Application of Microfluidic Devices to Proteomics Research
Bibliographic record
Abstract
This report describes an integrated and modular microsystem providing rapid analyses of trace-level tryptic digests for proteomics applications. This microsystem includes an autosampler, a microfabricated device comprising a large channel (2.4 microl total volume), an array of separation channels, together with a low dead volume enabling the interface to nanoelectrospray mass spectrometry. The large channel of this microfluidic device provides a convenient platform to integrate C(18) reverse phase packing or other type of affinity media such as immobilized antibodies or immobilized metal affinity chromatography beads thus enabling affinity selection of target peptides prior to electrophoretic separation and mass spectrometry analyses on a quadrupole/time-of-flight instrument. Sequential injection, preconcentration, and separation of peptide standards and tryptic digests are achieved with a throughput of up to 12 samples/per h and a concentration detection limit of approximately 5 nM (25 fmol on chip). Replicate injections of peptide mixtures indicated that reproducibility of migration time was 1.2-1.8%, whereas relative standard deviation ranging from 9.2 to 11.8% are observed on peak heights. The application of this device for trace-level protein identification is demonstrated for two-dimensional gel spots obtained from extracts of human prostatic cancer cells (LNCap) using both peptide mass-fingerprint data base searching and on-line tandem mass spectrometry. Enrichment of target peptides prior to mass spectral analyses is achieved using c-myc-specific antibodies immobilized on protein G-Sepharose beads and facilitates the identification of antigenic peptides spiked at a level of 20 ng/ml in human plasma. Affinity selection is also demonstrated for gel-isolated protein bands where tryptic phosphopeptides are captured on immobilized metal affinity chromatography beads and subsequently separated and characterized on this microfluidic system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".