Metabolomics Workbench : NIH Data Repository

MB Sample ID: SA169607

Local Sample ID:	id_155
Subject ID:	SU001904
Subject Type:	Human
Subject Species:	Homo sapiens
Taxonomy ID:	9606

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Subject:

Subject ID:	SU001904
Subject Type:	Human
Subject Species:	Homo sapiens
Taxonomy ID:	9606

Factors:

Local Sample ID	MB Sample ID	Factor Level ID	Level Value	Factor Name
id_155	SA169607	FL020219	30	gestage_serum

Collection:

Collection ID:	CO001897
Collection Summary:	At the time of metabolomics profiling (March 2018), 843 women had delivered a live born infant. The analytic sample includes a random subset of 410 mother-child pairs with maternal metabolomics measured during pregnancy (week of serum collection: 25th, 50th, 75th percentiles: 26, 29, 33 weeks).
Sample Type:	Blood (plasma)

Treatment:

Treatment ID:	TR001917
Treatment Summary:	Maternal blood was collected by venipuncture (mean ± standard deviation (SD): 29.6 ± 4.90 weeks) and serum aliquots were stored at −80℃ until assayed. Untargeted metabolomics analysis was conducted on 100µl of serum at Metabolon, Inc (Durham, NC, USA) with ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). The method utilized an ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds; the extract compound was gradient eluted from a C18 column using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was analyzed with the prior approach but it was chromatographically optimized for more hydrophobic compounds and operated at an overall higher organic content. A third aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column using a gradient consisting of water and acetonitrile with 10mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range between methods covered 70-1000 m/z. Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software. Peaks were quantified using area-under-the-curve. Batch adjustment to correct variation resulting from instrument inter-day tuning differences was performed for each compound in run-day blocks by dividing by the median of the values for the experimental samples for each instrument run day, then multiplying these values by the original median. In one serum sample with a lower volume (65µl instead of 80µl), metabolite intensities were scaled accounting for the volume of serum available, under the assumption that metabolite signal intensities scale linearly with the sample volume. We normalized all metabolomic data using first the natural base for log-scaling, thus removing skewness of the data. We then used a Pareto scaling approach, which incorporates a scaling factor equal to the square root of the standard deviation of individual metabolites so that larger fold changes were scaled more than smaller fold changes (Grace and Hudson, 2016). A total of 1,110 biochemicals were detected across all four assays. Potential sample outliers were examined using principal component analysis (PCA), though none were identified. Final data were presented as normalized levels to facilitate both linear and non-linear analyses and to harmonize all variables on a common scale.

Treatment ID:

TR001917

Treatment Summary:

Maternal blood was collected by venipuncture (mean ± standard deviation (SD): 29.6 ± 4.90 weeks) and serum aliquots were stored at −80℃ until assayed. Untargeted metabolomics analysis was conducted on 100µl of serum at Metabolon, Inc (Durham, NC, USA) with ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). The method utilized an ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds; the extract compound was gradient eluted from a C18 column using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was analyzed with the prior approach but it was chromatographically optimized for more hydrophobic compounds and operated at an overall higher organic content. A third aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column using a gradient consisting of water and acetonitrile with 10mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range between methods covered 70-1000 m/z. Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software. Peaks were quantified using area-under-the-curve. Batch adjustment to correct variation resulting from instrument inter-day tuning differences was performed for each compound in run-day blocks by dividing by the median of the values for the experimental samples for each instrument run day, then multiplying these values by the original median. In one serum sample with a lower volume (65µl instead of 80µl), metabolite intensities were scaled accounting for the volume of serum available, under the assumption that metabolite signal intensities scale linearly with the sample volume. We normalized all metabolomic data using first the natural base for log-scaling, thus removing skewness of the data. We then used a Pareto scaling approach, which incorporates a scaling factor equal to the square root of the standard deviation of individual metabolites so that larger fold changes were scaled more than smaller fold changes (Grace and Hudson, 2016). A total of 1,110 biochemicals were detected across all four assays. Potential sample outliers were examined using principal component analysis (PCA), though none were identified. Final data were presented as normalized levels to facilitate both linear and non-linear analyses and to harmonize all variables on a common scale.

Sample Preparation:

Sampleprep ID:	SP001910
Sampleprep Summary:	Following receipt, samples were inventoried and immediately stored at -80℃. Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier that was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at -80℃ until processed.

Sampleprep ID:

SP001910

Sampleprep Summary:

Following receipt, samples were inventoried and immediately stored at -80℃. Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier that was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at -80℃ until processed.

Combined analysis:

Analysis ID	AN002963
Analysis type	MS
Chromatography type	HILIC
Chromatography system	Thermo Scientific Q-Exactive
Column	SeQuant ZIC-HILIC (150 x 4.6mm,3.5um)
MS Type	ESI
MS instrument type	Orbitrap
MS instrument name	Thermo Q Exactive Orbitrap
Ion Mode	UNSPECIFIED
Units	pmoles/l

Chromatography:

Chromatography ID:	CH002196
Instrument Name:	Thermo Scientific Q-Exactive
Column Name:	SeQuant ZIC-HILIC (150 x 4.6mm,3.5um)
Chromatography Type:	HILIC

MS:

MS ID:	MS002753
Analysis ID:	AN002963
Instrument Name:	Thermo Q Exactive Orbitrap
Instrument Type:	Orbitrap
MS Type:	ESI
MS Comments:	Untargeted metabolomics analysis was conducted on 100µl of serum at Metabolon, Inc (Durham, NC, USA) with ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). The method utilized an ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds; the extract compound was gradient eluted from a C18 column using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was analyzed with the prior approach but it was chromatographically optimized for more hydrophobic compounds and operated at an overall higher organic content. A third aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column using a gradient consisting of water and acetonitrile with 10mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range between methods covered 70-1000 m/z. Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software. Peaks were quantified using area-under-the-curve. Batch adjustment to correct variation resulting from instrument inter-day tuning differences was performed for each compound in run-day blocks by dividing by the median of the values for the experimental samples for each instrument run day, then multiplying these values by the original median. In one serum sample with a lower volume (65µl instead of 80µl), metabolite intensities were scaled accounting for the volume of serum available, under the assumption that metabolite signal intensities scale linearly with the sample volume.
Ion Mode:	UNSPECIFIED