Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics

Granulin, a novel STAT3-interacting protein, enhances STAT3 transcriptional function and correlates with poorer prognosis in breast cancer

Since the neoplastic phenotype of a cell is largely driven by aberrant gene expression patterns, increasing attention has been focused on transcription factors that regulate critical mediators of tumorigenesis such as signal transducer and activator of transcription 3 (STAT3). As proteins that interact with STAT3 may be key in addressing how STAT3 contributes to cancer pathogenesis, we took a proteomics approach to identify novel STAT3-interacting proteins. We performed mass spectrometry-based profiling of STAT3-containing complexes from breast cancer cells that have constitutively active STAT3 and are dependent on STAT3 function for survival. We identified granulin (GRN) as a novel STAT3-interacting protein that was necessary for both constitutive and maximal leukemia inhibitory factor (LIF)induced STAT3 transcriptional activity. GRN enhanced STAT3 DNA binding and also increased the time-integrated amount of LIF-induced STAT3 activation in breast cancer cells. Furthermore, silencing GRN neutralized STAT3-mediated tumorigenic phenotypes including viability, clonogenesis, and migratory capacity. In primary breast cancer samples, GRN mRNA levels were positively correlated with STAT3 gene expression signatures and with reduced patient survival. These studies identify GRN as a functionally important STAT3-interacting protein that may serve as an important prognostic biomarker and potential therapeutic target in breast cancer

A methodology for discovering novel brain-relevant peptides : combination of ribosome profiling and peptidomics

Brain derived peptides function as signaling molecules in the brain and regulate various physiological and behavioral processes. The low abundance and atypical fragmentation of these brain derived peptides makes detection using traditional proteomic methods challenging. In this study, we introduce and validate a new methodology for the discovery of novel peptides derived from mammalian brain. This methodology combines ribosome profiling and mass spectrometry-based peptidomics. Using this framework, we have identified a novel peptide in mouse whole brain whose expression is highest in the basal ganglia, hypothalamus and amygdala. Although its functional role is unknown, it has been previously detected in peripheral tissue as a component of the mRNA decapping complex. Continued discovery and studies of novel regulating peptides in mammalian brain may also provide insight into brain disorders. (C) 2019 Published by Elsevier B.V

Binding Site Characterization of AM1336, a Novel Covalent Inverse Agonist at Human Cannabinoid 2 Receptor, Using Mass Spectrometric Analysis

Cannabinoid 2 receptor (CB2R), a Class-A G-protein coupled receptor (GPCR), is a promising drug target under a wide array of pathological conditions. Rational drug design has been hindered due to our poor understanding of the structural features involved in ligand binding. Binding of a high-affinity biarylpyrazole inverse agonist AM1336 to a library of the human CB2 receptor (hCB2R) cysteine-substituted mutants provided indirect evidence that two cysteines in transmembrane helix-7 (H7) were critical for the covalent attachment. We used proteomics analysis of the hCB2R with bound AM1336 to directly identify peptides with covalently attached ligand and applied in silico modeling for visualization of the ligand–receptor interactions. The hCB2R, with affinity tags (FlaghCB2His6), was produced in a baculovirus–insect cell expression system and purified as a functional receptor using immunoaffinity chromatography. Using mass spectrometry-based bottom-up proteomic analysis of the hCB2R-AM1336, we identified a peptide with AM1336 attached to the cysteine C284(7.38) in H7. The hCB2R homology model in lipid bilayer accommodated covalent attachment of AM1336 to C284(7.38), supporting both biochemical and mass spectrometric data. This work consolidates proteomics data and in silico modeling and integrates with our ligand-assisted protein structure (LAPS) experimental paradigm to assist in structure-based design of cannabinoid antagonist/inverse agonists

Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling

Conventional TopN data-dependent acquisition (DDA) LC–MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ∼10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC–MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (∼10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms

Diurnal Variations of Circulating Extracellular Vesicles Measured by Nano Flow Cytometry.

