Integrated System for Rapid Proteomics Analyses Using Microfluidic Devices Coupled to Nanoelectrospray Mass Spectrometry
Bibliographic record
Abstract
This chapter presents an integrated and modular microsystem providing rapid analyses of low femtomole of in-gel digests for proteomics applications. Enhancement of sample throughput is facilitated using an autosampler, a microfabricated device comprising a large (2.4-microL total volume) separation channel together with a low-dead-volume interface to nES mass spectrometry. Sample preconcentration is achieved by packing C18 reverse phase or immobilized metal affinity chromatography (IMAC) beads into the large channel of this microfluidic device to adsorb peptides or enrich the sample in phosphopeptides prior to capillary electrophoresis separation and MS detection. This integrated microfluidic systems enables a sample throughput of up to 12 samples/h with a detection limit of approx 5 nM (25 fmol inj.). Replicate injections of peptide standards indicated that reproducibility of migration time was typically 1.2-1.8%, whereas relative standard deviation (RSD) values of 9.2-11.8% were obtained on peak heights. The application of this device is demonstrated for 2D gel spots obtained from protein extracts of human astrocyte cells and for excised bands of membrane proteins from Neisseria meningitidis. A stepped acetonitrile gradient can be incorporated with the present microfluidic system to enhance selectivity during sample analysis.
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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 | 0.001 | 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 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".