Protein identification using a nanoUHPLC-AP-MALDI MS/MS workflow with CID of multiply charged proteolytic peptides

Liquid AP-MALDI can produce predominantly multiply charged ESI-like ions and stable durable analyte ion yields with samples allowing good shot-to-shot reproducibility and exhibiting self-healing properties during laser irradiation. In this study, LC-MALDI MS/MS workflows that utilize multiply charged ions are reported for the first time and compared with standard LC-ESI MS/MS for bottom-up proteomic analysis. The proposed method is compatible with trifluoroacetic acid as an LC ion pairing reagent and allows multiple MS/MS acquisitions of the LC-separated samples without substantial sample consumption. In addition, the method facilitates the storage of fully spotted MALDI target plates for months without significant sample degradation

Investigation and optimization of parameters affecting the multiply charged ion yield in AP-MALDI MS

Liquid matrix-assisted laser desorption/ionization (MALDI) allows the generation of predominantly multiply charged ions in atmospheric pressure (AP) MALDI ion sources for mass spectrometry (MS) analysis. The charge state distribution of the generated ions and the efficiency of the ion source in generating such ions crucially depend on the desolvation regime of the MALDI plume after desorption in the AP-tovacuum inlet. Both high temperature and a flow regime with increased residence time of the desorbed plume in the desolvation region promote the generation of multiply charged ions. Without such measures the application of an electric ion extraction field significantly increases the ion signal intensity of singly charged species while the detection of multiply charged species is less dependent on the extraction field. In general, optimization of high temperature application facilitates the predominant formation and detection of multiply charged compared to singly charged ion species. In this study an experimental setup and optimization strategy is described for liquid AP-MALDI MS which improves the ionization effi- ciency of selected ion species up to 14 times. In combination with ion mobility separation, the method allows the detection of multiply charged peptide and protein ions for analyte solution concentrations as low as 2 fmol/lL (0.5 lL, i.e. 1 fmol, deposited on the target) with very low sample consumption in the low nL-range

OpenMS – An open-source software framework for mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow.</p> <p>Results</p> <p>We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies.</p> <p>Conclusion</p> <p>OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at <url>http://www.openms.de</url>.</p

Optimal precursor ion selection for LC-MS/MS based proteomics

Shotgun proteomics with Liquid Chromatography (LC) coupled to Tandem Mass Spectrometry (MS/MS) is a key technology for protein identification and quantitation. Protein identification is done indirectly: detected peptide signals are fragmented byMS/MS and their sequence is reconstructed. Afterwards, the identified peptides are used to infer the proteins present in a sample. The problem of choosing the peptide signals that shall be identified with MS/MS is called precursor ion selection. Most workflows use data- dependent acquisition for precursor ion selection despite known drawbacks like data redundancy, limited reproducibility or a bias towards high-abundance proteins. In this thesis, we formulate optimization problems for different aspects of precursor ion selection to overcome these weaknesses. In the first part of this work we develop inclusion lists aiming at optimal precursor ion selection given different input information. We trace precursor ion selection back to known combinatorial problems and develop linear program (LP) formulations. The first method creates an inclusion list given a set of detected features in an LC-MS map. We show that this setting is an instance of the Knapsack Problem. The corresponding LP can be solved efficiently and yields inclusion lists that schedule more precursors than standard methods when the number of precursors per fraction is limited. Furthermore, we develop a method for inclusion list creation based on a list of proteins of interest. We employ retention time and detectability prediction to infer LC-MS features. Based on peptide detectability, we introduce protein detectabilities that reflect the likelihood of detecting and identifying a protein. By maximizing the sum of protein detectabilities we create an inclusion list of limited size that covers a maximum number of proteins. In the second part of the thesis, we focus on iterative precursor ion selection (IPS) with LC-MALDI MS/MS. Here, after a fixed number of acquired MS/MS spectra their identification results are evaluated and are used for the next round of precursor ion selection. We develop a heuristic which creates a ranked precursor list. The second method, IPS LP, is a combination of the two inclusion list scenarios presented in the first part. Additionally, a protein-based exclusion is part of the objective function. For evaluation, we compared both IPS methods to a static inclusion list (SPS) created before the beginning of MS/MS acquisition. We simulated precursor ion selection on three data sets of different complexity and show that IPS LP can identify the same number of proteins with fewer selected precursors. This improvement is especially pronounced for low abundance proteins. Additionally, we show that IPS LP decreases the bias to high abundance proteins. All presented algorithms were implemented in OpenMS, a software library for mass spectrometry. Finally, we present an online tool for IPS that has direct access to the instrument and controls the measurement

