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

Filter-Based Protein Digestion (FPD): A Detergent-Free and Scaffold-Based Strategy for TMT Workflows

High-throughput proteome profiling requires thorough optimization to achieve comprehensive analysis. We developed a filter aided sample preparation (FASP)-like, detergent-free method, termed Filter-Based Protein Digestion (FPD). We compared FPD to protein extraction methods commonly used in isobaric tag-based proteome profiling, namely trichloroacetic acid (TCA) and chloroform–methanol (C–M) precipitation. We divided a mammalian whole cell lysate from the SH-SY5Y neuroblastoma cell line for parallel protein processing with TCA (<i>n</i> = 3), C–M (<i>n</i> = 2), and FPD using either 10 kDa (<i>n</i> = 3) or 30 kDa (<i>n</i> = 3) molecular weight cutoff membranes. We labeled each sample with tandem mass tag (TMT) reagents to construct a TMT11-plex experiment. In total, 8654 proteins were quantified across all samples. Pairwise comparisons showed very little deviation for individual protein abundance measurements between the two FPD methods, whereas TCA and FPD showed the most difference. Specifically, membrane proteins were more readily quantified when samples were processed using TCA precipitation than other methods tested. However, globally, only 4% of proteins differed greater than 4-fold in the most divergent pair of protein extraction methods (i.e., FPD10 and TCA). We conclude that the detergent-free FPD strategy, particularly using the faster-flowing 30 kDa filter, is a seamless alteration to high-throughput TMT workflows

Filter-Based Protein Digestion (FPD): A Detergent-Free and Scaffold-Based Strategy for TMT Workflows

High-throughput proteome profiling requires thorough optimization to achieve comprehensive analysis. We developed a filter aided sample preparation (FASP)-like, detergent-free method, termed Filter-Based Protein Digestion (FPD). We compared FPD to protein extraction methods commonly used in isobaric tag-based proteome profiling, namely trichloroacetic acid (TCA) and chloroform–methanol (C–M) precipitation. We divided a mammalian whole cell lysate from the SH-SY5Y neuroblastoma cell line for parallel protein processing with TCA (<i>n</i> = 3), C–M (<i>n</i> = 2), and FPD using either 10 kDa (<i>n</i> = 3) or 30 kDa (<i>n</i> = 3) molecular weight cutoff membranes. We labeled each sample with tandem mass tag (TMT) reagents to construct a TMT11-plex experiment. In total, 8654 proteins were quantified across all samples. Pairwise comparisons showed very little deviation for individual protein abundance measurements between the two FPD methods, whereas TCA and FPD showed the most difference. Specifically, membrane proteins were more readily quantified when samples were processed using TCA precipitation than other methods tested. However, globally, only 4% of proteins differed greater than 4-fold in the most divergent pair of protein extraction methods (i.e., FPD10 and TCA). We conclude that the detergent-free FPD strategy, particularly using the faster-flowing 30 kDa filter, is a seamless alteration to high-throughput TMT workflows

MS3-IDQ: Utilizing MS3 Spectra beyond Quantification Yields Increased Coverage of the Phosphoproteome in Isobaric Tag Experiments

Protein phosphorylation is critically important for many cellular processes, including progression through the cell cycle, cellular metabolism, and differentiation. Isobaric labeling, for example, tandem mass tags (TMT), in phosphoproteomics workflows enables both relative and absolute quantitation of these phosphorylation events. Traditional TMT workflows identify peptides using fragment ions at the MS2 level and quantify reporter ions at the MS3 level. However, in addition to the TMT reporter ions, MS3 spectra also include fragment ions that can be used to identify peptides. Here we describe using MS3 spectra for both phosphopeptide identification and quantification, a process that we term MS3-IDQ. To maximize quantified phosphopeptides, we optimize several instrument parameters, including the modality of mass analyzer (i.e., ion trap or Orbitrap), MS2 automatic gain control (AGC), and MS3 normalized collision energy (NCE), to achieve the best balance of identified and quantified peptides. Our optimized MS3-IDQ method included the following parameters for the MS3 scan: NCE = 37.5 and AGC target = 1.5 × 10⁵, and scan range = 100–2000. Data from the MS3 scan were complementary to those of the MS2 scan, and the combination of these scans can increase phosphoproteome coverage by >50%, thereby yielding a greater number of quantified and accurately localized phosphopeptides

Filter-Based Protein Digestion (FPD): A Detergent-Free and Scaffold-Based Strategy for TMT Workflows

High-throughput proteome profiling requires thorough optimization to achieve comprehensive analysis. We developed a filter aided sample preparation (FASP)-like, detergent-free method, termed Filter-Based Protein Digestion (FPD). We compared FPD to protein extraction methods commonly used in isobaric tag-based proteome profiling, namely trichloroacetic acid (TCA) and chloroform–methanol (C–M) precipitation. We divided a mammalian whole cell lysate from the SH-SY5Y neuroblastoma cell line for parallel protein processing with TCA (<i>n</i> = 3), C–M (<i>n</i> = 2), and FPD using either 10 kDa (<i>n</i> = 3) or 30 kDa (<i>n</i> = 3) molecular weight cutoff membranes. We labeled each sample with tandem mass tag (TMT) reagents to construct a TMT11-plex experiment. In total, 8654 proteins were quantified across all samples. Pairwise comparisons showed very little deviation for individual protein abundance measurements between the two FPD methods, whereas TCA and FPD showed the most difference. Specifically, membrane proteins were more readily quantified when samples were processed using TCA precipitation than other methods tested. However, globally, only 4% of proteins differed greater than 4-fold in the most divergent pair of protein extraction methods (i.e., FPD10 and TCA). We conclude that the detergent-free FPD strategy, particularly using the faster-flowing 30 kDa filter, is a seamless alteration to high-throughput TMT workflows

Improved Method for Determining Absolute Phosphorylation Stoichiometry Using Bayesian Statistics and Isobaric Labeling

Phosphorylation stoichiometry, or occupancy, is one element of phosphoproteomics that can add useful biological context (Gerber et al. <i>Proc. Natl. Acad. Sci. U. S. A</i>. 2003, <i>100</i>, 6940–5). We previously developed a method to assess phosphorylation stoichiometry on a proteome-wide scale (Wu et al. <i>Nat. Methods</i> 2011, <i>8</i>, 677–83). The stoichiometry calculation relies on identifying and measuring the levels of each nonphosphorylated counterpart peptide with and without phosphatase treatment. The method, however, is problematic in that low stoichiometry phosphopeptides can return negative stoichiometry values if measurement error is larger than the percent stoichiometry. Here, we have improved the stoichiometry method through the use of isobaric labeling with 10-plex TMT reagents. In this way, five phosphatase treated and five untreated samples are compared simultaneously so that each stoichiometry is represented by five ratio measurements with no missing values. We applied the method to determine basal stoichiometries of HCT116 cells growing in culture. With this method, we analyzed five biological replicates simultaneously with no need for phosphopeptide enrichment. Additionally, we developed a Bayesian model to estimate phosphorylation stoichiometry as a parameter confined to an interval between 0 and 1 implemented as an R/Stan script. Consequently, both point and interval estimates are consistent with the plausible range of values for stoichiometry. Finally, we report absolute stoichiometry measurements with credible intervals for 6772 phosphopeptides containing at least a single phosphorylation site

Streamlined Tandem Mass Tag (SL-TMT) Protocol: An Efficient Strategy for Quantitative (Phospho)proteome Profiling Using Tandem Mass Tag-Synchronous Precursor Selection-MS3

Mass spectrometry (MS) coupled toisobaric labeling has developed rapidly into a powerful strategy for high-throughput protein quantification. Sample multiplexing and exceptional sensitivity allow for the quantification of tens of thousands of peptides and, by inference, thousands of proteins from multiple samples in a single MS experiment. Accurate quantification demands a consistent and robust sample-preparation strategy. Here, we present a detailed workflow for SPS-MS3-based quantitative abundance profiling of tandem mass tag (TMT)-labeled proteins and phosphopeptides that we have named the streamlined (SL)-TMT protocol. We describe a universally applicable strategy that requires minimal individual sample processing and permits the seamless addition of a phosphopeptide enrichment step (“mini-phos”) with little deviation from the deep proteome analysis. To showcase our workflow, we profile the proteome of wild-type <i>Saccharomyces cerevisiae</i> yeast grown with either glucose or pyruvate as the carbon source. Here, we have established a streamlined TMT protocol that enables deep proteome and medium-scale phosphoproteome analysis

Proteome-Wide Evaluation of Two Common Protein Quantification Methods

Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identifiedlet alone totalproteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewer missing values. These findings highlight the importance of refining proteome quantitation methods to bring the number of usefully quantified proteins into closer agreement with the number of total quantified proteins

Streamlined Tandem Mass Tag (SL-TMT) Protocol: An Efficient Strategy for Quantitative (Phospho)proteome Profiling Using Tandem Mass Tag-Synchronous Precursor Selection-MS3

Mass spectrometry (MS) coupled toisobaric labeling has developed rapidly into a powerful strategy for high-throughput protein quantification. Sample multiplexing and exceptional sensitivity allow for the quantification of tens of thousands of peptides and, by inference, thousands of proteins from multiple samples in a single MS experiment. Accurate quantification demands a consistent and robust sample-preparation strategy. Here, we present a detailed workflow for SPS-MS3-based quantitative abundance profiling of tandem mass tag (TMT)-labeled proteins and phosphopeptides that we have named the streamlined (SL)-TMT protocol. We describe a universally applicable strategy that requires minimal individual sample processing and permits the seamless addition of a phosphopeptide enrichment step (“mini-phos”) with little deviation from the deep proteome analysis. To showcase our workflow, we profile the proteome of wild-type <i>Saccharomyces cerevisiae</i> yeast grown with either glucose or pyruvate as the carbon source. Here, we have established a streamlined TMT protocol that enables deep proteome and medium-scale phosphoproteome analysis

Proteome-Wide Evaluation of Two Common Protein Quantification Methods

Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identifiedlet alone totalproteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewer missing values. These findings highlight the importance of refining proteome quantitation methods to bring the number of usefully quantified proteins into closer agreement with the number of total quantified proteins