Search

Top-down mass spectrometry (MS)-based proteomics has become a powerful tool for analyzing intact proteins and their associated post-translational modification (PTMs). In particular, membrane proteins play critical roles in cellular functions and represent the largest class of drug targets. However, the top-down MS characterization of endogenous membrane proteins remains challenging, mainly due to their intrinsic hydrophobicity and low abundance. Phospholamban (PLN) is a regulatory membrane protein located in the sarcoplasmic reticulum and is essential for regulating cardiac muscle contraction. PLN has diverse combinatorial PTMs and their dynamic regulation has significant influence on cardiac contractility and disease. Herein, we have developed a rapid and robust top-down proteomics method enabled by a photocleavable anionic surfactant, Azo, for the extraction and comprehensive characterization of endogenous PLN from cardiac tissue. We employed a two-pronged top-down MS approach using an online reversed-phase liquid chromatography tandem MS (LC-MS/MS) method on a quadrupole time-of-flight (Q-TOF) MS and a direct infusion method via an ultrahigh-resolution Fourier-transform ion cyclotron resonance (FTICR) MS. We have comprehensively characterized the sequence and combinatorial PTMs of endogenous human cardiac PLN. We have shown the site-specific localization of phosphorylation to Ser16 and Thr17 by MS/MS for the first time and the localization of S-palmitoylation to Cys36. Taken together, we have developed a streamlined top-down targeted proteomics method for comprehensive characterization of combinatorial PTMs in PLN toward better understanding the role of PLN in cardiac contractility.

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-sourced, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork’s utility by employing it to identify groups of proteins associated with prostate cancer from a proteomics analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments for experiments including fifteen or more samples.

The extracellular matrix (ECM) provides an architectural meshwork that surrounds and supports cells. The dysregulation of heavily post-translationally modified ECM proteins directly contributes to various diseases. Mass spectrometry (MS)-based proteomics is an ideal tool to identify ECM proteins and characterize their post-translational modifications, but ECM proteomics remains extremely challenging owing to the extremely low solubility of the ECM. Herein, enabled by effective solubilization of ECM proteins using our recently developed photocleavable surfactant, Azo, we have developed a streamlined ECM proteomic strategy that allows fast tissue decellularization, efficient extraction and enrichment of ECM proteins, and rapid digestion prior to reversed-phase liquid chromatography (RPLC)-MS analysis. A total of 173 and 225 unique ECM proteins from mouse mammary tumors have been identified using 1D and 2D RPLC-MS/MS, respectively. Moreover, 87 (from 1DLC-MS/MS) and 229 (from 2DLC-MS/MS) post-translational modifications of ECM proteins, including glycosylation, phosphorylation, and hydroxylation, were identified and localized. This Azo-enabled ECM proteomics strategy will streamline the analysis of ECM proteins and promote the study of ECM biology.

A series of poly(ethylene oxide)-LiOTf electrolyte films were prepared using a variety of hydrocarbon templates as nanofillers, resulting in observable nanophase separation in the polymer electrolyte. Upon partial extraction of the nanofiller template, an enhanced conductivity over 2 orders of magnitude was measured using ac impedance. Scanning electron microscopy, differential scanning calorimetry, and thermogravimetric analysis were employed to characterize the porosity, composition, and mass loss of template-extracted and nonextracted film samples.

Summary Pancreatic islets are essential for maintaining physiological blood glucose levels, and declining islet function is a hallmark of type 2 diabetes. We employ mass spectrometry-based proteomics to systematically analyze islets from 9 genetic or diet-induced mouse models representing a broad cross-section of metabolic health. Quantifying the islet proteome to a depth of >11,500 proteins, this study represents the most detailed analysis of mouse islet proteins to date. Our data highlight that the majority of islet proteins are expressed in all strains and diets, but more than half of the proteins vary in expression levels, principally due to genetics. Associating these varied protein expression levels on an individual animal basis with individual phenotypic measures reveals islet mitochondrial function as a major positive indicator of metabolic health regardless of strain. This compendium of strain-specific and dietary changes to mouse islet proteomes represents a comprehensive resource for basic and translational islet cell biology., Graphical abstract, Highlights • Most comprehensive mouse islet proteome library generated to date • Quantification of islet proteomic changes across 6 strains of mice on 2 diets • Islet mitochondrial function revealed as strain-independent regulator of metabolic health, Animal physiology; Diabetology; Proteomics.