MMIR: an open-source software for the registration of multimodal histological images.

BACKGROUND: Multimodal histology image registration is a process that transforms into a common coordinate system two or more images obtained from different microscopy modalities. The combination of information from various modalities can contribute to a comprehensive understanding of tissue specimens, aiding in more accurate diagnoses, and improved research insights. Multimodal image registration in histology samples presents a significant challenge due to the inherent differences in characteristics and the need for tailored optimization algorithms for each modality. RESULTS: We developed MMIR a cloud-based system for multimodal histological image registration, which consists of three main modules: a project manager, an algorithm manager, and an image visualization system. CONCLUSION: Our software solution aims to simplify image registration tasks with a user-friendly approach. It facilitates effective algorithm management, responsive web interfaces, supports multi-resolution images, and facilitates batch image registration. Moreover, its adaptable architecture allows for the integration of custom algorithms, ensuring that it aligns with the specific requirements of each modality combination. Beyond image registration, our software enables the conversion of segmented annotations from one modality to another.

Towards therapeutic drug monitoring of antibiotic levels - analyzing the pharmacokinetics of levofloxacin using DUV-resonance Raman spectroscopy.

Therapeutic drug monitoring (TDM) plays an important role in clinical practice. Here, pharmacokinetics has a decisive influence on the effective antibiotic concentration during treatment. Moreover, different kinetics exist for different administration forms. Accordingly, adjusting the correct concentration depends, in addition to gender, age, weight, clinical picture, etc., on the dosage form of the antibiotic. This study investigates the capability of deep UV resonance Raman spectroscopy (DUV-RRS) to simulate the pharmacokinetics of fluoroquinolone levofloxacin after two different administration forms (intravenous and oral). Three different pre-processing methods were applied, and the best agreement with the simulation was achieved using the extended multiplicative scatter correction. The resulting spectra were used for partial least squares (PLS) regression and ordinary least squares (OLS) regression. The kinetic parameters were compared with the simulated data, with PLS showing the best performance for intravenous administration and a comparable result to OLS for oral administration, while the errors are smaller. The acquired results show the potential of DUV-RRS in combination with PLS regression as a promising supportive method for TDM.

Label-Free SERS and MD Analysis of Biomarkers for Rapid Point-of-Care Sensors Detecting Head and Neck Cancer and Infections.

For the progress of point-of-care medicine, where individual health status can be easily and quickly monitored using a handheld sensor, saliva serves as one of the best-suited body fluids thanks to its availability and abundance of physiological indicators. Salivary biomarkers, combined with rapid and highly sensitive detection tools, may pave the way to new real-time health monitoring and personalized preventative therapy branches using saliva as a target matrix. Saliva is increasing in importance in liquid biopsy, a non-invasive approach that helps physicians diagnose and characterize specific diseases in patients. Here, we propose a proof-of-concept study combining the unique specificity in biomolecular recognition provided by surface-enhanced Raman spectroscopy (SERS) in combination with molecular dynamics (MD) simulations, which give leave to explore the biomolecular absorption mechanism on nanoparticle surfaces, in order to verify the traceability of two validated salivary indicators, i.e., interleukin-8 (IL-8) and lysozyme (LYZ), implicated in oropharyngeal squamous cell carcinoma (OSCC) and oral infection. This strategy simultaneously assures the detection and interpretation of protein biomarkers in saliva, ultimately opening a new route for the evolution of fast and accurate point-of-care SERS-based sensors of interest in precision medicine diagnostics.

Raman Spectroscopy Profiling of Splenic T-Cells in Sepsis and Endotoxemia in Mice.

Sepsis is a life-threatening condition that results from an overwhelming and disproportionate host response to an infection. Currently, the quality and extent of the immune response are evaluated based on clinical symptoms and the concentration of inflammatory biomarkers released or expressed by the immune cells. However, the host response toward sepsis is heterogeneous, and the roles of the individual immune cell types have not been fully conceptualized. During sepsis, the spleen plays a vital role in pathogen clearance, such as bacteria by an antibody response, macrophage bactericidal capacity, and bacterial endotoxin detoxification. This study uses Raman spectroscopy to understand the splenic T-lymphocyte compartment profile changes during bona fide bacterial sepsis versus hyperinflammatory endotoxemia. The Raman spectral analysis showed marked changes in splenocytes of mice subjected to septic peritonitis principally in the DNA region, with minor changes in the amino acids and lipoprotein areas, indicating significant transcriptomic activity during sepsis. Furthermore, splenocytes from mice exposed to endotoxic shock by injection of a high dose of lipopolysaccharide showed significant changes in the protein and lipid profiles, albeit with interindividual variations in inflammation severity. In summary, this study provided experimental evidence for the applicability and informative value of Raman spectroscopy for profiling the immune response in a complex, systemic infection scenario. Importantly, changes within the acute phase of inflammation onset (24 h) were reliably detected, lending support to the concept of early treatment and severity control by extracorporeal Raman profiling of immunocyte signatures.

Identification of inflammatory markers in eosinophilic cells of the immune system: fluorescence, Raman and CARS imaging can recognize markers but differently.

Eosinophils (Eos) play an important role in the immune system's response releasing several inflammatory factors and contributing to allergic rhinitis, asthma, or atopic dermatitis. Since Eos have a relatively short lifetime after isolation from blood, usually eosinophilic cell line (EoL-1) is used to study mechanisms of their activation and to test therapies. In particular, EoL-1 cells are examined in terms of signalling pathways of the inflammatory response manifested by the presence of lipid bodies (LBs). Here we examined the differences in response to inflammation modelled by various factors, between isolated human eosinophils and EoL-1 cells, as manifested in the number and chemical composition of LBs. The analysis was performed using fluorescence, Raman, and coherent anti-Stokes Raman scattering (CARS) microscopy, which recognised the inflammatory process in the cells, but it is manifested slightly differently depending on the method used. We showed that unstimulated EoL-1 cells, compared to isolated eosinophils, contained more LBs, displayed different nucleus morphology and did not have eosinophilic peroxidase (EPO). In EoL-1 cells stimulated with various proinflammatory agents, including butyric acid (BA), liposaccharide (LPS), or cytokines (IL-1ß, TNF-α), an increased production of LBs with a various degree of lipid unsaturation was observed in spontaneous Raman spectra. Furthermore, stimulation of EoL-1 cells resulted in alterations of the LBs morphology. In conclusion, a level of lipid unsaturation and eosinophilic peroxidase as well as LBs distribution among cell population mainly accounted for the biochemistry of eosinophils upon inflammation.

Hydrogen and C2-C6 Alkane Sensing in Complex Fuel Gas Mixtures with Fiber-Enhanced Raman Spectroscopy.

Power-to-gas is a heavily discussed option to store surplus electricity from renewable sources. Part of the generated hydrogen could be fed into the gas grid and lead to fluctuations in the composition of the fuel gas. Consequently, both operators of transmission networks and end users would need to frequently monitor the gas to ensure safety as well as optimal and stable operation. Currently, gas chromatography-based analysis methods are the state of the art. However, these methods have several downsides for time-resolved and distributed application and Raman gas spectroscopy is favorable for future point-of-use monitoring. Here, we demonstrate that fiber-enhanced Raman gas spectroscopy (FERS) enables the simultaneous detection of all relevant gases, from major (methane, CH4; hydrogen, H2) to minor (C2-C6 alkanes) fuel gas components. The characteristic peaks of H2 (585 cm-1), CH4 (2917 cm-1), isopentane (765 cm-1), i-butane (798 cm-1), n-butane (830 cm-1), n-pentane (840 cm-1), propane (869 cm-1), ethane (993 cm-1), and n-hexane (1038 cm-1) are well resolved in the broadband spectra acquired with a compact spectrometer. The fiber enhancement achieved in a hollow-core antiresonant fiber enables highly sensitive measurements with limits of detection between 90 and 180 ppm for different hydrocarbons. Both methane and hydrogen were quantified with high accuracy with average relative errors of 1.1% for CH4 and 1.5% for H2 over a wide concentration range. These results show that FERS is ideally suited for comprehensive fuel gas analysis in a future, where regenerative sources lead to fluctuations in the composition of gas.

Nondestructive molecular imaging by Raman spectroscopy vs. marker detection by MALDI IMS for an early diagnosis of HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide with a steadily increasing mortality rate. Fast diagnosis at early stages of HCC is of key importance for the improvement of patient survival rates. In this regard, we combined two imaging techniques with high potential for HCC diagnosis in order to improve the prediction of liver cancer. In detail, Raman spectroscopic imaging and matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) were applied for the diagnosis of 36 HCC tissue samples. The data were analyzed using multivariate methods, and the results revealed that Raman spectroscopy alone showed a good capability for HCC tumor identification (sensitivity of 88% and specificity of 80%), which could not be improved by combining the Raman data with MALDI IMS. In addition, it could be shown that the two methods in combination can differentiate between well-, moderately- and poorly-differentiated HCC using a linear classification model. MALDI IMS not only classified the HCC grades with a sensitivity of 100% and a specificity of 80%, but also showed significant differences in the expression of glycerophospholipids and fatty acyls during HCC differentiation. Furthermore, important differences in the protein, lipid and collagen compositions of differentiated HCC were detected using the model coefficients of a Raman based classification model. Both Raman and MALDI IMS, as well as their combination showed high potential for resolving concrete questions in liver cancer diagnosis.

Fiber-Enhanced Raman Gas Spectroscopy for the Study of Microbial Methanogenesis.

Microbial methanogenesis is a key biogeochemical process in the carbon cycle that is responsible for 70% of global emissions of the potent greenhouse gas methane (CH4). Further knowledge about microbial methanogenesis is crucial to mitigate emissions, increase climate model accuracy, or advance methanogenic biogas production. The current understanding of the substrate use of methanogenic microbes is limited, especially regarding the methylotrophic pathway. Here, we present fiber-enhanced Raman spectroscopy (FERS) of headspace gases as an alternate tool to study methanogenesis and substrate use in particular. The optical technique is nondestructive and sensitive to CH4, hydrogen (H2), and carbon dioxide with a large dynamic range from trace levels (demonstrated LoDs: CH4, 3 ppm; H2, 49 ppm) to pure gases. In addition, the portable FERS system can provide quantitative information about methanol concentration in the liquid phase of microbial cultures through headspace gas sampling (LoD 25 ppm). We demonstrate how FERS gas sensing could enable us to track substrate and product levels of microbial methanogenesis with just one instrument. The versatility of Raman gas spectroscopy could moreover help us to elucidate links between nitrogen and carbon cycle in microbial communities in the near future.

Towards an Interpretable Classifier for Characterization of Endoscopic Mayo Scores in Ulcerative Colitis Using Raman Spectroscopy.

Ulcerative colitis (UC) is one of the main types of chronic inflammatory diseases that affect the bowel, but its pathogenesis is yet to be completely defined. Assessing the disease activity of UC is vital for developing a personalized treatment. Conventionally, the assessment of UC is performed by colonoscopy and histopathology. However, conventional methods fail to retain biomolecular information associated to the severity of UC and are solely based on morphological characteristics of the inflamed colon. Furthermore, assessing endoscopic disease severity is limited by the requirement for experienced human reviewers. Therefore, this work presents a nondestructive biospectroscopic technique, for example, Raman spectroscopy, for assessing endoscopic disease severity according to the four-level Mayo subscore. This contribution utilizes multidimensional Raman spectroscopic data to generate a predictive model for identifying colonic inflammation. The predictive modeling of the Raman spectroscopic data is performed using a one-dimensional deep convolutional neural network (1D-CNN). The classification results of 1D-CNN achieved a mean sensitivity of 78% and a mean specificity of 93% for the four Mayo endoscopic scores. Furthermore, the results of the 1D-CNN are interpreted by a first-order Taylor expansion, which extracts the Raman bands important for classification. Additionally, a regression model of the 1D-CNN model is constructed to study the extent of misclassification and border-line patients. The overall results of Raman spectroscopy with 1D-CNN as a classification and regression model show a good performance, and such a method can serve as a complementary method for UC analysis.

Deep learning as phase retrieval tool for CARS spectra.

Finding efficient and reliable methods for the extraction of the phase in optical measurements is challenging and has been widely investigated. Although sophisticated optical settings, e.g. holography, measure directly the phase, the use of algorithmic methods has gained attention due to its efficiency, fast calculation and easy setup requirements. We investigated three phase retrieval methods: the maximum entropy technique (MEM), the Kramers-Kronig relation (KK), and for the first time deep learning using the Long Short-Term Memory network (LSTM). LSTM shows superior results for the phase retrieval problem of coherent anti-Stokes Raman spectra in comparison to MEM and KK.

Fiber-Enhanced Raman Gas Spectroscopy for 18O-13C-Labeling Experiments.

Stable isotopes are used in ecology to track and disentangle different processes and pathways. Especially for studies focused on the gas exchange of plants, sensing techniques that offer oxygen (O2) and carbon dioxide (CO2) sensitivity with isotopic discrimination are highly sought after. Addressing this challenge, fiber-enhanced Raman gas spectroscopy is introduced as a fast optical technique directly combining 13CO2 and 12CO2 as well as 18O2 and 16O2 measurements in one instrument. We demonstrate how a new type of optical hollow-core fiber, the so-called revolver fiber, is utilized for enhanced Raman gas sensing. Carbon dioxide and oxygen isotopologues were measured at concentrations expected when using 13C- and 18O-labeled gases in plant experiments. Limits of detection have been determined to be 25 ppm for CO2 and 150 ppm for O2. The combination of measurements with different integration times allows the creation of highly resolved broadband spectra. With the help of calculations based on density functional theory, the line at 1512 cm-1 occurring in the oxygen spectrum is assigned to 18O16O. The relative abundances of the isotopologues 18O16O and nitrogen 15N14N were in good agreement with typical values. For CO2, fiber-enhanced Raman spectra show the Fermi diad and hotbands of 12C16O2, 13C16O2, and 12C18O16O. Several weak lines were observed, and the line at 1426 cm-1 was identified as originating from the (0 4 0 2) → (0 2 0 2) transition of 12C16O2. With the demonstrated sensitivity and discriminatory power, fiber-enhanced Raman spectroscopy is a possible alternative means to investigate plant metabolism, directly combining 13CO2 and 12CO2 measurements with 18O2 and 16O2 measurements in one instrument. The presented method thus has large potential for basic analytical investigations as well as for applications in the environmental sciences.

Highly Sensitive Detection of the Antibiotic Ciprofloxacin by Means of Fiber Enhanced Raman Spectroscopy.

Sepsis and septic shock exhibit a rapid course and a high fatality rate. Antibiotic treatment is time-critical and precise knowledge of the antibiotic concentration during the patients' treatment would allow individual dose adaption. Over- and underdosing will increase the antimicrobial efficacy and reduce toxicity. We demonstrated that fiber enhanced Raman spectroscopy (FERS) can be used to detect very low concentrations of ciprofloxacin in clinically relevant doses, down to 1.5 µM. Fiber enhancement was achieved in bandgap shifted photonic crystal fibers. The high linearity between the Raman signals and the drug concentrations allows a robust calibration for drug quantification. The needed sample volume was very low (0.58 µL) and an acquisition time of 30 s allowed the rapid monitoring of ciprofloxacin levels in a less invasive way than conventional techniques. These results demonstrate that FERS has a high potential for clinical in-situ monitoring of ciprofloxacin levels.

Fiber-Array-Based Raman Hyperspectral Imaging for Simultaneous, Chemically-Selective Monitoring of Particle Size and Shape of Active Ingredients in Analgesic Tablets.

The particle shape, size and distribution of active pharmaceutical ingredients (API) are relevant quality indicators of pharmaceutical tablets due to their high impact on the manufacturing process. Furthermore, the bioavailability of the APIs from the dosage form depends largely on these characteristics. Routinely, particle size and shape are only analyzed in the powder form, without regard to the effect of the formulation procedure on the particle characteristics. The monitoring of these parameters improves the understanding of the process; therefore, higher quality and better control over the biopharmaceutical profile can be ensured. A new fiber-array-based Raman hyperspectral imaging technique is presented for direct simultaneous in-situ monitoring of three different active pharmaceutical ingredients- acetylsalicylic acid, acetaminophen and caffeine- in analgesic tablets. This novel method enables a chemically selective, noninvasive assessment of the distribution of the active ingredients down to 1 µm spatial resolution. The occurrence of spherical and needle-like particles, as well as agglomerations and the respective particle size ranges, were rapidly determined for two commercially available analgesic tablet types. Subtle differences were observed in comparison between these two tablets. Higher amounts of acetaminophen were visible, more needle-shaped and bigger acetylsalicylic acid particles, and a higher incidence of bigger agglomerations were found in one of the analgesic tablets.

Counterfeit and Substandard Test of the Antimalarial Tablet Riamet® by Means of Raman Hyperspectral Multicomponent Analysis.

The fight against counterfeit pharmaceuticals is a global issue of utmost importance, as failed medication results in millions of deaths every year. Particularly affected are antimalarial tablets. A very important issue is the identification of substandard tablets that do not contain the nominal amounts of the active pharmaceutical ingredient (API), and the differentiation between genuine products and products without any active ingredient or with a false active ingredient. This work presents a novel approach based on fiber-array based Raman hyperspectral imaging to qualify and quantify the antimalarial APIs lumefantrine and artemether directly and non-invasively in a tablet in a time-efficient way. The investigations were carried out with the antimalarial tablet Riamet® and self-made model tablets, which were used as examples of counterfeits and substandard. Partial least-squares regression modeling and density functional theory calculations were carried out for quantification of lumefantrine and artemether and for spectral band assignment. The most prominent differentiating vibrational signatures of the APIs were presented.

A specific spectral signature of serum and plasma-derived extracellular vesicles for cancer screening.

In cancer, extracellular vesicles (EV) contribute to tumor progression by regulating local and systemic effects. Being released into body fluids, EV may be used in nanomedicine as a valuable source for diagnostic biomarkers. In this work, infrared and Raman spectroscopy were used for comprehensive comparative analysis of cancer versus non-cancer EV and patient screening. Two different EV fractions enriched in exosomes and microvesicles were isolated by differential centrifugation from serum and plasma of cancer and non-cancer patients and from serum and plasma of a healthy donor. The EV fractions were then subjected to drop-coating deposition and drying on calcium fluoride substrates. Reduction of alpha-helix-rich proteins and enhancement of beta-sheet-rich proteins as a cancer-specific blood EV signature was determined, and subsequently this feature was applied for a pilot study aiming to detect prostate cancer in a test cohort of patients with high-grade prostate carcinoma and benign hypoplasia.

Lab-on-a-Chip-Surface Enhanced Raman Scattering Combined with the Standard Addition Method: Toward the Quantification of Nitroxoline in Spiked Human Urine Samples.

The emergence of antibacterial resistance and the development of new drugs lead to a continuous change of guidelines for medical treatments. Hence, new analytical tools are required for the detection of drugs in biological fluids. In this study, the first surface enhanced Raman scattering (SERS) detection of nitroxoline (NTX) in purified water and in spiked human urine samples is reported. Insights concerning the nature of the molecule-metal interaction and its influence on the overall SERS signal are provided. Furthermore, three randomly collected urine samples originating from a healthy volunteer were spiked to assess the limit of detection (LOD), the limit of quantification (LOQ), and the linear dynamic range of the lab-on-a-chip SERS (LoC-SERS) method for NTX detection in human urine. The LOD is ∼3 µM (0.57 mg/L), LOQ ∼ 6.5 µM (1.23 mg/L) while the linear range is between 4.28 and 42.8 µM (0.81-8.13 mg/L). This covers the minimum inhibitory concentration (MIC) values of the most commonly encountered uropathogens. Finally, seven clinical samples having an "unknown" NTX concentration were simulated. The LoC-SERS technique combined with the standard addition method and statistical data analysis provided a good prediction of the unknown concentrations. Additionally, it is also demonstrated that the predictions carried out by multicurve resolution alternating least-squares (MCR-ALS) algorithm provides reliable results, and it is preferred to a univariate statistical approach.

Studies of silicon nanoparticles uptake and biodegradation in cancer cells by Raman spectroscopy.

In-vitro Raman micro-spectroscopy was used for diagnostics of the processes of uptake and biodegradation of porous silicon nanoparticles (SiNPs) in breast cancer cells (MCF-7 cell line). Two types of nanoparticles, with and without photoluminescence in the visible spectral range, were investigated. The spatial distribution of photoluminescent SiNPs within the cells obtained by Raman imaging was verified by high-resolution structured-illumination optical microscopy. Nearly complete biodegradation of SiNPs inside the living cells was observed after 13days of the incubation. The results reveal new prospects of multi-modal visualization of SiNPs inside cancer cells for theranostic applications.

A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images.

Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance without exacerbating the first. To achieve this, we propose a modification of the copy-paste data augmentation technique, combined with weight-balancing methods in the loss function. This strategy was specifically tailored to improve the performance in datasets with a high instance density, where instance overlap could be detrimental. To validate our methodology, we applied it to a highly unbalanced dataset focused on nuclei detection. The results show that this hybrid approach improves the classification of minority classes without significantly compromising the performance of majority classes.