The identification of extracellular vesicles (EVs) as intercellular conveyors of biological information has recently emerged as a novel paradigm in signaling, leading to the exploitation of EVs and their contents as biomarkers of various diseases. However, whether there are diurnal variations in the size, number, and tissue of origin of blood EVs is currently not known, and could have significant implications when using EVs as biomarkers for disease progression. Currently available technologies for the measurement of EV size and number are either time consuming, require specialized equipment, or lack sufficient accuracy across a range of EV sizes. Flow cytometry represents an attractive alternative to these methods; however, traditional flow cytometers are only capable of measuring particles down to 500 nm, which is significantly larger than the average and median sizes of plasma EVs. Utilizing a Beckman Coulter MoFlo XDP flow cytometer with NanoView module, we employed nanoscale flow cytometry (termed nanoFCM) to examine the relative number and scatter distribution of plasma EVs at three different time points during the day in 6 healthy adults. Analysis of liposomes and plasma EVs proved that nanoFCM is capable of detecting biologically-relevant vesicles down to 100 nm in size. With this high resolution configuration, we observed variations in the relative size (FSC/SSC distributions) and concentration (proportions) of EVs in healthy adult plasma across the course of a day, suggesting that there are diurnal variations in the number and size distribution of circulating EV populations. The use of nanoFCM provides a valuable tool for the study of EVs in both health and disease; however, additional refinement of nanoscale flow cytometric methods is needed for use of these instruments for quantitative particle counting and sizing. Furthermore, larger scale studies are necessary to more clearly define the diurnal variations in circulating EVs, and thus further inform their use as biomarkers for disease

Decoding Angiotensin Receptors: TOMAHAQ‐Based Detection and Quantification of Angiotensin Type‐1 and Type‐2 Receptors

Background The renin‐angiotensin system plays a crucial role in human physiology, and its main hormone, angiotensin, activates 2 G‐protein–coupled receptors, the angiotensin type‐1 and type‐2 receptors, in almost every organ. However, controversy exists about the location, distribution, and expression levels of these receptors. Concerns have been raised over the low sensitivity, low specificity, and large variability between lots of commercially available antibodies for angiotensin type‐1 and type‐2 receptors, which makes it difficult to reconciliate results of different studies. Here, we describe the first non–antibody‐based sensitive and specific targeted quantitative mass spectrometry assay for angiotensin receptors. Methods and Results Using a technique that allows targeted analysis of multiple peptides across multiple samples in a single mass spectrometry analysis, known as TOMAHAQ (triggered by offset, multiplexed, accurate mass, high resolution, and absolute quantification), we have identified and validated specific human tryptic peptides that permit identification and quantification of angiotensin type‐1 and type‐2 receptors in biological samples. Several peptide sequences are conserved in rodents, making these mass spectrometry assays amenable to both preclinical and clinical studies. We have used this method to quantify angiotensin type‐1 and type‐2 receptors in postmortem frontal cortex samples of older adults (n=28) with Alzheimer dementia. We correlated levels of angiotensin receptors to biomarkers classically linked to renin‐angiotensin system activation, including oxidative stress, inflammation, amyloid‐β load, and paired helical filament‐tau tangle burden. Conclusions These robust high‐throughput assays will not only catalyze novel mechanistic studies in the angiotensin research field but may also help to identify patients with an unbalanced angiotensin receptor distribution who would benefit from angiotensin receptor blocker treatment

Host Cell Protein Profiling by Targeted and Untargeted Analysis of Data Independent Acquisition Mass Spectrometry Data with Parallel Reaction Monitoring Verification

Host cell proteins (HCPs) are process-related impurities of biopharmaceuticals that remain at trace levels despite multiple stages of downstream purification. Currently, there is interest in implementing LC-MS in biopharmaceutical HCP profiling alongside conventional ELISA, because individual species can be identified and quantitated. Conventional data dependent LC-MS is hampered by the low concentration of HCP-derived peptides, which are 5–6 orders of magnitude less abundant than the biopharmaceutical-derived peptides. In this paper, we present a novel data independent acquisition (DIA)-MS workflow to identify HCP peptides using automatically combined targeted and untargeted data processing, followed by verification and quantitation using parallel reaction monitoring (PRM). Untargeted data processing with DIA-Umpire provided a means of identifying HCPs not represented in the assay library used for targeted, peptide-centric, data analysis. An IgG1 monoclonal antibody (mAb) purified by Protein A column elution, cation exchange chromatography, and ultrafiltration was analyzed using the workflow with 1D-LC. Five protein standards added at 0.5 to 100 ppm concentrations were detected in the background of the purified mAb, demonstrating sensitivity to low ppm levels. A calibration curve was constructed on the basis of the summed peak areas of the three highest intensity fragment ions from the highest intensity peptide of each protein standard. Sixteen HCPs were identified and quantitated on the basis of the calibration curve over the range of low ppm to over 100 ppm in the purified mAb sample. The developed approach achieves rapid HCP profiling using 1D-LC and specific identification exploiting the high mass accuracy and resolution of the mass spectrometer

A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious, requiring 2 h of mass spectrometry time per single muscle fiber; 50 fibers would take approximately 4 days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 min total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 h. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Ninety-four proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, oxidative phosphorylation, and muscle structure and contractile function. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput