Xiaomeng Shen

Highly multiplexed and reproducible ion-current-based strategy for large-scale quantitative proteomics and the application to protein expression dynamics induced by methylprednisolone in 60 rats.

A proteome-level time-series study of drug effects (i.e., pharmacodynamics) is critical for understanding mechanisms of action and systems pharmacology, but is challenging, because of the requirement of a proteomics method for reliable quantification of many biological samples. Here, we describe a highly reproducible strategy, enabling a global, large-scale investigation of the expression dynamics of corticosteroid-regulated proteins in livers from adrenalectomized rats over 11 time points after drug dosing (0.5–66 h, N = 5/point). The analytical advances include (i) exhaustive tissue extraction with a Polytron/sonication procedure in a detergent cocktail buffer, and a cleanup/digestion procedure providing very consistent protein yields (relative standard deviation (RSD%) of 2.7%–6.4%) and peptide recoveries (4.1–9.0%) across the 60 animals; (ii) an ultrahigh-pressure nano-LC setup with substantially improved temperature stabilization, pump-noise suppression, and programmed interface cleaning, enabling excellent reproducibility for continuous analyses of numerous samples; (iii) separation on a 100-cm-long column (2-μm particles) with high reproducibility for days to enable both in-depth profiling and accurate peptide ion-current match; and (iv) well-controlled ion-current-based quantification. To obtain high-quality quantitative data necessary to describe the 11 time-points protein expression temporal profiles, strict criteria were used to define “quantifiable proteins”. A total of 323 drug-responsive proteins were revealed with confidence, and the time profiles of these proteins provided new insights into the diverse temporal changes of biological cascades associated with hepatic metabolism, response to hormone stimuli, gluconeogenesis, inflammatory responses, and protein translation processes. Most profile changes persisted well after the drug was eliminated. The developed strategy can also be broadly applied in preclinical and clinical research, where the analysis of numerous biological replicates is crucial.

Brief Myocardial Ischemia Produces Cardiac Troponin I Release and Focal Myocyte Apoptosis in the Absence of Pathological Infarction in Swine

Visual Abstract

Quantitative proteomics in cardiovascular research: global and targeted strategies.

Extensive technical advances in the past decade have substantially expanded quantitative proteomics in cardiovascular research. This has great promise for elucidating the mechanisms of cardiovascular diseases and the discovery of cardiac biomarkers used for diagnosis and treatment evaluation. Global and targeted proteomics are the two major avenues of quantitative proteomics. While global approaches enable unbiased discovery of altered proteins via relative quantification at the proteome level, targeted techniques provide higher sensitivity and accuracy, and are capable of multiplexed absolute quantification in numerous clinical/biological samples. While promising, technical challenges need to be overcome to enable full utilization of these techniques in cardiovascular medicine. Here, we discuss recent advances in quantitative proteomics and summarize applications in cardiovascular research with an emphasis on biomarker discovery and elucidating molecular mechanisms of disease. We propose the integration of global and targeted strategies as a high‐throughput pipeline for cardiovascular proteomics. Targeted approaches enable rapid, extensive validation of biomarker candidates discovered by global proteomics. These approaches provide a promising alternative to immunoassays and other low‐throughput means currently used for limited validation.

ICan: an optimized ion-current-based quantification procedure with enhanced quantitative accuracy and sensitivity in biomarker discovery.

The rapidly expanding availability of high-resolution mass spectrometry has substantially enhanced the ion-current-based relative quantification techniques. Despite the increasing interest in ion-current-based methods, quantitative sensitivity, accuracy, and false discovery rate remain the major concerns; consequently, comprehensive evaluation and development in these regards are urgently needed. Here we describe an integrated, new procedure for data normalization and protein ratio estimation, termed ICan, for improved ion-current-based analysis of data generated by high-resolution mass spectrometry (MS). ICan achieved significantly better accuracy and precision, and lower false-positive rate for discovering altered proteins, over current popular pipelines. A spiked-in experiment was used to evaluate the performance of ICan to detect small changes. In this study E. coli extracts were spiked with moderate-abundance proteins from human plasma (MAP, enriched by IgY14-SuperMix procedure) at two different levels to set a small change of 1.5-fold. Forty-five (92%, with an average ratio of 1.71 ± 0.13) of 49 identified MAP protein (i.e., the true positives) and none of the reference proteins (1.0-fold) were determined as significantly altered proteins, with cutoff thresholds of ≥1.3-fold change and p ≤ 0.05. This is the first study to evaluate and prove competitive performance of the ion-current-based approach for assigning significance to proteins with small changes. By comparison, other methods showed remarkably inferior performance. ICan can be broadly applicable to reliable and sensitive proteomic survey of multiple biological samples with the use of high-resolution MS. Moreover, many key features evaluated and optimized here such as normalization, protein ratio determination, and statistical analyses are also valuable for data analysis by isotope-labeling methods.

