Raman-Guided Bronchoscopy: Feasibility and Detection Depth Studies Using Ex Vivo Lung Tissues and SERS Nanoparticle Tags

Surface-enhanced Raman scattering (SERS) technology: Emerging applications in cancer imaging and precision medicine

Recent advancements in Surface-enhanced Raman Scattering (SERS) bioprobes have substantially enhanced their bioimaging capabilities for disease theranostics. This review systematically analyzes three categories of engineered SERS probes: noble metal nanostructures, metal oxide hybrids, and multifunctional composite materials, emphasizing their optimized designs for targeted tumor detection and image-guided surgery. Key developments include improved in vitro biosensing platforms for rapid tumor screening and advanced in vivo probes enabling real-time intraoperative imaging with molecular specificity. The integration of SERS with complementary modalities (fluorescence, photoacoustic, MRI) is critically examined as a strategy to overcome individual technical limitations and achieve multiscale tissue characterization. Technical progress in spatial resolution enhancement, multiplex biomarker detection, and biocompatibility optimization is quantitatively highlighted. Current challenges in signal consistency across biological systems and scalable probe manufacturing are discussed, proposing standardized evaluation frameworks as essential for clinical translation. This work establishes SERS as a multimodal imaging cornerstone for precision oncology, particularly in tumor margin delineation and metastatic lesion identification.

Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges

The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contribute significantly to global cancer mortality. Routine detection methods, including medical imaging, endoscopic examination, and pathological biopsy, often suffer from drawbacks such as low sensitivity and laborious and complex procedures. Raman spectroscopy is a non-invasive and label-free optical technique that provides highly sensitive biomolecular information to facilitate effective tumor identification. In this work, we report the use of fiber-optic Raman spectroscopy for the accurate and rapid diagnosis of gastric and esophageal cancers. Using a database of 14,000 spectra from 140 ex vivo tissue pieces of both tumor and normal tissue samples, we compare the random forest (RF) and our established Euclidean distance Raman spectroscopy (EDRS) model. The RF analysis achieves a sensitivity of 85.23% and an accuracy of 83.05% in diagnosing gastric tumors. The EDRS algorithm with improved diagnostic transparency further increases the sensitivity to 92.86% and accuracy to 89.29%. When these diagnostic protocols are extended to esophageal tumors, the RF and EDRS models achieve accuracies of 71.27% and 93.18%, respectively. Finally, we demonstrate that fewer than 20 spectra are sufficient to achieve good Raman diagnostic accuracy for both tumor tissues. This optimizes the balance between acquisition time and diagnostic performance. Our work, although conducted on ex vivo tissue models, offers valuable insights for in vivo in situ endoscopic Raman diagnosis of gastric and esophageal cancer lesions in the future. Our study provides a robust, rapid, and convenient method as a new paradigm in in vivo endoscopic medical diagnostics that integrates spectroscopic techniques and a Raman probe for detecting upper gastrointestinal malignancies.

Self-Supervised Learning for Generic Raman Spectrum Denoising

Raman and surface-enhanced Raman scattering (SERS) spectroscopy are highly specific and sensitive optical modalities that have been extensively investigated in diverse applications. Noise reduction is demanding in the preprocessing procedure, especially for weak Raman/SERS spectra. Existing denoising methods require manual optimization of parameters, which is time-consuming and laborious and cannot always achieve satisfactory performance. Deep learning has been increasingly applied in Raman/SERS spectral denoising but usually requires massive training data, where the true labels may not exist. Aiming at these challenges, this work presents a generic Raman spectrum denoising algorithm with self-supervised learning for accurate, rapid, and robust noise reduction. A specialized network based on U-Net is established, which first extracts high-level features and then restores key peak profiles of the spectra. A subsampling strategy is proposed to refine the raw Raman spectrum and avoid the underlying biased interference. The effectiveness of the proposed approach has been validated by a broad range of spectral data, exhibiting its strong generalization ability. In the context of photosafe detection of deep-seated tumors, our method achieved signal-to-noise ratio enhancement by over 400%, which resulted in a significant increase in the limit of detection thickness from 10 to 18 cm. Our approach demonstrates superior denoising performance compared to the state-of-the-art denoising methods. The occlusion method further showed that the proposed algorithm automatically focuses on characterized peaks, enhancing the interpretability of our approach explicitly in Raman and SERS spectroscopy.

