Immobilized enzyme reactors in proteomics

Fast, efficient characterization of proteins is becoming one of the hottest topics in the bioanalytical community, especially for large-scale proteomic studies. As an attractive approach, protein digestion by enzymes supported on various matrices (referred to as immobilized enzyme reactors, IMERs) has recently attracted much attention.In this article, we present a critical overview of some highly efficient IMERs and related analytical systems. We give major coverage to applications of IMERs in proteomic analysis, including protein-expression profiling, characterization of proteins with post-translational modifications, and protein quantification. We also comment on promising trends for IMERs in proteomics.     

Overview of software options for processing,analysis and interpretation of mass spectrometric proteomic data

Recently, the interests in proteomics have been intensively increased, and the proteomic methods have been widely applied to many problems in cell biology. If the age of 1990s is considered to be a decade of genomics, we can claim that the following years of the new century is a decade of proteomics. The rapid evolution of proteomics has continued through these years, with a series of innovations in separation techniques and the core technologies of two‐dimensional gel electrophoresis and MS. Both technologies are fueled by automation and high throughput computation for profiling of proteins from biological systems. As Patterson ever mentioned, ‘data analysis is the Achilles heel of proteomics and our ability to generate data now outstrips our ability to analyze it’. The development of automatic and high throughput technologies for rapid identification of proteins is essential for large‐scale proteome projects and automatic protein identification and characterization is essential for high throughput proteomics. This review provides a snap shot of the tools and applications that are available for mass spectrometric high throughput biocomputation. The review starts with a brief introduction of proteomics and MS. Computational tools that can be employed at various stages of analysis are presented, including that for data processing, identification, quantification, and the understanding of the biological functions of individual proteins and their dynamic interactions. The challenges of computation software development and its future trends in MS‐based proteomics have also been speculated. Copyright © 2014 John Wiley & Sons, Ltd.     

Fractionation‐dependent improvements in proteome resolution in the mouse hippocampus by IEF LC‐MS/MS

An assessment of fractionated mouse hippocampal peptides was conducted. Protein extract from a single mouse hippocampus was enzymatically digested and fractionated by IEF. Aliquots of fractions were pooled into fewer, more complex samples. The unfractionated lysate, fractions, and pooled fractions were subjected to LC‐MS/MS analysis. Samples consisting of many individual fractions had more protein identifications, greater protein sequence coverage, and quantified proteins with more spectral counts than protein extract that was unfractionated or pooled into fewer LC‐MS/MS samples. Additionally, prefractionation reduced the median CV for spectral counts as much as 33%. However, the relative gain in proteome resolution was found to saturate with increasing fractionation extent. This study demonstrates how prefractionation by offline IEF can improve the resolution of proteomic investigations of the mouse hippocampus, and that a data‐driven pooling methodology can reduce excessive and cost‐ineffective fractionation.     

Application of an improved proteomics method, fluorogenic derivatization-liquid chromatography-tandem mass spectrometry, to differential analysis of proteins in small regions of mouse brain

To identify age-related proteins in small regions of mouse brain, we improved a proteomics approach, fluorogenic derivatization-liquid chromatography-tandem mass spectrometry (FD-LC-MS/MS), and applied the method to the differential proteome analysis of aging in cerebral cortex, hippocampus and brainstem. The method showed good accuracy with RSDs <10% for between-day protein peak heights, and much better sensitivity for the detection of proteins compared to other proteomics approaches. The existence of 28 regionally specific age-related proteins in mouse brain was demonstrated. These results verified that the small brain regions could be the targets for proteome analysis by the FD-LC-MS/MS method.     

Peptide correlation: a means to identify high quality quantitative information in large-scale proteomic studies

A major challenge of proteomic studies is the accurate quantitation of proteins. LC-MS/MS-based methods are especially suited for profiling proteins in large sample sets. In this setup, the measurement of relative protein abundance relies on the correct quantitation of tryptic peptides. However, peptide intensities often do not unequivocally reflect the abundance of the native proteins in the sample. In this study, we show that peptides that accurately reflect relative protein abundances in large-scale sample sets can be selected based on the correlation to each other. This strategy was tested in a well-controlled experiment using a set of spiked serum samples as well as 55 clinical serum samples from schizophrenia patients and healthy volunteers. The peptide correlation analysis we present here provides an intuitive and simple procedure to obtain a high quality quantitative information from proteomics data.     

高通量蛋白质组学分析研究进展

基于质谱的蛋白质组学技术已经日趋成熟,可以对细胞和组织中的成千上万种蛋白质进行全面的定性和定量分析,逐步实现“深度覆盖”。随着生物医学日益增长的大队列蛋白质组学分析需求,如何在保持较为理想的覆盖深度下实现短时间、快速的“高通量”蛋白质组学分析已成为当前亟需解决的关键问题之一。常规的蛋白质组学分析流程通常包括样品前处理、色谱分离、质谱检测和数据分析。该文从以上4个方面展开介绍近10年以来高通量蛋白质组学分析技术取得的一系列研究进展,主要包括:(1)基于高通量、自动化移液工作站的蛋白质组样品前处理方法;(2)基于微升流速液相色谱与质谱联用的高通量蛋白质组检测方法;(3)利用灵敏度高、扫描速度快的质谱仪实现短色谱梯度分离下蛋白质组深度覆盖的分析方法;(4)基于人工智能、深度神经网络、机器学习等的蛋白质组学大数据分析方法。此外,对高通量蛋白质组学面临的挑战及其发展进行展望。总而言之,预期在不久的将来高通量蛋白质组学技术将会逐步“落地转化”,成为大队列蛋白质组学分析的利器。     

High-throughput profiling of the mitochondrial proteome using affinity fractionation and automation

Recent studies have demonstrated the need for complementing cellular genomic information with specific information on expressed proteins, or proteomics, since the correlation between the two is poor. Typically, proteomic information is gathered by analyzing samples on two-dimensional gels with the subsequent identification of specific proteins of interest by using trypsin digestion and mass spectrometry in a process termed peptide mass fingerprinting. These procedures have, as a rule, been labor-intensive and manual, and therefore of low throughput. The development of automated proteomic technology for processing large numbers of samples simultaneously has made the concept of profiling entire proteomes feasible at last. In this study, we report the initiation of the (eventual) complete profile of the rat mitochondrial proteome by using high-throughput automated equipment in combination with a novel fractionation technique using minispin affinity columns. Using these technologies, approximately one hundred proteins could be identified in several days. In addition, separate profiles of calcium binding proteins, glycoproteins, and hydrophobic or membrane proteins could be generated. Because mitochondrial dysfunction has been implicated in numerous diseases, such as cancer, Alzheimer's disease and diabetes, it is probable that the identification of the majority of mitochondrial proteins will be a beneficial tool for developing drug and diagnostic targets for associated diseases.     

化学生物学新前沿——化学蛋白质组学

随着包括人类在内的主要模式生物的基因组计划的完成,生命科学的研究重心转向蛋白质组的研究--在对应基因组的整体蛋白质水平上系统研究调控细胞生命活动的蛋白质.化学蛋白质组学是化学生物学在后基因组时代的最新发展:化学蛋白质组学利用化学小分子为工具和手段,以基于靶蛋白质功能的新战略探测体内蛋白质组,是新一代的功能蛋白质组学.本文综述了化学蛋白质组学的最新进展、有关技术及其在生物医学和药物研发等方面的应用,并对化学蛋白质组学的发展趋势和前景进行了讨论.     

基于质谱的单细胞蛋白质组学分析方法及应用

细胞是生命体的最小组成单位,遗传及外部环境等因素使单细胞异质性广泛存在于众多生物体中。传统的生物学实验获得的结果多是大量细胞的平均测量值,因此在单细胞层面开展研究对于精确理解细胞的生长发育以及疾病的诊断与治疗至关重要。而作为重要的细胞和生命活动的执行者,蛋白质由于其不具备扩增特性,且种类繁多、丰度低、动态分布范围宽,与核酸等其他生物大分子相比,其单细胞组学研究相对滞后。而在所有的检测手段中,荧光检测以及电化学分析方法具有极高的灵敏度,但是囿于其研究通量有限,以及电化学活性依赖,很难成为普适性的单细胞蛋白质组学研究方法。质谱分析作为传统蛋白质组学中最为核心的研究技术,由于其高灵敏、高通量、结构信息丰富等特点,在单细胞蛋白质组学研究中独树一帜。该文综述了近年来基于质谱的单细胞蛋白质组学研究中的代表性方法,根据质谱分析前蛋白质分离方式的差异,将其分为基于毛细管电泳分离、液相色谱分离和无分离手段的直接检测3类方法,在介绍研究现状的同时对这些方法在细胞通量、蛋白质鉴定数目、灵敏度以及方法应用方面进行了总结与比较。最后,基于目前研究中面临的挑战以及发展趋势对基于质谱的单细胞蛋白质组学的研究前景进行了展望。     

Evaluation of the suitability of archival Bouin-fixed paraffin-embedded tissue specimens to proteomic investigation

