ABSTRACT Raman spectroscopy is widely used in drug development and delivery due to its label‐free and non‐destructive character. Reliable application to drug delivery systems requires unambiguous assignment of Raman bands from both the drug and the carrier material. Here, we establish the Raman spectral signatures of docetaxel (DTX)‐loaded polymeric micelles (nanoparticles, NPs) based on Pluronic F‐127, Soluplus, and a mixed Soluplus–TPGS system. DTX was nanoformulated to improve its aqueous solubility and therapeutic performance. In vitro FT‐Raman measurements (1064 nm excitation) were performed on pure polymers, unloaded micelles, and DTX‐loaded formulations. Detailed band assignments of the pure drug and nanocarriers were carried out and systematically compared with the loaded systems. Four characteristic DTX marker bands at 617, 1003, 1601, and 3072 cm − 1 were clearly identified in Soluplus and Soluplus–TPGS nanoparticles, providing spectroscopic evidence of drug incorporation. Linear least‐squares spectral decomposition enabled estimation of the relative Raman contributions of drug and carrier. The results demonstrate a higher loading capacity for Soluplus—TPGS mixed nanoparticles compared to pure Soluplus systems and establish a chemically rigorous Raman reference framework for future label‐free studies of drug–polymer nanocarriers.

Raman spectroscopy is a powerful tool for the characterization of microplastics in environmental samples; however, measurements are typically performed directly on filter substrates, making substrate selection a critical factor for reliable identification. Here, we systematically investigate the influence of 18 commonly used filter substrates on Raman-based microplastic analysis using polystyrene particles with diameters of 7 and 1 μm as model systems. By combining confocal Raman microscopy, spatial Raman mapping, substrate background correction, and PCA-SVM-based classification, we demonstrate that substrate suitability is governed primarily by the geometric relationship between particle size and the confocal sampling volume. For particles substantially larger than the confocal point spread function, Raman spectra are sample-dominated and can be reliably acquired on a wide range of substrates, including polymeric membranes. At particle sizes approaching the confocal sampling volume, substrate contributions become unavoidable; however, mapping-based localization and local background subtraction enable reliable recovery of microplastic spectra even on chemically interfering polymeric filters. These findings challenge the common assumption that only nominally Raman-silent substrates are suitable and provide a practical, physically grounded framework for cost-efficient and flexible filter selection in Raman-based microplastic analysis. Furthermore, this work provides a practical substrate-selection guide for confocal Raman microspectroscopy across different sample types measured on supporting substrates.

Objective: Due to limitations in current imaging technologies detecting subtle cardiac microstructural changes that can lead to sudden cardiac death is a significant clinical challenge. To address this problem, we developed a forward-viewing optical coherence tomography (OCT) endoscope for the detection of relevant cardiac microstructures in the subendocardium, including Purkinje fibers, scar tissue, surviving myocytes, and adipose tissue. Methods: An endoscopic probe based on the scanning fiber principle was developed for OCT measurements in contact. The probe was evaluated in freshly excised ovine hearts exhibiting chronic myocardial infarction. Relevant regions within the cardiac chamber were measured, and distinctive microstructures were identified, characterized, and subsequently corroborated using Masson's trichrome staining. The volumetric imaging data were used to train a convolutional neural network (CNN) to detect Purkinje fibers, enabling the reconstruction of their 3D morphology. Results: We were able to distinguish between healthy myocardium, fibrotic remodeling, and critical elements of the cardiac conduction system. Our findings demonstrate the capability of this technology to provide detailed images of cardiac microstructures in large mammal hearts. Conclusion: A novel application of forward-viewing endoscopic OCT in cardiology is demonstrated by visualizing cardiac microstructures within the subendocardium at depths accessible by optical imaging modalities. Significance: By enhancing visualization at the cellular level, this method may contribute to a better understanding of cardiac physiology and pathology, potentially extending future diagnostic and therapeutic strategies.

