Melanin-rich zebrafish melanophores are used to study pigment development, human skin color, and as a large-scale screening phenotype. To facilitate more detailed whole-body, computational analyses of melanin content and morphology, we have combined X-ray microtomography (micro-CT), a non-destructive, full-volume imaging modality, with a novel application of ionic silver staining to characterize melanin distribution in whole zebrafish larvae. Normalized micro-CT reconstructions of silver-stained fish consistently reproduced pigment patterns seen by light microscopy, and allowed direct quantitative comparisons of melanin content across wild-type and mutant samples, for both dramatic and subtle phenotypes not previously described. Silver staining of melanin for micro-CT provides proof-of-principle for whole-body, three-dimensional computational phenomic analysis of a particular cell type at cellular resolution, with potential applications in other model organisms and human melanoma biopsies. Whole-organism, high-resolution phenotyping is a challenging ideal, but provides superior context for functional studies of mutations, diseases, and environmental influences.

Micro-CT imaging was performed on Beamline 8.3.2, Tomography (micro-CT), at the Advanced Light Source (ALS) at the Lawrence Berkeley National Laboratory (LBNL, Berkeley, CA) in February 2020. A double-multilayer monochromator was used to select an X-ray energy of 26 keV to optimize silver-based contrast (elemental silver K edge = 25.5 keV) (Hubbell and Seltzer, 2004). Acquisition was performed using a custom detector system based on a 6x objective lens with 0.6 numerical aperture and a 101 megapixel thermo-electrically cooled CMOS camera outputting a 11,648-pixel x 8,742-pixel image (Vision Systems Technology, Vista, CA). Horizontal FOV was approximately 5 mm, but projections were cropped to cover the width of the sample tube as they were acquired. Whole 5 dpf larval zebrafish were imaged over two scans, covering head and tail regions, respectively. Each constant motion scan resulted in 1017 projections over 180 degrees with an exposure time of 175 ms per projection. A sample to scintillator distance of 33 mm was chosen to optimize phase effect. Two flat field images were acquired per sample, one before the head segment and one after the tail segment, to be used for image normalization.

Flat field correction, ring artifact reduction, and image reconstruction were performed using the open source TomoPy toolkit (Gürsoy et al., 2014). Flat field correction was performed using the flat field image taken either before or after sample scanning, depending on which produced the best contrast on test projections. Images were reconstructed using Gridrec (Dowd et al., 1999; Rivers, 2012) with ring artifact reduction (Miqueles et al., 2014; Münch et al., 2009) and a 2^nd order Butterworth filter with a cutoff of 0.2 to reduce noise, resulting in a nominal isotropic voxel size of 0.52 μm, as estimated by the number of reconstructed voxels spanning the 1.03 mm outer diameter of the reconstructed polyimide sample tube.

32-bit reconstructed images were further processed using Fiji (Schindelin et al., 2012). Reconstructions were cropped and rotated, and regions containing the zebrafish sample were segmented on a per slice basis to remove the plastic tube and any bubbles which had formed near the sample. This “cleaned up” data was normalized to the LR White resin present in every sample to allow direct comparisons between scans as follows: An average attenuation coefficient for the resin in each reconstruction (μ_resin) was determined as the mean intensity value over all slices of a 100-pixel x 100-pixel square of empty resin, distant from the sample or sides of the tube. Subsequently each reconstruction was processed using the formula, μ_normalized = (μ - μ_resin)/μ_resin, where μ is the attenuation value at any given pixel in a reconstruction and μ_normalized is the normalized intensity. Normalized reconstructions were converted to 16-bit for visualization and analysis using the same minimum (−11) and maximum (155) μ_normalized for all reconstructions.

3D rendering and analysis, including registration of head and tail segments, were performed using Avizo versions 2020.1 and 2020.2 (Thermo Fisher Scientific). A combination of thresholding and manual segmentation was used to assign voxels in merged datasets to established larval pigmented regions, including left and right RPE, dorsal stripe, ventral stripe, yolk sac stripe, and left and right lateral stripes. Regions of staining intensity not associated with one of these regions was assigned to an “other pigment” category. To clean up the segmentations by closing small holes and reducing noise, the morphological operator 3D Closing (size = 5 px), followed by 3D Opening by Reconstruction (size = 1 px), was used on the label file for each dataset.

