{
"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST001415","ANALYSIS_ID":"AN002367","VERSION":"1","CREATED_ON":"June 30, 2020, 12:18 pm"},

"PROJECT":{"PROJECT_TITLE":"Untargeted metabolomics of primary mouse neutrophils","PROJECT_SUMMARY":"Untargeted metabolomics of primary neutrophils from young and old, male and female mice","INSTITUTE":"Stanford University","DEPARTMENT":"Genetics","LAST_NAME":"Contrepois","FIRST_NAME":"Kevin","ADDRESS":"300 Pasteur Dr, ALWAY bldg M302, STANFORD, California, 94305, USA","EMAIL":"kcontrep@stanford.edu","PHONE":"6507239914"},

"STUDY":{"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","EMAIL":"kcontrep@stanford.edu","PHONE":"6506664538"},

"SUBJECT":{"SUBJECT_TYPE":"Mammal","SUBJECT_SPECIES":"Mus musculus","TAXONOMY_ID":"10090","GENOTYPE_STRAIN":"C57BL/6J","AGE_OR_AGE_RANGE":"4 months & 20 months","GENDER":"Male and female","ANIMAL_ANIMAL_SUPPLIER":"Jackson Laboratories","ANIMAL_HOUSING":"SPF animal facility at USC"},
"SUBJECT_SAMPLE_FACTORS":[
{
"Subject ID":"-",
"Sample ID":"M_20m_4a",
"Factors":{"Sex":"Male","Age":"Old"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_M_20m_4a","RAW_FILE_NAME":"nHILIC_M_20m_4a","RAW_FILE_NAME":"pRPLC_M_20m_4a","RAW_FILE_NAME":"nRPLC_M_20m_4a"}
},
{
"Subject ID":"-",
"Sample ID":"F_20m_3a",
"Factors":{"Sex":"Female","Age":"Old"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_F_20m_3a","RAW_FILE_NAME":"nHILIC_F_20m_3a","RAW_FILE_NAME":"pRPLC_F_20m_3a","RAW_FILE_NAME":"nRPLC_F_20m_3a"}
},
{
"Subject ID":"-",
"Sample ID":"F_4m_1b",
"Factors":{"Sex":"Female","Age":"Young"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_F_4m_1b","RAW_FILE_NAME":"nHILIC_F_4m_1b","RAW_FILE_NAME":"pRPLC_F_4m_1b","RAW_FILE_NAME":"nRPLC_F_4m_1b"}
},
{
"Subject ID":"-",
"Sample ID":"M_4m_1a",
"Factors":{"Sex":"Male","Age":"Young"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_M_4m_1a","RAW_FILE_NAME":"nHILIC_M_4m_1a","RAW_FILE_NAME":"pRPLC_M_4m_1a","RAW_FILE_NAME":"nRPLC_M_4m_1a"}
},
{
"Subject ID":"-",
"Sample ID":"F_4m_1a",
"Factors":{"Sex":"Female","Age":"Young"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_F_4m_1a","RAW_FILE_NAME":"nHILIC_F_4m_1a","RAW_FILE_NAME":"pRPLC_F_4m_1a","RAW_FILE_NAME":"nRPLC_F_4m_1a"}
},
{
"Subject ID":"-",
"Sample ID":"M_20m_3a",
"Factors":{"Sex":"Male","Age":"Old"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_M_20m_3a","RAW_FILE_NAME":"nHILIC_M_20m_3a","RAW_FILE_NAME":"pRPLC_M_20m_3a","RAW_FILE_NAME":"nRPLC_M_20m_3a"}
},
{
"Subject ID":"-",
"Sample ID":"M_4m_2b",
"Factors":{"Sex":"Male","Age":"Young"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_M_4m_2b","RAW_FILE_NAME":"nHILIC_M_4m_2b","RAW_FILE_NAME":"pRPLC_M_4m_2b","RAW_FILE_NAME":"nRPLC_M_4m_2b"}
},
{
"Subject ID":"-",
"Sample ID":"F_20m_4b",
"Factors":{"Sex":"Female","Age":"Old"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_F_20m_4b","RAW_FILE_NAME":"nHILIC_F_20m_4b","RAW_FILE_NAME":"pRPLC_F_20m_4b","RAW_FILE_NAME":"nRPLC_F_20m_4b"}
},
{
"Subject ID":"-",
"Sample ID":"M_20m_3b",
"Factors":{"Sex":"Male","Age":"Old"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_M_20m_3b","RAW_FILE_NAME":"nHILIC_M_20m_3b","RAW_FILE_NAME":"pRPLC_M_20m_3b","RAW_FILE_NAME":"nRPLC_M_20m_3b"}
},
{
"Subject ID":"-",
"Sample ID":"M_4m_2a",
"Factors":{"Sex":"Male","Age":"Young"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_M_4m_2a","RAW_FILE_NAME":"nHILIC_M_4m_2a","RAW_FILE_NAME":"pRPLC_M_4m_2a","RAW_FILE_NAME":"nRPLC_M_4m_2a"}
},
{
"Subject ID":"-",
"Sample ID":"M_4m_1b",
"Factors":{"Sex":"Male","Age":"Young"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_M_4m_1b","RAW_FILE_NAME":"nHILIC_M_4m_1b","RAW_FILE_NAME":"pRPLC_M_4m_1b","RAW_FILE_NAME":"nRPLC_M_4m_1b"}
},
{
"Subject ID":"-",
"Sample ID":"F_20m_4a",
"Factors":{"Sex":"Female","Age":"Old"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_F_20m_4a","RAW_FILE_NAME":"nHILIC_F_20m_4a","RAW_FILE_NAME":"pRPLC_F_20m_4a","RAW_FILE_NAME":"nRPLC_F_20m_4a"}
},
{
"Subject ID":"-",
"Sample ID":"F_4m_2b",
"Factors":{"Sex":"Female","Age":"Young"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_F_4m_2b","RAW_FILE_NAME":"nHILIC_F_4m_2b","RAW_FILE_NAME":"pRPLC_F_4m_2b","RAW_FILE_NAME":"nRPLC_F_4m_2b"}
},
{
"Subject ID":"-",
"Sample ID":"F_20m_3b",
"Factors":{"Sex":"Female","Age":"Old"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_F_20m_3b","RAW_FILE_NAME":"nHILIC_F_20m_3b","RAW_FILE_NAME":"pRPLC_F_20m_3b","RAW_FILE_NAME":"nRPLC_F_20m_3b"}
},
{
"Subject ID":"-",
"Sample ID":"M_20m_4b",
"Factors":{"Sex":"Male","Age":"Old"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_M_20m_4b","RAW_FILE_NAME":"nHILIC_M_20m_4b","RAW_FILE_NAME":"pRPLC_M_20m_4b","RAW_FILE_NAME":"nRPLC_M_20m_4b"}
},
{
"Subject ID":"-",
"Sample ID":"F_4m_2a",
"Factors":{"Sex":"Female","Age":"Young"},
"Additional sample data":{"RAW_FILE_NAME":"pHILIC_F_4m_2a","RAW_FILE_NAME":"nHILIC_F_4m_2a","RAW_FILE_NAME":"pRPLC_F_4m_2a","RAW_FILE_NAME":"nRPLC_F_4m_2a"}
}
],
"COLLECTION":{"COLLECTION_SUMMARY":"The hindlimb bones of each mouse were harvested and kept on ice in D-PBS (Corning) supplemented with 1% Penicillin/Streptomycin (Corning) until further processing. Muscle tissue was removed from the bones, and the bone marrow from cleaned bones was collected into clean tubes (Amend et al., 2016). Red blood cells from the marrow were removed using Red Blood Cell Lysis (Miltenyi Biotech #130-094-183), according to the manufacturer’s instructions, albeit with no vortexing step to avoid unscheduled neutrophil activation. Neutrophils were isolated from other bone marrow cells using magnetic-assisted cell sorting (Miltenyi Biotech kit #130-097-658). Viability and yield were assessed using trypan blue exclusion and an automated COUNTESS cell counter (Thermo-Fisher Scientific). Purified cells were pelleted at 300g and snap-frozen in liquid nitrogen until processing for RNA, lipid or metabolite isolation.","SAMPLE_TYPE":"Bone marrow"},

