Surface-Enhanced Raman Scattering Dye-Labeled Au Nanoparticles for Triplexed Detection of Leukemia and Lymphoma Cells and SERS Flow Cytometry
Bibliographic record
Abstract
The labeling of cell surface receptors by fluorescent markers is an established method for the identification of cell phenotype in both research and clinical settings. Fluorescence dye labeling has inherent constraints, most notably the upper limit of labels per cell that may be probed using a single excitation source, in addition to a physical limit to the number of broad emission spectra that can be distinctly collected within the visible wavelength region. SERS labeling has the potential to mitigate these shortfalls. Herein, antibody-targeted, PEG-coated surface-enhanced Raman scattering (SERS) Au nanoparticles are used simultaneously to label three cell surface markers of interest on malignant B cells from the LY10 lymphoma cell line. The SERS probes were characterized by multiple methods to confirm their monodispersity and functionalization with both PEG and monoclonal antibodies. The specificity of the particles' cell labeling was demonstrated on both primary chronic lymphocytic leukemia and LY10 cells using SERS from cell suspensions and confocal Raman mapping, respectively. Fluorescence flow cytometry was employed to confirm the binding of SERS probes to LY10 over large cell populations, and the particles' SERS was collected directly from labeled cells using a commercial flow cytometer. To the best of our knowledge, this is the first demonstration of SERS flow cytometry from cells tagged with targeted SERS probes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".