Proteomic profiling of the retinas in a neonatal rat model of oxygen-induced retinopathy with a reproducible ion-current-based MS1 approach.

Investigation of the retina proteome during hypoxia-induced retinal neovascularization is valuable for understanding pathogenesis of retinopathy of prematurity (ROP). Here we employed a reproducible ion-current-based MS1 quantification approach (ICB) to explore the retinal proteomic changes in early stage of ROP in a rat model of oxygen-induced retinopathy (OIR). Retina proteins, which are rich in membrane proteins, were efficiently extracted by a detergent-cocktail and subjected to precipitation/on-pellet-digestion, followed by nano-LC-MS analysis on a 75-cm column with a 7-h gradient. The high reproducibility of sample preparation and chromatography separation enabled excellent peak alignment and contributed to the superior performance of ICB over parallel label-free approaches. In this study, sum-of-intensity with rejection was incorporated to determine the protein ratios. In total, 1325 unique protein groups were quantified from rat retinas (n = 4/group) with at least two distinct peptides at a protein FDR of 1%. Thirty-two significantly altered proteins were observed with confidence, and the elevated glial fibrillary acidic protein and decreased crystalline proteins in OIR retinas agree well with previous studies. Selected key alterations were further validated by Western blot analysis. Interestingly, Rab21/RhoA/ROCK2/moesin signaling pathway was found to be involved in retinal neovascularization of OIR. Moreover, highly elevated annexin A3, a potential angiogenic mediator, was observed in OIR retinas and may serve as a potential therapeutic target. In conclusion, reproducible ICB profiling enabled reliable discovery of many altered mediators and pathways in OIR retinas, thereby providing new insights into molecular mechanisms involved in pathogenesis of ROP.

Reproducible ion-current-based approach for 24-plex comparison of the tissue proteomes of hibernating versus normal myocardium in swine models.

Hibernating myocardium is an adaptive response to repetitive myocardial ischemia that is clinically common, but the mechanism of adaptation is poorly understood. Here we compared the proteomes of hibernating versus normal myocardium in a porcine model with 24 biological replicates. Using the ion-current-based proteomic strategy optimized in this study to expand upon previous proteomic work, we identified differentially expressed proteins in new molecular pathways of cardiovascular interest. The methodological strategy includes efficient extraction with detergent cocktail; precipitation/digestion procedure with high, quantitative peptide recovery; reproducible nano-LC/MS analysis on a long, heated column packed with small particles; and quantification based on ion-current peak areas. Under the optimized conditions, high efficiency and reproducibility were achieved for each step, which enabled a reliable comparison of 24 the myocardial samples. To achieve confident discovery of differentially regulated proteins in hibernating myocardium, we used highly stringent criteria to define “quantifiable proteins”. These included the filtering criteria of low peptide FDR and S/N > 10 for peptide ion currents, and each protein was quantified independently from ≥2 distinct peptides. For a broad methodological validation, the quantitative results were compared with a parallel, well-validated 2D-DIGE analysis of the same model. Excellent agreement between the two orthogonal methods was observed (R = 0.74), and the ion-current-based method quantified almost one order of magnitude more proteins. In hibernating myocardium, 225 significantly altered proteins were discovered with a low false-discovery rate (∼3%). These proteins are involved in biological processes including metabolism, apoptosis, stress response, contraction, cytoskeleton, transcription, and translation. This provides compelling evidence that hibernating myocardium adapts to chronic ischemia. The major metabolic mechanisms include a down-regulation of mitochondrial respiration and an increase in glycolysis. Meanwhile, cardioprotective and cytoskeletal proteins are increased, while cardiomyocyte contractile proteins are reduced. These intrinsic adaptations to regional ischemia maintain long-term cardiomyocyte viability at the expense of contractile function.

An IonStar Experimental Strategy for MS1 Ion Current-Based Quantification Using Ultrahigh-Field Orbitrap: Reproducible, In-Depth, and Accurate Protein Measurement in Large Cohorts

In-depth and reproducible protein measurement in many biological samples is often critical for pharmaceutical/biomedical proteomics but remains challenging. MS1-based quantification using quadrupole/ultrahigh-field Orbitrap (Q/UHF-Orbitrap) holds great promise, but the critically important experimental approaches enabling reliable large-cohort analysis have long been overlooked. Here we described an IonStar experimental strategy achieving excellent quantitative quality of MS1 quantification. Key features include: (i) an optimized, surfactant-aided sample preparation approach provides highly efficient (>75% recovery) and reproducible (<15% CV) peptide recovery across large cell/tissue cohorts; (ii) a long column with modest gradient length (2.5 h) yields the optimal balance of depth/throughput on a Q/UHF-Orbitrap; (iii) a large-ID trap not only enables highly reproducible gradient delivery as for the first time observed via real-time conductivity monitoring, but also increases quantitative loading capacity by >8-fold and quantified >25% more proteins; (iv) an optimized HCD-OT markedly outperforms HCD-IT when analyzing large cohorts with high loading amounts; (v) selective removal of hydrophobic/hydrophilic matrix components using a novel selective trapping/delivery approach enables reproducible, robust LC-MS analysis of >100 biological samples in a single set, eliminating batch effect; (vi) MS1 acquired at higher resolution (fwhm = 120 k) provides enhanced S/N and quantitative accuracy/precision for low-abundance species. We examined this pipeline by analyzing a 5 group, 20 samples biological benchmark sample set, and quantified 6273 unique proteins (≥2 peptides/protein) under stringent cutoffs without fractionation, 6234 (>99.4%) without missing data in any of the 20 samples. The strategy achieved high quantitative accuracy (3-6% media error), low intragroup variation (6-9% media intragroup CV) and low false-positive biomarker discovery rates (3-8%) across the five groups, with quantified protein abundances spanning >6.5 orders of magnitude. Finally, this strategy is straightforward, robust, and broadly applicable in pharmaceutical/biomedical investigations.

Experimental Null Method to Guide the Development of Technical Procedures and to Control False-Positive Discovery in Quantitative Proteomics

Comprehensive and accurate evaluation of data quality and false-positive biomarker discovery is critical to direct the method development/optimization for quantitative proteomics, which nonetheless remains challenging largely due to the high complexity and unique features of proteomic data. Here we describe an experimental null (EN) method to address this need. Because the method experimentally measures the null distribution (either technical or biological replicates) using the same proteomic samples, the same procedures and the same batch as the case-vs-contol experiment, it correctly reflects the collective effects of technical variability (e.g., variation/bias in sample preparation, LC-MS analysis, and data processing) and project-specific features (e.g., characteristics of the proteome and biological variation) on the performances of quantitative analysis. To show a proof of concept, we employed the EN method to assess the quantitative accuracy and precision and the ability to quantify subtle ratio changes between groups using different experimental and data-processing approaches and in various cellular and tissue proteomes. It was found that choices of quantitative features, sample size, experimental design, data-processing strategies, and quality of chromatographic separation can profoundly affect quantitative precision and accuracy of label-free quantification. The EN method was also demonstrated as a practical tool to determine the optimal experimental parameters and rational ratio cutoff for reliable protein quantification in specific proteomic experiments, for example, to identify the necessary number of technical/biological replicates per group that affords sufficient power for discovery. Furthermore, we assessed the ability of EN method to estimate levels of false-positives in the discovery of altered proteins, using two concocted sample sets mimicking proteomic profiling using technical and biological replicates, respectively, where the true-positives/negatives are known and span a wide concentration range. It was observed that the EN method correctly reflects the null distribution in a proteomic system and accurately measures false altered proteins discovery rate (FADR). In summary, the EN method provides a straightforward, practical, and accurate alternative to statistics-based approaches for the development and evaluation of proteomic experiments and can be universally adapted to various types of quantitative techniques.

