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MB Sample ID: SA125821
| Local Sample ID: | 25 |
| Subject ID: | SU001565 |
| Subject Type: | Human |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
| Gender: | Female |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN002470 | AN002471 | AN002472 | AN002473 |
|---|---|---|---|---|
| Analysis type | MS | MS | MS | MS |
| Chromatography type | HILIC | HILIC | Reversed phase | Reversed phase |
| Chromatography system | Thermo Dionex Ultimate 3000 RS | Thermo Dionex Ultimate 3000 RS | Thermo Dionex Ultimate 3000 RS | Thermo Dionex Ultimate 3000 RS |
| Column | SeQuant ZIC-HILIC (100 x 2.1mm,3.5um) | SeQuant ZIC-HILIC (100 x 2.1mm,3.5um) | Hypersil GOLD (150 x 2.1mm,1.9um) | Hypersil GOLD (150 x 2.1mm,1.9um) |
| MS Type | ESI | ESI | ESI | ESI |
| MS instrument type | Orbitrap | Orbitrap | Orbitrap | Orbitrap |
| MS instrument name | Thermo Q Exactive HF hybrid Orbitrap | Thermo Q Exactive HF hybrid Orbitrap | Thermo Q Exactive HF hybrid Orbitrap | Thermo Q Exactive HF hybrid Orbitrap |
| Ion Mode | POSITIVE | NEGATIVE | POSITIVE | NEGATIVE |
| Units | MS count | MS Counts | MS Counts | MS Counts |
MS:
| MS ID: | MS002290 |
| Analysis ID: | AN002470 |
| Instrument Name: | Thermo Q Exactive HF hybrid Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Data processing. Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics). Metabolic features from blanks and that did not show sufficient linearity upon dilution in QC samples (r < 0.6) were discarded. Only metabolic features present in > 2/3 of the samples were kept for further analysis. Inter- and intra-batch variations were corrected by applying locally estimated scatterplot smoothing local regression (LOESS) on pooled samples injected repetitively along the batches (span = 0.75). Data were acquired in four batches for HILIC and RPLC modes. Dilution effects were corrected using probabilistic quotient normalization (PQN) (Rosen Vollmar et al., 2019). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were then merged, producing a dataset containing 6,630 metabolic features. Metabolite abundances were reported as spectral counts. Metabolic feature annotation. Peak annotation was first performed by matching experimental m/z, retention time and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases including HMDB (http://www.hmdb.ca/), MoNA (http://mona.fiehnlab.ucdavis.edu/) and MassBank (http://www.massbank.jp/) using the R package ‘MetID’ (v0.2.0) (Shen et al., 2019). Briefly, metabolic feature tables from Progenesis QI were matched to fragmentation spectra with a m/z and a retention time window of ± 15 ppm and ± 30 s (HILIC) and ± 20 s (RPLC), respectively. When multiple MS/MS spectra match a single metabolic feature, all matched MS/MS spectra were used for the identification. Next, MS1 and MS2 pairs were searched against public databases and a similarity score was calculated using the forward dot–product algorithm which considers both fragments and intensities (Stein and Scott, 1994). Metabolites were reported if the similarity score was above 0.4. Spectra from metabolic features of interest important in random forest models (see below) were further investigated manually to confirm identification. |
| Ion Mode: | POSITIVE |
| Capillary Temperature: | 375C |
| Capillary Voltage: | 3.4kV |
| Collision Energy: | 25 & 35 NCE |
| Collision Gas: | N2 |
| Dry Gas Temp: | 310C |
| MS ID: | MS002291 |
| Analysis ID: | AN002471 |
| Instrument Name: | Thermo Q Exactive HF hybrid Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Data processing. Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics). Metabolic features from blanks and that did not show sufficient linearity upon dilution in QC samples (r < 0.6) were discarded. Only metabolic features present in > 2/3 of the samples were kept for further analysis. Inter- and intra-batch variations were corrected by applying locally estimated scatterplot smoothing local regression (LOESS) on pooled samples injected repetitively along the batches (span = 0.75). Data were acquired in four batches for HILIC and RPLC modes. Dilution effects were corrected using probabilistic quotient normalization (PQN) (Rosen Vollmar et al., 2019). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were then merged, producing a dataset containing 6,630 metabolic features. Metabolite abundances were reported as spectral counts. Metabolic feature annotation. Peak annotation was first performed by matching experimental m/z, retention time and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases including HMDB (http://www.hmdb.ca/), MoNA (http://mona.fiehnlab.ucdavis.edu/) and MassBank (http://www.massbank.jp/) using the R package ‘MetID’ (v0.2.0) (Shen et al., 2019). Briefly, metabolic feature tables from Progenesis QI were matched to fragmentation spectra with a m/z and a retention time window of ± 15 ppm and ± 30 s (HILIC) and ± 20 s (RPLC), respectively. When multiple MS/MS spectra match a single metabolic feature, all matched MS/MS spectra were used for the identification. Next, MS1 and MS2 pairs were searched against public databases and a similarity score was calculated using the forward dot–product algorithm which considers both fragments and intensities (Stein and Scott, 1994). Metabolites were reported if the similarity score was above 0.4. Spectra from metabolic features of interest important in random forest models (see below) were further investigated manually to confirm identification. |
