Pipelines and Systems for Threshold Avoiding Quantification of LC-MS/MS data

The accurate processing of complex LC-MS/MS data from biological samples is a major challenge for metabolomics, proteomics and related approaches. Here we present the Pipelines and Systems for Threshold Avoiding Quantification (PASTAQ) LC-MS/MS pre-processing toolset, which allows highly accurate quantification of data-dependent acquisition (DDA) LC-MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parametrization and automatic generation of quality control plots for data and pre-processing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios, and allows the detection of peptides over a larger dynamic concentration range compared to widely used proteomics preprocessing tools. The performance of the pipeline is also demonstrated in a biological human serum dataset for the identification of gender related proteins.

Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis

Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley (Hordeum vulgare) leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms.

SmartPeak automates targeted and quantitative metabolomics data processing

SmartPeak is an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of CE-, GC- and LC-MS(/MS) data, and HPLC data for targeted and semi-targeted metabolomics, lipidomics, and fluxomics experiments.HighlightsNovel algorithms for retention time alignment, calibration curve fitting, and peak integrationEnables reproducibility by reducing operator bias and ensuring high QC/QAAutomated pipeline for high throughput targeted and/or quantitative metabolomics, lipidomics, and fluxomics data processing from multiple analytical instrumentsManually curated data set of LC-MS/MS, GC-MS, and HPLC integrated peaks for further algorithm development and benchmarking

DIAlignR provides precise retention time alignment across distant runs in DIA and targeted proteomics

SWATH-MS has been widely used for proteomics analysis given its high-throughput and reproducibility but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human-plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data-acquisition, acquisition by different operators or instruments, intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and successively performance of SWATH-MS data analysis. Here, we present a novel algorithm for retention time alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order swapped peaks. Furthermore, allowing RT-mapping in a certain window around coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperforms the current state-of-the-art approaches. In addition, on a real-world clinical data, our approach outperforms global alignment methods by mapping 98% of peaks compared to 67% cumulatively and DIAlignR can reduce alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy has also been demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.Abbreviations:AUCArea Under the CurveDIAData-independent acquisitionLCLiquid chromatographyLOESSLocal weighted regressionRSEResidual Standard ErrorRTRetention timeXICExtracted ion chromatogramsData Availability:Raw chromatograms and features extracted by OpenSWATH are available on PeptideAtlas.Servername: ftp.peptideatlas.orgUsername: PASS01280Password: KQ2592b

Metabolomic profiling in culture media of day-5 human embryos

The aim of this study was to determine the changes of metabolomic profiles in embryonic culture media (ECM) for the evaluation of quality and implantation potential of human embryos. ECM (n=163) were collected on day 5 before transfer or cryopreservation. Some embryos were used in preimplantation genetic screening for detection of aneuploidy karyotypes. Samples were subdivided into groups according to embryo morphological classification (by Gardner), genetic analysis and implantation data. ECM were extracted with methanol, precipitates were separated by centrifugation and metabolite production of individual embryo was analysed by LC-MS (the positive ion mode). After peak detection and retention time alignment, data were analysed using the PCA algorithm. MS fingerprinting analysis of embryo culture medium showed significant differences between morphologically divided groups. Intragroup comparisons did not reveal differences between subclasses. Genetic screening of embryos revealed 33 aneuploid karyotypes. It was shown that chromosome number did not affect the metabolite profiles comparing with the normal group. The culture media of embryos that were positive or negative for successful implantation showed specific signatures that allowed to distinguish embryos with different outcomes.The characterization of ECMs by LC-MS may facilitate more accurate selection of the best embryo for the implantation, improving single-embryo transfer and thus eliminating the risk and undesirable effects of multiple pregnancies.