Reactive Semiconductor Nanocrystals for Chemoselective Biolabeling and Multiplexed Analysis
Bibliographic record
Abstract
Effective biological application of nanocrystalline semiconductor quantum dots continues to be hampered by the lack of easily implemented and widely applicable labeling chemistries. Here, we introduce two new orthogonal nanocrystal bioconjugation chemistries that overcome many of the labeling issues associated with currently utilized approaches. These chemistries specifically target either (1) the ubiquitous amines found on proteins or (2) thiols present in either antibody hinge regions or recombinantly introduced into other proteins to facilitate site-specific labeling. The amine chemistry incorporates aniline-catalyzed hydrazone bond formation, while the sulfhydryl chemistry utilizes nanocrystals displaying surface activated maleimide groups. Both reactive chemistries are rapidly implemented, yielding purified nanocrystal-protein bioconjugates in as little as 3 h. Following initial characterization of the nanocrystal materials, the wide applicability and strong multiplexing potential of these chemistries are demonstrated in an array of applications including immunoassays, immunolabeling in both cellular and tissue samples, in vivo cellular uptake, and flow cytometry. Side-by-side comparison of the immunolabeled cells suggested a functional equivalence between results generated with the amine and thiol-labeled antibody-nanocrystal bioconjugates in that format. Three-color labeling was achieved in the cellular uptake format, with no significant toxicity observed while simultaneous five-color labeling of different epitopes was demonstrated for the immunolabeled tissue sample. Novel labeling applications are also facilitated by these chemistries, as highlighted by the ability to directly label cellular membranes in adherent cell cultures with the thiol-reactive chemistry.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".