Summary of Study ST002240
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 PR001429. The data can be accessed directly via it's Project DOI: 10.21228/M80X3B 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 | ST002240 |
| Study Title | Use of HRMS and Dual Isotope Labels to Resolve Difficult-to Measure Fluxes |
| Study Type | Stable isotope enriched Metabolomics |
| Study Summary | Data analysis and mass spectrometry tools have advanced significantly in the last decade. This ongoing revolution has elevated the status of analytical chemistry within the big-data omics era. High resolution mass spectrometers (HRMS) can now distinguish different metabolites with mass to charge ratios (i.e. m/z) that differ by 0.01 Da or less. This unprecedented level of resolution not only enables identification of previously unknown compounds but also presents an opportunity to establish active metabolic pathways through quantification of isotope enrichment. Studies with stable isotope tracers continue to contribute to our knowledge of biological pathways in human, plant and bacterial species, however most current studies have been based on targeted analyses. The capacity of HRMS to resolve near-overlapping isotopologues and identify compounds with high mass precision presents a strategy to assess ‘active’ pathways de novo from data generated in an untargeted way, that is blind to the metabolic network and therefore unbiased. Currently, identifying metabolic features, enriched with stable isotopes, at an ‘omics’ level remains an experimental bottleneck, limiting our capacity to understand biological network operation at the metabolic level. We developed data analysis tools that: i) use labeling information and exact mass to determine the elemental composition of each isotopically enriched ion, ii) apply correlation-based approaches to cluster metabolite peaks with similar patterns of isotopic labels and, iii) leverage this information to build directed metabolic networks de novo. Using Camelina sativa, an emerging oilseed model, we demonstrate the power of stable isotope labeling in combination with imaging and HRMS to reconstruct lipid metabolic networks in developing seeds and are currently addressing questions about lipid and central metabolism. Tools developed in this study will have a broader application to assess context specific operation of metabolic pathways. |
| Institute | Donald Danforth Plant Science Center |
| Department | Allen/USDA lab |
| Laboratory | Allen Lab |
| Last Name | Shrikaar |
| First Name | Kambhampati |
| Address | 975 North Warson road, St. Louis, MO 63132 |
| skambhampati@danforthcenter.org | |
| Phone | 3144025550 |
| Submit Date | 2022-07-21 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-08-17 |
| Release Version | 1 |
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Sample Preparation:
| Sampleprep ID: | SP002332 |
| Sampleprep Summary: | Frozen Arabidopsis root tissue was homogenized using a tissue lyser, and extraction was carried out using 1 mL of 4:1 methanol: water (v/v) with incubation in an ultra-sonication bath for 30 min followed by shaking for 30 min at 4°C. The mixture was then centrifuged at 21,000 x g for 10 min at 4°C; supernatant was transferred into fresh tubes and evaporated to dryness using a speedvac centrifuge at ambient temperature. Dried residue was re-suspended in 200 µL of 1:1 methanol: water (v/v), filtered using 0.2 µm PTFE micro centrifuge filters and transferred to glass vials for HILIC-HRMS runs. |
| Processing Storage Conditions: | -80℃ |
| Extraction Method: | 4:1 Methanol:Water |
