Contreras, Jhonatan, and Thomas Bocklitz
In: In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM, 597–606, 2023
Convolutional Neural Networks (CNN) have shown remarkable results in several fields in recent years. Traditional performance metrics assess model performance but fail to detect biases in datasets and models. Explainable artificial intelligence (XAI) methods aim to evaluate models, identify biases, and clarify model decisions. We propose an agnostic XAI method based on the Volterra series that approximates models. Our model architecture is composed of three second-order Volterra layers. Relevant information can be extracted from the model to be approximated and used to generate relevance maps that explain the contribution of the input elements to the prediction. Our Volterra-XAI learns its Volterra kernels comprehensively and is trained using a target model outcome. Therefore, no labels are required, and even when training data is unavailable, it is still possible to generate an approximation utilizing similar data. The trustworthiness of our method can be measured by considering the reliability of the Volterra approximation in comparison with the original model. We evaluate our XAI method for the classification task on 1D Raman spectra and 2D images using two common CNN architectures without hyperparameter tuning. We present relevance maps indicating higher and lower contributions to the approximation prediction (logit).
Halim Bhuiyan, Abdul, Jean-Emmanuel Clément, Zannatul Ferdous, Kentaro Mochizuki, Koji Tabata, James Nicholas Taylor, Yasuaki Kumamoto, et al.
In: Analyst, 2023
A line illumination Raman microscope extracts the underlying spatial and spectral information of a sample, typically a few hundred times faster than raster scanning. This makes it possible to measure a wide range of biological samples such as cells and tissues – that only allow modest intensity illumination to prevent potential damage – within feasible time frame. However, a non-uniform intensity distribution of laser line illumination may induce some artifacts in the data and lower the accuracy of machine learning models trained to predict sample class membership. Here, using cancerous and normal human thyroid follicular epithelial cell lines, FTC-133 and Nthy-ori 3-1 lines, whose Raman spectral difference is not so large, we show that the standard pre-processing of spectral analyses widely used for raster scanning microscopes introduced some artifacts. To address this issue, we proposed a detrending scheme based on random forest regression, a nonparametric model-free machine learning algorithm, combined with a position-dependent wavenumber calibration scheme along the illumination line. It was shown that the detrending scheme minimizes the artifactual biases arising from non-uniform laser sources and significantly enhances the differentiability of the sample states, i.e., cancerous or normal epithelial cells, compared to the standard pre-processing scheme.
Houhou, Rola, Elsie Quansah, Tobias Meyer-Zedler, Michael Schmitt, Franziska Hoffmann, Orlando Guntinas-Lichius, Jürgen Popp, and Thomas Bocklitz.
In: Biomedical Optics Express 14, no. 7 (1 July 2023): 3259–78.
Biophotonic multimodal imaging techniques provide deep insights into biological samples such as cells or tissues. However, the measurement time increases dramatically when high-resolution multimodal images (MM) are required. To address this challenge, mathematical methods can be used to shorten the acquisition time for such high-quality images. In this research, we compared standard methods, e.g., the median filter method and the phase retrieval method via the Gerchberg-Saxton algorithm with artificial intelligence (AI) based methods using MM images of head and neck tissues. The AI methods include two approaches: the first one is a transfer learning-based technique that uses the pre-trained network DnCNN. The second approach is the training of networks using augmented head and neck MM images. In this manner, we compared the Noise2Noise network, the MIRNet network, and our deep learning network namely incSRCNN, which is derived from the super-resolution convolutional neural network and inspired by the inception network. These methods reconstruct improved images using measured low-quality (LQ) images, which were measured in approximately 2 seconds. The evaluation was performed on artificial LQ images generated by degrading high-quality (HQ) images measured in 8 seconds using Poisson noise. The results showed the potential of using deep learning on these multimodal images to improve the data quality and reduce the acquisition time. Our proposed network has the advantage of having a simple architecture compared with similar-performing but highly parametrized networks DnCNN, MIRNet, and Noise2Noise.
Królikowska, Milena, and Thomas Bocklitz.
In: Advanced Chemical Microscopy for Life Science and Translational Medicine 2023, PC12392:PC1239213. SPIE, 2023
Raman spectroscopy is a label-free, non-invasive spectroscopic technique, which can be utilized for many biomedical and diagnostic investigations. To do so, chemometric modelling strategies are used, but they lead to a low generalizability of the models. To tackle this issue we investigated transfer learning (TL) approaches for deep learning (DL) based modelling of Raman spectra for classification of three bacterial spore species. In initial test we found that TL can facilitate the usage of DL for time-consuming measurement modalities, because it can help to deal with low dataset sizes.
Luo, Ruihao, and Thomas Bocklitz.
In: Informatics in Medicine Unlocked 40 (1 January 2023): 101292.
Background and objective
With the rapid development of data science methods like deep learning, these methods have already been used into the field of healthcare and medicine. However, due to regulations and ethical issues, it is practically difficult to obtain large amount of medical data for training deep learning models. Transfer learning is a powerful tool to reuse the knowledge gained from different domains, which makes it possible to retrain deep learning models only with small datasets in medical image processing. In this contribution, a systematic study of model transfer techniques like fine tuning parts of the network or adding additional layers, for medical image data was conducted.
The study accomplished a binary classification task based on a colorectal cancer dataset, including microsatellite unstable or hypermutated (MSIMUT) and microsatellite stable (MSS) images. By using K-fold cross-validation, the performances of five pretrained models (DenseNet121, DenseNet201, InceptionV3, MobileNetV2 and ResNet50) were assessed according to balanced accuracy. As baseline methods, combinations of transfer learning as feature extractor and principal component analysis with linear discriminant analysis (PCA-LDA) or support vector machine (PCA-SVM) were utilised, and compared with the transfer learning counterparts.
The results have shown that adding convolutional layers perform obviously better than simply using the original network or fine-tuning some last layers of the network. Furthermore, a proposed bagging method performed well on a testing dataset. This study reduces the workload for future transfer learning tasks in the biomedical domain and allows to test promising transfer learning strategies first.
Mostafapour, Sara, Thomas Dörfer, Ralf Heinke, Petra Rösch, Jürgen Popp, and Thomas Bocklitz.
In: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 1 July 2023, 123100
Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532nm, 633nm and 830nm) are evaluated by angular difference index and the influence on a classification model is tested.
The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω4 correction doesn’t have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633nm a lower amount of pre-treatment steps is needed but in the dataset 830nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis.
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.
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.
Vetter, Marcel, Maximilian J. Waldner, Sebastian Zundler, Daniel Klett, Thomas Bocklitz, Markus F. Neurath, Werner Adler, and Daniel Jesper.
In: Ultraschall in Der Medizin - European Journal of Ultrasound, 12 May 2023.
Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.
Lorenz, Björn, Shuxia Guo, Christoph Raab, Patrick Leisching, Thomas Bocklitz, Petra Rösch, and Jürgen Popp
In: Journal of Biophotonics (07 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.
Escobar Díaz Guerrero, Rodrigo, Lina Carvalho, Thomas Bocklitz, Juergen Popp, and José Luis Oliveira.
In: Journal of Pathology Informatics 13 (1 January 2022): 100103.
At the end of the twentieth century, a new technology was developed that allowed an entire tissue section to be scanned on an objective slide. Originally called virtual microscopy, this technology is now known as Whole Slide Imaging (WSI). WSI presents new challenges for reading, visualization, storage, and analysis. For this reason, several technologies have been developed to facilitate the handling of these images. In this paper, we analyze the most widely used technologies in the field of digital pathology, ranging from specialized libraries for the reading of these images to complete platforms that allow reading, visualization, and analysis. Our aim is to provide the reader, whether a pathologist or a computational scientist, with the knowledge to choose the technologies to use for new studies, development, or research.
Pistiki, Aikaterini, Franziska Hornung, Anja Silge, Anuradha Ramoji, Oleg Ryabchykov, Thomas W. Bocklitz, Karina Weber, Bettina Löffler, Jürgen Popp, and Stefanie Deinhardt‐Emmer.
In: Clinical and Translational Medicine 12, no. 12 (19 December 2022): e1139.
Photonic data can be used to characterize the biochemical composition of samples and often in a non-destructive and label-free manner. To utilize these label-free measurements for applications like diagnostics or analytics, data driven modeling is utilized to translate photonic data into higher-level information. In this contribution, two scenarios of data driven modeling will be presented. We will present the translation of nonlinear multi-contrast images into diagnostic information like tissue types, disease types, and histopathological stainings. Additionally, we will demonstrate deep learning as tool for the extraction of the imaginary part of the third-order susceptibility of spectral CARS measurements.
Ryabchykov, Oleg, and Thomas Bocklitz
In: Advanced Chemical Microscopy for Life Science and Translational Medicine 2022, 11973:1197302. SPIE, 2022.
Photonic data can be used to characterize the biochemical composition of samples and often in a non-destructive and label-free manner. To utilize these label-free measurements for applications like diagnostics or analytics, data driven modeling is utilized to translate photonic data into higher-level information. In this contribution, two scenarios of data driven modeling will be presented. We will present the translation of nonlinear multi-contrast images into diagnostic information like tissue types, disease types, and histopathological stainings. Additionally, we will demonstrate deep learning as tool for the extraction of the imaginary part of the third-order susceptibility of spectral CARS measurements.
Tolstik, Elen, Nairveen Ali, Shuxia Guo, Paul Ebersbach, Dorothe Möllmann, Paula Arias-Loza, Johann Dierks, et al.
In: International Journal of Molecular Sciences 23, no. 10 (January 2022): 5345
Vibrational spectroscopy can detect characteristic biomolecular signatures and thus has the potential to support diagnostics. Fabry disease (FD) is a lipid disorder disease that leads to accumulations of globotriaosylceramide in different organs, including the heart, which is particularly critical for the patient’s prognosis. Effective treatment options are available if initiated at early disease stages, but many patients are late- or under-diagnosed. Since Coherent anti-Stokes Raman (CARS) imaging has a high sensitivity for lipid/protein shifts, we applied CARS as a diagnostic tool to assess cardiac FD manifestation in an FD mouse model. CARS measurements combined with multivariate data analysis, including image preprocessing followed by image clustering and data-driven modeling, allowed for differentiation between FD and control groups. Indeed, CARS identified shifts of lipid/protein content between the two groups in cardiac tissue visually and by subsequent automated bioinformatic discrimination with a mean sensitivity of 90–96%. Of note, this genotype differentiation was successful at a very early time point during disease development when only kidneys are visibly affected by globotriaosylceramide depositions. Altogether, the sensitivity of CARS combined with multivariate analysis allows reliable diagnostic support of early FD organ manifestation and may thus improve diagnosis, prognosis, and possibly therapeutic monitoring of FD.
Tolstik, Elen, Nairveen Ali, Irina Schuler, Johann Dierks, Dorothe Möllmann, Paula Arias-Loza, Hideo A. Baba, Peter Nordbeck, Thomas Bocklitz, and Kristina Lorenz.
In: Journal of Molecular and Cellular Cardiology, Abstracts from the XXIV World Congress of the International Society for Heart Research: 12–15 June 2022, Berlin, Germany, 173 (31 December 2022): 99
X-linked Morbus Fabry, commonly named Fabry disease (FD), is a rare inherited lysosomal storage disorder due to alpha-galactosidase A (α-Gal A) deficiency causing progressive multiorgan damage. Manifestation of FD in the heart is the leading cause of death in these patients. A disease-specific enzyme replacement therapy is available; though its efficiency strongly depends on its initiation before organ damage develops. Especially patients with residual α-Gal A activity (“non-classic” or “late onset” FD) are under- or late-diagnosed as the clinical manifestation often affects only a single organ as the heart, and is accordingly accompanied by less symptoms compared to “classical” FD patients.
Azam, Kazi Sultana Farhana, Oleg Ryabchykov, and Thomas Bocklitz.
In: Molecules 27, no. 21: 7448 (January 2022).
Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.
Pistiki, Aikaterini, Stefan Monecke, Haodong Shen, Oleg Ryabchykov, Thomas W. Bocklitz, Petra Rösch, Ralf Ehricht, and Jürgen Popp.
In: Microbiology Spectrum 10, no. 5: e00763-22(25 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.
IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination.
Marcel Dahms, Simone Eiserloh, Jürgen Rödel, Oliwia Makarewicz, Thomas Bocklitz, Jürgen Popp and Ute Neugebauer
In: Frontiers in Cellular and Infection Microbiology (27 July 2022)
Streptococcus pneumoniae, commonly referred to as pneumococci, can cause severe and invasive infections, which are major causes of communicable disease morbidity and mortality in Europe and globally. The differentiation of S. pneumoniae from other Streptococcus species, especially from other oral streptococci, has proved to be particularly difficult and tedious. In this work, we evaluate if Raman spectroscopy holds potential for a reliable differentiation of S. pneumoniae from other streptococci. Raman spectra of eight different S. pneumoniae strains and four other Streptococcus species (S. sanguinis, S. thermophilus, S. dysgalactiae, S. pyogenes) were recorded and their spectral features analyzed. Together with Raman spectra of 59 Streptococcus patient isolates, they were used to train and optimize binary classification models (PLS-DA). The effect of normalization on the model accuracy was compared, as one example for optimization potential for future modelling. Optimized models were used to identify S. pneumoniae from other streptococci in an independent, previously unknown data set of 28 patient isolates. For this small data set balanced accuracy of around 70% could be achieved. Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.
Ruihao Luo Juergen Popp and Thomas Bocklitz
In: Analytica 2022 3(3), 287-301 (19 July 2022)
Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman signal is weak and due to the label-free character of RS, the Raman data is untargeted. Therefore, the analysis of Raman spectra is challenging and machine learning based chemometric models are needed. As a subset of representation learning algorithms, deep learning (DL) has had great success in data science for the analysis of Raman spectra and photonic data in general. In this review, recent developments of DL algorithms for Raman spectroscopy and the current challenges in the application of these algorithms will be discussed.
Lorenz, Björn, Shuxia Guo, Christoph Raab, Patrick Leisching, Thomas Bocklitz, Petra Rösch, and Jürgen Popp
In: Journal of Raman Spectroscopy (16 April 2022)
Raman spectroscopy is an emerging tool for fast bacterial identification. However, Raman spectroscopy is depending on suitable preprocessing of the spectra, thereby background removal is a decisive step for conventional Raman spectroscopy. The background has to be estimated, which is challenging especially for high fluorescence backgrounds. Shifted Excitation Raman Difference Spectroscopy (SERDS) eliminates the background through the experimental procedure and hold as promising approach for fluorescent samples. Bacterial Raman spectra might be especially challenging since these spectra consists of a multitude of overlapping Raman bands from a large multiplicity of biomolecules, and only subtitle differences between the species Raman spectra enable the bacterial identification. Here, we investigate the benefits of SERDS compared to conventional Raman spectroscopy specific for the study and identification of bacteria. The comparison utilizes spectra sets of four bacterial species measured with conventional Raman spectroscopy and SERDS and covers three processing approaches for SERDS spectra, for example the reconstruction with a Non-Negative Least Square algorithm.
Oleg Ryabchykov, Iwan W. Schie, Jürgen Popp, Thomas Bocklitz
In: Spectroscopy, April 2022, Volume 37, Issue 4 Pages: 48–50 (1 April 2022)
Seven common mistakes in the analysis of Raman spectra can lead to overestimating the performance of a model.
Darina Storozhuk, Oleg Ryabchykov, Juergen Popp, Thomas Bocklitz
In: ArXiv:2201.07586 [Physics, Stat] (19 January 2022)
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.
Amir Nakar, Aikaterini Pistiki, Oleg Ryabchykov, Thomas Bocklitz, Petra Rösch and Jürgen Popp
In: Analytical and Bioanalytical Chemistry, 117973 (6 January 2022)
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.
Liu, Xiao-Yang, Shuxia Guo, Thomas Bocklitz, Petra Rösch, Jürgen Popp, and Han-Qing Yu.
In: Water Research, 117973 (17 December 2021)
Biofilms are ubiquitous in natural and engineered environments and of great importance in drinking water distribution and biological wastewater treatment systems. Simultaneously acquiring the chemical and structural information of the hydrated biofilm matrix is essential for the cognition and regulation of biofilms in the environmental field. However, the complexity of samples and the limited approaches prevent a holistic understanding of the biofilm matrix. In this work, an approach based on the confocal Raman mapping technique integrated with non-negative matrix factorization (NMF) analysis was developed to probe the hydrated biofilm matrix in situ. The flexibility of the NMF analysis was utilized to subtract the undesired water background signal and resolve the meaningful biological components from Raman spectra of the hydrated biofilms. Diverse chemical components such as proteins, bacterial cells, glycolipids and polyhydroxyalkanoates (PHA) were unraveled within the distinct Pseudomonas spp. biofilm matrices, and the corresponding 3-dimensional spatial organization was visualized and quantified. Of these components, glycolipids and PHA were unique to the P. aeruginosa and P. putida biofilm matrix, respectively. Furthermore, their high abundances in the lower region of the biofilm matrix were found to be related to the specific physiological functions and surrounding microenvironments. Overall, the results demonstrate that our NMF Raman mapping method could serve as a powerful tool complementary to the conventional approaches for identifying and visualizing the chemical components in the biofilm matrix. This work may facilitate the online characterization of the biofilm matrix widely present in the environment and advance the fundamental understanding of biofilm.
Dana Cialla-May, Christoph Krafft, Petra Rösch, Tanja Deckert-Gaudig, Torsten Frosch, Izabella J.Jahn,Susanne Pahlow, Clara Stiebing, Tobias Meyer-Zedler, Thomas Bocklitz, Iwan Schie, Volker Deckert, and Jurgen Popp
In: Analytical Chemistry. (17 December 2021)
Tolstik, E., N. Ali, T. Saeidi, M. Grahovac, S. Guo, P. Arias-Loza, P. Nordbeck, J. Debus, T. Bocklitz, and K. Lorenz.
In: Translational Biophotonics: Diagnostics and Therapeutics, 11919:71–73. SPIE. (7 December 2021)
Based on CARS-SHG spectroscopy biomolecular fingerprints of lipids/proteins were distinguished in isolated adult cardiomyocytes of α-Gal-A-Knockout and wild-type mice opening new prospects for diagnostic of cardiac manifestations of Morbus Fabry.
Taubert, Martin, Will A. Overholt, Beatrix M. Heinze, Georgette Azemtsop Matanfack, Rola Houhou, Nico Jehmlich, Martin von Bergen, Petra Rösch, Jürgen Popp, and Kirsten Küsel.
In: The ISME Journal, 1–10. (7 December 2021)
Current understanding of organic carbon inputs into ecosystems lacking photosynthetic primary production is predicated on data and inferences derived almost entirely from metagenomic analyses. The elevated abundances of putative chemolithoautotrophs in groundwaters suggest that dark CO2 fixation is an integral component of subsurface trophic webs. To understand the impact of autotrophically fixed carbon, the flux of CO2-derived carbon through various populations of subsurface microbiota must first be resolved, both quantitatively and temporally. Here we implement novel Stable Isotope Cluster Analysis to render a time-resolved and quantitative evaluation of 13CO2-derived carbon flow through a groundwater community in microcosms stimulated with reduced sulfur compounds. We demonstrate that mixotrophs, not strict autotrophs, were the most abundant active organisms in groundwater microcosms. Species of Hydrogenophaga, Polaromonas, Dechloromonas, and other metabolically versatile mixotrophs drove the production and remineralization of organic carbon. Their activity facilitated the replacement of 43% and 80% of total microbial carbon stores in the groundwater microcosms with 13C in just 21 and 70 days, respectively. The mixotrophs employed different strategies for satisfying their carbon requirements by balancing CO2 fixation and uptake of available organic compounds. These different strategies might provide fitness under nutrient-limited conditions, explaining the great abundances of mixotrophs in other oligotrophic habitats, such as the upper ocean and boreal lakes.
Fellner, Lea, Marian Kraus, Arne Walter, Frank Duschek, Thomas Bocklitz, Valentina Gabbarini, Riccardo Rossi, Alessandro Puleio, Andrea Malizia, and Pasquale Gaudio.
In: The European Physical Journal Plus 136, no. 11, 1122 (09 November 2021)
Laser-induced fluorescence (LIF) provides the ability to distinguish organic materials by a fast and distant in situ analysis. When detecting the substances directly in the environment, e.g., in an aerosol cloud or on surfaces, additional fluorescence signals of other fluorophores occurring in the surrounding are expected to mix with the desired signal. We approached this problem with a simplified experimental design for an evaluation of classification algorithms. An upcoming question for enhanced identification capabilities is the case of mixed samples providing different signals from different fluorophores. For this work, mixtures of up to four common fluorophores (NADH, FAD, tryptophan and tyrosine) were measured by a dual-wavelength setup and spectrally analyzed. Classification and regression are conducted with neural networks and show an excellent performance in predicting the ratios of the selected ingredients.
Guo, Shuxia, Jürgen Popp, and Thomas Bocklitz.
In: Nature Protocols 1–37 (05 November 2021)
Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning–based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.
Pistiki, A., Ramoji, A., Ryabchykov, O., Thomas-Rüddel, D., Press, A.T., Makarewicz, O., Giamarellos-Bourboulis, E.J., Bauer, M., Bocklitz, T., Popp, J., Neugebauer, U.
In: International Journal of Molecular Sciences 22, no. 19, 10481 (28 September 2021)
Biochemical information from activated leukocytes provide valuable diagnostic information. In this study, Raman spectroscopy was applied as a label-free analytical technique to characterize the activation pattern of leukocyte subpopulations in an in vitro infection model. Neutrophils, monocytes, and lymphocytes were isolated from healthy volunteers and stimulated with heat-inactivated clinical isolates of Candida albicans, Staphylococcus aureus, and Klebsiella pneumoniae. Binary classification models could identify the presence of infection for monocytes and lymphocytes, classify the type of infection as bacterial or fungal for neutrophils, monocytes, and lymphocytes and distinguish the cause of infection as Gram-negative or Gram-positive bacteria in the monocyte subpopulation. Changes in single-cell Raman spectra, upon leukocyte stimulation, can be explained with biochemical changes due to the leukocyte’s specific reaction to each type of pathogen. Raman spectra of leukocytes from the in vitro infection model were compared with spectra from leukocytes of patients with infection (DRKS-ID: DRKS00006265) with the same pathogen groups, and a good agreement was revealed. Our study elucidates the potential of Raman spectroscopy-based single-cell analysis for the differentiation of circulating leukocyte subtypes and identification of the infection by probing the molecular phenotype of those cells.
Hniopek, Julian; Bocklitz, Thomas; Schmitt, Michael; Popp, Jürgen.
In: Applied Spectroscopy, Volume 75, Issue 8, p 1043-1052 (1 August 2021)
The functionality of active centers in proteins is governed by the secondary and higher structure of proteins which often lead to structures in the active center that are different from the structures found in protein-free models of the active center. To elucidate this structure–function relationship, it is therefore necessary to investigate both the protein structure and the local structure of the active center. In this work, we investigate the application of hetero (resonance) Raman two-dimensional correlation spectroscopy (2D-COS) to this problem. By employing a combination of near-infrared-Fourier transform-Raman- and vis-resonance Raman spectroscopy, we could show that this combination of techniques is able to directly probe the structure–function relationship of proteins. We were able to correlate the transition of the heme center in cytochrome c from low to high spin with changes in the secondary structure with the above mentioned two spectroscopic in situ techniques and without sample preparation. Thereby, we were able to reveal that the combination of a spectroscopic method to selectively observe the active center with a technique that monitors the whole system offers a promising toolkit to investigate the structure–function relationship of proteins with photoactive centers in general.
Nairveen Ali, Christian Bolenz, Tilman Todenhöfer, Arnulf Stenzel, Peer Deetmar, Martin Kriegmair, Thomas Knoll, Stefan Porubsky, Arndt Hartmann, Jürgen Popp, Maximilian C. Kriegmair & Thomas Bocklitz
In: Scientific Reports volume 11, Article number: 11629 (2 June 2021)
Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of flat and small lesions. Here, we aim to demonstrate a BL image-based artificial intelligence (AI) diagnostic platform using 216 BL images, that were acquired in four different urological departments and pathologically identified with respect to cancer malignancy, invasiveness, and grading. Thereafter, four pre-trained convolution neural networks were utilized to predict image malignancy, invasiveness, and grading. The results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84%, while the mean sensitivity and mean specificity of tumor invasiveness are 88% and 96.56%, respectively. This small multicenter clinical study clearly shows the potential of AI based classification of BL images allowing for better treatment decisions and potentially higher detection rates.
Georgette Azemtsop Matanfack, Martin Taubert, Shuxia Guo, Thomas Bocklitz, Kirsten Küsel, Petra Rösch, and Jürgen Popp
In: Analytical Chemistry (20 May 2021)
Raman-stable isotope labeling using heavy water (Raman-D2O) is attracting great interest as a fast technique with various applications ranging from the identification of pathogens in medical samples to the determination of microbial activity in the environment. Despite its widespread applications, little is known about the fundamental processes of hydrogen–deuterium (H/D) exchange, which are crucial for understanding molecular interactions in microorganisms. By combining two-dimensional (2D) correlation spectroscopy and Raman deuterium labeling, we have investigated H/D exchange in bacterial cells under time dependence. Most C–H stretching signals decreased in intensity over time, prior to the formation of the C–D stretching vibration signals. The intensity of the C–D signal gradually increased over time, and the shape of the C–D signal was more uniform after longer incubation times. Deuterium uptake showed high variability between the bacterial genera and mainly led to an observable labeling of methylene and methyl groups. Thus, the C–D signal encompassed a combination of symmetric and antisymmetric CD2 and CD3 stretching vibrations, depending on the bacterial genera. The present study allowed for the determination of the sequential order of deuterium incorporation into the functional groups of proteins, lipids, and nucleic acids and hence understanding the process of biomolecule synthesis and the growth strategies of different bacterial taxa. We present the combination of Raman-D2O labeling and 2D correlation spectroscopy as a promising approach to gain a fundamental understanding of molecular interactions in biological systems.
Houhou, Rola, Petra Rösch, Jürgen Popp, and Thomas Bocklitz.
In: Analytical and Bioanalytical Chemistry (15 May 2021)
Raman spectral data are best described by mathematical functions; however, due to the spectroscopic measurement setup, only discrete points of these functions are measured. Therefore, we investigated the Raman spectral data for the first time in the functional framework. First, we approximated the Raman spectra by using B-spline basis functions. Afterwards, we applied the functional principal component analysis followed by the linear discriminant analysis (FPCA-LDA) and compared the results with those of the classical principal component analysis followed by the linear discriminant analysis (PCA-LDA). In this context, simulation and experimental Raman spectra were used. In the simulated Raman spectra, normal and abnormal spectra were used for a classification model, where the abnormal spectra were built by shifting one peak position. We showed that the mean sensitivities of the FPCA-LDA method were higher than the mean sensitivities of the PCA-LDA method, especially when the signal-to-noise ratio is low and the shift of the peak position is small. However, for a higher signal-to-noise ratio, both methods performed equally. Additionally, a slight improvement of the mean sensitivity could be shown if the FPCA-LDA method was applied to experimental Raman data.
Ramoji, Anuradha; Thomas-Rüddel, Daniel; Ryabchykov, Oleg; Bauer, Michael; Arend, Natalie; Giamarellos-Bourboulis, Evangelos J.; Eugen-Olsen, Jesper; Kiehntopf, Michael; Bocklitz, Thomas; Popp, Jürgen; Bloos, Frank; Neugebauer, Ute.
In: Critical Care Explorations, Volume 3, Issue 5, p e0394 (May 2021)
Objectives: Leukocytes are first responders to infection. Their activation state can reveal information about specific host immune response and identify dysregulation in sepsis. This study aims to use the Raman spectroscopic fingerprints of blood-derived leukocytes to differentiate inflammation, infection, and sepsis in hospitalized patients. Diagnostic sensitivity and specificity shall demonstrate the added value of the direct characterization of leukocyte’s phenotype.
Design: Prospective nonrandomized, single-center, observational phase-II study (DRKS00006265).
Setting: Jena University Hospital, Germany.
Patients: Sixty-one hospitalized patients (19 with sterile inflammation, 23 with infection without organ dysfunction, 18 with sepsis according to Sepsis-3 definition).
Interventions: None (blood withdrawal).
Measurements AND MAIN RESULTS: Individual peripheral blood leukocytes were characterized by Raman spectroscopy. Reference diagnostics included established clinical scores, blood count, and biomarkers (C-reactive protein, procalcitonin and interleukin-6). Binary classification models using Raman data were able to distinguish patients with infection from patients without infection, as well as sepsis patients from patients without sepsis, with accuracies achieved with established biomarkers. Compared with biomarker information alone, an increase of 10% (to 93%) accuracy for the detection of infection and an increase of 18% (to 92%) for detection of sepsis were reached by adding the Raman information. Leukocytes from sepsis patients showed different Raman spectral features in comparison to the patients with infection that point to the special immune phenotype of sepsis patients.
Conclusions: Raman spectroscopy can extract information on leukocyte’s activation state in a nondestructive, label-free manner to differentiate sterile inflammation, infection, and sepsis.
Hniopek, Julian; Müller, Carolin; Bocklitz, Thomas; Schmitt, Michael; Dietzek, Benjamin; Popp, Jürgen
In: The Journal of Physical Chemistry Letters, Volume 12, Issue 17, p 4148-4153 (23 April 2021)
Here, we present, to the best of our knowledge for the first time, a systematic study of utilizing 2D correlation analysis in the field of femtosecond transient absorption (fs-TA) spectroscopy. We present that the application of 2D correlation spectroscopy (2DCOS) to fs-TA spectroscopy enables a model-free means to analyze excited state kinetics, which is demonstrated on the model system [(tbbpy)2Ru(dppz)]2+ in different solvents. We show that TA-2DCOS is able to determine the number of processes contributing to the time-resolved spectral changes in fs-TA data sets, as well as extract the spectral response of these components. Overall, the results show that TA-2DCOS leads to the same results as obtained with methods relying on global lifetime analysis or multivariate curve resolution but without the need to specify a predetermined kinetic model. The work presented therefore highlights the potential of TA-2DCOS as a model-free approach for analyzing fs-TA spectral data sets.
Guo, Shuxia, Anja Silge, Hyeonsoo Bae, Tatiana Kirchberger-Tolstik, Tobias Meyer, Georg Matziolis, Michael Schmitt, Jürgen Popp, and Thomas Bocklitz.
In: Journal of Biomedical Optics 26 (7 January 2021)
The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis.
Houhou, Rola, and Thomas Bocklitz.
In: Anal. Sci. Adv. (2 February 2021)
Abstract Artificial intelligence-based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI-based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed.
Huang, Jing, Nairveen Ali, Elsie Quansah, Shuxia Guo, Michel Noutsias, Tobias Meyer-Zedler, Thomas Bocklitz, Jürgen Popp, Ute Neugebauer, and Anuradha Ramoji.
In: Vibrational Spectroscopic Investigation of Blood Plasma and Serum by Drop Coating Deposition for Clinical Application
In recent decades, vibrational spectroscopic methods such as Raman and FT-IR spectroscopy are widely applied to investigate plasma and serum samples. These methods are combined with drop coating deposition techniques to pre-concentrate the biomolecules in the dried droplet to improve the detected vibrational signal. However, most often encountered challenge is the inhomogeneous redistribution of biomolecules due to the coffee-ring effect. In this study, the variation in biomolecule distribution within the dried-sample droplet has been investigated using Raman and FT-IR spectroscopy and fluorescence lifetime imaging method. The plasma-sample from healthy donors were investigated to show the spectral differences between the inner and outer-ring region of the dried-sample droplet. Further, the preferred location of deposition of the most abundant protein albumin in the blood during the drying process of the plasma has been illustrated by using deuterated albumin. Subsequently, two patients with different cardiac-related diseases were investigated exemplarily to illustrate the variation in the pattern of plasma and serum biomolecule distribution during the drying process and its impact on patient-stratification. The study shows that a uniform sampling position of the droplet, both at the inner and the outer ring, is necessary for thorough clinical characterization of the patient’s plasma and serum sample using vibrational spectroscopy.
Kirchberger-Tolstik, Tatiana, Oleg Ryabchykov, Thomas Bocklitz, Olaf Dirsch, Utz Settmacher, Juergen Popp, and Andreas Stallmach
In: The Analyst 146 (21 February 2021)
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.
Pradhan, Pranita, Katharina Köhler, Shuxia Guo, Olga Rosin, Jürgen Popp, Axel Niendorf, and Thomas Wilhelm Bocklitz.
In: Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, 495–506. SciTePress, 2021
A combination of histological and immunohistochemical tissue features can offer better breast cancer diagnosis as compared to histological tissue features alone. However, manual identification of histological and immunohistochemical tissue features for cancerous and healthy tissue requires an enormous human effort which delays the breast cancer diagnosis. In this paper, breast cancer detection using the fusion of histological (H&E) and immunohistochemical (PR, ER, Her2 and Ki-67) imaging data based on deep convolutional neural networks (DCNN) was performed. DCNNs, including the VGG network, the residual network and the inception network were comparatively studied. The three DCNNs were trained using two transfer learning strategies. In transfer learning strategy 1, a pre-trained DCNN was used to extract features from the images of five stain types. In transfer learning strategy 2, the images of the five stain types were used as inputs to a pre-trained multi-input DCNN, and the last layer of the multi-input DCNN was optimized. The results showed that data fusion of H&E and IHC imaging data could increase the mean sensitivity at least by 2% depending on the DCNN model and the transfer learning strategy. Specifically, the pre-trained inception and residual networks with transfer learning strategy 1 achieved the best breast cancer detection.
Pradhan, Pranita, Tobias Meyer, Michael Vieth, Andreas Stallmach, Maximilian Waldner, Michael Schmitt, Juergen Popp, and Thomas Bocklitz
In: Biomedical Optics Express (17 February 2021)
Schleusener, Johannes, Shuxia Guo, Maxim E. Darvin, Gisela Thiede, Olga Chernavskaia, Florian Knorr, Jürgen Lademann, Jürgen Popp, and Thomas W. Bocklitz.
In: Biomed. Opt. Express 12, no. 2 (February 2021): 1123–35
Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of $\geq$85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was $\approx$65%.
Wichmann, Christina, Thomas Bocklitz, Petra Rösch, and Jürgen Popp.
In: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 248 (1 March 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.
Pradhan, Pranita; Guo, Shuxia; Ryabchykov, Oleg; Popp, Jürgen; Bocklitz, Thomas
In: Journal of Biophotonics 13, e201960186-1 (2020)
This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyse biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudo-staining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.
Sittig, Maria; Schmidt, Benny; Görls, Helmar; Bocklitz, Thomas; Wächtler, Maria; Zechel, Stefan; Hager, Martin D.; Dietzek,Benjamin
In: Physical Chemistry Chemical Physics (2020) 4072
Fluorescence upconversion by triplet-triplet annihilation is demonstrated for a fully polymer-integrated material, i.e. in the limit of restricted diffusion. Organic sensitizer and acceptor are covalently attached to a poly(methacrylate) backbone, yielding a metal-free macromolecular all-in-one system for fluorescence upconversion. Due to the spatial confinement of the optically active molecular components, i.e. annihilator and sensitizer, UC by TTA in the constrained polymer system in solution is achieved at exceptionally low averaged annihilator concentrations. However, the UC quantum yield in the investigated systems is found to be low, highlighting that only chromophores in specific local surroundings yield upconversion in the limit of restricted diffusion. A photophysical model is proposed taking the heterogeneous local environment within the polymers into account.
