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ARQiv-HTS, a versatile whole-organism screening platform enabling in vivo drug discovery at high-throughput rates

Nature Protocols volume 11, pages 2432–2453 (2016)Cite this article

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Abstract

The zebrafish has emerged as an important model for whole-organism small-molecule screening. However, most zebrafish-based chemical screens have achieved only mid-throughput rates. Here we describe a versatile whole-organism drug discovery platform that can achieve true high-throughput screening (HTS) capacities. This system combines our automated reporter quantification in vivo (ARQiv) system with customized robotics, and is termed 'ARQiv-HTS'. We detail the process of establishing and implementing ARQiv-HTS: (i) assay design and optimization, (ii) calculation of sample size and hit criteria, (iii) large-scale egg production, (iv) automated compound titration, (v) dispensing of embryos into microtiter plates, and (vi) reporter quantification. We also outline what we see as best practice strategies for leveraging the power of ARQiv-HTS for zebrafish-based drug discovery, and address technical challenges of applying zebrafish to large-scale chemical screens. Finally, we provide a detailed protocol for a recently completed inaugural ARQiv-HTS effort, which involved the identification of compounds that elevate insulin reporter activity. Compounds that increased the number of insulin-producing pancreatic beta cells represent potential new therapeutics for diabetic patients. For this effort, individual screening sessions took 1 week to conclude, and sessions were performed iteratively approximately every other day to increase throughput. At the conclusion of the screen, more than a half million drug-treated larvae had been evaluated. Beyond this initial example, however, the ARQiv-HTS platform is adaptable to almost any reporter-based assay designed to evaluate the effects of chemical compounds in living small-animal models. ARQiv-HTS thus enables large-scale whole-organism drug discovery for a variety of model species and from numerous disease-oriented perspectives.

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Figure 1: Development and implementation of HTS-ARQiv.
Figure 2: Predicted SSMD scores using bootstrapping.
Figure 3: Robotics platform schematic.
Figure 4: Example protocol flowchart.
Figure 5: Real-time ARQiv data processing.
Figure 6: Summary of beta-cell neogenesis screen.

References

  1. Swinney, D.C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Eder, A. et al. Effects of proarrhythmic drugs on relaxation time and beating pattern in rat engineered heart tissue. Basic Res. Cardiol. 109, 436 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Walker, S.L. et al. Automated reporter quantification in vivo: high-throughput screening method for reporter-based assays in zebrafish. PLoS One 7, e29916 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Inglese, J. et al. Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. Proc. Natl. Acad. Sci. USA 103, 11473–11478 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wang, G. et al. First quantitative high-throughput screen in zebrafish identifies novel pathways for increasing pancreatic β-cell mass. Elife 4 http://dx.doi.org/10.7554/eLife.08261 (2015).

  6. Scannell, J.W., Blanckley, A., Boldon, H. & Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11, 191–200 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Rishton, G.M. Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov. Today 8, 86–96 (2003).

    Article  CAS  PubMed  Google Scholar 

  8. Medina, O., Estrada, J.C. & Servin, M. Robust adaptive phase-shifting demodulation for testing moving wavefronts. Opt. Express 21, 29687–29694 (2013).

    Article  PubMed  Google Scholar 

  9. Walker, M.J.A., Barrett, T. & Guppy, L.J. Functional pharmacology: the drug discovery bottleneck? Drug Discov. Today TARGETS 3, 208–215 (2004).

    Article  CAS  Google Scholar 

  10. Rennekamp, A.J. & Peterson, R.T. From phenotype to mechanism after zebrafish small molecule screens. Drug Discov. Today. Dis. Models 10, e51–e55 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rennekamp, A.J. & Peterson, R.T. 15 years of zebrafish chemical screening. Curr. Opin. Chem. Biol. 24, 58–70 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Peterson, R.T., Link, B.A., Dowling, J.E. & Schreiber, S.L. Small molecule developmental screens reveal the logic and timing of vertebrate development. Proc. Natl. Acad. Sci. USA 97, 12965–12969 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Peterson, R.T. et al. Chemical suppression of a genetic mutation in a zebrafish model of aortic coarctation. Nat. Biotechnol. 22, 595–599 (2004).

