Raman Imaging of Cells Using Antibody-Derived Carbon Nanotubes Nanoprobes
Bibliographic record
Abstract
Microscopy of biological samples with fluorescent dyes exhibits strong signals, but also limitations such as photobleaching, poor long-term stability and low multicolour capabilities. In contrast, Raman imaging of molecules is better adapted for multiplexing, but its intrinsic signal is too weak to be useful for high resolution imaging. To compensate for the low sensitivity of Raman, contrast agents (probes) have been developed over the years to boost the Raman signal to levels near that of fluorescence. By encapsulating dyes inside single-walled carbon nanotubes (dyes@SWCNTs), our group has highlighted interesting optical properties that make them suitable as Raman nanoprobes [1]. Not only these probes show giant Raman signal from the dyes, but they are resistant also to photobleaching and offer multiplexing capabilities as well as versatile chemistry at their surfaces. Recently we show that PEGylated dyes@SWCNTs can indeed be used as nanoprobes having good Raman multiplexing properties for biological imaging [2]. Here, we will present recent results on their covalent functionalization with bifunctional polyethylene-glycols (NH2-PEG-R). A full physicochemical characterization of the probes carried out using TGA, Raman spectroscopy, AFM and 1H NMR will be presented. The covalent functionalization of these Dyes@PEGylated-SWCNTs nanoprobes with antibodies was developed to produce active targeting agents for cells. Using Raman hyperspectral imaging, we have demonstrated that these antibody-functionalized nanoprobes can effectively target living or fixed human and mouse cancer cells and that they can selectively attach, depending on the antibody, to specific targets immobilized on surfaces. [1] E. Gaufrès, N.Y.-W. Tang, F. Lapointe, J. Cabana, M.-A. Nadon, N. Cottenye, et al., Giant Raman scattering from J-aggregated dyes inside carbon nanotubes for multispectral imaging, Nat. Photonics. 8 (2013) 72–78. doi:10.1038/nphoton.2013.309. [2] N. Cottenye, N.Y.-W. Tang, E. Gaufrès, A. Leduc, J. Barbeau, R. Martel, Raman tags derived from dyes encapsulated inside carbon nanotubes for Raman imaging of biological samples, Phys. Status Solidi. 211 (2014) 2790–2794. doi:10.1002/pssa.201431401. [3] This work was made possible because of financial support from NSERC (Canada) and Photon Etc.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".