Comparison of Ex-Situ and In-Situ Nano Plasmonic Platforms for Capture and Detection of Exosomes
Notice bibliographique
Résumé
Over the past few decades, in the field of sensing, the fabrication of the optimal nanostructures for detecting specific bio-entities remained an active area of research. It is well-known that the structure and the plasmonic properties of noble metal nanoparticles can be customized for specific applications such as biosensing, diagnosis, imaging, etc. by tuning the size and shape of the nanoparticles. Thus, by using the plasmonic property of noble metals, the detection at the nano-scale is possible by monitoring the shift of the local resonance band with respect to the changes in refractive index of the surrounding medium. The present study is aimed at comparing the quality and performance of two nano plasmonic platforms for the capture and detection of exosomes. Exosomes are nanoscale heterogeneous vesicles that are released by different cells. These vesicles plays significant role in intercellular communications, transport of proteins, RNA, and other molecular informations. In the last decade, researchers have shown substantial interest in this field as there is a lack of specific methodology to isolate and detect them. Currently, the exact science behind the major standard techniques of isolation of exosomes is not clearly understood. Thus, limitations with low yield and poor quality exosomes compromise further molecular analysis for diagnosis. The ultracentrifugation method of isolation of exosomes is time consuming, laborious, infrastructure intensive and may lack specificity. Therefore, a lot of challenges are existing in this field in order to develop next generation affinity-based technologies to capture the exosomes selectively and use them for further diagnosis at the clinical level. The two different sensing platforms, developed and tested for sensing of exosomes are the ex-situ gold (Au) nano-islands on glass substrates and the in-situ prepared silver (Ag) - polydimethylsiloxane (PDMS) nano-composite. The gold platform is fabricated by depositing colloidal gold on glass by the thermal convection method, followed by morphology tuning of the formed nanoparticles by annealing. The second is the nano-composite platform developed by the in-situ synthesis of silver ions present in the silver nitrate solution and the curing agent present in the PDMS polymer. In both cases, the capture and detection of exosomes is based on the strong affinity of heat shock proteins contained by exosomes and a polypeptide called Vn96, specially synthesized for this purpose. The Vn96 peptide targets canonical heat shock proteins that are present on the surface of exosomes. The Vn96 - based exosomes-capture method is further validated for downstream analyses, clinical compatibility, and liquid biopsy assays (biomarker and mutation detection) and platform-versatility using cell-culture conditioned media and human body fluids as sources of exosomes. Vn96 provides multiple advantages over currently-available methods for exosomes isolation: the scalability, quality, platform versatility, and cost-effectiveness. By using the gold platform, biotinylated Vn96 peptide is bound onto the streptavidin-coated Au nano-islands, and the subsequent steps of binding of nano-sized vesicles (exosomes) are monitored through the localized surface plasmon resonance (LSPR) band of Au. The sensing process was modelled, taking into account the characteristics of the nano-island structure. It is found that the results of the sensing process depend on the two major steps: the molar ratios of streptavidin to biotin-PEG-Vn96 and, the final step, the capture of exosomes by the biotin-PEG-Vn96 complex. The Ag-PDMS platform is used in a similar way and the shift of the Ag LSPR band is monitored after each sensing step. It is found that the bio sensitivity of the ex-situ synthesized gold/glass platform is considerably higher than that of the in-situ synthesized Ag-PDMS nanocomposite and, consequently, this platform is much more performant for the sensing of exosomes. Two principal reasons were identified in order to account for this difference. It is thought that, because of the low temperature of annealing of Ag-PDMS, contrary to the nano-islands of gold, a non-suitable morphology is formed. It has been demonstrated that nano-island structures have a higher sensitivity due to their morphological characteristics. On the other hand, due to the in-situ formation mechanism, a large proportion of the surface Ag particles will diffuse inside the polymer layer, that is, they will not be available anymore for sensing. The morphology of Au nano-islands and Ag-PDMS composite were investigated by SEM and the LSPR techniques are discussed in the paper.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».