Summary of Study ST001886
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 PR001189. The data can be accessed directly via it's Project DOI: 10.21228/M8140K 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 | ST001886 |
| Study Title | Untargeted metabolomics of hypertrophic cardiomyopathy (part I) |
| Study Summary | Hypertrophic cardiomyopathy (HCM) is a complex disease partly explained by the effects of individual gene variants on sarcomeric protein biomechanics. At the cellular level, HCM mutations most commonly enhance force production, leading to higher energy demands. Despite significant advances in elucidating sarcomeric structure-function relationships, there is still much to be learned about the mechanisms that link altered cardiac energetics to HCM phenotypes. In this work, we test the hypothesis that changes in cardiac energetics represent a common pathophysiologic pathway in HCM. |
| Institute | Stanford University |
| Last Name | Contrepois |
| First Name | Kevin |
| Address | 300 Pasteur Dr |
| kcontrep@stanford.edu | |
| Phone | 6506664538 |
| Submit Date | 2021-07-13 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-07-13 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN003051 | AN003052 | AN003053 | AN003054 |
|---|---|---|---|---|
| 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 HF hybrid Orbitrap | Thermo Q Exactive HF hybrid Orbitrap | Thermo Q Exactive Orbitrap | Thermo Q Exactive Orbitrap |
| Ion Mode | POSITIVE | NEGATIVE | POSITIVE | NEGATIVE |
| Units | MS count (log2) | MS count (log2) | MS count (log2) | MS count (log2) |
MS:
| MS ID: | MS002838 |
| Analysis ID: | AN003051 |
| Instrument Name: | Thermo Q Exactive HF hybrid Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics, Durham, NC). Metabolic features from blanks and those that didn’t 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. Median normalization was applied to correct for differential starting material quantity. Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were merged and metabolites of interest were formally identified by matching fragmentation spectra and retention time to analytical-grade standards when possible or matching experimental MS/MS to fragmentation spectra in publicly available databases. |
| Ion Mode: | POSITIVE |
| MS ID: | MS002839 |
| Analysis ID: | AN003052 |
| Instrument Name: | Thermo Q Exactive HF hybrid Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics, Durham, NC). Metabolic features from blanks and those that didn’t 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. Median normalization was applied to correct for differential starting material quantity. Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were merged and metabolites of interest were formally identified by matching fragmentation spectra and retention time to analytical-grade standards when possible or matching experimental MS/MS to fragmentation spectra in publicly available databases. |
| Ion Mode: | NEGATIVE |
| MS ID: | MS002840 |
| Analysis ID: | AN003053 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics, Durham, NC). Metabolic features from blanks and those that didn’t 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. Median normalization was applied to correct for differential starting material quantity. Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were merged and metabolites of interest were formally identified by matching fragmentation spectra and retention time to analytical-grade standards when possible or matching experimental MS/MS to fragmentation spectra in publicly available databases. |
| Ion Mode: | POSITIVE |
| MS ID: | MS002841 |
| Analysis ID: | AN003054 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics, Durham, NC). Metabolic features from blanks and those that didn’t 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. Median normalization was applied to correct for differential starting material quantity. Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were merged and metabolites of interest were formally identified by matching fragmentation spectra and retention time to analytical-grade standards when possible or matching experimental MS/MS to fragmentation spectra in publicly available databases. |
| Ion Mode: | NEGATIVE |
