for capturing the landscape of neuropeptides and their modifications. Importantly, the review bridges technological innovation with practical applications, highlighting how these approaches have been utilized to uncover novel neuropeptides and elucidate their roles in systems biology and disease pathways.

Fig. 1. Experimental mass spectrometry-based neuropeptidomic workflow. Peptides are extracted from neural tissue or circulating biofluids and forego the use of enzymatic digestion, as commonly applied to bottom-up proteomic workflows. Neuropeptides are processed and cleaned for downstream LC-MS/MS analysis. MS¹ precursor ions are then isolated and fragmented into smaller fragment ions (MS²), where different fragment ions correspond to specific peptide bonds. MS² spectra are then analyzed via database searching and de novo sequencing for quantitative data analysis of neuropeptides.

Fig. 2. Overview of mass spectrometry-based strategies for neuropeptide quantification. Label-free quantification measures the intensity of peptide ions at the MS¹-level over an elution time. The area under the curve correlates with peptide abundance. Stable isotopes can be incorporated into peptides through isotopic labeling or metabolic incorporation (e.g. SILAC). Labeled and unlabeled samples are mixed, and the ratio intensities of labeled to unlabeled peptides are measured. Isobaric labeling incorporates stable isotopes where labeled peptides are indistinguishable at the MS¹-level. Upon fragmentation, reporter ions are generated, and their MS² intensities are used for quantification, enabling multiplexing of samples. Data-dependent acquisition (DDA) and data-independent acquisition (DIA) are commonly employed for neuropeptide analysis, with DDA enabling targeted fragmentation of the most abundant precursor ions for detailed sequencing. DIA systematically fragments all ions within a mass range, providing comprehensive coverage and improved detection of low-abundance neuropeptides.

Fig. 3. Chemical structures of common isobaric tags used in quantitative peptidomics. Isobaric tags follow a common structure motif consisting of an amine-reactive group (green), a mass balancing group (black), and a reporter ion group (blue). (A) Tandem mass tags (TMT), (B) isobaric tag for relative and absolute quantification (iTRAQ), and (C) N,N-dimethyl leucine.

Fig. 4. General overview of mass spectrometry imaging (MSI) workflows. Thin tissue sections are mounted on a plate and tissue washing can remove interfering ion signals. The tissue slices are then coated with or without a matrix, depending on the ionization technique employed. The produced ions are detected by a mass spectrometer, generating mass spectra at each spatial coordinate. MSI constructs mass spectrometric images corresponding to specific molecular weight values. This allows for the simultaneous imaging of many different molecules from a single tissue section.

Angel Erbey Ibarra

Wenxin Wu

Haoran Zhang

Lingjun Li