Nanoliter Solvent Extraction Combined with Microspot MALDI TOF Mass Spectrometry for the Analysis of Hydrophobic Biomolecules
Bibliographic record
Abstract
A nanoliter solvent extraction technique combined with microspot matrix-assisted laser desorption/ionization (MALDI) mass spectrometry is presented. This method involves the use of a nanoliter droplet containing organic solvents at the tip of a small capillary for extraction. The droplet is formed inside a microliter aqueous sample containing the analyte of interest. After extraction, the droplet is deposited onto a MALDI target precoated with a thin matrix layer. Since the nanoliter droplet never touches the sample container wall, any possible extraction of contaminants adsorbed on the plastic or glassware is avoided. In addition, there is no need to concentrate the organic phase after the extraction, thus avoiding any possible loss during the concentration step. The nanoliter volume can be readily deposited onto a MALDI target, producing a high analyte concentration within a microspot. Combined with microspot MALDI, this technique allows for very sensitive analysis of the extracted analyte. The performance of this technique is illustrated in several applications involving the detection of hydrophobic peptides or phospholipids. It is shown that very hydrophobic analytes can be extracted from small-volume samples containing a large amount of salts and/or more hydrophilic analytes, which tend to give dominant signals in conventional MALDI experiments. Nanoliter extraction of analyte from samples containing less than 100 nM hydrophobic analyte and over 1 microM easily ionized hydrophilic species is demonstrated. Finally, using the analysis of the ionophore valinomycin as an example, it is demonstrated that the technique is a more reliable tool for probing metal-peptide complexes than regular MALDI sample preparations.
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 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 | 0.001 |
| 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) | 0.005 | 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".