Development of a biosensing strategy for multiplex and dynamic quantification of a secretory fingerprint from human pancreatic islets
Notice bibliographique
Résumé
Diabetes mellitus is a chronic disorder occurring when elevated levels of blood glucose, known as hyperglycemia, result from the body's impaired ability to produce or regulate insulin. If left untreated, chronic hyperglycemia can cause cardiovascular disease, neuropathy, nephropathy and eye disease, leading to retinopathy and blindness. In 2017 the number of people with diabetes reached 425 million worldwide. This disease arises from deficiencies in the secretory pathways of the pancreatic islets, a micro-organ constituting 1-2% of the pancreas mass. Recent studies have shown that the cells comprising the islets possess an intricate communication system, in which their secreted hormones exert paracrine interactions on neighbor cells. However, little is understood about the consequences of such communications. Up-to-date most research in the field has focused on understanding the processes related with insulin and glucagon secretion, the main hormones secreted by the two major cell types present in the islets. Thus, monitoring a secretory fingerprint (SF) contemplating more than two hormones, presents a research opportunity to increase our current understanding of diabetes. Due to their simplicity, ease of use, non-invasive and label-free nature, biosensors provide an excellent basis for the development of analytical tools capable of detecting the SF of islets. Therefore, the main objective of the present thesis was to develop a biosensing strategy for the multiplex detection of a SF composed of the hormones secreted by the three major cell types contained in the pancreatic islets. At first, we explored the use of a capacitance-based biosensor for the detection of insulin. This biosensing technique was selected, since it could offer high sensitivity, potential for multiplexing and capabilities for integration with microelectronic technologies. Since the performance of this biosensor critically depends on the surface chemistry design of the bioreceptor immobilization, a systematic study was performed to evaluate the effect of common architectures reported in literature. These chemistries included the covalent immobilization of biomolecules on the electrodes, in the gaps between electrodes and a conformal coating covering both. The development of this capacitive biosensor provided valuable knowledge on the effect of various parameters for the detection of insulin, however its implementation for islet continuous SF analysis proved difficult due to its long analysis time. Thus, we explored surface plasmon resonance imaging (SPRi) as an alternative to fully reach the thesis objective. By combining a competitive immunoassay with SPRi and the optimization of the sensor's surface chemistry it was possible to detect, for the first time, insulin, glucagon and somatostatin simultaneously. This biosensing strategy presented a limit of detection (LOD) comparable to previous reports detecting insulin and glucagon secretions individually with a short analysis time. However, detecting the smallest hormone, somatostatin, remained a challenge due to the obtained high LOD compared to insulin and glucagon and a lack of reports providing a desirable reference for its performance. Thus, to address this pitfall and ensure the detection of all targeted hormones in a biologically relevant concentration range, we performed a study comparing three different signal amplification strategies based on gold nanoparticles (GNPs). These strategies included GNPs immobilized on the sensor surface, GNPs conjugated with primary antibodies and GNPs conjugated with a secondary antibody for post competitive assay amplification. Here, multiplexed detection of the three hormones was achieved with an improved LOD of 9 fold for insulin, 10 fold for glucagon and 200 fold for somatostatin when compared to the SPRi biosensor without GNPs signal amplification, successfully addressing the aforementioned challenge.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».