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      Three-dimensional virtual histology of human cerebellum by X-ray phase-contrast tomography

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          Significance

          The complex cytoarchitecture of human brain tissue is traditionally studied by histology, providing structural information in 2D planes. This can be partly extended to 3D by inspecting many parallel slices, however, at nonisotropic resolution. This work shows that propagation-based X-ray phase-contrast tomography, both at the synchrotron and even at a compact laboratory source, can be used to perform noninvasive 3D virtual histology on unstained paraffin-embedded human cerebellum at isotropic subcellular resolution. The resulting data quality is high enough to visualize and automatically locate ∼10 6 neurons within the different layers of the cerebellum, providing unprecedented data on its 3D cytoarchitecture and spatial organization.

          Abstract

          To quantitatively evaluate brain tissue and its corresponding function, knowledge of the 3D cellular distribution is essential. The gold standard to obtain this information is histology, a destructive and labor-intensive technique where the specimen is sliced and examined under a light microscope, providing 3D information at nonisotropic resolution. To overcome the limitations of conventional histology, we use phase-contrast X-ray tomography with optimized optics, reconstruction, and image analysis, both at a dedicated synchrotron radiation endstation, which we have equipped with X-ray waveguide optics for coherence and wavefront filtering, and at a compact laboratory source. As a proof-of-concept demonstration we probe the 3D cytoarchitecture in millimeter-sized punches of unstained human cerebellum embedded in paraffin and show that isotropic subcellular resolution can be reached at both setups throughout the specimen. To enable a quantitative analysis of the reconstructed data, we demonstrate automatic cell segmentation and localization of over 1 million neurons within the cerebellar cortex. This allows for the analysis of the spatial organization and correlation of cells in all dimensions by borrowing concepts from condensed-matter physics, indicating a strong short-range order and local clustering of the cells in the granular layer. By quantification of 3D neuronal “packing,” we can hence shed light on how the human cerebellum accommodates 80% of the total neurons in the brain in only 10% of its volume. In addition, we show that the distribution of neighboring neurons in the granular layer is anisotropic with respect to the Purkinje cell dendrites.

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          Most cited references28

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          The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography.

          We present the ASTRA Toolbox as an open platform for 3D image reconstruction in tomography. Most of the software tools that are currently used in electron tomography offer limited flexibility with respect to the geometrical parameters of the acquisition model and the algorithms used for reconstruction. The ASTRA Toolbox provides an extensive set of fast and flexible building blocks that can be used to develop advanced reconstruction algorithms, effectively removing these limitations. We demonstrate this flexibility, the resulting reconstruction quality, and the computational efficiency of this toolbox by a series of experiments, based on experimental dual-axis tilt series.
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            Noninterferometric Phase Imaging with Partially Coherent Light

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              Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs).

              Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. (2010), a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU's cache is used more efficiently, making more effective use of the available memory bandwidth. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                3 July 2018
                18 June 2018
                18 June 2018
                : 115
                : 27
                : 6940-6945
                Affiliations
                [1] aInstitute for X-Ray Physics, University of Göttingen , 37077 Göttingen, Germany;
                [2] bCenter for Nanoscopy and Molecular Physiology of the Brain , 37073 Göttingen, Germany;
                [3] cInstitute for Neuropathology, University Medical Center Göttingen , 37075 Göttingen, Germany
                Author notes
                1To whom correspondence should be addressed. Email: tsaldit@ 123456gwdg.de .

                Edited by Martin Bech, Lund University, Lund, Sweden, and accepted by Editorial Board Member John W. Sedat May 25, 2018 (received for review January 30, 2018)

                Author contributions: M.T. and T.S. designed research; M.T. performed laboratory experiments; M.T. and T.S. performed synchrotron experiments; M.T. analyzed data; M.T., C.S., and T.S. wrote the paper; F.v.d.M. prepared the samples; and C.S. provided the samples and neurological data interpretation.

                Article
                201801678
                10.1073/pnas.1801678115
                6142271
                29915047
                88de2326-f27d-4932-9975-c76bca5b6a7d
                Copyright © 2018 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 6
                Funding
                Funded by: Deutsche Forschungsgemeinschaft (DFG) 501100001659
                Award ID: SFB755/C1
                Award Recipient : Mareike Töpperwien Award Recipient : Tim Salditt
                Funded by: Deutsche Forschungsgemeinschaft (DFG) 501100001659
                Award ID: EXC 171/A
                Award Recipient : Mareike Töpperwien Award Recipient : Tim Salditt
                Categories
                Physical Sciences
                Biophysics and Computational Biology
                From the Cover

                x-ray phase-contrast tomography,3d virtual histology,human brain cytoarchitecture,automatic cell counting

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