Guo, Shuxia; Rösch, Petra; Popp, Jürgen; Bocklitz, Thomas
In: Journal of Chemometrics 34, e3202-1 (2020)
Raman spectra of biological samples often exhibit variations originating from changes of spectrometers, measurement conditions, and cultivation conditions. Such unwanted variations make a classification extremely challenging, especially if they are more significant compared with the differences between groups to be separated. A classifier is prone to such unwanted variations (ie, intragroup variations) and can fail to learn the patterns that can help separate different groups (ie, intergroup differences). This often leads to a poor generalization performance and a degraded transferability of the trained model. A natural solution is to separate the intragroup variations from the intergroup differences and build the classifier based on merely the latter information, for example, by a well-designed feature extraction. This forms the idea of this contribution. Herein, we modified two commonly applied feature extraction approaches, principal component analysis (PCA) and partial least squares (PLS), in order to extract merely the features representing the intergroup differences. Both of the methods were verified with two Raman spectral datasets measured from bacterial cultures and colon tissues of mice, respectively. In comparison to ordinary PCA and PLS, the modified PCA was able to improve the prediction on the testing data that bears significant difference to the training data, while the modified PLS could help avoid overfitting and lead to a more stable classification.
Hniopek, Julian; Schmitt, Michael; Popp, Jürgen; Bocklitz, Thomas
In: Applied Spectroscopy (2020) 460
This paper introduces the newly developed principal component powered two-dimensional (2D) correlation spectroscopy (PC 2D-COS) as an alternative approach to 2D correlation spectroscopy taking advantage of a dimensionality reduction by principal component analysis. It is shown that PC 2D-COS is equivalent to traditional 2D correlation analysis while providing a significant advantage in terms of computational complexity and memory consumption. These features allow for an easy calculation of 2D correlation spectra even for data sets with very high spectral resolution or a parallel analysis of multiple data sets of 2D correlation spectra. Along with this reduction in complexity, PC 2D-COS offers a significant noise rejection property by limiting the set of principal components used for the 2D correlation calculation. As an example for the application of truncated PC 2D-COS a temperature-dependent Raman spectroscopic data set of a fullerene-anthracene adduct is examined. It is demonstrated that a large reduction in computational cost is possible without loss of relevant information, even for complex real world data sets.
Pahlow, Susanne; Orasch, Thomas; Zukovskaja, Olga; Bocklitz, Thomas; Haas, Hubertus; Weber, Karina
In: Analytical and Bioanalytical Chemistry (2020) -
Triacetylfusarinine C (TAFC) is a siderophore produced by certain fungal species and might serve as a highly useful biomarker for the fast diagnosis of invasive aspergillosis. Due to its renal elimination, the biomarker is found in urine samples of patients suffering from Aspergillus infections. Accordingly, non-invasive diagnosis from this easily obtainable body fluid is possible. Within our contribution, we demonstrate how Raman microspectroscopy enables a sensitive and specific detection of TAFC. We characterized the TAFC iron complex and its iron-free form using conventional and interference-enhanced Raman spectroscopy (IERS) and compared the spectra with the related compound ferrioxamine B, which is produced by bacterial species. Even though IERS only offers a moderate enhancement of the Raman signal, the employment of respective substrates allowed lowering the detection limit to reach the clinically relevant range. The achieved limit of detection using IERS was 0.5 ng of TAFC, which is already well within the clinically relevant range. By using an extraction protocol, we were able to detect 1.4 μg/mL TAFC via IERS from urine within less than 3 h including sample preparation and data analysis. We could further show that TAFC and ferrioxamine B can be clearly distinguished by means of their Raman spectra even in very low concentrations.
Liu, Xiao-Yang; Guo, Shuxia; Ramoji, Anuradha; Bocklitz, Thomas; Rösch, Petra; Popp, Jürgen; Yu, Han-Qing
In: Analytical Chemistry (2020) 707
Biofilms are microbial aggregates of microorganisms surrounded by a hydrogel-like matrix formed by extracellular polymeric substances (EPS). The formation of biofilms is intrinsically complex, from the attachment of microbial cells to the dispersion of the biofilm. Meanwhile, the three-dimensional framework built up by EPS changes with time and protects the microorganisms against environmental stress. Simultaneous acquiring chemical and structural information within the biofilm matrix is vital for the cognition and regulation of biofilms, yet it remains a great challenge due to the sample complexity and the limited approaches. In this study, confocal Raman microscopy and non-negative matrix factorization (NMF) analysis were combined to investigate spatiotemporal organization of Escherichia coli biofilms during development at molecular-level detail. The alternating non-negative least square (ANLS) approach was incorporated with the sequential coordinate-wise descent (SCD) algorithm to realize the NMF analysis for the large-scale hyperspectral dataset. As a result, three components, including bacteria, protein and polyhydroxybutyrate (PHB) were successfully resolved from the spectra of E. coli biofilm. Furthermore, the structural changes of biofilms could be visualized and quantified by their abundances derived from the NMF analysis, which might be related to the nutrient and oxygen gradient and physiological functions. This methodology provides a comprehensive understanding of the chemical constituents and their spatiotemporal distribution within the biofilm matrix. Furthermore, it also shows great potential for the analysis of unknown and complex biological samples with 3D Raman mapping.
Zukovskaja, Olga; Ryabchykov, Oleg; Straßburger, Maria; Heinekamp, Thorsten; Brakhage, Axel A.; Hennings, J. Christopher; Hübner, Christian A.; Wegmann, Michael; Cialla-May, Dana; Bocklitz, Thomas; Weber, Karina; Popp, Jürgen
In: Journal of Biophotonics (2020) e201900143-1
For the screening purposes urine is an especially attractive biofluid, since it offers easy and non-invasive sample collection and provides a snapshot of the whole metabolic status of the organism, which may change under different pathological conditions. Raman spectroscopy (RS) has the potential to monitor these changes and utilize them for disease diagnostics. The current study utilizes mouse models aiming to compare the feasibility of the urine based RS combined with chemometrics for diagnosing kidney diseases directly influencing urine composition and respiratory tract diseases having no direct connection to urine formation. The diagnostic models for included diseases were built using principal component analysis with linear discriminant analysis and validated with a leave-one-mouse-out cross-validation approach. Considering kidney disorders, the accuracy of 100 % was obtained in discrimination between sick and healthy mice, as well as between two different kidney diseases. For asthma and invasive pulmonary aspergillosis achieved accuracies were noticeably lower, being, respectively, 77.27 % and 78.57 %. In conclusion, our results suggest that RS of urine samples not only provides a solution for a rapid, sensitive and non-invasive diagnosis of kidney disorders, but also holds some promises for the screening of non-urinary tract diseases.
Huang, Jing; Ramoji, Anuradha; Guo, Shuxia; Bocklitz, Thomas; Boivin-Jahns, Valérie; Möller, Jan; Kiehntopf, Michael; Noutsias, Michel; Popp, Jürgen; Neugebauer, Ute
In: Analyst (2020) 486
Dilated cardiomyopathy (DCM) is a leading cardiomyopathy entity and is the leading reason for heart transplantation. Due to high etiologic and genetic heterogeneity of the pathologies, different therapeutic treatment strategies are available and succeed in terms of different treatments. Immunoadsorption (IA) therapy removes the circulating anticardiac antibodies and improves the left ventricular function in substantial propotion of DCM patients. Powerful, non-invasive analytical tools are highly desired to investigate the efficiency and success of IA therapy. In this contribution, we followed changes of a female DCM patient undergoing IA therapy at different treatment time-points in a label-free, non-invasive manner directly from blood samples (plasma and serum) on the basis of vibrational spectroscopy (Raman scattering and IR absorption). Chemometric methods including dimension reduction and statistical modeling were used to interpret spectral data. Different IA treatment time points can be differentiated with a high accuracy. Removal of antibodies of IgG group during IA therapy and their restoration was reflected in both Raman and FT-IR spectra. Relative changes in the spectral bands assigned to IgG correlated well with ELISA measurement of total IgG. The successful clinical treatment was accompanied by the spectral differences between vibrational spectra measured at initial disease state and 11 months after the IA treatment. Long-term follow-up of the patient reveals stabilization of the health state after therapy. Noteworthy, the treatment time points were distinguished with a better accuracy using spectra from plasma than those from serum samples, might indicate the involvement of corresponding proteins in the coagulation. Vibrational spectroscopy is a powerful tool to follow-up the treatment success of IA therapy in DCM disorder.
Schleusener, Johannes; Carrer, V.; Patzelt, A.; Guo, Shuxia; Bocklitz, Thomas; Coderch, L.; Lademann, Jürgen; Darvin, Maxim E.
In: Quantum Electronics (2019) 6
Confocal Raman microscopy (CRM) is applied ex vivo for imaging of the spatial distribution of different skin components in skin sections containing hair follicles. For multivariate data analysis, different methods are used in order to spectrally decom¬pose the reference spectra of the skin components (dermis, viable epidermis, stratum corneum and hair). Classical least squares regression (CLS) and multivariate curve resolution – alternating least squares (MCR-ALS) are chosen as suitable methods. In com-parison to other optical methods, the advantage of CRM is molecu¬lar specificity and dispensability of labelling dyes, which is e.g. nec¬essary in fluorescence microscopy. Therefore, a useful future appli¬cation of CRM in combination with multivariate data analysis lies in the analysis of penetration routes of topically applied substances, such as cosmetic formulations or drugs into the skin, which is par¬ticularly interesting in and around hair follicles.
Geitner, Robert; Fritzsch, Robby; Bocklitz, Thomas; Popp, Jürgen
In: Journal of Statistical Software (2019) 1
In the package corr2D two-dimensional correlation analysis is implemented in R. This paper describes how two-dimensional correlation analysis is done in the package and how the mathematical equations are translated into R code. The paper features a simple tutorial with executable code for beginners, insight into the calculations done before the correlation analysis, a detailed look at the parallelization of the fast Fourier transformation based correlation analysis and a speed test of the calculation. The package corr2D offers the possibility to preprocess, correlate and postprocess spectroscopic data using exclusively the R language. Thus, corr2D is a welcome addition to the toolbox of spectroscopists and makes two-dimensional correlation analysis more accessible and transparent.
Rodner, Erik; Bocklitz, Thomas; von Eggeling, Ferdinand; Ernst, Günther; Chernavskaia, Olga; Popp, Jürgen; Denzler, Joachim; Guntinas-Lichius, Orlando
In: Head and Neck-Journal for the Sciences and Specialities of the Head and Neck (2019) 116
Background: A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed. Methods: Head and neck cancer sections were used for standard histopathology and co-registered with multimodal images from the same sections using the combination of coherent anti-Stokes Raman scattering, two-photon excited fluorescence, and second harmonic generation microscopy. The images analyzed with semantic segmentation using a FCN for four classes: cancer, normal epithelium, background, and other tissue types. Results: A total of 114 images of 12 patients were analyzed. Using a patch score aggregation, the average recognition rate and an overall recognition rate or the four classes were 88.9% and 86.7%, respectively. A total of 113 seconds were needed to process a whole-slice image in the dataset. Conclusion: Multimodal nonlinear microscopy in combination with automated image analysis using FCN seems to be a promising technique for objective differentiation between head and neck cancer and noncancerous epithelium.
Yarbakht, Melina; Pradhan, Pranita; Köse-Vogel, Nilay; Bae, Hyeonsoo; Stengel, Sven; Meyer, Tobias; Schmitt, Michael; Stallmach, Andreas; Popp, Jürgen; Bocklitz, Thomas; Bruns, Tony
In: Analytical Chemistry (2019) 11116
Sepsis constitutes a life-threatening organ failure caused by a deregulated host response to infection. Identifying early biomolecular indicators of organ dysfunction may improve clinical decision-making and outcome of patients. Herein we utilized label-free nonlinear multimodal imaging, combining coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), and second-harmonic generation (SHG) to investigate the consequences of early septic liver injury in a murine model of polymicrobial abdominal infection. Liver tissue sections from mice with and without abdominal sepsis were analyzed using multimodal nonlinear microscopy, immunofluorescence, immunohistochemistry, and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Twenty-four hours after the induction of sepsis, hepatic mRNA of inflammatory cytokines and acute phase proteins was upregulated, and liver-infiltrating myeloid cells could be visualized alongside hepatocellular cytoplasmic translocation of high mobility group box 1. According to the statistical analysis based on texture feature extraction followed by the combination of dimension reduction and linear discriminant analysis, CARS (AUC = 0.93) and TPEF (AUC = 0.83) showed an excellent discrimination between liver sections from septic mice and sham-treated mice in contrast to SHG (AUC = 0.49). Spatial analysis revealed no major differences in the distribution of sepsis-associated changes between periportal and pericentral zones. These data suggest early alterations in hepatic lipid distribution and metabolism during liver injury and confirm nonlinear multimodal imaging as a promising complementary method for the real-time, label-free study of septic liver damage.
Ramoji, Anuradha; Ryabchykov, Oleg; Galler, Kerstin; Tannert, Astrid; Markwart, Robby; Requardt, Robert Pascal; Rubio, Ignacio; Bauer, Michael; Bocklitz, Thomas; Popp, Jürgen; Neugebauer, Ute
In: ImmunoHorizons (2019) 45
T lymphocytes (T cells) are highly specialized members of the adaptive immune system and hold the key to the understanding the hosts’ response toward invading pathogen or pathogen-associated molecular patterns such as LPS. In this study, noninvasive Raman spectroscopy is presented as a label-free method to follow LPS-induced changes in splenic T cells during acute and postacute inflammatory phases (1, 4, 10, and 30 d) with a special focus on CD4+ and CD8+ T cells of endotoxemic C57BL/6 mice. Raman spectral analysis reveals highest chemical differences between CD4+ and CD8+ T cells originating from the control and LPS-treated mice during acute inflammation, and the differences are visible up to 10 d after the LPS insult. In the postacute phase, CD4+ and CD8+ T cells from treated and untreated mice could not be differentiated anymore, suggesting that T cells largely regained their original status. In sum, the biological information obtained from Raman spectra agrees with immunological readouts demonstrating that Raman spectroscopy is a well-suited, label-free method for following splenic T cell activation in systemic inflammation from acute to postacute phases. Themethod can also be applied to directly study tissue sections as is demonstrated for spleen tissue one day after LPS insult.
Wichmann, Christina; Chhallani, Mehul A.; Bocklitz, Thomas; Rösch, Petra; Popp, Jürgen
In: Analytical Chemistry (2019) 13688
Recently, Raman spectroscopy has become more and more into the focus of bacterial identification as it is a culture-independent, non-destructive and contact-less method. Since Raman spectroscopy is a phenotypic method lots of parameters can influence the spectra. One of the least controllable factors is transport and storage but it is often not taken into account and therefore these influences on the Raman spectra of bacteria are unknown. In order to investigate this effect, we simulated the transport and storage of bacteria under different conditions and investigated them with Raman spectroscopy. With a look at the mean spectra, only one bacterium showed differences during the storage conditions. However, after applying chemometric methods, changes in the data could be found within all bacteria during storage times. This study shows how drastic the effect will influence a database, depending on the different handling or storage. Therefore, it is of utmost importance to consider these non-biological influences when planning further experiments and evaluating the resulting data.
Guo, Shuxia; Kohler, Achim; Zimmermann, Boris; Heinke, Ralf; Stöckel, Stephan; Rösch, Petra; Popp, Jürgen; Bocklitz, Thomas
In: Analytical Chemistry (2018) 9787
The chemometric analysis of Raman spectra of biological materials is hampered by spectral variations due to the instrumental setup that overlay the subtle biological changes of interest. Thus, an established statistical model may fail when applied to Raman spectra of samples acquired with a different device. Therefore, model transfer strategies are essential. Herein we report a model transfer approach based on extended multiplicative signal correction (EMSC). As opposed to existing model transfer methods, the EMSC based approach does not require group information on the secondary data sets, thus no extra measurements are required. The proposed model-transfer approach is a preprocessing procedure and can be combined with any method for regression and classification. The performance of EMSC as a model transfer method was demonstrated with a data set of Raman spectra of three Bacillus bacteria spore species ( B. mycoides, B. subtilis, and B. thuringiensis), which were acquired on four Raman spectrometers. A three-group classification by partial least-squares discriminant analysis (PLS-DA) with leave-one-device-out external cross-validation (LODCV) was performed. The mean sensitivities of the prediction on the independent device were considerably improved by the EMSC method. Besides the mean sensitivity, the model transferability was additionally benchmarked by the newly defined numeric markers: (1) relative Pearson's correlation coefficient and (2) relative Fisher's discriminant ratio. We show that these markers have led to consistent conclusions compared to the mean sensitivity of the classification. The advantage of our defined markers is that the evaluation is more effective and objective, because it is independent of the classification models.
Ryabchykov, Oleg; Popp, Jürgen; Bocklitz, Thomas
In: Frontiers in Chemistry (2018) 1
Despite of a large number of imaging techniques for the characterization of biological samples, no universal one has been reported yet. In this work, a data fusion approach was investigated for combining Raman spectroscopic data with matrix-assisted laser desorption/ionization (MALDI) mass spectrometric data. It betters the imaging analysis of biological samples because Raman and MALDI information can be complementary to each other. While MALDI spectrometry yields detailed information regarding the lipid content, Raman spectroscopy provides valuable information about the overall chemical composition of the sample. The combination of Raman spectroscopic and MALDI spectrometric imaging data helps distinguish different regions within the sample with a higher precision than would be possible by using either technique. We demonstrate that a data weighting step within the data fusion is necessary to reveal additional spectral features. The selected weighting approach was evaluated by examining the proportions of variance within the data explained by the first principal components of a principal component analysis (PCA) and visualizing the PCA results for each data type and combined data. In summary, the presented data fusion approach provides a concrete guideline on how to combine Raman spectroscopic and MALDI spectrometric imaging data for biological analysis.
