Automated preparation of plasma lipids, metabolites, and proteins for LC/MS-based analysis of a high-fat diet in mice.

Blood plasma is one of the most commonly analyzed and easily accessible biological samples. Here, we describe an automated liquid-liquid extraction (LLE) platform that generates accurate, precise, and reproducible samples for metabolomic, lipidomic, and proteomic analyses from a single aliquot of plasma while minimizing hands-on time and avoiding contamination from plasticware. We applied mass spectrometry to examine the metabolome, lipidome, and proteome of 90 plasma samples to determine the effects of age, time of day, and a high-fat diet in mice. From 25 µL of mouse plasma, we identified 907 lipid species from 16 different lipid classes and subclasses, 233 polar metabolites, and 344 proteins. We found that the high-fat diet induced only mild changes in the polar metabolome, upregulated Apolipoproteins, and induced substantial shifts in the lipidome, including a significant increase in arachidonic acid (AA) and a decrease in eicosapentaenoic acid (EPA) content across all lipid classes.

A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.

We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.

High-Throughput Proteome Profiling of Plasma and Native Plasma Complexes Using Native Chromatography.

We describe a high-throughput method for co-fractionation mass spectrometry (CF-MS) profiling for native plasma protein profiling. CF-MS allows the profiling of endogenous protein complexes between samples. Proteins often interact with other proteins and form macromolecular complexes that are different in disease states as well as cell states and cell types. This protocol describes an example for the sample preparation of 954 individual size exclusion chromatography (SEC) fractions, derived from 18 plasma samples that were separated into 53 fractions. Eighteen plasma samples were chosen based on the TMTpro multiplexing, but this methodology can be adapted for fewer or larger numbers of samples as appropriate. Our automated sample preparation method allows for high-throughput native plasma profiling, and we provide detailed methods for both a label-free and an isobaric labeling approach, discuss the merits of each approach, and detail the advantages of combining these strategies for comprehensive native plasma proteome profiling.

Triple-threat quantitative multiplexed plasma proteomics analysis on immune complex disease MRL-lpr mice.

Systemic lupus erythematosus is a common autoimmune inflammatory disease which is associated with increases in autoantibodies and immune complexes that deposit in the kidney. The MRL-lpr mouse is a common mouse model used for the study of lupus and immune complex glomerulonephritis but very little is known about the plasma proteome changes in this model. We performed in-depth quantitative proteome profiling on MRL-lpr and control (strain MpJ) mice to investigate the changes in the proteome, immunoglobulins and their glycoproteome as well as protein and immune complexes. Methodologies used included immunohistochemistry, immunoglobulin isotyping, multiplexed proteome profiling, immunoglobulin immunoprecipitation with glycoproteome profiling, and size exclusion chromatography (SEC) profiling to enable a comprehensive proteome profiling of proteins and protein complexes. We also used a novel native multiplexed plasma proteome profiling (NativeMP3) method that relies on native enrichment of plasma proteins enabling ultra-deep single shot profiling where we identified 922 plasma proteins at 1% false discovery rate (FDR) in a single shot mass spectrometry run. We observed many large plasma protein differences between the MRL-lpr and control strain including differences in the immunoglobulins, immunoglobulins against specific antigens, chemokines, and proteases as well as changes in protein complexes such as the immunoproteasome.

Automated 16-Plex Plasma Proteomics with Real-Time Search and Ion Mobility Mass Spectrometry Enables Large-Scale Profiling in Naked Mole-Rats and Mice.

