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Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts

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Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts

Juan Rodríguez-Coira et al. Metabolites.

Abstract

Metabolomics, understood as the science that manages the study of compounds from the metabolism, is an essential tool for deciphering metabolic changes in disease. The experiments rely on the use of high-throughput analytical techniques such as liquid chromatography coupled to mass spectrometry (LC-ToF MS). This hyphenation has brought positive aspects such as higher sensitivity, specificity and the extension of the metabolome coverage in a single run. The analysis of a high number of samples in a single batch is currently not always feasible due to technical and practical issues (i.e., a drop of the MS signal) which result in the MS stopping during the experiment obtaining more than a single sample batch. In this situation, careful data treatment is required to enable an accurate joint analysis of multi-batch data sets. This paper summarizes the analytical strategies in large-scale metabolomic experiments; special attention has been given to QC preparation troubleshooting and data treatment. Moreover, labeled internal standards analysis and their aim in data treatment, and data normalization procedures (intra- and inter-batch) are described. These concepts are exemplified using a cohort of 165 patients from a study in asthma.

Keywords: LC-QToF-MS; asthma; large-scale; metabolomics; normalization.

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Experimental worklists and batches followed for (A) ESI+ and (B) ESI− modes. (A) Experimental samples were measured into two batches for ESI+ while (B) in the case of ESI− mode, 3 batches were obtained due to an ‘instrument communication error’. NOTE. The source of the equipment was not cleaned up between batches 2 and 3 in ESI− mode.
Figure 2
Figure 2
Quality control charts of the abundance of IS compounds in each sample according to the injection order for ESI+ mode. (A) Carnitine-D3 RT 0.70min, RSD = 22.95%. (B) Isoleucine-13C, 15N RT 0.77 min, RSD = 8.40%. (C) Sphingosine-D7 RT 14.38 min, RSD = 5.15%. (D) LPC18:1-D7 RT 19.30 min, RSD = 7.04%. (E) LPC18:1-D7 RT 20.00 min, RSD = 8.82%. Legend. Y-axis: Abundance. X-axis: Sample order. Red circles: QCs; blue and green circles: Experimental samples measured in batch 1 and batch 2, respectively. Dotted lines represent the mean +/− 2 and 3 SD for each batch independently.
Figure 3
Figure 3
Quality control charts of the abundance of IS compounds in each sample according to the injection order for ESI− mode. (A) Isoleucine-13C, 15N at RT 0.77 min, RSD =25.01%. Grey rectangle signals samples with low levels of IS mix. (B) LPC18:1-D7 RT 19.30 min, RSD = 23.93%. (C) LPC18:1-D7 RT 20.00 min, RSD = 21.22%. (D) Stearic acid-D5 RT 34.54 min, RSD = 30.03% Legend. Y-axis: Abundance. X-axis: Sample order. Red circles: QCs; blue and green circles: experimental samples measured in batch 1 and batch 2, respectively. Dotted lines represent the mean +/− 2 and 3 SD for each batch independently.
Figure 4
Figure 4
Outcome of the data normalization strategy after QC-SVRC algorithm for both ESI+ and ESI− modes. (A,C) Quality control chart of ESI+ and ESI− modes. Samples with a TUS higher than 3 SD of the mean or lower than −3 SD were removed from PCA model. (B,D) PCA plots of ESI+ and ESI− mode, respectively Signals with %RSD < 30% on QCs were kept, and UV scaling was used. Features in ESI+ and ESI− modes, respectively: 1056 and 394. Legend. TUS plot: Blue and green dots: samples of batch1 and batch2, respectively, red dots: QCs. Dotted lines represent the mean +/− 2 and 3 SD for each batch independently. PCA: Blue dots and dark blue triangles are samples and QCs measured in batch1, respectively; green dots and dark green triangles are samples and QCs from batch2, respectively. Grey circle are samples with low levels of IS mix.
Figure 5
Figure 5
Outcome of the data normalization strategy after QC-SVRC algorithm and QC-norm for both: ESI+ and ESI− modes. (A,C) Quality control chart of ESI+ and ESI− modes, respectively. Samples with a TUS higher than 3 SD of the mean or lower than −3 SD were removed from the PCA model. (B,D) PCA plots of ESI+ and ESI− mode, respectively. Signals with %RSD < 30% on QCs were kept, and UV scaling was used. Features in ESI+ and ESI− modes respectively: 880 and 525. Legend. TUS plot: Blue and green dots: samples of batch1 and batch2, respectively, red dots: QCs. Dotted lines represent the mean +/− 2 and 3 SD for each batch independently. PCA: Blue dots and dark blue triangles are samples and QCs measured in batch1, respectively; green dots and dark green triangles are samples and QCs from batch2, respectively. Grey circle signals samples with low levels of IS mix.
Figure 6
Figure 6
RSD distribution across QC samples from the complete dataset after each normalization method. (A) ESI+ mode and (B) ESI− mode. The red and grey bars indicate peaks that fall under RSD < 30% and RSD > 100%, respectively.
Figure 7
Figure 7
Normalization impact evaluated by HCA test on repeated experimental samples for (A,D) raw data; (B,E) after normalization by QC-SVRC and; (C,F) QC-SVRC + QC-norm for both, ESI+ and ESI− modes, respectively. Legend. Every pair of samples represents one patient and is depicted in a different color for each polarity mode.

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