From single cells to complex tissues in applications of surface-enhanced Raman scattering
Bibliographic record
Abstract
As surface-enhanced Raman scattering (SERS) continues to grow in popularity, more work needs to be done to evaluate its compatibility with a wider scope of applications. With such a strong emphasis on SERS being used for biosensing, it is important to examine how SERS is used in bioanalytical nanoscience, and more importantly, look towards where SERS is heading. For many, the initial steps involve demonstrating in vivo sensing by SERS using cultures of live cells. To further and better demonstrate the capabilities of SERS as a technique in bioanalytical nanoscience, it is necessary to transition away from studies involving single cells or small quantities of cells. This means working with tissue, typically as an ex vivo slice or a spheroid, before moving onto in vivo animal models. Although working with tissue as opposed to single cells introduces new challenges, the types of approaches developed for single cell studies serve as the foundation for the more complex biomaterials. The aim of this tutorial review is to better facilitate the transition from single cells to complex tissues by demonstrating the similarities in the methodologies that have been used and how to overcome some of the challenges of working with tissue. Specifically, we explore how three of the most common methods of working with nanoparticles and cells have been adapted and incorporated for experiments involving different types of tissues. Overall, this review highlights a variety of methods that can be readily implemented for those wishing to perform SERS measurements with or in complex tissues.
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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) | 0.001 | 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 | 0.000 | 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".