A novel platform for metabolomics using barcoded structure-switching aptamers
Bibliographic record
Abstract
Abstract Small organic molecules like metabolites and drugs are critical for diagnostics, treatment, and synthetic biology. Measuring them presents two key challenges however: they are biochemically highly diverse and there is no method to amplify them. Mass spectrometry has been the workhorse of metabolomics for decades but is costly and slow and single-cell metabolomics remains very challenging. Here we describe an alternative platform for metabolomics based on structure-switching aptamers (SSAs). SSAs are short nucleic acid molecules that each recognise a specific target ligand and undergo a major conformational change on ligand binding. This conformational change can drive detection such as fluorescence allowing SSAs to be used as sensors. We adapted conventional SSAs to a novel readout: barcode release. Each SSA recognises a unique ligand and each SSA releases a unique barcode allowing many ligands to be detected in parallel. We show that these barcode SSAs (bSSAs) can be multiplexed and act as independent sensors and that barcode release can be massively amplified to allow high sensitivity. Finally, we establish methods for the generation of large collections of bSSAs where barcode-SSA matching is completely directed. We believe that this novel platform which converts metabolite detection into barcode sequencing will allow the deep multiplexed detection of metabolites and drugs down to the scale of single cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".