READ ME File For 'Dataset for X-ray micro-computed tomography for non-destructive 3D X-ray histology' Dataset DOI: 10.5258/SOTON/D0902 ReadMe Author: Orestis Katsamenis, University of Southampton 0000-0003-4367-4147 This dataset supports the publication: AUTHORS: Orestis L. Katsamenis, Michael Olding, Jane A. Warner, David S. Chatelet, Mark G. Jones, Giacomo Sgalla, Bennie Smit, Oliver J. Larkin, Ian Haig, Luca Richeldi, Ian Sinclair, Peter M. Lackie, Philipp Schneider TITLE: X-ray micro-computed tomography for non-destructive 3D X-ray histology JOURNAL: The American Journal of Pathology PAPER DOI IF KNOWN Abstract of the paper: Historically, micro-computed tomography has been considered unsuitable for histological analysis of unstained formalin-fixed and paraffin-embedded (FFPE) soft tissue biopsies due to a lack of image contrast between the tissue and the paraffin. However, we recently demonstrated that µCT can successfully resolve microstructural detail in routinely prepared tissue specimens. Here, we illustrate how µCT imaging of standard FFPE biopsies can be seamlessly integrated into conventional histology workflows, enabling non-destructive three-dimensional (3D) X-ray histology, the use and benefits of which we showcase for the exemplar of human lung biopsy specimens. This technology advancement was achieved through manufacturing a first-of-kind µCT scanner for X-ray histology and developing optimised imaging protocols, which do not require any additional sample preparation. 3D X-ray histology allows for non-destructive 3D imaging of tissue microstructure, resolving structural connectivity and heterogeneity of complex tissue networks, such as the vascular or the respiratory tract. We also demonstrate that 3D X-ray histology can yield consistent and reproducible image quality, enabling quantitative assessment of tissue’s 3D microstructures, which is inaccessible to conventional two-dimensional histology. Being non-destructive the technique does not interfere with histology workflows, permitting subsequent tissue characterisation by means of conventional light microscopy-based histology, immunohistochemistry, and immunofluorescence. 3D X-ray histology can be readily applied to a plethora of archival materials, yielding unprecedented opportunities in diagnosis and research of disease. This dataset contains: * 40_landmarks_backup.csv csv file of the landmark pairs used to to elastically register the histology image (warped source image) to the μCT slice (target image) to account for physical distortions caused during mechanical sectioning of the tissue block. * CAL_SPST10024_InPlane_1566x950x360_16bit_MO_slice60.tif Single CT slice (slice 60) which best matched the corresponding histology slide * Screenshot_2019_03_07_16.52.41.png Screenshot showing the position of the landmark pairs on both the histology and CT slices prior to warping the source (histology) image * CAL_SPST10024_InPlane_1566x950x360_16bit_MO_slice60_scaledtoHisto.tif Single CT slice (slice 60) which best matched the corresponding histology slide, oversampled to match Histology image dimensions. * SPST10024_15_OriginalHistology.tif Histology Image of the the control specimen used in Figure 3 * Info_for_Med3D_HPass_CAL_20161209_MEDX_1381_SPST10081_960x1800x418x16bit.txt Information for importing the raw 3D X-Ray Histology volume of the IPF specimen * Info_for_Med3D_HPass_CAL_20170120_MEDX_1381_SPST10024_1566x950x360x16bit.txt Information for importing the raw 3D X-Ray Histology volume of the control specimen * SPST10024_15_OriginalHistology.tif_xfm_0.tif Histology Image of the the control specimen used in Figure 3 * Med3D_HPass_CAL_20161209_MEDX_1381_SPST10081_960x1800x418x16bit.raw Raw 3D X-Ray Histology volume of the IPF specimen * Med3D_HPass_CAL_20170120_MEDX_1381_SPST10024_1566x950x360x16bit.raw Raw 3D X-Ray Histology volume of the control specimen * 10024_Thickness_tissue_Stack_Tb.tif Resulted thickness map (volume) for the control specimen as produced using BoneJ plugin in ImageJ. * 10081_Thickness_Tissue_Stack_Tb.tif Resulted thickness map (volume) for the IPF specimen as produced using BoneJ plugin in ImageJ. Date of data collection: 2016 -2019 Information about geographic location of data collection: University of Southampton, U.K. Licence: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/ Date that the file was created: May, 2019