Simple chemical tools to expand the range of proteomics applications

Proteomics is an expanding technology with potential applications in many research fields. Even though many research groups do not have direct access to its main analytical technique, mass spectrometry, they can interact with proteomics core facilities to incorporate this technology into their projects. Protein identification is the analysis most frequently performed in core facilities and is, probably, the most robust procedure. Here we discuss a few chemical reactions that are easily implemented within the conventional protein identification workflow. Chemical modification of proteins with N-hydroxysuccinimide esters, 4-sulfophenyl isothiocyanate, O-methylisourea or through β-elimination/Michael addition can be easily performed in any laboratory. The reactions are quite specific with almost no side reactions. These chemical tools increase considerably the number of applications and have been applied to characterize protein-protein interactions, to determine the N-terminal residues of proteins, to identify proteins with non-sequenced genomes or to locate phosphorylated and O-glycosylated.     

Challenges for proteomics core facilities

Many analytical techniques have been executed by core facilities established within academic, pharmaceutical and other industrial institutions. The centralization of such facilities ensures a level of expertise and hardware which often cannot be supported by individual laboratories. The establishment of a core facility thus makes the technology available for multiple researchers in the same institution. Often, the services within the core facility are also opened out to researchers from other institutions, frequently with a fee being levied for the service provided. In the 1990s, with the onset of the age of genomics, there was an abundance of DNA analysis facilities, many of which have since disappeared from institutions and are now available through commercial sources. Ten years on, as proteomics was beginning to be utilized by many researchers, this technology found itself an ideal candidate for being placed within a core facility. We discuss what in our view are the daily challenges of proteomics core facilities. We also examine the potential unmet needs of the proteomics core facility that may also be applicable to proteomics laboratories which do not function as core facilities.     

The proteomics standards initiative

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and verification. Progress has been made in the development of common standards for data exchange in the fields of both mass spectrometry and protein-protein interaction. A proteomics-specific extension is being created for the emerging American Society for Tests and Measurements mass spectrometry standard, which will be supported by manufacturers of both hardware and software. A data model for proteomics experimentation is under development and discussions on a public repository for published proteomics data are underway. The Protein-Protein Interactions group expects to publish the Level 1 PSI data exchange format for protein-protein interactions soon and discussions as to the content of Level 2 have been initiated.     

From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions

Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data‐independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in Escherichia coli for > 2,000 proteins over > 60 growth conditions, including nutrient limitations, non‐metabolic stresses, and non‐planktonic states. The resulting high‐quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low‐abundant proteins under various metabolic limitations, the non‐specificity of catabolic enzymes upregulated under carbon limitation, the lack of large‐scale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain‐dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi‐omics studies of gene regulation and metabolic control in E. coli.     

The HUPO proteomics standards initiative--overcoming the fragmentation of proteomics data

Proteomics is a key field of modern biomolecular research, with many small and large scale efforts producing a wealth of proteomics data. However, the vast majority of this data is never exploited to its full potential. Even in publicly funded projects, often the raw data generated in a specific context is analysed, conclusions are drawn and published, but little attention is paid to systematic documentation, archiving, and public access to the data supporting the scientific results. It is often difficult to validate the results stated in a particular publication, and even simple global questions like "In which cellular contexts has my protein of interest been observed?" can currently not be answered with realistic effort, due to a lack of standardised reporting and collection of proteomics data. The Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organisation (HUPO), defines community standards for data representation in proteomics to facilitate systematic data capture, comparison, exchange and verification. In this article we provide an overview of PSI organisational structure, activities, and current results, as well as ways to get involved in the broad-based, open PSI process.     

Information management for proteomics: a perspective

Proteomics is a data-rich discipline that makes extensive use of separation tools, mass spectrometry and bioinformatics to analyze and interpret the features and dynamics of the proteome. A major challenge for the field is how proteomics data can be stored and managed, such that data become permanent and can be mined with current and future tools. This article details our experience in the development of a commercial proteomic information management system. We identify the challenges faced in data acquisition, workflow management, data permanence, security, data interpretation and analysis, as well as the solutions implemented to address these issues. We finally provide a perspective on data management in proteomics and the implications for academic and industry-based researchers working in this field.     

Quality matters: International standards for biobanking

Human biospecimens provide the basis for research, leading to a better understanding of human disease biology and discovery of new treatments that are tailored to individual patients with cancer or other common complex diseases. The collection, processing, preservation, storage and providing access to these resources are key activities of biobanks. Biobanks must ensure proper quality of samples and data, ethical and legal compliance as well as transparent and efficient access procedures. The standards for biobanking outlined herein are intended to be implemented in biobanks and to supply researchers with high‐quality samples fitted for an intended use.

Several variables in the pre‐analytical phase can affect the quality of biological samples. © K. Zatloukal.     

Supporting tool suite for production proteomics

SUMMARY: The large amount of data produced by proteomics experiments requires effective bioinformatics tools for the integration of data management and data analysis. Here we introduce a suite of tools developed at Vanderbilt University to support production proteomics. We present the Backup Utility Service tool for automated instrument file backup and the ScanSifter tool for data conversion. We also describe a queuing system to coordinate identification pipelines and the File Collector tool for batch copying analytical results. These tools are individually useful but collectively reinforce each other. They are particularly valuable for proteomics core facilities or research institutions that need to manage multiple mass spectrometers. With minor changes, they could support other types of biomolecular resource facilities.     

Advances in fungal proteomics

Proteomics, the global analysis of proteins, will contribute greatly to our understanding of gene function in the post-genomic era. This review summarizes recent developments in fungal proteomics and also generalizes protocols for sample preparation from plant pathogenic fungi. Challenges and future perspectives of proteomics are discussed as well.     

