Analysis of human liver proteome using replicate shotgun strategy

In this study, a liquid-based shotgun strategy was used to comprehensively identify the expression of human liver proteome. Proteins were extracted from human liver tissue and digested in-solution. The tryptic digest mixture was desalted and separated by off-line strong cation exchange (SCX) chromatography with a 60-min elution. The MS/MS spectra were acquired in data-dependent mode after an RP chromatographic separation combined with linear IT MS analysis. To obtain the most comprehensive human liver proteome, each SCX fraction was run six times in RPLC MS/MS manner. Finally, more than 6,000,000 MS/MS spectra were collected. Using a relatively strict filter criteria, 24,311 proteins (48.42% of the predicted human proteome from human International Protein Index (IPI) protein database 3.07) corresponding to 13,150 nonredundant proteins were successfully identified, in which 7001 proteins (53.24%) were identified by two or more peptides, which could be considered as a high-confident dataset. Among the 6149 proteins (46.76%) identified by single peptide, 3812 proteins (61.99%) were detected more than twice in six repeated runs. Comparative analysis between different runs shows that the overlap of identified proteins between any two runs ranged from 25 to 44%. Of the nonredundant proteins identified, 8919 proteins (67.83%) were detected more than twice and 4231 proteins (32.17%) were detected only once in six RPLC MS/MS runs. The Gene Ontology annotation shows that the identified proteins come from various subcellular components. In addition, a large number of low abundant proteins were identified. The dynamic range of the approach reached at least nine orders of magnitude by estimating the concentration of proteins.     

Advances in shotgun proteomics and the analysis of membrane proteomes

The emergence of shotgun proteomics has facilitated the numerous biological discoveries made by proteomic studies. However, comprehensive proteomic analysis remains challenging and shotgun proteomics is a continually changing field. This review details the recent developments in shotgun proteomics and describes emerging technologies that will influence shotgun proteomics going forward. In addition, proteomic studies of integral membrane proteins remain challenging due to the hydrophobic nature in integral membrane proteins and their general low abundance levels. However, there have been many strategies developed for enriching, isolating and separating membrane proteins for proteomic analysis that have moved this field forward. In summary, while shotgun proteomics is a widely used and mature technology, the continued pace of improvements in mass spectrometry and proteomic technology and methods indicate that future studies will have an even greater impact on biological discovery.     

A comparison of the bovine uterine and plasma proteome using iTRAQ proteomics

Early embryo loss is a key factor affecting fertility in dairy and beef herds. Prior to implantation, the bovine embryo spends around 16 days free-floating in the uterine environment and is dependent on the composition of uterine fluid for normal growth and development. However, there is a lack of information regarding the protein composition of the bovine uterus and how it relates to plasma. In this study, uterine flushings (UF) (n = 6) and blood plasma (n = 4) were collected from beef heifers on day 7 of the oestrous cycle, albumin depleted and compared using iTRAQ proteomics. A total of 35 proteins were higher and 18 were lower in UF including metabolic enzymes, proteins with anti-oxidant activity and those involved in modulation of the immune response. This study confirms the dynamic nature of the bovine uterine proteome and that it differs from plasma. Factors affecting the uterine proteome and how it impacts on embryo survival warrant further study.     

Enhanced recovery of lyophilized peptides in shotgun proteomics by using an LC‐ESI‐MS compatible surfactant

LC‐ESI/MS/MS‐based shotgun proteomics is currently the most commonly used approach for the identification and quantification of proteins in large‐scale studies of biomarker discovery. In the past several years, the shotgun proteomics technologies have been refined toward further enhancement of proteome coverage. In the complex series of protocols involved in shotgun proteomics, however, loss of proteolytic peptides during the lyophilization step prior to the LC/MS/MS injection has been relatively neglected despite the fact that the dissolution of the hydrophobic peptides in lyophilized samples is difficult in 0.05–0.1% TFA or formic acid, causing substantial loss of precious peptide samples. In order to prevent the loss of peptide samples during this step, we devised a new protocol using Invitrosol (IVS), a commercially available surfactant compatible with ESI‐MS; by dissolving the lyophilized peptides in IVS, we show improved recovery of hydrophobic peptides, leading to enhanced coverage of proteome. Thus, the use of IVS in the recovery step of lyophilized peptides will help the shotgun proteomics analysis by expanding the proteome coverage, which would significantly promote the discovery and development of new diagnostic markers and therapeutic targets.     

Quantitative shotgun proteomics reveals extensive changes to the proteome of the orbitofrontal cortex in rats that are hyperactive following withdrawal from a high sugar diet

In most Westernized societies, there has been an alarming increase in the consumption of sugar‐sweetened drinks. For many adults these drinks represent a substantial proportion of their total daily caloric intake. Here we investigated whether extended exposure to sugar changes behavior and protein expression in the orbitofrontal cortex (OFC). Male adult Sprague‐Dawley rats (n = 8 per group) were treated for 26 days with either water or a 10% sucrose solution. Locomotor behavior was measured on the first and last day of treatment, then 1 week after treatment. Following the 1‐week period free from treatment, sucrose treated rats were significantly more active than the control. Two hours following final behavioral testing, brains were rapidly removed and prepared for proteomic analysis of the OFC. Label free quantitative shotgun proteomic analyses of three rats from each group found 290 proteins were differentially expressed in the sucrose treated group when compared to the control group. Major changes in the proteome were seen in proteins related to energy metabolism, mitochondrial function and the cellular response to stress. This research does not seek to suggest that sugar will cause specific neurological disorders, however similar changes in proteins have been seen in neurological disorders such as Alzheimer's disease, Parkinson's disease and schizophrenia.     

甲氨蝶呤对大鼠尿液蛋白质组的影响

甲氨蝶呤是常见的免疫抑制剂,曾被大剂量用于治疗癌症,近年来其被小剂量用于治疗类风湿性关节炎。文中尝试研究甲氨蝶呤对大鼠尿蛋白质组的影响。大鼠口服甲氨蝶呤构造用药模型,再收集大鼠10 h内的尿液,并且采用液相色谱串联质谱(liquid chromatography tandem mass spectrometry,LC-MS/MS)分析大鼠的尿蛋白。总共鉴定到31个差异蛋白,其中7个蛋白与甲氨蝶呤药物作用和类风湿性关节炎症状有关。部分大鼠的生物学过程反映了甲氨蝶呤对机体谷胱甘肽代谢以及对JAK/STAT信号通路的影响。结果显示,尿蛋白具有反映甲氨蝶呤对大鼠机体影响的能力。对单个大鼠个体差异蛋白的分析体现出不同个体对该药物的反应有较大差异。     

Proteomic analysis of human gastric juice: a shotgun approach

Gastric juice is the most proximal fluid surrounding the stomach tissue. The analysis of gastric juice protein contents will thus be able to accurately reflect the pathophysiology of the stomach. This biological fluid is also a potential reservoir of secreted biomarkers in higher concentration as compared to the serum. Unlike the rest of the gastrointestinal fluids, there were very few studies reported on gastric juice proteome. To date, the proteins that routinely populate this biofluid are largely unknown. This is partly due to the technical difficulties in processing a sample that contains a collection of other gastrointestinal fluids, especially saliva. In this study, we attempt to profile the protein components of the gastric fluids from chronic gastritis patients using a direct shotgun proteomics approach. These data represent the first report of the proteome of human gastric juice with gastritis background.     

The challenge of the proteome dynamic range and its implications for in‐depth proteomics

The dynamic range of the cellular proteome approaches seven orders of magnitude—from one copy per cell to ten million copies per cell. Since a proteome's abundance distribution represents a nearly symmetric bell‐shape curve on the logarithmic copy number scale, detection of half of the expressed cellular proteome, i.e. approximately 5000 proteins, should be a relatively straightforward task with modern mass spectrometric instrumentation that exhibits four orders of magnitude of the dynamic range, while deeper proteome analysis should be progressively more difficult. Indeed, metaanalysis of 15 recent papers that claim detection of >5000 protein groups reveals that the half‐proteome analyses currently requires ≈5 h of chromatographic separation, while deeper analyses yield on average ≤20 new proteins per hour of chromatographic gradient. Therefore, a typical proteomics experiment consists of a “high‐content” part, with the detection rate of approximately 1000 proteins/h, and a “low‐content” tail with much lower rate of discovery and respectively, lower cost efficiency. This result calls for disruptive innovation in deep proteomics analysis.     

Comparison of a Salmonella typhimurium proteome defined by shotgun proteomics directly on an LTQ-FT and by proteome pre-fractionation on an LCQ-DUO.

Shotgun proteomics is rapidly becoming one of the most efficient and popular tools to examine protein expression in cells. Numerous laboratories now have a wide array of low- and high-performance mass spectrometry instrumentation necessary to complete proteome-wide projects. Often these laboratories have time and financial constraints that prohibit all projects from being conducted on high-performance state-of-the-art mass spectrometers. Here, we compare shotgun proteomic results using a direct 'lyse, digest and analyse' approach on a high-performance mass spectrometer (i.e. the LTQ-FT) with the results from a much lower-performance instrument (i.e. the LCQ-DUO) where, for the latter, various traditional protein pre-fractionation steps and gas-phase fractionation were used to increase the proteome coverage. Our results demonstrate that shotgun proteomic analyses conducted on the lower-performance LCQ-DUO mass spectrometer could adequately characterize a PhoP constitutive strain of Salmonella typhimurium if proteome pre-fractionation steps and gas-phase fractionation were included.     

Clinical application of urinary proteomics/peptidomics

The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or ‘potential’ biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.     

Concepts and strategies of soybean seed proteomics using the shotgun proteomics approach

ABSTRACT

Introduction: The last decade has yielded significant developments in the field of proteomics, especially in mass spectrometry (MS) and data analysis tools. In particular, a shift from gel-based to MS-based proteomics has been observed, thereby providing a platform with which to construct proteome atlases for all life forms. Nevertheless, the analysis of plant proteomes, especially those of samples that contain high-abundance proteins (HAPs), such as soybean seeds, remains challenging.

Areas covered: Here, we review recent progress in soybean seed proteomics and highlight advances in HAPs depletion methods and peptide pre-fractionation, identification, and quantification methods. We also suggest a pipeline for future proteomic analysis, in order to increase the dynamic coverage of the soybean seed proteome.

Expert opinion: Because HAPs limit the dynamic resolution of the soybean seed proteome, the depletion of HAPs is a prerequisite of high-throughput proteome analysis, and owing to the use of two-dimensional gel electrophoresis-based proteomic approaches, few soybean seed proteins have been identified or characterized. Recent advances in proteomic technologies, which have significantly increased the proteome coverage of other plants, could be used to overcome the current complexity and limitation of soybean seed proteomics.     

Comparison between procedures using SDS for shotgun proteomic analyses of complex samples

Filter-aided sample preparation (FASP) and a new sample preparation method using a modified commercial SDS removal spin column are quantitatively compared in terms of their performance for shotgun proteomic experiments in three complex proteomic samples: a Saccharomyces cerevisiae lysate (insoluble fraction), a Caenorhabditis elegans lysate (soluble fraction), and a human embryonic kidney cell line (HEK293T). The characteristics and total number of peptides and proteins identified are compared between the two procedures. The SDS spin column procedure affords a conservative fourfold improvement in throughput, is more reproducible, less expensive (i.e. requires less materials), and identifies between 30 and 107% more peptides at q≤0.01, than the FASP procedure. The peptides identified by SDS spin column are more hydrophobic than species identified by the FASP procedure as indicated by the distribution of GRAVY scores. Ultimately, these improvements correlate to as great as a 50% increase in protein identifications with two or more peptides.     

PepDistiller: A quality control tool to improve the sensitivity and accuracy of peptide identifications in shotgun proteomics

In this study, we presented a quality control tool named PepDistiller to facilitate the validation of MASCOT search results. By including the number of tryptic termini, and integrating a refined false discovery rate (FDR) calculation method, we demonstrated the improved sensitivity of peptide identifications obtained from semitryptic search results. Based on the analysis of a complex data set, approximately 7% more peptide identifications were obtained using PepDistiller than using MASCOT Percolator. Moreover, the refined method generated lower FDR estimations than the percentage of incorrect target (PIT) fixed method applied in Percolator. Using a standard data set, we further demonstrated the increased accuracy of the refined FDR estimations relative to the PIT-fixed FDR estimations. PepDistiller is fast and convenient to use, and is freely available for academic access. The software can be downloaded from http://www.bprc.ac.cn/pepdistiller.     

Expanding the zebrafish embryo proteome using multiple fractionation approaches and tandem mass spectrometry

The proteome of zebrafish, Danio rerio, embryos has not been studied in great detail mainly due to the presence of high abundance yolk proteins in embryos. Here we report the highest number of the zebrafish embryo proteins identified so far to our knowledge, through a combination of a protein-level fractionation approach (1D SDS-PAGE) and two different peptide-level fractionation approaches (IEF and strong anion exchange (SAX)) of deyolked zebrafish embryos followed by LC-MS/MS. We detected 5267 proteins in total of which 3464 proteins were identified with at least two peptides (less than 1% peptide false discovery rate). The analysis of proteome coverage from each method showed that 56% of detected proteins were common to all approaches and 95% of the detected proteome was obtained from 1D SDS-PAGE approach alone. Bioinformatics analysis of the detected proteome demonstrated that nucleocytoplasmic transport (biological process) and ribosomal proteins (cellular component) were the most over-represented proteins, whereas cell-cell signaling (biological process) and extracellular space proteins (cellular component) were the most under-represented proteins in the identified proteome.     

膜整合蛋白质的“鸟枪法”蛋白质组学分析策略

疾病状态下生物膜表面蛋白质分子标记的表达量和修饰状态会发生改变。但由于其低丰度和不易溶解等特性,制约了膜蛋白质组学的研究,同时也制约了相关药物靶标的设计。近年来,为克服这些困难,学者们提出了"鸟枪法"的膜蛋白质组学研究策略。基于此,本文论述了"鸟枪法"的蛋白质组学分析的基本过程及其后续的部分改进工作。随着新的策略不断被采用,更多膜蛋白质的拓扑学特征和功能的相关研究一定会走上新的台阶。     

Exome‐based proteogenomics of HEK‐293 human cell line: Coding genomic variants identified at the level of shotgun proteome

Genomic and proteomic data were integrated into the proteogenomic workflow to identify coding genomic variants of Human Embryonic Kidney 293 (HEK‐293) cell line at the proteome level. Shotgun proteome data published by Geiger et al. (2012), Chick et al. (2015), and obtained in this work for HEK‐293 were searched against the customized genomic database generated using exome data published by Lin et al. (2014). Overall, 112 unique variants were identified at the proteome level out of ～1200 coding variants annotated in the exome. Seven identified variants were shared between all the three considered proteomic datasets, and 27 variants were found in any two datasets. Some of the found variants belonged to widely known genomic polymorphisms originated from the germline, while the others were more likely resulting from somatic mutations. At least, eight of the proteins bearing amino acid variants were annotated as cancer‐related ones, including p53 tumor suppressor. In all the considered shotgun datasets, the variant peptides were at the ratio of 1:2.5 less likely being identified than the wild‐type ones compared with the corresponding theoretical peptides. This can be explained by the presence of the so‐called “passenger” mutations in the genes, which were never expressed in HEK‐293 cells. All MS data have been deposited in the ProteomeXchange with the dataset identifier PXD002613 ( http://proteomecentral.proteomexchange.org/dataset/PXD002613 ).     

Insights from the shotgun-based proteomics analysis of Biomphalaria glabrata

Biomphalaria glabrata is an important host in the transmission of human schistosomiasis in the Caribbean and South America. Therefore, it is of interest to analyse the proteome data of Biomphalaria glabrata hemolymph to identify immunity related proteins in host-pathogen relationship. We used shotgun proteomic and bioinformatic analyses of the non-depleted and depleted [0.5 and 0.75% Trifluoroacetic acid (TFA) depletion] hemolymph of B. glabrata (LE strain). Analysis showed 148 proteins from the hemolymph. 148 were obtained from the 0.5% TFA-depleted sample. 62 proteins follow this from the 0.75% TFA-depleted sample. However, only 59 were found from non-depleted hemolymph. A number of proteins were identified from the hemolymph of this schistosomiasis snail vector linked to immunity related functions. This provides insights to the understanding of schistosome-snail interaction.     

Strategy for surveying the proteome using affinity proteomics and mass spectrometry

Antibody‐based microarrays is a rapidly evolving technology that has gone from the first proof‐of‐concept studies to more demanding proteome profiling applications, during the last years. Miniaturized microarrays can be printed with large number of antibodies harbouring predetermined specificities, capable of targeting high‐ as well as low‐abundant analytes in complex, nonfractionated proteomes. Consequently, the resolution of such proteome profiling efforts correlate directly to the number of antibodies included, which today is a key limiting factor. To overcome this bottleneck and to be able to perform in‐depth global proteome surveys, we propose to interface affinity proteomics with MS‐based read‐out, as outlined in this technical perspective. Briefly, we have defined a range of peptide motifs, each motif being present in 5–100 different proteins. In this manner, 100 antibodies, binding 100 different motifs commonly distributed among different proteins, would potentially target a protein cluster of 10⁴ individual molecules, i.e. around 50% of the nonredundant human proteome. Notably, these motif‐specific antibodies would be directly applicable to any proteome in a specie independent manner and not biased towards abundant proteins or certain protein classes. The biological sample is digested, exposed to these immobilized antibodies, whereby motif‐containing peptides are specifically captured, enriched and subsequently detected and identified using MS.     

A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across an LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities.