Quantitative Detection of Biological Nanoparticles Using Twilight Off-Axis Holographic Microscopy: Insights on Complex Formation between PEGylated Gold Nanoparticles and Lipid Vesicles
Bibliographic record
Abstract
The detection of biological nanoparticles (NPs), such as viruses and extracellular vesicles (EVs), plays a critical role in medical diagnostics. However, these particles are optically faint, making microscopic detection in complex solutions challenging. Recent advancements have demonstrated that distinguishing between metallic and dielectric signals with twilight off-axis holographic microscopy makes it possible to differentiate between metal and biological NPs and to quantify complexes formed from metal and biological NPs binding together. Here, this method is employed to investigate complex formation through specific interactions between streptavidin (StrAv)-modified gold NPs (StrAv-AuNPs) and large biotin-containing unilamellar lipid vesicles (biotin-LUVs), serving as virus and EV mimics. To minimize AuNP self-aggregation during functionalization of PEGylated 25 nm radius AuNPs with tetrameric StrAv, 0.06% biotin-PEG (∼5 biotin per AuNP) was used, which also serves to ensure that aggregation involving multiple LUVs is effectively prevented. While the StrAv-biotin ratio did not significantly affect AuNP self-aggregation upon fabrication of StrAv-AuNPs, a 1000-fold StrAv excess with respect to biotin-PEG on the AuNPs was required to fabricate StrAv-AuNPs with the anticipated reactivity with biotin-LUVs. Through a combination of waveguide scattering microscopy, surface plasmon resonance, and twilight off-axis holographic microscopy, we demonstrate that this likely stems from a dramatic reduction in the association rate constant between StrAv and biotin within the PEG layer. Furthermore, by using a mixture of 3 kDa nonbiotinylated PEG and 5 kDa biotin-PEG, functional StrAv-AuNPs were successfully fabricated at an orders of magnitude lower StrAv-to-biotin ratio, enabling a sub-pM limit of detection of biotin-LUVs using off-axis holography.
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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 | 0.001 | 0.001 |
| 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".