Summary of Study ST002977
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 PR001853. The data can be accessed directly via it's Project DOI: 10.21228/M86B06 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 | ST002977 |
| Study Title | Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome following Fecal Microbiota Transplant |
| Study Summary | In this study, we describe a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using an offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach, and successfully identified several rare and novel compounds including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable, but not identifiable, in the original single-dimensional data. An evaluation of this approach in the context of patients with recurrent Clostridioides difficile infection receiving fecal microbiota transplants is also included. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with common commercially-available instrumentation, and should be applicable to any dataset requiring deeper annotation of the metabolome. |
| Institute | University of Michigan |
| Department | Michigan Compound Identification Development Core |
| Last Name | Anderson |
| First Name | Brady |
| Address | 1000 Wall St, Ann Arbor, MI 48105 |
| anderbra@umich.edu | |
| Phone | 734-232-8177 |
| Submit Date | 2023-06-02 |
| Num Groups | 2 |
| Total Subjects | 8 |
| Publications | Publication to come later |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML, raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2024-03-11 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR001853 |
| Project DOI: | doi: 10.21228/M86B06 |
| Project Title: | Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome following Fecal Microbiota Transplant |
| Project Summary: | In this study, we describe a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using an offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach, and successfully identified several rare and novel compounds including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable, but not identifiable, in the original single-dimensional data. An evaluation of this approach in the context of patients with recurrent Clostridioides difficile infection receiving fecal microbiota transplants is also included. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with common commercially-available instrumentation, and should be applicable to any dataset requiring deeper annotation of the metabolome. |
| Institute: | University of Michigan |
| Department: | Michigan Compound Identification Development Core |
| Last Name: | Anderson |
| First Name: | Brady |
| Address: | 1000 Wall St, Ann Arbor, MI 48105 |
| Email: | anderbra@umich.edu |
| Phone: | 734-232-8177 |
| Funding Source: | NIH U2CES030164 and P41-GM108538 |
| Publications: | Publication to come later |
| Contributors: | Alexander Raskind, Rylan Hissong, Michael K. Dougherty, Sarah K. McGill, Ajay Gulati, Casey M. Theriot, Robert T. Kennedy, Charles R. Evans |
