Lewy pathology in Parkinson's disease consists of crowded organelles and lipid membranes.

Parkinson's disease, the most common age-related movement disorder, is a progressive neurodegenerative disease with unclear etiology. Key neuropathological hallmarks are Lewy bodies and Lewy neurites: neuronal inclusions immunopositive for the protein α-synuclein. In-depth ultrastructural analysis of Lewy pathology is crucial to understanding pathogenesis of this disease. Using correlative light and electron microscopy and tomography on postmortem human brain tissue from Parkinson's disease brain donors, we identified α-synuclein immunopositive Lewy pathology and show a crowded environment of membranes therein, including vesicular structures and dysmorphic organelles. Filaments interspersed between the membranes and organelles were identifiable in many but not all α-synuclein inclusions. Crowding of organellar components was confirmed by stimulated emission depletion (STED)-based super-resolution microscopy, and high lipid content within α-synuclein immunopositive inclusions was corroborated by confocal imaging, Fourier-transform coherent anti-Stokes Raman scattering infrared imaging and lipidomics. Applying such correlative high-resolution imaging and biophysical approaches, we discovered an aggregated protein-lipid compartmentalization not previously described in the Parkinsons' disease brain.

An open-source code for Mie extinction extended multiplicative signal correction for infrared microscopy spectra of cells and tissues.

Infrared spectroscopy of single cells and tissue is affected by Mie scattering. During recent years, several methods have been proposed for retrieving pure absorbance spectra from such measurements, while currently no user-friendly version of the state-of-the-art algorithm is available. In this work, an open-source code for correcting highly scatter-distorted absorbance spectra of cells and tissues is presented, as well as several improvements of the latest version of the Mie correction algorithm based on extended multiplicative signal correction (EMSC) published by Konevskikh et al. In order to test the stability of the code, a set of apparent absorbance spectra was simulated. To this purpose, pure absorbance spectra based on a Matrigel spectrum are simulated. Scattering contributions where obtained by mimicking the scattering features observed in a set of experimentally obtained spectra . It can be concluded that the algorithm is not depending strongly on the reference spectrum used for initializing the algorithm and retrieves well the underlying pure absorbance spectrum. The calculation time of the algorithm is considerably improved with respect to the resonant Mie scattering EMSC algorithm used by the community today.

Label-free identification of myopathological features with coherent anti-Stokes Raman scattering.

INTRODUCTION: The aim of this study was the label-free identification of distinct myopathological features with coherent anti-Stokes Raman scattering (CARS) imaging, which leaves the sample intact for further analysis. METHODS: The protein distribution was determined without labels by CARS at 2,930 cm-1 and was compared with the results of standard histological staining. RESULTS: CARS imaging allowed the visualization of glycogen accumulation in glycogen storage disease type 5 (McArdle disease) and of internal nuclei in centronuclear myopathy. CARS identified an inhomogeneous protein distribution within muscle fibers in sporadic inclusion body myositis that was not shown with standard staining. In Duchenne muscular dystrophy, evidence for a higher protein content at the border of hypercontracted fibers was detected. DISCUSSION: CARS enables the label-free identification of distinct myopathological features, possibly paving the way for subsequent proteomic, metabolic, and genomic analyses. Muscle Nerve 58: 457-460, 2018.

Specific Substates of Ras To Interact with GAPs and Effectors: Revealed by Theoretical Simulations and FTIR Experiments.

The oncogenic Ras protein adopts various specific conformational states to execute its function in signal transduction. The large number of Ras structures obtained from X-ray and NMR experiments illustrates the diverse conformations that Ras adopts. It is difficult, however, to connect specific structural features with Ras functions. We report the free-energy landscape of Ras·GTP based on extensive explicit solvent simulations. The free-energy map clearly shows that the functional state 2 of Ras·GTP in fact has two distinct substates, denoted here as "Tyr32in" and "Tyr32out". Unbiased MD simulations show that the two substrates interconvert on the submicrosecond scale in solution, pointing to a novel mechanism for Ras·GTP to selectively interact with GAPs and effectors. This proposal is further supported by time-resolved FTIR experiments, which demonstrate that Tyr32 destabilizes the Ras·GAP complex and facilitates an efficient termination of Ras signaling.

Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections.

BACKGROUND: In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools. RESULTS: We propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images. CONCLUSIONS: Our validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.

A method for the comparison of multi-platform spectral histopathology (SHP) data sets.

Results of a study comparing infrared imaging data sets collected on different instruments or instrument platforms are reported, along with detailed methods developed to permit such comparisons. It was found that different instrument platforms, although employing different detector technologies and pixel sizes, produce highly similar and reproducible spectral results. However, differences in the absolute intensity values of the reflectance data sets were observed that were caused by heterogeneity of the sample substrate in terms of reflectivity and planarity.

Similarity maps and hierarchical clustering for annotating FT-IR spectral images.

BACKGROUND: Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization. RESULTS: We introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy. CONCLUSIONS: We demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward's clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.

Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections.

During the past years, many studies have shown that infrared spectral histopathology (SHP) can distinguish different tissue types and disease types independently of morphological criteria. In this manuscript, we report a comparison of immunohistochemical (IHC), histopathological and spectral histopathological results for colon cancer tissue sections. A supervised algorithm, based on the "random forest" methodology, was trained using classical histopathology, and used to automatically identify colon tissue types, and areas of colon adenocarcinoma. The SHP images subsequently were compared to IHC-based images. This comparison revealed excellent agreement between the methods, and demonstrated that label-free SHP detects compositional changes in tissue that are the basis of the sensitivity of IHC.