| Ion Mode: | NEGATIVE |
| Capillary Temperature: | 375C |
| Capillary Voltage: | 3.4kV |
| Collision Energy: | 25 & 35 NCE |
| Collision Gas: | N2 |
| Dry Gas Temp: | 310C |
| MS ID: | MS002292 |
| Analysis ID: | AN002472 |
| Instrument Name: | Thermo Q Exactive HF hybrid Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Data processing. Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics). Metabolic features from blanks and that did not show sufficient linearity upon dilution in QC samples (r < 0.6) were discarded. Only metabolic features present in > 2/3 of the samples were kept for further analysis. Inter- and intra-batch variations were corrected by applying locally estimated scatterplot smoothing local regression (LOESS) on pooled samples injected repetitively along the batches (span = 0.75). Data were acquired in four batches for HILIC and RPLC modes. Dilution effects were corrected using probabilistic quotient normalization (PQN) (Rosen Vollmar et al., 2019). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were then merged, producing a dataset containing 6,630 metabolic features. Metabolite abundances were reported as spectral counts. Metabolic feature annotation. Peak annotation was first performed by matching experimental m/z, retention time and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases including HMDB (http://www.hmdb.ca/), MoNA (http://mona.fiehnlab.ucdavis.edu/) and MassBank (http://www.massbank.jp/) using the R package ‘MetID’ (v0.2.0) (Shen et al., 2019). Briefly, metabolic feature tables from Progenesis QI were matched to fragmentation spectra with a m/z and a retention time window of ± 15 ppm and ± 30 s (HILIC) and ± 20 s (RPLC), respectively. When multiple MS/MS spectra match a single metabolic feature, all matched MS/MS spectra were used for the identification. Next, MS1 and MS2 pairs were searched against public databases and a similarity score was calculated using the forward dot–product algorithm which considers both fragments and intensities (Stein and Scott, 1994). Metabolites were reported if the similarity score was above 0.4. Spectra from metabolic features of interest important in random forest models (see below) were further investigated manually to confirm identification. |
| Ion Mode: | POSITIVE |
| Capillary Temperature: | 375C |
| Capillary Voltage: | 3.4kV |
| Collision Energy: | 25 & 50 NCE |
| Collision Gas: | N2 |
| Dry Gas Temp: | 310C |
| MS ID: | MS002293 |
| Analysis ID: | AN002473 |
| Instrument Name: | Thermo Q Exactive HF hybrid Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Data processing. Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics). Metabolic features from blanks and that did not show sufficient linearity upon dilution in QC samples (r < 0.6) were discarded. Only metabolic features present in > 2/3 of the samples were kept for further analysis. Inter- and intra-batch variations were corrected by applying locally estimated scatterplot smoothing local regression (LOESS) on pooled samples injected repetitively along the batches (span = 0.75). Data were acquired in four batches for HILIC and RPLC modes. Dilution effects were corrected using probabilistic quotient normalization (PQN) (Rosen Vollmar et al., 2019). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were then merged, producing a dataset containing 6,630 metabolic features. Metabolite abundances were reported as spectral counts. Metabolic feature annotation. Peak annotation was first performed by matching experimental m/z, retention time and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases including HMDB (http://www.hmdb.ca/), MoNA (http://mona.fiehnlab.ucdavis.edu/) and MassBank (http://www.massbank.jp/) using the R package ‘MetID’ (v0.2.0) (Shen et al., 2019). Briefly, metabolic feature tables from Progenesis QI were matched to fragmentation spectra with a m/z and a retention time window of ± 15 ppm and ± 30 s (HILIC) and ± 20 s (RPLC), respectively. When multiple MS/MS spectra match a single metabolic feature, all matched MS/MS spectra were used for the identification. Next, MS1 and MS2 pairs were searched against public databases and a similarity score was calculated using the forward dot–product algorithm which considers both fragments and intensities (Stein and Scott, 1994). Metabolites were reported if the similarity score was above 0.4. Spectra from metabolic features of interest important in random forest models (see below) were further investigated manually to confirm identification. |
| Ion Mode: | NEGATIVE |
| Capillary Temperature: | 375C |
| Capillary Voltage: | 3.4kV |
| Collision Energy: | 25 & 50 NCE |
| Collision Gas: | N2 |
| Dry Gas Temp: | 310C |
