Integration of immobilized trypsin bead beds for protein digestion within a microfluidic chip incorporating capillary electrophoresis separations and an electrospray mass spectrometry interface
Bibliographic record
Abstract
A microfluidic device is described in which an electrospray interface to a mass spectrometer is integrated with a capillary electrophoresis channel, an injector and a protein digestion bed on a monolithic substrate. A large channel, 800 microm wide, 150 microm deep and 15 mm long, was created to act as a reactor bed for trypsin immobilized on 40-60 microm diameter beads. Separation was performed in channels etched 10 microm deep, 30 microm wide and about 45 mm long, feeding into a capillary, attached to the chip with a low dead volume coupling, that was 30 mm in length, with a 50 microm i.d. and 180 microm o.d. Sample was pumped through the reactor bed at flow rates between 0.5 and 60 microL/min. The application of this device for rapid digestion, separation and identification of proteins is demonstrated for melittin, cytochrome c and bovine serum albumin (BSA). The rate and efficiency of digestion was related to the flow rate of the substrate solution through the reactor bed. A flow rate of 1 or 0.5 microL/min was found adequate for complete consumption of cytochrome c or BSA, corresponding to a digestion time of 3-6 min at room temperature. Coverage of the amino acid sequence ranged from 92% for cytochrome c to 71% for BSA, with some missed cleavages observed. Melittin was consumed within 5 s. In contrast, a similar extent of digestion of melittin in a cuvet took 10-15 min. The kinetic limitations associated with the rapid digestion of low picomole levels of substrate were minimized using an integrated digestion bed with hydrodynamic flow to provide an increased ratio of trypsin to sample. This chip design thus provides a convenient platform for automated sample processing in proteomics applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".