Targeting CLL Cells Using Rituximab-Conjugated Surface Enhanced Raman Scattering (SERS) Gold Nanoparticles
Bibliographic record
Abstract
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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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".