Tetrameric Antibody Complexes and Affinity Tag Peptides for the Selective Immobilization and Imaging of Single Quantum Dots
Bibliographic record
Abstract
Colloidal semiconductor quantum dots (QDs) are of widespread interest as fluorescent labels for bioanalysis and imaging applications. Single-particle measurements have proven to be a very powerful tool for better understanding the fundamental properties and behaviors of QDs and their bioconjugates; however, a recurring challenge is the immobilization of QDs in a solution-like environment that minimizes interactions with a bulk surface. Immobilization strategies for QD-peptide conjugates are particularly underdeveloped within this context. Here, we present a novel strategy for the selective immobilization of single QD-peptide conjugates using a combination of tetrameric antibody complexes (TACs) and affinity tag peptides. A glass substrate is modified with an adsorbed layer of concanavalin A (ConA) that binds a subsequent layer of dextran that minimizes nonspecific binding. A TAC with anti-dextran and anti-affinity tag antibodies binds to the dextran-coated glass surface and to the affinity tag sequence of QD-peptide conjugates. The result is spontaneous and sequence-selective immobilization of single QDs without any chemical activation or cross-linking. Controlled immobilization of multiple colors of QDs is possible using multiple affinity tag sequences. Experiments confirmed that this approach positions the QD away from the bulk surface. The method supports real-time imaging of binding and dissociation, measurements of Förster resonance energy transfer (FRET), tracking of dye photobleaching, and detection of proteolytic activity. We anticipate that this immobilization strategy will be useful for studies of QD-associated photophysics, biomolecular interactions and processes, and digital assays.
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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) | 0.000 | 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 | 0.001 | 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".