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Analyzing proteome topology and function by automated multidimensional fluorescence microscopy

Nature Biotechnology volume 24, pages 1270–1278 (2006)Cite this article

Abstract

Temporal and spatial regulation of proteins contributes to function. We describe a multidimensional microscopic robot technology for high-throughput protein colocalization studies that runs cycles of fluorescence tagging, imaging and bleaching in situ. This technology combines three advances: a fluorescence technique capable of mapping hundreds of different proteins in one tissue section or cell sample; a method selecting the most prominent combinatorial molecular patterns by representing the data as binary vectors; and a system for imaging the distribution of these protein clusters in a so-called toponome map. By analyzing many cell and tissue types, we show that this approach reveals rules of hierarchical protein network organization, in which the frequency distribution of different protein clusters obeys Zipf's law, and state-specific lead proteins appear to control protein network topology and function. The technology may facilitate the development of diagnostics and targeted therapies.

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Figure 1: Protein mapping by MELC.
Figure 2: Construction of toponome maps.
Figure 3: Robustness of CMP detection and 3D colocalization mapping.
Figure 4: Toponome maps of skin biopsies from patients with inflammatory skin disease.
Figure 5: Zipf's plot of the relationship between the rank and the frequency of CMP motifs in human skin.
Figure 6: Toponome maps of lamina I/II in rat spinal cords with and without CCI as a model of chronic neuropathic pain.
Figure 7: Toponome maps of TE671 rhabdomyosarcoma cells before and during transition to the migratory state.

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Acknowledgements

We thank K. Neubert for supplying bis-ala-rhodamine 110, B.P. Roques for RB3014, and A. Borissenko and P. Karcher for computational help. The study was supported by the Bundesministerium für Bildung und Forschung (NBL-3 FKZ: 01ZZ0107, 01ZZ0407; NGFN2 01 GR 0446, CELLECT 0312844, BioChance 0312452), the DFG (627/1-8; INK “Bildinformation”) and Land Saxony-Anhalt. A.W.M. Dress thanks the Center for Combinatorics at Nankai University for hospitality during the preparation of this manuscript. We are grateful for animal tissue provided by F. Hucho/O. Bogen and fruitful discussions, and appreciate critical reading by A. Leech. We thank Anja Bastian, Katrin Brennecke and Franziska Böckelmann for technical assistance.

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Author notes

  1. Manuela Friedenberger, Marcus Bode and Andreas W M Dress: These authors contributed equally to this work.

Authors and Affiliations

  1. Molecular Pattern Recognition Research (MPRR) Group, Institute of Medical Neurobiology, Otto-von-Guericke-University Magdeburg, Magdeburg, D-39120, Germany

    Walter Schubert, Manuela Friedenberger & Marcus Bode

  2. Clinic for Dermatology and Venereology, Otto-von-Guericke-University Magdeburg, Magdeburg, D-39120, Germany

    Bernd Bonnekoh, Raik Böckelmann & Harald Gollnick

  3. CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences (SIBS), Shanghai, CN-200031, China

    Andreas W M Dress

  4. MPI for Mathematics in the Sciences, Leipzig, D-04103, Germany

    Andreas W M Dress

  5. ZENIT Technology Park, Magdeburg, D-39120, Germany

    Walter Schubert, Ansgar J Pommer, Lars Philipsen, Yanina Malykh & Marcus Bode

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Contributions

W.S. invented MELC and designed the study. M.F. and M.B. closely cooperated with L.P., A.J.P. and W.S. to develop robots and corresponding biological assays. A.W.M.D. performed mathematical image analyses. W.S., M.F. and M.B. designed, performed and interpreted data of multidimensional analyses of protein locations. CCI tissue material was analyzed and interpreted by W.S. and corresponding statistical motif analyses were done by L.P. Experiments with rhabdomyosarcoma cells were performed by M.F. and analyzed and interpreted in close cooperation with M.B., L.P. and W.S. 3D-images were generated by M.B. in close cooperation with W.S. W.S., M.B. and M.F. prepared the corresponding parts of the manuscript. B.B., R.B. and H.G. closely cooperated with A.J.P., Y.M. and L.P. in the dermatological and skin tissue–related investigations. In detail, B.B., A.J.P., R.B. and H.G. designed these clinico-experimental studies. B.B. and H.G. performed the clinical study part. R.B. did most of the accompanying conventional immunohistology. A.J.P., Y.M. and L.P. did the MELC experiments. R.B. binarized most of the MELC images and performed statistics. L.P. contributed all computer analyses and visualizations. B.B., A.J.P., L.P., R.B., Y.M. and H.G. interpreted these data and prepared the corresponding parts of the manuscript.

Corresponding author

Correspondence to Walter Schubert.

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Competing interests

The authors declare that their competing financial interests in this work are too numerous to itemize.

Supplementary information

Supplementary Fig. 1

Principle of the MELC Procedure. (PDF 713 kb)

Supplementary Fig. 2

Control Experiment. (PDF 467 kb)

Supplementary Fig. 3

Performance and Validation Tests for MELC. (PDF 147 kb)

Supplementary Fig. 4

Toponome Map of the Upper Dermis. (PDF 80 kb)

Supplementary Fig. 5

Colocalization Map of Keratinocyte Stem Cells. (PDF 851 kb)

Supplementary Fig. 6

Efalizumab Binding Sites in Skin. (PDF 13577 kb)

Supplementary Table 1

MELC Tag Libraries. (PDF 22 kb)

Supplementary Table 2

CMP and Motif Lists. (PDF 1523 kb)

Supplementary Table 3

MELC Toponome Data Base. (PDF 10 kb)

Supplementary Table 4

CD11a as Lead Protein in Inflammatory Skin Disease. (PDF 24 kb)

Supplementary Table 5

Demographic Data of Individuals Subjected to MELC Analysis of Skin Biopsies. (PDF 17 kb)

Supplementary Video 1

Exploring a Human Hepatocyte at 3D. (MOV 8902 kb)

Supplementary Video 2

3D Rocking Images of Nervous and Muscle Tissue. (MOV 9781 kb)

Supplementary Video 3

100 MELC Cycles on Skin Tissue and 3D Skin Imaging. (MOV 9064 kb)

Supplementary Video 4

Lead Protein Inhibition and Time-resolved Toponomics of Rhabdomyosarcoma Cells. (MOV 8291 kb)

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Schubert, W., Bonnekoh, B., Pommer, A. et al. Analyzing proteome topology and function by automated multidimensional fluorescence microscopy. Nat Biotechnol 24, 1270–1278 (2006). https://doi.org/10.1038/nbt1250

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