Effect of Sampling Rate and Data Pretreatment for Targeted and Nontargeted Analysis by Means of Liquid Chromatography Coupled to Drift Time Ion Mobility Quadruple Time-of-Flight Mass Spectrometry
datasetposted on 13.09.2021, 15:05 by Kristina Tötsch, John C. Fjeldsted, Sarah M. Stow, Oliver J. Schmitz, Sven W. Meckelmann
Ion mobility as an additional separation dimension can help to resolve and annotate metabolite and lipid biomarkers and provides important information about the components in a sample. Identifying relevant information in the resulting data is challenging because of the complexity of the data and data evaluation strategies for both targeted or nontargeted workflows. Frequently, feature analysis is used as a first step to search for differences between samples in discovery workflows. However, follow-up experimentation often leads to more targeted data extraction methods. In both cases, optimizing data sets for data extraction can make an important contribution to the overall results. In this work, we evaluate the effect of experimental conditions including acquisition sampling rate and data pretreatment on lipid standards and lipid extracts as examples of complex biological samples analyzed by liquid chromatography coupled to drift time ion mobility quadrupole time-of-flight mass spectrometry. The results show that a reduction of both peak variation and background noise can be achieved by optimizing the sampling rate. The use of data pretreatment including data smoothing, intensity thresholding, and spike removal also play an important role in improving detection and annotation of analytes from complex biological samples, whereas nonoptimal data sampling rates and preprocessing can lead to adverse effects including the loss or alternation of small, or closely eluting, low-abundant peaks.
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liquid chromatography coupledidentifying relevant informationflight mass spectrometryexperimentation often leadsadverse effects includingadditional separation dimensionprovides important informationdata evaluation strategiescomplex biological samplesoptimizing data setsimportant roleimportant contributionresulting datadata pretreatmentdata extractionsampling rateresults showpeak variationoverall resultsnontargeted workflowsnontargeted analysislipid standardslipid extractslipid biomarkersintensity thresholdingimproving detectionfirst stepfeature analysisdiscovery workflowsclosely elutingbackground noiseannotate metaboliteabundant peaks