Bocklitz, Thomas; Meyer, Tobias; Schmitt, Michael; Rimke, Ingo; Hoffmann, Franziska; von Eggeling, Ferdinand; Ernst, G.; Guntinas-Lichius, Orlando; Popp, Jürgen
In: APL Photonics (2018) 092404-1
Raman scattering based imaging represents a very powerful optical tool for biomedical diagnostics. Different Raman signatures obtained by distinct tissue structures and disease induced changes provoke sophisticated analysis of the hyperspectral Raman datasets. While the analysis of linear Raman spectroscopic tissue datasets is quite established the evaluation of hyperspectral nonlinear Raman data has not been evaluated in great detail yet. The two most common nonlinear Raman methods are CARS (coherent anti-Stokes Raman scattering) and SRS (stimulated Raman scattering) spectroscopy. Specifically the linear concentration dependence of SRS as compared to the quadratic dependence of CARS has fostered the application of SRS tissue imaging. Here, we applied spectral processing to hyperspectral SRS and CARS data for tissue characterization. We could demonstrate for the first time that similar cluster distributions can be obtained for multispectral CARS and SRS data, but that clustering is based on different spectral features. It is shown, that a direct combination of CARS and SRS data does not improve the clustering results.
Guo, Shuxia; Heinke, Ralf; Stöckel, Stephan; Rösch, Petra; Popp, Jürgen; Bocklitz, Thomas
In: Journal of Raman Spectroscopy (2018) 627
Raman spectroscopy is gaining increasing attention in biomedical diagnostics thanks to instrumental development and chemometric approaches enhancing the accuracy and speed of this technique. Meanwhile, it is demanding to construct a statistical model based on one dataset (primary conditions) and use it to predict another dataset measured under different (secondary) conditions. Thus, model transfer becomes extremely important to improve prediction with minimal or no training samples measured under secondary conditions. Methods that have been proposed and applied for near-infrared spectroscopy, for example, spectral standardization, lead to poor performance in Raman spectroscopy. This is because Raman bands are sharper and more sensitive to noise introduced by the spectral standardization. Our recently reported Tikhonov regularization based on a partial least squares regression (TR-PLSR) approached this problem. In the present work, we showed that the TR-PLSR model transfer also works for Raman spectra of vegetative bacteria. This was demonstrated by the Raman spectra of three species of bacteria acquired on three different Raman spectrometers. Afterward, we report two newly developed model transfer methods: movement of principal components scores (MS) and spectral augmentation (SA). Both methods were validated based on the Raman spectra of bacterial spores and vegetative bacteria, where a significant improvement of the model transferability was observed. The MS method yielded comparable results to the TR-PLSR. However, the new methods are superior to TR-PLSR in two ways: first, no training samples with the secondary conditions are necessary, and second, the methods are not restricted to PLSR but can also be applied to other models. Both advantages are important in real applications and are a big step to enhance the performance of model transfer.
Guo, Shuxia; Pfeifenbring, Susanne; Meyer, Tobias; Ernst, Günther; Maio, Vincenza; von Eggeling, Ferdinand; Massi, Daniela; Cicchi, Ricardo; Pavone, Francesco Saverio; Popp, Jürgen; Bocklitz, Thomas
In: Journal of Chemometrics (2018) e2963-1
Early diagnosis is the corner stone for a successful treatment of most diseases including melanoma, which cannot be achieved by traditional histopathological inspection. In this respect, multimodal imaging, the combination of TPEF and SHG features a high diagnostic potential as an alternative approach. Multimodal imaging generates molecular contrast, but to use this technique in clinical practice, the optical signals must be translated into diagnostic relevant information. This translation requires automatic image analysis techniques. Within this contribution, we established an analysis pipeline for multimodal images to achieve melanoma diagnostics of skin tissue. The first step of the image analysis was the pre-treatment, where the mosaicking artifacts were corrected and a standardization was performed. Afterwards, the local histogram based first order texture features and the local gray-level co-occurrence matrix (GLCM) texture features were extracted in multiple scales. Thereafter we constructed a local hierarchical statistical model to distinguish melanoma, normal epithelium, and other tissue types. The results demonstrated the capability of multimodal imaging combined with image analysis to differentiate different tissue types. Furhermore, we compared the histogram and the GLCM based texture feature sets according to the Fisher’s discriminant ratio (FDR) and the prediction of the classification, which demonstrated that the histogram based texture features are superior to the GLCM features for the given task. Finally, we performed a global classification to achieve a patient diagnostics with the clinical diagnosis as ground truth. The agreement of the prediction and the clinical results demonstrated the great potential of multimodal imaging for melanoma diagnostics.
Ryabchykov, Oleg; Bräutigam, Katharina; Galler, Kerstin; Neugebauer, Ute; Mosig, Alexander; Bocklitz, Thomas; Popp, Jürgen
In: Journal of Raman Spectroscopy (2018) 935
In this work, Raman spectroscopic cell imaging approaches and a discrimination between HepG2, non-differentiated HepaRG, and differentiated hepatocyte-like HepaRG cells are presented. Raman spectroscopic imaging was used to visualize the cell nuclei by means of false color imaging of a marker band and a cell segmentation was performed by means of clustering. Furthermore, a 3-class-classification model based on the mean Raman spectra of individual cells was established for a classification between different cell types. A high average sensitivity of 96% was achieved by the applied classification model. Based on the results of clustering and classification, the main spectral contributions to different cell types and cell segments were analyzed in detail. Thereby, HepG2, non-differentiated HepaRG, and differentiated hepatocyte-like HepaRG cells were Raman spectroscopic characterized and proven to be significantly different.
Silge, Anja; Bocklitz, Thomas; Becker, Bjoern; Matheis, Walter; Popp, Jürgen; Bekeredjian-Ding, Isabelle
In: NPJ Vaccines (2018) 50-1
Vaccines are complex biomedicines. Manufacturing is time consuming and requires a high level of quality control (QC) to guarantee consistent safety and potency. An increasing global demand has led to the need to reduce time and cost of manufacturing. The evolving concepts for QC and the upcoming threat of falsification of biomedicines define a new need for methods that allow the fast and reliable identification of vaccines. Raman spectroscopy is a non-invasive technology already established in QC of classical medicines. We hypothesized that Raman spectroscopy could be used for identification and differentiation of vaccine products. Raman maps obtained from air-dried samples of combination vaccines containing antigens from tetanus, diphtheria and pertussis (DTaP vaccines) were summarized to compile product-specific Raman signatures. Sources of technical variance were identified. The data management approach corrects for spatial inhomogeneities in the dried sample while offering a proper representation of the original samples inherent chemical signature. Reproducibility of the identification was validated by a leave-one replicate-out cross-validation. The results highlighted the high specificity and sensitivity of Raman measurements in identifying DTaP vaccine products. The results pave the way for further exploitation of the Raman technology for identification of vaccines in batch release and cases of suspected falsification.
Ali, Nairveen; Girnus, Sophie; Rösch, Petra; Popp, Jürgen; Bocklitz, Thomas
In: Analytical Chemistry (2018) 12485
The goal of sample size planning is to determine the number of measurements needed for a statistical analysis. This is necessary to achieve robust and significant results, while a minimal number of measurements need to be collected. This is a common procedure for univariate measurements, while for multivariate measurements, like spectra or time traces, no general sam-ple size planning method exists. Sample size planning (SSP) becomes more important for bio-spectroscopic data because the data generation is time consuming and costly. Additionally, ethical reasons don’t allow the use of unnecessary samples and measure an unnecessary number of spectra. In this paper, sample size planning for Raman-spectroscopic data is achieved by utilizing learning curves. The learning curve quantifies the improvement of a classifier for an increasing training set size. These curves are fitted by the inverse power law while the parameters of this fit can be utilized to predict the necessary training set size. The sample size planning is demonstrated for a bio-spectroscopic task of differentiating 6 different bacteria species including E. coli, K. terrigena,P. stutzeri, L. innocua, S. warneri, and S. cohnii based on their Raman spectra. Thereby, we estimate the required number of Raman spectra and biological replicates to train a classification model, which consists of principal component analysis (PCA) combined with a linear discriminant analysis (LDA). The presented algorithm revealed that 142 Raman spectra per specie and 7 biological replicates are needed for the above mentioned bio-spectroscopic classification task. Even though it was not demonstrated, the learning curve based sample size planning algorithm can be applied to all bio-spectroscopic classification tasks.
Guo, Shuxia; Chernavskaia, Olga; Popp, Jürgen; Bocklitz, Thomas
In: Talanta (2018) 372
Fluorescence emission has been one of the major obstacles to apply Raman spectroscopy in biological investigations. It is usually several orders more intense than Raman scattering and hampers further analysis. In cases where the fluorescence emission is too intense to be efficiently removed via routine mathematical baseline correction algorithms, an alternative approach is needed. One alternative approach is shifted-excitation Raman difference spectroscopy (SERDS), where two Raman spectra are recorded with two slightly different excitation wavelengths. Ideally, the fluorescence emission at the two excitations does not change while the Raman spectrum shifts according to the excitation wavelength. Hence the fluorescence is removed in the difference of the two recorded Raman spectra. For better interpretability a spectral reconstruction procedure is necessary to recover the fluorescence-free Raman spectrum. This is challenging due to the intensity variations between the two recorded Raman spectra caused by unavoidable experimental changes, as well as the presence of noise. Existent approaches suffer from drawbacks like spectral resolution loss, fluorescence residual, and artefacts. In this contribution, we proposed a reconstruction method based on non-negative least squares (NNLS), where the intensity variations between the two measurements are utilized in the reconstruction model. The method achieved fluorescence-free reconstruction on three real-world SERDS datasets without significant information loss. Thereafter, we quantified the performance of the reconstruction based on artificial datasets from four aspects: reconstructed spectral resolution, precision of reconstruction, signal to-noise-ratio (SNR), and fluorescence residual. The artificial datasets were constructed with varied Raman to fluorescence intensity ratio(RFIR), SNR, full-width at half-maximum (FWHM), excitation wavelength shift, and fluorescence variation between the two spectra. It was demonstrated that the NNLS approach provides a faithful reconstruction without significantly changing the spectral resolution. Meanwhile, the reconstruction is almost robust to fluorescence variations between the two spectra. Last but not the least the SNR was improved after reconstruction for extremely noisy SERDS datasets.
Dugandžić, Vera; Drikermann, Denis; Ryabchykov, Oleg; Undisz, Andreas; Vilotijevic, Ivan; Lorkowski, Stefan; Bocklitz, Thomas; Matthäus, Christian; Weber, Karina; Cialla-May, Dana; Popp, Jürgen
In: Journal of Biophotonics (2018) 1
Atherosclerosis is a process of thickening and stiffening of the arterial walls through the accumulation of lipids and fibrotic material, as a consequence of aging and unhealthy life style. However, not all arterial plaques lead to complications, which can lead to life-threatening events such as stroke and myocardial infarction. Diagnosis of the disease in early stages and identification of unstable atherosclerotic plaques are still challenging. It has been shown that the development of atherosclerotic plaques is an inflammatory process, where the accumulation of macrophages in the arterial walls is immanent in the early as well as late stages of the disease. We present a novel surface enhanced Raman spectroscopy (SERS)-based strategy for the detection of early stage atherosclerosis, based on the uptake of tagged gold nanoparticles by macrophages and subsequent detection by means of SERS. The results presented here provide a basis for future in vivo studies in animal models. The workflow of tracing the SERS-active nanoparticle uptake by macrophages employing confocal Raman imaging
Silge, Anja; Heinke, Ralf; Bocklitz, Thomas; Wiegand, Cornelia; Hipler, Uta-Christina; Rösch, Petra; Popp, Jürgen
In: Analytical and Bioanalytical Chemistry (2018) 5839
Background: Candida-related infections have become a major problem in hospitals. The species identification of yeast is the prerequisite for the initiation of adequate antifungal therapy. Objectives: In the present study the connection between inherent UV Resonance Raman (UV RR)spectral profiles of Candida species and taxonomic differences were investigated for the first time. Methods: UV RR spectroscopy in combination with statistical modelling was applied to extract taxonomic information from the spectral fingerprints for subsequent differentiation. The identification accuracies of independent batch cultures were determined by applying a leave-one batch-out cross validation. Results: The quality of differentiation can be divided into three levels. Within a defined taxonomic group comprising the species C. glabrata, C. guilliermondii and C. haemulonii, the identification accuracy was low. On the next level the identification results of C. albicans and C. tropicalis were characterized by high sensitivities of 98 and 95% but simultaneously challenged by false positive predictions due to the misallocation of C. spherica (as C. albicans) and C. viswanathii (as C. tropicalis).The highest level of identification accuracies was reached for the species C. dubliniensis, C. krusei,C. africana, C. novergica and C. parapsilosis. Reliable identification results were observed with accuracies ranging from 93 up to 100%. The species allocation based on the UV RR spectral profiles could be reproduced by the identification of independent batch cultures. Conclusions: We conclude that the introduced spectroscopic approach is capable to transform the high dimensional UV RR data of Candida species into clinically useful decision parameters.
Talkenberg, Olga; Kloß, Sandra; Ryabchykov, Oleg; Kniemeyer, Olaf; Brakhage, Axel A.; Bocklitz, Thomas; Cialla-May, Dana; Weber, Karina; Popp, Jürgen
In: Analytical Chemistry (2018) 8912
Fungal spores are one of several environmental factors responsible for causing respiratory diseases like asthma ,chronic obstructive pulmonary disease (COPD), and aspergillosis. These spores also are able to trigger exacerbations during chronic forms of disease. Different fungal spores may contain different allergens and mycotoxins, therefore the health hazards are varying between the species. Thus it is highly important quickly to identify the composition of fungal spores in the air. In this study, UV-Raman spectroscopy with an excitation wavelength of 244 nm was applied to investigate eight different fungal species implicated in respiratory diseases worldwide. Here, we demonstrate that darkly colored spores can be directly examined, and UV-Raman spectroscopy provides the information sufficient for classifying fungal spores. Classification models on the genus, species, and strain levels were built using a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) followed by evaluation with leave-one-batch-out-cross-validation (LBOCV). At the genus level an accuracy of 97.5% was achieved, whereas on the species level four different Aspergillus species were classified with 100% accuracy. Finally, classifying three strains of Aspergillus fumigatus an accuracy of 89.4% was reached. These results demonstrate that UV-Raman spectroscopy in combination with innovative chemometrics allows for fast identification of fungal spores and can be a potential alternative to currently used time-consuming cultivation.
Geitner, Robert; Legesse, Fisseha Bekele; Kuhl, Natascha; Bocklitz, Thomas; Zechel, Stefan; Vitz, Jürgen; Hager, Martin D.; Schubert, Ulrich S.; Dietzek, Benjamin; Schmitt, Michael; Popp, Jürgen
In: Chemistry-A European Journal (20 December, 2017) 2493
The self-healing ability of self-healing materials is often analyzed via morphologic microscopy images. Here it was possible to show that morphologic information alone is not sufficient to judge the status of a self-healing process and molecular information is required as well. When comparing molecular coherent anti-Stokes Raman scattering (CARS) and morphological laser reflection images during a standard scratch healing test of an intrinsic self-healing polymer network it was found that the morphologic closing of the scratch and the molecular crosslinking of the material do not take place simultaneously. This important observation can be explained by the fact that the self-healing process of the thiol-ene based polymer network is limited by the mobility of alkene-containing compounds, which can only be monitored by molecular CARS microscopy and not via standard morphological imaging. Additionally, the recorded CARS images indicate a mechano-chemical activation of the self-healing material by the scratching/damaging process, which leads to an enhanced self-healing behavior in the vicinity of the scratch.
Guo, Shuxia; Bocklitz, Thomas; Neugebauer, Ute; Popp, Jürgen
In: Analytical Methods (2017) 4410
The common mistakes of cross-validation (CV) for the development of chemometric models for Raman based biological applications were investigated. We focused on two common mistakes: the first mistake occurs when splitting the dataset into training and validation datasets improperly; and the second mistake is regarding the wrong position of a dimension reduction procedure with respect to the CV loop. For the first mistake, we split the dataset either randomly or each technical replicate was used as one fold of the CV and compared the results. To check the second mistake, we employed two dimension reduction methods including principal component analysis (PCA) and partial least squares regression (PLS). These dimension reduction models were constructed either once for the whole training data outside the CV loop or rebuilt inside the CV loop for each iteration. We based our study on a benchmark dataset of Raman spectra of three cell types, which included nine technical replicates respectively. Two binary classification models were constructed with a two-layer CV. For the external CV, each replicate was used once as the independent testing dataset. The other replicates were used for the internal CV, where different methods of data splitting and different positions of the dimension reduction were studied. The conclusions include two points. The first point is related to the reliability of the model evaluation by the internal CV, illustrated by the differences between the testing accuracies from the external CV and the validation accuracies from the internal CV. It was demonstrated that the dataset should be split at the highest hierarchical level, which means the biological/technical replicate in this manuscript. Meanwhile, the dimension reduction should be redone for each iteration of the internal CV loop. The second point is the optimization of the performance of the internal CV, benchmarked by the prediction accuracy of the optimized model on the testing dataset. Comparable results were observed for different methods of data splitting and positions of dimension reduction in the internal CV. This means if the internal CV is used for optimizing the model parameters, the two mistakes are less influential in contrast to the model evaluation.
Chernavskaia, Olga; Guo, Shuxia; Meyer, Tobias; Vogler, Nadine; Akimov, Denis; Heuke, Sandro; Heintzmann, Rainer; Bocklitz, Thomas; Popp, Jürgen
In: Journal of Chemometrics (2017) e2901-1
Recent advances in nonlinear multimodal imaging, eg, the combination of coherent anti-Stokes Raman scattering, 2-photon excited autofluorescence, and second-harmonic generation, have shown the great potential of this imaging technique for medical diagnostics. To extract reliable diagnostic information from these multimodal images, a complex image-processing pipeline is necessary. A major part of this image-processing pipeline is the elimination of the mosaicking artifact caused by an uneven illumination within the images. While this problem is well known in image processing of photographic images and methods to solve it were developed, their direct application to multimodal images does not yield satisfactory results. This fact results from the nonlinearity of the measurement modalities and characteristics of the multimodal images itself. In this contribution, different approaches to correct the mosaicking are considered and adapted to multimodal images. In this tutorial article, an investigation and comparative analysis of correction methods were performed, and practical recommendations for the application of different methods are given. The results of this paper can be applied to the development of complete or partial automatic software for medical diagnostics using nonlinear multimodal imaging techniques.