Optimale Prekursor-Ionenauswahl für die LC-MS/MS basierte Proteomik

Shotgun proteomics with Liquid Chromatography (LC) coupled to Tandem Mass Spectrometry (MS/MS) is a key technology for protein identification and quantitation. Protein identification is done indirectly: detected peptide signals are fragmented byMS/MS and their sequence is reconstructed. Afterwards, the identified peptides are used to infer the proteins present in a sample. The problem of choosing the peptide signals that shall be identified with MS/MS is called precursor ion selection. Most workflows use data- dependent acquisition for precursor ion selection despite known drawbacks like data redundancy, limited reproducibility or a bias towards high-abundance proteins. In this thesis, we formulate optimization problems for different aspects of precursor ion selection to overcome these weaknesses. In the first part of this work we develop inclusion lists aiming at optimal precursor ion selection given different input information. We trace precursor ion selection back to known combinatorial problems and develop linear program (LP) formulations. The first method creates an inclusion list given a set of detected features in an LC-MS map. We show that this setting is an instance of the Knapsack Problem. The corresponding LP can be solved efficiently and yields inclusion lists that schedule more precursors than standard methods when the number of precursors per fraction is limited. Furthermore, we develop a method for inclusion list creation based on a list of proteins of interest. We employ retention time and detectability prediction to infer LC-MS features. Based on peptide detectability, we introduce protein detectabilities that reflect the likelihood of detecting and identifying a protein. By maximizing the sum of protein detectabilities we create an inclusion list of limited size that covers a maximum number of proteins. In the second part of the thesis, we focus on iterative precursor ion selection (IPS) with LC-MALDI MS/MS. Here, after a fixed number of acquired MS/MS spectra their identification results are evaluated and are used for the next round of precursor ion selection. We develop a heuristic which creates a ranked precursor list. The second method, IPS LP, is a combination of the two inclusion list scenarios presented in the first part. Additionally, a protein-based exclusion is part of the objective function. For evaluation, we compared both IPS methods to a static inclusion list (SPS) created before the beginning of MS/MS acquisition. We simulated precursor ion selection on three data sets of different complexity and show that IPS LP can identify the same number of proteins with fewer selected precursors. This improvement is especially pronounced for low abundance proteins. Additionally, we show that IPS LP decreases the bias to high abundance proteins. All presented algorithms were implemented in OpenMS, a software library for mass spectrometry. Finally, we present an online tool for IPS that has direct access to the instrument and controls the measurement.Flüssigkeitschromatographie (LC) gekoppelt mit Tandemmassenspektrometrie (MS/MS) ist eine Schlüsseltechnologie für die Proteinidentifikation und Quantifizierung in proteomischen Proben. Dabei werden Proteine indirekt identifiziert: detektierte Peptidsignale werden durch MS/MS fragmentiert und anschließend wird die Peptidsequenz rekonstruiert. Über die identifizierten Peptide werden schließlich die Proteine in der Probe identifiziert. Das Problem der Auswahl der Peptidsignale, die über MS/MS sequenziert werden sollen, heißt Precursor-Ionen-Selektion (PS). Die meisten Selektionsverfahren benutzen rein intensitätsbasierte Ansätze – sogenannte Datenabhängige Akquisition (DDA) – trotz bekannter Schwächen wie Datenredundanz, begrenzter Reproduzierbarkeit oder einer Neigung zur Identifikation häufiger Proteine. In dieser Arbeit entwickeln wir für unterschiedliche Aspekte der PS Formulierungen als Optimierungsprobleme mit dem Ziel den bekannten Schwächen entgegenzusteuern. Im ersten Teil der Arbeit werden für unterschiedliche Anfangsinformationen optimale Inklusionslisten erstellt. Dabei führen wir PS auf bekannte kombinatorische Probleme zurück und entwickeln Formulierungen als Lineare Programme (LP) zur Lösung der Probleme. Die erste Methode basiert auf einer Liste von LC-MS-Features. Wir zeigen, dass sich diese Situation auf das Rucksackproblem zurückführen läßt. Das zugehörige LP erstellt effiziente Inklusionslisten, die mehr Precursor enthalten als Standardmethoden, wenn die Anzahl an Precursor-Ionen pro Fraktion begrenzt ist. Außerdem entwickeln wir eine Methode basierend auf einer Liste an zu identifizierenden Proteinsequenzen. Wir benutzen Schätzverfahren für RT und Detektierbarkeit um repräsentative LC-MS-Features für diese Proteine vorherzusagen. Basierend auf der Peptiddetektierbarkeit führen wir eine Proteindetektierbarkeit ein. Indem wir die Summe dieser maximieren, erstellen wir eine größenbeschränkte Inklusionsliste, die eine maximale Anzahl an Proteinen abdeckt. Im zweiten Teil der Arbeit beschäftigen wir uns mit iterativer PS (IPS) mit LC-MALDI MS/MS. Dabei werden nach einer bestimmten Anzahl an aufgenommenen MS/MS- Spektren deren Identifikationsergebnisse ausgewertet und diese zur weiteren PS benutzt. Wir entwickeln einerseits eine Heuristik, die eine priorisierte Inklusionsliste erstellt.Für die zweite Methode, IPS_LP, kombinieren wir die beiden LP-Formulierungen aus dem ersten Teil und erweitern sie um eine proteinbasierte Exklusion. Für die Auswertung vergleichen wir unsere IPS- Methoden mit einer statischen Inklusionsliste (SPS), die vor Beginn der MS/MS- Messung erstellt wurde. Wir simulieren die PS auf drei Datensätzen mit unterschiedlicher Komplexität und zeigen, dass IPS_LP die gleiche Proteinanzahl wie SPS identifiziert, dabei aber weniger MS/MS-Messungen benötigt. Diese Verbesserung wird insbesondere für Proteine mit geringer Abundanz deutlich. Außerdem können wir zeigen, dass die Neigung zur Identifikation häufiger Proteine gesenkt wird. Unsere Algorithmen wurden als Teil von OpenMS, einer Softwarebibliothek für Massenspektrometrie, implementiert. Im letzten Teil stellen wir außerdem ein Onlinetool vor, dass direkten Zugriff auf das Massenspektrometer hat und die Messungen steuert

Optimal precursor ion selection for LC-MALDI MS/MS

Background Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins. Results We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time. Conclusions We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well-defined protein standard and a complex human cell lysate, we demonstrate that our methods outperform standard approaches. Our algorithms are implemented as part of OpenMS and are available under http://www.openms.de