DOI: 10.5061/dryad.7h44j0zvn
title: Quantitative 3D OPT and LSFM datasets of pancreata from mice with streptozotocin-induced diabetes: Sample data sets

description: Mouse models for streptozotocin (STZ) induced diabetes probably represent
 the most widely used systems for preclinical diabetes research, owing to
 the compound’s toxic effect on pancreatic ß-cells. However, a
 comprehensive view of pancreatic β-cell mass distribution subject to STZ
 administration is lacking. Previous assessments have largely relied on the
 extrapolation of stereological sections, which provide limited 3D-spatial
 and quantitative information. This data descriptor presents multiple ex
 vivo tomographic optical image data sets of the full β-cell mass
 distribution in mice subject to single high and multiple low doses of STZ
 administration, and in glycaemia recovered mice. The data further include
 information about structural features, such as individual islet β-cell
 volumes, spatial coordinates, and shape as well as signal intensities for
 both insulin and GLUT2. Together, they provide the most comprehensive
 anatomical record of the effects of STZ administration on the islet of
 Langerhans in mice. As such, this data descriptor may serve as reference
 material to facilitate the planning, use and (re)interpretation of this
 widely used disease model.
description: <em>Animals, STZ
 administration and organ isolation</em>
 The data presented in this
 data descriptor was acquired for Hahn et al., 2020, and all experiments were performed following the
 European Union guidelines for care and use of animals in research. All
 procedures were approved by the Animal Review Board at the Court of Appeal
 of Northern Norrland and of Northern Stockholm. Streptozotocin (STZ,
 Sigma-Aldrich) dissolved freshly in 0.1 M sodium citrate buffer (pH 4.5)
 was administered to 8-week-old male C57BL/6J mice by intraperitoneal
 (i.p.) injection, either as a single high dose (SHD, 150 mg/kg) or as multiple low doses (MLD, 50 mg/kg
 over 5 consecutive days). For data reliability, untreated control mice
 were compared with mice receiving an i.p. injection of the vehicle
 solution only (0.1 M sodium citrate, pH 4.5). No difference in BCM, islet
 number or blood glucose levels could be detected (see Hahn et al.,
 2020, suppl. Fig 2).
 Glucose measurements were regularly performed from tail vein blood with
 OneTouch (LifeScan, USA) or Accu-Chek (Roche, Switzerland) glucometers
 until death (see Table 1 and 2). Animals were killed by cervical
 dislocation and pancreata from diabetic groups of SHD or MLD and healthy
 control groups were isolated at 1-,2- and 3-weeks post administration of
 STZ (n=3-5, n=3-5 and n=5 respectively). Harvested pancreata were fixed in
 4% paraformaldehyde (PFA, Sigma Aldrich) for 2 h, washed in 1x PBS and
 divided into the splenic, gastric, and duodenal lobular
 compartments (see also Fig. 2) before processing for whole mount
 immunohistochemistry and 3D imaging. 
 
 To
 delineate the long-term effects of hyperglycemia on GLUT2 expression, on
 β-cell function and islet size distribution generated data from islet
 transplantation experiments (see Fig. 2, data set 2) was performed as
 previously described. In
 short, pancreatic islets of Langerhans were obtained from healthy
 (normoglycemic) mice with the same genetic background via collagenase
 treatment. SHD-treated animals with the highest blood glucose levels 4
 days post-STZ administration were then transplanted with 100 – 150 islets
 per animal into the anterior chamber of the eye under isoflurane
 anaesthesia to revert hyperglycemia. Once the SHD treated (n=8) and islet
 transplanted cohort (SHD+Tx, n=4) reached normoglycemic levels to the
 control (n=7), organs of all animal cohorts were harvested (28 days
 post-STZ administration, see above). 
 
 <em>Pancreas processing, whole mount
 immunohistochemistry and tissue
 clearing.</em> <em>
 </em> Tissue
 processing, staining procedure, and preparation for OPT/LSFM imaging were
 performed as described. In
 brief, isolated and fixed pancreata were separated into the main lobes
 (SL, GL and DL respectively, see Fig. 2) permeabilized by freeze/thawing cycles, bleached
 to reduce autofluorescence, stained with primary and secondary antibodies,
 mounted in a cylinder of low melting point agarose, dehydrated with
 methanol and made transparent by matching the refractive index of
 proteins, lipids, and other cellular components with a 1:2 mixture of
 benzyl alcohol and benzyl benzoate (BABB), respectively. All specimens
 were blinded and randomized after organ harvest for all downstream
 processes. Primary antibody used was guinea pig anti-insulin (DAKO A0594,
 dilution 1:500), and secondary antibody was goat Alexa 594 anti-guinea pig
 (Molecular Probes, A11076, dilution 1:500). For co-expression assessments
 of insulin and GLUT2 (see Fig. 1 and Fig. 2, Dataset 2), pancreata were in
 addition to insulin labelled with primary rabbit anti-GLUT2 (Millipore,
 07-1402-l, dilution 1:500) and secondary IRDye 680RD goat anti-rabbit
 (Licor, 926-68071, dilution 1:500). 
 <em>3D imaging: Optical
 projection tomography (OPT) and Light sheet fluorescent microscopy
 (LSFM)</em> OPT scanning of pancreatic specimen (Dataset 1)
 was performed as described using a Bioptonics 3001 OPT scanner (SkyScan,
 Belgium) with varying exposure times (see folder “Metadata for all groups”
 for exposure times) of Insulin staining (filter set “insulin”:
 Ex:560/20nm, Em.:610nm LP) and autofluorescence (filter set “anatomy”: Ex:
 425/20nm, Em.:475nm LP). The image data was generated using SkyScanner
 3001 (v1.3.13, SkyScan). Samples from co-expression experiments (Dataset
 2) were scanned in our custom build Near Infrared-OPT
 setup using LabVIEW
 (v20.0f1) to retrieve image data. For comparison of intensities in 3D, all
 images in dataset 2 were generated using equal exposure times of Insulin
 staining (filter set: Ex: HQ 565/30 nm, Em: HQ 620/60 nm, exp. t = 4000
 ms), GLUT2 staining (filter set: Ex: HQ 665/45 nm, Em: HQ 725/50 nm, exp.
 t = 8000 ms) and endogenous fluorescent anatomy (filter set: Ex: 425/60 nm
 Em: LP 480 nm, exp. t = 500 ms). 
 Additional high-resolution
 scans (Dataset 3) of volumes of interest from representative pancreata
 that were OPT scanned (see above) were reimaged in a LaVision biotech 2nd
 generation UltraMicroscope (LaVision BioTec BmbH, Germany) with a 1x
 Olympus objective (Olympuse PLAPO 2XC) coupled to an Olympus MVX10 zoom
 body, providing between 0.36x and 6.3x magnification with a lens corrected
 dipping cap MVPLAPO 2x DC DBE objective. Samples mounted in low melting
 point SeaPlaque Agarose (39346-81-1, Lonza) were trimmed in BABB to fit
 the LSFM sample holder. Scans were acquired using 6.3
 x magnification, which rendered a pixel
 size of 0.48 µm in x and y dimensions. Depending on the scan locations,
 the exposure time was 120-300 ms, light sheet-width was between 10-20%
 with 3.78 µm thickness (NA of 0.14) with a z-step size of 5 µm. Image data
 acquisition was performed using ImSpectorPro (version 5.0.164, LaVision
 BioTec GmbH, Germany). Representative islets of Langerhans with different
 sizes and locations in the gland were chosen based on 3D rendered OPT data
 sets. 
 <em>Image processing,
 reconstruction, and 3D volume rendering</em>
 Insulin-based projection views
 retrieved from the Bioptonics 3001 scanner (Dataset 1) and volumetric
 assessments on β-cell volumes retrieved from the custom build NIR-OPT
 scanner (Dataset 2) were first processed with a contrast limited adaptive
 histogram equalization (CLAHE) algorithm, with a tile size of 64 x 64 to increase the
 signal-to-noise ratio for downstream islet segmentation. Secondly, a
 discrete Fourier transform alignment (DFTA) was performed to align opposing projection
 images to the same axis of rotation of a sample. However, for combined
 assessments of insulin and GLUT2 expression (Dataset 2) and analysis of
 the effect of STZ on GLUT2 staining intensity in β-cells, the CLAHE
 normalisation routine was not implemented. Reconstruction of OPT
 projection views to tomographic sections was performed using a filtered
 back projection algorithm in the NRecon software (V1.6.9.18, Bruker
 microCT, Belgium) with ring artefact correction set to 4. Resulting
 tomographic sections (*.bmp and *.tif Datasets 1 & 2, Record B)
 and raw z-sectional images (*.ome.tif, Dataset 3, Record B) generated with
 the Ultramicroscope II (LSFM) of each channel were converted with Imaris
 converter (Bitplane, UK) and the subsequential *.ims files of each channel
 for each sample were incorporated into one Imaris file. Individual
 insulin-positive islet volumes and lobular anatomies were quantified using
 an automated surfacing algorithm within the Imaris software (version
 9.3.1, Bitplane, UK). Surface segmentation was performed using the
 ‘background subtraction’ function in Imaris with varying thresholding
 between samples. Individual threshold values for islet volume segmentation
 are displayed in Supplementary Table 1 and Supplementary Table 2 for
 Datasets 1 and 2, respectively. Surfaced islet volumes were arbitrarily
 categorized into small (&lt;1 x 10<sup>6</sup>
 µm<sup>3</sup>), medium (1-5 x 10<sup>6</sup>
 µm<sup>3</sup>) and large (&gt;5 x
 10<sup>6</sup> µm<sup>3</sup>) islets of
 Langerhans as previously described. Volumes of 10 voxels or less were filtered out
 from quantification data sets (Datasets 1 and 2) to avoid inclusions of
 artefacts in the data analysis.
description: This is a representative sample of all data sets and data
 records. Raw image projections (*.tiff) and tomographic
 sections (*.bmp or *ome.tiff) can be converted and imported into most 3D
 visualisation/quantification software such as ImageJ (https://imagej.nih.gov/ij/, NIH, USA), Arivis (Munich, Germany), Imaris (Bitplane, UK) or 3D slicer (https://www.slicer.org/). When importing multiple channels generated by OPT or LSFM the numerical value of the starting image of the projection views (OPT) or z-stacks (LSFM) should always be the same for the anatomy, insulin or GLUT2 channels. The Imaris files (*.ims) contain all iso-surfaces (anatomy, insulin or GLUT2) used for quantification and numerical data mining and can be visualised for free using the Imaris viewer (https://imaris.oxinst.com/imaris-viewer). <em>Code availability </em> Custom generated scripts used for processing OPT data including COM-AR (alignment of axis of rotation during OPT scan setup), DFTA (alignment of axis of rotation post-OPT scanning) and CLAHE (improving islet segmentation) is compiled as a software package (together with video instructions on their implementation) at GitHub, Link https://github.com/ARDISDataset/DSPOPT. There are two other Dryad datasets associated with this dataset: Dataset 1 (https://doi.org/10.5061/dryad.51c59zw8g) and Datasets 2 & 3 (https://doi.org/10.5061/dryad.9s4mw6mh5).