A High-Sensitivity Lanthanide Nanoparticle Reporter for Mass Cytometry: Tests on Microgels as a Proxy for Cells
Bibliographic record
Abstract
This paper addresses the question of whether one can use lanthanide nanoparticles (e.g., NaHoF4) to detect surface biomarkers expressed at low levels by mass cytometry. To avoid many of the complications of experiments on live or fixed cells, we carried out proof-of-concept experiments using aqueous microgels with a diameter on the order of 700 nm as a proxy for cells. These microgels were used to test whether nanoparticle (NP) reagents would allow the detection of as few as 100 proteins per "cell" in cell-by-cell assays. Streptavidin (SAv), which served as the model biomarker, was attached to the microgel in two different ways. Covalent coupling to surface carboxyls of the microgel led to large numbers (>10(4)) of proteins per microgel, whereas biotinylation of the microgel followed by exposure to SAv led to much smaller numbers of SAv per microgel. Using mass cytometry, we compared two biotin-containing reagents, which recognized and bound to the SAvs on the microgel. One was a metal chelating polymer (MCP), a biotin end-capped polyaspartamide containing 50 Tb(3+) ions per probe. The other was a biotinylated NaHoF4 NP containing 15 000 Ho atoms per probe. Nonspecific binding was determined with bovine serum albumin (BSA) conjugated microgels. The MCP was effective at detecting and quantifying SAvs on the microgel with covalently bound SAv (20 000 SAvs per microgel) but was unable to give a meaningful signal above that of the BSA-coated microgel for the samples with low levels of SAv. Here the NP reagent gave a signal 2 orders of magnitude stronger than that of the MCP and allowed detection of NPs ranging from 100 to 500 per microgel. Sensitivity was limited by the level of nonspecific adsorption. This proof of concept experiment demonstrates the enhanced sensitivity possible with NP reagents in cell-by-cell assays by mass cytometry.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".