Summary of Study ST002993
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 PR001863. The data can be accessed directly via it's Project DOI: 10.21228/M8WX4S 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 | ST002993 |
Study Title | Identifying subgroups of childhood obesity by using multiplatform metabotyping |
Study Summary | Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios. |
Institute | Universidad CEU San Pablo |
Laboratory | CEMBIO |
Last Name | Chamoso-Sánchez |
First Name | David |
Address | Urb. Montepríncipe. 28925 Alcorcón, Madrid (España) |
david.chamososanchez@usp.ceu.es | |
Phone | (+34)913724769 |
Submit Date | 2023-11-07 |
Num Groups | 2 |
Total Subjects | 110 |
Num Males | 53 |
Num Females | 57 |
Raw Data Available | Yes |
Raw Data File Type(s) | mzXML |
Analysis Type Detail | GC/LC-MS |
Release Date | 2023-12-04 |
Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
Project ID: | PR001863 |
Project DOI: | doi: 10.21228/M8WX4S |
Project Title: | Identifying subgroups of childhood obesity by using multiplatform metabotyping |
Project Summary: | Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios. |
Institute: | Universidad CEU San Pablo |
Laboratory: | CEMBIO |
Last Name: | Chamoso-Sánchez |
First Name: | David |
Address: | Urb. Montepríncipe. 28925 Alcorcón, Madrid (España) |
Email: | david.chamososanchez@usp.ceu.es |
Phone: | (+34)913724769 |
Subject:
Subject ID: | SU003106 |
Subject Type: | Human |
Subject Species: | Homo sapiens |
Taxonomy ID: | 9606 |
Species Group: | Mammals |
Factors:
Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)
mb_sample_id | local_sample_id | Factor |
---|---|---|
SA325834 | C624 | Idiopathic obesity |
SA325835 | C615 | Idiopathic obesity |
SA325836 | C628 | Idiopathic obesity |
SA325837 | C636 | Idiopathic obesity |
SA325838 | C668 | Idiopathic obesity |
SA325839 | C578 | Idiopathic obesity |
SA325840 | C1133 | Idiopathic obesity |
SA325841 | C2312 | Idiopathic obesity |
SA325842 | C2310 | Idiopathic obesity |
SA325843 | C2318 | Idiopathic obesity |
SA325844 | C2319 | Idiopathic obesity |
SA325845 | C756 | Idiopathic obesity |
SA325846 | C572 | Idiopathic obesity |
SA325847 | C1171 | Idiopathic obesity |
SA325848 | C1222 | Idiopathic obesity |
SA325849 | C1204 | Idiopathic obesity |
SA325850 | C1229 | Idiopathic obesity |
SA325851 | C1238 | Idiopathic obesity |
SA325852 | C842 | Idiopathic obesity |
SA325853 | C947 | Idiopathic obesity |
SA325854 | C904 | Idiopathic obesity |
SA325855 | C791 | Idiopathic obesity |
SA325856 | C2296 | Idiopathic obesity |
SA325857 | C820 | Idiopathic obesity |
SA325858 | C841 | Idiopathic obesity |
SA325859 | C877 | Idiopathic obesity |
SA325860 | C772 | Idiopathic obesity |
SA325861 | C726 | Idiopathic obesity |
SA325862 | C1539 | Idiopathic obesity |
SA325863 | C1538 | Idiopathic obesity |
SA325864 | C1548 | Idiopathic obesity |
SA325865 | C1030 | Idiopathic obesity |
SA325866 | C1564 | Idiopathic obesity |
SA325867 | C1556 | Idiopathic obesity |
SA325868 | C1520 | Idiopathic obesity |
SA325869 | C1453 | Idiopathic obesity |
SA325870 | C2289 | Idiopathic obesity |
SA325871 | C1306 | Idiopathic obesity |
SA325872 | C1331 | Idiopathic obesity |
SA325873 | C1336 | Idiopathic obesity |
SA325874 | C1366 | Idiopathic obesity |
SA325875 | C1768 | Idiopathic obesity |
SA325876 | C1317 | Idiopathic obesity |
SA325877 | C2249 | Idiopathic obesity |
SA325878 | C1131 | Idiopathic obesity |
SA325879 | C2259 | Idiopathic obesity |
SA325880 | C2275 | Idiopathic obesity |
SA325881 | C1782 | Idiopathic obesity |
SA325882 | C2226 | Idiopathic obesity |
SA325883 | C2284 | Idiopathic obesity |
SA325884 | C1937 | Idiopathic obesity |
SA325885 | C2218 | Idiopathic obesity |
SA325886 | C2002 | Idiopathic obesity |
SA325887 | C2158 | Idiopathic obesity |
SA325888 | C2188 | Idiopathic obesity |
SA325889 | M393 | Monogenic obesity |
SA325890 | M37 | Monogenic obesity |
SA325891 | M538 | Monogenic obesity |
SA325892 | M2126 | Monogenic obesity |
SA325893 | M516 | Monogenic obesity |
SA325894 | M489 | Monogenic obesity |
SA325895 | M2220 | Monogenic obesity |
SA325896 | M2178 | Monogenic obesity |
SA325897 | M539 | Monogenic obesity |
SA325898 | M2195 | Monogenic obesity |
SA325899 | M1104 | Monogenic obesity |
SA325900 | M2253 | Monogenic obesity |
SA325901 | M2258 | Monogenic obesity |
SA325902 | M913 | Monogenic obesity |
SA325903 | M1145 | Monogenic obesity |
SA325904 | M1120 | Monogenic obesity |
SA325905 | M1147 | Monogenic obesity |
SA325906 | M1176 | Monogenic obesity |
SA325907 | M2105 | Monogenic obesity |
SA325908 | M996 | Monogenic obesity |
SA325909 | M962 | Monogenic obesity |
SA325910 | M1116 | Monogenic obesity |
SA325911 | M759 | Monogenic obesity |
SA325912 | M803 | Monogenic obesity |
SA325913 | M881 | Monogenic obesity |
SA325914 | M908 | Monogenic obesity |
SA325915 | M637 | Monogenic obesity |
SA325916 | M796 | Monogenic obesity |
SA325917 | M1361 | Monogenic obesity |
SA325918 | M1344 | Monogenic obesity |
SA325919 | M1380 | Monogenic obesity |
SA325920 | M1044 | Monogenic obesity |
SA325921 | M1518 | Monogenic obesity |
SA325922 | M1322 | Monogenic obesity |
SA325923 | M1309 | Monogenic obesity |
SA325924 | M1223 | Monogenic obesity |
SA325925 | M1202 | Monogenic obesity |
SA325926 | M1239 | Monogenic obesity |
SA325927 | M1272 | Monogenic obesity |
SA325928 | M1294 | Monogenic obesity |
SA325929 | M1558 | Monogenic obesity |
SA325930 | M1605 | Monogenic obesity |
SA325931 | M1884 | Monogenic obesity |
SA325932 | M1078 | Monogenic obesity |
SA325933 | M1986 | Monogenic obesity |
Collection:
Collection ID: | CO003099 |
Collection Summary: | A 12‐hour fasting serum sample (drawn, immediately processed, aliquoted and stored at −80°C until assayed) was used. |
Sample Type: | Blood (serum) |
Storage Conditions: | -80℃ |
Treatment:
Treatment ID: | TR003115 |
Treatment Summary: | N/A |
Sample Preparation:
Sampleprep ID: | SP003112 |
Sampleprep Summary: | For LC-MS analysis, 40 µL of serum was mixed with 800 µL of a cold mixture (-20ºC) of methanol:MTBE:Chloroform (1.33:1:1, v/v/v) with Sphinganine (D17:0) and palmitic acid-d31 as internal standards. Samples were vortexed for 30 s and shaken for 20 min at maximum speed at room temperature. Next, samples were centrifuged (13,200 rpm, room temperature, 5 min). After centrifugation, supernatant was directly injected into the system. For GC-MS analysis, protein precipitation was achieved by mixing 1 volume of serum with 3 volumes of cold (-20ºC) acetonitrile with 25 ppm of palmitic acid-d31 as internal standard, followed by methoximation with O-methoxyamine hydrochloride (15 mg/mL) in pyridine, and sylation with BSTFA: TMCS (99:1). Finally, 20 ppm of tricosane in heptane was added as second internal standard. For CE-MS analysis, 100 µL of serum was mixed with 100 µL of 0.2 M formic acid containing 5% acetonitrile and 0.4 mM methionine sulfone, 2 mM paracetamol and 0.5 mM 4-Morpholineethanesulfonic acid, 2-(N-Morpholino) ethanesulfonic acid (MES) as internal standards. The sample was transferred to an ultracentrifugation device (Millipore Ireland Ltd., Carrigtohill, Ireland) with a 30 kDa protein cutoff for deproteinization through centrifugation (2000 × g, 4 °C, 90 min). |
