Raman Spectroscopy for Chemical Biology Research

Single-cell Raman spectroscopy as a novel platform for unveiling the heterogeneity of mesenchymal stem cells

Despite the significant potential of mesenchymal stem cells (MSC) therapy in clinical settings, challenges persist regarding the efficient detection of consistency and uniformity of MSC populations. Raman spectroscopy is a fast, convenient, and nondestructive technique to acquire molecular properties of biomolecules across laboratory and mass-production settings. Here we utilized Raman spectroscopy to evaluate the heterogeneity of primary MSC from varying donors, passages, and distinct culture conditions, and compared its effectiveness with conventional techniques such as flow cytometry. Although these MSC exhibited insignificant differences in morphology and surface markers in flow cytometry analysis, they could be distinctly clustered into different populations by Raman spectroscopy and the subsequent machine learning using linear discriminant analysis. Principal component analysis demonstrated limited efficiency in clustering Raman data from diverse sources, which could be enhanced through combination with support vector machine or deterministic finite automation. These findings highlight the sensitivity of Raman spectroscopy in detecting subtle differences. Moreover, the analysis of characteristic Raman peaks attributed to cellular biomolecules in MSC from passages 2 (P2) to P10 revealed a gradual decrease in the levels of nucleic acids, lipids, and proteins with increasing passages, and a significant increase in carotenoids from P8. These results suggest the potential use of Raman spectroscopy to assess cellular biochemical characteristics such as aging, with carotenoids emerging as a potential marker of cell aging. In conclusion, Raman spectroscopy demonstrates the ability to rapidly and non-invasively detect cellular heterogeneity and biochemical status, offering significant potential for quality control in stem cell therapy.

Structure and Dissociation of Water at the Electrode-Solution Interface Studied by In Situ Vibrational Spectroscopic Techniques

In aqueous electrochemistry, water in contact with charged surfaces is ubiquitous and indispensable, dictating the binding of solutes to electrode surfaces as well as the transport process of protons and electrons in the interfacial region. A comprehensive understanding of the structure and dissociation of interfacial water at the molecular level is extremely important yet challenging, given its critical role in various physical, chemical, and biological processes. In situ vibrational spectroscopic techniques serve as a powerful tool for acquiring the molecular structure of electrode surfaces and probing interfacial reaction mechanisms in real time. In this review, we briefly summarize the latest advances in the electric double layer model and the experimental methods employed at the electrode-solution interface. Particular emphasis is placed on in situ vibrational spectroscopic techniques that have unveiled new insights into the molecular structure of interfacial water across diverse electrode surfaces under ambient conditions. And then, it also provides an overview of recent progress on the subtle relationship between the structure of interfacial water and its dissociation activity, aiming to provide novel insights into the fields of electrochemistry, energy and catalysis.

SpecRecFormer: Deep Learning-Driven Adaptive Component Identification of PAH Mixtures Based on Single-Component Raman Spectra

The identification of components in mixed spectra is a fundamental challenge in spectral analysis, complicated by factors such as spectral peak overlap due to structural similarities, shifts in characteristic peaks from molecular interactions, and interferences caused by matrix effects. While deep learning offers robust feature extraction capabilities and notable advantages in addressing these challenges, it still faces significant obstacles, including the limited availability of labeled spectral data for effective training and the difficulty of applying fixed-threshold predictive models to spectra containing uncertain components. This paper established a deep learning model, SpecRecFormer, for the rapid identification of individual components in mixed polycyclic aromatic hydrocarbons (PAHs) based on their Raman spectra. The model integrates a dual-channel convolutional neural network (CNN) for local feature extraction with a Transformer module for global representation. It is trained on a reference database composed of single-component spectra, with simulated mixed spectra generated through data augmentation to expand and diversify the training set. This architecture enables the model to evaluate the similarity between unknown mixed spectra and known single-component references. To further enhance recognition accuracy, an adaptive threshold strategy is introduced, dynamically adjusting decision thresholds based on spectral characteristics to retain only components exceeding the threshold as candidate predictions. Experimental results demonstrate that with training data derived from only four single-component reference spectra, the model generalizes effectively to three real-world PAH data sets, achieving accuracies of 93.75%, 89.21%, and 93.63%, respectively, significantly outperforming conventional neural network models. These findings present an innovative and highly effective approach to mixed spectral analysis, with substantial potential for advancing applications in environmental science and chemical analysis.

Photocatalytic and Chemoselective H/D Exchange at α-Thio C(sp3)-H Bonds

Deuterated compounds used in drug discovery and live-cell imaging have recently gained the attention of various scientific fields. Although hydrogen-deuterium (H/D) exchange reactions are straightforward deuteration methods, achieving perfect chemoselectivity is challenging. We report the highly chemoselective deuteration of α-thio C(sp3)-H bonds using a thioxanthone or anthraquinone organic photocatalyst bearing an aromatic ketone skeleton and D2O as an inexpensive deuterium source under 390 nm irradiation. Notably, incorporation of deuterium at the α-positions of the O/N atoms, benzylic positions, and aromatic rings was not observed. The present chemoselectivity was accomplished via a single electron transfer mechanism between the photocatalyst and S-containing substrates, as proven by laser-induced time-resolved transient absorption spectroscopic measurements. Furthermore, the proposed deuteration method could be applied to various S-containing substrates, including pharmaceuticals and biologically active compounds with high regioselectivities. The available deuterated compounds as novel deuterated alkylation reagents for future drug discovery and materials for Raman imaging were also demonstrated.

Identification of marine microplastics by a combined method of principal component analysis and random forest for fluorescence spectrum processing

The severely overlapped laser-induced fluorescence spectra between different microplastics pose significant challenges on fluorescence-based particle identification and quantification. To address this problem, this paper proposes a combined method of principal component analysis (PCA) and random forest (RF) for fluorescence spectrum processing. The key idea is to identify the overlapped PCA scores of the first three principal components of fluorescence spectra by the random forest method. Both pure and mixed microplastics samples were used to verify the accuracy of this method. It was demonstrated that both the compositions of the samples and mass concentration of one specific microplastics can be accurately identified. The accuracy for component identification reaches 99.7 % and the correlation coefficient between the predicted and actual concentration exceeds 0.99. Furthermore, the PCA-RF model established with commercial plastic samples was also applied for real marine microplastics identification with good identification results obtained.

Monitoring Enzymatic Reactions through Probing Chemical Bond Changes by the CD3 Group Using Multiplex Coherent Anti-Stokes Raman Scattering

We propose a method to measure enzymatic reactions using the C-D stretching vibrational spectra of CD3 groups that exist next to the site where a chemical bond changes. In the proposed method, temporal changes in the concentrations of the substrate and product are measured from changes in the C-D stretching spectra of the CD3 groups. Vibrational spectra are obtained using multiplex coherent anti-Stokes Raman scattering (CARS) spectroscopy, which can obtain vibrational spectra much faster than spontaneous Raman spectroscopy. We tested the proposed method by examining the dehydrogenation reaction of isopropyl alcohol-1,1,1,3,3,3-D6 (IPA-D6) catalyzed by alcohol dehydrogenase and found that the change in concentrations of the substrate and product, IPA-D6 and acetone-D6, respectively, was successfully measured from the C-D stretching spectra.

Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media

Maintaining consistent quality in biomanufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) often fall short in achieving rapid and accurate monitoring of small-molecule critical process parameters and critical quality attributes. Surface-enhanced Raman spectroscopy (SERS) holds great promise but faces challenges like intensity fluctuations, compromising reproducibility. Herein, we propose a deep learning-powered colloidal digital SERS platform. This innovation converts SERS spectra into binary "ON/OFF" signals based on defined intensity thresholds, which allows single-molecule event visualization and reduces false positives. Through integration with deep learning, this platform enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks. Furthermore, we demonstrate its accuracy and reproducibility for studying AMBIC 1.1 mammalian cell culture media. These results highlight its rapidity, accuracy, and precision, paving the way for widespread adoption and scale-up as a novel PAT tool in biomanufacturing and diagnostics.

Multiple Deuterium Atom Transfer Perdeuteration of Unactivated Alkenes under Base-Assisted Cobalt/Photoredox Dual Catalysis

A radical approach for hydrogenative perdeuteration of unactivated alkenes under cobalt/photoredox dual catalysis is described. The addition of a suitable base plays a key role in controlling two competing pathways by switching the catalytic performance of cobalt/photoredox catalysis. Base-assisted cobalt/photoredox dual catalysis promoted a hydrogen isotope exchange reaction of alkenes to afford deuterated alkenes via multiple repeating deuterium atom transfer/hydrogen atom abstraction processes, while consecutive reductive deuteration of alkenes proceeded in the absence of a base to afford polydeuterated alkanes. One-pot hydrogenative perdeuteration and perdeutero-arylation were also developed, providing access to various polydeuterated aliphatic compounds.

Nanophotonic sensors and AI for a new possible approach for accurate diagnosis of salivary glands tumors: a technical note

Currently, diagnosing salivary gland tumors in their early stages presents significant challenges. This paper aims to outline a feasibility analysis of a novel approach utilizing advanced nanophotonic sensors and AI to address these diagnostic issues. The proposed approach integrates new nanophotonic sensors to tackle the complexities encountered in the early detection of salivary gland tumors. By leveraging these cutting-edge sensors, we seek to offer a potential solution to the existing diagnostic problems associated with identifying these tumors at their initial stages.

Cancer Cell Line Classification Using Raman Spectroscopy of Cancer-Derived Exosomes and Machine Learning

Liquid biopsies are an emerging, noninvasive tool for cancer diagnostics, utilizing biological fluids for molecular profiling. Nevertheless, the current methods often lack the sensitivity and specificity necessary for early detection and real-time monitoring. This work explores an advanced approach to improving liquid biopsy techniques through machine learning analysis of the Raman spectra measured to classify distinct exosome solutions by their cancer origin. This was accomplished by conducting principal component analysis (PCA) of the Raman spectra of exosomes from three cancer cell lines (COLO205, A375, and LNCaP) to extract chemically significant features. This reduced set of features was then utilized to train a linear discriminant analysis (LDA) classifier to predict the source of the exosomes. Furthermore, we investigated differences in the lipid composition in these exosomes by their spectra. This spectral similarity analysis revealed differences in lipid profiles between the different cancer cell lines as well as identified the predominant lipids across all exosomes. Our PCA-LDA framework achieved 93.3% overall accuracy and F1 scores of 98.2%, 91.1%, and 91.0% for COLO205, A375, and LNCaP, respectively. Our results from spectral similarity analysis were also shown to support previous findings of lipid dynamics due to cancer pathology and pertaining to exosome function and structure. These findings underscore the benefits of enhancing Raman spectroscopy analysis with machine learning, laying the groundwork for the development of early noninvasive cancer diagnostics and personalized treatment strategies. This work potentially establishes the foundation for refining the classification model and optimizing exosome extraction and detection from clinical samples for clinical translation.

Deep Learning-Assisted SERS for Therapeutic Drug Monitoring of Clozapine in Serum on Plasmonic Metasurfaces

Clozapine is widely regarded as one of the most effective therapeutics for treatment-resistant schizophrenia. Despite its proven efficacy, the therapeutic use of clozapine is complicated by its narrow therapeutic index, which necessitates rapid and precise therapeutic drug monitoring (TDM) to optimize patient outcomes and minimize adverse effects. However, conventional techniques, such as high-performance liquid chromatography, are limited by their high costs, complex instrumentation, and long turnaround times. Herein, we propose a novel approach that integrates artificial neural networks (ANNs) with surface-enhanced Raman spectroscopy (SERS) on a plasmonic metasurface for rapid TDM of clozapine and its two primary metabolites, norclozapine and clozapine-N-oxide, in human serum. The ANN-SERS strategy enables accurate classification and robust concentration prediction of the three analytes. We envision that the integrated ANN-SERS framework could deliver a scalable biomedical diagnostic and therapeutic tool for studying a wide variety of chemical and biological molecules in clinical settings.

Application of raman spectroscopy in the non-invasive diagnosis of urological diseases via urine

OBJECTIVES

The objective of this review is to provide a comprehensive overview of the utilization of Raman spectroscopy in urinary system diseases, highlighting its potential in non-invasive diagnostic methodologies for early diagnosis and prognostic assessment of urinary ailments.

METHODS

We searched PubMed, Web of Science, and Google Scholar using 'raman,' 'bladder,' 'kidney,' 'prostate,' 'cancer,' 'infection,' 'stone or urinary calculi,' and 'urine or urinary,' along with 'AND' and 'OR' to refine our search. We excluded irrelevant articles and screened potential ones based on titles and abstracts before assessing the full texts for relevance and quality.

FINDINGS

The findings indicate that RS can furnish data on biomolecules in urine, which is significant for non-invasive diagnostic approaches. It has shown potential within non-invasive diagnostic methodologies and is expected to play a pivotal role in the early diagnosis and prognostic assessment of urinary system diseases, such as malignancies, urinary tract infections, kidney diseases, urolithiasis, and other urinary conditions.

CONCLUSIONS

Raman spectroscopy has demonstrated significant potential in providing precise and rapid diagnostic approaches for clinical use in the context of urinary system diseases. Its ability to analyze biomolecules non-invasively positions it as an increasingly important tool in the early diagnosis and prognostic assessment of these conditions.

Toward cancer detection by label-free microscopic imaging in oncological surgery: Techniques, instrumentation and applications

This review examines the clinical application of label-free microscopy and spectroscopy, which are based on optical signals emitted by tissue components. Over the past three decades, a variety of techniques have been investigated with the aim of developing an in situ histopathology method that can rapidly and accurately identify tumor margins during surgical procedures. These techniques can be divided into two groups. One group encompasses techniques exploiting linear optical signals, and includes infrared and Raman microspectroscopy, and autofluorescence microscopy. The second group includes techniques based on nonlinear optical signals, including harmonic generation, coherent Raman scattering, and multiphoton autofluorescence microscopy. Some of these methods provide comparable information, while others are complementary. However, all of them have distinct advantages and disadvantages due to their inherent nature. The first part of the review provides an explanation of the underlying physics of the excitation mechanisms and a description of the instrumentation. It also covers endomicroscopy and data analysis, which are important for understanding the current limitations in implementing label-free techniques in clinical settings. The second part of the review describes the application of label-free microscopy imaging to improve oncological surgery with focus on brain tumors and selected gastrointestinal cancers, and provides a critical assessment of the current state of translation of these methods into clinical practice. Finally, the potential of confocal laser endomicroscopy for the acquisition of autofluorescence is discussed in the context of immediate clinical applications.

Raman investigation of in vivo radiation exposure on melanin in murine hair

Determining the effects of ionizing radiation from unintended exposure in a nuclear event requires the identification of relevant biomarkers and development of methods to retrospectively estimate the absorbed dose. Melanin, a biologically important natural pigment found in hair, shows promise as a biomarker to assess potential radiation exposure. We investigated Raman spectroscopy as a rapid and noninvasive technique to assess changes in melanin from the hair of C57BL/6 mice to gamma radiation between 0 and 4 Gy. Two excitation wavelengths (532 and 785 nm) were employed to probe the melanin response for changes with radiation exposure. Excitation wavelength-dependent variation in Raman features indicates resonance Raman effects, where a 785-nm excitation is more sensitive to the effects of gamma radiation. Melanin-specific Raman features were identified as potential biomarkers for gamma-radiation exposure and used to distinguish between irradiated and nonirradiated mice. Partial least square discriminant analysis models of exposure exhibited enhanced sensitivity to irradiation at 785 nm excitation and yielded a sensitivity of 88% and a specificity of 83%. Mice were classified with 100% sensitivity and specificity up to day 7 at a known time point. A decline in specificity and classification accuracy correlated with alterations in melanin's spectra after >7 days following irradiation. Regression models of the Raman spectrum determined the exposed dose with a precision of <1 Gy at a known exposure time point. This noninvasive approach offers promising applications in radiation biodosimetry and medical monitoring, providing retrospective detection of gamma-radiation exposure at clinically relevant doses.

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis

Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical processes can provide critical insights into the mechanisms of labor and potentially inform better clinical practices for monitoring and managing pregnancy and childbirth. Fourier-transform infrared (FT-IR) spectroscopy as a non-invasive optical technique, it has the potential sensibility to detect biochemical components. This technology operates by meansuring the vibrational energy of molecular composition and structural changes occurring in the tissue. A total of 30 pregnant participants undergoing either spontaneous or induced labor were recruited. We detected several biochemical changes during labor, including a significant decrease in FT-IR spectral features associated with collagen and other extracellular matrix (ECM) proteins, attributed to collagen dispersion. Specifically, the amide I and amide II bands, which are indicative of protein secondary structure, showed marked reductions. Our results have demonstrated that FT-IR spectroscopy is sensitive to multiple biochemical remodeling changes in the cervix during labor. Traditional methods have limitations, either due to their invasiveness or insufficient sensitivity to detect subtle biochemical alterations, therefore, FT-IR spectroscopy may be a valuable noninvasive tool for objective cervical assessment to potentially guide clinical labor management.

Molecular Engineering of a SICTERS Small Molecule with Superior In Vivo Raman Imaging and Photothermal Performance

Raman-based theranostics has demonstrated great potential for sensitive real-time imaging and treatment. However, these advanced materials, primarily depending on the SERS technique, encounter clinical concerns regarding substrate biosafety. Herein, we molecularly engineered a de novo substrate-free SICTERS small molecule, namely BTT-TPA (bis-thienyl-substituted benzotriazole selenadiazole derivative structures), possessing both ultrasensitive Raman signals and excellent photothermal effects based on self-stacking. The mechanistic studies confirm that BTT maintains the planar structure with polycyclic distorted vibrations required for SICTERS. TPA enhances the donor-acceptor interaction, yielding a Raman sensitivity of BTT higher than previously reported SICTERS molecules; it also acts as a molecular rotor, increasing the photothermal conversion efficiency to 67.44%, which is superior to most of the existing SERS-based photothermal materials. In the tumor model of mouse orthotopic colon cancer, BTT-TPA NPs demonstrate a great Raman imaging-guided photothermal therapy effect in eliminating primary and metastatic tumors, remarkably decreasing the recurrence rate. This work puts forward substrate-free SICTERS small molecules toward Raman-based theranostic applications in vivo.

Mapping Selenium Nanoparticles Distribution Inside Cells through Confocal Raman Microspectroscopy

Selenium nanoparticles (SeNPs) exhibit significant potential in biomedical applications due to their antimicrobial, anticancer, and anti-inflammatory properties. In this study, we synthesized biocompatible SeNPs and employed confocal Raman microspectroscopy to map their distribution within human dermal fibroblast (HDF) cells. SeNPs possess a distinctive Raman band placed outside the cellular fingerprint region, which facilitates its detection and precise Raman imaging. Viability assays revealed that SeNPs exhibit cytotoxic effects only at the highest concentrations and for long exposure times while resulting in no harmful effects during all of the other treatments. For the first time, we achieved three-dimensional (3D) Raman mapping of SeNPs within cells, providing insights into their cellular penetration. Additionally, two-dimensional (2D) Raman mapping performed at different times and at sublethal concentrations demonstrated dynamic uptake and confirmed internalization. These findings highlight the effectiveness of SeNPs for biomedical imaging and therapeutic applications, offering an additional approach to studying nanoparticle-cell interactions.

The Role of Inorganic Nanomaterials in Overcoming Challenges in Colorectal Cancer Diagnosis and Therapy

Colorectal cancer poses a significant threat to human health due to its high aggressiveness and poor prognosis. Key factors impacting patient outcomes include post-surgical recurrence, chemotherapeutic drug resistance, and insensitivity to immunotherapy. Consequently, early diagnosis and the development of effective targeted therapies are essential for improving prevention and treatment strategies. Inorganic nanomaterials have gained prominence in the diagnosis and treatment of colorectal cancer owing to their unique size, advantageous properties, and high modifiability. Various types of inorganic nanomaterials-such as metal-based, metal oxide, quantum dots, magnetic nanoparticles, carbon-based, and rare-earth nanomaterials-have demonstrated significant potential in enhancing multimodal imaging, drug delivery, and synergistic therapies. These advancements underscore their critical role in improving therapeutic outcomes. This review highlights the properties and development of inorganic nanomaterials, summarizes their recent applications and progress in colorectal cancer diagnosis and treatment, and discusses the challenges in translating these materials into clinical use. It aims to provide valuable insights for future research and the clinical application of inorganic nanomaterials in colorectal cancer management.

Boosting Visible-Light-Driven Hydrogen Evolution Enabled by Iodine-Linked Magnetically Curved Graphene with Mobius-like Electronic Paths

Materials with high electron transfer performance remain a key focus in photocatalytic research, as they can effectively promote the separation of photogenerated carriers and enhance the utilization efficiency of photogenerated electrons. To enhance the effective utilization of photogenerated electrons, the MSIG material was prepared by incorporating the iodine clusters and magnetic Fe3O4 into the as-synthesized crumpled graphene oxide (CGO) to construct Möbius-like electronic transmission pathways. The introduction of magnetic groups optimized the spin orientation of electrons, facilitating directional electron transport and thereby enhancing the photocatalytic efficiency of the material. Experimental results reveal that, in visible light-driven hydrogen production reactions, the eosin Y (EY)-sensitized Pt-Fe3O4-MSIG catalyst exhibits outstanding catalytic performance, with a hydrogen production rate of 1.48 mL/h, which is 15 times higher than that of the Pt-Fe3O4 catalyst. Photoelectrochemical analyses show a significant increase in the catalyst's fluorescence lifetime, attributed to the Möbius strip-like electron transport channels within the material. Theoretical calculations further support this by demonstrating that the bandgap widening of the CGO reduces the recombination probability of photogenerated carriers, thereby improving their average lifetime. This study offers a novel approach for the design of visible-light-driven photocatalytic materials.

Diagnosis and activity prediction of SLE based on serum Raman spectroscopy combined with a two-branch Bayesian network

Objective

This study aims to examine the impact of systemic lupus erythematosus (SLE) on various organs and tissues throughout the body. SLE is a chronic autoimmune disease that, if left untreated, can lead to irreversible damage to these organs. In severe cases, it can even be life-threatening. It has been demonstrated that prompt diagnosis and treatment are crucial for improving patient outcomes. However, applying spectral data in the classification and activity assessment of SLE reveals a high degree of spectral overlap and significant challenges in feature extraction. Consequently, this paper presents a rapid and accurate method for disease diagnosis and activity assessment, which has significant clinical implications for achieving early diagnosis of the disease and improving patient prognosis.

Methods

In this study, a two-branch Bayesian network (DBayesNet) based on Raman spectroscopy was developed for the rapid identification of SLE. Serum Raman spectra samples were collected from 80 patients with SLE and 81 controls, including those with dry syndrome, undifferentiated connective tissue disease, aortitis, and healthy individuals. Following the pre-processing of the raw spectra, the serum Raman spectral data of SLE were classified using the deep learning model DBayes. DBayesNet is primarily composed of a two-branch structure, with features at different levels extracted by the Bayesian Convolution (BayConv) module, Attention module, and finally, feature fusion performed by Concate, which is performed by the Bayesian Linear Layer (BayLinear) output to obtain the result of the classification prediction.

Results

The two sets of Raman spectral data were measured in the spectral wave number interval from 500 to 2000 cm-1. The characteristic peaks of serum Raman spectra were observed to be primarily located at 1653 cm-1 (amide I), 1432 cm-1 (lipid), 1320 cm-1 (protein), 1246 cm-1 (amide III, proline), and 1048 cm-1 (glycogen). The following peaks were identified: 1653 cm-1 (amide), 1432 cm-1 (lipid), 1320 cm-1 (protein), 1246 cm-1 (amide III, proline), and 1048 cm-1 (glycogen). A comparison was made between the proposed DBayesNet classification model and traditional machine and deep learning algorithms, including KNN, SVM, RF, LDA, ANN, AlexNet, ResNet, LSTM, and ResNet. The results demonstrated that the DBayesNet model achieved an accuracy of 85.9%. The diagnostic performance of the model was evaluated using three metrics: precision (82.3%), sensitivity (91.6%), and specificity (80.0%). These values demonstrate the model's ability to accurately diagnose SLE patients. Additionally, the model's efficacy in classifying SLE disease activity was assessed.

Conclusion

This study demonstrates the feasibility of Raman spectroscopy combined with deep learning algorithms to differentiate between SLE and non-SLE. The model's potential for clinical applications and research value in early diagnosis and activity assessment of SLE is significant.

Photocatalytic Multiple Deuteration of Polyethylene Glycol Derivatives Using Deuterium Oxide

Deuterated molecules are of growing interest because of the specific characteristics of deuterium, such as stronger C-D bonds being stronger than C-H bonds. Polyethylene glycols (PEGs) are widely utilized in scientific fields (e. g., drug discovery and material sciences) as linkers and for the improvement of various properties (solubility in water, stability, etc.) of mother compounds. Therefore, deuterated PEGs can be used as novel tools for drug discovery. Although the H/D exchange reaction (deuteration) is a powerful and straightforward method to produce deuterated compounds, the deuteration of PEGs bearing many unactivated C(sp3)-H bonds has not been developed. Herein, we report the photocatalytic deuteration of multiple sites of PEGs using tetra-n-butylammonium decatungstate (TBADT) and D2O as an inexpensive deuterium source. This deuteration can be adapted to PEG derivatives bearing various substituents ((hetero)aryl, benzoyl, alkyl, etc.). The deuteration efficiencies of the α-oxy C(sp3)-H bonds at the terminal positions of the PEGs were strongly influenced by the substituents. These reactivities were elucidated by density functional theory calculations of the reaction barriers towards the formation of radical intermediates, induced by the excited state of TBADT and the PEG substrate. In addition, the applicability of deuterated PEGs to internal standard experiments and Raman spectroscopy was demonstrated.

Application of Surface-Enhanced Raman Spectroscopy in Head and Neck Cancer Diagnosis

Surface-enhanced Raman spectroscopy (SERS) has emerged as a crucial analytical tool in the field of oncology, particularly presenting significant challenges for the diagnosis and treatment of head and neck cancer. This Review provides an overview of the current status and prospects of SERS applications, highlighting their profound impact on molecular biology-level diagnosis, tissue-level identification, HNC therapeutic monitoring, and integration with emerging technologies. The application of SERS for single-molecule assays such as epidermal growth factor receptors and PD-1/PD-L1, gene expression analysis, and tumor microenvironment characterization is also explored. This Review showcases the innovative applications of SERS in liquid biopsies such as high-throughput lateral flow analysis for ctDNA quantification and salivary diagnostics, which can offer rapid and highly sensitive assays suitable for immediate detection. At the tissue level, SERS enables cancer cell visualization and intraoperative tumor margin identification, enhancing surgical precision and decision-making. The role of SERS in radiotherapy, chemotherapy, and targeted therapy is examined along with its use in real-time pharmacokinetic studies to monitor treatment response. Furthermore, this Review delves into the synergistic relationship between SERS and artificial intelligence, encompassing machine learning and deep learning algorithms, marking the dawn of a new era in precision oncology. The integration of SERS with genomics, metabolomics, transcriptomics, proteomics, and single-cell omics at the multiomics level will revolutionize our comprehension and management of HNC. This Review offers an overview of the transformative impacts of SERS and examines future directions as well as challenges in this dynamic research field.

Machine learning with label-free Raman microscopy to investigate ferroptosis in comparison with apoptosis and necroptosis

Human and animal health rely on balancing cell division and cell death to maintain normal homeostasis. This process is accomplished by regulated cell death (RCD), whose imbalance can lead to disease. Currently, the most frequently used method for analyzing RCD is fluorescence microscopy. This method has limitations and potential side effects due to the presence of fluorescent labels. Furthermore, fluorescence often lacks specificity and may have side effects. In the quest to overcome such difficulties, label-free approaches have come into focus.Here, Raman microscopy in combination with machine learning is used to investigate RCDs, where biochemical molecular "fingerprints" are investigated with a focus on the vibrations of atoms in molecules. Three different and unique RCD types with different genetic and biochemical machinery, namely, ferroptosis is studied in comparison with apoptosis, and necroptosis in the murine fibroblast line L929sAhFas. Interestingly, during ferroptosis, a decrease in the wavenumber at 939 cm-1 was observed, which is associated with a potential reduction in the expression of collagen - a compound essential in multiple diseases. Data analysis was performed by machine learning (ML), here SVMs, where the model utilizing the spectra directly into a support vector machine (SVM) outperforms other SVM strategies correctly predicting 73% of all spectra. Other methods: PCA-SVM (principal component analysis-SVM), peak fitting-AUC-SVM (area under the curve) and peak fitting-spectral reconstruction-SVM rendered prediction accuracies of ~52%, ~43%, and 61%, respectively. Peak fitting has the additional benefit of enabling the biological interpretation of Raman scattering peaks by using the area under the curve, although at a loss of general accuracy. The potential of Raman microscopy in biology, in combination with machine learning pipelines, can be applied to a broader field of cell biology, not limited to regulated cell death.

Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media

Maintaining consistent quality in biopharmaceutical manufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) struggle to achieve rapid and highly accurate monitoring of small molecule critical process parameters and critical quality attributes. While Raman spectroscopy holds great promise as a highly sensitive and specific bioanalytical tool for PAT applications, its conventional implementation, surface-enhanced Raman spectroscopy (SERS), is constrained by considerable temporal and spatial intensity fluctuations, limiting the achievable reproducibility and reliability. Herein, we introduce a deep learning-powered colloidal digital SERS platform to address these limitations. Rather than addressing the intensity fluctuations, the approach leverages their very stochastic nature, arising from highly dynamic analyte-nanoparticle interactions. By converting the temporally fluctuating SERS intensities into digital binary "ON/OFF" signals using a predefined intensity threshold by analyzing the characteristic SERS peak, this approach enables digital visualization of single-molecule events and significantly reduces false positives and background interferences. By further integrating colloidal digital SERS with deep learning, the applicability of this platform is significantly expanded and enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks for certain analytes. We further implement this approach for studying AMBIC 1.1, a chemically-defined, serum-free complete media for mammalian cell culture. The obtained highly accurate and reproducible results demonstrate the unique capabilities of this platform for rapid and precise cell culture media monitoring, paving the way for its widespread adoption and scaling up as a new PAT tool in biopharmaceutical manufacturing and biomedical diagnostics.

Penile Cancer: Innovations in Ultrastructural and Vibrational Markers

Penile cancer (PCa) is a disease that manifests predominantly as squamous cell carcinomas (SCCs), which, although rare, represents a significant public health problem, especially in regions with less socioeconomic development. One of the biggest challenges in managing this disease is the difficulty in differentiating tumor subtypes, making accurate diagnosis and treatment challenging. In this context, new characterization techniques are needed to investigate these tumors more completely. Atomic force microscopy (AFM) and Raman spectroscopy (RS) are valuable in this context, providing quantitative and qualitative ultrastructural data and vibrational signatures of the analyzed samples. In this study, AFM and RS techniques were employed to investigate subtypes of penile cancer, including the highly aggressive basaloid subtype, which is closely associated with human papillomavirus (HPV), and the sarcomatoid subtype, comparing them with nontumorous tissues. The AFM results revealed nanoscale changes in the ultrastructural properties of tumor samples, such as increased roughness in tumor tissues, with emphasis on the basaloid type associated with the HPV virus, and reduction in the surface area and volume of tumor tissues at the nanoscale, suggesting deeper tissue infiltration and greater deformability of tumor samples at the nanoscale. RS results detected significant spectral differences between normal and cancerous tissues and between tumor subtypes, particularly in vibrational modes related to proteins and lipids. Principal component analysis (PCA) confirmed a strong discriminative power between control and PCa groups. The data presented here offers new insights into the characteristics of penile tumors that, when integrated with clinical analyses, could improve the understanding of penile cancer behavior, contributing to more accurate diagnostic methods and targeted treatments.

Monitoring kinetic processes of drugs and metabolites: Surface-enhanced Raman spectroscopy

Monitoring the kinetic changes of drugs and metabolites plays a crucial role in fundamental research, preclinical and clinical application. Raman spectroscopy (RS) is regarded as a fingerprinting technique that can reflect molecular structures but limited in applications due to poor sensitivity. Surface-enhanced Raman spectroscopy (SERS) significantly amplifies the detection sensitivity by plasmonic substrates, facilitating the identification and quantification of small molecules in biological samples, such as serum, urine, and living cells. This review will focus on advances in how SERS has been utilized to monitor the dynamic processes of small molecule drugs and metabolites in recent years. We first provide readers with a comprehensive overview of the mechanism and practical considerations of SERS, including enhancement theory, substrate design, sample pretreatment, molecule-substrate interactions and spectral analysis. Then we describe the latest advances in SERS for the detection and analysis of metabolites and drugs in cells, dynamic monitoring of drug in various biological matrices, and metabolic profiling for health assessment in biological fluids. We believe that high-performance SERS substrates, standardized technical regulations, and artificial intelligence spectral analysis will boost sensitive, accurate, reproducible, and universal molecular detection in the future. We hoped this review could inspire researchers working in related fields to better understand and utilize SERS for the analytical detection of drugs and metabolites.

Rapid Raman spectroscopy-based test for antimicrobial resistance

Antimicrobial resistance (AMR) is one of the top global health threats. In 2019, AMR was associated with 4.95 million deaths, of which 1.97 million were caused by drug-resistant infections directly. The main subset of AMR is antibiotic resistance, that is, the resistance of bacteria to antibiotic treatment. Traditional and most commonly used antibiotic susceptibility tests are based on the detection of bacterial growth and its inhibition in the presence of an antimicrobial. These tests typically take over 1-2 days to perform, so empirical therapy schemes are often administered before proper testing. Rapid tests for AMR are necessary to optimize the treatment of bacterial infection. Here, we combine the MTT test with Raman spectroscopy to provide a 1.5 h long test for minimal inhibitory concentration determination. Several Escherichia coli and Klebsiella pneumoniae strains were tested with three types of antibiotics, including ampicillin from penicillin family, kanamycin from aminoglycoside family and levofloxacin from fluoroquinolone family. The test provided the same minimal inhibitory concentrations as traditional Etest confirming its robustness.

To Acquire or Not to Acquire: Evaluating Compressive Sensing for Raman Spectroscopy in Biology

Raman spectroscopy has revolutionized the field of chemical biology by providing detailed chemical and compositional information with minimal sample preparation. Despite its advantages, the technique suffers from low throughput due to the weak Raman effect, necessitating long acquisition times and expensive equipment. This limitation is particularly acute in time-sensitive applications like bioprocess monitoring and dynamic studies. Compressive sensing offers a promising solution by reducing the burden on measurement hardware, lowering costs, and decreasing measurement times. It allows for the collection of sparse data, which can be computationally reconstructed later. This paper explores the practical application of compressive sensing in spontaneous Raman spectroscopy across various biological samples. We demonstrate its benefits in scenarios requiring portable hardware, rapid acquisition, and minimal storage, such as skin hydration prediction and cellular studies involving drug molecules. Our findings highlight the potential of compressive sensing to overcome traditional limitations of Raman spectroscopy, paving the way for broader adoption in biological research and clinical diagnostics.

Label-Free Raman Probing of the Intrinsic Electric Field for High-Efficiency Screening of Electricity-Producing Bacteria at the Single-Cell Level

The fabrication of high-performance microbial fuel cells requires the evaluation of the activity of electrochemically active bacteria. However, this is challenging because of the time-consuming nature of biofilm formation and the invasive nature of labeling. To address this issue, we developed a fast, label-free, single-cell Raman spectroscopic method. This method involves investigating the "pure" linear Stark effect of endogenous CO in the silent region of biological samples, which allows for probing the intrinsic electric field in the outer-membrane cytochromes of live bacterial cells. We found that reduced outer-membrane cytochromes can generate an additional intrinsic electric field equivalent to an applied potential of +0.29 V. We also found that the higher the electrical activity of the cell, the larger the generated electric field. This was also reflected in the output current of the constructed microbial fuel cells. Raman spectroscopy was employed to facilitate the assessment of electrochemical activity at the single-cell level in highly-diluted bacterial samples. After analysis, inactive bacteria were ablated by laser heating, and 20 active cells were cultured for further testing. The rapid and high-throughput probing of the intrinsic electric field offers a promising platform for high-efficiency screening of electrochemically active bacterial cells for bioenergetic and photosynthetic research.

Raman Spectroscopy and Exosome-Based Machine Learning Predicts the Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer

Early prediction of the neoadjuvant therapy efficacy for HER2-positive breast cancer is crucial for personalizing treatment and enhancing patient outcomes. Exosomes, which play a role in tumor development and treatment response, are emerging as potential biomarkers for cancer diagnosis and efficacy prediction. Despite their promise, current exosome detection and isolation methods are cumbersome and time-consuming and often yield limited purity and quantity. In this study, we employed Raman spectroscopy to analyze the molecular changes in exosomes from the sera of HER2-positive breast cancer patients before and after two cycles of neoadjuvant therapy. Utilizing machine learning techniques (PCA, LDA, and SVM), we developed a predictive model with an AUC value exceeding 0.89. Additionally, we introduced an innovative HER2-positive exosome capture and detection system, termed Magnetic beads@HER2-Exos@HER2-SERS detection nanoprobes (HER2-MEDN). This system enabled us to efficiently extract and analyze HER2-positive exosomes, refining our predictive model to achieve an accuracy greater than 0.94. Our study has demonstrated the potential of the HER2-MEDN system in accurately predicting early treatment response, offering novel insights and methodologies for assessing the efficacy of neoadjuvant therapy in HER2-positive breast cancer.

Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy

This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcinoma. Raman spectroscopy-based machine learning was applied in real-time during endoscopy in 19 patients (aged 51-85 years) with high-risk for gastric adenocarcinoma. Raman spectra were captured from suspicious lesions and adjacent normal mucosa, which were biopsied for matched histopathologic diagnosis. Spectral data were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA) with leave-one-out cross-validation (LOOCV) to develop a machine learning model for diagnosing gastric adenocarcinoma. High-quality spectra (800-3300 cm⁻¹) revealed distinct patterns: adenocarcinoma tissues had higher intensities below 3150 cm⁻¹, while benign tissues exhibited higher intensities between 3150 and 3290 cm⁻¹ (p < 0.001). The model achieved diagnostic accuracy, sensitivity, specificity, and AUC values of 0.905, 0.942, 0.787, and 0.957, respectively. Biochemical correlations suggested adenocarcinoma tissues had increased protein (e.g., phenylalanine), reduced lipids, and lower water content compared to benign tissues. This study highlights the potential of Raman spectroscopy with machine learning as a real-time diagnostic tool for gastric adenocarcinoma. Further validation could establish this technique as a non-invasive, accurate method to aid clinical decision-making during endoscopy.

Surface-enhanced Raman scattering for HSP 70A mRNA detection in live cells using silica nanoparticles and DNA-modified gold nanoparticles

Real-time monitoring of mRNA in living cells is crucial for understanding dynamic biological processes. Traditional methods such as northern blotting, PCR, and sequencing require cell lysis and do not allow for continuous observation. Fluorescence-based techniques have advanced this field, but they are limited by photobleaching, which hinders long-term monitoring. In this study, we designed a dual-probe system combining fluorescence and surface-enhanced Raman scattering (SERS) signals to monitor mRNA in living cells. Our system uses silica nanoparticles (SiNPs) with DNA sequences which are hybridized with fluorescent DNA sequences and DNA-modified gold nanoparticles (AuNPs) to detect heat shock protein 70A mRNA, which can be induced by photothermal damage from laser exposure. Following nanoparticle uptake and induction of heat shock, we observed a time-dependent decrease in fluorescence intensity and increase in SERS intensity, indicating successful mRNA monitoring in living cells. These findings suggest that our dual-probe system with SiNPs and AuNPs is a promising nanotechnological platform for sensitive, long-term monitoring of gene expression in living cells, offering significant potential for future biological and medical research.

Highly Biocompatible Lamellar Liquid Crystals Based on Hempseed or Flaxseed Oil with Incorporated Betamethasone Dipropionate: A Bioinspired Multi-Target Dermal Drug Delivery System for Atopic Dermatitis Treatment

Purpose

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease that severely impairs patient's life quality and represents significant therapeutic challenge due to its pathophysiology arising from skin barrier dysfunction. Topical corticosteroids, the mainstay treatment for mild to moderate AD, are usually formulated into conventional dosage forms that are impeded by low drug permeation, resulting in high doses with consequent adverse effects, and also lack properties that would strengthen the skin barrier. Herein, we aimed to develop biomimetic lamellar lyotropic liquid crystals (LLCs), offering a novel alternative to conventional AD treatment.

Methods

In screening studies, pseudoternary phase diagrams alongside polarized light microscopy (PLM) and viscosity measurements were utilized. Next, the selected LCCs underwent comprehensive characterization via PLM, small-angle X-ray scattering, differential scanning calorimetry, and rheological analysis. Lastly, their performance was evaluated and compared with the commercially available reference medicine in chemical stability study, in vitro permeation testing, in vitro safety assessment using cell proliferation assay, inverted light microscopy, and Raman mapping of keratinocytes, besides gap closure assay performed by live-cell imaging.

Results

Formulation (L/T)Ho30, containing the highest amount of lecithin/Tween 80 mixture (21%) and hempseed oil (28%), demonstrated lamellar microstructure with high skin hydration potential and favourable rheological features for skin administration. Moreover, in comparison with the reference medicine, it stood out by providing suitable chemical BD (betamethasone dipropionate) stability, improved 3-fold BD permeation, and excellent biocompatibility with over 85% cell proliferation at all tested concentrations, ensuring keratinocytes' integrity, as well as promoting skin healing with gap closure observed after 36 hours.

Conclusion

Unique multi-target drug delivery strategy depicted in newly developed bioinspired lamellar LCCs structurally resembling stratum corneum intercellular lipids, with incorporated BD drug, and composed of multifunctional components that synergistically strengthen skin barrier, was presented here and shows a promising approach for improved AD treatment.

Spatially Quantitative Imaging of Enzyme Activity in a Living Cell

Enzyme activity plays a key role in cell heterogeneity. Its spatially quantitative imaging in a living cell not only directly displays but also helps people to understand cell heterogeneity. Current methods are hard to achieve due to the short intracellular retention or lack of internal reference of the imaging probes. Herein, we rationally designed a self-referenced Raman probe Val-Cit-Cys(StBu)-Pra-Gly-CBT (Yne-CBT) which takes an intracellular cathepsin B (CTSB)-initiated CBT-Cys click reaction to yield a long-retained cyclic dimer in cell. In the meantime, Raman signal changes of its two chemical bonds (C≡C and C≡N) after the reaction are used for self-referencing and quantitative Raman imaging of CTSB activity. In vitro experiments demonstrated that, with shell-isolated nanoparticle-enhanced Raman spectroscopy technique, 20 μM Yne-CBT was able to quantitatively detect CTSB activity with a limit of detection of 61.4 U L-1. Under a homemade microfluidic channel, Yne-CBT was successfully applied for spatially quantitative imaging CTSB activity in a living cell. Our strategy provides people with a facile method to directly and quantitatively display cell heterogeneity.

Use of optical techniques to evaluate the ionizing radiation effects on biological specimens

Radiation induces various changes in biological specimens; however, the evaluation of these changes is usually complicated and can be achieved only through investment in time and labor. Optical methods reduce the cost of such evaluations as they require less pretreatment of the sample, are adaptable to high-throughput screening and are easy to automate. Optical methods are also advantageous, owing to their real-time and onsite evaluation capabilities. Here, we discuss three optical technologies to evaluate the effects of radiation on biological samples: single-molecule tracking microscopy to evaluate the changes in the physical properties of DNA, Raman spectral microscopy for dosimetry using human hair and second-harmonic generation microscopy to evaluate the effect of radiation on the differentiation of stem cells. These technologies can also be combined for more detailed information and are applicable to other biological samples. Although optical methods are not commonly used to evaluate the effects of radiation, advances in this technology may facilitate the easy and rapid assessment of radiation effects on biological samples.

Regulating Electron Acceptor Unit to Construct Conjugated Polymer Probe for Raman Imaging of Tumor and Sentinel Lymph Nodes

Donor-acceptor (D-A) conjugated polymers have large Stokes shifts, high photostability, and good biocompatibility and thus are ideal for use as Raman probes in vivo. However, few D-A conjugated polymers are used as Raman probes for Raman imaging due to their weak Raman signal intensity and intrinsic fluorescence interference. Here, we developed a D-A conjugated polymer probe (CDT-TT) containing alternating cyclopentadithiophene-thienothiophene units for Raman imaging of tumor and sentinel lymph nodes (SLNs). The CDT-TT shows a strong Raman signal at 1389 cm-1 without fluorescence and lipid background signal interference under 785 nm near-infrared light. Moreover, the CDT-TT loaded nanoparticles realized the accurate imaging of tumor cells and tumor tissues. In addition, a high-resolution margin imaging of in situ SLNs was acquired. Taken together, the established method is effective for accurate Raman detection of tumors and SLNs, which may shed new light on the development of D-A polymer Raman probes for clinical imaging.

Multiplexed SERS Detection of Serum Cardiac Markers Using Plasmonic Metasurfaces

Surface-enhanced Raman spectroscopy (SERS) possesses exquisite molecular-specific properties with single-molecule sensitivity. Yet, translation of SERS into a quantitative analysis technique remains elusive owing to considerable fluctuation of the SERS intensity, which can be ascribed to the SERS uncertainty principle, a tradeoff between "reproducibility" and "enhancement". To provide a potential solution, herein, an integrated multiplexed SERS biosensing strategy is proposed, which features two distinct advantages. First, a subwavelength-structured plasmonic metasurface consisting of alternately stacked metal-dielectric pyramidal meta-atoms is fabricated and could provide simultaneously enhanced electric and magnetic fields to enable spatially extended and weakly wavelength-dependent SERS. Second, nanomechanical perturbations are harnessed to transduce signals in the form of SERS frequency shifts, which are not directly affected by the SERS uncertainty principle. By also employing 3D printing methods, a proof-of-concept study of multiplexed detection of a panel of serum cardiac biomarkers for acute myocardial infarction is provided. Success in the development of both the electric and magnetic fields-active plasmonic metasurfaces could transform future designs of SERS substrates with newly endowed functionalities, and frequency shift-based SERS multiplexing could open new opportunities to develop innovative quantitative optical techniques for applications in chemistry, biology, and medicine.

Advances in Super-resolution Stimulated Raman Scattering Microscopy

Super-resolution optical imaging overcomes the diffraction limit in light microscopy to enable the visualization of previously invisible molecular details within a sample. The realization of super-resolution imaging based on stimulated Raman scattering (SRS) microscopy represents a recent area of fruitful development that has been used to visualize cellular structures in three dimensions, with multiple spectroscopic colors at the nanometer scale. Several fundamental approaches to achieving super-resolution SRS imaging have been reported, including optical engineering strategies, expansion microscopy, deconvolution image analysis, and photoswitchable SRS reporters as methods to break the diffraction limit. These approaches have enabled the visualization of biological structures, cellular interactions, and dynamics with unprecedented detail. In this Perspective, an overview of the current strategies and capabilities for achieving super-resolution SRS imaging will be highlighted together with an outlook on potential directions of this rapidly evolving field.

A Polyphenol Decorated Triplex Hybrid Biomaterial: Structure-Function, Release Profiles, Sorption, and Antipathogenic Effects

Herein, nonwoven alkali modified flax substrates were coated with incremental levels of chitosan, followed by immobilization of tannic acid, via a facile "dip-coating" strategy to yield a unique hierarchal "triplex" hybrid biomaterial, denoted as "THB". The characterization of the physicochemical properties of THB employed complementary spectroscopic (IR, Raman, and NMR) techniques, which support the role of hydrogen bonding and electrostatic interactions between the components: chitosan as the secondary biopolymer coating and the tertiary adsorbed polyphenols. XRD and SEM techniques provide further structural insight that confirms the unique semicrystalline nature and porous hierarchal structure of the biocomposite. The THBs present a polyphenol kinetic release profile that follows the Korsmeyer-Peppas model that concurs with Fickian diffusion for heterogeneous polymer systems. Furthermore, these systems demonstrate a tailored solvent uptake capacity (up to 4 g/g) in aqueous PBS media. Antipathogenic activity tests revealed 95% elimination of pathogens (E. coli, S. aureus, and C. albicans) at a dose of 50 mg for the THB system. The trend in the structure-property relationships for the THB systems indicates synergistic effects of electrostatic multiform interactions between protonated chitosan and the polyphenol units. Herein, we report the first example of a unique hierarchal biomaterial via a facile design strategy for diversiform roles as responsive adsorbents for environmental remediation to biomedical applications (e.g., controlled release, topical administration, or antimicrobial surface coatings).

Highly sensitive microfluidic sensor using integrated optical fiber and real-time single-cell Raman spectroscopy for diagnosis of pancreatic cancer

Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer.

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness, and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

A Novel Method for Rapid and High-Performance SERS Substrate Fabrication by Combination of Cold Plasma and Laser Treatment

In this paper, we report a simple yet efficient method for rapid and high-performance SERS substrate fabrication by a combination of cold plasma and laser treatment. Our analysis reveals that cold plasma pre-treatment significantly reduced surface roughness, transforming 200 nm spikes into an almost perfectly uniform surface, while enhancing the substrate's surface energy by lowering the water contact angle from 59° to 0°, all achieved within just 30 s of 0.9-mW plasma treatment, while 15-min green-laser treatment facilitated more uniform deposition of AuNPs across the entire treated area, effectively creating the SERS substrates. The combined treatments result in enhancement of the Raman intensity (11 times) and consistency over the whole area of the SERS substrates, and their reusability (up to 10 times). The fabricated SERS substrates exhibit a significant enhancement factor of approximately 3 × 10⁸ with R6G, allowing detection down to a concentration of 10-12 M. We demonstrate the application of these SERS substrates by detecting amoxicillin-an antibiotic used worldwide to treat a diversity of bacterial infections-in a dynamic expanded linear range of seven orders (from 10-3 to 10-9 M) with high reliability (R2 = 0.98), and a detection limit of 9 × 10-10 M. Our approach to high-performance SERS substrate fabrication holds potential for further expansion to other metallic NPs like Ag, or magnetic NPs (Fe3O4).

Retinol semisolid preparations in cosmetics: transcutaneous permeation mechanism and behaviour

Retinol is widely used to treat skin ageing because of its effect on cell differentiation, proliferation and apoptosis. However, its potential benefits appear to be limited by its skin permeability. Herein, we investigated the transcutaneous behavior of retinol in semisolid cosmetics, in both in vitro and in vivo experiments. In vitro experiments used the modified Franz diffusion cell combined with Raman spectroscopy. In in vivo experiments, the content of retinol in rat skin and plasma was detected with HPLC. Retinol in semisolid cosmetics was mainly concentrated in the stratum corneum in the skin of the three animal models tested, and in any case did not cross the skin barrier after a 24 h dermatologic topical treatment in Franz diffusion cells tests. Similar results were obtained in live mice and rats, where retinol did not cross the skin barrier and did not enter the blood circulation. Raman spectroscopy was used to test the penetration depth of retinol in skin, which reached 16 μm out of 34 μm in pig skin, whereas the skin of mouse and rat showed too strong bakground interference. To explore epidermal transport mechanism and intradermal residence, skin transcriptomics was performed in rats, which identified 126 genes upregulated related to retinol transport and metabolism, relevant to the search terms "retinoid metabolic process" and "transporter activity". The identity of these upregulated genes suggests that the mechanism of retinol action is linked to epidermis, skin, tissue and epithelium development.

Biomimetic Gold Nanorods-Manganese Porphyrins with Surface-Enhanced Raman Scattering Effect for Photoacoustic Imaging-Guided Photothermal/Photodynamic Therapy

Surface-enhanced Raman scattering (SERS) imaging integrating photothermal and photodynamic therapy (PTT/PDT) is a promising approach for achieving accurate diagnosis and effective treatment of cancers. However, most available Raman reporters show multiple signals in the fingerprint region, which overlap with background signals from cellular biomolecules. Herein, a 4T1 cell membrane-enveloped gold nanorods-manganese porphyrins system (GMCMs) is designed and successfully fabricated as a biomimetic theranostic nanoplatform. Manganese porphyrins are adsorbed on the surface of Au nanorods via the terminal alkynyl group. Cell membrane encapsulation protects the manganese porphyrins from falling off the gold nanorods. The biomimetic GMCMs confirm specific homologous targeting to 4T1 cells with good dispersibility, excellent photoacoustic (PA) imaging properties, and preferable photothermal and 1O2 generation performance. GMCMs exhibit distinct SERS signals in the silent region without endogenous biomolecule interference both in vitro and in vivo. Manganese ions could not only quench the fluorescence of porphyrins to enhance the SERS imaging effect but also deplete cellular GSH to increase 1O2 yield. Both in vitro and in vivo studies demonstrate that GMCMs effectively eradicate tumors through SERS/PA imaging-guided PTT/PDT. This study provides a feasible strategy for augmenting the Raman imaging effects of the alkynyl group and integrating GSH-depletion to enhance PTT/PDT efficacy.

Benchtop IR Imaging of Live Cells: Monitoring the Total Mass of Biomolecules in Single Cells

Absolute quantity imaging of biomolecules on a single cell level is critical for measurement assurance in biosciences and bioindustries. While infrared (IR) transmission microscopy is a powerful label-free imaging modality capable of chemical quantification, its applicability to hydrated biological samples remains challenging due to the strong IR absorption by water. Traditional IR imaging of hydrated cells relies on powerful light sources, such as synchrotrons, to mitigate the light absorption by water. However, we overcome this challenge by applying a solvent absorption compensation (SAC) technique to a home-built benchtop IR microscope based on an external-cavity quantum cascade laser. SAC-IR microscopy adjusts the incident light using a pair of polarizers to precompensate the IR absorption by water while retaining the full dynamic range. Integrating the IR absorbance over a cell yields the total mass of biomolecules per cell. We monitor the total mass of the biomolecules of live fibroblast cells over 12 h, demonstrating promise for advancing our understanding of the biomolecular processes occurring in live cells on the single-cell level.

Seeing the unseen: The role of bioimaging techniques for the diagnostic interventions in intervertebral disc degeneration

Intervertebral Disc Degeneration is a pathophysiological condition that primarily affects the spinal discs, causing back pain and neurological deficits. It is caused by the contribution of several factors such as genetic predisposition, age-related degeneration, and lifestyle choices like obesity and physical activity. Even though there are medications to treat pain, there is a lack of medicines for a complete cure. The main difficulty lies in poor diagnosis of the morphological and functional changes in the disc. With the ever-increasing research on bioimaging techniques, new techniques are being developed and repurposed to evaluate disc shape and composition, and their defects like thinning or deformities on the disc, leading to the proper diagnostic intervention in intervertebral disc degeneration. In this review, we aim to present a comprehensive overview of the imaging techniques used in the pre-clinical and clinical stages for the diagnosis of intervertebral disc degeneration. First, we will discuss about patho-anatomy and the pathophysiology of degenerative disc disease with the significance and a brief description of various dyes and tracers utilized for bioimaging. Then we will shed light on the latest advancements in diagnostic modalities in intervertebral disc degeneration; concluded by an analysis of the repercussions of the methodologies and experimental systems employed in identifying mechanisms and developing therapeutic strategies in intervertebral disc degeneration.

Deuterated Alkyl Sulfonium Salt Reagents; Importance of H/D Exchange Methods in Drug Discovery

Deuterated drugs (heavy drugs) have recently been spotlighted as a new modality for small-molecule drugs because the pharmacokinetics of pharmaceutical drugs can be enhanced by replacing C-H bonds with more stable C-D bonds at metabolic positions. Therefore, deuteration methods for drug candidates are a hot topic in medicinal chemistry. Among them, the H/D exchange reaction (direct transformation of C-H bonds to C-D bonds) is a useful and straightforward method for creating novel deuterated target molecules, and over 20 reviews on the synthetic methods related to H/D exchange reactions have been published in recent years. Although various deuterated drug candidates undergo clinical trials, approved deuterated drugs possess CD3 groups in the same molecule. However, less diversification, except for the CD3 group, is a problem for future medicinal chemistry. Recently, we developed various deuterated alkyl (dn-alkyl) sulfonium salts based on the H/D exchange reaction of the corresponding hydrogen form using D2O as an inexpensive deuterium source to introduce CD3, CH3CD2, and ArCH2CD2 groups into drug candidates. This concept summarises recent reviews related to H/D exchange reactions and novel reagents that introduce the CD3 group, and our newly developed electrophilic dn-alkyl reagents are discussed.

DNA origami-templated gold nanorod dimer nanoantennas: enabling addressable optical hotspots for single cancer biomarker SERS detection

The convergence of DNA origami and surface-enhanced Raman spectroscopy (SERS) has opened a new avenue in bioanalytical sciences, particularly in the detection of single-molecule proteins. This breakthrough has enabled the development of advanced sensor technologies for diagnostics. DNA origami offers a highly controllable framework for the precise positioning of nanostructures, resulting in superior SERS signal amplification. In our investigation, we have successfully designed and synthesized DNA origami-based gold nanorod monomer and dimer assemblies. Moreover, we have evaluated the potential of dimer assemblies for label-free detection of a single biomolecule, namely epidermal growth factor receptor (EGFR), a crucial biomarker in cancer research. Our findings have revealed that the significant Raman amplification generated by DNA origami-assembled gold nanorod dimer nanoantennas facilitates the label-free identification of Raman peaks of single proteins, which is a prime aim in biomedical diagnostics. The present work represents a significant advancement in leveraging plasmonic nanoantennas to realize single protein SERS for the detection of various cancer biomarkers with single-molecule sensitivity.

Alkyne-tagged imidazolium-based membrane cholesterol analogs for Raman imaging applications

Cholesterol is an important lipid playing a crucial role in mediating essential cellular processes as well as maintaining the basic structural integrity of biological membranes. Given its vast biological importance, there is an unabated need for sophisticated strategies to investigate cholesterol-mediated biological processes. Raman-tagged sterol analogs offer the advantage of being visualizable without the need for a bulky dye that potentially affects natural membrane integration and cellular interactions as it is the case for many conventionally used fluorescent analogs. Herein, we report a series of alkyne-tagged imidazolium-based cholesterol analogs (CHIMs) with large Raman scattering cross-sections that readily integrate into HEK cells and primary monocyte-derived macrophages and allow (multiplexed) cellular Raman imaging. We envision Raman-tagged CHIM analogs to be a powerful platform for the investigation of cholesterol-mediated cellular processes complementary to other established methods, such as the use of fluorescent analogs.