Liposome-Encapsulated NaLnF<sub>4</sub> Nanoparticles for Mass Cytometry: Evaluating Nonspecific Binding to Cells
Bibliographic record
Abstract
We are interested in developing lanthanide nanoparticles (NPs) as high sensitivity tagging reagents for antibodies to analyze cells by mass cytometry (MC). Two key prerequisites for this application are that the NPs have to be colloidally stable in phosphate-containing buffers and the free NPs must have very low levels of nonspecific binding to cells. These are the issues we address here. We describe the synthesis of 30 nm diameter NaYF 4:Yb,Er nanoparticles, their transfer to aqueous solution via citrate exchange, and their encapsulation in liposomes to minimize their interaction with live cells. The lipid coating consisted of a 2:2:1 mol ratio mixture of dioleoylphosphatidyl choline (DOPC), egg sphingomyelin (ESM), and ovine cholesterol (Chol), referred to as DEC221. Since encapsulating 30 nm NPs in liposomes is an unprecedented challenge, we added varying amounts of 1,2-distearoyl- sn -glycero-3-phosphoethanolamine- N -[methoxyPEG-2000] (mPEG2K-DSPE) to the lipid formulation, both to promote curvature of the lipid coating and to use the polyethylene glycol (PEG) chains to impart stealth and minimize interaction with cells. We succeeded in coating individual NPs with the lipid bilayer and showed that, after coating, the NPs were colloidally stable in PBS buffer for up to one month. We used MC to measure nonspecific binding of the lipid-coated NPs to three different suspension cell lines, Ramos, THP-1, and KG1a cells. For dosages of 50, 100, and 1000 NPs/cell, the measured signals were barely above background. For dosages of 10 000 and 30 000 NPs/cell, nonspecific binding levels were on the order of 10–15 NPs per cell, less than 0.1% of the applied dose. Dopant ions such as Yb also provide a measurable signal, indicating that NaYF 4 NPs can serve as a useful host matrix for different lanthanide dopants for multiparameter experiments. These are very encouraging results for future experiments in which specific antibodies will be incorporated into the lipid coating.
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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".