Digital microfluidic hydrogel microreactors for proteomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proteolytic digestion is an essential step in proteomic sample processing. While this step has traditionally been implemented in homogeneous (solution) format, there is a growing trend to use heterogeneous systems in which the enzyme is immobilized on hydrogels or other solid supports. Here, we introduce the use of immobilized enzymes in hydrogels for proteomic sample processing in digital microfluidic (DMF) systems. In this technique, preformed cylindrical agarose discs bearing immobilized trypsin or pepsin were integrated into DMF devices. A fluorogenic assay was used to optimize the covalent modification procedure for enzymatic digestion efficiency, with maximum efficiency observed at 31 μg trypsin in 2-mm diameter agarose gel discs. Gel discs prepared in this manner were used in an integrated method in which proteomic samples were sequentially reduced, alkylated, and digested, with all sample and reagent handling controlled by DMF droplet operation. Mass spectrometry analysis of the products revealed that digestion using the trypsin gel discs resulted in higher sequence coverage in model analytes relative to conventional homogenous processing. Proof-of-principle was demonstrated for a parallel digestion system in which a single sample was simultaneously digested on multiple gel discs bearing different enzymes. We propose that these methods represent a useful new tool for the growing trend toward miniaturization and automation in proteomic sample processing.
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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) | 0.000 | 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 it