"TREATMENT":{"TREATMENT_SUMMARY":"There was no treatment."},

"SAMPLEPREP":{"SAMPLEPREP_SUMMARY":"Metabolites and lipids were extracted from neutrophil cell pellets and analyzed in a randomized order. Extraction was performed using a biphasic separation protocol with ice-cold methanol, methyl tert-butyl ether (MTBE) and water (Contrepois et al., 2018). Briefly, 300μL of methanol spiked-in with 54 deuterated internal standards provided with the Lipidyzer platform (SCIEX, cat #5040156, LPISTDKIT-101) was added to the cell pellet, samples were vigorously vortexed for 20 seconds and sonicated in a water bath 3 times for 30 seconds on ice. Lipids were solubilized by adding 1000μL of MTBE and incubated under agitation for 1h at 4°C. After addition of 250μL of ice-cold water, the samples were vortexed for 1 min and centrifuged at 14,000g for 5 min at 20°C. The upper phase containing the lipids was then collected and dried down under nitrogen. The dry lipid extracts were reconstituted with 300μL of 10 mM ammonium acetate in 9:1 methanol:toluene for analaysis. The lower phase containing metabolites was subjected to further protein precipitation by adding 4 times of ice-cold 1:1:1 isopropanol:acetonitrile:water spiked in with 17 labeled internal standards and incubating for 2 hours at -20°C. The supernatant was dried down to completion under nitrogen and re-suspended in 100μL of 1:1 MeOH:Water for analysis."},

"CHROMATOGRAPHY":{"CHROMATOGRAPHY_SUMMARY":"RPLC experiments were performed using a Zorbax SBaq column 2.1 x 50 mm, 1.7 μm, 100Å (Agilent Technologies) and mobile phase solvents consisting of 0.06% acetic acid in water (A) and 0.06% acetic acid in methanol (B). (Contrepois et al., 2015)","CHROMATOGRAPHY_TYPE":"Reversed phase","INSTRUMENT_NAME":"Thermo Dionex Ultimate 3000 RS","COLUMN_NAME":"Agilent Zorbax SBaq (50 x 2.1 mm, 1.7 µm)","FLOW_RATE":"0.6 ml/min","COLUMN_TEMPERATURE":"60","SOLVENT_A":"0.06% acetic acid in water","SOLVENT_B":"0.06% acetic acid in methanol"},

"ANALYSIS":{"ANALYSIS_TYPE":"MS","OPERATOR_NAME":"Kevin Contrepois","DETECTOR_TYPE":"Orbitrap","DATA_FORMAT":".RAW"},

"MS":{"INSTRUMENT_NAME":"Thermo Q Exactive Plus Orbitrap","INSTRUMENT_TYPE":"Orbitrap","MS_TYPE":"ESI","ION_MODE":"POSITIVE","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.","CAPILLARY_TEMPERATURE":"375C","CAPILLARY_VOLTAGE":"3.4kV","COLLISION_ENERGY":"25 & 50 NCE","COLLISION_GAS":"N2","DRY_GAS_TEMP":"310C","MS_RESULTS_FILE":"ST001415_AN002367_Results.txt UNITS:MS count Has m/z:Yes Has RT:Yes RT units:Minutes"}

}