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MB Sample ID: SA167778

Local Sample ID:Dm_polneg_LCO_1_33
Subject ID:SU001884
Subject Type:Invertebrate
Subject Species:Daphnia magna
Taxonomy ID:35525
Age Or Age Range:neonates < 9 h old

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Combined analysis:

Analysis ID AN002929 AN002930
Analysis type MS MS
Chromatography type None (Direct infusion) None (Direct infusion)
Chromatography system LTQ Orbitrap Elite (Thermo Fisher Scientific) LTQ Orbitrap Elite (Thermo Fisher Scientific)
Column none none
MS Type ESI ESI
MS instrument type LTQ-FT LTQ-FT
MS instrument name Thermo Orbitrap Elite Hybrid Ion Trap-Orbitrap Thermo Orbitrap Elite Hybrid Ion Trap-Orbitrap
Ion Mode POSITIVE NEGATIVE
Units arbitrary units arbitrary units

MS:

MS ID:MS002720
Analysis ID:AN002929
Instrument Name:Thermo Orbitrap Elite Hybrid Ion Trap-Orbitrap
Instrument Type:LTQ-FT
MS Type:ESI
MS Comments:For both positive and negative ion analyses, samples were randomized and 5 µL of sample supernatant was pipetted into a pre-washed 96-well sample plate in quadruplicate. Three quality control (QC) samples (a mixture with equal volume from all samples) and a blank were also included on each plate. Loaded plates were covered with a foil seal using heat sealer and loaded into a TriVersa Nano-Mate® nanoelectro-spray ion source (Advion) with the cooler set at 10 ºC. Non-targeted analysis was carried out on polar fractions by direct infusion mass spectrometry (DIMS) using an LTQ Orbitrap Elite (Thermo Fisher Scientific). 21 overlapping selected ion monitoring (SIM) windows were collected covering m/z values from 50 to 620. Data are available in the NIH National Metabolomics Data Repository (NMDR). The Galaxy pipeline at the University of Birmingham was used to process raw data collected. SIM windows were assembled into single spectra for each sample (SIM-Stitching). Filtering A signal to noise ratio (SNR) of 10 was selected to filter out background noise from the data. A replicate filter was applied to retain only peaks found in at least 3 out of 4 technical replicates, and samples aligned across biological samples. A blank filter was applied to only retain peaks if they are a specified % larger than blank values. Finally a sample filter was applied to keep only those peaks found in greater than 80% of biological samples. Missing-value imputation, normalization, and quality assessment Probabilistic quotient normalization (PQN) was applied to normalize the DIMS metabolomics data to account for differences in dilution between samples. A K-nearest neighbor (KNN) algorithm was then applied to impute missing values. A generalized-log transformation was then applied to stabilize the technical variance of the DIMS measurements. To assess data quality, the median relative standard deviation (RSD) was measured across technical replicates and an RSD cutoff value of 10 was specified. Data analysis Univariate ANOVAs were carried out on metabolite data with a false discovery rate (FDR) correction to account for the large number of possible comparisons. Peaks were annotated using the Functional Analysis tool for MS peaks on the MetaboAnalyst 5.0 online web server.4 Peak list files were uploaded containing m/z values and FDR corrected p-values obtained by the processing above, and analyzed in the respective (positive or negative) ion mode with a 5.0 ppm mass tolerance. For enrichment analysis, the Mummichog algorithm was applied with a p-value cutoff of p < 0.1 and analyzed against the KEGG database for Homo sapiens and Drosophila melanogaster. References (1) Bozich, J.; Hang, M.; Hamers, R.; Klaper, R. Core Chemistry Influences the Toxicity of Multicomponent Metal Oxide Nanomaterials, Lithium Nickel Manganese Cobalt Oxide, and Lithium Cobalt Oxide to Daphnia Magna. Environ. Toxicol. Chem. 2017, 36 (9), 2493–2502. https://doi.org/10.1002/etc.3791. (2) Niemuth, N. J. N. J.; Curtis, B. J. B. J.; Hang, M. N. M. N.; Gallagher, M. J. M. J.; Fairbrother, D. H. H.; Hamers, R. J. R. J.; Klaper, R. D. R. D. Next-Generation Complex Metal Oxide Nanomaterials Negatively Impact Growth and Development in the Benthic Invertebrate Chironomus Riparius upon Settling. Environ. Sci. Technol. 2019, 53 (7), 3860–3870. https://doi.org/10.1021/acs.est.8b06804. (3) Davidson, R. L.; Weber, R. J. M.; Liu, H.; Sharma-Oates, A.; Viant, M. R. Galaxy-M: A Galaxy Workflow for Processing and Analyzing Direct Infusion and Liquid Chromatography Mass Spectrometry-Based Metabolomics Data. Gigascience 2016, 5 (1), 10. https://doi.org/10.1186/s13742-016-0115-8. (4) Xia, J.; Psychogios, N.; Young, N.; Wishart, D. S. MetaboAnalyst: A Web Server for Metabolomic Data Analysis and Interpretation. Nucleic Acids Res. 2009, 37 (Web Server), W652–W660. https://doi.org/10.1093/nar/gkp356.
Ion Mode:POSITIVE
  
