Wall-less sample preparation of <i>μ</i>m-sized sample spots for femtomole detection limits of proteins from liquid based UV-MALDI matrices
Bibliographic record
Abstract
Previously, we introduced wall-less sample preparation (WaSP), technology that involves the use of an electrodynamic balance (EDB) to prepare microm-sized sample spots for analysis by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). In that work we demonstrated the detection of femtomole quantities of a low molecular weight peptide and a hydrophobic ester (both <600 Da). Here we use WaSP to test the hypothesis that the use of small sample spot sizes and an instrument equipped with delayed extraction would increase the analytical utility of liquid sample spots for peptide and protein (>2500 Da) analysis by UV-MALDI-TOF-MS (Sze et al.; J. Am. Soc. Mass Spectrom. 1998, 9, 166-174). To aid the optimization of preparing microm-sized sample spots by WaSP, optical microscopy and mass spectrometry were used to investigate nonvolatile solute concentration effects on droplet fission and sample spot size, modifications of the EDB electric field to control droplet ejection, and the use of multiple droplet deposition to increase sample loading. Also described is a rapid deposition mode of operation for WaSP that allows single droplets generated at 1 Hz to be levitated briefly ( approximately 500 ms) before being ejected autonomously and deposited as a concentrated sample spot with a spatial accuracy of +/-5 microm. To test the sensitivity of the method, one hundred glycerol droplets (270 pL each, 27 nL total) each containing 32 amol lysozyme were deposited on top of each other one-at-a-time to create a single sample spot. Using a mass spectrometer equipped with delayed extraction to analyze this sample spot, we verified the hypothesis of Sze et al. by achieving detection limits three orders of magnitude below that previously observed for the detection of a protein by UV-MALDI-TOF-MS with a chemical-doped liquid matrix sample preparation.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".