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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".