Bouin's solution has been used for over a century as a common fixative in several pathology laboratories worldwide. Therefore, a considerable number of Bouin-fixed paraffin-embedded (BFPE) tumor samples of various origin are available in hospital repositories as a powerful information mine for clinical investigations. To date, however, such archived tissues have not been subjected to a systematic study aimed to evaluate their potential use in proteomics. In this report, we investigated whether archival BFPE tissue specimens could be exploited for proteomic studies, upon application of protein extraction and proteomic analysis methods previously optimized for formalin-fixed samples. As a result, gastric BFPE protein extracts exhibited poor suitability for 2D-PAGE analysis, whereas over 300 unique proteins could be successfully detected when extracts were subjected to SDS-PAGE followed by LC-MS/MS (GeLC-MS/MS). Among these, several known markers for gastric cancer and normal gastric functionality were identified, indicative of biological and clinical significance of proteomic data mined from BFPE tissues. A quantitative and qualitative comparison of FFPE and BFPE tissue proteomes was also performed, and results are reported. In conclusion, we demonstrated that BFPE specimens can be analyzed by means of a proteomic approach such as GeLC-MS/MS. Although considerable molecular biases and technical constraints exist, BFPE tissue archives can be fruitfully exploited for gathering proteomic data from particularly precious samples.     

Recent advances in computational analysis of mass spectrometry for proteomic profiling

The proteome, defined as an organism's proteins and their actions, is a highly complex end-effector of molecular and cellular events. Differing amounts of proteins in a sample can be indicators of an individual's health status; thus, it is valuable to identify key proteins that serve as 'biomarkers' for diseases. Since the proteome cannot be simply inferred from the genome due to pre- and posttranslational modifications, a direct approach toward mapping the proteome must be taken. The difficulty in evaluating a large number of individual proteins has been eased with the development of high-throughput methods based on mass spectrometry (MS) of peptide or protein mixtures, bypassing the time-consuming, laborious process of protein purification. However, proteomic profiling by MS requires extensive computational analysis. This article describes key issues and recent advances in computational analysis of mass spectra for biomarker identification.     

A tandem orthogonal proteolysis strategy for high-content chemical proteomics

The field of proteomics aims to assign functions to the numerous protein products encoded by eukaryotic and prokaryotic genomes. Toward this end, chemical strategies have emerged as a powerful means to enrich specific classes of proteins based on shared functional properties, such as catalytic activity [activity-based protein profiling (ABPP)], and post-translational modification state. The theoretical information content in chemical proteomic experiments greatly exceeds the actual data procured, due in large part to limitations in existing analytical technologies. Here, we present a tandem orthogonal proteolysis (TOP) strategy for high-content chemical proteomics that enables the parallel characterization of probe-labeled proteins and sites of probe modification. The TOP approach exploits "click chemistry" to introduce a multifunctional tag onto probe-labeled proteins that contains both a biotin group for protein enrichment and a tobacco etch virus (TEV) protease cleavage site for selective release of probe-modified peptides. Following capture on streptavidin beads, protein targets of probes and their sites of labeling are sequentially identified by a two-step proteolysis strategy (trypsin and TEV, respectively). We apply the TOP method to characterize targets of sulfonate ester ABPP probes in tissue proteomes, resulting in the discovery of numerous active site-labeled enzymes. Enzymes modified on regulatory sites and proteins of unknown function were also identified. These findings indicate that a wide range of functional residues are targeted by sulfonate ester probes and highlight the value of TOP-based chemical proteomics for the characterization of proteins and the residues that regulate their activity.     

High-Throughput Proteomics Enabled by a Photocleavable Surfactant

Mass spectrometry (MS)-based proteomics provides unprecedented opportunities for understanding the structure and function of proteins in complex biological systems; however, protein solubility and sample preparation before MS remain a bottleneck preventing high-throughput proteomics. Herein, we report a high-throughput bottom-up proteomic method enabled by a newly developed MS-compatible photocleavable surfactant, 4-hexylphenylazosulfonate (Azo) that facilitates robust protein extraction, rapid enzymatic digestion (30 min compared to overnight), and subsequent MS-analysis following UV degradation. Moreover, we developed an Azo-aided bottom-up method for analysis of integral membrane proteins, which are key drug targets and are generally underrepresented in global proteomic studies. Furthermore, we demonstrated the ability of Azo to serve as an “all-in-one” MS-compatible surfactant for both top-down and bottom-up proteomics, with streamlined workflows for high-throughput proteomics amenable to clinical applications.     

High‐Throughput Proteomics Enabled by a Photocleavable Surfactant

Mass spectrometry (MS)‐based proteomics provides unprecedented opportunities for understanding the structure and function of proteins in complex biological systems; however, protein solubility and sample preparation before MS remain a bottleneck preventing high‐throughput proteomics. Herein, we report a high‐throughput bottom‐up proteomic method enabled by a newly developed MS‐compatible photocleavable surfactant, 4‐hexylphenylazosulfonate (Azo) that facilitates robust protein extraction, rapid enzymatic digestion (30 min compared to overnight), and subsequent MS‐analysis following UV degradation. Moreover, we developed an Azo‐aided bottom‐up method for analysis of integral membrane proteins, which are key drug targets and are generally underrepresented in global proteomic studies. Furthermore, we demonstrated the ability of Azo to serve as an “all‐in‐one” MS‐compatible surfactant for both top‐down and bottom‐up proteomics, with streamlined workflows for high‐throughput proteomics amenable to clinical applications.     

Comparative Bioinformatics Analyses and Profiling of Lysosome-Related Organelle Proteomes

Complete and accurate profiling of cellular organelle proteomes, while challenging, is important for the understanding of detailed cellular processes at the organelle level. Mass spectrometry technologies coupled with bioinformatics analysis provide an effective approach for protein identification and functional interpretation of organelle proteomes. In this study, we have compiled human organelle reference datasets from large-scale proteomic studies and protein databases for 7 lysosome-related organelles (LROs), as well as the endoplasmic reticulum and mitochondria, for comparative organelle proteome analysis. Heterogeneous sources of human organelle proteins and rodent homologs are mapped to human UniProtKB protein entries based on ID and/or peptide mappings, followed by functional annotation and categorization using the iProXpress proteomic expression analysis system. Cataloging organelle proteomes allows close examination of both shared and unique proteins among various LROs and reveals their functional relevance. The proteomic comparisons show that LROs are a closely related family of organelles. The shared proteins indicate the dynamic and hybrid nature of LROs, while the unique transmembrane proteins may represent additional candidate marker proteins for LROs. This comparative analysis, therefore, provides a basis for hypothesis formulation and experimental validation of organelle proteins and their functional roles.     

A label-free differential proteomic analysis of mouse bronchoalveolar lavage fluid exposed to ultrafine carbon black

Ultrafine carbon black (ufCB) is a potential hazard to the lung. It causes changes in protein expression and it increases alveolar-capillary permeability in the lung. Label-free quantitative proteomic methods allow a sensitive and accurate analytical method for identifying and quantifying proteins in a protein mixture without chemically modifying the proteins. We used a label-free quantitative proteomic approach that combined and aligned LC-MS and LC-MS/MS spectra to analyze mouse bronchoalveolar lavage fluid (BALF) protein changes associated with exposure to ufCB. We developed a simple normalization method for quantification without spiking the internal standard. The intensities of unchanged peptides were used as normalization factors based on a statistical method to avoid the influence of peptides changed because of ufCB. LC-MS/MS spectra and then database searching were used to identify proteins. The relative abundances of the aligned peptides of identified proteins were determined using LC-MS spectra. We identified 132 proteins, of which 77 are reported for the first time. In addition, the expression of 15 inflammatory proteins and surfactant-associated proteins was regulated (i.e., 7 upregulated and 8 downregulated) compared with the controls. Several proteins not previously reported provide complementary information on the proteins present in mouse BALF, and they are potential biomarkers for the understanding of mechanisms involved in ufCB-induced lung disorders hypothesize that using the label-free quantitative proteomic approach introduced here is well suited for more rigorous, large-scale quantitative analysis of biological samples. We hypothesize that this label-free quantitative proteomic approach will be suited for a large-scale quantitative analysis of biological samples.     

Trends in proteomic analysis of human vitreous humor samples

Proteomic analysis of human vitreous humor (VH) may elucidate the pathogenesis of retinal ocular diseases and may provide information for the development of potential therapeutic targets due to its pivotal location near lens and retina. The discovery of whole VH proteome involves a complex analysis of thousands of proteins simultaneously. Therefore, in proteomic studies the protein fractionation is important for reducing sample complexity, facilitating the access to the low‐abundant proteins, and recognizing them as biotargets for clinical research. Although several separation methods have been used, gel‐based proteomics are the most popular and versatile ones applied for global protein separation. However, chromatographic methods and its combination with other separation techniques are now beginning to be used as promising set‐ups for VH protein identification. This review attempts to offer an overview of the techniques currently used with VH, exploring its methodological demands, exposing its advantages, and helping the reader to plan future experiences. Moreover, this review shows the relevance of VH proteomic analysis as a tool for the study of the mechanisms underlying some ocular diseases and for the development of new therapeutic approaches.     

Comprehensive profiling of CTP-binding proteins using a biotinylated CTP affinity probe

Recent studies have shown that CTP may act as a ligand to regulate the activity of its target proteins in many biological processes. However, proteome-wide identification of CTP-binding proteins remains challenging. Here, we employed a biotinylated CTP affinity probe coupled with stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics approach to capture, identify and quantify CTP-binding proteins in human cells. By performing two types of competitive SILAC experiments with high vs. low concentrations of CTP probe (100 vs. 10 µmol/L) or with CTP probe in the presence of free CTP, we identified 90 potential CTP-binding proteins which are involved in a variety of biological processes, including protein folding, nucleotide binding and cell-cell adhesion. Together, we developed a chemical proteomic method for uncovering the CTP-binding proteins in human cells, which could be widely applicable for profiling CTP-binding proteins in other biological samples.