Summary of Study ST001415
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000971. The data can be accessed directly via it's Project DOI: 10.21228/M85X14 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST001415 |
| Study Title | Multi-omic profiling of primary mouse neutrophils reveals a pattern of sex and age-related functional regulation |
| Study Summary | Neutrophils are the most abundant white blood cells in humans and constitute one of the first lines of defense in the innate immune response. Neutrophils are extremely short-lived cells, which survive less than a day after reaching terminal differentiation. Thus, little is known about how organismal aging, rather than the daily cellular aging process, may impact neutrophil biology. In addition, accumulating evidence suggests that both immunity and organismal aging are extremely sex-dimorphic. Here, we describe a multi-omic resource of mouse primary bone marrow neutrophils from young and old female and male animals, at the transcriptomic, metabolomic and lipidomic levels. Importantly, we identify widespread age-related and sex-dimorphic regulation of ‘omics’ in neutrophils, specifically regulation of chromatin metabolism. We leverage machine-learning and identify candidate molecular drivers of age-related and sex-dimorphic transcriptional regulation of neutrophils. We leverage our resource to predict increased levels/release of neutrophil elastase in male mice. To date, this dataset represents the largest multi-omic resource for the study of neutrophils across biological sex and ages. This resource identifies molecular states linked to neutrophil characteristics linked to organismal age or sex, which could be leveraged to improve immune responses across individuals. |
| Institute | Stanford University |
| Last Name | Contrepois |
| First Name | Kevin |
| Address | 300 Pasteur Dr |
| kcontrep@stanford.edu | |
| Phone | 6506664538 |
| Submit Date | 2020-06-30 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2021-06-30 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN002365 | AN002366 | AN002367 | AN002368 |
|---|---|---|---|---|
| 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) | Agilent Zorbax SBaq (50 x 2.1mm,1.7um) | Agilent Zorbax SBaq (50 x 2.1mm,1.7um) |
| MS Type | ESI | ESI | ESI | ESI |
| MS instrument type | Orbitrap | Orbitrap | Orbitrap | Orbitrap |
| MS instrument name | Thermo Q Exactive Plus Orbitrap | Thermo Q Exactive Plus Orbitrap | Thermo Q Exactive Plus Orbitrap | Thermo Q Exactive Plus Orbitrap |
| Ion Mode | POSITIVE | NEGATIVE | POSITIVE | NEGATIVE |
| Units | MS count | MS count | MS count | MS count |
MS:
| MS ID: | MS002207 |
| Analysis ID: | AN002365 |
| Instrument Name: | Thermo Q Exactive Plus 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 didn’t show sufficient linearity upon dilution were discarded. Only metabolic features present in >33% of the samples in each group were kept for further analysis and missing values were imputed by drawing from a random distribution of small values in the corresponding sample (Tyanova et al., 2016). Metabolic feature annotation. Annotation confidence levels for each metabolite were provided following the Metabolomics Standards Initiative (MSI) confidence scheme. 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 (level 1). 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) (PMID: 30944337) (level 2). 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 takes into account both fragments and intensities. Metabolites were reported if the similarity score was above 0.4. Level 3 corresponds to unknown metabolites. |
| Ion Mode: | POSITIVE |
| Capillary Temperature: | 375C |
| Capillary Voltage: | 3.4kV |
| Collision Energy: | 25 & 35 NCE |
| Collision Gas: | N2 |
| Dry Gas Temp: | 310C |
| MS ID: | MS002208 |
| Analysis ID: | AN002366 |
| Instrument Name: | Thermo Q Exactive Plus 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 didn’t show sufficient linearity upon dilution were discarded. Only metabolic features present in >33% of the samples in each group were kept for further analysis and missing values were imputed by drawing from a random distribution of small values in the corresponding sample (Tyanova et al., 2016). Metabolic feature annotation. Annotation confidence levels for each metabolite were provided following the Metabolomics Standards Initiative (MSI) confidence scheme. 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 (level 1). 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) (PMID: 30944337) (level 2). 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 takes into account both fragments and intensities. Metabolites were reported if the similarity score was above 0.4. Level 3 corresponds to unknown metabolites. |
| Ion Mode: | NEGATIVE |
| Capillary Temperature: | 375C |
| Capillary Voltage: | 3.4kV |
| Collision Energy: | 25 & 35 NCE |
| Collision Gas: | N2 |
| Dry Gas Temp: | 310C |
| MS ID: | MS002209 |
| Analysis ID: | AN002367 |
| Instrument Name: | Thermo Q Exactive Plus 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 didn’t show sufficient linearity upon dilution were discarded. Only metabolic features present in >33% of the samples in each group were kept for further analysis and missing values were imputed by drawing from a random distribution of small values in the corresponding sample (Tyanova et al., 2016). Metabolic feature annotation. Annotation confidence levels for each metabolite were provided following the Metabolomics Standards Initiative (MSI) confidence scheme. 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 (level 1). 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) (PMID: 30944337) (level 2). 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 takes into account both fragments and intensities. Metabolites were reported if the similarity score was above 0.4. Level 3 corresponds to unknown metabolites. |
| Ion Mode: | POSITIVE |
| Capillary Temperature: | 375C |
| Capillary Voltage: | 3.4kV |
| Collision Energy: | 25 & 50 NCE |
| Collision Gas: | N2 |
| Dry Gas Temp: | 310C |
| MS ID: | MS002210 |
| Analysis ID: | AN002368 |
| Instrument Name: | Thermo Q Exactive Plus 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 didn’t show sufficient linearity upon dilution were discarded. Only metabolic features present in >33% of the samples in each group were kept for further analysis and missing values were imputed by drawing from a random distribution of small values in the corresponding sample (Tyanova et al., 2016). Metabolic feature annotation. Annotation confidence levels for each metabolite were provided following the Metabolomics Standards Initiative (MSI) confidence scheme. 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 (level 1). 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) (PMID: 30944337) (level 2). 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 takes into account both fragments and intensities. Metabolites were reported if the similarity score was above 0.4. Level 3 corresponds to unknown metabolites. |
| Ion Mode: | NEGATIVE |
| Capillary Temperature: | 375C |
| Capillary Voltage: | 3.4kV |
| Collision Energy: | 25 & 50 NCE |
| Collision Gas: | N2 |
| Dry Gas Temp: | 310C |
