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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.

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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).

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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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