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

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.

In Vivo Surface-Enhanced Transmission Raman Spectroscopy and Impact of Frozen Biological Tissues on Lesion Depth Prediction

Plasmonic surface-enhanced transmission Raman spectroscopy (SETRS) has emerged as a promising optical technique for detecting and predicting the depths of deep-seated lesions in biological tissues. However, in vivo studies using SETRS are scarce and typically show shallow penetration depths. Moreover, the optical parameters used in the prediction process are often derived from frozen samples and there is limited understanding of how freezing affects the optical properties of biological tissues and the accuracy of depth prediction in living models. In this work, we conduct in vivo SETRS measurements on thick abdominal tissue region of the live rats to investigate the impact of freezing on the measured optical properties for the purpose of depth prediction. First, we fabricated ultrahigh bright surface-enhanced Raman spectroscopy (SERS) nanotags and utilized a custom transmission Raman system. We then measured the change of optical attenuation at two different wavelengths (Δμ) for four types of rat tissues (including skin, fat, muscle, and liver) following freezing. The freezing process dramatically affects Δμ values, even after only 1 day of freezing. In contrast, Δμ values obtained from fresh samples enable precise localization of SERS lesion phantoms in the live rat with only 5% deviation. The total thickness of the live rat is 2.6 cm, which, to the best of our knowledge, is the highest value of in vivo SETRS studies so far. This work helps to fill the gap in the SERS field of tissue localization and optical coefficient studies in highly heterogeneous tissues, and demonstrates the potential of the SETRS technique to achieve precise clinical localization of deep lesions.

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.

Material-agnostic characterization of spatially offset Raman spectroscopy in turbid media via Monte Carlo simulations

Spatially offset Raman spectroscopy (SORS) is a transformative method for probing subsurface chemical compositions in turbid media. This systematic study of Monte Carlo simulations provides closed-form characterizations of key SORS parameters, such as the distribution of spatial origins of collected Raman photons and optimal SORS geometry to selectively interrogate a subsurface region of interest. These results are unified across an extensive range of material properties by multiplying spatial dimensions by the medium's effective attenuation coefficient, which can be calculated when the absorption and reduced scattering coefficients are known from the literature or experimentation. This method of spatial nondimensionalization is validated via goodness-of-fit analysis on the aggregate models and by training a subsurface sample localization model on a heterogeneous population of materials. The findings reported here advance the understanding of SORS phenomena while providing a quantitative and widely applicable foundation for designing and interpreting SORS experiments, facilitating its application in disciplines such as biomedical, materials science, and cultural heritage fields.

NIR-II Surface-Enhanced Raman Scattering Nanoprobes in Biomedicine: Current Impact and Future Directions

The field of second near-infrared (NIR-II) surface-enhanced Raman scattering (SERS) nanoprobes has made commendable progress in biomedicine. This article reviews recent advances and future development of NIR-II SERS nanoprobes. It introduces the fundamental principles of SERS nanoprobes and highlights key advances in the NIR-II window, including reduced tissue attenuation, deep penetration, maximized allowable exposure, and improved photostability. The discussion of future directions includes the refinement of nanoprobe substrates, emphasizing the tailoring of optical properties of metallic SERS-active nanoprobes, and exploring non-metallic alternatives. The intricacies of designing Raman reporters for the NIR-II resonance and the potential of these reporters to advance the field are also discussed. The integration of artificial intelligence (AI) into nanoprobe design represents a cutting-edge approach to overcome current challenges. This article also examines the emergence of deep Raman techniques for through-tissue SERS detection, toward NIR-II SERS tomography. It acknowledges instrumental advancements like improved charge-coupled device sensitivity and accelerated imaging speeds. The article concludes by addressing the critical aspects of biosafety, ease of functionalization, compatibility, and the path to clinical translation. With a comprehensive overview of current achievements and future prospects, this review aims to illuminate the path for NIR-II SERS nanoprobes to innovate diagnostic and therapeutic approaches in biomedicine.

Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications.

Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples

The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk of false-negative results. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms holds promise for cancer diagnosis. In this study, we develop a label-free SERS liquid biopsy method with machine learning for the rapid and accurate diagnosis of thyroid cancer by using thyroid FNA washout fluids. These liquid supernatants are mixed with silver nanoparticle colloids, and dispersed in quartz capillary for SERS measurements to discriminate between healthy and malignant samples. We collect Raman spectra of 36 thyroid FNA samples (18 malignant and 18 benign) and compare four classification models: Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The results show that the CNN algorithm is the most precise, with a high accuracy of 88.1%, sensitivity of 87.8%, and the area under the receiver operating characteristic curve of 0.953. Our approach is simple, convenient, and cost-effective. This study indicates that label-free SERS liquid biopsy assisted by deep learning models holds great promise for the early detection and screening of thyroid cancer.

Label-Free Fiber-Optic Raman Spectroscopy for Intravascular Coronary Atherosclerosis and Plaque Detection

The rupture of atherosclerotic plaques remains one of the leading causes of morbidity and mortality worldwide. The plaques have certain pathological characteristics including a fibrous cap, inflammation, and extensive lipid deposition in a lipid core. Various invasive and noninvasive imaging techniques can interrogate structural aspects of atheroma; however, the composition of the lipid core in coronary atherosclerosis and plaques cannot be accurately detected. Fiber-optic Raman spectroscopy has the capability of in vivo rapid and accurate biomarker detection as an emerging omics technology. Previous studies demonstrated that an intravascular Raman spectroscopic technique may assess and manage the therapeutic and medication strategies intraoperatively. The Raman spectral information identified plaque depositions consisting of lipids, triglycerides, and cholesterol esters as the major components by comparing normal region and early plaque formation region with histology. By focusing on the composition of plaques, we could identify the subgroups of plaques accurately and rapidly by Raman spectroscopy. Collectively, this fiber-optic Raman spectroscopy opens up new opportunities for coronary atherosclerosis and plaque detection, which would assist optimal surgical strategy and instant postoperative decision-making. In this paper, we will review the advancement of label-free fiber-optic Raman probe spectroscopy and its applications of coronary atherosclerosis and atherosclerotic plaque detection.

Sentinel lymph node identification using NIR-II ultrabright Raman nanotags on preclinical models

Surface-enhanced Raman spectroscopy (SERS) nanotags have garnered much attention as promising bioimaging contrast agent with ultrahigh sensitivity, but their clinical translation faces challenges including biological and laser safety. As breast sentinel lymph node (SLN) imaging agents, SERS nanotags used by local injection and only accumulation in SLNs, which were removed during surgery, greatly reduce biological safety concerns. But their clinical translation lacks pilot demonstration on large animals close to humans. The laser safety requires irradiance below the maximum permissible exposure threshold, which is currently not achievable in most SERS applications. Here we report the invention of the core-shell SERS nanotags with ultrahigh brightness (1 pM limit of detection) at the second near-infrared (NIR-II) window for SLN identification on pre-clinical animal models including rabbits and non-human primate. We for the first time realize the intraoperative SERS-guided SLN navigation under a clinically safe laser (1.73 J/cm2) and identify multiple axillary SLNs on a non-human primate. No evidence of biosafety issues was observed in systematic examinations of these nanotags. Our study unveils the potential of NIR-II SERS nanotags as appropriate SLN tracers, making significant advances toward the accurate positioning of lesions using the SERS-based tracer technique.

SERS in 3D cell models: a powerful tool in cancer research

Unraveling the cellular and molecular mechanisms underlying tumoral processes is fundamental for the diagnosis and treatment of cancer. In this regard, three-dimensional (3D) cancer cell models more realistically mimic tumors compared to conventional 2D cell cultures and are more attractive for performing such studies. Nonetheless, the analysis of such architectures is challenging because most available techniques are destructive, resulting in the loss of biochemical information. On the contrary, surface-enhanced Raman spectroscopy (SERS) is a non-invasive analytical tool that can record the structural fingerprint of molecules present in complex biological environments. The implementation of SERS in 3D cancer models can be leveraged to track therapeutics, the production of cancer-related metabolites, different signaling and communication pathways, and to image the different cellular components and structural features. In this review, we highlight recent progress in the use of SERS for the evaluation of cancer diagnosis and therapy in 3D tumoral models. We outline strategies for the delivery and design of SERS tags and shed light on the possibilities this technique offers for studying different cellular processes, through either biosensing or bioimaging modalities. Finally, we address current challenges and future directions, such as overcoming the limitations of SERS and the need for the development of user-friendly and robust data analysis methods. Continued development of SERS 3D bioimaging and biosensing systems, techniques, and analytical strategies, can provide significant contributions for early disease detection, novel cancer therapies, and the realization of patient-tailored medicine.