This research used resources of the Advanced Light Source, a U.S. DOE Office of Science User Facility under contract no. DE-AC02-05CH11231. The Pennsylvania Department of Health specifically disclaims responsibility for any analysis, interpretations, or conclusions.

A ReadMe file has been included to describe the dataset organization.

DATA & FILE OVERVIEW

1. Folder List

Unstained wild-type samples:

.\wt_5dpf_unstained_1_(AAA406).7z

Silver-stained wild-type samples:

.\wt_5dpf_silver_1_(AAA411).7z

.\wt_5dpf_silver_2_(AAA412).7z

.\wt_5dpf_silver_3_(AAA413).7z

Silver-stained golden mutant samples:

.\gol_5dpf_silver_1_(AAA421).7z

.\gol_5dpf_silver_2_(AAA422).7z

.\gol_5dpf_silver_3_(AAA423).7z

Silver-stained nacre/casper mutant samples:

.\nac_5dpf_silver_1_(AAA431).7z

.\nac_5dpf_silver_2_(AAA432).7z

.\nac_5dpf_silver_3_(AAA433).7z

Source Code:

.\Source_Code.7z

2. Folder Architecture

All sample folders* are organized in the following organization:

.\Sample_name_(ID)\

ID_Avizo_Source_Files\ --> contains necessary files to open the Avizo Project File for each sample

ID_colormap.am

ID_final_mask.labels.am --> segmented label file after all morphological operators have been applied

ID_final_mask.MaterialStatistics.am --> extracted intensity statistics for segmented regions

<recon_name>.tif.am --> Merged reconstruction used for intensity analysis

<recon_name>.tif.labels --> manually segmented label file

<recon_name>.tif.labels.closing --> segmented label file after Closing morhphological operator has been applied

ID_head\

rotated and cropped 32-bit reconstruction folder --> contains image sequence of 32-bit tiffs with nominal 0.52 um voxel resolution

cleaned and normalized 16-bit reconstruction folder --> contains image sequence of 16-bit tiffs with nominal 0.52 um voxel resolution

ID_tail\

rotated and cropped 32-bit reconstruction folder

cleaned and normalized 16-bit reconstruction folder

Avizo_Project_File.hx --> Avizo projects used to analyze each merged zebrafish sample

*NOTE: nacre/casper samples only contain head reconstructions

3. Additional Data: Raw projection and micro-CT reconstructions (~100 Gb per scan) are available upon request from the authors as digital download or physical media.

 

METHODOLOGICAL INFORMATION

1. Description of Methods for Collection and Processing of Data: Available at https://doi.org/10.1101/2021.03.11.434673

2. Software Information for Data:

These folders (.7z) were compressed using the LZMA2 Ultra algorithm of 7-Zip, a free, open-source software available here: https://www.7-zip.org/

We recommend opening 32- or 16-bit reconstructions (as .tif series) in the free, open-source software Fiji (Fiji is Just ImageJ, https://imagej.net/Fiji)

Avizo Project Files (.hx) were generated in Avizo 2020.1 and 2020.2 (ThermoFisher Scientific)

Avizo source files (.am) were generated in Avizo 2020.1 and 2020.2, but may also be opened using Fiji using the File > Import > Amira

 

SOURCE CODE

We have included example scripts for our reconstruction and data processing pipeline:

hdf5_extractor.py --> Python script for extracting projection images from hdf5 database

allinwonderful_v1.1.py --> Python script for reconstruction from raw projections in hdf5 database format

allinwonderful_v1.1_center_find.py --> Python script for automatically estimating reconstruction center used for reconstruction script above

clear_outside.ijm --> Fiji/ImageJ script used to manually clean up reconstruction data by removing data outside a designated selection and moving to the next slice in an image series

measure_all.ijm --> Fiji/ImageJ script used to take measurements throughout an entire image series