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/tomography on post-mortem 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 aSyn inclusions. Crowding of organellar components was confirmed by STED-based superresolution microscopy, and high lipid content within α-synuclein immunopositive inclusions was corroborated by confocal imaging, CARS/FTIRimaging and lipidomics. Applying such correlative high-resolution imaging and biophysical approaches, we discovered an aggregated protein-lipid compartmentalization not previously described in the PD brain.

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.

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.

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 "Tyr32" and "Tyr32". 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.

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.

BackgroundIn 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.ResultsWe 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0804-9) contains supplementary material, which is available to authorized users.

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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.