Tandem Analysis of Transcriptome and Proteome Changes after a Single Dose of Corticosteroid: A Systems Approach to Liver Function in Pharmacogenomics

Corticosteroids (CS) such as methylprednisolone (MPL) affect almost all liver functions through multiple mechanisms of action, and long-term use results in dysregulation causing diverse side effects. The complexity of involved molecular mechanisms necessitates a systems approach. Integration of information from the transcriptomic and proteomic responses has potential to provide deeper insights into CS actions. The present report describes the tandem analysis of rich time-series transcriptomic and proteomic data in rat liver after a single dose of MPL. Hierarchical clustering of the common genes represented in both mRNA and protein datasets displayed two dominant patterns. One of these patterns exhibited complementary mRNA and protein expression profiles indicating that MPL affected the regulation of these genes at the transcriptional level. Some of the classic pharmacodynamic markers for CS actions, including tyrosine aminotransferase (TAT), were among this group, together with genes encoding urea cycle enzymes and ribosomal proteins. The other pattern was rather unexpected. For this group of genes, MPL had distinctly observable effects at the protein expression level, although a change in the reverse direction occurred at the transcriptional level. These genes were functionally associated with metabolic processes that might be essential to elucidate side effects of MPL on liver, most importantly including modulation of oxidative stress, fatty acid oxidation, and bile acid biosynthesis. Furthermore, profiling of gene and protein expression data was also done independently of one another by a two-way sequential approach. Prominent temporal shifts in expression and relevant cellular functions were described together with the assessment of changes in the complementary side.

Optimization of Search Engines and Postprocessing Approaches to Maximize Peptide and Protein Identification for High-Resolution Mass Data

The two key steps for analyzing proteomic data generated by high-resolution MS are database searching and postprocessing. While the two steps are interrelated, studies on their combinatory effects and the optimization of these procedures have not been adequately conducted. Here, we investigated the performance of three popular search engines (SEQUEST, Mascot, and MS Amanda) in conjunction with five filtering approaches, including respective score-based filtering, a group-based approach, local false discovery rate (LFDR), PeptideProphet, and Percolator. A total of eight data sets from various proteomes (e.g., E. coli, yeast, and human) produced by various instruments with high-accuracy survey scan (MS1) and high- or low-accuracy fragment ion scan (MS2) (LTQ-Orbitrap, Orbitrap-Velos, Orbitrap-Elite, Q-Exactive, Orbitrap-Fusion, and Q-TOF) were analyzed. It was found combinations involving Percolator achieved markedly more peptide and protein identifications at the same FDR level than the other 12 combinations for all data sets. Among these, combinations of SEQUEST-Percolator and MS Amanda-Percolator provided slightly better performances for data sets with low-accuracy MS2 (ion trap or IT) and high accuracy MS2 (Orbitrap or TOF), respectively, than did other methods. For approaches without Percolator, SEQUEST-group performs the best for data sets with MS2 produced by collision-induced dissociation (CID) and IT analysis; Mascot-LFDR gives more identifications for data sets generated by higher-energy collisional dissociation (HCD) and analyzed in Orbitrap (HCD-OT) and in Orbitrap Fusion (HCD-IT); MS Amanda-Group excels for the Q-TOF data set and the Orbitrap Velos HCD-OT data set. Therefore, if Percolator was not used, a specific combination should be applied for each type of data set. Moreover, a higher percentage of multiple-peptide proteins and lower variation of protein spectral counts were observed when analyzing technical replicates using Percolator-associated combinations; therefore, Percolator enhanced the reliability for both identification and quantification. The analyses were performed using the specific programs embedded in Proteome Discoverer, Scaffold, and an in-house algorithm (BuildSummary). These results provide valuable guidelines for the optimal interpretation of proteomic results and the development of fit-for-purpose protocols under different situations.