A review of recent advances in the use of complex metal nanostructures for biomedical applications from diagnosis to treatment

Complex metal nanostructures represent an exceptional category of materials characterized by distinct morphologies and physicochemical properties. Nanostructures with shape anisotropies, such as nanorods, nanostars, nanocages, and nanoprisms, are particularly appealing due to their tunable surface plasmon resonances, controllable surface chemistries, and effective targeting capabilities. These complex nanostructures can absorb light in the near-infrared, enabling noteworthy applications in nanomedicine, molecular imaging, and biology. The engineering of targeting abilities through surface modifications involving ligands, antibodies, peptides, and other agents potentiates their effects. Recent years have witnessed the development of innovative structures with diverse compositions, expanding their applications in biomedicine. These applications encompass targeted imaging, surface-enhanced Raman spectroscopy, near-infrared II imaging, catalytic therapy, photothermal therapy, and cancer treatment. This review seeks to provide the nanomedicine community with a thorough and informative overview of the evolving landscape of complex metal nanoparticle research, with a specific emphasis on their roles in imaging, cancer therapy, infectious diseases, and biofilm treatment. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Diagnostic Tools > Diagnostic Nanodevices.

In vivo imaging using surface enhanced spatially offset raman spectroscopy (SESORS): balancing sampling frequency to improve overall image acquisition

In the field of optical imaging, the ability to image tumors at depth with high selectivity and specificity remains a challenge. Surface enhanced resonance Raman scattering (SERRS) nanoparticles (NPs) can be employed as image contrast agents to specifically target cells in vivo; however, this technique typically requires time-intensive point-by-point acquisition of Raman spectra. Here, we combine the use of "spatially offset Raman spectroscopy" (SORS) with that of SERRS in a technique known as "surface enhanced spatially offset resonance Raman spectroscopy" (SESORRS) to image deep-seated tumors in vivo. Additionally, by accounting for the laser spot size, we report an experimental approach for detecting both the bulk tumor, subsequent delineation of tumor margins at high speed, and the identification of a deeper secondary region of interest with fewer measurements than are typically applied. To enhance light collection efficiency, four modifications were made to a previously described custom-built SORS system. Specifically, the following parameters were increased: (i) the numerical aperture (NA) of the lens, from 0.2 to 0.34; (ii) the working distance of the probe, from 9 mm to 40 mm; (iii) the NA of the fiber, from 0.2 to 0.34; and (iv) the fiber diameter, from 100 μm to 400 μm. To calculate the sampling frequency, which refers to the number of data point spectra obtained for each image, we considered the laser spot size of the elliptical beam (6 × 4 mm). Using SERRS contrast agents, we performed in vivo SESORRS imaging on a GL261-Luc mouse model of glioblastoma at four distinct sampling frequencies: par-sampling frequency (12 data points collected), and over-frequency sampling by factors of 2 (35 data points collected), 5 (176 data points collected), and 10 (651 data points collected). In comparison to the previously reported SORS system, the modified SORS instrument showed a 300% improvement in signal-to-noise ratios (SNR). The results demonstrate the ability to acquire distinct Raman spectra from deep-seated glioblastomas in mice through the skull using a low power density (6.5 mW/mm2) and 30-times shorter integration times than a previous report (0.5 s versus 15 s). The ability to map the whole head of the mouse and determine a specific region of interest using as few as 12 spectra (6 s total acquisition time) is achieved. Subsequent use of a higher sampling frequency demonstrates it is possible to delineate the tumor margins in the region of interest with greater certainty. In addition, SESORRS images indicate the emergence of a secondary tumor region deeper within the brain in agreement with MRI and H&E staining. In comparison to traditional Raman imaging approaches, this approach enables improvements in the detection of deep-seated tumors in vivo through depths of several millimeters due to improvements in SNR, spectral resolution, and depth acquisition. This approach offers an opportunity to navigate larger areas of tissues in shorter time frames than previously reported, identify regions of interest, and then image the same area with greater resolution using a higher sampling frequency. Moreover, using a SESORRS approach, we demonstrate that it is possible to detect secondary, deeper-seated lesions through the intact skull.

Highly Sensitive and Wide-Range Detection of Thiabendazole via Surface-Enhanced Raman Scattering Using Bimetallic Nanoparticle-Functionalized Nanopillars

Thiabendazole (TBZ) is a benzimidazole; owing to its potent antimicrobial properties, TBZ is extensively employed in agriculture as a fungicide and pesticide. However, TBZ poses environmental risks, and excessive exposure to TBZ through various leakage pathways can cause adverse effects in humans. Therefore, a method must be developed for early and sensitive detection of TBZ over a range of concentrations, considering both human and environmental perspectives. In this study, we used silver nanopillar structures (SNPis) and Au@Ag bimetallic nanoparticles (BNPs) to fabricate a BNP@SNPi substrate. This substrate exhibited a broad reaction surface with significantly enhanced surface-enhanced Raman scattering hotspots, demonstrating excellent Raman performance, along with high reproducibility, sensitivity, and selectivity for TBZ detection. Ultimately, the BNP@SNPi substrate successfully detected TBZ across a wide concentration range in samples of tap water, drinking water, juice, and human serum, with respective limits of detection of 146.5, 245.5, 195.6, and 219.4 pM. This study highlights BNP@SNPi as a promising sensor platform for TBZ detection in diverse environments and contributes to environmental monitoring and bioanalytical studies.

Engineered aptamers for molecular imaging

Molecular imaging, including quantification and molecular interaction studies, plays a crucial role in visualizing and analysing molecular events occurring within cells or organisms, thus facilitating the understanding of biological processes. Moreover, molecular imaging offers promising applications for early disease diagnosis and therapeutic evaluation. Aptamers are oligonucleotides that can recognize targets with a high affinity and specificity by folding themselves into various three-dimensional structures, thus serving as ideal molecular recognition elements in molecular imaging. This review summarizes the commonly employed aptamers in molecular imaging and outlines the prevalent design approaches for their applications. Furthermore, it highlights the successful application of aptamers to a wide range of targets and imaging modalities. Finally, the review concludes with a forward-looking perspective on future advancements in aptamer-based molecular imaging.

Multiplexing potential of NIR resonant and non-resonant Raman reporters for bio-imaging applications

Multiplexed imaging, which allows for the interrogation of multiple molecular features simultaneously, is vital for addressing numerous challenges across biomedicine. Optically unique surface-enhanced Raman scattering (SERS) nanoparticles (NPs) have the potential to serve as a vehicle to achieve highly multiplexed imaging in a single acquisition, which is non-destructive, quantitative, and simple to execute. When using laser excitation at 785 nm, which allows for a lower background from biological tissues, near infrared (NIR) dyes can be used as Raman reporters to provide high Raman signal intensity due to the resonance effect. This class of imaging agents are known as surface-enhanced resonance Raman scattering (SERRS) NPs. Investigators have predominantly utilized two classes of Raman reporters in their nanoparticle constructs for use in biomedical applications: NIR-resonant and non-resonant Raman reporters. Herein, we investigate the multiplexing potential of five non-resonant SERS: BPE, 44DP, PTT, PODT, and BMMBP, and five NIR resonant SERRS NP flavors with heptamethine cyanine dyes: DTTC, IR-770, IR-780, IR-792, and IR-797, which have been extensively used for biomedical imaging applications. Although SERRS NPs display high Raman intensities, due to their resonance properties, we observed that non-resonant SERS NP concentrations can be quantitated by the intensity of their unique emissions with higher accuracy. Spectral unmixing of five-plex mixtures revealed that the studied non-resonant SERS NPs maintain their detection limits more robustly as compared to the NIR resonant SERRS NP flavors when introducing more components into a mixture.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering

BACKGROUND

Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

Locating Three-Dimensional Position of Deep-Seated SERS Phantom Lesions in Thick Tissues Using Tomographic Transmission Raman Spectroscopy

Locating distinct objects within a thick scattering medium remains a long-standing challenge in the fields of materials science, health, and engineering. Transmission Raman spectroscopy (TRS) with the use of surface-enhanced Raman scattering (SERS) nanoparticles has proven to be an effective approach to detect deep-seated lesions inside thick biological tissues. However, it has not yet been proven to spatially locate deep lesions in three dimensions using optical modalities. Herein, we present the concept of tomographic TRS and report its successful use for accurately locating SERS nanoparticles in elongated rod-like thick tissues. Our work starts with theoretical simulations of Raman photon propagation in tissues. We discovered a linear relationship between the Raman spectral peak ratio and propagation distance of Raman photons in tissues, allowing us to predict the location of lesions tagged by SERS NPs. Based on this, we propose a two-step tomographic TRS strategy, which includes axial scanning and ring scanning. We demonstrate the robustness of our approach using ex vivo thick tissue (4.5 cm in thickness) and locate an embedded SERS phantom lesion, with a ring scanning step of 10-30°. We successfully locate multiple SERS phantom lesions in the ex vivo porcine muscle stack with high accuracy (absolute error of <2 mm). Our method is rapid, efficient, and of low cost compared to current tomographic medical imaging techniques. This work advances Raman techniques for three-dimensional positioning and offers new insights toward practical diagnosis applications.

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.