31 research outputs found

    Ancient DNA suggests modern wolves trace their origin to a late Pleistocene expansion from Beringia.

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    Grey wolves (Canis lupus) are one of the few large terrestrial carnivores that have maintained a wide geographic distribution across the Northern Hemisphere throughout the Pleistocene and Holocene. Recent genetic studies have suggested that, despite this continuous presence, major demographic changes occurred in wolf populations between the late Pleistocene and early Holocene, and that extant wolves trace their ancestry to a single late Pleistocene population. Both the geographic origin of this ancestral population and how it became widespread remain unknown. Here, we used a spatially and temporally explicit modelling framework to analyse a dataset of 90 modern and 45 ancient mitochondrial wolf genomes from across the Northern Hemisphere, spanning the last 50,000 years. Our results suggest that contemporary wolf populations trace their ancestry to an expansion from Beringia at the end of the Last Glacial Maximum, and that this process was most likely driven by Late Pleistocene ecological fluctuations that occurred across the Northern Hemisphere. This study provides direct ancient genetic evidence that long-range migration has played an important role in the population history of a large carnivore, and provides an insight into how wolves survived the wave of megafaunal extinctions at the end of the last glaciation. Moreover, because late Pleistocene grey wolves were the likely source from which all modern dogs trace their origins, the demographic history described in this study has fundamental implications for understanding the geographical origin of the dog.L.L., K.D. and G.L. were supported by the Natural Environment Research Council, UK (grant numbers NE/K005243/1, NE/K003259/1); LL was also supported by the European Research Council grant (339941‐ADAPT); A.M. and A.E. were supported by the European Research Council Consolidator grant (grant number 647787‐LocalAdaptation); L.F. and G.L. were supported by the European Research Council grant (ERC‐2013‐StG 337574‐UNDEAD); T.G. was supported by a European Research Council Consolidator grant (681396‐Extinction Genomics) & Lundbeck Foundation grant (R52‐5062); O.T. was supported by the National Science Center, Poland (2015/19/P/NZ7/03971), with funding from EU's Horizon 2020 programme under the Marie Skłodowska‐Curie grant agreement (665778) and Synthesys Project (BETAF 3062); V.P., E.P. and P.N. were supported by the Russian Science Foundation grant (N16‐18‐10265 RNF); A.P. was supported by the Max Planck Society; M.L‐G. was supported by a Czech Science Foundation grant (GAČR15‐06446S)

    X-ray micro-tomography for investigations of brain tissues on cellular level

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    X-ray imaging in absorption contrast mode is well established for hard tissue visualization. However, performance for lower density materials is limited due to a reduced contrast. Our aim is three-dimensional (3D) characterization of micro-morphology of human brain tissues down to (sub-)cellular resolution within a laboratory environment. Using the laboratory-based microtomography (μCT) system nanotom® m (GE Sensing &amp; Inspection Technologies GmbH, Wunstorf, Germany) and synchrotron radiation at the Diamond-Manchester Imaging Branchline I13-2 (Diamond Light Source, Didcot, UK), we have acquired 3D data with a resolution down to 0.45 μm for visualization of a human cerebellum specimen down to cellular level. We have shown that all selected modalities, namely laboratory-based absorption contrast micro-tomography (LBμCT), synchrotron radiation based in-line single distance phase contrast tomography (SDPR) and synchrotron radiation based single-grating interferometry (GI), can reach cellular resolution for tissue samples with a size in the mm-range. The results are discussed qualitatively in comparison to optical microscopy of haematoxylin and eosin (H&amp;E) stained sections. As phase contrast yields to a better data quality for soft tissues and in order to overcome restrictions of limited beamline access for phase contrast measurements, we have equipped the μCT system nanotom® m with a double-grating phase contrast set-up. Preliminary experimental results of a knee sample consisting of a bony part and a cartilage demonstrate that phase contrast data exhibits better quality compared to absorption contrast. Currently, the set-up is under adjustment. It is expected that cellular resolution would also be achieved. The questions arise (1) what would be the quality gain of laboratory-based phase contrast in comparison to laboratory-based absorption contrast tomography and (2) could laboratory-based phase contrast data provide comparable results to synchrotron radiation based phase contrast data.</p

    Computational cell quantification in the human brain tissues based on hard X-ray phase-contrast tomograms

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    Cell visualization and counting plays a crucial role in biological and medical research including the study of neurodegenerative diseases. The neuronal cell loss is typically determined to measure the extent of the disease. Its characterization is challenging because the cell density and size already differs by more than three orders of magnitude in a healthy cerebellum. Cell visualization is commonly performed by histology and fluorescence microscopy. These techniques are limited to resolve complex microstructures in the third dimension. Phase-contrast tomography has been proven to provide sufficient contrast in the three-dimensional imaging of soft tissue down to the cell level and, therefore, offers the basis for the three-dimensional segmentation. Within this context, a human cerebellum sample was embedded in paraffin and measured in local phase-contrast mode at the beamline ID19 (ESRF, Grenoble, France) and the Diamond Manchester Imaging Branchline I13-2 (Diamond Light Source, Didcot, UK). After the application of Frangi-based filtering the data showed sufficient contrast to automatically identify the Purkinje cells and to quantify their density to 177 cells per mm3 within the volume of interest. Moreover, brain layers were segmented in a region of interest based on edge detection. Subsequently performed histological analysis validated the presence of the cells, which required a mapping from the two-dimensional histological slices to the three-dimensional tomogram. The methodology can also be applied to further tissue types and shows potential for the computational tissue analysis in health and disease.</p
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