ProxyPhos sensors for the detection of negatively charged membranes
Bibliographic record
Abstract
Membrane-embedded negatively charged phospholipids (MENCP) can be used as biomarkers for a range of biological processes, including early detection of apoptosis in animal cells, drug-induced phospholipidosis, and selective detection of bacterial over animal cells. Currently, several technologies for the detection of apoptosis and bacterial cells are based on the recognition of MENCPs, including the AnnexinV stain and PSVue™ probes. As probes, these technologies have limitations, the most significant of which is the need for washing the unbound probe away to achieve optimal signal. In contrast, a turn-on chemosensor selective for MENCP would address this shortcoming, and allow for a more rapid protocol for the detection of apoptosis, bacteria and for other relevant applications. In this work, the aim was to explore whether ProxyPhos chemosensors, previously reported by our group for the detection of proximally phosphorylated peptides and proteins, could be re-purposed for the detection of MENCPs. Six lead ProxyPhos sensors were screened against synthetic vesicles containing biologically relevant negatively charged phospholipids including phosphatidic acid (PA), phosphatidylglycerol (PG), cardiolipin (CL) and phosphatidylserine (PS). Through these screens, ProxyPhos sensors exhibiting high selectivity for the detection of MENCPs over zwitterionic lipids were identified. Particular selectivity was observed for PA and CL. Sensitivity of the lead sensors for MENCPs was suitable for the detection of apoptosis: ProxyPhos detected vesicles containing as little as 2.5% PS and detected camptothecin-induced apoptosis in mammalian cells in flow cytometry experiments. The results suggest that ProxyPhos sensors can be used for the detection of MENCPs in synthetic vesicles and live mammalian cells.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".