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Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples
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.
Using machine-learning to optimize phase contrast in a low-cost cellphone microscope
Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light’s phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. Dedicated illumination approaches, tailored to the sample under investigation help to boost the contrast. This is achieved by a programmable illumination source, which also allows to measure the phase gradient using the differential phase contrast (DPC) [1, 2] or even the quantitative phase using the derived qDPC approach [3]. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 $ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements.
Revealing the Chemical Composition of Birch Pollen Grains by Raman Spectroscopic Imaging
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.
DNA-Biofunctionalization of CTAC-Capped Gold Nanocubes
Clinical diagnostics and disease control are fields that strongly depend on technologies for rapid, sensitive, and selective detection of biological or chemical analytes. Nanoparticles have become an integral part in various biomedical detection devices and nanotherapeutics. An increasing focus is laid on gold nanoparticles as they express less cytotoxicity, high stability, and hold unique optical properties with the ability of signal transduction of biological recognition events with enhanced analytical performance. Strong electromagnetic field enhancements can be found in close proximity to the nanoparticle that can be exploited to enhance signals for e.g., metal-enhanced fluorescence or Raman spectroscopy. Even stronger field enhancements can be achieved with sharp-edged nanoparticles, which are synthesized with the help of facet blocking agents, such as cetyltrimethylammonium bromide/chloride (CTAB/CTAC). However, chemical modification of the nanoparticle surface is necessary to reduce the particle’s cytotoxicity, stabilize it against aggregation, and to bioconjugate it with biomolecules to increase its biocompatibility and/or specificity for analytical applications. Here, a reliable two-step protocol following a ligand exchange with bis (p-sulfonatophenyl) phenyl phosphine (BSPP) as the intermediate capping-agent is demonstrated, which results in the reliable biofunctionalization of CTAC-capped gold nanocubes with thiol-modified DNA. The functionalized nanocubes have been characterized regarding their electric potential, plasmonic properties, and stability against high concentrations of NaCl and MgCl2.
cellSTORM - Cost-effective Super-Resolution on a Cellphone using dSTORM
High optical resolution in microscopy usually goes along with costly hardware components, such as lenses, mechanical setups and cameras. Several studies proved that Single Molecular Localization Microscopy can be made affordable, relying on off-the-shelf optical components and industry grade CMOS cameras. Recent technological advantages have yielded consumer-grade camera devices with surprisingly good performance. The camera sensors of smartphones have benefited of this development. Combined with computing power smartphones provide a fantastic opportunity for “imaging on a budget”. Here we show that a consumer cellphone is capable of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we used a trained image-to-image generative adversarial network (GAN) to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance directly on the smartphone. We believe that “cellSTORM” paves the way to make super-resolution microscopy not only affordable but available due to the ubiquity of cellphone cameras.High optical resolution in microscopy usually goes along with costly hardware components, such as lenses, mechanical setups and cameras. Several studies proved that Single Molecular Localization Microscopy can be made affordable, relying on off-the-shelf optical components and industry grade CMOS cameras. Recent technological advantages have yielded consumer-grade camera devices with surprisingly good performance. The camera sensors of smartphones have benefited of this development. Combined with computing power smartphones provide a fantastic opportunity for “imaging on a budget”. Here we show that a consumer cellphone is capable of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we used a trained image-to-image generative adversarial network (GAN) to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance directly on the smartphone. We believe that “cellSTORM” paves the way to make super-resolution microscopy not only affordable but available due to the ubiquity of cellphone cameras.
Signal and noise analysis for chiral structured illumination microscopy
Recently, chiral structured illumination microscopy has been proposed to image fluorescent chiral domains at sub-wavelength resolution. Chiral structured illumination microscopy is based on the combination of structured illumination microscopy, fluorescence-detected circular dichroism, and optical chirality engineering. Since circular dichroism of natural chiral molecules is typically weak, the differential fluorescence is also weak and can be easily buried by the noise, hampering the fidelity of the reconstructed images. In this work, we systematically study the impact of the noise on the quality and resolution of chiral domain images obtained by chiral SIM. We analytically describe the signal-to-noise ratio of the reconstructed chiral SIM image in the Fourier domain and verify our theoretical calculations with numerical demonstrations. Accordingly, we discuss the feasibility of chiral SIM in different experimental scenarios and propose possible strategies to enhance the signal-to-noise ratio for samples with weak circular dichroism.
