Press Kit
Publication
RAMANMETRIX: a delightful way to analyze Raman spectra
ArXiv:2201.07586 [Physics, Stat]
Darina Storozhuk, Oleg Ryabchykov, Juergen Popp, Thomas Bocklitz
Although Raman spectroscopy is widely used for the investigation of biomedical samples and has a high potential for use in clinical applications, it is not common in clinical routines. One of the factors that obstruct the integration of Raman spectroscopic tools into clinical routines is the complexity of the data processing workflow. Software tools that simplify spectroscopic data handling may facilitate such integration by familiarizing clinical experts with the advantages of Raman spectroscopy. Here, RAMANMETRIX is introduced as a user-friendly software with an intuitive web-based graphical user interface (GUI) that incorporates a complete workflow for chemometric analysis of Raman spectra, from raw data pretreatment to a robust validation of machine learning models. The software can be used both for model training and for the application of the pretrained models onto new data sets. Users have full control of the parameters during model training, but the testing data flow is frozen and does not require additional user input. RAMANMETRIX is available in two versions: as standalone software and web application. Due to the modern software architecture, the computational backend part can be executed separately from the GUI and accessed through an application programming interface (API) for applying a preconstructed model to the measured data. This opens up possibilities for using the software as a data processing backend for the measurement devices in real-time. The models preconstructed by more experienced users can be exported and reused for easy one-click data preprocessing and prediction, which requires minimal interaction between the user and the software. The results of such prediction and graphical outputs of the different data processing steps can be exported and saved.
Supporting files
Dataset used as an example: Sample-Size Planning for Multivariate Data: A Raman-Spectroscopy-Based Example, N. Ali, S. Girnus, P. Rösch, J. Popp, T. Bocklitz, , Anal. Chem. 90 (2018) 12485–12492.
Metadata file
Training report
Presentations
User publications
Raman Spectroscopy for Infection Diagnosis
Salbreiter, Markus, Aikaterini Pistiki, Dana Cialla-May, Petra Rösch, and Jürgen Popp.
Book: In Raman Spectroscopy in Human Health and Biomedicine, 337–410. WORLD SCIENTIFIC, 2022.
Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy
Pahlow, Susanne, Marie Richard-Lacroix, Franziska Hornung, Nilay Köse-Vogel, Thomas G. Mayerhöfer, Julian Hniopek, Oleg Ryabchykov, et al.
In: Biosensors 13, no. 6 (June 2023): 594
We introduce a magnetic bead-based sample preparation scheme for enabling the Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-positive and -negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as a recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. The subsequent Raman measurements directly enable discriminating SARS-CoV-2-positive and -negative samples. The proposed approach is also applicable for other virus species when the specific recognition element is exchanged. A series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All of the spectra are dominated by the magnetic bead substrate and no obvious differences between the sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized cross correlation coefficient. By comparing the correlation with the negative control, differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.
Data science for biomedical studies based on the Raman effect
Darina Storozhuk, Oleg Ryabchykov, Thomas W. Bocklitz.
In: Analytical Science Article DO Series 2023
Spectroscopic techniques are increasingly used in various disciplines, such as biology, biomedicine, and diagnostics. This increase in applications is triggered in part by the development of computational data science methods. Using these data science methods, the extraction of information and knowledge from small differences in Raman spectra is possible. To realize the full potential of Raman spectroscopy, the entire data life cycle of spectroscopic data from generation to data modeling to archiving is important and must be considered holistically. Relevant points within the data life cycle are experimental design, data pretreatment, chemometrics, and machine learning-based data modeling. All procedures are combined in a data pipeline that standardizes the data and extracts reliable information from the Raman spectral data.
Use of Polymers as Wavenumber Calibration Standards in Deep-UVRR
Pistiki, Aikaterini, Oleg Ryabchykov, Thomas W. Bocklitz, Petra Rösch, and Jürgen Popp.
In: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 287 (15 February 2023): 122062
Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400–1900 cm−1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.
Specific Intracellular Signature of SARS-CoV-2 Infection Using Confocal Raman Microscopy’
Salehi, Hamideh, Anuradha Ramoji, Said Mougari, Peggy Merida, Aymeric Neyret, Jurgen Popp, Branka Horvat, Delphine Muriaux, and Frederic Cuisinier.
In: Communications Chemistry 5, no. 1 (25 July 2022): 1–10.
SARS-CoV-2 infection remains spread worldwide and requires a better understanding of virus-host interactions. Here, we analyzed biochemical modifications due to SARS-CoV-2 infection in cells by confocal Raman microscopy. Obtained results were compared with the infection with another RNA virus, the measles virus. Our results have demonstrated a virus-specific Raman molecular signature, reflecting intracellular modification during each infection. Advanced data analysis has been used to distinguish non-infected versus infected cells for two RNA viruses. Further, classification between non-infected and SARS-CoV-2 and measles virus-infected cells yielded an accuracy of 98.9 and 97.2 respectively, with a significant increase of the essential amino-acid tryptophan in SARS-CoV-2-infected cells. These results present proof of concept for the application of Raman spectroscopy to study virus-host interaction and to identify factors that contribute to the efficient SARS-CoV-2 infection and may thus provide novel insights on viral pathogenesis, targets of therapeutic intervention and development of new COVID-19 biomarkers.
Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates
Aikaterini Pistiki, Stefan Monecke, Haodong Shen, Oleg Ryabchykov, Thomas W. Bocklitz, Petra Rösch, Ralf Ehricht, Jürgen Popp
In: Microbiology Spectrum (22 August 2022)
Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation.
3D Hyperspectral Data Analysis with Spatially Aware Deep Learning for Diagnostic Applications
Luo, Ruihao, Shuxia Guo, Julian Hniopek, and Thomas Bocklitz.
In Analytical Chemistry 97, no. 14 (15 April 2025): 7729–37.
Nowadays, with the rise of artificial intelligence (AI), deep learning algorithms play an increasingly important role in various traditional fields of research. Recently, these algorithms have already spread into data analysis for Raman spectroscopy. However, most current methods only use 1-dimensional (1D) spectral data classification, instead of considering any neighboring information in space. Despite some successes, this type of methods wastes the 3-dimensional (3D) structure of Raman hyperspectral scans. Therefore, to investigate the feasibility of preserving the spatial information on Raman spectroscopy for data analysis, spatially aware deep learning algorithms were applied into a colorectal tissue data set with 3D Raman hyperspectral scans. This data set contains Raman spectra from normal, hyperplasia, adenoma, carcinoma tissues as well as artifacts. First, a modified version of 3D U-Net was utilized for segmentation; second, another convolutional neural network (CNN) using 3D Raman patches was utilized for pixel-wise classification. Both methods were compared with the conventional 1D CNN method, which worked as baseline. Based on the results of both epithelial tissue detection and colorectal cancer detection, it is shown that using spatially neighboring information on 3D Raman scans can increase the performance of deep learning models, although it might also increase the complexity of network training. Apart from the colorectal tissue data set, experiments were also conducted on a cholangiocarcinoma data set for generalizability verification. The findings in this study can also be potentially applied into future tasks regarding spectroscopic data analysis, especially for improving model performance in a spatially aware way.
Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms
Tewes, Thomas J., Mario Kerst, Svyatoslav Pavlov, Miriam A. Huth, Ute Hansen, and Dirk P. Bockmühl.
In Heliyon 10, no. 6 (30 March 2024)
In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%–88%. The six remaining species were correctly predicted by only 0%–49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.
Lighting the Path: Raman Spectroscopy’s Journey Through the Microbial Maze
Salbreiter, Markus, Sandra Baaba Frempong, Sabrina Even, Annette Wagenhaus, Sophie Girnus, Petra Rösch, and Jürgen Popp.
In Molecules 29, no. 24 (January 2024): 5956.
The rapid and precise identification of microorganisms is essential in environmental science, pharmaceuticals, food safety, and medical diagnostics. Raman spectroscopy, valued for its ability to provide detailed chemical and structural information, has gained significant traction in these fields, especially with the adoption of various excitation wavelengths and tailored optical setups. The choice of wavelength and setup in Raman spectroscopy is influenced by factors such as applicability, cost, and whether bulk or single-cell analysis is performed, each impacting sensitivity and specificity in bacterial detection. In this study, we investigate the potential of different excitation wavelengths for bacterial identification, utilizing a mock culture composed of six bacterial species: three Gram-positive (S. warneri, S. cohnii, and E. malodoratus) and three Gram-negative (P. stutzeri, K. terrigena, and E. coli). To improve bacterial classification, we applied machine learning models to analyze and extract unique spectral features from Raman data. The results indicate that the choice of excitation wavelength significantly influences the bacterial spectra obtained, thereby impacting the accuracy and effectiveness of the subsequent classification results.
12 - Recent innovations in signal and image processing and data analysis in Raman spectroscopy
Ryabchykov, Oleg, Dana Cialla-May, Anja Silge, Sara Mostafapour, Azadeh Mokari, Ruihao Luo, Pegah Dehbozorgi, Jhonatan Contreras, Jürgen Popp, and Thomas Bocklitz.
In Biophotonics and Biosensing, edited by Andrea Armani, Tatevik Chalyan, and David D. Sampson, 391–416. Photonic Materials and Applications Series. Elsevier, 2024.
This chapter, “Recent innovations in signal and image processing and data analysis in Raman spectroscopy” aims at an overview of data modeling techniques used for Raman data. This includes data modeling in the spectral domain, e.g., when Raman spectra are analyzed, and data modeling in the image domain, where a small number of variables are measured over a larger spatial region. The chapter starts with an introduction meant as overview on Raman-based techniques and in the following two sections, i.e., “Signal processing for spectral analysis” and “Image processing,” introduce methods to handle Raman spectra and Raman images. In both sections, the pretreatment, the preprocessing, and machine learning for analysis are combined and explained separately. In every section the recent trends for the specific methods are described as well. Finally, a summary of the chapter and the topic is given.
SERS-Driven Ceftriaxone Detection in Blood Plasma: A Protein Precipitation Approach
Dwivedi, Aradhana, Oleg Ryabchykov, Chen Liu, Edoardo Farnesi, Michael Stenbæk Schmidt, Thomas Bocklitz, Jürgen Popp, and Dana Cialla-May.
In: Chemosensors 12, no. 10 (October 2024): 213
Accurate detection of antibiotics in biological samples is essential for clinical diagnoses and therapeutic drug monitoring. This research examines how proteins and other substances in blood plasma affect the detection of the antibiotic ceftriaxone using surface-enhanced Raman spectroscopy (SERS). We detected ceftriaxone spiked in blood plasma without sample preparation within the range of 1 mg/mL to 50 µg/mL. By employing a pretreatment approach involving methanol-based protein precipitation to eliminate interfering substances from a spiked blood plasma solution, we could detect ceftriaxone down to 20 µg/mL. The comparative analysis demonstrates that the protein precipitation step enhances the sensitivity of SERS-based detection of drugs in the matrix blood plasma. The insights derived from this study are highly beneficial and can prove advantageous in developing new antibiotic detection methods that are both sensitive and selective in complex biological matrices. These methods can have important implications for clinical treatments.
Label-free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy
Nakar, Amir, Aikaterini Pistiki, Oleg Ryabchykov, Thomas Bocklitz, Petra Rösch, and Jürgen Popp.
In: Journal of Biophotonics (7 April 2022)
Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli, Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV-Resonance Raman spectroscopy (UVRR) and single-cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine-learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications.
Trends in pharmaceutical analysis and quality control by modern Raman spectroscopic techniques
Silge, A., Karina Weber, D. Cialla-May, L. Müller-Bötticher, D. Fischer, and J. Popp.
In: TrAC Trends in Analytical Chemistry, 116623 (12 April 2022)
The application of Raman spectroscopic methods in life science applications has entered a new era due to advances in instrumentation, miniaturization and, most importantly, increased interdisciplinary dialogue between spectroscopists and end users such as clinicians or pharmacists. Advances in laser sampling technologies, optical instruments and data analysis provide new possibilities for optical sensing and chemical imaging. Raman-based technologies are being driven by these new developments, opening new application for advanced pharmaceutical development and manufacturing. In addition, non-destructive and label free quality testing of drug formulations, Raman sensing is of particular interest for process-related aspects in bioprocessing. The trend to personalized medicine, particularly for pediatric and geriatric applications increased the number of prescriptions of individually tailored medicines manufactured in public pharmacies. This article discusses trends and updates of Raman based analytics and how Raman spectroscopic information can create value for modern pharmaceutical applications and processes. The first section reviews Raman modalities that have great potential for on-site applications in the pharmaceutical industry as well as in research and development. The second section provides an overview of innovative Raman spectroscopic devices and the many possibilities in terms of analytical capabilities and sample properties. Photonic data science has been an important driver for Raman based technologies breakthroughs in the last decade and help to standardize Raman spectral analysis, which is discussed in the third section. In the last section we discuss how to translate Raman-based innovations into user-friendly on-site applications.
Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy
Amir Nakar, Aikaterini Pistiki, Oleg Ryabchykov, Thomas Bocklitz, Petra Rösch and Jürgen Popp
In: Analytical and Bioanalytical Chemistry (6 January 2022): 117973
In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.
Noise Sources and Requirements for Confocal Raman Spectrometers in Biosensor Applications
Jahn, Izabella J., Alexej Grjasnow, Henry John, Karina Weber, Jürgen Popp, and Walter Hauswald.
In: Sensors 21, no. 15 (January 2021): 5067
Raman spectroscopy probes the biochemical composition of samples in a non-destructive, non-invasive and label-free fashion yielding specific information on a molecular level. Nevertheless, the Raman effect is very weak. The detection of all inelastically scattered photons with highest efficiency is therefore crucial as well as the identification of all noise sources present in the system. Here we provide a study for performance comparison and assessment of different spectrometers for confocal Raman spectroscopy in biosensor applications. A low-cost, home-built Raman spectrometer with a complementary metal-oxide-semiconductor (CMOS) camera, a middle price-class mini charge-coupled device (CCD) Raman spectrometer and a laboratory grade confocal Raman system with a deeply cooled CCD detector are compared. It is often overlooked that the sample itself is the most important “optical” component in a Raman spectrometer and its properties contribute most significantly to the signal-to-noise ratio. For this purpose, different representative samples: a crystalline silicon wafer, a polypropylene sample and E. coli bacteria were measured under similar conditions using the three confocal Raman spectrometers. We show that biosensor applications do not in every case profit from the most expensive equipment. Finally, a small Raman database of three different bacteria species is set up with the middle price-class mini CCD Raman spectrometer in order to demonstrate the potential of a compact setup for pathogen discrimination.
Bacterial Phenotype Dependency from CO2 Measured by Raman Spectroscopy
Wichmann, Christina, Thomas Bocklitz, Petra Rösch, and Jürgen Popp.
In: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 248 (2021): 119170.
In recent years, Raman spectroscopy has become an established method to study medical, biological or environmental samples. Since Raman spectroscopy is a phenotypic method, many parameters can influence the spectra. One of these parameters is the concentration of CO2, as this never remains stable in nature, but always adjusts itself in a dynamic equilibrium. So, it is obvious that the concentration of CO2 cannot be controlled but it might have a big impact on the bacteria and bacterial composition in medical samples. When using a phenotypic method like Raman spectroscopy it is also important to know the influence of CO2 to the dataset. To investigate the influence of CO2 towards Raman spectra we cultivated E. coli at different concentration of CO2 since this bacterium is able to switch metabolism from aerobic to microaerophilic conditions. After applying statistic methods small changes in the spectra became visible and it was even possible to observe the change of metabolism in this species according to the concentration of CO2.
Isolation of bacteria from artificial bronchoalveolar lavage fluid using density gradient centrifugation and their accessibility by Raman spectroscopy
Wichmann, Christina, Petra Rösch, and Jürgen Popp.
In: Analytical and Bioanalytical Chemistry 413, no. 20 (August 2021): 5193–5200
Raman spectroscopy is an analytical method to identify medical samples of bacteria. Because Raman spectroscopy detects the biochemical properties of a cell, there are many factors that can influence and modify the Raman spectra of bacteria. One possible influence is a proper method for isolation of the bacteria. Medical samples in particular never occur in purified form, so a Raman-compatible isolation method is needed which does not affect the bacteria and thus the resulting spectra. In this study, we present a Raman-compatible method for isolation of bacteria from bronchoalveolar lavage (BAL) fluid using density gradient centrifugation. In addition to measuring the bacteria from a patient sample, the yield and the spectral influence of the isolation on the bacteria were investigated. Bacteria isolated from BAL fluid show additional peaks in comparison to pure culture bacteria, which can be attributed to components in the BAL sample. The isolation gradient itself has no effect on the spectra, and with a yield of 63% and 78%, the method is suitable for isolation of low concentrations of bacteria from a complex matrix. Graphical abstract.
Raman Stable Isotope Probing of Bacteria in Visible and Deep UV-Ranges
Azemtsop Matanfack, Georgette, Aikaterini Pistiki, Petra Rösch, and Jürgen Popp.
In: Life 11, no. 10 (October 2021): 1003