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- ItemRaman spectroscopy follows time-dependent changes in T lymphocytes isolated from spleen of endotoxemic mice(Rockville : American Association of Immunologists, 2019) Ramoji, Anuradha; Ryabchykov, Oleg; Galler, Kerstin; Tannert, Astrid; Markwart, Robby; Requardt, Robert Pascal; Rubio, Ignacio; Bauer, Michael; Bocklitz, Thomas W.; Popp, Jürgen; Neugebauer, UteT 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. The method can also be applied to directly study tissue sections as is demonstrated for spleen tissue one day after LPS insult.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. The method can also be applied to directly study tissue sections as is demonstrated for spleen tissue one day after LPS insult.
- ItemNew perspectives for viability studies with high-content analysis Raman spectroscopy (HCA-RS)(Berlin : Nature Publishing, 2019) Mondol, Abdullah S.; Töpfer, Natalie; Rüger, Jan; Neugebauer, Ute; Popp, Jürgen; Schie, Iwan W.Raman spectroscopy has been widely used in clinical and molecular biological studies, providing high chemical specificity without the necessity of labels and with little-to-no sample preparation. However, currently performed Raman-based studies of eukaryotic cells are still very laborious and time-consuming, resulting in a low number of sampled cells and questionable statistical validations. Furthermore, the approach requires a trained specialist to perform and analyze the experiments, rendering the method less attractive for most laboratories. In this work, we present a new high-content analysis Raman spectroscopy (HCA-RS) platform that overcomes the current challenges of conventional Raman spectroscopy implementations. HCA-RS allows sampling of a large number of cells under different physiological conditions without any user interaction. The performance of the approach is successfully demonstrated by the development of a Raman-based cell viability assay, i.e., the effect of doxorubicin concentration on monocytic THP-1 cells. A statistical model, principal component analysis combined with support vector machine (PCA-SVM), was found to successfully predict the percentage of viable cells in a mixed population and is in good agreement to results obtained by a standard cell viability assay. This study demonstrates the potential of Raman spectroscopy as a standard high-throughput tool for clinical and biological applications.
- ItemRevealing the Chemical Composition of Birch Pollen Grains by Raman Spectroscopic Imaging(Basel : Molecular Diversity Preservation International (MDPI), 2022) Stiebing, Clara; Post, Nele; Schindler, Claudia; Göhrig, Bianca; Lux, Harald; Popp, Jürgen; Heutelbeck, Astrid; Schie, Iwan W.The investigation of the biochemical composition of pollen grains is of the utmost interest for several environmental aspects, such as their allergenic potential and their changes in growth conditions due to climatic factors. In order to fully understand the composition of pollen grains, not only is an in-depth analysis of their molecular components necessary but also spatial information of, e.g., the thickness of the outer shell, should be recorded. However, there is a lack of studies using molecular imaging methods for a spatially resolved biochemical composition on a single-grain level. In this study, Raman spectroscopy was implemented as an analytical tool to investigate birch pollen by imaging single pollen grains and analyzing their spectral profiles. The imaging modality allowed us to reveal the layered structure of pollen grains based on the biochemical information of the recorded Raman spectra. Seven different birch pollen species collected at two different locations in Germany were investigated and compared. Using chemometric algorithms such as hierarchical cluster analysis and multiple-curve resolution, several components of the grain wall, such as sporopollenin, as well as the inner core presenting high starch concentrations, were identified and quantified. Differences in the concentrations of, e.g., sporopollenin, lipids and proteins in the pollen species at the two different collection sites were found, and are discussed in connection with germination and other growth processes.
- ItemFusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples(Lausanne : Frontiers Media, 2018) Ryabchykov, Oleg; Popp, Jürgen; Bocklitz, Thomas W.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 image 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 distinguishing 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.
