Biotinylated Lipid-Coated NaLnF<sub>4</sub> Nanoparticles: Demonstrating the Use of Lanthanide Nanoparticle-Based Reporters in Suspension and Imaging Mass Cytometry
Bibliographic record
Abstract
Lanthanide nanoparticles (LnNPs) have the potential to be used as high-sensitivity mass tag reporters in mass cytometry immunoassays. For this application, however, the LnNPs must be made colloidally stable in aqueous buffers, demonstrate minimal non-specific binding to cells, and have functional groups to attach antibodies or other targeting agents. One possible approach to address these requirements is by using lipid coating to modify the surface of the LnNPs. In this work, 39 nm diameter NaYF 4:Yb, Er NPs (LnNPs) were coated with a lipid formulation consisting of egg sphingomyelin, 1,2-dioleoyl- sn -glycero-3-phosphocholine, 1,2-dioleoyl-3-trimethylammonium propane, cholesterol-(polyethylene glycol-600), and 1,2-distearoyl- sn -glycero-3-phosphoethanolamine- N -[biotinyl(polyethylene glycol)-2000]. The resulting biotinylated lipid-coated LnNPs were characterized by dynamic light scattering to determine the hydrodynamic size and stability in phosphate buffered saline, and the composition of the lipid coatings was quantified by liquid chromatography–tandem mass spectrometry. The specific and non-specific binding of the biotinylated lipid-coated LnNPs to a model system of functionalized polystyrene microbeads were then tested by both suspension and imaging mass cytometry. We found that targeted binding with minimal non-specific binding can be achieved with the lipid-coated LnNPs and that the lipid composition of the coating has an impact on the performance of the LnNPs as mass cytometry reporters. These results additionally establish the importance of quantifying the composition of lipid-coated nanomaterials to optimize them more effectively for their desired application.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".