Preparation of Functionalized Mesoporous Silica Nanoparticles as Mass Tags for Potential Applications in Mass Cytometry
Bibliographic record
Abstract
Mass cytometry (MC) is an emerging and powerful bioanalytical technique for high-dimensional single-cell analysis. While metal-chelating polymers (MCPs) have been the most successful mass tag reagents for MC, nanoparticle (NP)-based mass tag reagents are of great interest to improve the sensitivity of MC toward low-abundance biomarkers. Here we present a new structure design for potential MC probes using multifunctionalized mesoporous silica nanoparticles (MSNs), modified with zwitterionic sulfobetaine silanes and long-chain polyethylene glycol silanes (PEG 5k, M = 5000). The resulting methoxy-terminated NPs (PMSN-Zwi-mPEG 5k ) displayed uniform size, good redispersibility and colloidal stability, as well as versatility in accommodating 13 types of lanthanides. They exhibited the capacity to carry up to 7.4 × 10 4 Tb ions per NP, with negligible ion loss observed in both H 2 O and 1× PBS buffer. We investigated the interactions of these functionalized NPs with serum proteins using UV–vis, and with peripheral blood mononuclear cells (PBMCs) using MC. By varying PEG 5k chain density on NP surface, we could minimize their nonspecific binding (NSB) to human serum albumin proteins, while also reducing their NSB to PBMCs at the titer of 1000 NPs/cell. Additionally, unconjugated NPs showed good compatibility with commercial Maxpar MCP mass tags in a 10-plex assay for PBMCs staining. Under these optimized conditions, N 3 -terminated NPs (PMSN-Zwi-PEG 5k -N 3 ) were synthesized and conjugated with antibiotin antibodies (Abs), resulting in effective binding of biotin Cy5 molecules. We finally compared eight different bioconjugation conditions to maximize bioconjugation efficiency with retention of Ab function. These preliminary findings demonstrate the fundamental technical capabilities and promising prospects for the future application of MSN-based mass tag reagents in MC.
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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".