Multiplexed Homogeneous Assays of Proteolytic Activity Using a Smartphone and Quantum Dots
Bibliographic record
Abstract
Semiconductor quantum dot (QD) bioconjugates, with their unique and highly advantageous physicochemical and optical properties, have been extensively utilized as probes for bioanalysis and continue to generate widespread interest for these applications. An important consideration for expanding the utility of QDs and making their use routine is to make assays with QDs more accessible for laboratories that do not specialize in nanomaterials. Here, we show that digital color imaging of QD photoluminescence (PL) with a smartphone camera is a viable, easily accessible readout platform for quantitative, multiplexed, and real-time bioanalyses. Red-, green-, and blue-emitting CdSeS/ZnS QDs were conjugated with peptides that were labeled with a deep-red fluorescent dye, Alexa Fluor 647, and the dark quenchers, QSY9 and QSY35, respectively, to generate Förster resonance energy transfer (FRET) pairs sensitive to proteolytic activity. Changes in QD PL caused by the activity of picomolar to nanomolar concentrations of protease were detected as changes in the red-green-blue (RGB) channel intensities in digital color images. Importantly, measurements of replicate samples made with smartphone imaging and a sophisticated fluorescence plate reader yielded the same quantitative results, including initial proteolytic rates and specificity constants. Homogeneous two-plex and three-plex assays for the activity of trypsin, chymotrypsin, and enterokinase were demonstrated with RGB imaging. Given the ubiquity of smartphones, this work largely removes any instrumental impediments to the adoption of QDs as routine tools for bioanalysis in research laboratories and is a critical step toward the use of QDs for point-of-care diagnostics. This work also adds to the growing utility of smartphones in analytical methods by enabling multiplexed fluorimetric assays within a single sample volume and across multiple samples in parallel.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".