Exosomes Detection by a Label-free Localized Surface Plasmonic Resonance Method
Bibliographic record
Abstract
Exosomes contain disease biomarkers for cancer and other pathological conditions. They are groups of nano-scale extracellular communication organelles which dynamically transport cargoes of molecules and genetic materials between cells. Exosomes circulating in our body-fluids provide a snap-shot in real-time for virtually all aspects of our physiology. Isolation detection and quantification methods of exosomes from various bio-fluids are challenging for clinical applications, like liquid biopsy. Here, we present a simple label-free technique to capture and detect exosomes integrating an exosome-capturing synthetic polypeptide (called Vn96) into microfluidic device. We use our in-house developed nanocomposite plasmonic sensing platform, prepared by in-situ synthesis of Ag-PDMS (silver- polydimethylsiloxane), using silver nitrate as the silver nanoparticle precursor by the in-situ reduction of silver ions. The Ag-PDMS nanocomposites are subsequently high-temperature annealed to improve the uniformity of morphology to accomplish improved sensing properties. We grafted the Vn96 peptide on the Ag-PDMS nanoparticle using functionalization chemistry to covalently attach streptavidin followed by affinity-grafting of biotinylated Vn96 peptide on streptavidin. The biotinylated Vn96 peptide has a polyethylene glycol linker between the peptide and biotin moieties providing flexibility to Vn96 to access and capture exosomes from contacting bio-fluids. We measured localized surface plasmon resonance (LSPR) bands of Ag for all the chip development steps mentioned above and after exosome-capture. The Ag plasmon band shifts to longer wavelengths (redshift) as the refractive index of the surrounding medium is increased at each step of the protocol till exosome-capture-step. The extent of the red shift of the Ag LSPR band is proportional to the concentration of exosome in the fluid till saturation of Vn96 on the Ag-PDMS nanocomposite. Hence, the concentration of exosomes in a bio-fluid can be measured by using calibration curves without pre- or post- labelling exosomes. This exosome-capture chip can efficiently quantitate exosomes present in a fluid and will be used for subsequent molecular analysis (protein and nucleic acid) to facilitate liquid biopsy.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".