Detection of 25,000 molecules of Substance P by MALDI-TOF mass spectrometry and investigations into the fundamental limits of detection in MALDI
Bibliographic record
Abstract
Mass spectrometric analysis of peptides with a total sample loading of several tens of thousands of molecules (i.e., low zeptomoles) is demonstrated. At this low level of sample loading, it becomes important to consider several very unique technical and fundamental aspects that are not obvious in compatible experiments with a higher amount of sample loading. We demonstrate that prudent matrix preparation allows analysis of peptides from solutions with picomolar concentrations in matrix-assisted laser desorption ionization (MALDI) mass spectrometry. Using microspot MALDI we demonstrate the introduction and detection of 25,000 molecules of Substance P in a time-of-flight mass spectrometer. A method based on probability theory is presented to estimate the minimum number of ions required for generating a statistically significant isotope peak pattern of peptide ions. It is found that the low boundary of ionization efficiency is 1–2% for Substance P. In addition, comparison of macro- and microspot sample deposition techniques for Substance P shows that under the experimental conditions used, a minimum of ∼5 analyte molecules per μm 2 are necessary to generate useful signals. Implications of these results on further mass spectrometric developments towards even more sensitive detection are discussed.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".