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A pooling-deconvolution strategy for biological network elucidation

Nature Methods volume 3, pages 183–189 (2006)Cite this article

Abstract

The generation of large-scale data sets is a fundamental requirement of systems biology. But despite recent advances, generation of such high-coverage data remains a major challenge. We developed a pooling-deconvolution strategy that can dramatically decrease the effort required. This strategy, pooling with imaginary tags followed by deconvolution (PI-deconvolution), allows the screening of 2n probe proteins (baits) in 2 × n pools, with n replicates for each bait. Deconvolution of baits with their binding partners (preys) can be achieved by reading the prey's profile from the 2 × n experiments. We validated this strategy for protein-protein interaction mapping using both proteome microarrays and a yeast two-hybrid array, demonstrating that PI-deconvolution can be used to identify interactions accurately with fewer experiments and better coverage. We also show that PI-deconvolution can be used to identify protein-small molecule interactions inferred from profiling the yeast deletion collection. PI-deconvolution should be applicable to a wide range of library-against-library approaches and can also be used to optimize array designs.

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Figure 1: Scheme for PI-deconvolution.
Figure 2: PI-deconvolution applied to protein interaction mapping.
Figure 3: PI-deconvolution applied to drug resistance screening of 128 (=27) yeast deletion strains in 14 pools (64 strains per pool).
Figure 4: PI-deconvolution simulated on yeast interactome (DIP).

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Acknowledgements

We thank H. Herschman, C. Miller, E. O'Shea, F. Fox, C. Stanyon and members of the Huang laboratory for critical readings and suggestions on the manuscript, the anonymous reviewer for introducing to us the idea of communication systems, Y. Du for assistance with drug screening and K. Scanlan for faithfully supporting our work. This research was partially supported by a Singleton Developmental Grant (to J.H.), University of California Systemwide Biotechnology Research & Education Program, Graduate Research and Education in Adaptive bioTechnology (GREAT) Training Grant 2005-268 (F.J. and J.H.), and a grant from the US National Center for Research Resources of the National Institutes of Health, P41 RR11823 (S.F.).

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Author notes

  1. Tony Hazbun

    Present address: Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, 47907, USA

Authors and Affiliations

  1. Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, and the Molecular Biology Institute, University of California, Los Angeles, 90095, California, USA

    Fulai Jin & Jing Huang

  2. Departments of Genome Sciences and Medicine, Howard Hughes Medical Institute (HHMI), University of Washington, Box 357730, Seattle, 98195, Washington, USA

    Tony Hazbun & Stanley Fields

  3. Protein Microarray Center, Invitrogen Life Technologies, 688 East Main Street, Branford, 06405, Connecticut, USA

    Gregory A Michaud, Michael Salcius & Paul F Predki

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Corresponding authors

Correspondence to Paul F Predki, Stanley Fields or Jing Huang.

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Competing interests

Some Invitrogen products (including preotein microarrays) were used druing the course of this research. G.A.M, M.S. and P.F.P. are employed by Invitrogen Life technologies.

Supplementary information

Supplementary Table 1

Design of partially overlapping pools. (XLS 16 kb)

Supplementary Table 2

Pooling scheme for Y2H array screens. (XLS 16 kb)

Supplementary Table 3

Comparison of single bait screening data and PI-Deconvolution data. (XLS 154 kb)

Supplementary Table 4

Unambiguously deconvoluted prey-bait pairs in PI-Deconvolution data. (XLS 29 kb)

Supplementary Table 5

Possible causes for profile transformation. (XLS 11 kb)

Supplementary Table 6

List of 128 yeast strains used for drug resistance PI-Deconvolution screening and generation of pools. (XLS 43 kb)

Supplementary Table 7

Proteome microarray spot data. (XLS 31 kb)

Supplementary Note (PDF 326 kb)

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Jin, F., Hazbun, T., Michaud, G. et al. A pooling-deconvolution strategy for biological network elucidation. Nat Methods 3, 183–189 (2006). https://doi.org/10.1038/nmeth859

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