High-Frequency Microfluidic Fractionation for Compound-Resolved Bioactivity-Based Metabolomics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Specialized metabolites represent a prolific source of potential drug candidates. However, the process from detecting bioactivity in a crude metabolite extract to unambiguously identifying the active agent is a tedious and expensive endeavor. Speeding up this procedure is crucial, as new drugs, such as antibiotics, are urgently needed. Furthermore, the systematic functional assessment of complex metabolome samples represents a key bottleneck in nontargeted metabolomics, which once solved, holds the potential to fundamentally advance our systematic understanding of biology. To tackle this central bioanalytical challenge, we developed a compound-resolved bioactivity-based metabolomics workflow that combines nontargeted liquid chromatography tandem mass spectrometry (LC-MS/MS), high frequency fractionation on microfluidic devices and subsequent readout with luminescent bioreporter strains. Central for this workflow is a custom high-speed (∼1 Hz frequency) fractionation device that spots the mobile phase onto a microfluidic paper-analytical device (μPAD) in parallel to MS/MS data acquisition. Subsequently, the μPAD can be overlaid with a bioreporter strain, which displays cellular stress by expressing luciferase. The luminescence signal can then be correlated to MS signals through their chromatographic profiles. We evaluated five different luciferase-expressing bioreporter strains which provide information about different antibacterial modes of action, and tested the workflow with different antibiotic standards and mixtures thereof, as well as crude extracts from the known antibiotic producer Saccharopolyspora erythraea . Our results demonstrated high sensitivity (up to 1 ng/spot, depending on compound and bioreporter) and the rapid identification of multiple antimicrobial compounds out of crude extracts, highlighting the practicality and high-throughput capability of this compound-resolved bioactivity-based metabolomics approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".