    Article  CAS  PubMed  Google Scholar 

  14. Burns, C.G. et al. High-throughput assay for small molecules that modulate zebrafish embryonic heart rate. Nat. Chem. Biol. 1, 263–264 (2005).

    Article  CAS  PubMed  Google Scholar 

  15. Stern, H.M. et al. Small molecules that delay S phase suppress a zebrafish bmyb mutant. Nat. Chem. Biol. 1, 366–370 (2005).

    Article  CAS  PubMed  Google Scholar 

  16. Murphey, R.D. & Zon, L.I. Small molecule screening in the zebrafish. Methods 39, 255–261 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. Anderson, C. et al. Chemical genetics suggests a critical role for lysyl oxidase in zebrafish notochord morphogenesis. Mol. Biosyst. 3, 51–59 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. Molina, G.A., Watkins, S.C. & Tsang, M. Generation of FGF reporter transgenic zebrafish and their utility in chemical screens. BMC Dev. Biol. 7, 62 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Owens, K.N. et al. Identification of genetic and chemical modulators of zebrafish mechanosensory hair cell death. PLoS Genet. 4, e1000020 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Cao, Y. et al. Chemical modifier screen identifies HDAC inhibitors as suppressors of PKD models. Proc. Natl. Acad. Sci. USA 106, 21819–21824 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Molina, G. et al. Zebrafish chemical screening reveals an inhibitor of Dusp6 that expands cardiac cell lineages. Nat. Chem. Biol. 5, 680–687 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yeh, H.H. et al. Ha-ras oncogene-induced Stat3 phosphorylation enhances oncogenicity of the cell. DNA Cell Biol. 28, 131–139 (2009).

    Article  CAS  PubMed  Google Scholar 

  23. Coffin, A.B. et al. Chemical screening for hair cell loss and protection in the zebrafish lateral line. Zebrafish 7, 3–11 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. de Groh, E.D. et al. Inhibition of histone deacetylase expands the renal progenitor cell population. J. Am. Soc. Nephrol. 21, 794–802 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kokel, D. et al. Rapid behavior-based identification of neuroactive small molecules in the zebrafish. Nat. Chem. Biol. 6, 231–237 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Paik, E.J., de Jong, J.L.O., Pugach, E., Opara, P. & Zon, L.I. A chemical genetic screen in zebrafish for pathways interacting with cdx4 in primitive hematopoiesis. Zebrafish 7, 61–68 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rihel, J. et al. Zebrafish behavioral profiling links drugs to biological targets and rest/wake regulation. Science 327, 348–351 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Milan, D.J., Peterson, T.A., Ruskin, J.N., Peterson, R.T. & MacRae, C.A. Drugs that induce repolarization abnormalities cause bradycardia in zebrafish. Circulation 107, 1355–1358 (2003).

    Article  PubMed  Google Scholar 

  29. Ali, S., van Mil, H.G.J. & Richardson, M.K. Large-scale assessment of the zebrafish embryo as a possible predictive model in toxicity testing. PLoS One 6, e21076 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Langheinrich, U. Zebrafish: a new model on the pharmaceutical catwalk. Bioessays 25, 904–912 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. North, T.E. et al. Prostaglandin E2 regulates vertebrate haematopoietic stem cell homeostasis. Nature 447, 1007–1011 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Peravali, R. et al. Automated feature detection and imaging for high-resolution screening of zebrafish embryos. Biotechniques 50, 319–324 (2011).

    Article  CAS  PubMed  Google Scholar 

  33. MacRae, C.A. & Peterson, R.T. Zebrafish as tools for drug discovery. Nat. Rev. Drug Discov. 14, 721–731 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Mathias, J.R., Saxena, M.T. & Mumm, J.S. Advances in zebrafish chemical screening technologies. Future Med. Chem. 4, 1811–1822 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Leung, C.K. et al. An ultra high-throughput, whole-animal screen for small molecule modulators of a specific genetic pathway in Caenorhabditis elegans. PLoS One 8, e62166 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Goktug, A.N., Chai, S.C. & Chen, T. Data analysis approaches in high throughput screening. Drug Discovery (ed. El-Shemy, H.A.). (InTech, 2013).