Combined analysis:
Analysis ID | AN004913 | AN004914 | AN004915 | AN004916 | AN004917 |
---|---|---|---|---|---|
Analysis type | MS | MS | MS | MS | MS |
Chromatography type | Reversed phase | Reversed phase | GC | CE | CE |
Chromatography system | Agilent 1290 Infinity II | Agilent 1290 Infinity II | Agilent 8890 GC System | Agilent 7100 CE | Agilent 7100 CE |
Column | Agilent InfinityLab Poroshell 120 EC-C18 (100 x 3mm,2.7um) | Agilent InfinityLab Poroshell 120 EC-C18 (100 x 3mm,2.7um) | Agilent DB5-MS (30m x 0.25mm, 0.25um) | Agilent Technologies fused silica capillary (total length, 100 cm; internal diameter, 50 µm) | Agilent Technologies fused polyvinyl alcohol capillary PVA (total length, 97.6 cm; internal diameter, 50 µm) |
MS Type | ESI | ESI | EI | ESI | ESI |
MS instrument type | QTOF | QTOF | Single quadrupole | TOF | TOF |
MS instrument name | Agilent 6545 QTOF | Agilent 6545 QTOF | Agilent 5977B | Agilent 6230 TOF | Agilent 6224 TOF |
Ion Mode | POSITIVE | NEGATIVE | POSITIVE | POSITIVE | NEGATIVE |
Units | Corrected areas | Corrected areas | Corrected areas | Corrected areas | Corrected areas |
Chromatography:
Chromatography ID: | CH003708 |
Chromatography Summary: | UHPLC-QTOF-MS POS |
Instrument Name: | Agilent 1290 Infinity II |
Column Name: | Agilent InfinityLab Poroshell 120 EC-C18 (100 x 3mm,2.7um) |
Column Temperature: | 50 °C |
Flow Gradient: | Started at 70% of B at 0-1 min, 86% B at 3.5-10 min, 100% B at 11-17 min. The starting conditions were recovered by minute 17. |
Flow Rate: | 0.6 mL/min |
Internal Standard: | Sphinganine (D17:0) and palmitic acid-d31 |
Solvent A: | 90% water/10% methanol; 10 mM ammonium acetate; 0.2 mM ammonium fluoride |
Solvent B: | 20% acetonitrile/30% methanol/50% isopropanol; 10 mM ammonium acetate; 0.2 mM ammonium fluoride |
Analytical Time: | 19 min |
Chromatography Type: | Reversed phase |
Chromatography ID: | CH003709 |
Chromatography Summary: | UHPLC-QTOF-MS NEG |
Instrument Name: | Agilent 1290 Infinity II |
Column Name: | Agilent InfinityLab Poroshell 120 EC-C18 (100 x 3mm,2.7um) |
Column Temperature: | 50 °C |
Flow Gradient: | Started at 70% of B at 0-1 min, 86% B at 3.5-10 min, 100% B at 11-17 min. The starting conditions were recovered by minute 17. |
Flow Rate: | 0.6 mL/min |
Internal Standard: | Sphinganine (D17:0) and palmitic acid-d31 |
Solvent A: | 90% water/10% methanol; 10 mM ammonium acetate; 0.2 mM ammonium fluoride |
Solvent B: | 20% acetonitrile/30% methanol/50% isopropanol; 10 mM ammonium acetate; 0.2 mM ammonium fluoride |
Analytical Time: | 19 min |
Chromatography Type: | Reversed phase |
Chromatography ID: | CH003710 |
Chromatography Summary: | GC-MS |
Instrument Name: | Agilent 8890 GC System |
Column Name: | Agilent DB5-MS (30m x 0.25mm, 0.25um) |
Column Temperature: | The temperature of the column was initially set at 60 °C for 1 minute, then raised to 10 °C/min to 325 °C, which was maintained for 10 minutes before cooling |
Flow Gradient: | Constant |
Flow Rate: | 0.5508 mL/min |
Internal Standard: | palmitic acid-d31 and tricosane |
Solvent A: | Helium |
Solvent B: | N/A |
Analytical Time: | 37 min |
Chromatography Type: | GC |
Chromatography ID: | CH003711 |
Chromatography Summary: | CE-MS POS |
Instrument Name: | Agilent 7100 CE |
Column Name: | Agilent Technologies fused silica capillary (total length, 100 cm; internal diameter, 50 µm) |
Column Temperature: | 20 ºC |
Flow Gradient: | None |
Flow Rate: | None |
Internal Standard: | methionine sulfone, paracetamol and 4-morpholineethanesulfonic acid, 2-(N-morpholino)ethanesulfonic acid (MES) |
Internal Standard Mt: | methionine sulfone |
Solvent A: | BGE (1 M formic acid solution in 10% methanol (v/v)) |
Solvent B: | N/A |
Analytical Time: | 26 min |
Capillary Voltage: | 30 KV |
Sheath Liquid: | Methanol: water (1:1, v/v) and two reference masses (20 μL of purine: 121.0509 and 20 μL of HP-0922: 922.0098) at a flow rate of 0.6 mL/min (1:100 of split ratio) |
Chromatography Type: | CE |
Chromatography ID: | CH003712 |
Chromatography Summary: | CE-MS NEG |
Instrument Name: | Agilent 7100 CE |
Column Name: | Agilent Technologies fused polyvinyl alcohol capillary PVA (total length, 97.6 cm; internal diameter, 50 µm) |
Column Temperature: | 20 ºC |
Flow Gradient: | None |
Flow Rate: | None |
Internal Standard: | methionine sulfone, paracetamol and 4-morpholineethanesulfonic acid, 2-(N-morpholino)ethanesulfonic acid (MES) |
Internal Standard Mt: | methionine sulfone |
Solvent A: | BGE (0.1 M formic acid solution) |
Solvent B: | N/A |
Analytical Time: | 55 min |
Capillary Voltage: | -30 KV |
Sheath Liquid: | Methanol: water (1:1, v/v) and two reference masses (20 μL of purine: 121.0509 and 20 μL of HP-0922: 922.0098) at a flow rate of 0.6 mL/min (1:100 of split ratio) |
Chromatography Type: | CE |
MS:
MS ID: | MS004656 |
Analysis ID: | AN004913 |
Instrument Name: | Agilent 6545 QTOF |
Instrument Type: | QTOF |
MS Type: | ESI |
MS Comments: | The Agilent 6545 QTOF mass spectrometer equipped with a dual AJS ESI ion source was set with the following parameters: 150 V fragmentor, 65 V skimmer, 3500 V capillary voltage, 750 V octopole radio frequency voltage, 10 L/min nebulizer gas flow, 200 °C gas temperature, 50 psi nebulizer gas pressure, 12 L/min sheath gas flow, and 300 °C sheath gas temperature. Data were collected in positive ESI mode, operated in full scan mode from 40 to 1200 m/z with a scan rate of 3 spectra/s. We use two reference mass compounds throughout the whole analysis: purine (C5H4N4) at m/z 121.0509; and HP-0921 (C18H18O6N3P3F24) at m/z 922.0098. These masses were continuously infused into the system through an Agilent 1260 Iso Pump at a 1 mL/min (split ratio 1:100) to provide a constant mass correction. Data was acquired using Agilent MassHunter Workstation Software LC/MS Data Acquisition for 6200 series TOF/6500 series Q-TOF B 9.0.9044.0 (Agilent Technologies). The raw data were processed using Agilent Technologies MassHunter Profinder B.10.0.2.162 (Santa Clara, United States) to clean the background noise and unrelated ions. |
Ion Mode: | POSITIVE |
MS ID: | MS004657 |
Analysis ID: | AN004914 |
Instrument Name: | Agilent 6545 QTOF |
Instrument Type: | QTOF |
MS Type: | ESI |
MS Comments: | The Agilent 6545 QTOF mass spectrometer equipped with a dual AJS ESI ion source was set with the following parameters: 150 V fragmentor, 65 V skimmer, 3500 V capillary voltage, 750 V octopole radio frequency voltage, 10 L/min nebulizer gas flow, 200 °C gas temperature, 50 psi nebulizer gas pressure, 12 L/min sheath gas flow, and 300 °C sheath gas temperature. Data were collected in negative ESI mode, operated in full scan mode from 40 to 1200 m/z with a scan rate of 3 spectra/s. We use two reference mass compounds throughout the whole analysis: purine (C5H4N4) at m/z 119.0363 and HP-0921 (C18H18O6N3P3F24) at m/z 980.0163 (HP-0921 + acetate). These masses were continuously infused into the system through an Agilent 1260 Iso Pump at a 1 mL/min (split ratio 1:100) to provide a constant mass correction. Data was acquired using Agilent MassHunter Workstation Software LC/MS Data Acquisition for 6200 series TOF/6500 series Q-TOF B 9.0.9044.0 (Agilent Technologies). |
Ion Mode: | NEGATIVE |
MS ID: | MS004658 |
Analysis ID: | AN004915 |
Instrument Name: | Agilent 5977B |
Instrument Type: | Single quadrupole |
MS Type: | EI |
MS Comments: | The operating parameters of electronic impact ionization were established as follows: filament source temperature at 230 ° C and electronic ionization energy at 70 eV. Mass spectra were collected in a mass range of 50 to 600 m/z at a scan rate of 2 spectra per second. Data was acquired using Agilent MassHunter Workstation GC/MS Data Acquisition B 10.0.384.1 software (Agilent Technologies). |
Ion Mode: | POSITIVE |
MS ID: | MS004659 |
Analysis ID: | AN004916 |
Instrument Name: | Agilent 6230 TOF |
Instrument Type: | TOF |
MS Type: | ESI |
MS Comments: | Mass spectrometry was operated in positive polarity, with a mass range 70–1000 m/z at a rate of 1.36 spectrum /s. Other parameters for the MS were: fragmentor at 125 V, skimmer at 65 V, OctopoleRFPeak at 750 V, drying gas temperature at 200 °C, flow at 10 L/min, nebulizer at 0 psig and capillary voltage at 3500 V. The sheath liquid used consisted of methanol: water (1:1, v/v) and two reference masses (20 μL of purine: 121.0509 and 20 μL of HP-0922: 922.0098) at a flow rate of 0.6 mL/min (1:100 of split ratio). The MS data in positive ionization were acquired using the Agilent MassHunter Workstation Software LC/MS Data Acquisition for 6200 series TOF/6500 series Q-TOF B 9.0.9044.0 (Agilent Technologies), and the raw data were inspected with the MassHunter Qualitative software (version B.08.00, Agilent Technologies) before data processing. |
Ion Mode: | POSITIVE |
MS ID: | MS004660 |
Analysis ID: | AN004917 |
Instrument Name: | Agilent 6224 TOF |
Instrument Type: | TOF |
MS Type: | ESI |
MS Comments: | . Mass spectrometry was operated in negative polarity, with a mass range 60–1000 m/z at a rate of 1.0 spectrum /s. Other parameters for the MS were: fragmentor at 125 V, skimmer at 65 V, OctopoleRFPeak at 750 V, drying gas temperature at 275 °C, flow at 10 L/min, nebulizer at 0 psig and capillary voltage at 2000 V. The sheath liquid used consisted of methanol: water (1:1, v/v) and two reference masses (20 μL of purine: 121.0509 and 20 μL of HP-0922: 922.0098) at a flow rate of 0.6 mL/min (1:100 of split ratio). The MS data in negative ionization were acquired using the Agilent MassHunter Workstation Software LC/MS Data Acquisition for 6200 series TOF/6500 series Q-TOF B 6.01.6172 SP1 (Agilent Technologies), and the raw data were inspected with the MassHunter Qualitative software (version B.08.00, Agilent Technologies) before data processing. |
Ion Mode: | NEGATIVE |