Krafft, Christoph; Schmitt, Michael; Schie, Iwan W.; Cialla-May, Dana; Matthäus, Christian; Bocklitz, Thomas; Popp, Jürgen
In: Angewandte Chemie-International Edition (2017) 4392
Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials due to its unique spectroscopic fingerprint capabilities. Imaging of cells and tissues by Raman microspectroscopy represents a non-destructive and label-free approach. All components of cells or tissues contribute to the Raman signals giving complex spectral signatures. Long acquisitions times are often required due to the relatively small Raman scattering cross sections. To overcome these limitations, Raman signal enhancing methods like resonance Raman scattering and surface enhanced Raman scattering can be applied that also reduce the spectral complexity because the enhancement is often restricted to selected bands. Raman-active labels can be introduced to increase specificity and multimodality. In addition, non-linear coherent Raman scattering such as coherent anti-Stokes Raman scattering and stimulated Raman scattering offer higher sensitivities which enable rapid imaging of larger sampling areas. Finally, fiber based imaging techniques open the way towards in vivo applications of Raman spectroscopy. This critical review summarizes theory, instrumentation, data processing and progress of medical Raman imaging since 2012.
Krafft, Christoph; Schmitt, Michael; Schie, Iwan W.; Cialla-May, Dana; Matthäus, Christian; Bocklitz, Thomas; Popp, Jürgen
In: Angewandte Chemie (2017) 4458
Raman-Spektroskopie ist eine aufstrebende Technik in der Analyse und Bildgebung von Biomaterialien, da sie in der Lage ist, einen einzigartigen spektroskopischen Fingerabdruck zu erzeugen. Die Darstellung von Zellen und Geweben durch Raman-Mikrospektroskopie stellt einen zerstörungsfreien und markerfreien Ansatz dar. Dabei tragen alle Komponenten zu den Raman-Signalen bei, die komplexe spektrale Signaturen liefern. Verfahren wie Resonanz-Raman-Streuung und oberflächenverstärkte Raman-Streuung verstärken die Signale und ergeben einfachere Spektren. Raman-aktive Markierungen können eingeführt werden, um die Spezifität und die Multimodalität zu erhöhen. Nichtlineare kohärente Raman-Streuung bietet höhere Empfindlichkeiten, die eine schnelle Abbildung größerer Probenflächen ermöglichen. Schließlich eröffnen faserbasierte Bildgebungsverfahren den Weg zu In-vivo-Anwendungen der Raman-Spektroskopie. Dieser Aufsatz fasst die Grundlagen der medizinischen Raman-Bildgebung und ihre Fortschritte seit 2012 zusammen.
Pohling, Christoph; Bocklitz, Thomas; Duarte, Alex S.; Emmanuello, Cinzia; Ishikawa, Mariana S.; Dietzek, Benjamin; Buckup, Tiago; Uckermann, Ortrud; Schackert, Gabriele; Kirsch, Matthias; Schmitt, Michael; Popp, Jürgen; Motzkus, Marcus
In: Journal of Biomedical Optics (2017) 066005-1
Multiplex Coherent Anti Stokes Raman Scattering (MCARS) microscopy was carried out to map a solid tumor in mouse brain tissue. The border between normal and tumor tissue was visualized using support vector machines (SVM) as a higher ranking type of data classification. Training data was collected separately in both tissue types, the image contrast is based on class affiliation of the single spectra. Color coding in the image generated by SVM is then related to pathological information instead of single spectral intensities or spectral differences within the data set. The results show good agreement with the HE stained reference and spontaneous Raman microscopy proving the validity of the MCARS-approach in combination with SVM.
Kampe, Bernd; Kloß, Sandra; Bocklitz, Thomas; Rösch, Petra; Popp, Jürgen
In: Frontiers of Optoelectronics (2017) 273
The presence of irrelevant and correlated data points in a Raman spectrum can lead to a decline in classifier performance. We introduce support vector machine-based recursive feature elimination into the field of Raman spectroscopy and demonstrate its performance on a dataset of spectra of clinically relevant microorganisms in urine samples, along with patient samples. As the original technique is only suitable for two-class problems, we adapt it to the multi-class setting. It is shown that a large amount of spectral points can be removed without degrading the prediction accuracy of the resulting model notably.
Jahn, Izabella-Jolan; Talkenberg, Olga; Zheng, Xiao-Shan; Weber, Karina; Bocklitz, Thomas; Cialla-May, Dana; Popp, Jürgen
In: Analyst (2017) 1022
The exhaustive body of literature published in the last four years on the development and application of sytems based on surface enhanced Raman spectroscopy (SERS) combined with microfluidic devices demonstrates that this research field is a current hot-topic. This synergy, also referred to as lab-on-a-chip SERS (LoC-SERS) or nano-, micro-optofluidics SERS,opened the door for new opportunities where both techniques can profit of. On one hand, SERS measurements are considerably improved because processes previously done on a large scale in the laboratory and human error prone arenow carried out in nanoliter volumes in an automatic and reproducible manner. On the other hand, microfluidic platforms need detection methods able to sense in small volumes and therefore, SERS is ideal for this task. The present review does not only aim to give the reader an overview of the recent developments and advancements in the field, but it also addressess aspects of fundamental SERS theory influencing the interpretation of SERS spectra, challenges brought by experimental conditions and chemometric data analysis.
Guo, Shuxia; Heinke, Ralf; Stöckel, Stephan; Rösch, Petra; Bocklitz, Thomas; Popp, Jürgen
In: Vibrational spectroscopy (2017) 111
One of the most important issues for the application of Ramanspectroscopy for biological diagnostics is how to deal efficiently with large datasets. The best solution is chemometrics, where statistical models are built based on a certain number of known samples and used topredict unknown datasets in future. However, the prediction may fail if the new datasets are measured under different conditions as those used for establishing the model. In this case, model transfer methods are required to obtain high prediction accuracy for both datasets. Knownmodel transfer methods, for instance standard calibration and trainingmodels with datasets measured under multiple conditions, do not providesatisfactory results. Therefore, we studied two approaches to improvemodel transfer: wavenumber adjustment by a genetic algorithm (GA) after the standard calibration and model updating based on the Tikhonov regularization (TR). We based our investigation on Raman spectra of three spore species measured on four spectrometers. The methods were tested regarding two aspects. First, the wavenumber alignment is checked by computing Euclidean distances between the mean Raman spectra from different devices. Second, we evaluated the model transferability by means of the accuracy of a three-class classification system. According to the results, the model transferability was significantly improved by the wavenumber adjustment, even though the Euclidean distances were almost the same compared with those after the standard calibration. Forthe TR2 method the model transferability was dramatically improved by updating current models with very few samples from the new datasets. This improvement was not significantly lowered even if no spectral standardization was implemented beforehand. Nevertheless, the modelt ransferability was enhanced by combining different model transform mechanisms.
Ryabchykov, Oleg; Bocklitz, Thomas; Ramoji, Anuradha A.; Neugebauer, Ute; Förster, Martin; Kroegel, Claus; Bauer, Michael; Kiehntopf, Michael; Popp, Jürgen
In: Chemometrics and Intelligent Laboratory Systems (2016) 1
Raman spectroscopy as a technique has high potential for biological applications, e.g. cell and tissue analysis. In these applications, large data sets are normally recorded which require automated analysis. Unfortunately, a lot of disturbing external influences exist, which negatively affect the analysis of Raman spectra. A problematic corrupting effect in big data sets is cosmic ray noise, which usually produces intense spikes within the Ramanspectra. In order to exploit Raman spectroscopy in real world applications, detection and removing of spikes should be stable, data-independent and performed without manual control. Herein, an automatic algorithm for cosmic ray noise correction is presented.The algorithm distinguishes spikes from spectra based on their response to a Laplacian, e.g. their sharpness. Manual rating of the spike presence was used as a benchmark for algorithmvalidation. The algorithm's sensitivity was estimated to be above 99%.
Chernavskaia, Olga; Heuke, Sandro; Vieth, Michael; Friedrich, Oliver; Schürmann, Sebastian; Atreya, Raja; Stallmach, Andreas; Neurath, Markus F.; Waldner, Maximilian; Petersen, Iver; Schmitt, Michael; Bocklitz, Thomas; Popp, Jürgen
In: Scientific Reports (2016) 29239-1
Assessing disease activity is a prerequisite for an adequate treatment of inflammatory bowel diseases (IBD) such as Crohn’s disease and ulcerative colitis. In addition to endoscopic mucosal healing, histologic remission poses a promising end-point of IBD therapy. However, evaluating histological remission harbors the risk for complications due to the acquisition of biopsies and results in a delay of diagnosis because of tissue processing procedures. In this regard, non-linear multimodal imaging techniques might serve as an unparalleled technique that allows the real-time evaluation of microscopic IBD activity in the endoscopy unit. In this study, tissue sections were investigated using the non-linear multimodal microscopy combination of coherent anti-Stokes Raman scattering (CARS), two-photon excited auto fluorescence (TPEF) and second-harmonic generation (SHG). After the measurement a gold-standard assessment of histological indexes was carried out based on a conventional HE stain. Subsequently, various geometry and intensity related features were extracted from the multimodal images. An optimized set of features was selected and utilized to predict levels of histological indexes on the basis of a linear discriminant analysis (LDA) with excellent results. Based on the automated prediction the diagnosis time interval is decreased. Therefore, non-linear multimodal imaging may provide a real-time diagnosis of IBD activity suited to assist clinical decision making within the endoscopy unit.
Kumar, Vinay B.N.; Guo, Shuxia; Bocklitz, Thomas; Rösch, Petra; Popp, Jürgen
In: Analytical Chemistry (2016) 7574
Carbon catabolite repression (CCR) is a regulatory phenomenon occurring in both lower organisms like bacteria andhigher organisms like yeast which allows them to preferentially utilize a specific carbon source to achieve highest metabolicactivity and cell growth. CCR has been intensely studied in the model organisms Escherichia coli and Bacillus subtilis by followingdiauxic growth curves, assays to estimate the utilization or depletion of carbon sources, enzyme assays, Western blotting and massspectrometric analysis to monitor and quantify the involvement of specific enzymes and proteins involved in CCR. In this study, wehave visualized this process in three species of naphthalene degrading soil bacteria at a single cell level via Raman spectroscopybased stable isotope probing (Raman-SIP) using a single and double labeling approach. This is achieved using a combination of 2Hand 13C isotope labeled carbon sources like glucose, galactose, fructose and naphthalene. Time dependent metabolic flux of 13C and2H isotopes has been followed via relative quantification and 2D Raman correlation analysis. For this, the relative intensities of Raman marker bands corresponding to 2H and 13C incorporation in newly synthesized macromolecules like proteins and lipids have been utilized. The 2D correlation analysis of time dependent Raman spectra readily identified small sequential changes resultingfrom isotope incorporation. Overall, we show that Raman-SIP has the potential to be used to obtain information about regulatory processes like CCR in bacteria at a single cell level within a time span of 3 h in fast growing bacteria. We also demonstrate the potential of this approach in identifying the most efficient naphthalene degraders asserting its importance for use in bioremediation.
Koprowski, Robert; Bocklitz, Thomas
In: Journal of Biophotonics (2016) 434
Hidi, Izabella Jolan; Jahn, Martin; Weber, Karina; Bocklitz, Thomas; Pletz, Mathias W.; Cialla-May, Dana; Popp, Jürgen
In: Analytical Chemistry (2016) 9173
The emergence of antibacterial resistance and the development of new drags, 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 similar to 3 mu M (0.57 mg/L), LOQ similar to 6.5 mu M (1.23 mg/L) while the linear range is between 4.28 and 42.8 mu M (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 (MORALS) algorithm provides reliable results, and it is preferred to a univariate statistical approach.
Mühlig, Anna; Bocklitz, Thomas; Labugger, Ines; Dees, Stefan; Henk, Sandra; Richter, Elvira; Andres, Sönke; Merker, Matthias; Stöckel, Stephan; Weber, Karina; Cialla-May, Dana; Popp, Jürgen
In: Analytical Chemistry (2016) 7998
A closed droplet based lab-on-a-chip (LOC) device has been developed for the differentiation of six species of my-cobacteria, i.e., both Mycobacterium tuberculosis complex (MTC) and nontuberculous mycobacteria (NTM) using surface-enhanced Raman spectroscopy (SERS). The combination of a fast and simple bead-beating module for the disruption of the bacterial cell with the LOC-SERS device enables the application of an easy and reliable system for bacteria discrimination. Without extraction or further treatment of the sample, the obtained SERS spectra are dominated by the cell-wall component mycolic acid. For the differentiation, a robust data set was recorded using a droplet based LOC-SERS device. Thus, more than 2100 individual SERS spectra of the bacteria suspension were obtained in one hour. The differentiation of bacteria using LOC-SERS provides helpful information for physicians to define the conditions for the treatment of individual patients.
Geitner, Robert; Kötteritzsch, Julia; Siegmann, Michael; Fritzsch, Robby; Bocklitz, Thomas; Hager, Martin D.; Schubert, Ulrich S.; Gräfe, Stefanie; Dietzek, Benjamin; Schmitt, Michael; Popp, Jürgen
In: Physical Chemistry Chemical Physics (2016) 17973
The self-healing polymer P(LMA-co-MeAMMA) cross linked with C60-fullerene has been studied by FT-Raman spectroscopy in combination with two-dimensional (2D) correlation analysis and density functional theory calculations. To unveil the molecular changes during the self-healing process mediated by the Diels-Alder equilibrium between 10-methyl-9-anthracenyl groups and C60-fullerene different anthracene-C60-fullerene adducts have been synthesized and characterized by time-, concentration- and temperature-dependent FT-Raman measurements. The self-healing process could be monitored via the C60-fullerene vibrations at270, 432 and 1469 cm-1. Furthermore, the detailed analysis of the concentration-dependent FT Raman spectra point towards the formation of anthracene-C60-fullerene adducts with an unusual high amount of anthracene bound to C60-fullerene in the polymer film, while the 2Dcorrelation analysis of the temperature-dependent Raman spectra suggests a stepwise degradation of anthracene-C60-fullerene adducts, which are responsible for the self-healing of the polymer.
Heuke, Sandro; Chernavskaia, Olga; Bocklitz, Thomas; Legesse, Fisseha Bekele; Meyer-Zedler, Tobias; Akimov, Denis; Dirsch, Olaf; Ernst, Günther; von Eggeling, Ferdinand; Petersen, Iver; Guntinas-Lichius, Orlando; Schmitt, Michael; Popp, Jürgen
In: Head and Neck-Journal for the Sciences and Specialities of the Head and Neck (2016) 1545
Background. Treatment of early cancer stages is deeply connected to a good prognosis, a moderate reduction of the quality oflife, and comparably low treatment costs. Methods. Head and neck squamous cell carcinomas were investigated using the multimodal combination of coherent anti-Stokes Raman scattering(CARS), two-photon excited fluorescence (TPEF), and secondharmonic generation (SHG) microscopy.Results. An increased median TPEF to CARS contrast was found comparingcancerous and healthy squamous epithelium with a p value of 1.8_10210. A following comprehensive image analysis was able to predict the diagnosis of imaged tissue sections with an overall accuracy of90% for a 4-class model.Conclusion. Nonlinear multimodal imaging is verified objectively as a valuable diagnostic tool that complements conventional staining protocols and can serve as filter in future clinical routine reducing the pathologist’sworkload.
Guo, Shuxia; Bocklitz, Thomas; Popp, Jürgen
In: Analyst (2016) 2396
In the last decade Raman-spectroscopy has become an invaluable tool for biomedical diagnostics. However, a manual rating of the subtle spectral differences between normal and abnormal disease states is not possible or practical. Thus it is necessary to combine Raman-spectroscopy with chemometrics in order to build statistical models predicting the disease states directly without manual intervention. Within chemometrical analysis a number of corrections have to be applied to receive robust models. Base line correction is an important step of the pre-processing, which should remove spectral contributions of fluorescence effects and improve the performance and robustness of statistical models. However, it is demanding, time-consuming, and depends on expert knowledge to select an optimal baseline correction method and its parameters every time working with a new dataset. To circumvent this issue we proposed a genetic algorithm based method to automatically optimize the baseline correction. The investigation was carried out in three main steps. Firstly, a numerical quantitative marker was defined to evaluate the baseline estimation quality. Secondly, a genetic algorithm based methodology was established to search the optimal baseline estimation with the defined quantitative marker as evaluation function. Finally, classification models were utilized to benchmark the performance of the optimized baseline. For comparison, model based baseline optimization was carried out applying the same classifiers. It was proven that our method could provide a semi-optimal and stable baseline estimation without any chemical knowledge required or any additional spectral information used.
Bocklitz, Thomas; Subhi Salah, Firas; Vogler, Nadine; Heuke, Sandro; Chernavskaia, Olga; Schmidt, Carsten; Waldner, Maximilian; Greten, Florian; Bräuer, Rolf; Schmitt, Michael; Stallmach, Andreas; Petersen, Iver; Popp, Jürgen
In: BMC CANCER (2016) 534-1
Background: Due to the steadily increasing number of cancer patients worldwide the early diagnosis and treatment of cancer is a major field of research. The diagnosis of cancer is mostly performed by an experienced pathologist via the visual inspection of histo-pathological stained tissue sections. To save valuable time, low quality cryosection are frequently analyzed with diagnostic accuracies that are below those of high quality embedded tissue section. Thus, alternative means have to be found that enable for fast and accurate diagnosis as the basis of following clinical decision making. Methods: In this contribution, we will show that the combination of the three label-free non-linear imaging modalities CARS (coherent anti-Stokes Raman-scattering), TPEF (two-photon excited autofluorescence) and SHG (second harmonic generation) yields information that can be translated into computational hematoxylin and eosin (HE) images by multivariate statistics. Thereby, a computational HE stain is generated resulting in pseudo-HE overview images that allow for identification of suspicious regions. The latter are analyzed further by Raman-spectroscopy retrieving the tissue's molecular fingerprint. Results: The results suggest that the combination of non-linear multimodal imaging and Raman-spectroscopy possesses the potential as a precise and fast tool in routine histopathology. Conclusions: As the key advantage, both optical methods are non-invasive enabling for further pathological investigations of the same tissue section, e.g. a direct comparison with the current pathological gold-standard.
Bocklitz, Thomas; Guo, Shuxia; Vogler, Nadine; Popp, Jürgen
In: Analytical Chemistry (2016) 133
The Raman effect was predicted by Schmekal1 in 1923 and independently discovered in 1928 by two Indian physicists, Raman and Krishna.2,3 In principle, monochromatic light is inelastically scattered at a quantiffed structure like the vibrational states of a molecule. The occurring energy shifts are an indirect representation of the vibrational states of the molecule and, thus, are molecule specific. If this principle is spectroscopically used, an ensemble of molecules is measured and the result is called a Stokes-Raman spectrum, or shorter a Raman spectrum. The Stokes-Raman spectrum is the part of inelastically scattered light, which is shifted to lower energies.4,5 This is the dominant effect at room temperatures, which is the reason for skipping the attribute. Due to the ensemble mixing the Raman spectrum is not representing the vibrational states of one molecule but of a mixture of molecules. Thus, the Raman spectrum is a superposition of Raman spectra of substances within the excitation focus. Because the unmixing of this superposition is only possible for limited cases, the Raman spectrum is used as a vibrational fingerprint. This fingerprint is either interpreted with a certain set of reference Raman spectra or evaluated by means of statistical methods. The latter procedure is often applied, if heterogonous mixtures like cells or tissues are investigated, while the former method is used, if pure substances or easy mixtures are studied. As investigations on biological samples, like cells or tissue are the topic of the review, we will focus on biological samples in the following. Therefore, a Raman spectrum is used as vibrational fingerprint.
Galler, Kerstin; Requardt, Robert Pascal; Glaser, Uwe; Markwart, Robby; Bocklitz, Thomas; Bauer, Michael; Popp, Jürgen; Neugebauer, Ute
In: Scientific Reports (2016) 241551-1
Hepatic stellate cells (HSCs) are retinoid storing cells in the liver: The retinoid content of those cells changes depending on nutrition and stress level. There are also differences with regard to a HSC’s anatomical position in the liver. Up to now, retinoid levels were only accessible from bulk measurements of tissue homogenates or cell extracts. Unfortunately, they do not account for the intercellular variability. Herein, Raman spectroscopy relying on excitation by the minimally destructive wavelength 785 nm is introduced for the assessment of the retinoid state of single HSCs in freshly isolated, unprocessed murine liver lobes. A quantitative estimation of the cellular retinoid content is derived. Implications of the retinoid content on hepatic health state are reported. The Raman-based results are integrated with histological assessments of the tissue samples. This spectroscopic approach enables single cell analysis regarding an important cellular feature in unharmed tissue.
Kämmer, Evelyn; Götz, Isabel; Bocklitz, Thomas; Stöckel, Stephan; Dellith, Andrea; Cialla-May, Dana; Weber, Karina; Zell, Roland; Dellith, Jan; Deckert, Volker; Popp, Jürgen
In: Analytical and Bioanalytical Chemistry (2016) 4035
Currently, two types of direct methods to characterize and identify single virions are available: electron microscopy (EM) and scanning probe techniques, especially atomic force microscopy (AFM). AFM in particular provides morphologic information even of the ultrastructure of viral specimens without the need to cultivate the virus and to invasively alter the sample prior to the measurements. Thus, AFM can play a critical role as a frontline method in diagnostic virology. Interestingly, varying morphological parameters for virions of the same type can be found in the literature, depending on whether AFM or EM was employed and according to the respective experimental conditions during the AFM measurements. Here, an inter-methodological proof of principle is presented, in which the same single virions of herpes simplex virus 1 were probed by AFM previously and after they were measured by scanning electron microscopy (SEM). Sophisticated chemometric analyses then allowed a calculation of morphological parameters of the ensemble of single virions and a comparison thereof. A distinct decrease in the virions' dimensions was found during as well as after the SEM analyses and could be attributed to the sample preparation for the SEM measurements. Graphical abstract The herpes simplex virus is investigated with scanning electron and atomic force microscopy in view of varying dimensions.
Latorre, Federico; Kupfer, Stephan; Bocklitz, Thomas; Kinzel, Daniel; Trautmann, Steffen; Gräfe, Stefanie; Deckert, Volker
In: Nanoscale (2016) 10229
Experimental evidence of extremely high spatial resolution of tip-enhanced Raman scattering (TERS) has been recently demonstrated. Here, we present a full quantum chemical description (at the density functional level of theory) of the non-resonant chemical effects on the Raman spectrum of an adenine molecule mapped by a tip, modeled as a single silver atom or a small silver cluster. We show pronounced changes in the Raman pattern and its intensities depending on the conformation of the nanoparticle-substrate system, concluding that the spatial resolution of the chemical contribution of TERS can be in the sub-nm range.
Vogler, Nadine; Bocklitz, Thomas; Subhi Salah, Firas; Schmidt, Carsten; Bräuer, Rolf; Cui, Tiantian; Mireskandari, Masoud; Greten, Florian; Schmitt, Michael; Stallmach, Andreas; Petersen, Iver; Popp, Jürgen
In: Journal of Biophotonics (2016) 533
Being among the most common cancers worldwide screening and early diagnosis of colorectal cancer is of high interest for the health system, the patients and for research. Raman microspectroscopy as a label-free, noninvasive and non-destructive technique is a promising tool for an early diagnosis. However, to ensure a reliable diagnosis specially designed statistical analysis workflows are required. Several statistical approaches have been introduced leading to varying results in the overall accuracy, sensitivity and specificity. In this study a systematic evaluation of different statistical analysis approaches has been performed using a colon cancer mouse model with genotypic identical individuals. Based on the inter-individual Raman spectral variances a measure for the biological variance can be estimated. By applying a leave-one-individual-out cross-validation a clinically relevant discrimination of healthy tissue versus adenoma and carcinoma with an accuracy of 95% is shown. Furthermore, the transfer of a model from tissue to biopsy specimen is demonstrated.
Silge, Anja; Bocklitz, Thomas; Ossig, Rainer; Schnekenburger, Jürgen; Rösch, Petra; Popp, Jürgen
In: Analytical and Bioanalytical Chemistry (2016) 5935
Metal oxide nanoparticles (NP) are applied in the fields of biomedicine, pharmaceutics, and in consumer products as textiles, cosmetics, paints, or fuels. In this context, the functionalization of the NP surface is a common method to modify and modulate the product performance. A chemical surface modification of NP such as an aminofunctionalization can be used to achieve a positively chargedand hydrophobic surface. Surface functionalization is known to affect the interaction of nanomaterials (NM) with cellular macromolecules and the responses of tissues or cells, like the uptake of particles by phagocytic cells. Therefore, it is important to assess the possible risk of those modified NP for human hea l t h and environment . By appl y i n g Raman microspectroscopy, we verified in situ the interaction of amino-modified ZrO2 NP with cultivated macrophages. The results demonstrated strong adhesion properties of the NP to the cell membrane and internalization into the cells. The intra cellular localization of the NP was visualized via Raman depth scans of single cells. After the cells were treated with sodiumazide (NaN3) and 2-deoxy-glucose to inhibit the phagocytic activity, NP were still detected inside cells to comparable percentages. The observed tendency of amino-modified ZrO2 NP to interact with the cultivated macrophages may influence membrane integrity and cellular functions of alveolar macrophages in the respiratory system.
Radu, Andreea; Bocklitz, Thomas; Hübner, Uwe; Weber, Karina; Cialla-May, Dana; Popp, Jürgen
In: Analyst (2016) 4447
Carotenoids are molecules that play important roles in both plant development and in the well-being of mammalian organisms. Due to this fact, different studies have been performed towards understanding their properties, distribution in nature and their health benefits, upon ingestion. Nevertheless, there is a gap regarding a fast detection of them at the plant phase. Within this contribution we report the results obtained regarding the application of surface enhanced Raman spectroscopy (SERS) towards the differentiation of two carotenoid molecules (namely lycopene and beta-carotene) out of tomato samples. In order to achieve this, an e-beam lithography (EBL) SERS active substrate and a 488 nm excitation source were employed. For this, a relevant simulated matrix was prepared (by mixing the two carotenoids in defined percentages) and measured. Next, carotenoids were extracted from tomato plants and measured as well. Finally, a combination of principal component analysis and partial least squares regression (PCA-PLSR) was applied to process the data and the obtained results were compared with HPLC measurements of the same extracts. A good agreement was obtained in between the HPLC and the SERS results for most of the tomato samples.
Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen
In: Nanoscale (2015) 4545
Rapid techniques for virus identification are more relevant today than ever. The conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows a characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which also a successful discrimination of the two viral species based on TERS spectra of single virus particles was realised with a classification accuracy of 91%.
Dochow, Sebastian; Ma, Dinglong; Latka, Ines; Bocklitz, Thomas; Hartl, Brad; Bec, Julien; Fatakdawala, Hussain; Marple, Eric T.; Urmey, Kirk; Wachsmann-Hogiu, Sebastian; Schmitt, Michael; Marcu, Laura; Popp, Jürgen
In: Analytical and Bioanalytical Chemistry (2015) 8291
In this contribution we present a dual modality fiber optic probe combining fluorescence lifetime imaging (FLIm) and Raman spectroscopy for in vivo endoscopic applications. The presented EmVision LLC multispectroscopy probe enables efficient excitation and collection of fluorescence lifetime signals for FLIm in the UV/visible wavelength region, as well as of Raman spectra in the near IR for simultaneous Raman/FLIm imaging. The probe has been characterized for its lateral resolution and distance dependency of the Raman and FLIm signals. In addition, the feasibility of the probe for in vivo FLIm and Raman spectral characterization of tissue was demonstrated.
Zhang, Ying; Kupfer, Stephan; Zedler, Linda; Schindler, Julian; Bocklitz, Thomas; Guthmuller, Julien; Rau, Sven; Dietzek, Benjamin
In: Physical Chemistry Chemical Physics (2015) 29637
Terpyridine 4H-imidazole-ruthenium(II) complexes are considered promising candidates for sensitizer in dye sensitized solar cells (DSSCs) by displaying a broad absorption in the visible range, where the dominant absorption features are due to metal-to-ligand charge transfer (MLCT) transitions. The ruthenium(III) intermediates resulting from photoinduced MLCT transitions are essential intermediates in the photoredox-cycle of the DSSC. However, their photophysics is much less studied compared to the ruthenium(II) parent systems. To this end, the structural alterations accompanying one-electron oxidation of the RuIm dye series (including non-carboxylic RuIm precursor, and, carboxylic RuImCOO in solution and anchored to a nanocrystalline TiO2 film) are investigated via in situ experimental and theoretical UV-Vis absorption and resonance Raman (RR) spectroelectrochemistry. The excellent agreement between the experimental and the TDDFT spectra derived in this work allows for an in-depth assignment of UV-Vis and RR spectral features of the dyes. A concordant pronounced wavelength dependence with respect to the charge transfer character has been observed for the model system RuIm, and both RuImCOO in solution and attached on TiO2 surface: Excitation at long wavelengths leads to the population of ligand-to-metal charge transfer states, i.e. photoreduction of the central ruthenium(III) ion, while high-energy excitation features an intra-ligand charge transfer state localized on the 4H-imidazole moiety. Therefore, these 4H-imidazole ruthenium complexes investigated here are potential multi-photoelectron donors: One electron is donated from MLCT states, and additionally, the 4H-imidazole ligand reveals electron-donating character with a significant contribution to the excited states of the ruthenium(III) complexes upon blue-light irradiation.
Vogler, Nadine; Heuke, Sandro; Bocklitz, Thomas; Schmitt, Michael; Popp, Jürgen
In: Annual Review of Analytical Chemistry (2015) 359
Advanced optical imaging technologies have experienced increased visibility in medical research as they allow for a label-free and nondestructive investigation of tissue either in an excised state or in living organisms. Besides a multitude of ex vivo studies proving the applicability of these optical imaging approaches a transfer of various modalities toward in vivo diagnosis is currently in progress. Furthermore, combining optical imaging techniques, referred to as multimodal imaging, allows for an improved diagnostic reliability due to the complementary nature of retrieved information. In this review, we provide a summary of multifold ongoing efforts in multimodal tissue imaging with particular attention to in vivo applications for medical diagnostic purposes. We also discuss the advantages and potential limitations of the imaging methods and outline opportunities for future developments.
Bocklitz, Thomas; Bräutigam, Katharina; , ; Hoffmann, Franziska; von Eggeling, Ferdinand; Ernst, Günther; Schmitt, Michael; Schubert, Ulrich S.; Guntinas-Lichius, Orlando; Popp, Jürgen
In: Analytical and Bioanalytical Chemistry (2015) 7865
Molecular heterogeneity of cancer is a major obstacle in tumor diagnosis and treatment. To deal with this heterogeneity, a multidisciplinary combination of different analysis techniques is of urgent need because a combination enables the creation of a multimodal image of a tumor. Here, we develop a computational workflow in order to combine matrixassisted laser desorption/ionization mass spectrometric (MALDI-MS) imaging and Raman microspectroscopic imaging for tissue based studies. The computational workflow can be used to confirm a spectral histopathology (SHP) based on one technique with another technique. In this contribution, we confirmed a Raman spectroscopic based SHP with MALDI-imaging. Owing to this combination, we could demonstrate, for a larynx carcinoma sample, that tissue types and different metabolic states could be extracted from the Raman spectra. Further investigations with the help of MALDI spectra yield a better characterization of variable epithelial differentiation and a better understanding of ongoing dysplastic alterations.
Kämmer, Evelyn; Olschewski, Konstanze; Stöckel, Stephan; Rösch, Petra; Weber, Karina; Cialla-May, Dana; Bocklitz, Thomas; Popp, Jürgen
In: Analytical and Bioanalytical Chemistry (2015) 8925
Here, we report on a proof-of-concept study highlighting a new approach for quantitative surface enhanced Raman spectroscopy (SERS) measurements. This has been achieved by implementing the standard addition method (SAM) within a lab-on-a-chip (LOC) system. The approach has been successfully tested to quantify congo red as a model analyte even in the presence of the chemically related mole- cule methyl red. Thus, the developed concept demonstrates its potential to quantify analytes via SERS in the presence of other SERS active molecules.
Legesse, Fisseha Bekele; Chernavskaia, Olga; Heuke, Sandro; Bocklitz, Thomas; Meyer-Zedler, Tobias; Popp, Jürgen; Heintzmann, Rainer
In: Journal of Microscopy (2015) 223
For diagnostic purposes, optical imaging techniques need to obtain high-resolution images of extended biological specimens in reasonable time. The field of view of an objective lens, however, is often smaller than the sample size. To image the whole sample, laser scanning microscopes acquire tile scans that are stitched into larger mosaics. The appearance of such image mosaics is affected by visible edge artefacts that arise from various optical aberrations which manifest in grey level jumps across tile boundaries. In this contribution, a technique for stitching tiles into a seamless mosaic is presented. The stitching algorithm operates by equilibrating neighbouring edges and forcing the brightness at corners to a common value. The corrected image mosaics appear to be free from stitching artefacts and are, therefore, suited for further image analysis procedures. The contribution presents a novel method to seamlessly stitch tiles captured by a laser scanning microscope into a large mosaic. The motivation for the work is the failure of currently existing methods for stitching nonlinear, multimodal images captured by our microscopic setups. Our method eliminates the visible edge artefacts that appear between neighbouring tiles by taking into account the overall illumination differences among tiles in such mosaics. The algorithm first corrects the nonuniform brightness that exists within each of the tiles. It then compensates for grey level differences across tile boundaries by equilibrating neighbouring edges and forcing the brightness at the corners to a common value. After these artefacts have been removed further image analysis procedures can be applied on the microscopic images. Even though the solution presented here is tailored for the aforementioned specific case, it could be easily adapted to other contexts where image tiles are assembled into mosaics such as in astronomical or satellite photos.
Silge, Anja; Abdou, Elias; Schneider, Kilian; Meisel, Susann; Bocklitz, Thomas; Lu-Walther, Hui-Wen; Heintzmann, Rainer; Rösch, Petra; Popp, Jürgen
In: Cellular Microbiology (2015) 832
Macrophages are the primary habitat of pathogenic mycobacteria during infections. The current research about the host pathogen interaction on the cellular level is still going on. The present study proves the potential of Raman microspectroscopy as a label-free and non- invasive method to investigate intracellular mycobacteria in situ. Therefore, macrophages were infected with M. gordonae, a mycobacterium known to cause inflammation linked to intracellular survival in macrophages. Here, we show that Raman maps provided spatial and spectral information about the position of bacteria within determined cell margins of macrophages in two dimensional scans and in three dimensional image stacks. Simultaneously, the relative intracellular concentration and distributions of cellular constituents such as DNA, proteins and lipids provided phenotypic information about the infected macrophages. Locations of bacteria outside or close to the outer membrane of the macrophages were notably different in their spectral pattern compared to intracellular once. Furthermore, accumulations of bacteria inside of macrophages exhibit distinct spectral/molecular information due to the chemical composition of the intracellular microenvironment. The data show that the connection of microscopically and chemically gained information provided by Raman microspectroscopy offers a new analytical way to detect and to characterize the mycobacterial infection of macrophages.
Bocklitz, Thomas; Dörfer, Thomas; Heinke, Ralf; Schmitt, Michael; Popp, Jürgen
In: Spectrochimica Acta Part A-Molecular and Biomolecular Spectroscopy (2015) 544
The combination of Raman spectroscopy with chemometrics has gained significant importance within the last years to address a broad variety of biomedical and life science questions. However, the routine application of chemometric models to analyze Raman spectra recorded with Raman devices different from the device used to establish the model is extremely challenging due to Raman device specific influences on the recorded Raman spectra. Here we report on the influence of different non-resonant excitation wavelengths on Raman spectra and propose a calibration routine, which corrects for the main part of the spectral differences between Raman spectra recorded with different (non-resonant) excitation wavelengths. The calibration routine introduced within this contribution is an improvement to the known ‘standard’ calibration routines and is a starting point for the development of a calibration protocol to generate spectrometer independent Raman spectra. The presented routine ensures that a chemometric model utilizes only Raman information of the sample and not artifacts from small shifts in the excitation wavelength. This is crucial for the application of Raman-spectroscopy in real-world-settings, such as diagnostics of diseases or identification of bacteria.