  37. Zhang, X.D. et al. Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens. J. Biomol. Screen. 13, 378–389 (2008).

    Article  CAS  PubMed  Google Scholar 

  38. Zhang, J., Chung, T. & Oldenburg, K. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen. 4, 67–73 (1999).

    Article  CAS  PubMed  Google Scholar 

  39. Zhang, X.D. Novel analytic criteria and effective plate designs for quality control in genome-scale RNAi screens. J. Biomol. Screen. 13, 363–377 (2008).

    Article  CAS  PubMed  Google Scholar 

  40. Zhang, X.D. Illustration of SSMD, z score, SSMD*, z* score, and t statistic for hit selection in RNAi high-throughput screens. J. Biomol. Screen. 16, 775–785 (2011).

    Article  PubMed  Google Scholar 

  41. Graf, S.F., Hötzel, S., Liebel, U., Stemmer, A. & Knapp, H.F. Image-based fluidic sorting system for automated zebrafish egg sorting into multiwell plates. J. Lab. Autom. 16, 105–111 (2011).

    Article  PubMed  Google Scholar 

  42. Parsons, M.J. et al. Notch-responsive cells initiate the secondary transition in larval zebrafish pancreas. Mech. Dev. 126, 898–912 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lawrence, M. & Lang, D. RGtk2: a graphical user interface toolkit for R. J. Stat. Softw. 37, 1–52 (2010).

    Article  Google Scholar 

  44. Borchers, H.W. pracma: Practical Numerical Math Functions. (2015). https://rdrr.io/rforge/pracma/ (2016).

  45. Rovira, M. et al. Chemical screen identifies FDA-approved drugs and target pathways that induce precocious pancreatic endocrine differentiation. Proc. Natl. Acad. Sci. USA 108, 19264–19269 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ariga, J., Walker, S.L. & Mumm, J.S. Multicolor time-lapse imaging of transgenic zebrafish: visualizing retinal stem cells activated by targeted neuronal cell ablation. J. Vis. Exp. 43, e2093 (2010).

    Google Scholar 

  47. Halsey, L.G., Curran-Everett, D., Vowler, S.L. & Drummond, G.B. The fickle P value generates irreproducible results. Nat. Methods 12, 179–185 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Westerfield, M. A guide for the laboratory use of zebrafish (Danio rerio) (Eugene, OR: University of Oregon Press, 2007).

  49. Adatto, I., Lawrence, C., Thompson, M. & Zon, L.I. A new system for the rapid collection of large numbers of developmentally staged zebrafish embryos. PLoS One 6, e21715 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hallare, A., Nagel, K., Köhler, H.-R. & Triebskorn, R. Comparative embryotoxicity and proteotoxicity of three carrier solvents to zebrafish (Danio rerio) embryos. Ecotoxicol. Environ. Saf. 63, 378–388 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. David, R.M. et al. Interference with xenobiotic metabolic activity by the commonly used vehicle solvents dimethylsulfoxide and methanol in zebrafish (Danio rerio) larvae but not Daphnia magna. Chemosphere 88, 912–917 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhang, X.D. A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays. Genomics 89, 552–561 (2007).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We are grateful to R. L. Daily, 3rd (integration designer, Hudson Robotics) for creating the ARQiv-HTS workstation schematics. A huge thanks goes to our in vivo drug discovery collaborators J. Liu (Department of Pharmacology and Molecular Science, Johns Hopkins School of Medicine) and M. Parsons (Department of Surgery, Johns Hopkins School of Medicine) and their research teams. We also thank M. Saxena for her editorial assistance, as well as other past and present Mumm and Luminomics laboratory members for helpful discussions. This work was supported by grants to J.S.M. from the NIH (RC4DK090816, R01EY022810, R41TR000945, and F31EY021713), DoD (MR130301), and the Foundation Fighting Blindness (TA-NMT-0614-0643-JHU-WG).

Author information

Author notes

  1. Steven L Walker

    Present address: Present address: The Chinese University of Hong Kong, Hong Kong, China.,

  2. David T White, Arife Unal Eroglu and Guohua Wang: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    David T White, Arife Unal Eroglu, Guohua Wang, Liyun Zhang, Sumitra Sengupta, Jiang Qian & Jeff S Mumm

  2. Department of Cellular Biology and Anatomy, Augusta University, Augusta, Georgia, USA

    David T White, Surendra K Rajpurohit, Steven L Walker & Jeff S Mumm

  3. Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA

    Ding Ding & Hongkai Ji

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Contributions

J.S.M. conceived of the ARQiv-HTS platform, designed reporter-based assay systems, and supervised the project. D.T.W., A.U.E., L.Z., S.S., S.K.R., and S.L.W. accomplished large-scale implementation of the ARQiv-HTS system and analyzed data. G.W., D.D., S.L.W., H.J., and J.Q. developed the software packages for assay optimization and real-time data analysis. D.T.W., A.U.E., and J.S.M. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Jeff S Mumm.

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Competing interests

J.S.M. is a founder and stakeholder of Luminomics, a company that is collaborating with Hudson Robotics and Union Biometrica to develop a series of benchtop AQRiv-HTS assay platforms as commercial products.

Integrated supplementary information

Supplementary Figure 1 Data transformation.

A: Representative data from an ARQiv-HTS assay quality test with arbitrary fluorescent units (AFU) expressed on an arithmetic scale. The data show unequal variance between the negative (green) and positive (red) controls. B: Base 2 log transformation of the data shown in A. Log transformation achieves more symmetrically distributed data around the respective means, as in a normal distribution, and can also facilitate a larger dynamic range of the effect size metrics used to evaluate tested compounds (e.g., increased SSMD scores, compare upper right in A and B).

Supplementary Figure 2 Economic large-scale egg production units.

A: Top view of simple grouped breeding system assembled from plastic storage units showing the uppermost mating chamber (empty). Dashed boxes represent areas where inserted mesh screen allows eggs to pass through to a middle collection chamber; dashed circle shows area where micron mesh is inserted in the collection chamber to allow water drainage. B: Close-up side view of all three chambers, mating, collection, and water, seated one inside the other. Arrows indicate egg movement from mating to collection chamber. C: Grouped breeding unit in use, egg production is correlated to density of breeders in mating chamber, plastic ‘plants’ can be dropped in to further stimulate breeding. C’: Measurement of eggs with graduated cylinder (once settled, 500-600eggs/mL). D: Top view of empty drop-in mating chambers. E: Side view of drop-in mating chamber (in use). Arrows indicate egg movement from tank to collection chamber.

Supplementary Figure 3 ARQiv package graphical user interface (GUI).

Upon installing the ARQiv R-based package, the GUI above is available for simplifying ARQiv data processing. The use of this GUI is detailed within relevant sections of the protocol. Briefly, the ARQiv R package includes functions that fall into two categories - those applied to ‘Pre-screening Assay Optimization' (upper panel) and 'Compound Analysis' (lower panel). The functions allow the user to calculate background signal, determine sample size, run quality control tests, perform virtual experiments to simulate compound efficacy - and finally, to perform compound analysis during iterative drug screen cycles.

Supplementary Figure 4 Titration-based ARQiv-HTS assay diagram.

Compounds are tested at a total six concentrations at a sample number of 16 per compound concentration, thus one 96 well plate per compound (center 96 well plate). To account for the possibility of signal changes over time, positive and negative control plates (red and green, respectively) ‘bookend’ every set of 10 tested compound plates. This process is reiterated for each series of tested compounds.

Supplementary Figure 5 COPAS-based larval fish sorting and microtiter plate dispensing.

Sorting and dispensing of transgenic larvae into microtiter plates can be automated using the COPAS-XL system. Fish are illuminated with a 561 solid-state nm laser as they pass through a flow cell (analysis chamber) wherein they are gated/sorted based on extinction (i.e., size and internal structure of object), time of flight (i.e., length of object), and fluorescence (emission at 610 nm +/-10). Upper panels are ‘gating dot plots’ denoting extinction (Ext, y-axis) and time of flight (Tof, x-axis) parameters used for size-based sorting of fish/non-fish objects as determined by user-defined ‘gate region’ (e.g., interior to dashed line in upper panel). Lower panel is fluorescence-based sorting of transgenic fish via user-defined ‘gate region’ in ‘sorting dot plot’ denoting ‘RFP’ signal (Red, y-axis) and time of flight (Tof, x-axis). Red bracket represents transgenic fish sorted into wells.

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White, D., Eroglu, A., Wang, G. et al. ARQiv-HTS, a versatile whole-organism screening platform enabling in vivo drug discovery at high-throughput rates. Nat Protoc 11, 2432–2453 (2016). https://doi.org/10.1038/nprot.2016.142

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