MS ID:MS002721
Analysis ID:AN002930
Instrument Name:Thermo Orbitrap Elite Hybrid Ion Trap-Orbitrap
Instrument Type:LTQ-FT
MS Type:ESI
MS Comments:For both positive and negative ion analyses, samples were randomized and 5 µL of sample supernatant was pipetted into a pre-washed 96-well sample plate in quadruplicate. Three quality control (QC) samples (a mixture with equal volume from all samples) and a blank were also included on each plate. Loaded plates were covered with a foil seal using heat sealer and loaded into a TriVersa Nano-Mate® nanoelectro-spray ion source (Advion) with the cooler set at 10 ºC. Non-targeted analysis was carried out on polar fractions by direct infusion mass spectrometry (DIMS) using an LTQ Orbitrap Elite (Thermo Fisher Scientific). 21 overlapping selected ion monitoring (SIM) windows were collected covering m/z values from 50 to 620. Data are available in the NIH National Metabolomics Data Repository (NMDR). The Galaxy pipeline at the University of Birmingham was used to process raw data collected. SIM windows were assembled into single spectra for each sample (SIM-Stitching). Filtering A signal to noise ratio (SNR) of 10 was selected to filter out background noise from the data. A replicate filter was applied to retain only peaks found in at least 3 out of 4 technical replicates, and samples aligned across biological samples. A blank filter was applied to only retain peaks if they are a specified % larger than blank values. Finally a sample filter was applied to keep only those peaks found in greater than 80% of biological samples. Missing-value imputation, normalization, and quality assessment Probabilistic quotient normalization (PQN) was applied to normalize the DIMS metabolomics data to account for differences in dilution between samples. A K-nearest neighbor (KNN) algorithm was then applied to impute missing values. A generalized-log transformation was then applied to stabilize the technical variance of the DIMS measurements. To assess data quality, the median relative standard deviation (RSD) was measured across technical replicates and an RSD cutoff value of 10 was specified. Data analysis Univariate ANOVAs were carried out on metabolite data with a false discovery rate (FDR) correction to account for the large number of possible comparisons. Peaks were annotated using the Functional Analysis tool for MS peaks on the MetaboAnalyst 5.0 online web server.4 Peak list files were uploaded containing m/z values and FDR corrected p-values obtained by the processing above, and analyzed in the respective (positive or negative) ion mode with a 5.0 ppm mass tolerance. For enrichment analysis, the Mummichog algorithm was applied with a p-value cutoff of p < 0.1 and analyzed against the KEGG database for Homo sapiens and Drosophila melanogaster. References (1) Bozich, J.; Hang, M.; Hamers, R.; Klaper, R. Core Chemistry Influences the Toxicity of Multicomponent Metal Oxide Nanomaterials, Lithium Nickel Manganese Cobalt Oxide, and Lithium Cobalt Oxide to Daphnia Magna. Environ. Toxicol. Chem. 2017, 36 (9), 2493–2502. https://doi.org/10.1002/etc.3791. (2) Niemuth, N. J. N. J.; Curtis, B. J. B. J.; Hang, M. N. M. N.; Gallagher, M. J. M. J.; Fairbrother, D. H. H.; Hamers, R. J. R. J.; Klaper, R. D. R. D. Next-Generation Complex Metal Oxide Nanomaterials Negatively Impact Growth and Development in the Benthic Invertebrate Chironomus Riparius upon Settling. Environ. Sci. Technol. 2019, 53 (7), 3860–3870. https://doi.org/10.1021/acs.est.8b06804. (3) Davidson, R. L.; Weber, R. J. M.; Liu, H.; Sharma-Oates, A.; Viant, M. R. Galaxy-M: A Galaxy Workflow for Processing and Analyzing Direct Infusion and Liquid Chromatography Mass Spectrometry-Based Metabolomics Data. Gigascience 2016, 5 (1), 10. https://doi.org/10.1186/s13742-016-0115-8. (4) Xia, J.; Psychogios, N.; Young, N.; Wishart, D. S. MetaboAnalyst: A Web Server for Metabolomic Data Analysis and Interpretation. Nucleic Acids Res. 2009, 37 (Web Server), W652–W660. https://doi.org/10.1093/nar/gkp356.
Ion Mode:NEGATIVE
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