Cytoplasmic Protein-Powered In Situ Fluorescence Amplification for Intracellular Assay of Low-Abundance Analyte
Bibliographic record
Abstract
Fluorescence amplification is critical for in situ and real-time detection of intracellular low abundance biological species. However, current intracellular amplification techniques mainly rely on synthetic nucleic acid-based nanodevices, manipulating them in living cells remains challenging. To solve this problem, herein, a new signal amplification concept named cytoplasmic protein-powered in situ fluorescence amplification (CPFA) is proposed. CPFA takes cytoplasmic protein as cell-self-power for signal amplification enabling it to operate in living cells. To establish a prototype of CPFA, an amplifiable sensor for hydroxyl radicals (•OH) was designed by entrapping the screened cytoplasmic protein-enhanced fluorophore (PBF1) inside mesoporous silica (MSN) nanocontainer with ssDNA/PTAD-based signal switch. When encountered with •OH in living cells, the ssDNA was cleaved to separate PTAD from MSN, liberating multiple copies of the loaded PBF1 to light up the fluorescence. Furthermore, these released PBF1 molecules can instantly bind with cytoplasmic proteins to amplify their fluorescence signals. Take advantage of this two-stage amplification mode, the sensor in response to •OH exhibited remarkable fluorescence enhancement (near 400-fold) in cell lysates, and the •OH was linearly determined from 0 to 800 nM with a detection limit of 6.4 pM. Moreover, this sensor can track basal level and fluctuation of •OH in living cells on account of its high sensitivity. To our knowledge, this is the first effort to use cytoplasmic protein for amplifying detection signals, which will provide a new dimension to current methodologies for low-abundance biomarkers discovery and regulation for chemical biology and medical diagnostics.
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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 | 0.000 | 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".