No abstract

The detection of deep‐seated lesions is of great significance for biomedical applications. However, due to the strong photon absorption and scattering of biological tissues, it is challenging to realize in vivo deep optical detections, particularly for those using the safe laser irradiance below clinical maximum permissible exposure (MPE). In this work, the combination of ultra‐bright surface‐enhanced Raman scattering (SERS) nanotags and transmission Raman spectroscopy (TRS) is reported to achieve the non‐invasive and photosafe detection of “phantom” lesions deeply hidden in biological tissues, under the guidance of theoretical calculations showing the importance of SERS nanotags’ brightness and the expansion of laser beam size. Using a home‐built TRS system with a laser power density of 0.264 W cm−2 (below the MPE criteria), we successfully demonstrated the detection of SERS nanotags through up to 14‐cm‐thick ex vivo porcine tissues, as well as in vivo imaging of “phantom” lesions labeled by SERS nanotags in a 1.5‐cm‐thick unshaved mouse under MPE. This work highlights the potential of transmission Raman‐guided identification and non‐invasive imaging toward clinically photosafe cancer diagnoses.

Metabolites are important biomarkers in human body fluids, conveying direct information of cellular activities and physical conditions. Metabolite detection has long been a research hotspot in the field of biology and medicine. Surface-enhanced Raman spectroscopy (SERS), based on the molecular “fingerprint” of Raman spectrum and the enormous signal enhancement (down to a single-molecule level) by plasmonic nanomaterials, has proven to be a novel and powerful tool for metabolite detection. SERS provides favorable properties such as ultra-sensitive, label-free, rapid, specific, and non-destructive detection processes. In this review, we summarized the progress in recent 10 years on SERS-based sensing of endogenous metabolites at the cellular level, in tissues, and in biofluids, as well as drug metabolites in biofluids. We made detailed discussions on the challenges and optimization methods of SERS technique in metabolite detection. The combination of SERS with modern biomedical technology were also anticipated.

Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been demonstrated to be a powerful tool for tumor identification. However, the analysis and classification of Raman spectra for biological samples with complex compositions are still challenges. In addition, the signal-to-noise ratio of the spectra also influences the accuracy of the classification. Herein, we applied the variational autoencoder (VAE) to Raman spectra for downscaling and noise reduction simultaneously. We validated the performance of the VAE algorithm at the cellular and tissue levels. VAE successfully downscaled high-dimensional Raman spectral data to two-dimensional (2D) data for three subtypes of non-small cell lung cancer cells and two subtypes of kidney cancer tissues. Gaussian naïve bayes was applied to subtype discrimination with the 2D data after VAE encoding at both the cellular and tissue levels, significantly outperforming the discrimination results using original spectra. Therefore, the analysis of Raman spectroscopy based on VAE and machine learning has great potential for rapid diagnosis of tumor subtypes.