Proteomics Facilities Research Articles

Standardizing Protein Corona Characterization in Nanomedicine: A Multicenter Study to Enhance Reproducibility and Data Homogeneity.

We recently revealed significant variability in protein corona characterization across various proteomics facilities, indicating that data sets are not comparable between independent studies. This heterogeneity mainly arises from differences in sample preparation protocols, mass spectrometry workflows, and raw data processing. To address this issue, we developed standardized protocols and unified sample preparation workflows, distributing uniform protein corona digests to several top-performing proteomics centers from our previous study. We also examined the influence of using similar mass spectrometry instruments on data homogeneity and standardized database search parameters and data processing workflows. Our findings reveal a remarkable stepwise improvement in protein corona data uniformity, increasing overlaps in protein identification from 11% to 40% across facilities using similar instruments and through a uniform database search. We identify the key parameters behind data heterogeneity and provide recommendations for designing experiments. Our findings should significantly advance the robustness of protein corona analysis for diagnostic and therapeutics applications.

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package.

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

A uniform data processing pipeline enables harmonized nanoparticle protein corona analysis across proteomics core facilities

Protein corona, a layer of biomolecules primarily comprising proteins, forms dynamically on nanoparticles in biological fluids and is crucial for predicting nanomedicine safety and efficacy. The protein composition of the corona layer is typically analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Our recent study, involving identical samples analyzed by 17 proteomics facilities, highlighted significant data variability, with only 1.8% of proteins consistently identified across these centers. Here, we implement an aggregated database search unifying parameters such as variable modifications, enzyme specificity, number of allowed missed cleavages and a stringent 1% false discovery rate at the protein and peptide levels. Such uniform search dramatically harmonizes the proteomics data, increasing the reproducibility and the percentage of consistency-identified unique proteins across distinct cores. Specifically, out of the 717 quantified proteins, 253 (35.3%) are shared among the top 5 facilities (and 16.2% among top 11 facilities). Furthermore, we note that reduction and alkylation are important steps in protein corona sample processing and as expected, omitting these steps reduces the number of total quantified peptides by around 20%. These findings underscore the need for standardized procedures in protein corona analysis, which is vital for advancing clinical applications of nanoscale biotechnologies.

An analysis of proteomics and its applications

Proteomics is the study of the identification and measurement of total protein content in a cell, tissue, or organism using various methods. It works in tandem with other “omics” technologies like genomics and transcriptomics to determine the identification of an organism's proteins and to understand their structure and activities. Proteomics-based technologies are used in a variety of research settings, including the detection of various diagnostic markers, vaccine candidates, pathogenicity mechanisms, the alteration of expression patterns in response to various signals, and the interpretation of functional protein pathways in various diseases. Proteomics is very complicated since it entails analyzing and categorizing a genome's total protein signatures. Mass spectrometry, with LC–MS–MS and MALDI-TOF/TOF as common equipment, is at the heart of today's proteomics. However, the expenses of using proteomics facilities, such as software for equipment, databases, and the need for trained people, significantly rise, limiting their usage, particularly in poor countries. Furthermore, because of intricate regulatory mechanisms that regulate protein expression levels, the proteome is extremely dynamic. This study aims to explain the different proteomics methods, as well as their current advancements and applications in research and analysis.

Filter-Aided Sample Preparation (FASP) for Improved Proteome Analysis of Recombinant Chinese Hamster Ovary Cells.

Chinese hamster ovary (CHO) cells are the most commonly used mammalian host cell line for biopharmaceutical production because of their ability to correctly fold and posttranslationally modify recombinant proteins that are compatible with human use. Proteomics, along with other 'omic platforms, are being used to understand the biology of CHO cells with the ultimate aim of enhancing CHO cell factories for more efficient production of biopharmaceuticals. In this chapter, we will describe an efficient protocol called Filter Aided Sample Preparation (FASP) for the extraction of proteins from CHO cells for proteomic studies. FASP uses a common ultrafiltration device whereby the membrane pores are small enough to allow contaminating detergents to pass through, while proteins are too large and are retained and concentrated in the filter unit. This method of sample preparation and protein digestion is universally applicable and can be easily employed in any proteomics facilities as standard everyday laboratory reagents and equipment are used.

Proteomics: Technologies and Their Applications.