Heterogeneity in recombinant protein expression is a critical challenge in baculovirus expression vector system (BEVS) bioprocessing, yet many platforms lack an analytical approach that can simultaneously report infection status, endogenous biochemical state, and cell morphology on individual cells. We present an automated multimodal platform combining high-throughput Raman spectroscopy, phase-contrast microscopy, and fluorescence imaging through a single high-NA objective, acquiring coregistered spectra and images from approximately 1000 individual Spodoptera frugiperda (Sf) 9 cells in ∼32 min. An integrated data-processing pipeline links phase-contrast-derived morphological features, fluorescence-validated reporter abundance, and label-free Raman spectra at single-cell resolution. Applied to three BEVS conditions, i.e. noninfected cells, cells expressing mCherry (BC), and cells coexpressing mCherry with a GLUT6-related transporter (B8), a PCA-LDA model trained on Raman spectra achieved 93% cross-validation accuracy. Cross-modal analysis shows Raman spectroscopy captures biochemical information inaccessible to fluorescence. The mCherry-associated Raman band correlated strongly with fluorescence in BC (Spearman ρ = 0.83) but not in B8 (ρ = -0.16), indicating that Raman captures construct-dependent biochemical states that complement fluorescence-based reporter measurements. Phase-contrast-derived radiomic features independently corroborated this, with amplified cell swelling in high-expressing B8 cells relative to BC (Cliff's δ = -0.69 versus -0.51) pointing to an additional biosynthetic burden in the dual-expression sample. By resolving construct-specific biochemical, morphological, and expression heterogeneity at single-cell resolution, this multimodal workflow demonstrates the analytical potential of correlated single-cell measurements for BEVS characterization, establishing an analytical basis for future at-line adaptation.

Optical photothermal infrared (O-PTIR) spectroscopy is an emerging technique to analyze submicron mid-infrared absorption. It can be combined with concurrent, co-located Raman spectroscopy on the same instrument platform, offering considerable potential for microplastic analysis. However, since measurements are typically performed directly on collection filters, substrate selection becomes a critical yet un-examined factor for these concurrent modalities. Here, we characterized nine commercially available filter substrates, namely Anodisc, silicon, gold-coated PET, gold-coated PC, silver, glass and quartz microfiber, PC, and cellulose, for O-PTIR and Raman analysis using 14 µm and 7 µm PMMA microplastic beads. Spectral fidelity was quantified via hit quality indices against a CaF2 reference. Our results show that substrate suitability is governed by modality-specific requirements. O-PTIR demands mid-infrared spectral neutrality and is additionally sensitive to the substrate surface, whereas the quality of Raman spectra primarily depends on the absence of substrate Raman bands. Beyond spectral match quality, the reproducibility of PMMA spectra is consistently higher in the Raman channel than in the O-PTIR channel. This difference is attributed to O-PTIR’s sensitivity to local thermal contact and surface morphology, which introduce measurement variability absent in direct Raman scattering. Of the tested substrates, Anodisc and gold-coated PET performed the best in both modalities and with both particle sizes. These findings demonstrate that recommendations for substrates established for conventional FTIR or Raman microscopy cannot be transferred directly to O-PTIR. They also provide an evidence-based framework for selecting substrates in multimodal microplastic analysis workflows.

As first part of an ongoing prospective feasibility trial (DRKS00028114) this work explored the integration of in vivo Raman spectroscopy (RS) into the routine setting workflow of head and neck cancer (HNC) surgery. In vivo RS was performed intraoperatively on 30 patients with HNC cell carcinoma and 10 patients with inflammatory diseases as a control group. A standardized process was established using a Raman system complied with stringent medical device regulatory standards. Spectra were collected in vivo from the tumor site, the tumor margins, and healthy tissue. The learning curve of the HNC team significantly improved measurement times from over 30 min initially to 2 min after 15 patients. Data from 35 patients were interpretable, demonstrating clear spectral differences between tumor and healthy tissues. The intraoperative in vivo RS workflow is now well established and is being used in the ongoing clinical trial.

Long-term stability of Raman setups is one of the critical criteria for using Raman spectroscopy in real-world applications. Substantial differences from long-term drifts of a device can largely reduce the reliability of the technology and lead to serious consequences in scenarios such as disease diagnostics. A systematic investigation of long-term device stability is urgently needed to understand the device-related variations and to help improve the situation. In this study, 13 substances were measured as quality control references weekly for 10 months on a Raman device to investigate instrumental stability over time. The 13 substances were selected to be stable and to cover a wide range of standards, solvents, lipids, and carbohydrates. Approximately 50 Raman spectra of each substance were acquired per measurement day. A data pipeline was constructed to discover the variability (i.e., instability) of the device for the covered time window. Therein, the stability of the measurement was benchmarked from multiple perspectives, including the intensity variations, the correlation coefficients, the clustering, and the classification. The results suggested the device variability to be more random than systematic. Nonetheless, we demonstrated the possibility of decreasing the variations from the data via computational methods. In particular, we estimated the spectral variations by a network adapted from the variational autoencoder (VAE) and suppressed them from the measured data by the extensive multiplicative scattering correction (EMSC) method. This could improve the prediction of independent measurement days for three representative classification tasks.

Microplastic pollution poses a significant environmental challenge, with particles ranging from micrometers to millimeters contaminating ecosystems worldwide. Traditional Raman microspectroscopy struggles to balance spatial resolution, field of view, and throughput, especially at low particle concentrations. Here, we present a high-throughput Raman spectroscopy (HTS-RS) platform that overcomes these limitations by combining a 3.15 × 2.10 mm2 field of view with a spatial resolution of 1.4 μm, enabling rapid, label-free detection and classification of microplastics across a wide size range. The system integrates automated particle recognition, autofocus correction, and Raman spectral acquisition into a seamless workflow, reducing user intervention and accelerating data acquisition. Validation on reference microplastic mixtures demonstrated precise detection from 7 μm to over 400 μm, with robust morphological and chemical characterization. With its high sensitivity, throughput, and automation, our platform sets a new benchmark for microplastic monitoring and provides a scalable solution for environmental screening applications.

Sepsis remains a major clinical challenge, often resulting in long-term physiological and immunological disturbances. This study employed high-throughput single-cell Raman spectroscopy to analyze the biochemical profiles of peripheral blood leukocytes from patients with non-COVID-19 and COVID-19-associated sepsis. Leukocytes were assessed at multiple timepoints, including the acute phase (Days 3 and 7 after sepsis onset) and late recovery phase (6 and 12 months after sepsis onset). Raman spectroscopic profiles of leukocytes showed clear separation between healthy controls and sepsis patients during the acute phase with high balanced accuracy (BAcc: 95%-98%). Spectral differences between acute and recovery phases (BAcc: 84%-97%) and between recovery-phase leukocytes and those from healthy controls (BAcc: 81%-90%) were also observed, indicating long-lasting molecular alterations. Furthermore, distinct profiles were identified between non-COVID-19 and COVID-19-associated sepsis during the acute phase (BAcc: 65%-71%) and in the late-recovery phase (BAcc: 71%-83%). These findings demonstrate that Raman spectroscopy enables label-free, high-throughput profiling of leukocyte biochemistry across the sepsis trajectory. This suggests that Raman spectroscopy is a promising tool for high-throughput screening, offering insights into the biomolecular changes in sepsis and providing a diagnostic platform to differentiate between sepsis etiologies, a significant advancement in the field of sepsis diagnostics.

Understanding the process of fibrotic scarring of the myocardium is critical for the diagnosis and risk stratification of life-threatening cardiac dysfunction. Complex changes in structure, composition, and conductivity occurring at different stages of fibrogenesis diversify the biomedical characteristics of the myocardium. We present a multimodal optical imaging approach including cardiac optical mapping (COM), optical coherence tomography (OCT), multiphoton microscopy (MPM), and line scan Raman microspectroscopy (LSRM) for multiparametric assessment of the myocardium with radiomic analysis to link electrophysiologic, morphologic, functional, and molecular changes in ischemic cardiac tissue and validate our results with histology. COM is used to map the electrical behavior across myocardial tissue. Second harmonic generation and two-photon excitation fluorescence imaging as MPM techniques provide additional unique contrast of collagen, the extracellular matrix, and cardiac cells, such as cardiomyocytes playing a critical role in cardiac fibrosis. Our machine learning model based on radiomic features extracted from MPM data addresses the need for automated fast high-throughput classification between healthy and pathologic cardiac tissues and achieved an accuracy of 0.99. In addition, LSRM assesses the molecular contrast and is used to evaluate the development stage of fibrotic scarring and multiclass classification by utilizing partial least-squares discriminant analysis, achieving sensitivity and specificity values of 0.94. OCT is used for fast navigation through the sample, for intermodal referencing, and easy coregistration between the complementary imaging techniques operating at different fields of view and resolutions ranging from cm2 down to μm2.

Colorectal cancer is one of the most prevalent forms of cancer globally. The most common routine diagnostic methods are the examination of the interior of the colon during colonoscopy or sigmoidoscopy, which frequently includes the removal of a biopsy sample. Optical methods, such as Raman spectroscopy (RS) and optical coherence tomography (OCT), can help to improve diagnostics and reduce the number of unnecessary biopsies. For in vivo use, we have developed fiber-optic probes, one for single-point Raman measurements and one for volumetric OCT. Here, we present the results of a clinical study using these fiber-optic probes in an ex vivo setting. The goal was to evaluate the beneficial effect of combining these two modalities on the AUC ROC score of the machine learning models for the discrimination of cancerous and healthy tissue. In the initial stage of the investigation, both modalities were validated separately using linear discriminant analysis. RS was subjected to spectral preprocessing, while OCT underwent texture feature extraction. Subsequently, both modalities were integrated using the Bayes rule, resulting in an enhanced area under the curve score of 0.93, representing an improvement over the 0.77 score for Raman spectroscopy and 0.86 for OCT.

This work reports on an in vivo Raman-based endoscopy system, invaScope, enabling Raman measurements of healthy and tumor bladder tissue during an endoscopic procedure in the operating theatre. The presented study outlines the progression from the initial concept (validated through previously performed ex vivo studies) to the approval and implementation of a clinical investigational device according to the requirement within the framework of the European Medical Device Regulation (MDR2017/745). The study’s primary objective was to employ the invaScope Raman system within the bladder, capturing in vivo spectroscopic Raman data followed by standard histo- and cytopathological examinations of urological tissue (considered the gold standard). The collected data were analyzed and correlated with histopathological findings post-procedure. Additionally, the study aimed to assess the feasibility of using diagnostic equipment, probes, and software for application in a clinical setting, evaluating usability aspects that are important during surgical procedures. This research represents a pivotal step toward advancing Raman spectroscopy for routine clinical use in characterizing bladder lesions.

T cells are considered to be critical drivers of intestinal inflammation in mice and people. The so called intra-epithelial lymphocyte (IEL) compartment largely consist of T cells. Interestingly, the specific regulation and contribution of IELs in the context of inflammatory bowel disease remains poorly understood, in part due to the lack of appropriate analysis tools. Powerful, label-free methods could ultimately provide access to this cell population and hence give valuable insight into IEL biology and even more to their disease-related functionalities. Raman spectroscopy has demonstrated over the last few years its potential for reliable cell characterization and differentiation, but its utility in regard to IEL exploration remains unknown. To address this question experimentally, we utilized a murine, T cell-driven experimental model system which is accepted to model human gut inflammation. Here, we repopulated the small intestinal IEL compartment (SI IELs) of Rag1-deficient mice endogenously lacking T cells by transferring naïve CD4+ T helper cells intraperitoneally. Using multivariate statistical analysis, high-throughput Raman spectroscopy managed to define a cell subpopulation ex vivo within the SI IEL pool of mice previously receiving T cells in vivo that displayed characteristic spectral features of lymphocytes. Raman data sets matched flow cytometry analyses with the latter identifying T cell receptor (TCR)αβ+ CD4+ T cell population in SI IELs from T cell-transferred mice, but not from control mice, in an abundance comparable to the one detected by Raman spectroscopy. Hence, in this study, we provide experimental evidence for high-throughput Raman spectroscopy to be a novel, future tool to reliably identify and potentially further characterize the T cell pool of small intestinal IELs ex vivo.

In this paper, we summarize our previous relevant works and demonstrate various approaches to overcome the common drawbacks of applying Raman probes for many applications. A handheld fiber-optic Raman probe with an autofocus unit was presented to overcome the problem arising from using fixed-focus lenses, by using a liquid lens as the objective lens, which allows dynamical adjustment of the focal length of the probe. An implementation of a computer vision-based positional tracking to co-register the regular Raman spectroscopic measurements with the spatial location enables fast recording of a Raman image from a large tissue sample by combining positional tracking of the laser spot through brightfield images. The visualization of the Raman image has been extended to augmented and mixed reality and combined with a 3D reconstruction method and projector-based visualization to offer an intuitive and easily understandable way of presenting the Raman image. All these advances are substantial and highly beneficial to further drive the clinical translation of Raman spectroscopy as potential image-guided instrumentation.

Raman spectroscopy (RS) has been widely used in a variety of biomedical applications. Nevertheless, there have been multiple technological and regulatory hurdles to move RS from the research lab to the bedside. From the regulatory point of view, especially in Europe, the new EU regulation on medical devices (Medical Device Regulation, MDR2017/745) complicates the translation. From the technical point of view the lack of imaging capabilities for fiber-optic Raman probes, the lack of depth information and the low imaging speed create challenges. Here, we present some of our recent developments to address current challenges.

The investigation of the biochemical composition of pollen grains is of the utmost interest for several environmental aspects, such as their allergenic potential and their changes in growth conditions due to climatic factors. In order to fully understand the composition of pollen grains, not only is an in-depth analysis of their molecular components necessary but also spatial information of, e.g., the thickness of the outer shell, should be recorded. However, there is a lack of studies using molecular imaging methods for a spatially resolved biochemical composition on a single-grain level. In this study, Raman spectroscopy was implemented as an analytical tool to investigate birch pollen by imaging single pollen grains and analyzing their spectral profiles. The imaging modality allowed us to reveal the layered structure of pollen grains based on the biochemical information of the recorded Raman spectra. Seven different birch pollen species collected at two different locations in Germany were investigated and compared. Using chemometric algorithms such as hierarchical cluster analysis and multiple-curve resolution, several components of the grain wall, such as sporopollenin, as well as the inner core presenting high starch concentrations, were identified and quantified. Differences in the concentrations of, e.g., sporopollenin, lipids and proteins in the pollen species at the two different collection sites were found, and are discussed in connection with germination and other growth processes.