Search Results
Detection of Protein Glycosylation Using Tip-Enhanced Raman Scattering
2016, Cowcher, David P., Deckert-Gaudig, Tanja, Brewster, Victoria L., Ashton, Lorna, Deckert, Volker, Goodacre, Royston
The correct glycosylation of biopharmaceutical glycoproteins and their formulations is essential for them to have the desired therapeutic effect on the patient. It has recently been shown that Raman spectroscopy can be used to quantify the proportion of glycosylated protein from mixtures of native and glycosylated forms of bovine pancreatic ribonuclease (RNase). Here we show the first steps toward not only the detection of glycosylation status but the characterization of glycans themselves from just a few protein molecules at a time using tip-enhanced Raman scattering (TERS). While this technique generates complex data that are very dependent on the protein orientation, with the careful development of combined data preprocessing, univariate and multivariate analysis techniques, we have shown that we can distinguish between the native and glycosylated forms of RNase. Many glycoproteins contain populations of subtly different glycoforms; therefore, with stricter orientation control, we believe this has the potential to lead to further glycan characterization using TERS, which would have use in biopharmaceutical synthesis and formulation research.
Discrimination of different cell monolayers before and after exposure to nanosecond pulsed electric fields based on Cole-Cole and multivariate analysis
2019, Shi, Fukun, Zhuang, Jie, Kolb, Juergen F.
Normal and cancer cells, which were grown in monolayers, were investigated and discriminated by electrical bioimpedance spectroscopy (EBIS) before and after exposures to nanosecond pulsed electric fields (nsPEFs). Bioimpedance data were analysed with a Cole-Cole model and the principal component analysis (PCA). Normal and cancer cells could be clearly distinguished from each other either from Cole parameters (R 0, a, t) or from two dominant principal components. The trend of changes for Cole parameters indicated distinctively different post-nsPEF-effects between normal and cancer cells. PCA was also able to distinguish characteristic impedance spectra 30 min after exposures. The first principal component suggested that post-nsPEF-effects for normal cells were revealed especially at lower frequencies. The results indicated further that the extracellular resistance, which is dominated by cell-cell connections, might be an important factor with respect to selective nsPEF-effects on cancer cells that are organized in a monolayer or a tissue, respectively. Accordingly, the results support the application of EBIS as an early, non-invasive, label-free, and time-saving approach for the classification of cells to provide in particular predictive information on the success of cancer treatments with nsPEFs. © 2019 IOP Publishing Ltd.
On the abundance and source contributions of dicarboxylic acids in size-resolved aerosol particles at continental sites in central Europe
2014, van Pinxteren, D., Neusüß, C., Herrmann, H.
Dicarboxylic acids (DCAs) are among the most abundant organic compounds observed in atmospheric aerosol particles and have been extensively studied at many places around the world. The importance of the various primary sources and secondary formation pathways discussed in the literature is often difficult to assess from field studies, though. In the present study, a large data set of size-resolved DCA concentrations from several inland sites in Germany is combined with results from a recently developed approach of statistical back-trajectory analysis and additional data. Principal component analysis is then used to reveal the most important factors governing the abundance of DCAs in different particle size ranges. The two most important sources revealed are (i) photochemical formation during intense radiation days in polluted air masses, likely occurring in the gas phase on short timescales (gasSOA), and (ii) secondary reactions in anthropogenically influenced air masses, likely occurring in the aqueous phase on longer timescales (aqSOA). While the first source strongly impacts DCA concentrations mainly in small and large particles, the second one enhances accumulation mode DCAs and is responsible for the bulk of the observed concentrations. Primary sources were found to be minor (sea salt, soil resuspension) or non-existent (biomass burning, traffic). The results can be regarded as representative for typical central European continental conditions.
Regional and inter-regional effects in evolving climate networks
2014, Hlinka, J., Hartman, D., Jajcay, N., Vejmelka, M., Donner, R., Marwan, N., Kurths, J., Paluš, M.
Complicated systems composed of many interacting subsystems are frequently studied as complex networks. In the simplest approach, a given real-world system is represented by an undirected graph composed of nodes standing for the subsystems and non-oriented unweighted edges for interactions present among the nodes; the characteristic properties of the graph are subsequently studied and related to the system's behaviour. More detailed graph models may include edge weights, orientations or multiple types of links; potential time-dependency of edges is conveniently captured in so-called evolving networks. Recently, it has been shown that an evolving climate network can be used to disentangle different types of El Niño episodes described in the literature. The time evolution of several graph characteristics has been compared with the intervals of El Niño and La Niña episodes. In this study we identify the sources of the evolving network characteristics by considering a reduced-dimensionality description of the climate system using network nodes given by rotated principal component analysis. The time evolution of structures in local intra-component networks is studied and compared to evolving inter-component connectivity.
Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo
2021-1-28, Schleusener, Johannes, Guo, Shuxia, Darvin, Maxim E., Thiede, Gisela, Chernavskaia, Olga, Knorr, Florian, Lademann, Jürgen, Popp, Jürgen, Bocklitz, Thomas W.
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 ≥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 ≈65%.
Single cell analysis in native tissue: Quantification of the retinoid content of hepatic stellate cells
2016, Galler, Kerstin, Requardt, Robert Pascal, Glaser, Uwe, Markwart, Robby, Bocklitz, Thomas, Bauer, Michael, Popp, Jürgen, Neugebauer, Ute
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.