Proteomics involves the applications of technologies for the identification and quantification of overall proteins present content of a cell, tissue or an organism. It supplements the other "omics" technologies such as genomic and transcriptomics to expound the identity of proteins of an organism, and to cognize the structure and functions of a particular protein. Proteomics-based technologies are utilized in various capacities for different research settings such as detection of various diagnostic markers, candidates for vaccine production, understanding pathogenicity mechanisms, alteration of expression patterns in response to different signals and interpretation of functional protein pathways in different diseases. Proteomics is practically intricate because it includes the analysis and categorization of overall protein signatures of a genome. Mass spectrometry with LC-MS-MS and MALDI-TOF/TOF being widely used equipment is the central among current proteomics. However, utilization of proteomics facilities including the software for equipment, databases and the requirement of skilled personnel substantially increase the costs, therefore limit their wider use especially in the developing world. Furthermore, the proteome is highly dynamic because of complex regulatory systems that control the expression levels of proteins. This review efforts to describe the various proteomics approaches, the recent developments and their application in research and analysis.

Multicenter experiment for quality control of peptide-centric LC–MS/MS analysis — A longitudinal performance assessment with nLC coupled to orbitrap MS analyzers

Proteomic technologies based on mass spectrometry (MS) have greatly evolved in the past years, and nowadays it is possible to routinely identify thousands of peptides from complex biological samples in a single LC–MS/MS experiment. Despite the advancements in proteomic technologies, the scientific community still faces important challenges in terms of depth and reproducibility of proteomics analyses. Here, we present a multicenter study designed to evaluate long-term performance of LC–MS/MS platforms within the Spanish Proteomics Facilities Network (ProteoRed-ISCIII). The study was performed under well-established standard operating procedures, and demonstrated that it is possible to attain qualitative and quantitative reproducibility over time. Our study highlights the importance of deploying quality assessment metrics routinely in individual laboratories and in multi-laboratory studies. The mass spectrometry data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000205.This article is part of a Special Issue entitled: HUPO 2014.

Processing Shotgun Proteomics Data on the Amazon Cloud with the Trans-Proteomic Pipeline

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost.

The amino acid's backup bone — Storage solutions for proteomics facilities

Proteomics methods, especially high-throughput mass spectrometry analysis have been continually developed and improved over the years. The analysis of complex biological samples produces large volumes of raw data. Data storage and recovery management pose substantial challenges to biomedical or proteomic facilities regarding backup and archiving concepts as well as hardware requirements. In this article we describe differences between the terms backup and archive with regard to manual and automatic approaches. We also introduce different storage concepts and technologies from transportable media to professional solutions such as redundant array of independent disks (RAID) systems, network attached storages (NAS) and storage area network (SAN). Moreover, we present a software solution, which we developed for the purpose of long-term preservation of large mass spectrometry raw data files on an object storage device (OSD) archiving system. Finally, advantages, disadvantages, and experiences from routine operations of the presented concepts and technologies are evaluated and discussed. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Cloud CPFP: A Shotgun Proteomics Data Analysis Pipeline Using Cloud and High Performance Computing

We have extended the functionality of the Central Proteomics Facilities Pipeline (CPFP) to allow use of remote cloud and high performance computing (HPC) resources for shotgun proteomics data processing. CPFP has been modified to include modular local and remote scheduling for data processing jobs. The pipeline can now be run on a single PC or server, a local cluster, a remote HPC cluster, and/or the Amazon Web Services (AWS) cloud. We provide public images that allow easy deployment of CPFP in its entirety in the AWS cloud. This significantly reduces the effort necessary to use the software, and allows proteomics laboratories to pay for compute time ad hoc, rather than obtaining and maintaining expensive local server clusters. Alternatively the Amazon cloud can be used to increase the throughput of a local installation of CPFP as necessary. We demonstrate that cloud CPFP allows users to process data at higher speed than local installations but with similar cost and lower staff requirements. In addition to the computational improvements, the web interface to CPFP is simplified, and other functionalities are enhanced. The software is under active development at two leading institutions and continues to be released under an open-source license at http://cpfp.sourceforge.net.

