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Multiparameter screening method for developing optimized red-fluorescent proteins

Nature Protocols volume 15, pages 450–478 (2020)Cite this article

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Abstract

Genetically encoded fluorescent proteins (FPs) are highly utilized in cell biology research to study proteins of interest or signal processes using biosensors. To perform well in specific applications, these FPs require a multitude of tailored properties. It is for this reason that they need to be optimized by using mutagenesis. The optimization process through screening is often based solely on bacterial colony brightness, but multiple parameters ultimately determine the performance of an optimal FP. Instead of characterizing other properties after selection, we developed a multiparameter screening method based on four critical parametersscreened simultaneously: fluorescence lifetime, cellular brightness, maturation efficiency, and photostability. First, a high-throughput primary screen (based on fluorescence lifetime and cellular brightness using a mutated FP library) is performed in bacterial colonies. A secondary multiparameter screen based on all four parameters, using a novel bacterial–mammalian dual-expression vector enables expression of the best FP variants in mammalian cell lines. A newly developed automated multiparameter acquisition and cell-based analysis approach for 96-well plates further increased workflow efficiency. We used this protocol to yield the record-bright mScarlet, a fast-maturating mScarlet-I, and a photostable mScarlet-H. This protocol can also be applied to other FP classes or Förster resonance energy transfer (FRET)-based biosensors with minor adaptations. With an available mutant library of a template FP and a complete and tested laboratory setup, a single round of multiparameter screening (including the primary bacterial screen, secondary mammalian cell screen, sequencing, and data processing) can be performed within 2 weeks.

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Fig. 1
Fig. 2: Plasmid map of expression vector pDRESS.
Fig. 3: Anticipated results of a primary screen on bacterial colonies.
Fig. 4: Obtaining lifetime information from frequency-domain signals.
Fig. 5: Anticipated result of a fluorescence lifetime screening assay in mammalian cells.
Fig. 6: Anticipated result of a cellular brightness screening assay in mammalian cells.
Fig. 7: Anticipated result of a photostability screening assay in mammalian cells.
Fig. 8: The evolution path of mScarlet in a 3D landscape.
Fig. 9: Adapting a microscope to image Petri dishes.

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Data availability

The test data for the different screening macros and output can be found at the Zenodo repository: https://doi.org/10.5281/zenodo.3347150.

Code availability

The ImageJ macros and MATLAB script used for the test data accompanying this manuscript are available at the repositories Zenodo and GitHub; repository updates with the latest versions can be found at GitHub. The specific links to the software have been provided in the Equipment section.

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Acknowledgements

We thank M. Hink for proofreading the manuscript. We thank R. Breedijk for technical assistance with the microscopes. We thank J. Hertz and J. Bosch (Lambert Instruments) and Nikon Netherlands for their assistance in setting up the automated FLIM screening using MATLAB API. This work was supported by the NWO CW-Echo Grant 711.011.018 (M.A.H. and T.W.J.G.), Grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands, and the ALW-VIDI Grant 864.09.015 (M.P.) from the Netherlands Organization for Scientific Research (NWO).

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Authors and Affiliations

  1. Section of Molecular Cytology, van Leeuwenhoek Centre for Advanced Microscopy, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands

    Daphne S. Bindels, Marten Postma, Lindsay Haarbosch & Theodorus W. J. Gadella Jr

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  1. Daphne S. Bindels
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  4. Laura van Weeren
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Contributions

The general approach was designed by T.W.J.G. and D.S.B.; D.S.B. designed the dual expression vector; D.S.B., L.H., and L.V.W. cloned the constructs, and performed and tested the mutagenesis and screening experiments. T.W.J.G. developed the ImageJ macros for ratio, bleach, and FLIM analysis. D.S.B. analyzed the data. M.P. and D.S.B. developed the MATLAB scripts for the automated FLIM screening pipeline and the 5D scatterplot. D.S.B., M.P., and T.W.J.G. wrote the manuscript, and all authors reviewed the manuscript.

Corresponding author

Correspondence to Theodorus W. J. Gadella Jr.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Alexander S. Mishin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Bindels D. S. et al. Nat. Methods 14, 53–56 (2017): https://www.nature.com/articles/nmeth.4074

Bindels D. S. et al. Methods Mol. Biol. 1076, 371–417 (2013): https://link.springer.com/protocol/10.1007%2F978-1-62703-649-8_16

Goedhart J. et al. Nat. Methods 7, 137–139 (2010): https://www.nature.com/articles/nmeth.1415

Integrated supplementary information

Supplementary Fig. 1 Automated 96-well lifetime acquisition GUI.

In the MATLAB GUI specific excitation and emission filters can be selected together with autoexposure and output file settings (a), Each well can be selected or deselected by using the well selector (b), the lifetime files are exported to a large TIF hyperstack together with the metadata that includes labels for each well (c).

Supplementary Fig. 2 Cell segmentation and fluorescence lifetime visualization ImageJ macro.

An ImageJ macro allows to set several processing and display parameters and generates a TIF stack with display figures for all wells present in the hyperstack.

Supplementary Fig. 3 Ratio analysis ImageJ macro.

This macro performs cell segmentation and determines ratio of red over cyan and red over green fluorescence intensity by linear regression using the fluorescence intensities of each cell.

Supplementary Fig. 4 Photostability analysis ImageJ macro.

This macro performs cell segmentation and determines the first frame in which the intensity is more than 50% decreased relative to the initial intensity and calculates the residual fluorescence intensity.

Supplementary Fig. 5 MATLAB ScatterGUI.

A customized MATLAB GUI interface to explore the multi-parameter space of all screening results using scatterplots.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5.

Reporting Summary

Supplementary Data 1

Underlying figure data

Supplementary Video 1

Scatterplot movie of mScarlet evolution

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Bindels, D.S., Postma, M., Haarbosch, L. et al. Multiparameter screening method for developing optimized red-fluorescent proteins. Nat Protoc 15, 450–478 (2020). https://doi.org/10.1038/s41596-019-0250-7

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