Performing large-scale plasma proteome profiling is challenging due to limitations imposed by lengthy preparation and instrument time. We present a fully automated multiplexed proteome profiling platform (AutoMP3) using the Hamilton Vantage liquid handling robot capable of preparing hundreds to thousands of samples. To maximize protein depth in single-shot runs, we combined 16-plex Tandem Mass Tags (TMTpro) with high-field asymmetric waveform ion mobility spectrometry (FAIMS Pro) and real-time search (RTS). We quantified over 40 proteins/min/sample, doubling the previously published rates. We applied AutoMP3 to investigate the naked mole-rat plasma proteome both as a function of the circadian cycle and in response to ultraviolet (UV) treatment. In keeping with the lack of synchronized circadian rhythms in naked mole-rats, we find few circadian patterns in plasma proteins over the course of 48 h. Furthermore, we quantify many disparate changes between mice and naked mole-rats at both 48 h and one week after UV exposure. These species differences in plasma protein temporal responses could contribute to the pronounced cancer resistance observed in naked mole-rats. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD022891.

Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae.

The global proteomic alterations in the budding yeast Saccharomyces cerevisiae due to differences in carbon sources can be comprehensively examined using mass spectrometry-based multiplexing strategies. In this study, we investigate changes in the S. cerevisiae proteome resulting from cultures grown in minimal media using galactose, glucose, or raffinose as the carbon source. We used a tandem mass tag 9-plex strategy to determine alterations in relative protein abundance due to a particular carbon source, in triplicate, thereby permitting subsequent statistical analyses. We quantified more than 4700 proteins across all nine samples; 1003 proteins demonstrated statistically significant differences in abundance in at least one condition. The majority of altered proteins were classified as functioning in metabolic processes and as having cellular origins of plasma membrane and mitochondria. In contrast, proteins remaining relatively unchanged in abundance included those having nucleic acid-related processes, such as transcription and RNA processing. In addition, the comprehensiveness of the data set enabled the analysis of subsets of functionally related proteins, such as phosphatases, kinases, and transcription factors. As a resource, these data can be mined further in efforts to understand better the roles of carbon source fermentation in yeast metabolic pathways and the alterations observed therein, potentially for industrial applications, such as biofuel feedstock production.

Global Analysis of Protein Expression and Phosphorylation Levels in Nicotine-Treated Pancreatic Stellate Cells.

Smoking is a risk factor in pancreatic disease; however, the biochemical mechanisms correlating smoking with pancreatic dysfunction remain poorly understood. Strategies using multiplexed isobaric tag-based mass spectrometry facilitate the study of drug-induced perturbations on biological systems. Here, we present the first large-scale analysis of the proteomic and phosphoproteomic alterations in pancreatic stellate cells following treatment with two nicotinic acetylcholine receptor (nAChR) ligands: nicotine and α-bungarotoxin. We treated cells with nicotine or α-bungarotoxin for 12 h in triplicate and compared alterations in protein expression and phosphorylation levels to mock-treated cells using a tandem mass tag (TMT9plex)-based approach. Over 8100 proteins were quantified across all nine samples, of which 46 were altered in abundance upon treatment with nicotine. Proteins with increased abundance included those associated with neurons, defense mechanisms, indicators of pancreatic disease, and lysosomal proteins. In addition, we measured differences for ∼16 000 phosphorylation sites across all nine samples using a titanium dioxide-based strategy, of which 132 sites were altered with nicotine and 451 with α-bungarotoxin treatment. Many altered phosphorylation sites were involved in nuclear function and transcriptional events. This study supports the development of future targeted investigations to establish a better understanding for the role of nicotine and associated receptors in pancreatic disease.

iFASP: combining isobaric mass tagging with filter-aided sample preparation.

Careful, clean and controlled preparation of samples for mass spectrometry proteomics is crucial to obtain reproducible and reliable data. This is especially important when carrying out quantitative proteomics by chemical isobaric labeling (aka tandem mass tagging), since the differentially labeled samples are combined quite late during the sample processing. Addressing this need for robust and reliable sample processing for quantitative proteomics, we describe here iFASP, a simple protocol for combining isobaric mass tagging with the recently introduced filter-aided sample preparation (FASP) method. iFASP provides a quick, simple and effective method for obtaining clean samples, ensuring efficient digestion and providing excellent labeling yields for quantitative proteomics experiments. We have carried out our iFASP protocol using several highly complex Xenopus laevis egg and embryo lysates and compared the labeling yields and number of high-confidence peptide identifications to a standard in-solution digestion and labeling protocol. Although the labeling efficiency with both techniques is in the 99+% range, the number of peptides identified with a 1% false discovery rate and the corresponding number of quantified peptide spectral matches are as much as doubled with iFASP compared to the corresponding non-FASP-based method.

