XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules

Transcription

1 SUPPLEMENTARY Brief Communication INFORMATION In the format provided by the authors and unedited. XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules Xavier Domingo-Almenara 1,8, J. Rafael Montenegro-Burke 1,8, Julijana Ivanisevic 2, Aurelien Thomas 3,4, Jonathan Sidibé 3,4, Tony Teav 2, Carlos Guijas 1, Aries E. Aisporna 1, Duane Rinehart 1, Linh Hoang 1, Anders Nordström 5, María Gómez-Romero 6, Luke Whiley 6, Matthew R. Lewis 6, Jeremy K. Nicholson 6, H. Paul Benton 1 * and Gary Siuzdak 1,7 * 1 Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA, USA. 2 Metabolomics Unit, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland. 3 Unit of Toxicology, CURML, Lausanne University Hospital, Geneva University Hospitals, Lausanne, Switzerland. 4 Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland. 5 Department of Molecular Biology, Umeå University, Umeå, Sweden. 6 The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre, Department of Surgery and Cancer, Imperial College London, London, UK. 7 Department of Molecular and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA. 8 These authors contributed equally: Xavier Domingo-Almenara, J. Rafael Montenegro-Burke. * hpbenton@scripps.edu; siuzdak@scripps.edu Nature Methods

2 Supplementary Figure 1 METLIN-MRM screenshot. Transitions for Tryptophan are shown. Each transition type (EO, CO or PR) is displayed in a separated table.

3 Supplementary Figure 2 XCMS-MRM transition-peak-processing algorithm. a, qualitative and quantitative signals as seen in a typical MRM experiment. The quantitative transition has three peaks, but only one corresponds to the target molecule as observed from the qualitative transition signal. XCMS-MRM first examines the signal of all the transitions, including qualitative transitions, and uses that information to b, drive the integration of the qualitative transition in a more efficient manner. XCMS-MRM also subtracts the baseline that might be affecting the chromatographic signals.

4 Supplementary Figure 3 XCMS-MRM workflow and output. Different type of samples including quality controls, calibration and blanks can be uploaded into XCMS-MRM for its further processing. This processing implies automated transition peak area integration, absolute concentration computation with calibration curves and results display (a, b, c). Results by XCMS-MRM (d) include analytical quality descriptors to assess the method performance and quality via quality control coefficients of variation, specificity, linear ranges and limits of detection and quantification among others. These descriptors allow validating the experiment assay to allow further cooperative biological interpretation via the cloud with the statistical results provided by XCMS-MRM. In a, the use of non-selective transition fragments (m/z 86.1) masked both leucine and isoleucine hampering its correct quantification. The use of computationally optimized selective transitions from METLIN-MRM (m/z 43.1 and 69.1) allowed their quantification in this particular example. In b, XCMS-MRM uses calibration (cal) samples to convert relative values into absolute concentration with calibration curves, allowing different calibration methods to be used. In c, XCMS-MRM integrates and aligns the detected fragment peaks of corresponding transitions. In this example, N1,N12-Diacetylspermine (DAS) was quantified in serum samples from mice before and after its administration, where as expected, DAS is seen increased in mice serum after its administration (see Supplementary Methods for details).

5 Supplementary Figure 4 METLIN-MRM statistical ranking. a, the transitions of the target molecule are compared to putative interfering molecules, which consist of all the molecules in the METLIN library with precursors within ±0.7 Da window the precursor of the target molecule. Selective transitions are selected based on their fragment specificity (how unique is that fragment for that molecule). This specificity or selectivity is determined with a density estimation b, where the density of the intensity of a given fragment is computed. Fragments with a low-density area indicate that this fragment has a low intensity or is not present in the interfering molecules (e.g., fragment A), and thus this fragment has a high degree of selectivity. Moreover, in cases where the most selective transition for a given molecule is not selective enough (fragment A in c) METLIN-MRM is designed to provide a second transition that provides more selectivity, in cases where the first transition is present (Fragment B in c). This approach decreases the likelihood of the two transitions appearing together if the target molecule is not present, or enables the detection of interferences if the two transitions do not show a linear relation.

6 Supplementary Figure 5 Results of metabolite quantification by XCMS-MRM. Results of quantification by XCMS-MRM compared to these obtained in a certified laboratory. For each molecule, the plot shows the number of samples for each relative error range.

7 Supplementary Table 1. R-squared values for metabolites with both experimental (EO) and computationally optimized (CO) experimental transitions. No. Name Mode R 2 CO R 2 EO R 2 CO/ EO 1 3-Methyl-L-histidine Acetyl-L-glutamic acid Adenine Adenosine Adenosine monophosphate ADP Alanine Aminoadipic acid Arginine Aspartic acid ATP Beta-alanine Carnosine Citrulline Creatine Cysteine-S-sulfate Cystine Fructose 1-6-bisphosphate Fructose 6-phosphate Fumaric acid Glucose Glucose 6-phosphate Glutamate Glutamine Glutathione, oxidized Glyceraldehyde-3-phosphate Glycine GTP Guanidineacetic acid Guanidylic acid (guanosine monophosphate) Guanosine Histidine Hypoxanthine IMP Inosine Isoleucine Kynurenic acid L-Cysteine Lactic acid Leucine Lysine Malic acid

8 43 Methionine N-Acetyl-D-glucosamine N-Methyl-L-glutamate NAD NADP Niacinamide Nicotinamide adenine dinucleotide (NAD) Norleucine Oxoglutaric acid phenylalanine Phosphocholine Phosphoenol pyruvate Pimelic acid Proline Spermidine Succinic acid Taurine Thiamine Threonine Tryptophan Tyrosine UDP UDP-D-galactose UMP Uric acid Uridine Uridine diphosphate-n-acetylglucosamine UTP Valine Xanthine CTP Ribose 5-phosphate R 2 CO and R 2 EO values denote the linear relation between the transition peak area and the real concentration for the experimental (EO) and computationally optimized (CO) experimental transitions, respectively. R 2 CO/ EO denotes the linear relationship between the experimental (EO) and computationally optimized (CO) experimental transitions peak areas. In cases were more than one transition per metabolite was used, only the best R 2 value is shown. 2

9 Supplementary Table 2. R-squared values for metabolites with computationally optimized (CO) experimental transitions. No. Name Mode R 2 CO 75 5-Hyroxy-L-tryptophan Acetyl-CoA Citric acid Cystathionine DL-homocysteine DL-o-Tyrosine Isocitrate L-glutathione oxidized L-Methionine S-oxide m-coumaric acid N-Acetyl-L-Histidine N-Acetyl-phenylalanine N1-Acetylspermidine N2-Acetyl-L-ornithine O-acteylserine Ornithine Purine S1P Saccharopine Spermine Sphinganine Sphingosine Linoleic acid p-coumaric acid R 2 CO values denote the linear relation between the transition peak area and the real concentration for the computationally optimized (CO) experimental transitions. In cases were more than one transition per metabolite was used, only the best R 2 value is shown. Supplementary Table 3. Transition list of the interlaboratory dataset Metabolite Precursor Quant. CE (ev) Qual. 1 CE (ev) Qual. 2 CE (ev) Tyrosine Isoleucine Leucine Phenylalanine Vitamin B Tryptophan Caffeine Palmitoylcarnitine Arachidonic acid Cholesterol The table shows the metabolites with their precursor, quantitative (quant.) and two qualitative (qual.) ion fragments with their corresponding collision energies (CE). 3

10 Supplementary Table 4. Quantitative results summary of the interlaboratory dataset analysis by XCMS- MRM Metabolite TSRI Umea Unil Imp. C. Mean SD (µm) CV (%) Tyrosine Isoleucine Leucine Phenylalanine Vitamin B Tryptophan Caffeine Palmitoyl-carnitine Arachidonic acid The table shows the quantitative concentration (µm) of the metabolites in the same pool serum sample (six replicates of the same pool of human serum sample spiked with ten standards), after calibration by standard addition and analysis by XCMS-MRM by each independent laboratory (TSRI, The Scripps Research Institute; Umea, University of Umea; Unil, University of Lausanne; and Imp. C., Imperial College). The mean, standard deviation (SD) and coefficient of variation (CV) are also shown. Of note, cholesterol was removed from the list since its concentration was under the limits of quantification. 4