High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cellsNat Biotechnol


Malte Wachsmuth, Christian Conrad, Jutta Bulkescher, Birgit Koch, Robert Mahen, Mayumi Isokane, Rainer Pepperkok, Jan Ellenberg
Molecular Medicine / Applied Microbiology and Biotechnology / Biotechnology / Biomedical Engineering / Bioengineering


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A ll rig ht s re se rv ed . nature biotechnology  advance online publication  l e t t e r s

To understand the function of cellular protein networks, spatial and temporal context is essential. Fluorescence correlation spectroscopy (FCS) is a single-molecule method to study the abundance, mobility and interactions of fluorescence-labeled biomolecules in living cells. However, manual acquisition and analysis procedures have restricted live-cell FCS to short-term experiments of a few proteins.

Here, we present high-throughput (HT)-FCS, which automates screening and time-lapse acquisition of FCS data at specific subcellular locations and subsequent data analysis1,2. We demonstrate its utility by studying the dynamics of 53 nuclear proteins3,4. We made 60,000 measurements in 10,000 living human cells, to obtain biophysical parameters that allowed us to classify proteins according to their chromatin binding and complex formation. We also analyzed the cell-cycle-dependent dynamics of the mitotic kinase complex Aurora B/INCENP5 and showed how a rise in Aurora concentration triggers two-step complex formation. We expect that throughput and robustness will make HT-FCS a broadly applicable technology for characterizing protein network dynamics in cells.

Characterizing proteins in their natural environment is essential for understanding their roles in cellular processes. Imaging of fluorescently labeled proteins in living cells is a powerful method for studying protein localization and interactions in their spatial and functional context, and genome editing methods now also allow physiological expression of such fusion proteins in human cell lines6. Various techniques have been developed to extract from cells quantitative information about the dynamic behavior of labeled biomolecules7–9.

Among these, FCS measures a particularly comprehensive set of parameters by analyzing local concentration fluctuations of single molecules to obtain information about free and bound fractions of labeled molecules, their diffusion properties and absolute concentrations8.

The method can be extended to fluorescence cross-correlation spectroscopy (FCCS) experiments, where the fluctuation of differentially labeled molecules reveals quantitative bi- or multimolecular interaction properties2,10. Thus, FC(C)S measurements at specific subcellular localizations identified by confocal fluorescence microscopy is, in principle, ideally suited to explore the localization, diffusion and changing composition of protein complexes, allowing one, for example, to build three-dimensional cellular concentration maps11.

Since its implementation on some commercial confocal microscopes, FC(C)S has successfully enabled studies, for example, on nuclear export–competent mRNA-protein particles12, transcription factors in developing embryos13, chromatin-associated and chromatinmodifying proteins14, and morphogen gradients15.

However, confocal FC(C)S experiments in live cells are based upon a manual, labor-intensive workflow of image optimization and acquisition, decisions of where and when to acquire data, and the actual fluctuation-spectra recording. After acquisition, the processing and evaluation of the data is tedious manual work, because the method’s high sensitivity and time resolution reveal the strong variability of single-molecule properties in the cellular interior, requiring careful corrections to extract accurate information16. Also, biological variations in time and space have to be taken into account properly. In summary, because acquisition and analysis of FC(C)S data are carried out manually, require a great deal of time and are prone to bias, it has been difficult to exploit the potential of the technique for in vivo proteomics, that is, to study the dynamics of many different proteins or of the same protein at many different time points with sufficient sampling for robust statistical measurements.

Recent developments in the automation of screening microscopy17,18 have made it possible to overcome this challenge and fully automate live-cell FC(C)S with correlated confocal imaging and achieve high throughput and experimental and statistical robustness. Real-time image analysis by machine vision has already enabled unattended cellular classification19 and, combined with online feedback procedures using, for example, the ‘Micropilot’ software, has allowed fully automated high-resolution confocal imaging experiments20,21. In addition, automatic FCS acquisition has allowed the analysis of a yeast library of more than 4,000 proteins22, and more efficient processing of fluorescence fluctuation data became feasible for imaging FCS data23,24. Here, we have achieved full automation of the entire workflow of correlated confocal imaging and FC(C)S acquisition of live cells as well as data analysis to accomplish high-throughput characterization of a large

High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cells

Malte Wachsmuth1,5, Christian Conrad2,3,5, Jutta Bulkescher2,4, Birgit Koch1, Robert Mahen1, Mayumi Isokane1,

Rainer Pepperkok1,2 & Jan Ellenberg1 1Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany. 2Advanced Light Microscopy Facility, European Molecular Biology

Laboratory, Heidelberg, Germany. 3Theoretical Bioinformatics, German Cancer Research Center/BioQuant, Heidelberg, Germany. 4Present address: Protein Imaging

Center, Novo Nordisk Foundation Center for Protein Research and Danish Stem Cell Center, University of Copenhagen, Copenhagen, Denmark. 5These authors contributed equally to this work. Correspondence should be addressed to J.E. (jan.ellenberg@embl.de) or R.P. (rainer.pepperkok@embl.de).

Received 6 October 2014; accepted 24 December 2014; published online 16 March 2015; doi:10.1038/nbt.3146 © 20 15

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A ll rig ht s re se rv ed .   advance online publication nature biotechnology l e t t e r s set of proteins, as well as time-resolved characterization of protein complex dynamics in single cells over an entire cell cycle.