Analysis of endoscopic pancreatic function test (ePFT)-collected pancreatic fluid proteins precipitated via ultracentrifugation.

CONTEXT: We have shown previously that trichloroacetic acid precipitation is an effective method of protein extraction from pancreatic fluid for downstream biomarker discovery, compared to other common extraction methods tested. OBJECTIVE: We aim to assess the utility of ultracentrifugation as an alternative method of protein extraction from pancreatic fluid. DESIGN: Proteins extracted from trichloroacetic acid- and ultracentrifugation-precipitated pancreatic fluid were identified using mass spectrometry techniques (in-gel tryptic digestion followed by liquid chromatography-tandem mass spectrometry; GeLC-MS/MS). Data were analyzed using Proteome Discoverer and Scaffold 3. SETTING: This is a proteomic analysis experiment of endoscopically collected fluid in an academic center. PATIENTS: The study population included adult patients referred to the Center for Pancreatic Disease at Brigham and Women's Hospital, Boston, MA, USA for the evaluation of abdominal pain and gastrointestinal symptoms. INTERVENTIONS: Secretin-stimulated pancreatic fluid was collected as standard of care for the evaluation of abdominal pain and gastrointestinal symptoms. MAIN OUTCOME MEASURES: We compared proteins identified via standard trichloroacetic acid precipitation and this alternative ultracentrifugation strategy. RESULTS: A subset of pancreatic fluid proteins was identified via the ultracentrifugation method. Of these proteins, similar numbers were obtained from fully tryptic or semi-tryptic database searching. Proteins identified in the ultracentrifugation-precipitated samples included previously identified biomarker candidates of chronic pancreatitis. CONCLUSIONS: This alternative ultracentrifugation strategy requires less time and fewer handling procedures than standard trichloroacetic acid precipitation, at the expense of higher sample volume. As such, this method is well suited for targeted assays (i.e., dot blotting or targeted mass spectrometry) if the protein of interest is among those readily identified by ultracentrifugation-promoted precipitation.

Subcellular fractionation enhances proteome coverage of pancreatic duct cells.

OBJECTIVES: Subcellular fractionation of whole cell lysates offers a means of simplifying protein mixtures, potentially permitting greater depth of proteomic analysis. Here we compare proteins identified from pancreatic duct cells (PaDC) following organelle enrichment to those identified from PaDC whole cell lysates to determine if the additional procedures of subcellular fractionation increase proteome coverage. METHODS: We used differential centrifugation to enrich for nuclear, mitochondrial, membrane, and cytosolic proteins. We then compared - via mass spectrometry-based analysis - the number of proteins identified from these four fractions with four biological replicates of PaDC whole cell lysates. RESULTS: We identified similar numbers of proteins among all samples investigated. In total, 1658 non-redundant proteins were identified in the replicate samples, while 2196 were identified in the subcellular fractionation samples, corresponding to a 30% increase. Additionally, we noted that each organelle fraction was in fact enriched with proteins specific to the targeted organelle. CONCLUSIONS: Subcellular fractionation of PaDC resulted in greater proteome coverage compared to PaDC whole cell lysate analysis. Although more labor intensive and time consuming, subcellular fractionation provides greater proteome coverage, and enriches for compartmentalized sub-populations of proteins. Application of this subcellular fractionation strategy allows for a greater depth of proteomic analysis and thus a better understanding of the cellular mechanisms of pancreatic disease.