Targeting CLL Cells Using Rituximab-Conjugated Surface Enhanced Raman Scattering (SERS) Gold Nanoparticles
Notice bibliographique
Résumé
Abstract Abstract 2691 Measuring the expression of cell surface markers is crucial to the effective diagnosis of lymphoproliferative disorders such as chronic lymphocytic leukemia (CLL). However, conventional fluorescent probes are constrained by the degradative effect of photobleaching and the broad emission spectra of dyes, which restricts the multiplexing capacity of marker detection. Recent developments in nanoparticle-based technology may confer significant advantages over these traditional tools. In particular, surface enhanced Raman scattering (SERS) nanoparticles (NPs) can now be targeted to cells via conjugation to monoclonal antibodies. These particles are composed of colloidal gold cores surrounded by an organic dye with a distinct Raman-scattering signature. In addition to providing stable long-term signals, Raman probes typically exhibit spectral bands more than 30 times narrower than those of fluorescence techniques, thereby greatly increasing the multiplexing potential of phenotypic analysis. In this study, we developed SERS NPs conjugated to the monoclonal antibody rituximab in order to target the surface marker CD20. Rituximab has been established as an effective therapeutic antibody in the treatment of several B-cell disorders, though its precise mechanism of action is unclear. The preparation of SERS probes was achieved by coating 60 nm gold particles with the Raman-active reporter malachite green isothiocyanate (MGITC) followed by a stabilizing layer of polyethylene glycol (PEG). These particles were then covalently linked to rituximab using ethyl dimethylaminopryl carbiimide (EDC) and sulfo-NHS chemistry. Following the incubation of CLL cells with rituximab conjugates, samples were examined using darkfield microscopy, and Raman scatter analyzed using a Raman spectroscope. The resulting spectra were concordant with the successful retention of SERS probes, as indicated by an increase in the intensity of MGITC Raman peaks as the staining concentration of conjugates increased. However, the significant background signal obtained using unconjugated control NPs highlights the necessity to incorporate more rigorous steps to remove unbound particles in future studies. Darkfield imaging strongly confirmed the successful binding of SERS probes to CLL cells, which notably failed to retain control NPs. Conjugate targeting was also disrupted by blocking CD20 binding sites with unconjugated rituximab prior to SERS probe staining, thereby confirming that NP targeting was not the product of non-specific binding. Together, these results strongly indicate the successful incorporation of a therapeutic antibody into the NP-based targeting of CD20. In conjunction with SERS probes directed at other markers, this novel diagnostic approach could have a profound impact on the multiplexing capacity of cell surface marker detection during the diagnosis of lymphoproliferative disorders. In addition, the long-term stability of these probes might facilitate the use of NP conjugates as tracers to examine the effects of rituximab binding, thereby providing valuable insight into the mechanisms of antibody-based immunotherapy. Disclosures: No relevant conflicts of interest to declare.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».