Egodage-Dampali, Kokila; Dochow, Sebastian; Bocklitz, Thomas; Chernavskaia, Olga; Matthäus, Christian; Schmitt, Michael; Popp, Jürgen
In: Journal of Biomedical Photonics & Enginering (2015) 169
The visualization as well as characterization of diseased tissue are of vital diagnostic interest for an early diagnosis to increase patients’ survival rate. In this study we introduce an imaging device combining optical coherence tomography (OCT) and Raman spectroscopy (RS), allowing to record 2D and 3D OCT cross sectional images of bulk tissue samples, as well as the acquisition of Raman spectra from small areas of interest in order to aid the detection process with molecular information. The design of the OCT/RS imaging device consists of commercially available cage components. The probe head involves a CCD camera chip for visualization purposes and galvanic mirrors for scanning the sample in x and y directions with a scan line rate up to 76 kHz. The OCT/RS imaging approach has been successfully evaluated by investigating pork samples. OCT and Raman data were correlated and different tissue types within the samples were successfully identified and clustered separately. Finally, pork skin samples with visual defects were characterized. Overall, the presented OCT/RS device allows for recording of large morphological overview OCT images to define points of interest, which are afterwards characterized in more detail on a molecular level by means of Raman spectroscopy.
Jahn, Martin; Patze, Sophie; Bocklitz, Thomas; Weber, Karina; Cialla-May, Dana; Popp, Jürgen
In: Analytica Chimica Acta (2015) 43
Food safety is an actual topic of great importance for our society which places high demands on analytical methods. Surface enhanced Raman spectroscopy (SERS) meets the requirements for a rapid, sensitive and specific detection technique. The fact that metallic colloids, one of the most often used SERS substrates, are usually prepared in aqueous solution makes the detection of water-insoluble substances challenging. In this paper we present a SERS based approach for the detection of water-insoluble molecules by applying a hydrophobic surface modification onto the surface of enzymatic generated silver nanoparticles. By this approach the detection of the illegal water-insoluble food dyes, such as Sudan III in presence of riboflavin, as water-soluble competitor, is possible. Moreover, we demonstrate the usability of this kind of SERS substrates for determination of Sudan III out of spiked paprika extracts.
Berry, David; Mader, Esther; Lee, Tae Kwon; Woebken, Dagmar; Wang, Yun; Zhu, Di; Palatinszky, Marton; Schintlmeister, Arno; Schmid, Markus C.; Hanson, Buck T.; Shterzer, Naama; Mizrahi, Itzhak; Rauch, Isabella; Decker, Thomas; Bocklitz, Thomas; Popp, Jürgen; Gibson, Christopher M.; Fowler, Patrick W.; Huang, Wei E.; Wagner, Michael
In: Proceedings of the National Academy of Sciences of The United States of America (2015) E194
Microbial communities are essential to the function of virtually all ecosystems and eukaryotes, including humans. However, it is still a major challenge to identify microbial cells active under natural conditions in complex systems. In this study, we developed a new method to identify and sort active microbes on the single-cell level in complex samples using stable isotope probing with heavy water (D2O) combined with Raman microspectroscopy. Incorporation of D2O-derived D into the biomass of autotrophic and heterotrophic bacteria and archaea could be unambiguously detected via C-D signature peaks in single-cell Raman spectra, and the obtained labeling pattern was confirmed by nanoscale resolution secondary ion MS. In fast-growing Escherichia coli cells, label detection was already possible after 20 min. For functional analyses of microbial communities, the detection of D incorporation from D2O in individual microbial cells via Raman microspectroscopy can be directly combined with FISH for the identification of active microbes. Applying this approach to mouse cecal microbiota revealed that the host-compound foragers Akkermansia muciniphila and Bacteroides acidifaciens exhibited distinctive response patterns to amendments of mucin and sugars. By Raman based cell sorting of active (deuterated) cells with optical tweezers and subsequent multiple displacement amplification and DNA sequencing, novel cecal microbes stimulated by mucin and/ or glucosamine were identified, demonstrating the potential of the nondestructive D2O-Raman approach for targeted sorting of microbial cells with defined functional properties for single cell genomics.
Geitner, Robert; Kötteritzsch, Julia; Siegmann, Michael; Bocklitz, Thomas; Hager, Martin D.; Schubert, Ulrich S.; Gräfe, Stefanie; Dietzek, Benjamin; Schmitt, Michael; Popp, Jürgen
In: Physical Chemistry Chemical Physics (2015) 22587
The thermally healable polymer P(LMA-co-FMA-co-MIMA) has been studied by temperature-dependent FT-Raman spectroscopy, two-dimensional Raman correlation analysis and density functional theory (DFT) calculations. To the best of our knowledge this study reports for the first time on the investigation of a self-healing polymer by means of two-dimensional correlation techniques. The synchronous correlation spectrum reveals that the spectrally overlapping CQC stretching vibrations at 1501, 1575, 1585 and 1600 cm_1 are perfect marker bands to monitor the healing process which is based on a Diels–Alder reaction of furan and maleimide. The comparison between experimental and calculated Raman spectra as well as their correlation spectra showed a good agreement between experiment and theory. The data presented within this study nicely demonstrate that Raman correlation analysis combined with a band assignment based on DFT calculations presents a powerful tool to study the healing process of self-healing polymers.
Silge, Anja; Bräutigam, Katharina; Bocklitz, Thomas; Rösch, Petra; Vennemann, Antje; Schmitz, Inge; Popp, Jürgen; Wiemann, Martin
In: Analyst (2015) 5120
ZrO2 nanoparticles are frequently used in composite materials such as dental fillers from where they may be released and inhaled upon polishing and grinding. Since the overall distribution of ZrO2 NP inside the lung parenchyma can hardly be observed by routine histology, here a labeling with a fluorphore was used secondary to the adsorption of serum proteins. Particles were then intratracheally instilled into rat lungs. After 3 h fluorescent structures consisted of agglomerates scattered throughout the lung parenchyma, which were mainly concentrated in alveolar macrophages after 3 d. A detection method based on Raman microspectroscopy was established to investigate the chemical composition of those fluorescent structures in detail. Raman measurements were arranged such that no spectral interference with the protein-bound fluorescence label was evident. Using an "expert knowledge analysis" of Raman maps, signals of the ZrO2 nanomaterial were co-localized with the fluorescence label, indicating the stability of the nanomaterial-protein-dye complex inside the rat lung. The combination of Raman microspectroscopy and adsorptive fluorescence labeling may, therefore, become a useful tool for studying the localization of protein-coated nanomaterials in cells and tissues.
Kämmer, Evelyn; Olschewski, Konstanze; Bocklitz, Thomas; Rösch, Petra; Weber, Karina; Popp, Jürgen; Cialla-May, Dana
In: Physical Chemistry Chemical Physics (2014) 9056
This study demonstrates a new concept of calibrating surface enhanced Raman scattering (SERS) intensities without an additional substances as an internal standard and explores the factors such as laser fluctuation and different Ag substrates, which affect the results of quantitative analyses based on SERS. To demonstrate the capabilities of the concept, the model analyte adenine has been chosen. A Lab-on-a-Chip device is applied for the measurements to guarantee consistent data recording. In order to simulate varied measuring conditions, two various silver colloids (batch 1 and 2) are utilized as SERS substrate and two different laser power levels (25 or 55 mW) are applied on the sample. A concentration gradient was generated which allows to use the analyte itself for the correction of the resulting SERS spectra regarding intensity deviations caused by different ambient conditions. In doing so, a vast improvement in the quantification using SERS, especially in view of the comparability, reproducibility and repeatability, is demonstrated
Bräutigam, Katharina; Bocklitz, Thomas; Silge, Anja; Dierker, Christian; Ossig, Rainer; Schnekenburger, Jürgen; Cialla, Dana; Rösch, Petra; Popp, Jürgen
In: Journal of Molecular Structure (2014) 44
The increasing production and application of engineered nanomaterials requires a detailed understanding of the potential toxicity of nanoparticles and their uptake in living cells and tissue. For that purpose, a highly sensitive and selective method for detecting single nonlabeled nanoparticles and nanoparticle agglomerations in cells and animal tissue is required. Here, we show that Raman microspectroscopy allows for the specific detection of TiO2 nanoparticles inside cultured NIH/3T3 fibroblasts and RAW 264.7 macrophages. The spatial position of TiO2 nanoparticles and in parallel the relative intracellular concentration and distribution of cellular constituents such as proteins or DNA residues were identified and displayed by construction of two- and three-dimensional Raman maps. The resulting Raman images reflected the significant differences in nanoparticle uptake and intracellular storage of fibroblasts and macrophages. Furthermore, TiO2 nanomaterials could be characterized and the presence of rutile- and anatase-phase TiO2 were determined inside cells. Together, the data shown here prove that Raman spectroscopic imaging is a promising technique for studying the interaction of nanomaterials with living cells and for differentiating intracellular nanoparticles from those localized on the cell membrane. The technology provides a label-free, non-destructive, material-specific analysis of whole cells with high spatial resolution, along with additional information on the current status of the material properties.
Neugebauer, Ute; Trenkmann, Sabine; Bocklitz, Thomas; Schmerler, Diana; Kiehntopf, Michael; Popp, Jürgen
In: Journal of Biophotonics (2014) 232
Currently, there is no biomarker that can reliable distinguish between infectious and non-infectious systemic inflammatory response syndrome (SIRS). However, such a biomarker would be of utmost importance for early identification and stratification of patients at risk to initiate timely and appropriate antibiotic treatment. Within this proof of principle study, the high potential of Raman spectroscopy for the fast differentiation of noninfectious SIRS and sepsis is demonstrated. Blood plasma collected from 70 patients from the intensive care unit (31 patients with sepsis and 39 patients classified with SIRS without infection) was analyzed by means of Raman spectroscopy. A PCA-LDA based classification model was trained with Raman spectra from test samples and yielded for sepsis a sensitivity of 1.0 and specificity of 0.82. These results have been confirmed with an independent dataset (prediction accuracy 80%).
Neugebauer, Ute; Kurz, Christian; Bocklitz, Thomas; Berger, Tina; Velten, Thomas; Clement, Joachim; Krafft, Christoph; Popp, Jürgen
In: Micromachines (2014) 204
Circulating tumor cells (CTCs) from blood of cancer patients are valuable prognostic markers and enable monitoring responses to therapy. The extremely low number of CTCs makes their isolation and characterization a major technological challenge. For label-free cell identification a novel combination of Raman spectroscopy with a microhole array platform is described that is expected to support high-throughput and multiplex analyses. Raman spectra were registered from regularly arranged cells on the chip with low background noise from the silicon nitride chip membrane. A classification model was trained to distinguish leukocytes from lymphoblasts (OCI-AML3) and breast cancer cells (MCF-7 and BT-20). The model was validated by Raman spectra of a mixed cell population. The high spectral quality, low destructivity and high classification accuracy suggests that this approach is promising for Raman activated cell sorting.
Bocklitz, Thomas; Kämmer, Evelyn; Stöckel, Stephan; Cialla-May, Dana; Weber, Karina; Zell, Roland; Deckert, Volker; Popp, Jürgen
In: Journal of Structural Biology (2014) 30
In the present contribution virions of five different virus species, namely Varicella-zoster virus, Porcine teschovirus, Tobacco mosaic virus, Coliphage M13 and Enterobacteria phage PsP3, are investigated using atomic force microscopy (AFM). From the resulting height images quantitative features like maximal height, area and volume of the viruses could be extracted and compared to reference values. Subsequently, these features were accompanied by image moments, which quantify the morphology of the virions. Both types of features could be utilized for an automatic discrimination of the five virus spe- cies. The accuracy of this classification model was 96.8%. Thus, a virus detection on a single-particle level using AFM images is possible. Due to the application of advanced image analysis the morphology could be quantified and used for further analysis. Here, an automatic recognition by means of a classification model could be achieved in a reliable and objective manner.
Schwarz, Martha; Pahlow, Susanne; Bocklitz, Thomas; Steinbrücker, Carolin; , ; Weber, Karina; Popp, Jürgen
In: Analyst (2013) 5866
An easy and inexpensive detection method for DNA hybridization assays combining magnetic beads and enzymatically generated silver nanoparticles is introduced. The main advantage of this approach is the possibility to distinguish between positive and negative test results with the naked eye. In the case of complementary DNA sequences the sample will turn black within a few minutes, allowing readout without any hardware. In order to illustrate the applicability of the assay genomic DNA isolated fromE. coli contaminated Ringer's solution was used for testing the sensitivity as well as specificity.
Bocklitz, Thomas; Crecelius, Anna C.; Matthäus, Christian; Tarcea, Nicolae; von Eggeling, Ferdinand; Schmitt, Michael; Schubert, Ulrich S.; Popp, Jürgen
In: Analytical Chemistry (2013) 10829
In order to achieve a comprehensive description of biological tissue, spectral information about proteins, lipids, nucleic acids, and other biochemical components need to be obtained concurrently. Different analytical techniques may be combined to record complementary information of the same sample. Established techniques, which can be utilized to elucidate the biochemistry of tissue samples are, for instance, MALDI-TOF-MS and Raman microscopic imaging. With this contribution, we combine these two techniques for the first time. The combination of both techniques allows the utilization and interpretation of complementary information (i.e., the information about the protein composition derived from the Raman spectra with data of the lipids analyzed by the MALDI-TOF measurements). Furthermore, we demonstrate how spectral information from MALDI-TOF experiments can be utilized to interpret Raman spectra.
Bräutigam, Katharina; Bocklitz, Thomas; Schmitt, Michael; Rösch, Petra; Popp, Jürgen
In: ChemPhysChem, European Journal of chemical physics and physical chemistry (2013) 550
There is an urgent need for methods allowing for a fast, noninvasive, sensitive and selective monitoring of the effectiveness of anticancer drugs during the course of a chemotherapeutic treatment of cancer patients. The possibility of predicting and controlling the efficiency of chemotherapeutic agents for every patient individually enables a personalized therapy with largely improved success rates. The results presented herein demonstrate that Raman microspectroscopy is perfectly suited to monitor the impact of chemotherapeutic agents on living cells. The influence of the clinically well-established chemotherapeutic docetaxel on both the morphology and also biochemistry of living colon cancer cells (HT-29) has been studied by means of Raman spectroscopy in combination with modern chemometric approaches. The work presented paves the way for establishing Raman spectroscopy as a monitoring tool of the effectiveness of a chemotherapy treatment and can therefore be seen as a step towards personalized therapy.
Beleites, Claudia; Neugebauer, Ute; Bocklitz, Thomas; Krafft, Christoph; Popp, Jürgen
In: Analytica Chimica Acta (2013) 25
In biospectroscopy, suitably annotated and statistically independent samples (e. g. patients, batches, etc.) for classifier training and testing are scarce and costly. Learning curves show the model performance as function of the training sample size and can help to determine the sample size needed to train good classifiers. However, building a good model is actually not enough: the performance must also be proven. We discuss learning curves for typical small sample size situations with 5 - 25 independent samples per class. Although the classication models achieve acceptable performance, the learning curve can be completely masked by the random testing uncertainty due to the equally limited test sample size. In consequence, we determine test sample sizes necessary to achieve reasonable precision in the validation and find that 75 - 100 samples will usually be needed to test a good but not perfect classifier. Such a data set will then allow refined sample size planning on the basis of the achieved performance. We also demonstrate how to calculate necessary sample sizes in order to show the superiority of one classifier over another: this often requires hundreds of statistically independent test samples or is even theoretically impossible. We demonstrate our findings with a data set of ca. 2550 Raman spectra of single cells (ve classes erythrocytes, leukocytes and three di erent tumour cell lines) as well as by an extensive simulation that allows precise determination of the actual performance of the models in question.
Becker-Putsche, Melanie; Bocklitz, Thomas; Clement, Joachim; Rösch, Petra; Popp, Jürgen
In: Journal of Biomedical Optics (2013) 047001-1
Medical diagnosis of biopsies performed by fine needle aspiration has to be very reliable. Therefore, pathologists/cytologists need additional biochemical information on single cancer cells for an accurate diagnosis. Accordingly, we applied three different classification models for discriminating various features of six breast cancer cell lines by analyzing Raman microspectroscopic data. The statistical evaluations are implemented by linear discriminant analysis (LDA) and support vector machines (SVM). For the first model, a total of 61,580 Raman spectra from 110 single cells are discriminated at the cell-line level with an accuracy of 99.52% using an SVM. The LDA classification based on Raman data achieved an accuracy of 94.04% by discriminating cell lines by their origin (solid tumor versus pleural effusion). In the third model, Raman cell spectra are classified by their cancer subtypes. LDA results show an accuracy of 97.45% and specificities of 97.78%, 99.11%, and 98.97% for the subtypes basallike, HER2 þ ∕ER−, and luminal, respectively. These subtypes are confirmed by gene expression patterns, which are important prognostic features in diagnosis. This work shows the applicability of Raman spectroscopy and statistical data handling in analyzing cancer-relevant biochemical information for advanced medical diagnosis on the single-cell level.