Common interchange standards for proteomics data: Public availability of tools and schema

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparision, exchange and verification. To this end, a Level 1 Molecular Interaction XML data exchange format has been developed which has been accepted for publication and is freely available at the PSI website (http.//psidev.sf.net/). Several major protein interaction databases are already making data available in this format. A draft XML interchange format for mass spectrometry data has been written and is currently undergoing evaluation whilst work is ongoing to develop a proteomics data integration model, MIAPE.     

Data standards for proteomics: mitochondrial two-dimensional polyacrylamide gel electrophoresis data as a model system

Proteomics has emerged as a major discipline that led to a re-examination of the need for consensus and a nationally sanctioned set of proteomics technology standards. Such standards for databases and data reporting may be applied to two-dimensional polyacrylamide gel electrophoresis (2D PAGE) technology as a pilot project for assessing global and national needs in proteomics, and the role of the National Institute of Standards and Technology (NIST) and other similar standards and measurement organizations. The experience of harmonizing the heterogeneous data included in the Protein Data Bank (PDB) provides a paradigm for technology in an area where significant heterogeneity in technical detail and data storage has evolved. Here we propose an approach toward standardizing mitochondrial 2D PAGE data in support of a globally relevant proteomics consensus.     

Role and challenges of proteomics in pharma and biotech: technical, scientific and commercial perspective

Contemporary proteomics, currently in its exponential growth phase, is a bewildering array of tools. Proteomic methods are the result of a convergence of rapidly improving mass spectrometry technologies, protein chemistry and separation sciences, genomics and bioinformatics. Strides in improving proteomics technologies to map and measure proteomes and subproteomes are being made. However, no single proteomic platform appears ideally suited to address all research needs or accomplish ambitious goals satisfactorily. However, proteomics is in a unique position to contribute to protein discovery and to public health in terms of better biomarkers, diagnostics and treatment of disease. While the potential is great, many challenges and issues remain to be solved. Fundamental issues, such as biological variability, pre-analytic factors and analytical reproducibility, remain to be resolved. Neither an all-genetic approach nor an all-proteomic approach will solve biological complexity. Proteomics will be the foundation for constructing and extracting useful knowledge to pharma and biotech depicted in the following path: data --> structured data --> information --> information architecture --> knowledge --> useful knowledge.     

A genotyping‐in‐thousands by sequencing panel to inform invasive deer management using noninvasive fecal and hair samples

Studies in ecology, evolution, and conservation often rely on noninvasive samples, making it challenging to generate large amounts of high‐quality genetic data for many elusive and at‐risk species. We developed and optimized a Genotyping‐in‐Thousands by sequencing (GT‐seq) panel using noninvasive samples to inform the management of invasive Sitka black‐tailed deer (Odocoileus hemionus sitkensis) in Haida Gwaii (Canada). We validated our panel using paired high‐quality tissue and noninvasive fecal and hair samples to simultaneously distinguish individuals, identify sex, and reconstruct kinship among deer sampled across the archipelago, then provided a proof‐of‐concept application using field‐collected feces on SGang Gwaay, an island of high ecological and cultural value. Genotyping success across 244 loci was high (90.3%) and comparable to that of high‐quality tissue samples genotyped using restriction‐site associated DNA sequencing (92.4%), while genotyping discordance between paired high‐quality tissue and noninvasive samples was low (0.50%). The panel will be used to inform future invasive species operations in Haida Gwaii by providing individual and population information to inform management. More broadly, our GT‐seq workflow that includes quality control analyses for targeted SNP selection and a modified protocol may be of wider utility for other studies and systems where noninvasive genetic sampling is employed.     

Integrating systematic biological and proteomics strategies to explore the pharmacological mechanism of danshen yin modified on atherosclerosis

This research utilized the systematic biological and proteomics strategies to explore the regulatory mechanism of Danshen Yin Modified (DSYM) on atherosclerosis (AS) biological network. The traditional Chinese medicine database and HPLC was used to find the active compounds of DSYM, Pharmmapper database was used to predict potential targets, and OMIM database and GeneCards database were used to collect AS targets. String database was utilized to obtain the other protein of proteomics proteins and the protein‐protein interaction (PPI) data of DSYM targets, AS genes, proteomics proteins and other proteins. The Cytoscape 3.7.1 software was utilized to construct and analyse the network. The DAVID database is used to discover the biological processes and signalling pathways that these proteins aggregate. Finally, animal experiments and proteomics analysis were used to further verify the prediction results. The results showed that 140 active compounds, 405 DSYM targets and 590 AS genes were obtained, and 51 differentially expressed proteins were identified in the DSYM‐treated ApoE‐/‐ mouse AS model. A total of 4 major networks and a number of their derivative networks were constructed and analysed. The prediction results showed that DSYM can regulate AS‐related biological processes and signalling pathways. Animal experiments have also shown that DSYM has a therapeutic effect on ApoE‐/‐mouse AS model (P < .05). Therefore, this study proposed a new method based on systems biology, proteomics, and experimental pharmacology, and analysed the pharmacological mechanism of DSYM. DSYM may achieve therapeutic effects by regulating AS‐related signalling pathways and biological processes found in this research.     

非模式植物蛋白质组学研究进展

蛋白质组学研究是对基因组学研究的重要补充,它是在蛋白质水平定量、动态、整体性研究生物体。该文简要介绍了蛋白质组学的含义,蛋白质组学及植物蛋白质组学产生的科学背景,蛋白质组学的研究内容。概述了非模式植物蛋白质组学的研究进展,主要包括非模式植物个体及群体蛋白质组学,组织和器官蛋白质组学,亚细胞蛋白质组学,响应环境变化的蛋白质组学以及非模式植物生物环境因子的蛋白质组学的研究情况,同时对植物蛋白质组学的发展前景进行了展望。     

Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial

Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.