- ItemThermal illumination limits in 3D Raman microscopy: A comparison of different sample illumination strategies to obtain maximum imaging speed(San Francisco : Public Library of Science, 2019) Hauswald, Walter; Förster, Ronny; Popp, Jürgen; Heintzmann, RainerConfocal Raman microscopy is a powerful tool for material science and biomedical research. However, the low Raman scattering cross-section limits the working speed, which reduces the applicability for large and sensitive samples. Here, we discuss the fundamental physical limits of Raman spectroscopy with respect to signal-to-noise, sample load and how to achieve maximal imaging speed. For this, we develop a simple model to describe arbitrary far field light microscopes and their thermal influence on the sample. This model is used to compare the practical applicability of point- and line-confocal microscopes as well as wide-field-, light sheet- and light line illumination, for the measurement of 3D biological samples. The parallelization degree of the illumination can positively affect the imaging speed as long as it is not limited by thermal sample heating. In case of heat build-up inside the sample, the advantages of parallelization can be lost due to the required attenuation of excitation and the working speed can drop below that of a sequential method. We show that for point like illumination, the exposure time is thermally not as critical for the sample as the irradiance, while for volume like illumination, the exposure time and irradiance result in the same thermal effect. The results of our theoretical study are experimentally confirmed and suggest new concepts of Raman microscopy, thus extending its applicability. The developed model can be applied to Raman imaging as well as to other modes (e.g. two- or three- photon imaging, STED, PALM/STORM, MINFLUX) where thermal effects impose a practical limit due to the high irradiance required.
- ItemFiber-array-based Raman hyperspectral imaging for simultaneous chemical selective monitoring of particle size and shape of active ingredients in analgesic tablets(Basel : MDPI, 2019) Frosch, Timea; Wyrwich, Elisabeth; Yan, Di; Popp, Jürgen; Frosch, TorstenThe particle shape, size and distribution of active pharmaceutical ingredients (API) are relevant quality indicators of pharmaceutical tablets due to their high impact on the manufacturing process. Furthermore, the bioavailability of the APIs from the dosage form depends largely on these characteristics. Routinely, particle size and shape are only analyzed in the powder form, without regard to the effect of the formulation procedure on the particle characteristics. The monitoring of these parameters improves the understanding of the process; therefore, higher quality and better control over the biopharmaceutical profile can be ensured. A new fiber-array-based Raman hyperspectral imaging technique is presented for direct simultaneous in-situ monitoring of three different active pharmaceutical ingredients- acetylsalicylic acid, acetaminophen and caffeine- in analgesic tablets. This novel method enables a chemically selective, noninvasive assessment of the distribution of the active ingredients down to 1 µm spatial resolution. The occurrence of spherical and needle-like particles, as well as agglomerations and the respective particle size ranges, were rapidly determined for two commercially available analgesic tablet types. Subtle differences were observed in comparison between these two tablets. Higher amounts of acetaminophen were visible, more needle-shaped and bigger acetylsalicylic acid particles, and a higher incidence of bigger agglomerations were found in one of the analgesic tablets.
- ItemSurface-Enhanced Raman Spectroscopy to Characterize Different Fractions of Extracellular Vesicles from Control and Prostate Cancer Patients(Basel : MDPI, 2021) Osei, Eric Boateng; Paniushkina, Liliia; Wilhelm, Konrad; Popp, Jürgen; Nazarenko, Irina; Krafft, ChristophExtracellular vesicles (EVs) are membrane-enclosed structures ranging in size from about 60 to 800 nm that are released by the cells into the extracellular space; they have attracted interest as easily available biomarkers for cancer diagnostics. In this study, EVs from plasma of control and prostate cancer patients were fractionated by differential centrifugation at 5000× g, 12,000× g and 120,000× g. The remaining supernatants were purified by ultrafiltration to produce EV-depleted free-circulating (fc) fractions. Spontaneous Raman and surface-enhanced Raman spectroscopy (SERS) at 785 nm excitation using silver nanoparticles (AgNPs) were employed as label-free techniques to collect fingerprint spectra and identify the fractions that best discriminate between control and cancer patients. SERS spectra from 10 µL droplets showed an enhanced Raman signature of EV-enriched fractions that were much more intense for cancer patients than controls. The Raman spectra of dehydrated pellets of EV-enriched fractions without AgNPs were dominated by spectral contributions of proteins and showed variations in S-S stretch, tryptophan and protein secondary structure bands between control and cancer fractions. We conclude that the AgNPs-mediated SERS effect strongly enhances Raman bands in EV-enriched fractions, and the fractions, EV12 and EV120 provide the best separation of cancer and control patients by Raman and SERS spectra.
- ItemFLIM data analysis based on Laguerre polynomial decomposition and machine-learning(Bellingham, Wash. : SPIE, 2021) Guo, Shuxia; Silge, Anja; Bae, Hyeonsoo; Tolstik, Tatiana; Meyer, Tobias; Matziolis, Georg; Schmitt, Michael; Popp, Jürgen; Bocklitz, ThomasSignificance: 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. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components.