Sara Spaziani

Surface-enhanced Raman scattering (SERS) has established itself as a powerful analytical technique for detecting specific molecular structures and analyzing the composition of biological and chemical substances with high sensitivity. However, the practical deployment of SERS beyond specialized laboratories remains challenging, largely due to the limited portability of conventional planar SERS substrates and the need for in situ, real-time measurements in confined or complex environments. To address this gap, significant advances have been made in integrating SERS-active nanostructures with optical fibers, combining the strong signal enhancement of SERS with the versatility and potential of lab-on-fiber technology. In this perspective, we review the recent progress, emerging opportunities, and current challenges in applying lab-on-fiber technology to the development of SERS optrodes. Particular emphasis is placed on the most recent advances, remaining limitations, and prospects for biomedical applications.

We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.

Lab-on-fiber (LoF) optrodes offer several advantages over conventional techniques for point-of-care platforms aimed at real-time and label-free detection of clinically relevant biomarkers. Moreover, the easy integration of LoF platforms in medical needles, catheters, and nano endoscopes offer unique potentials for in vivo biopsies and tumor microenvironment assessment. The main barrier to translating the vision close to reality is the need to further lower the final limit of detection of developed optrodes. For immune-biosensing purposes, the assay sensitivity significantly relies on the capability to correctly immobilize the capture antibody in terms of uniform coverage and correct orientation of the bioreceptor, especially when very low detection limits are requested as in the case of cancer diagnostics. Here, we investigated the possibility to improve the immobilization strategies through the use of hinge carbohydrates by involving homemade antibodies that demonstrated a significantly improved recognition of the antigen with ultra-low detection limits. In order to create an effective pipeline for the improvement of biofunctionalization protocols to be used in connection with LoF platforms, we first optimized the protocol using a microfluidic surface plasmon resonance (mSPR) device and then transferred the optimized strategy onto LoF platforms selected for the final validation. Here, we selected two different LoF platforms: a biolayer interferometry (BLI)-based device (commercially available) and a homemade advanced LoF biosensor based on optical fiber meta-tips (OFMTs). As a clinically relevant scenario, here we focused our attention on a promising serological biomarker, Cripto-1, for its ability to promote tumorigenesis in breast and liver cancer. Currently, Cripto-1 detection relies on laborious and time-consuming immunoassays. The reported results demonstrated that the proposed approach based on oriented antibody immobilization was able to significantly improve Cripto-1 detection with a 10-fold enhancement versus the random approach. More interestingly, by using the oriented antibody immobilization strategy, the OFMTs-based platform was able to reveal Cripto-1 at a concentration of 0.05 nM, exhibiting detection capabilities much higher (by a factor of 250) than those provided by the commercial LoF platform based on BLI and similar to the ones shown by the commercial and well-established bench-top mSPR Biacore 8K system. Therefore, our work opened new avenues into the development of high-sensitivity LoF biosensors for the detection of clinically relevant biomarkers in the sub-ng/mL range.