Hartmann, Katharina; Putsche, Melanie; Bocklitz, Thomas; Pachmann, Katharina; Niendorf, Axel; Rösch, Petra; Popp, Jürgen
In: Analytical and bioanalytical chemistry (2012) 745
Chemotherapies feature a low success rate of about 25%, and therefore, the choice of the most effective cytostatic drug for the individual patient and monitoring the efficiency of an ongoing chemotherapy are important steps towards personalized therapy. Thereby, an objective method able to differentiate between treated and untreated cancer cells would be essential. In this study, we provide molecular insights into Docetaxel-induced effects in MCF-7 cells, as a model system for adenocarcinoma, by means of Raman
Bielecki, Christiane; Bocklitz, Thomas; Schmitt, Michael; Krafft, Christoph; Marquardt, Claudio; Gharbi, A.; Knösel, T.; Stallmach, Andreas; Popp, Jürgen
In: Journal of Biomedical Optics (2012) 076030-1
The present study reports about a Raman microspectroscopic characterization of the inflammatory bowel diseases Crohn’s disease and ulcerative colitis. Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches: first support vector machines were applied to highlight the tissue morphology (= Raman spectroscopic histopathology). In a second step the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy controls (n=11), Crohn’s disease (n=14) and ulcerative colitis (n=13). These three groups exhibit significantly different molecular specific Raman signatures allowing to establish a classifier (support-vectormachine). By utilizing this classifier it was possible to separate between healthy controls, Crohn’s disease and ulcerative colitis with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of inflammatory bowel diseases) paves the way for an objective clinical diagnosis of inflammatory bowel diseases by means of Raman spectroscopy in combination with chemometric approaches.
Bergner, Norbert; Bocklitz, Thomas; Romeike, Bernd F.M.; Reichart, Rupert; Kalff, Rolf; Krafft, Christoph; Popp, Jürgen
In: Chemometrics and Intelligent Laboratory Systems (2012) 224
Vibrational spectroscopic imaging techniques are new tools for visualizing chemical components in tissue without staining. The spectroscopic signature can be used as a molecular fingerprint of pathological tissues. Fourier transform infrared imaging which is more common than Raman imaging so far has already been applied to identify the primary tumor of brain metastases. The current study introduces a two level classification model for Raman microspectroscopic images to distinguish normal brain, necrosis and tumor tissue, and subsequently to determine the primary tumor. 20 specimens of normal brain tissue and brain metastasis of bladder carcinoma, lung carcinoma, mamma carcinoma, colon carcinoma prostate carcinoma and renal cell carcinoma were snap frozen, and thin tissue sections were prepared. Raman microscopic images were collected with 785 nm laser excitation at 10 μm step size. Cluster analysis, vertex component analysis and principal component analysis were applied for data preprocessing. Then, data of 17 specimens were used to train the classification model based on support vector machines. The re-classification rate was better than 99%. Finally, the classification model correctly predicted three independent specimens.
Walter, Angela; Kuhri, Susanne; Reinicke, Martin; Bocklitz, Thomas; Schumacher, Wilm; Rösch, Petra; Merten, Dirk; Büchel, Georg; Kothe, Erika; Popp, Jürgen
In: Journal of Raman Spectroscopy (2012) 1058
Heavy metal contamination of soil has an immense impact on the surrounding environment, such as the ground water, and hence, has become an important issue within bioremediation. Therefore, heavy metal contamination has to be determined preferably cost-efficiently, rapidly, and reliably. Here, soil bacteria of the genus Streptomyces are used as bioindicators for heavy metal contamination investigated via micro-Raman spectroscopy. A single cell approach is studied to avoid timeconsuming culturing and plate counting. Bacteria of Streptomyces galilaeus were incubated in Ni2+ enriched media and single cell spectra were recorded. Supervised statistics linear discriminant analysis was used to evaluate the influence of the culture age and the anion on bacterial cells, which has been determined to be minor compared with the spectral impact of Ni2+. The identification of the Raman spectra according to different Ni2+ concentration ranges is accomplished with a prediction accuracy of about 88%. Therefore, we conclude that Streptomyces can be used as a bioindicator to predict Ni2+ concentrations in the micromolar range.
Ramoji, Anuradha A.; Neugebauer, Ute; Bocklitz, Thomas; Förster, Martin; Kiehntopf, Michael; Bauer, Michael; Popp, Jürgen
In: Analytical Chemistry (2012) 5335
The first response to infection in the blood is mediated by leukocytes. As a result crucial information can be gained from a hemogram. Conventional methods such as blood smears and automated sorting procedures are not capable to record detailed biochemical information of the different leukocytes. In this study, Raman spectroscopy has been applied to investigate the differences between the leukocyte subtypes which have been obtained from healthy donors. Raman imaging was able to visualize the same morphological features as standard staining methods without the need of any label. Unsupervised statistical methods such as principal component analysis and hierarchical cluster analysis were able to separate Raman spectra of the two most abundant leukocytes, the neutrophils and lymphocytes, (with a special focus on CD4+ T-lymphocytes). For the same cells a classification model was built to allow an automated Raman-based differentiation of the cell type in the future. The classification model could achieve an accuracy of 94 % in the validation step and could predict the identity of unknown cells from a completely different donor with an accuracy of 81% when using single spectra and with an accuracy of 97 % when using the majority vote from all individual spectra of the cell. This marks a promising step towards automated Raman spectroscopic blood analysis which holds the potential not only to assign the numbers of the cells, but also yield important biochemical information.
Dörfer, Thomas; Bocklitz, Thomas; Tarcea, Nicolae; Schmitt, Michael; Popp, Jürgen
In: Zeitschrift Für Physikalische Chemie-INTERNATIONAL JOURNAL OF RESEARCH IN PHYSICAL CHEMISTRY & CHEMICAL PHYSICS (2011) 753
A wavenumber and intensity calibration procedure of Raman spectra by using chemometric techniques is presented. This approach allows the fine tuning of calibration parameters and routines with the final goal to eliminate setup dependent differences within experimentally recorded Raman spectra. This seems to be necessary since more and more Raman databases are needed for different analytical tasks, like identification of minerals or bacteria. Minimizing the impact of the applied experimental Raman setup on the reference (database stored) Raman spectra allows the databases to be enlarged very easily by feeding the database with Raman spectra recorded with different setups. Furthermore the chemometric analysis performance increases due to the larger number and better quality of reference spectra.
Walter, Angela; Schumacher, Wilm; Bocklitz, Thomas; Reinicke, Martin; Rösch, Petra; Kothe, Erika; Popp, Jürgen
In: Applied Spectroscopy (2011) 1116
Classification of Raman spectra recorded from single cells is commonly applied to bacteria that exhibit small sizes of approximately 1 to 2 μm. Here, we study the possibility to adopt this classification approach to filamentous bacteria of the genus Streptomyces. The hyphae can reach extensive lengths of up to 35 μm, which can correspond to a single cell identified in light microscopy. The classification of Raman bulk spectra will be demonstrated. Here, ultraviolet resonance Raman (UV RR) spectroscopy is chosen to classify six Streptomyces species by the application of a tree-like classifier. For each knot of the hierarchical classifier, estimated classification accuracies of over 94% are accomplished. In contrast to the classification of bulk spectra, the classification of single-cell spectra requires a homogenous substance distribution within the cell. Consequently, the bacterial cell chemistry can be represented by one individual spectrum. This requirement is not fulfilled when different spectra are processed from different locations within the cell. Bacteria of the investigated genus Streptomyces exhibit, besides the normal bacterial spectra, lipid-rich spectra. The occurrence of lipid enrichment depends on culture age and nutrition availability. With this study, we investigate the cell substance distribution, especially of lipid-rich fractions. The classification utilizing a tree-like classifier is also applied to the Streptomyces single-cell spectra, resulting in classification accuracies between 80 and 93% for the investigated Streptomyces species.
Bocklitz, Thomas; Walter, Angela; Hartmann, Katharina; Rösch, Petra; Popp, Jürgen
In: Analytica Chimica Acta (2011) 47
Raman spectroscopy in combination with chemometrics is gaining more and more importance for answering biological questions. This results from the fact that Raman spectroscopy is non-invasive, marker-free and water is not corrupting Raman spectra significantly. However, Raman spectra contain despite Raman fingerprint information other contributions like fluorescence background, Gaussian noise, cosmic spikes and other effects dependent on experimental parameters, which have to be removed prior to the analysis, in order to ensure that the analysis is based on the Raman measurements and not on other effects. Here we present a comprehensive study of the influence of pre-processing procedures on statistical models. We will show that a large amount of possible and physically meaningful pre-processing procedures leads to bad results. Furthermore a method based on genetic algorithms (GAs) is introduced, which chooses the spectral pre-processing according to the carried out analysis task without trying all possible pre-processing approaches (grid-search). This was demonstrated for the two most common tasks, namely for a multivariate calibration model and for two classification models. However, the presented approach can be applied in general, if there is a computational measure, which can be optimized. The suggested GA procedure results in models, which have a higher precision and are more stable against corrupting effects.
März, Anne; Bocklitz, Thomas; Popp, Jürgen
In: Analytical Chemistry (2011) 8337
Concerning the usability of lab-on-a-chip surface enhanced Raman spectroscopy (LOC-SERS) for analytical tasks applying chemometric data evalutation, a secure, reproducible, and stable data output independent of inconsistent ambient conditions has to be accomplished. In this contribution, we present a new approach to achieve reliable and robust measurements based on segmented flow LOC-SERS via online wave number calibration.
Walter, Angela; Reinicke, Martin; Bocklitz, Thomas; Schumacher, Wilm; Rösch, Petra; Kothe, Erika; Popp, Jürgen
In: Analytical and bioanalytical chemistry (2011) 2763
Bacterial resistances against antibiotics are increasingly problematic for medical treatment of pathogenic bacteria, e.g., in hospitals. Resistances are, among other genes, often encoded on plasmids which can be transmitted between bacteria not only within one species, but also between different species, genera, and families. The plasmid pDrive is transformed into bacteria of the model strain Escherichia coli DH5α. Within this investigation, we applied micro-Raman spectroscopy with two different excitation wavelengths in combination with support vector machine (SVM) and linear discriminant analysis (LDA) to differentiate between bacterial cultures according to their cultural plasmid content. Recognition rates of about 92% and 90% are achieved by Raman excitation at 532 and 244 nm, respectively. The SVM loadings reveal that the pDrive transformed bacterial cultures exhibit a higher DNA content.
Dochow, Sebastian; Krafft, Christoph; Neugebauer, Ute; Bocklitz, Thomas; Henkel, Thomas; Mayer, Günter; Albert, Jens; Popp, Jürgen
In: Lab on a chip (2011) 1484
Raman spectroscopy has been recognized to be a powerful tool for label-free discrimination of cells. Sampling methods are under development to utilize the unique capabilities to identify cells in body fluids such as saliva, urine or blood. The current study applied optical traps in combination with Raman spectroscopy to acquire spectra of single cells in microfluidic glass channels. Optical traps were realized by two 1070 nm single mode fibre lasers. Microflows were controlled by a syringe pump system. A novel microfluidic glass chip was designed to inject single cells, modify the flow speed, accommodate the laser fibres and sort cells after Raman based identification. Whereas the integrated microchip setup used 514 nm for excitation of Raman spectra, a quartz capillary setup excited spectra with 785 nm laser wavelength. Classification models were trained using linear discriminant analysis to differentiate erythrocytes, leukocytes, acute myeloid leukaemia cells (OCI-AML3), and breast tumour cells BT-20 and MCF-7 with accuracies that are comparable with previous Raman experiments of dried cells and fixed cells in a Petri dish. Implementation into microfluidic environments enables a high degree of automation that is required to improve the throughput of the approach for Raman activated cell sorting.
Neugebauer, U., T. Bocklitz, J. H. Clement, C. Krafft, and J. Popp
In: Analyst 135, no. 12 (2010): 3178–82
Body fluids are easily accessible and contain valuable indices for medical diagnosis. Fascinating tools are tumour cells circulating in the peripheral blood of cancer patients. As these cells are extremely rare, they constitute a challenge for clinical diagnostics. In this contribution we present the Raman spectroscopic-based identification of different single cells in suspension that are found in peripheral blood of cancer patients including healthy cells like leukocytes and erythrocytes, and tumour cells like leukaemic cells and cells originating from solid tumours. Leukocytes and erythrocytes were isolated from the peripheral blood of healthy donors while myeloid leukaemia cells (OCI-AML3) and breast carcinoma derived cells (MCF-7, BT-20) were obtained from cell cultures. A laser emitting 785 nm light was used for optical trapping the single cells in the laser focus and to excite the Raman spectrum. Support vector machines were applied to develop a supervised classification model with spectra of 1210 cells originating from three different donors and three independent cultivation batches. Distinguishing tumour cells from healthy cells was achieved with a sensitivity of >99.7% and a specificity of >99.5%. In addition, the correct cell types were predicted with an accuracy of approximately 92%.
Neugebauer, U., J. H. Clement, T. Bocklitz, C. Krafft, and J. Popp
In: Journal of Biophotonics 3 (2010): 579–87
Medical diagnosis can be improved significantly by fast, highly sensitive and quantitative cell identification from easily accessible body fluids. Prominent examples are disseminated tumour cells circulating in the peripheral blood of cancer patients. These cells are extremely rare and therefore difficult to detect. In this contribution we present the Raman spectroscopic characterization of different cells that can be found in peripheral blood such as leukocytes, leukemic cells and solid tumour cells. Leukocytes were isolated from the peripheral blood from healthy donors. Breast carcinoma derived tumour cells (MCF-7, BT-20) and myeloid leukaemia cells (OCI-AML3) were prepared from cell cultures. Raman images were collected from dried cells on calcium fluoride slides using 785 nm laser excitation. Unsupervised statistical methods (hierarchical cluster analysis and principal component analysis) were used to visualize spectral differences and cluster formation according to the cell type. With the help of supervised statistical methods (support vector machines) a classification model with 99.7% accuracy rates for the differentiation of the cells was built. The model was successfully applied to identify single cells from an independent mixture of cells based on their vibrational spectra. The classification was validated by fluorescence staining of the cells after the Raman measurement.
Walter, Angela, Susann Erdmann, Thomas Bocklitz, Elke-Martina Jung, Nadine Vogler, Denis Akimov, Benjamin Dietzek, Petra Rösch, Erika Kothe, and Jürgen Popp
In: Analyst 135, no. 5 (26 April 2010): 908–17
The cytochrome distribution in hyphal tip cells of Schizophyllum commune was visualized using resonance Raman mapping and CARS microscopy. For comparison, resonance Raman mapping and CARS imaging of cytochrome was also performed during branch formation and in completely developed central hyphae. Cytochrome, as an essential component of the electron transport chain in mitochondria, plays an important role in providing energy to actively growing mycelia. It could be shown that mitochondria at the growing hyphal tips and at branching regions are more active, i.e. contain more cytochrome, as compared to those in older parts of the hyphae. This finding is compatible with the idea of high energy consumption for biosynthesis and intracellular transport at the growing tip, while older hyphae have lower needs for ATP generated via the respiratory chain in mitochondria. To the best of our knowledge this is the first study reporting about the localization and distribution of cytochrome, as an indirect mitochondria localization within S. commune or other basidiomycetous fungi, by means of resonance Raman microspectroscopy and CARS microscopy. These Raman methods bear the potential of label-free in vivo mitochondria localization and investigation.
Vogler, Nadine, Thomas Bocklitz, Melissa Mariani, Volker Deckert, Aneta Markova, Peter Schelkens, Petra Rösch, Denis Akimov, Benjamin Dietzek, and Jürgen Popp
In: JOSA A 27, no. 6 (1 June 2010): 1361–71
Coherent anti-Stokes Raman scattering (CARS) gained a lot of importance in chemical imaging. This is due to the fast image acquisition time, the high spatial resolution, the non-invasiveness, and the molecular sensitivity of this method. By using the single-line CARS in contrast to the multiplex CARS, different signal contributions stemming from resonant and non-resonant light–matter interactions are indistinguishable. Here a numerical method is presented in order to extract more information from univariate CARS images: vibrational composition, morphological information, and contributions from index-of-refraction steps can be separated from single-line CARS images. The image processing algorithm is based on the physical properties of CARS process as reflected in the shape of the intensity histogram of univariate CARS images. Because of this the comparability of individual CARS images recorded with different experimental parameters is achieved. The latter is important for a quantitative evaluation of CARS images.
Bocklitz, Thomas, Melanie Putsche, Carsten Stüber, Josef Käs, Axel Niendorf, Petra Rösch, and Jürgen Popp
In: Journal of Raman Spectroscopy 40 (2009): 1759–65
In this model study, we developed a method to distinguish between breast cancer cells and normal epithelial cells, which is in principal suitable for online diagnosis by Raman spectroscopy. Two cell lines were chosen as model systems for cancer and normal tissue. Both cell lines consist of epithelial cells, but the cells of the MCF-7 series are carcinogenic, where the MCF-10A cells are normal growing. An algorithm is presented for distinguishing cells of the MCF-7 and MCF-10A cell lines, which has an accuracy rate of above 99%. For this purpose, two classification steps are utilized. The first step, the so-called top-level classifier searches for Raman spectra, which are measured in the nuclei region. In the second step, a wide range of discriminant models are possible and thesemodels are compared. The classification rates are always estimated using a cross-validation and a holdout-validation procedure to ensure the ability of the routine diagnosis to work in clinical environments.
März, Anne, Katrin R. Ackermann, Daniéll Malsch, Thomas Bocklitz, Thomas Henkel, and Jürgen Popp
In: Journal of Biophotonics 2, no. 4 (2009): 232–42
In this contribution a new approach for quantitative measurements using surface-enhanced Raman spectroscopy (SERS) is presented. Combining the application of isotope-edited internal standard with the advantages of the liquid–liquid segmented-flow-based approach for flow-through SERS detection seems to be a promising means for quantitative SERS analysis. For the investigations discussed here a newly designed flow cell, tested for ideal mixing efficiency on the basis of grayscale-value measurements, is implemented. Measurements with the heteroaromatics nicotine and pyridine using their respective deuterated isotopomers as internal standards show that the integration of an isotopically labeled internal standard in the used liquid–liquid two-phase segmented flow leads to reproducible and comparable SERS spectra independent from the used colloid. With the implementation of an internal standard into the microfluidic device the influence of the properties of the colloid on the SERS activity can be compensated. Thus, the problem of a poor batch-to-batch reproducibility of the needed nanoparticle solutions is solved. To the best of our knowledge these are the first measurements combining the above mentioned concepts in order to correct for differences in the enhancement behaviour of the respective colloid. (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)