ModLS: Post-Translational Modification Localization Scoring with Automatic Specificity Expansion

Probability-based localisation scoring of fragment mass-spectrum phosphorylation site identifications has become common practice to confirm search engine modification assignments, and indicate the degree of certainty with which they are defined. Localisation of modifications other than phosphorylation is also required but is less commonly supported by current tools. These other modifications, such as hydroxylation, may have broad aminoacid specificity, and can be misassigned when the correct specificity is not considered in an MS database search. In addition, localisation software is often specific to a particular MS/MS search engine, and cannot be used to localise modifications identified by multiple search engines. ModLS, a new tool within our freely-available Central Proteomics Facilities Pipeline (CPFP), applies a localisation scoring method to arbitrary post-translational modifications (PTMs). As well as localising PTMs based on amino-acid specificities are included in the initial search, ModLS can automatically consider additional specificities from UniMod. This can help avoid ‘correct modification, incorrect amino-acid’ errors which can occur when data is searched using only a subset of PTM specificities. Localisation scoring can be performed on the results from any search engine incorporated within the pipeline, or where the output of individual search engines is combined to give increased coverage. We demonstrate the performance of ModLS using a publicly available phosphorylated peptide dataset, showing that it outperforms the recently characterised Mascot Delta Score approach for CID and MSA data, and is comparable for HCD data. In addition, we show the utility of automatic specificity expansion using hydroxylated and methylated peptide data. ModLS is a user-friendly localisation tool for arbitrary modifications. Its inclusion within CPFP allows PTM localisation to be performed quickly and easily on large or small result sets, from multiple search engines. Specificity expansion, introduced in ModLS, allows misassignments of modifications due to incomplete consideration of specificities to be identified and minimised.

Cysteine Cathepsins S and L Modulate Anti-angiogenic Activities of Human Endostatin

Human endostatin, a potent anti-angiogenic protein, is generated by release of the C terminus of collagen XVIII. Here, we propose that cysteine cathepsins are involved in both the liberation and activation of bioactive endostatin fragments, thus regulating their anti-angiogenic properties. Cathepsins B, S, and L efficiently cleaved in vitro FRET peptides that encompass the hinge region corresponding to the N terminus of endostatin. However, in human umbilical vein endothelial cell-based assays, silencing of cathepsins S and L, but not cathepsin B, impaired the generation of the ∼22-kDa endostatin species. Moreover, cathepsins L and S released two peptides from endostatin with increased angiostatic properties and both encompassing the NGR sequence, a vasculature homing motif. The G10T peptide (residues 1455-1464: collagen XVIII numbering) displayed compelling anti-proliferative (EC(50) = 0.23 nm) and proapoptotic properties. G10T inhibited aminopeptidase N (APN/CD13) and reduced tube formation of endothelial cells in a manner similar to bestatin. Combination of G10T with bestatin resulted in no further increase in anti-angiogenic activity. Taken together, these data suggest that endostatin-derived peptides may represent novel molecular links between cathepsins and APN/CD13 in the regulation of angiogenesis.

Building and Searching Tandem Mass Spectral Libraries for Peptide Identification

Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. Conceptually, the premise of this approach is that the tandem MS fragmentation pattern of a peptide under some fixed conditions is a reproducible fingerprint of that peptide, such that unknown spectra acquired under the same conditions can be identified by spectral matching. In actual practice, a spectral library is first meticulously compiled from a large collection of previously observed and identified tandem MS spectra, usually obtained from shotgun proteomics experiments of complex mixtures. Then, a query spectrum is then identified by spectral matching using recently developed spectral search engines. This review discusses the basic principles of the two pillars of this approach: spectral library construction, and spectral library searching. An overview of the software tools available for these two tasks, as well as a high-level description of the underlying algorithms, will be given. Finally, several new methods that utilize spectral libraries for peptide identification in ways other than straightforward spectral matching will also be described.

The ProteoRed MIAPE web toolkit: A User-friendly Framework to Connect and Share Proteomics Standards

The development of the HUPO-PSI's (Proteomics Standards Initiative) standard data formats and MIAPE (Minimum Information About a Proteomics Experiment) guidelines should improve proteomics data sharing within the scientific community. Proteomics journals have encouraged the use of these standards and guidelines to improve the quality of experimental reporting and ease the evaluation and publication of manuscripts. However, there is an evident lack of bioinformatics tools specifically designed to create and edit standard file formats and reports, or embed them within proteomics workflows. In this article, we describe a new web-based software suite (The ProteoRed MIAPE web toolkit) that performs several complementary roles related to proteomic data standards. First, it can verify that the reports fulfill the minimum information requirements of the corresponding MIAPE modules, highlighting inconsistencies or missing information. Second, the toolkit can convert several XML-based data standards directly into human readable MIAPE reports stored within the ProteoRed MIAPE repository. Finally, it can also perform the reverse operation, allowing users to export from MIAPE reports into XML files for computational processing, data sharing, or public database submission. The toolkit is thus the first application capable of automatically linking the PSI's MIAPE modules with the corresponding XML data exchange standards, enabling bidirectional conversions. This toolkit is freely available at http://www.proteored.org/MIAPE/.

