Journal:ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis

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Full article title ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis
Journal Cytometry Part A
Author(s) Auer, Norbert; Hrdina, Astrid; Hiremath, Chaitra; Vcelar, Sabine; Baumann, Martina; Borth, Nicole; Jadhav, Vaibhav
Author affiliation(s) Austrian Centre for Industrial Biotechnology
Primary contact Email: nicole dot borth at boku dot ac dot at
Year published 2018
Volume and issue 93(7)
Page(s) 749–54
DOI 10.1002/cyto.a.23505
ISSN 1552-4930
Distribution license Creative Commons Attribution-NonCommercial 4.0 International
Website https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23505
Download https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.23505 (PDF)

Abstract

Multicolor image analysis finds its applications in a broad range of biological studies. Specifically, multiplex fluorescence in situ hybridization (M‐FISH) for chromosome painting facilitates the analysis of individual chromosomes in complex metaphase spreads and is widely used to detect both numerical and structural aberrations. While this is well established for human and mouse karyotypes, for which species sophisticated software and analysis tools are available, other organisms and species are less well served. Commercially available software is proprietary and not easily adaptable to other karyotypes. Therefore, a publicly available open-source software that combines flexibility and customizable functionalities is needed. Here we present such a tool, called “ChromaWizard,” which is based on popular scientific image analysis libraries (OpenCV, scikit‐image, and NumPy). We demonstrate its functionality on the example of primary Chinese hamster (Cricetulus griseus) fibroblasts metaphase spreads and on Chinese hamster ovary cell lines, known for their large number of chromosomal rearrangements. The application can be easily adapted to any kind of available labeling kits and is independent of the organism and instrumentation used. It allows direct inspection of the original hybridization signals and enables either manual or automatic assignment of colors, making it a functional and versatile tool that can also be used for other multicolor applications.

Keywords: fluorescence in situ hybridization (FISH), chromosome painting, open-source image analysis software

Introduction

fluorescence in situ hybridization (FISH) has greatly facilitated the characterization of gene position, chromosomes, and genome organization.[1] FISH involves the generation and hybridization of loci-specific fluorescence‐labeled nucleic acid probes on to metaphase chromosomes or onto interphase nuclei.[2] The use of this method made gene mapping possible at much higher resolution compared with other physical mapping technologies.[3] FISH was further developed into chromosome painting to simultaneously and unequivocally distinguish chromosomes and their rearrangements in complex karyotypes. Using chromosome-specific probes and combining multiple fluorophores allows the visualization of all chromosomes with individual and unique color combinations.[4] Chromosome painting has proven to be very important not only in diagnostics and in cancer research, but also in biological research for numerical and structural aberrations, as analysis can be done at the entire genome level.[5][6] There have been considerable technical improvements for analysis of human and murine samples. However, even though chromosome painting probes become available for a larger number of non‐model species from commercial or specialized labs[7], this method is still not widely applied due to the absence of open‐source software for analysis.[8]

Specialized cytogenetics labs are equipped with the necessary infrastructure, including highly sophisticated software and instruments specifically set up for the purpose of karyotype analysis and chromosome painting, including examples such as Isis (by Metasystems), CytoVision (by Leica Biosystems), and Hyperspectral (by Applied Spectral Imaging). However, if this method needs to be established and used as a side project in a lab not thus equipped, this might turn into a challenge; while imaging facilities are typically accessible, software packages come at a cost that may not be budgeted for given the restricted budgets of most core facilities.

Only to a limited extent can laboratories turn to free (or open‐source) tools such as ImageJ[9] for general applications, as they are technically challenging and require programming skills to write macros to customize the user application, which again limits their use. Therefore, we introduce a stand‐alone, simple open‐source program called ChromaWizard for analysis and visualization of multicolor fluorescence images that addresses the needs of custom non‐model organism samples. This software integrates image processing, multicolor integration, chromosome separation, and visualization with false color assignments. It processes images for any number of color channels/probes with flexible configuration. One of the important features of ChromaWizard is the use of a GUI to access full functionality so that the user can refine results. The software works with standard image formats (TIFF, PNG, and JPEG) taken either by epifluorescence or confocal microscopes. This open‐source tool is filling a gap for users lacking the ability or time to write macro programs, while being freely accessible for advanced users for further customization to enable specific or enhanced imaging applications. It can be connected with other image processing tools to improve the quality of final images as suggested.[10][11]

In this article we present an overview of the functionality of the program, as well as more detailed descriptions of selected features using chromosome painting analysis performed on primary fibroblasts from the Chinese hamster and on Chinese hamster ovary (CHO‐K1) cells as examples.

Materials and methods

Cell lines and sample preparation

Chinese hamster lung fibroblast cells[12] were maintained in DMEM medium containing 10% of FCS and 2 mM Glutamine, and the cells were incubated at 37°C in 7% CO2.

Suspension and protein-free adapted CHO‐K1 (ECACC CCL‐61) cells were grown in CD‐CHO (Gibco/Life Technologies) supplemented with 8 mM L‐Glu and Anti‐Clumping Agent (Gibco/Life Technologies) in flasks shaken at 140 rpm at 37°C in 7% CO2. For the generation of metaphase spreads, 10 ml of exponentially growing cells were treated with 25 μl of Colcemid stock (100 μg/ml in PBS, Sigma‐Aldrich, USA) to a final concentration of 0.25 μg/ml for up to 16 hours. Mitotic cells were spun down and 5 ml of a 75 M hypotonic potassium chloride solution were added under continuous soft agitation, followed by incubation at 37°C for 20 minutes. Cells were fixed by the addition of 500 μl of a 3:1 cold methanol/acetic acid mixture. The fixation was repeated four times and centrifuged after each addition. Metaphase cells were stored at −80°C in 1 ml fixative.

Chromosome painting

Painting of metaphase chromosome spreads was performed using the 12XCHamster mFISH probe kit (MetaSystems, Germany) according to the vendor's protocol. In this kit, probes for each of the 12 Chinese hamster chromosomes are labeled by a unique combination of five fluorophores (FITC = green; Spectrum Orange = orange; TexasRed = red; DEAC (Diethylamino‐coumarin) = aqua; and Cy5 = NIR). As modification, the incubation period for hybridization was increased from 24 to 48 hours, which provided the best quality images. No DAPI counterstaining was used. After hybridization and washing of the slide, 20 μl antifade mounting reagent (SlowFade Gold Antifade, Thermo Fisher Scientific, Waltham, MA, USA) was applied onto the spread, which was carefully overlaid with a 76 × 26 mm2 coverslip and sealed with transparent nail varnish.

Images of the fluorochrome‐labeled chromosome spreads were recorded with a Leica SP8‐gSTED super‐resolution microscope (Leica Microsystems, Germany) using the following excitation and emission filters: NIR (ex: 644 nm, em: 655–697 nm); RED (ex: 595, em: 625–655 nm); ORANGE (ex: 552 nm, em: 580–611 nm); GREEN (ex: 505 nm, em: 537‐570 nm); AQUA (ex: 405 nm, em: 441–498 nm); mask layer (ex: 518, 551, and 581 nm, em: 600–744 nm). All pictures were taken with 100× magnification (objective: HC PL APO CS2 100×/1.40 OIL) at a scan speed of 400 Hz. The images from the six different channels were recorded sequentially from higher to lower excitation wavelength to reduce crosstalk (Table 1). Images were exported in the TIFF format with LAS X 2.0.1.14392 software (Leica Microsystems, Germany) for further analysis.

Results

Software description

ChromaWizard is written in Python 3 and can be installed on all operating systems supporting Conda or the Python Index Wheel format. The software has no special hardware requirements and should work on any standard personal computer. ChromaWizard makes use of openCV (Open Source Computer Vision Library, version > = 3.0.0), a versatile platform which includes several libraries and hundreds of algorithms for image analysis. Additionally, NumPy[12] and scikit‐image[13] were used, which are primary platforms for scientific computing and image analysis. Finally, a GUI was generated with the Qt library version 5.7. Fig. 1 shows the overall workflow of the software.


Fig1 Auer CytometryPartA2018 93-7.jpg

Figure 1. Workflow of the ChromaWizard software

ChromaWizard configuration

ChromaWizard is highly configurable and can be adapted to virtually any number of color channels (probes) for M‐FISH analysis in the configuration file. The default configuration file is found in the program's home folder, named “.c.default.conf.json,” after the first run. This file defines the requirements for the used labeling kit and thus for the organism of interest. The default configuration is set up for the six-channel 12XCHamster labeling kit as an example (Fig. 2A). Numbers and names of the channels and the chromosome to color association can be set in the configuration file. The configuration file is a simple text file in JSON (JavaScript Object Notation) format. A JSON editor like JSON Editor Online for changing the file is recommended; however, a standard text editor is sufficient.


Fig2 Auer CytometryPartA2018 93-7.jpg

Figure 2. Configuration of ChromaWizard for Chinese hamster painting analysis. (A) Labeling schemes of Metasystem Hamster 12X painting probes. Each row represents a single chromosome (1–10 autosomes and X‐Y sex chromosomes) for the Chinese hamster. The first column represents the assigned false color, while all other columns show the labeling combinations of the five fluorochromes. Both combinations and false colors can be individually adapted to the desired application. (B) Examples of configurations of the “.c.default.conf.json” file in ChromaWizard. Listing 1: where a number of channels can be set with their respective bit‐mask and channel name which will appear in the GUI in the order in which the images are loaded. Listing 2: where the false color can be set to each chromosome with the respective channel and bit‐mask combination. For example; Chromosome 4 is a combination of channel aqua and orange, thus bit‐mask 1010 (1000; orange and 10; Aqua).


Acknowledgements

The authors acknowledge support from the Austrian BMWFW, BMVIT, SFG, Standortagentur Tirol, Government of Lower Austria, and Business Agency Vienna through the Austrian FFG‐COMET‐ Funding Program.

References

  1. ↑ Fauth, C.; Speicher, M.R. (2001). "Classifying by colors: FISH-based genome analysis". Cytogenetics and Cell Genetics 93 (1–2): 1–10. doi:10.1159/000056937. PMID 11474168. 
  2. ↑ Volpi, E.V.; Bridger, J.M. (2008). "FISH glossary: An overview of the fluorescence in situ hybridization technique". Biotechniques 45 (4): 385–6. doi:10.2144/000112811. PMID 18855767. 
  3. ↑ Cui, C.; Shu, W.; Li, P. (2016). "Fluorescence in situ hybridization: Cell-based genetic diagnostic and research applications". Frontiers in Cell and Developmental Biology 4: 89. doi:10.3389/fcell.2016.00089. PMC PMC5011256. PMID 27656642. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5011256. 
  4. ↑ Liehr, T.; Starke, H.; Weise, A. et al. (2004). "Multicolor FISH probe sets and their applications". Histology and Histopathology 19 (1): 229–37. doi:10.14670/HH-19.229. PMID 14702191. 
  5. ↑ Schröck, E.; Veldman, T.; Padilla-Nash, H. et al. (1997). "Spectral karyotyping refines cytogenetic diagnostics of constitutional chromosomal abnormalities". Human Genetics 101 (3): 255–62. doi:10.1007/s004390050626. PMID 9439652. 
  6. ↑ Ried, T.; Schröck, E.; Ning, Y. et al. (1998). "Chromosome painting: A useful art". Human Molecular Genetics 7 (10): 1619–26. doi:10.1093/hmg/7.10.1619. PMID 9735383. 
  7. ↑ Liehr, T. (2018). "Basics and literature on multicolor fluorescence in situ hybridization application". fish-tl.com. http://fish-tl.com/mfish.html. 
  8. ↑ Liehr, T.; Starke, H.; Heller, A. et al. (2006). "Multicolor fluorescence in situ hybridization (FISH) applied to FISH-banding". Cytogenetic and Genome Research 114 (3–4): 240–4. doi:10.1159/000094207. PMID 16954660. 
  9. ↑ Schindelin, J.; Rueden, C.T.; Hiner, M.C. et al. (2015). "The ImageJ ecosystem: An open platform for biomedical image analysis". Molecular Reproduction & Development 82 (7–8): 518-529. doi:10.1002/mrd.22489. PMC PMC5428984. PMID 26153368. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428984. 
  10. ↑ Partipilo, G.; D'Addabbo, P.; Lacalandra, G.M. et al. (2011). "Refinement of Bos taurus sequence assembly based on BAC-FISH experiments". BMC Genomics 12: 639. doi:10.1186/1471-2164-12-639. PMC PMC3268123. PMID 22208360. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268123. 
  11. ↑ Vcelar, S.; Jadhav, V.; Melcher, M. et al. (2018). "Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting". Biotechnology and Bioengineering 115 (1): 165-173. doi:10.1002/bit.26453. PMID 28921524. 
  12. ↑ 12.0 12.1 van der Walt, S.; Colbert, S.C.; Varoquaux, G. (2011). "The NumPy Array: A Structure for Efficient Numerical Computation". Computing in Science & Engineering 13 (2): 22–30. doi:10.1109/MCSE.2011.37. 
  13. ↑ van der Walt, S.; Schönberger, J.L.; Nunez-Iglesias, J. et al. (2014). "scikit-image: Image processing in Python". PeerJ 2: e453. doi:10.7717/peerj.453. PMC PMC4081273. PMID 25024921. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4081273. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, spelling, and grammar. We also added PMCID and DOI when they were missing from the original reference.

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