Characterisation of a novel composite SCCmec-SCCfus element in an emerging Staphylococcus aureus strain from the Arabian Gulf region
Fusidic acid is a steroid antibiotic known since the 1960s. It is frequently used in topical preparations, i.e., ointments, for the treatment of skin and soft tissue infections caused by Staphylococcus aureus. There is an increasing number of methicillin-resistant S. aureus (MRSA) strains that harbour plasmid-borne fusB/far1 or fusC that is localised on SCC elements. In this study we examined a series of related CC30-MRSA isolates from the Arabian Gulf countries that presented with SCCmec elements and fusC, including a variant that—to the best of our knowledge—has not yet formally been described. It consisted of a class B mec complex and ccrA/B-4 genes. The fusidic acid resistance gene fusC was present, but contrary to the previously sequenced element of HDE288, it was not accompanied by tirS. This element was identified in CC30 MRSA from Kuwait, Saudi Arabia and the United Arab Emirates that usually also harbour the Panton-Valentin leukocidin (PVL) genes. It was also identified in CC8 and ST834 isolates. In addition, further CC30 MRSA strains with other SCCmec VI elements harbouring fusC were found to circulate in the Arabian Gulf region. It can be assumed that MRSA strains with SCCmec elements that include fusC have a selective advantage in both hospital and community settings warranting a review of the use of topical antibiotics and indicating the necessity of reducing over-the-counter sale of antibiotics, including fusidic acid, without prescription.Fusidic acid is a steroid antibiotic known since the 1960s. It is frequently used in topical preparations, i.e., ointments, for the treatment of skin and soft tissue infections caused by Staphylococcus aureus. There is an increasing number of methicillin-resistant S. aureus (MRSA) strains that harbour plasmid-borne fusB/far1 or fusC that is localised on SCC elements. In this study we examined a series of related CC30-MRSA isolates from the Arabian Gulf countries that presented with SCCmec elements and fusC, including a variant that—to the best of our knowledge—has not yet formally been described. It consisted of a class B mec complex and ccrA/B-4 genes. The fusidic acid resistance gene fusC was present, but contrary to the previously sequenced element of HDE288, it was not accompanied by tirS. This element was identified in CC30 MRSA from Kuwait, Saudi Arabia and the United Arab Emirates that usually also harbour the Panton-Valentin leukocidin (PVL) genes. It was also identified in CC8 and ST834 isolates. In addition, further CC30 MRSA strains with other SCCmec VI elements harbouring fusC were found to circulate in the Arabian Gulf region. It can be assumed that MRSA strains with SCCmec elements that include fusC have a selective advantage in both hospital and community settings warranting a review of the use of topical antibiotics and indicating the necessity of reducing over-the-counter sale of antibiotics, including fusidic acid, without prescription.
Aluminum-Doped Zinc Oxide Improved by Silver Nanowires for Flexible, Semitransparent and Conductive Electrodes on Textile with High Temperature Stability
In order to facilitate the design freedom for the implementation of textile-integrated electronics, we seek flexible transparent conductive electrodes (TCEs) that can withstand not only the mechanical stresses encountered during use but also the thermal stresses of post-treatment. The transparent conductive oxides (TCO) typically used for this purpose are rigid in comparison to the fibers or textiles they are intended to coat. In this paper, a TCO, specifically aluminum-doped zinc oxide (Al:ZnO), is combined with an underlying layer of silver nanowires (Ag-NW). This combination brings together the advantages of a closed, conductive Al:ZnO layer and a flexible Ag-NW layer, forming a TCE. The result is a transparency of 20–25% (within the 400–800 nm range) and a sheet resistance of 10 Ω/sq that remains almost unchanged, even after post-treatment at 180 °C.
FLIM data analysis based on Laguerre polynomial decomposition and machine-learning
Significance: 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.
Raman spectroscopy follows time-dependent changes in T lymphocytes isolated from spleen of endotoxemic mice
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.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.