Comparative evaluation of label‐free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline

Normalized spectral index quantification was recently presented as an accurate method of label-free quantitation, which improved spectral counting by incorporating the intensities of peptide MS/MS fragment ions into the calculation of protein abundance. We present SINQ, a tool implementing this method within the framework of existing analysis software, our freely available central proteomics facilities pipeline (CPFP). We demonstrate, using data sets of protein standards acquired on a variety of mass spectrometers, that SINQ can rapidly provide useful estimates of the absolute quantity of proteins present in a medium-complexity sample. In addition, relative quantitation of standard proteins spiked into a complex lysate background and run without pre-fractionation produces accurate results at amounts above 1 fmol on column. We compare quantitation performance to various precursor intensity- and identification-based methods, including the normalized spectral abundance factor (NSAF), exponentially modified protein abundance index (emPAI), MaxQuant, and Progenesis LC-MS. We anticipate that the SINQ tool will be a useful asset for core facilities and individual laboratories that wish to produce quantitative MS data, but lack the necessary manpower to routinely support more complicated software workflows. SINQ is freely available to obtain and use as part of the central proteomics facilities pipeline, which is released under an open-source license.

CPFP: a central proteomics facilities pipeline

The central proteomics facilities pipeline (CPFP) provides identification, validation, and quantitation of peptides and proteins from LC-MS/MS datasets through an easy to use web interface. It is the first analysis pipeline targeted specifically at the needs of proteomics core facilities, reducing the data analysis load on staff, and allowing facility clients to easily access and work with their data. Identification of peptides is performed using multiple search engines, their output combined and validated using state-of-the-art techniques for improved results. Cluster execution of jobs allows analysis capacity to be increased easily as demand grows. Released under the Common Development and Distribution License at http://cpfp.sourceforge.net/. Demonstration available at https://cpfp-master.molbiol.ox.ac.uk/cpfp_demo.

Ranking Methods for the Prediction of Frequent Top Scoring Peptides from Proteomics Data

Proteomics facilities accumulate large amounts of proteomics data that are archived for documentation purposes. Since proteomics search engines, e.g. Mascot or Sequest, are used for peptide sequencing resulting in peptide hits that are ranked by a score, we apply ranking algorithms to combine archived search results into predictive models. In this way peptide sequences can be identified that frequently achieve high scores. Using our approach they can be predicted directly from their molecular structure and then be used to support protein identification or perform experiments that require reliable peptide identification. We prepared all peptide sequences and Mascot scores from a four year period of proteomics experiments on Homo sapiens of the Proteome Center Tuebingen for training. To encode the peptides MacroModel and DragonX were used for molecular descriptor computation. All features were ranked by ranking-specific feature selection using the Greedy Search Algorithm to significantly improve the performance of RankNet and FRank. Model evaluation on hold-out test data resulted in a Mean Average Precision up to 0.59 and a Normalized Discounted Cumulative Gain up to 0.81. Therefore we demonstrate that ranking algorithms can be used for the analysis of long term proteomics data to identify frequently top scoring peptides.

Profiling constitutive proteolytic events in vivo

Most known organisms encode proteases that are crucial for constitutive proteolytic events. In the present paper, we describe a method to define these events in proteomes from Escherichia coli to humans. The method takes advantage of specific N-terminal biotinylation of protein samples, followed by affinity enrichment and conventional LC (liquid chromatography)-MS/MS (tandem mass spectrometry) analysis. The method is simple, uses conventional and easily obtainable reagents, and is applicable to most proteomics facilities. As proof of principle, we demonstrate profiles of proteolytic events that reveal exquisite in vivo specificity of methionine aminopeptidase in E. coli and unexpected processing of mitochondrial transit peptides in yeast, mouse and human samples. Taken together, our results demonstrate how to rapidly distinguish real proteolysis that occurs in vivo from the predictions based on in vitro experiments.

Geographical Focus. Proteomics Initiatives in Spain: ProteoRed

The Spanish National Network of Proteomic Facilities--ProteoRed has been created as an initiative for the coordination, integration and development of the proteomics facilities and laboratories distributed throughout Spain. ProteoRed's main objective is to give support to the scientific community allowing them wide access to emerging proteomics technologies and thus encouraging the science of proteomics. In addition, standardization of protocols and robustness of workflows are addressed by multi-centric laboratory activities. Educational, training and dissemination issues are part of the core activities of ProteoRed. To reach these objectives, specific activities have been developed through six working groups (WG1-WG6) covering functional, technical, educational and scientific aspects of proteomics.