Biomimetic-Membrane-Protected Plasmonic Nanostructures as Dual-Modality Contrast Agents for Correlated Surface-Enhanced Raman Scattering and Photoacoustic Detection of Hidden Tumor Lesions

Optical imaging and spectroscopic modalities are of considerable current interest for in vivo cancer detection and image-guided surgery, but the turbid or scattering nature of biomedical tissues has severely limited their abilities to detect buried or occluded tumor lesions. Here we report the development of a dual-modality plasmonic nanostructure based on colloidal gold nanostars (AuNSs) for simultaneous surface-enhanced Raman scattering (SERS) and photoacoustic (PA) detection of tumor phantoms embedded (hidden) in ex vivo animal tissues. By using red blood cell membranes as a naturally derived biomimetic coating, we show that this class of dual-modality contrast agents can provide both Raman spectroscopic and PA signals for the detection and differentiation of hidden solid tumors with greatly improved depths of tissue penetration. Compared to previous polymer-coated AuNSs, the biomimetic coatings are also able to minimize protein adsorption and cellular uptake when exposed to human plasma without compromising their SERS or PA signals. We further show that tumor-targeting peptides (such as cyclic RGD) can be noncovalently inserted for targeting the ανβ3-integrin receptors expressed on metastatic cancer cells and tracked via both SERS and PA imaging (PAI). Finally, we demonstrate image-guided resections of tumor-mimicking phantoms comprising metastatic tumor cells buried under layers of skin and fat tissues (6 mm in thickness). Specifically, PAI was used to determine the precise tumor location, while SERS spectroscopic signals were used for tumor identification and differentiation. This work opens the possibility of using these biomimetic dual-modality nanoparticles with superior signal and biological stability for intraoperative cancer detection and resection.

Non-Invasive Detection, Precise Localization, and Perioperative Navigation of In Vivo Deep Lesions Using Transmission Raman Spectroscopy

Non-invasive detection and precise localization of deep lesions have attracted significant attention for both fundamental and clinical studies. Optical modality techniques are promising with high sensitivity and molecular specificity, but are limited by shallow tissue penetration and the failure to accurately determine lesion depth. Here the authors report in vivo ratiometric surface-enhanced transmission Raman spectroscopy (SETRS) for non-invasive localization and perioperative surgery navigation of deep sentinel lymph nodes in live rats. The SETRS system uses ultrabright surface-enhanced Raman spectroscopy (SERS) nanoparticles with a low detection limit of 10 pM and a home-built photosafe transmission Raman spectroscopy setup. The ratiometric SETRS strategy is proposed based on the ratio of multiple Raman spectral peaks for obtaining lesion depth. Via this strategy, the depth of the phantom lesions in ex vivo rat tissues is precisely determined with a mean-absolute-percentage-error of 11.8%, and the accurate localization of a 6-mm-deep rat popliteal lymph node is achieved. The feasibility of ratiometric SETRS allows the successful perioperative navigation of in vivo lymph node biopsy surgery in live rats under clinically safe laser irradiance. This study represents a significant step toward the clinical translation of TRS techniques, providing new insights for the design and implementation of in vivo SERS applications.

Euclidean distance-based Raman spectroscopy (EDRS) for the prognosis analysis of gastric cancer: A solution to tumor heterogeneity

The prognosis analysis of gastric cancer is critical for selection of treatments and development of advanced therapeutic methods. A prognosis approach that is accurate, fast, convenient, and of low cost for gastric cancers is in high demand. Raman spectroscopy is a label-free and non-destructive technique to provide molecular fingerprints of biological samples, holding promises for cancer prognosis. However, the major challenge of gastric cancer prognosis lies in the widely existing tumor heterogeneity, which leads to unexpected spectral variations within one type of samples. In this work, we have developed the Euclidean distance (ED)-based Raman spectroscopy (EDRS) method for the prognosis analysis of gastric cancer to eliminate the influence of tumor heterogeneity. Raman spectra were first collected on the slices of paraffin-preserved tumor tissues from gastric cancer patients. A standard spectrum to represent the 'worst prognostic tumor cells' was then established. The similarity between each spectrum of tissues and the standard spectrum was assessed by ED, to provide a direct assessment on the prognosis status. We have successfully classified the patients into poor and favorable prognosis groups, either based on the averaged regional ED values (sensitivity of 75 %, specificity of 96.8 %), or based on the minimal ED values at the patient level (sensitivity of 90 %, specificity of 100 %). EDRS was also investigated for survival analysis (AUC = 0.955), much better than the commonly applied post-neoadjuvant therapy (ypTNM) category (AUC = 0.718). Our work highlights EDRS as a rapid, accurate, low-cost and robust tool for heterogeneous cancer-related prognosis assessment and survival prediction, providing new insights for spectroscopic tumor analysis.

In Vivo Surface-Enhanced Transmission Raman Spectroscopy under Maximum Permissible Exposure: Toward Photosafe Detection of Deep-Seated Tumors

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.