Proteome-wide characterization of the RNA-binding protein RALY-interactome using the in vivo-biotinylation-pulldown-quant (iBioPQ) approach

RALY is a member of the heterogeneous nuclear ribonucleoproteins, a family of RNA-binding proteins generally involved in many processes of mRNA metabolism. No quantitative proteomic analysis of RALY-containing ribonucleoparticles (RNPs) has been performed so far, and the biological role of RALY remains elusive. Here, we present a workflow for the characterization of RALY's interaction partners, termed iBioPQ, that involves in vivo biotinylation of biotin acceptor peptide (BAP)-fused protein in the presence of the prokaryotic biotin holoenzyme synthetase of BirA so that it can be purified using streptavidin-coated magnetic beads, circumventing the need for specific antibodies and providing efficient pulldowns. Protein eluates were subjected to tryptic digestion and identified using data-independent acquisition on an ion-mobility enabled high-resolution nanoUPLC-QTOF system. Using label-free quantification, we identified 143 proteins displaying at least 2-fold difference in pulldown compared to controls. Gene Ontology overrepresentation analysis revealed an enrichment of proteins involved in mRNA metabolism and translational control. Among the most abundant interacting proteins, we confirmed RNA-dependent interactions of RALY with MATR3, PABP1 and ELAVL1. Comparative analysis of pulldowns after RNase treatment revealed a protein-protein interaction of RALY with eIF4AIII, FMRP, and hnRNP-C. Our data show that RALY-containing RNPs are much more heterogeneous than previously hypothesized

Algorithmen zur labelfreien quantitativen Proteomanalyse auf Basis datenunabhängig-akquirierter LC-MS-Daten

Moderne ESI-LC-MS/MS-Techniken erlauben in Verbindung mit Bottom-up-Ansätzen eine qualitative und quantitative Charakterisierung mehrerer tausend Proteine in einem einzigen Experiment. Für die labelfreie Proteinquantifizierung eignen sich besonders datenunabhängige Akquisitionsmethoden wie MSE und die IMS-Varianten HDMSE und UDMSE. Durch ihre hohe Komplexität stellen die so erfassten Daten besondere Anforderungen an die Analysesoftware. Eine quantitative Analyse der MSE/HDMSE/UDMSE-Daten blieb bislang wenigen kommerziellen Lösungen vorbehalten. rn| In der vorliegenden Arbeit wurden eine Strategie und eine Reihe neuer Methoden zur messungsübergreifenden, quantitativen Analyse labelfreier MSE/HDMSE/UDMSE-Daten entwickelt und als Software ISOQuant implementiert. Für die ersten Schritte der Datenanalyse (Featuredetektion, Peptid- und Proteinidentifikation) wird die kommerzielle Software PLGS verwendet. Anschließend werden die unabhängigen PLGS-Ergebnisse aller Messungen eines Experiments in einer relationalen Datenbank zusammengeführt und mit Hilfe der dedizierten Algorithmen (Retentionszeitalignment, Feature-Clustering, multidimensionale Normalisierung der Intensitäten, mehrstufige Datenfilterung, Proteininferenz, Umverteilung der Intensitäten geteilter Peptide, Proteinquantifizierung) überarbeitet. Durch diese Nachbearbeitung wird die Reproduzierbarkeit der qualitativen und quantitativen Ergebnisse signifikant gesteigert.rn| Um die Performance der quantitativen Datenanalyse zu evaluieren und mit anderen Lösungen zu vergleichen, wurde ein Satz von exakt definierten Hybridproteom-Proben entwickelt. Die Proben wurden mit den Methoden MSE und UDMSE erfasst, mit Progenesis QIP, synapter und ISOQuant analysiert und verglichen. Im Gegensatz zu synapter und Progenesis QIP konnte ISOQuant sowohl eine hohe Reproduzierbarkeit der Proteinidentifikation als auch eine hohe Präzision und Richtigkeit der Proteinquantifizierung erreichen.rn| Schlussfolgernd ermöglichen die vorgestellten Algorithmen und der Analyseworkflow zuverlässige und reproduzierbare quantitative Datenanalysen. Mit der Software ISOQuant wurde ein einfaches und effizientes Werkzeug für routinemäßige Hochdurchsatzanalysen labelfreier MSE/HDMSE/UDMSE-Daten entwickelt. Mit den Hybridproteom-Proben und den Bewertungsmetriken wurde ein umfassendes System zur Evaluierung quantitativer Akquisitions- und Datenanalysesysteme vorgestellt.Modern ESI-LC-MS/MS techniques allow for qualitative and quantitative characterization of thousands of proteins in a single bottom-up proteomics experiment. The data-independent LC-MS acquisition method MSE and its ion-mobility successors HDMSE and UDMSE are particularly suitable for label-free quantification of proteins. However, the high complexity of the acquired data poses a special challenge for the data analysis software. Only view and only commercially available softwares could approach the analysis of MSE/HDMSE/UDMSE data in the past.rn| In this work, we present a workflow consisting of a set of novel methods for the quantitative analysis of label-free MSE/HDMSE/UDMSE proteomics data. The developed methods were implemented in Java programming language and tied to an analysis pipeline as part of open-source software ISOQuant. Initially, the commercial software package PLGS is used for the feature detection and the peptide and protein identification. Then, the PLGS-preprocessed data is automatically imported into a relational database for the downstream processing. To solve data specific problems, the analysis workflow applies dedicated algorithms, namely pairwise and multiple retention time alignment, clustering corresponding features, multiple data filters, annotation of feature clusters, normalization of feature intensities, analysis of protein inference problem, redistribution of peptide intensities and absolute protein quantification. The data analysis using ISOQuant significantly increases the reproducibility of qualitative and quantitative results.rn| For benchmarking the performance of quantitative data analysis, we developed a set of exactly defined hybrid proteome benchmark samples. We acquired the benchmark samples using MSE and UDMSE methods and analyzed the data by Progenesis QIP, synapter and ISOQuant. In contrast to the competitors, ISOQuant generated highly reproducible results, both identifying high numbers of proteins and quantifying them with high precision and high accuracy.rn| In conclusion, the developed algorithms and workflow allow for accurate, reproducible qualitative and quantitative proteome analyses. With the ISOQuant software package, we present an easy and efficient tool for daily routine high-throughput analysis of label-free MSE/HDMSE/UDMSE proteomics data. With the hybrid proteome sample set and the evaluation metrics, we present a complete methodology for benchmarking quantitative label-free acquisition and data analysis systems

Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics

We present a data-independent acquisition mass spectrometry method, ultradefinition (UD) MS(E). This approach utilizes ion mobility drift time-specific collision-energy profiles to enhance precursor fragmentation efficiency over current MS(E) and high-definition (HD) MS(E) data-independent acquisition techniques. UDMS(E) provided high reproducibility and substantially improved proteome coverage of the HeLa cell proteome compared to previous implementations of MS(E), and it also outperformed a state-of-the-art data-dependent acquisition workflow. Additionally, we report a software tool, ISOQuant, for processing label-free quantitative UDMS(E) data

Rapid formation of plasma protein corona critically affects nanoparticle pathophysiology

In biological fluids, proteins bind to the surface of nanoparticles to form a coating known as the protein corona, which can critically affect the interaction of the nanoparticles with living systems. As physiological systems are highly dynamic, it is important to obtain a time-resolved knowledge of protein-corona formation, development and biological relevancy. Here we show that label-free snapshot proteomics can be used to obtain quantitative time-resolved profiles of human plasma coronas formed on silica and polystyrene nanoparticles of various size and surface functionalization. Complex time- and nanoparticle-specific coronas, which comprise almost 300 different proteins, were found to form rapidly (<0.5 minutes) and, over time, to change significantly in terms of the amount of bound protein, but not in composition. Rapid corona formation is found to affect haemolysis, thrombocyte activation, nanoparticle uptake and endothelial cell death at an early exposure time

Nanoparticle size is a critical physicochemical determinant of the human Blood plasma corona : a comprehensive quantitative proteomic analysis

In biological fluids, proteins associate with nanoparticles, leading to a protein "corona" defining the biological identity of the particle. However, a comprehensive knowledge of particle-guided protein fingerprints and their dependence on nanomaterial properties is Incomplete. We studied the long-lived ("hard") blood plasma derived corona on monodispersed amorphous silica nanoparticles differing in size (20, 30, and 100 nm). Employing label-free liquid chromatography mass spectrometry, one- and two-dimensional gel electrophoresis, and immunoblotting the composition of the protein corona was analyzed not only qualitatively but also quantitatively. Detected proteins were bioinformatically classified according to their physicochemical and biological properties. finding of the 125 identified proteins did not simply reflect their relative abundance in the plasma but revealed an enrichment of specific lipoproteins as well as proteins involved in coagulation and the complement pathway. In contrast, immunoglobulins and acute phase response proteins displayed a lower affinity tor the panicles. Protein decoration of the negatively charged particles did not correlate with protein sin or charge, demonstrating that electrostatic effects alone are not the major driving force regulating the nanoparticle-protein Interaction. Remarkably, even differences in particle size of only 10 nm significantly determined the nanoparticle corona, although no clear correlation with particle surface volume, Protein size, or charge was evident. Particle size quantitatively influenced the panicle&#039;s decoration with 37% of all identified proteins, including (patho)biologically relevant candidates. We demonstrate the complexity of the plasma corona and its still unresolved physicochemical regulation, which need to be considered in nanobioscience in the future

A multicenter study benchmarks software tools for label-free proteome quantification

Consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH 2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from sequential window acquisition of all theoretical fragment-ion spectra (SWATH)-MS, which uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test data sets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation-window setups. For consistent evaluation, we developed LFQbench, an R package, to calculate metrics of precision and accuracy in label-free quantitative MS and report the identification performance, robustness and specificity of each software tool. Our reference data sets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics