Search

Deze studie werd opgezet als een “proof of concept” of een pilootproject om een complexe studieopzet met metingen in meerdere compartimenten bij dezelfde deelnemers uit te testen en met als doel i) een zicht te krijgen op de PFAS-gehalten in deze compartimenten verspreid in Vlaanderen, ii) hypotheses te formuleren voor de bijdrage van deze compartimenten tot de PFAS-blootstelling en iii) hypotheses te formuleren voor mogelijke factoren die een invloed kunnen hebben op de waargenomen PFAS-gehalten.

Mass spectrometry quality control: Instrument monitoring and pattern mining insights Introduction Because of the inherent complexity of mass spectrometry, the results of an experiment can be subject to a large variability. In order to assess the quality of a mass spectrometry experiment, several different quality control metrics have been defined. These metrics can be computed from the experimental spectral-derived data, and they capture the important operational characteristics of a mass spectrometer. Additionally, besides these metrics based on the experimental data, there is a separate part of qualitative information available in the form of the mass spectrometer instrument parameters. This information provides complementary insights into the operational characteristics of a mass spectrometer compared to the aforementioned quality control metrics that originate from the mass spectral data. Methods The core of the instrument monitoring functionality consists of a central database that is optimized to store a vast amount of instrument parameters, called the iMonDB (Instrument MONitoring DataBase). Software tools exist to set up and populate the database in an automated fashion, and to visualize the instrument parameters stored in the iMonDB to perform a downstream analysis. Such structured recording of instrument parameters results in a large set of data detailing the instrument performance over time. Because of the high-dimensional nature of this data, advanced techniques are required to interpret it. For example, pattern mining algorithms that take into account the relationships between various parameters, such as subspace clustering, can be employed to analyze the high-dimensional data. Preliminary Data For each particular instrument the iMonDB contains the instrument parameters that were in effect during the execution of the experiments performed on the instrument. Possible parameters are for example the status of the ion source, the vacuum, or a turbo pump, depending on the type of instrument. These instrument parameters can be automatically extracted from experimental raw files and stored in the database to keep the iMonDB continuously up to date. Another type of information that is related to the operation of a mass spectrometer contained in the iMonDB are external events that may occur. For example, machine calibrations, periodic maintenance events, or even unexpected incidents that an operator likes to report. Unlike the instrument parameters, this type of information cannot be automatically retrieved and instead it has to be manually provided by the user. However, this information is vital when interpreting the evolution of the instrument parameters over time. It will be shown that the instrument parameters can be employed to detect/predict instrument failure. Monitoring of the instrument parameters showed that the FT turbo pump 4 of a Thermo Scientific Orbitrap Velos exhibited an increased power consumption. The reason for this was that while the turbo pump was deteriorating it tried to increase the power consumption to remain fully functional. However, this was insufficient to continuously achieve the required speed, after which the instrument finally broke down. By consistently monitoring the instrument parameters, the malfunctioning could have been predicted as the increase in power consumption serves as a proxy for turbo pump retrogression. Hence, by closely monitoring the instrument parameters and defining a normal range of operation, a suspected malfunctioning can be diagnosed early on and be reported. Based on this diagnosis, a timely intervention can prevent a highly undesirable loss of precious sample, analysis time, and effort. Novel Aspect Illustration how instrument monitoring can be used for quality control to detect instrument failure, introduction of instrument monitoring software.

