| Chapter title |
LC-MS-Based Metabolomics of Biofluids Using All-Ion Fragmentation (AIF) Acquisition
|
|---|---|
| Chapter number | 3 |
| Book title |
Clinical Metabolomics
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7592-1_3 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7591-4, 978-1-4939-7592-1
|
| Authors |
Romanas Chaleckis, Shama Naz, Isabel Meister, Craig E. Wheelock, Chaleckis, Romanas, Naz, Shama, Meister, Isabel, Wheelock, Craig E. |
| Abstract |
The field of liquid chromatography-mass spectrometry (LC-MS)-based nontargeted metabolomics has advanced significantly and can provide information on thousands of compounds in biological samples. However, compound identification remains a major challenge, which is crucial in interpreting the biological function of metabolites. Herein, we present a LC-MS method using the all-ion fragmentation (AIF) approach in combination with a data processing method using an in-house spectral library. For the purposes of increasing accuracy in metabolite annotation, up to four criteria are used: (1) accurate mass, (2) retention time, (3) MS/MS fragments, and (4) product/precursor ion ratios. The relative standard deviation between ion ratios of a metabolite in a biofluid vs. its analytical standard is used as an additional metric for confirming metabolite identity. Furthermore, we include a scheme to distinguish co-eluting isobaric compounds. Our method enables database-dependent targeted as well as nontargeted metabolomics analysis from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biological information. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Comoros | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 19% |
| Professor > Associate Professor | 3 | 14% |
| Student > Ph. D. Student | 3 | 14% |
| Student > Bachelor | 2 | 10% |
| Professor | 2 | 10% |
| Other | 2 | 10% |
| Unknown | 5 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 24% |
| Chemistry | 3 | 14% |
| Agricultural and Biological Sciences | 1 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 5% |
| Medicine and Dentistry | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 9 | 43% |