Single amino acid resolution of proteolytic fragments generated in individual cells
Bibliographic record
Abstract
The complex nature of enzyme regulation mandates that enzyme activity profiles be measured in the context of the intact cell. Single-cell capillary electrophoresis (CE) coupled with laser-induced fluorescence is a powerful approach for quantitation and separation of analytes present in small samples and single live cells; however, it does not allow for the definitive identification of the reaction products. On the other hand, mass spectrometry (MS) is able to identify analytes but still lacks the requisite sensitivity for most single-cell analysis applications. Thus, it follows that by determining the relative amounts of reaction products generated in single cells using CE and by producing larger quantities of these products using bulk cell populations to identify them using MS, it is possible to determine enzyme activity profiles in single cells. In this study, the applicability of this approach was demonstrated by examining the intracellular fate of a protease substrate derived from the beta-amyloid precursor protein (beta-APP). In single live TF-1 cells, three distinct fragments were generated from the beta-APP peptide, which differed by a single uncharged amino acid. The CE measurements indicated that the proteolytic fragment profiles (i.e., the relative amounts of each fragment) were consistent from cell to cell but that they were different from those obtained in cell lysates. Furthermore, measurements obtained at the single cell level made it possible to observe a modest but statistically significant negative correlation between the total amount of beta-APP peptide loaded in cells and the fraction of peptide that remained intact. This study demonstrates how single-cell CE, MS, and peptide substrates can be combined to identify and measure enzyme activities in single live cells.
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 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 | 0.000 | 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 | 0.000 | 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 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".