Targeted vs untargeted MS2 data-dependent acquisition for automated peak annotation in LC-MS metabolomics

Published: 10 January 2020| Version 1 | DOI: 10.17632/fnzbxmkv83.1
Contributors:
Guillermo Quintas,
Isabel Ten,
Teresa Martínez-Sena,
Marta Moreno-Torres,
Juan Daniel Sanjuan-Herráez,
José Castell,
Anna Parra Llorca,
Maximo Vento,
Julia Kuligowski

Description

The data set includes MS and MSMS data collected from the analysis of human milk samples, using four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted liquid chromatography-mass spectrometry (LC-MS). These strategies include (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database (HMDB). MS data sets: 2 blanks and a set of QCs were injected at the beginning of the sequence for system conditioning and MS2 data acquisition (data set: I0). Then, the sample batch including 42 milk samples, 13 QCs (1 QC every 6 samples) and 3 blanks were analyzed (data set: B0). Peak table generation was carried out using XCMS software. The centWave method was used for peak detection with the following parameters: mass accuracy, 20 ppm; peak width, (5,25); snthresh, 12; prefilter, (5,5000); minimum difference for overlapping peaks: 7.5 mDa; intensity weighted m/z values of each feature were calculated using the wMean function; Peak limits used for integration: Mexican hat filtered data. Grouping before and after RT correction was carried out using the nearest method and 9 s as rtCheck argument. Missing data points were filled by reintegrating the raw data files in the regions of the missing peaks using the fillPeaks method. The CAMERA package was used for the identification of pseudospectra based on peak shape analysis, isotopic information and intensity correlation across samples. MSMS data sets: Two untargeted and two targeted DDA strategies for automated MS2 data acquisition based on the algorithm depicted in Figure 1B were employed: (i) untargeted selection of precursors in the 70-1500 Da range (DDA); (ii) untargeted iterated DDA, in which MS2 spectra were acquired in consecutive QC replicates using untargeted DDA in the [70-200], [200-400], [400-600], [600-800], [800-1000], [1000-1250], and [1250-1500] Da ranges (i-DDA); (iii) targeted dynamic iterated DDA, in which MS2 spectra were acquired by automated selection of precursor ions using an inclusion list generated after the injection of two blanks and three QCs during system conditioning (xcms-DDA). Raw MSMS data (.D) was converted into .ms2 and .mgf format using ProteoWizard. ms2 data was directly imported into MATLAB (ms2_dda, ms2_hmdb, ms2_idda and ms2_xcms data structures).

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Steps to reproduce

Sample preparation: Human Milk (HM) samples were thawed at RT followed by heating in a water bath (33°C, 10 min). 5 μL of an internal standard solution containing oleic acid-D9 and prostaglandin F2α-D4 in H2O were added to 45 μL HM and then, 175 µL MeOH followed by 175 µL MTBE were added to each sample. The mixture was shaken (1400 rpm, 20 °C, 1 min) and centrifuged (4000xg, 15°C, 15 min). 20 µL of supernatant were added to 80 µL of MeOH:MTBE (1:1, v/v). A blank extract was prepared replacing HM with water. A pooled QC sample was prepared by mixing 20 μL of each HM sample extract. Batch design: 2 blanks and a set of QCs were injected at the beginning of the sequence for system conditioning and MS2 data acquisition. Then, the sample batch including 42 HM samples, 13 QCs (1 QC every 6 HMs) and 3 blanks were analyzed. Sample analysis: LCMS analysis: 1290 Infinity system (Agilent Tech.) equipped with a UPLC BEH C18 column (50x2.1 mm, 1.7 µm, Waters). Flow rate: 400 µL min-1. Binary mobile phase gradient starting at 98% of mobile phase A (5:1:4 IPA:MeOH:water 5 mM ammonium acetate, 0.1%v/v formic acid) during 0.5 min followed by a linear gradient from 2 to 20% of mobile phase B (99:1 IPA:water 5 mM ammonium acetate, 0.1%v/v formic acid) during 3.5 min and from 20 to 95% v/v of mobile phase B in 4 min; 95%v/v of mobile phase B was maintained during 1 min; return to initial conditions was achieved in 0.25 min and were maintained for a total run time of 14 min. Column and autosampler were kept at 55 and 4 ˚C, respectively, and the injection volume was 2 µL. MS detection: 6550 Agilent iFunnel QTOF-MS. Mode: ESI+. Full scan MS range: 70-1500 m/z. Scan frequency: 5 Hz. Gas T, 200 °C; drying gas, 14 L/min; nebulizer, 37 psi; sheath gas T, 350 °C; sheath gas flow, 11 L min-1. MS spectra recalibration: 149.02332 (background contaminant), 121.050873 (purine), and 922.009798 (HP-0921) m/z. DDA methods: (i) untargeted selection of precursors in the 70-1500 Da range (DDA); (ii) untargeted iterated DDA, in which MS2 spectra were acquired in consecutive QC replicates using untargeted DDA in the [70-200], [200-400], [400-600], [600-800], [800-1000], [1000-1250], and [1250-1500] Da ranges (i-DDA); (iii) targeted dynamic iterated DDA, in which MS2 spectra were acquired by automated selection of precursor ions using an inclusion list generated during system conditioning (xcms-DDA). Here, LCMS features were added to the inclusion list if the ratio between the minimum values in QCs and the maximum value in blanks was higher than 6; and (iv) targeted dynamic iterated DDA, where MS2 spectra were acquired using an inclusion list of (pre)annotated features after the injection of two blanks and three QCs during system conditioning (hmdb-DDA). In this case, LC-MS features were added to the inclusion list if they were not detected in blanks and could be (pre)annotated based on m/z as, at least, one of the 95688 metabolites included in the HMDB (m/z error <20 ppm).

Institutions

Laboratorio de Ensayos e Investigaciones Textiles del Acondicionamiento Tarransese, Instituto de Investigacion Sanitaria La Fe

Categories

Mass Spectrometry, Lipidomics, Metabolomics, Tandem Mass Spectrometry, Ultra Performance Liquid Chromatography-Tandem Mass Spectrometry

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