2019, Alhogail, Sahar, Suaifan, Ghadeer A.R.Y, Bikker, Floris J., Kaman, Wendy E., Weber, Karina, Cialla-May, Dana, Popp, Jürgen, Zourob, Mohammed M.

A rapid, sensitive, and specific colorimetric biosensor based on the use of magnetic nanoparticles (MNPs) was designed for the detection of Pseudomonas aeruginosa in clinical samples. The biosensing platform was based on the measurement of P. aeruginosa proteolytic activity using a specific protease substrate. At the N-terminus, this substrate was covalently bound to MNPs and was linked to a gold sensor surface via cystine at the C-terminus of the substrates. The golden sensor appears black to naked eyes because of the coverage of the MNPs. However, upon proteolysis, the cleaved peptide–MNP moieties will be attracted by an external magnet, revealing the golden color of the sensor surface, which can be observed by the naked eye. In vitro, the biosensor was able to detect specifically and quantitatively the presence of P. aeruginosa with a detection limit of 102 cfu/mL in less than 1 min. The colorimetric biosensor was used to test its ability to detect in situ P. aeruginosa in clinical isolates from patients. This biochip is anticipated to be useful as a rapid point-of-care device for the diagnosis of P. aeruginosa-related infections.

2019, Mayerhöfer, Thomas G., Pipa, Andrei V., Popp, Jürgen

As derived by Max Planck in 1903 from dispersion theory, Beer's law has a fundamental limitation. The concentration dependence of absorbance can deviate from linearity, even in the absence of any interactions or instrumental nonlinearities. Integrated absorbance, not peak absorbance, depends linearly on concentration. The numerical integration of the absorbance leads to maximum deviations from linearity of less than 0.1 %. This deviation is a consequence of a sum rule that was derived from the Kramers-Kronig relations at a time when the fundamental limitation of Beer's law was no longer mentioned in the literature. This sum rule also links concentration to (classical) oscillator strengths and thereby enables the use of dispersion analysis to determine the concentration directly from transmittance and reflectance measurements. Thus, concentration analysis of complex samples, such as layered and/or anisotropic materials, in which Beer's law cannot be applied, can be achieved using dispersion analysis. ©2019 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

2019, Silge, Anja, Moawad, Amira A., Bocklitz, Thomas, Fischer, Katja, Rösch, Petra, Roesler, Uwe, Elschner, Mandy C., Popp, Jürgen, Neubauer, Heinrich

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay. © 2019 by the authors

2017, Hassoun, Mohamed, Schie, Iwan W., Tolstik, Tatiana, Stanca, Sarmiza E., Krafft, Christoph, Popp, Jürgen

The throughput of spontaneous Raman spectroscopy for cell identification applications is limited to the range of one cell per second because of the relatively low sensitivity. Surface-enhanced Raman scattering (SERS) is a widespread way to amplify the intensity of Raman signals by several orders of magnitude and, consequently, to improve the sensitivity and throughput. SERS protocols using immuno-functionalized nanoparticles turned out to be challenging for cell identification because they require complex preparation procedures. Here, a new SERS strategy is presented for cell classification using non-functionalized silver nanoparticles and potassium chloride to induce aggregation. To demonstrate the principle, cell lysates were prepared by ultrasonication that disrupts the cell membrane and enables interaction of released cellular biomolecules to nanoparticles. This approach was applied to distinguish four cell lines – Capan-1, HepG2, Sk-Hep1 and MCF-7 – using SERS at 785 nm excitation. Six independent batches were prepared per cell line to check the reproducibility. Principal component analysis was applied for data reduction and assessment of spectral variations that were assigned to proteins, nucleotides and carbohydrates. Four principal components were selected as input for classification models based on support vector machines. Leave-three-batches-out cross validation recognized four cell lines with sensitivities, specificities and accuracies above 96%. We conclude that this reproducible and specific SERS approach offers prospects for cell identification using easily preparable silver nanoparticles.

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.