Analyzing Unique Detection in High-throughput Metabolomics and Lipidomics using Ion Mobility-enhanced Mass Spectrometry
Bibliographic record
Abstract
Accurately quantifying small molecules present in complex mixtures continues to pose a significant challenge in analytical chemistry. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is recognized as a gold standard method for the unbiased analysis of small molecules within complex samples in high throughput. Despite these advancements, current targeted and untargeted metabolomics workflows are limited by ambiguous identification based on the precursor alone or relatively few fragment ions. To address this gap, I investigated unique compound detection in traditional LC-MS approaches and developed novel multidimensional mass spectrometry methods using additional separation techniques. First, I quantitatively investigated the conditions necessary for unique metabolite detection in complex backgrounds by comparing traditional LC-MS methods in metabolomics. I exhibited the power of using both high-resolution precursor and fragment ion mass-to-charge (m/z) for unambiguous compound detection and the selectivity of data-independent acquisition (DIA). These findings were subsequently applied to help improve novel data analysis tools for DIA metabolomics (DIAMetAlyzer). Overall, this work provides a comprehensive outlook on compound uniqueness in metabolomics and presents a robust framework for researchers to analyze unambiguous compound detection quantitatively. Next, I developed two novel methods for lipidomics using fragment ion information (MS2) and ion mobility separation. My data-dependent acquisition method, DDA-PASEF, was developed for library generation using lipid standards, and my data-independent acquisition method, diaPASEF, was optimized for plasma lipidomics, demonstrating improved reproducibility, peak sampling, and peak shape. diaPASEF improved selectivity and sensitivity by using comprehensive MS2 spectra for quantification and collision cross section as a reproducible molecular identifier for lipids. These experimental developments exhibited the positive impact of multiplexing on quantitative performance in both the m/z and ion mobility dimensions. Using my optimized method, I demonstrate the potential to acquire, process and quantify a large number of lipids from human plasma in diaPASEF mode. In conclusion, this thesis presents computational and experimental advances that promote the unique identification of metabolites and lipids in complex samples. As unambiguous identification is an essential requirement in many biological studies, these advancements have the potential to be applied to a wide array of research, furthering the understanding of functional interactions in complex metabolic consortia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".