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Biochemical Characterization of Mouse Retina of an Alzheimer's Disease Model by Raman Spectroscopy
The presence of biomarkers characteristic for Alzheimer's disease in the retina is a controversial topic. Raman spectroscopy offers information on the biochemical composition of tissues. Thus, it could give valuable insight into the diagnostic value of retinal analysis. Within the present study, retinas of a double transgenic mouse model, that expresses a chimeric mouse/human amyloid precursor protein and a mutant form of human presenilin 1, and corresponding control group were subjected to ex vivo Raman imaging. The Raman data recorded on cross sections of whole eyes highlight the layered structure of the retina in a label-free manner. Based on the Raman information obtained from en face mounted retina samples, a discrimination between healthy and Alzheimer's disease retinal tissue can be done with an accuracy of 85.9%. For this a partial least squares-linear discriminant analysis was applied. Therefore, although no macromolecular changes in form of, i.e., amyloid beta plaques, can be noticed based on Raman spectroscopy, subtle biochemical changes happening in the retina could lead to Alzheimer's disease identification. ©
Photophysics of Anionic Bis(4H-imidazolato)CuI Complexes
In this paper, the photophysical behavior of four panchromatically absorbing, homoleptic bis(4H-imidazolato)CuI complexes, with a systematic variation in the electron-withdrawing properties of the imidazolate ligand, were studied by wavelength-dependent time-resolved femtosecond transient absorption spectroscopy. Excitation at 400, 480, and 630 nm populates metal-to-ligand charge transfer, intraligand charge transfer, and mixed-character singlet states. The pump wavelength-dependent transient absorption data were analyzed by a recently established 2D correlation approach. Data analysis revealed that all excitation conditions yield similar excited-state dynamics. Key to the excited-state relaxation is fast, sub-picosecond pseudo-Jahn-Teller distortion, which is accompanied by the relocalization of electron density onto a single ligand from the initially delocalized state at Franck-Condon geometry. Subsequent intersystem crossing to the triplet manifold is followed by a sub-100 ps decay to the ground state. The fast, nonradiative decay is rationalized by the low triplet-state energy as found by DFT calculations, which suggest perspective treatment at the strong coupling limit of the energy gap law.
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.
Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning
Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.
Shape-Memory Metallopolymers Based on Two Orthogonal Metal–Ligand Interactions
A new shape-memory polymer is presented, in which both the stable phase as well as the switching unit consist of two different metal complexes. Suitable metal ions, which simultaneously form labile complexes with histidine and stable ones with terpyridine ligands, are identified via isothermal titration calorimetry (ITC) measurements. Different copolymers are synthesized, which contain butyl methacrylate as the main monomer and the metal-binding ligands in the side chains. Zn(TFMS)2 and NiCl2 are utilized for the dual crosslinking, resulting in the formation of metallopolymer networks. The switching temperature can simply be tuned by changing the composition as well as by the choice of the metal ion. Strain fixity rates (about 99%) and very high strain recovery rates (up to 95%) are achieved and the mechanism is revealed using different techniques such as Raman spectroscopy. © 2021 The Authors. Advanced Materials published by Wiley-VCH GmbH
Novel Biobased Self-Healing Ionomers Derived from Itaconic Acid Derivates
This article presents novel biobased ionomers featuring self-healing abilities. These smart materials are synthesized from itaconic acid derivates. Large quantities of itaconic acid can be produced from diverse biomass like corn, rice, and others. This study presents a comprehensive investigation of their thermal and mechanical properties via differential scanning calorimetry (DSC), thermo gravimetric analysis (TGA), and FT-Raman and FT-IR measurements as well as dynamic mechanic analysis. Within all these measurements, different kinds of structure-property relationships could be derived from these measurements. For example, the proportion of ionic groups enormously influences the self-healing efficiency. The investigation of the self-healing abilities reveals healing efficiencies up to 99% in 2 h at 90 °C for the itaconic acid based ionomer with the lowest ionic content. © 2020 The Authors. Macromolecular Rapid Communications published by Wiley-VCH GmbH
Imaging the invisible—Bioorthogonal Raman probes for imaging of cells and tissues
A revolutionary avenue for vibrational imaging with super-multiplexing capability can be seen in the recent development of Raman-active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug-cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence-based imaging without the need of bulky fluorescent tags. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Multimodal Molecular Imaging and Identification of Bacterial Toxins Causing Mushroom Soft Rot and Cavity Disease
Soft rot disease of edible mushrooms leads to rapid degeneration of fungal tissue and thus severely affects farming productivity worldwide. The bacterial mushroom pathogen Burkholderia gladioli pv. agaricicola has been identified as the cause. Yet, little is known about the molecular basis of the infection, the spatial distribution and the biological role of antifungal agents and toxins involved in this infectious disease. We combine genome mining, metabolic profiling, MALDI-Imaging and UV Raman spectroscopy, to detect, identify and visualize a complex of chemical mediators and toxins produced by the pathogen during the infection process, including toxoflavin, caryoynencin, and sinapigladioside. Furthermore, targeted gene knockouts and in vitro assays link antifungal agents to prevalent symptoms of soft rot, mushroom browning, and impaired mycelium growth. Comparisons of related pathogenic, mutualistic and environmental Burkholderia spp. indicate that the arsenal of antifungal agents may have paved the way for ancestral bacteria to colonize niches where frequent, antagonistic interactions with fungi occur. Our findings not only demonstrate the power of label-free, in vivo detection of polyyne virulence factors by Raman imaging, but may also inspire new approaches to disease control. © 2021 The Authors. ChemBioChem published by Wiley-VCH GmbH
Ultra-compact tunable fiber laser for coherent anti-Stokes Raman imaging
This work describes the construction of an ultra-compact narrowband fiber laser source for coherent anti-Stokes Raman scattering microscopy of Raman tags, that is, for addressing Raman resonances of deuterated molecules and alkyne tags in the spectral range from 2080 to 2220 cm−1. A narrowband and fast electronically tunable cw seed source based on a semiconductor optical amplifier (SOA) emitting around 1335 nm has been employed to seed four-wave mixing (FWM) in an endlessly single mode fiber (ESM) pumped by a ps pulse duration Yb-fiber laser. A conversion efficiency of 50% is demonstrated. This compact fiber optical parametric amplifier (FOPA) has been used to perform coherent anti-Stokes Raman imaging experiments of crystalline deuterated palmitic acid.
In vivo coherent anti-Stokes Raman scattering microscopy reveals vitamin A distribution in the liver
Here we present a microscope setup for coherent anti-Stokes Raman scattering (CARS) imaging, devised to specifically address the challenges of in vivo experiments. We exemplify its capabilities by demonstrating how CARS microscopy can be used to identify vitamin A (VA) accumulations in the liver of a living mouse, marking the positions of hepatic stellate cells (HSCs). HSCs are the main source of extracellular matrix protein after hepatic injury and are therefore the main target of novel nanomedical strategies in the development of a treatment for liver fibrosis. Their role in the VA metabolism makes them an ideal target for a CARS-based approach as they store most of the body's VA, a class of compounds sharing a retinyl group as a structural motive, a moiety that is well known for its exceptionally high Raman cross section of the C=C stretching vibration of the conjugated backbone.