Introduction Because of the inherent complexity of mass spectrometry, the results of an experiment can be subject to a large variability. As a means of quality control, several qualitative metrics have been defined. These quality control metrics can be derived from the experimental data, and they capture the important operational characteristics of a mass spectrometer. Additionally, besides these metrics based on the experimental data, there is a separate part of qualitative information available in the form of the mass spectrometer instrument settings. This information can provide complementary insights into the operational characteristics of a mass spectrometer compared to the aforementioned quality control metrics. Instrument settings Several tools exist to compute quality control metrics based on the experimental data, however, unfortunately, until now the instrument settings have been largely ignored. In fact, this potentially important source of quality control information is still largely untapped. Therefore, we have developed tools to extract instrument settings from experimental raw files, and store this qualitative information in an optimized relational database. This allows for monitoring historical instrument settings for a large range of experiments over the course of a considerable time period in order to detect and confirm instrument failure, fostering additional methods of quality control. Pattern mining Both the experimental-based quality control metrics and the instrument settings can be used to monitor instrument behavior, in order to detect emerging problems in a timely fashion. Previous approaches mostly evaluated each metric individually; however, looking at specific metrics individually is insufficient because the different stages of a mass spectrometry experiment do not function in isolation, instead they can have an effect an each other. Therefore, specific pattern mining algorithms, which aim to take into account the relationship between various metrics, can be employed to provide additional insights when analyzing quality control data. In particular, subspace clustering can be used to find subspaces of related metrics, based on the experimental data as well as the instrument settings, and provide a comprehensive analysis of various different sources of qualitative data. Conclusion The awareness has risen that suitable quality control information is mandatory to assess the validity of a mass spectrometry experiment. Over the past few years, effort has been expended to define a suitable set of qualitative metrics based on the experimental results. We have complemented this source of information with the mass spectrometer instrument settings, which provide complementary insights into the operational characteristics of a mass spectrometer. Furthermore, we have developed specialized pattern mining algorithms to interpret this high-dimensional data. The derived patterns could subsequently be used to optimize experimental design and mass spectrometry instrument settings.

Pattern mining of mass spectrometry quality control data Introduction Mass spectrometry is widely used to identify proteins based on the mass distribution of their peptides. Unfortunately, because of its inherent complexity, the results of a mass spectrometry experiment can be subject to a large variability. As a means of quality control, recently several qualitative metrics have been defined [1]. Prior research either evaluated these quality control metrics individually through traditional statistical means [1], or globally, using a multivariate statistics approach [2]. Unfortunately, looking at specific metrics individually is insufficient because the different stages of a mass spectrometry experiment do not function in isolation, instead they can have an effect on each other. Therefore, a multivariate approach tries to overcome this defect by evaluating all metrics simultaneously. However, this global analysis might obfuscate potentially interesting observations that are only expressed by a subset of metrics. As a result, specialized pattern mining techniques that take this duality into account can provide additional insights when analyzing quality control data. Pattern mining Both traditional univariate statistics and multivariate statistics are insufficient to capture all interesting patterns in very high-dimensional data. Specifically, because mass spectrometry is a complicated process, a single quality metric will be insufficient to detect problems in a timely manner. On the other hand, the different parts of a mass spectrometry experiment can be considered to be produced by a different generating mechanism, which means that specific sets of metrics can exhibit dissimilar behavior. On the other hand, subspace mining algorithms [3] try to find a suitable subset of the original feature space in which (dis)similar items can be found. Unfortunately, current subspace mining algorithms originating from the theoretical data mining field are often not applicable on real-world data. For example, the mass spectrometry quality control data is characterized by a high dimensionality. Due to the curse of dimensionality, most existing algorithms either fail or have an excessive run time, or result into a degenerate subspace cluster detection. Therefore, practical and sound algorithms are mandatory, which is why we have developed a novel subspace clustering algorithm that takes these considerations into account. Using this subspace clustering algorithm we can detect prevalent subspace clusters indicating normal mass spectrometer behavior, as well as subspace outliers indicating unexpected or faulty behavior. Conclusion The awareness has risen that suitable quality control information is mandatory to assess the validity of a mass spectrometry experiment. Recently there have been efforts to standardize this quality control information, which will facilitate its dissemination along with experimental data [4]. This will result in a large amount of as of yet untapped information, which can be leveraged by making use of specific data mining techniques in order to harness the full power of this new information. We have developed a novel and practical subspace clustering algorithm which takes into account the high dimensionality of the data. Current results yield a wide range of quality control parameters indicating correct or faulty behavior. These patterns could subsequently be used to optimized mass spectrometry instrument settings. References 1) Rudnick, P. A. et al. Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses. Molecular & Cellular Proteomics 9, 225–241 (2010). 2) Wang, X. et al. QC metrics from CPTAC raw LC-MS/MS data interpreted through multivariate statistics. Analytical Chemistry 86, 2497–2509 (2014). 3) Müller, E., Günnemann, S., Assent, I. & Seidl, T. Evaluating clustering in subspace projections of high dimensional data. Proceedings of the VLDB Endowment 2, 1270–1281 (2009). 4) Walzer, M. et al. qcML: An exchange format for quality control metrics from mass spectrometry experiments. Molecular & Cellular Proteomics 13, 1905–1913 (2014).