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Deducing the mechanism of action of compounds identified in phenotypic screens by integrating their multiparametric profiles with a reference genetic screen

Nature Protocols volume 9, pages 474–490 (2014)Cite this article

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Abstract

Cell-based high-content screens are increasingly used to discover bioactive small molecules. However, identifying the mechanism of action of the selected compounds is a major bottleneck. Here we describe a protocol consisting of experimental and computational steps to identify the cellular pathways modulated by chemicals, and their mechanism of action. The multiparametric profiles from a 'query' chemical screen are used as constraints to select genes with similar profiles from a 'reference' genetic screen. In our case, the query screen is the intracellular survival of mycobacteria and the reference is a genome-wide RNAi screen of endocytosis. The two disparate screens are bridged by an 'intermediate' chemical screen of endocytosis, so that the similarity in the multiparametric profiles between the chemical and the genetic perturbations can generate a testable hypothesis of the cellular pathways modulated by the chemicals. This approach is not assay specific, but it can be broadly applied to various quantitative, multiparametric data sets. Generation of the query system takes 3–6 weeks, and data analysis and integration with the reference data set takes an 3 additional weeks.

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Figure 1: An overview of the distinct stages involved in the protocol.
Figure 2: Schematics of the HC assays for endocytosis and intracellular mycobacterial survival.
Figure 3: Image analysis using MotionTracking.
Figure 4: Scheme for integration of multiparametric profiles from the mycobacteria HC chemical screen and the genome-wide reference endocytosis screen.

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Acknowledgements

We thank J. Pieters (Biozentrum, Basel) for M. bovis BCG-GFP strain and A. Roesen-Wolff (Technische Universität Dresden (TUD)) for the help with monocyte isolation. We are indebted to High-Throughput Technology Development Studio (HT-TDS), MPI-CBG for expert technical assistance, to the High-Performance Computing Center at TUD and the Computer Department for outstanding IT support and to H. Nonaka for critical comments on the manuscript. The work was supported by the EU-FP7–funded projects NATT, PHAGOSYS and APO-SYS, and by the Max Planck Society.

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Authors and Affiliations

  1. Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Dresden, Germany

    Varadharajan Sundaramurthy, Rico Barsacchi, Mikhail Chernykh, Martin Stöter, Nadine Tomschke, Marc Bickle, Yannis Kalaidzidis & Marino Zerial

  2. Faculty of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russia

    Yannis Kalaidzidis

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  1. Varadharajan Sundaramurthy
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Contributions

V.S. and M.Z. conceived the project; V.S., R.B., M.S. and N.T. developed and performed the experimental steps; M.C. and Y.K. developed the computational tools and performed the analysis; and M.B. and M.Z. coordinated and supervised the project.

Corresponding author

Correspondence to Marino Zerial.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Organization of the endosomics database.

The endosomics database (http://endosomics.mpi-cbg.de) is a repository for the high content data from the genome-wide screen of endocytosis. Quantitative multi-parametric profile for individual genes can be searched and exported. This figure details the central aspects of the organization and representation of the dataset.

Supplementary Figure 2 Screenshots from http://endosomics.mpi-cbg.de/gws/search (version 0.7.8.3).

This tool is used to compare the multi-parametric profiles from the query or intermediate systems with the endocytosis genome-wide reference system. (a) Users can generate the multi-parametric profiles for the query or intermediate system through the interactive graphic window. (b) Screen-shot of the list of parameters used in the endocytosis reference dataset. (c) Screenshot of the window to feed in correlation and phenoscore values.

Supplementary Figure 3 Screenshots from MotionTracking software (version 8.81.15) for the comparison of multiparametric profiles from reference dataset provided by the users.

(a) Screenshot of the step involved in importing an external reference dataset. (b-d) Screenshots of the steps involved in computing the phenoscore and correlations.

Supplementary information

Supplementary Figure 1

Organization of the endosomics database. (PDF 2416 kb)

Supplementary Figure 2

Screenshots from http://endosomics.mpi-cbg.de/gws/search (version 0.7.8.3). (PDF 1669 kb)

Supplementary Figure 3

Screenshots from MotionTracking software (version 8.81.15) for the comparison of multiparametric profiles from reference dataset provided by the users. (PDF 731 kb)

Supplementary Discussion

(PDF 1139 kb)

Supplementary Data 1

This is a compressed file that contains a sample MotionTracking project, nature_protocols.mtj, and some test images of mycobacteria infected human primary macrophages. Users are required to open the .mtj file in MotionTracking to access the images. Mycobacteria and cells have been pre-identified and the object search parameters are stored in the file nature_protocols_mt.srp. The parameters for object statistics are provided in nature_protocols_mt.stp. (ZIP 25508 kb)

Supplementary Data 2

This is a compressed file that contains a sample CellProfiler project, TbExample_pipeline.cp and some test images of mycobacteria-infected human primary macrophages that have been stained for lysosomes. (ZIP 16469 kb)

Supplementary Data 3

This is a compressed file that contains a .csv file having normalized multiparametric profiles of a subset of 1000 genes from the genome-wide endocytosis screen1. (ZIP 158 kb)

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Sundaramurthy, V., Barsacchi, R., Chernykh, M. et al. Deducing the mechanism of action of compounds identified in phenotypic screens by integrating their multiparametric profiles with a reference genetic screen. Nat Protoc 9, 474–490 (2014). https://doi.org/10.1038/nprot.2014.027

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