The subcellular arrangement of alpha-synuclein proteoforms in the Parkinson's disease brain as revealed by multicolor STED microscopy.

Various post-translationally modified (PTM) proteoforms of alpha-synuclein (aSyn)-including C-terminally truncated (CTT) and Serine 129 phosphorylated (Ser129-p) aSyn-accumulate in Lewy bodies (LBs) in different regions of the Parkinson's disease (PD) brain. Insight into the distribution of these proteoforms within LBs and subcellular compartments may aid in understanding the orchestration of Lewy pathology in PD. We applied epitope-specific antibodies against CTT and Ser129-p aSyn proteoforms and different aSyn domains in immunohistochemical multiple labelings on post-mortem brain tissue from PD patients and non-neurological, aged controls, which were scanned using high-resolution 3D multicolor confocal and stimulated emission depletion (STED) microscopy. Our multiple labeling setup highlighted a consistent onion skin-type 3D architecture in mature nigral LBs in which an intricate and structured-appearing framework of Ser129-p aSyn and cytoskeletal elements encapsulates a core enriched in CTT aSyn species. By label-free CARS microscopy we found that enrichments of proteins and lipids were mainly localized to the central portion of nigral aSyn-immunopositive (aSyn+) inclusions. Outside LBs, we observed that 122CTT aSyn+ punctae localized at mitochondrial membranes in the cytoplasm of neurons in PD and control brains, suggesting a physiological role for 122CTT aSyn outside of LBs. In contrast, very limited to no Ser129-p aSyn immunoreactivity was observed in brains of non-neurological controls, while the alignment of Ser129-p aSyn in a neuronal cytoplasmic network was characteristic for brains with (incidental) LB disease. Interestingly, Ser129-p aSyn+ network profiles were not only observed in neurons containing LBs but also in neurons without LBs particularly in donors at early disease stage, pointing towards a possible subcellular pathological phenotype preceding LB formation. Together, our high-resolution and 3D multicolor microscopy observations in the post-mortem human brain provide insights into potential mechanisms underlying a regulated LB morphogenesis.

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.

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.

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.

Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study.

A major promise of Raman microscopy is the label-free detailed recognition of cellular and subcellular structures. To this end, identifying colocalization patterns between Raman spectral images and fluorescence microscopic images is a key step to annotate subcellular components in Raman spectroscopic images. While existing approaches to resolve subcellular structures are based on fluorescence labeling, we propose a combination of a colocalization scheme with subsequent training of a supervised classifier that allows label-free resolution of cellular compartments. Our colocalization scheme unveils statistically significant overlapping regions by identifying correlation between the fluorescence color channels and clusters from unsupervised machine learning methods like hierarchical cluster analysis. The colocalization scheme is used as a pre-selection to gather appropriate spectra as training data. These spectra are used in the second part as training data to establish a supervised random forest classifier to automatically identify lipid droplets and nucleus. We validate our approach by examining Raman spectral images overlaid with fluorescence labelings of different cellular compartments, indicating that specific components may indeed be identified label-free in the spectral image. A Matlab implementation of our colocalization software is available at .

Automated identification of subcellular organelles by coherent anti-stokes Raman scattering.

Coherent anti-Stokes Raman scattering (CARS) is an emerging tool for label-free characterization of living cells. Here, unsupervised multivariate analysis of CARS datasets was used to visualize the subcellular compartments. In addition, a supervised learning algorithm based on the "random forest" ensemble learning method as a classifier, was trained with CARS spectra using immunofluorescence images as a reference. The supervised classifier was then used, to our knowledge for the first time, to automatically identify lipid droplets, nucleus, nucleoli, and endoplasmic reticulum in datasets that are not used for training. These four subcellular components were simultaneously and label-free monitored instead of using several fluorescent labels. These results open new avenues for label-free time-resolved investigation of subcellular components in different cells, especially cancer cells.