Time-Lapse Proteomics

Isotope labeling and mass spectrometry unite to track proteome dynamics.

Chandra Shekhar
Feb 28, 2007
<figcaption>Matthias Mann and colleagues used a time-based proteomic approach for mapping interaction in the nucleolus.</figcaption>
Matthias Mann and colleagues used a time-based proteomic approach for mapping interaction in the nucleolus.

Thousands of experiments focusing on the dynamics of single proteins and their closest interacting partners have provided valuable but fragmented views of the cell. Meanwhile, quantitative proteomics continues to build increasingly comprehensive but static inventories. To get a complete picture of the cell, one needs to characterize the dynamics and interactions of the entire proteome.

To address this, Matthias Mann of the Max Planck Institute for Biochemistry in Germany developed a method that combines large-scale quantitative proteomics with a technique for time-labeling cell cultures. First used to deduce tyrosine phosphorylation in growth factor signaling1 and later to describe the activity of a cellular substructure, the nucleolus,2 the method has helped map the in vivo phosphoproteome3 as well as the network of interactions in the nucleolus.4 Used in conjunction with traditional microscopy, it is providing a more complete view of the dramatic changes in protein composition in response to metabolic stimuli. "The beauty of the method is that it allows you to look at dynamics globally," says Tom Misteli of the National Institutes of Health in Bethesda, Md. "This sort of approach is absolutely essential for generating the data to model cellular behavior."

Time Stamping

The method is based on Mann?s earlier work with stable isotope-labeled amino acids in cell culture (SILAC),5 which grows cells with one of three forms of arginine and lysine tagged with different stable isotopes of carbon and nitrogen. Mann combined proteomes from three cell batches fed with the different versions and jointly analyzed them by mass spectrometry. He could then identify peptides from each batch by the offsets in their mass spectra. Since the three samples are analyzed under identical conditions, this allows their protein levels to be quantified relative to one another. By stimulating the batches for different lengths of time, Mann could measure the relative levels of proteins as a function of time. "For the first time, we could see the time course of a proteome," he says.

Any method of tracking a proteome in time has to deal with the vast number of components and their high dynamic range. Signaling proteins, usually of low abundance, are particularly hard to track. In 2003, when Mann was attempting to quantify the dynamics of epidermal growth factor receptor (EGFR) signaling, new instrumentation that had just become available provided the necessary accuracy and sensitivity for his experiments, when combined with software developed in his laboratory.

To characterize the dynamics of growth factor signaling, Mann stimulated isotope-tagged batches of cells with EGF for different time intervals and analyzed the combined lysate using mass spectrometry. Among the 80-odd tyrosine-phosphorylated effectors that showed up were many proteins not previously linked to EGFR signaling. Only a third of the effectors played a direct role in signal transduction; at least as many were involved in remodeling the cytoskeleton. Further, several RNA-binding proteins were unexpectedly activated. "The cell expends as much effort for the RNA binding proteins as it does for signaling to the nucleus," Mann says. "That has been neglected in the growth factor signaling field."

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B. Blagoev et al., "Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics," Nat Biotechnol, 22:1139-45, 2004. (Cited in 113 papers) J.S. Andersen et al., "Nucleolar proteome dynamics," Nature, 433:77-83, 2005. (Cited in 113 papers)

Nucleolar Dynamics

In 2002, Mann and erstwhile colleague Angus Lamond of the University of Dundee in Scotland had used SILAC to map several hundred proteins in the nucleolus.1 After Mann?s success in applying SILAC to characterizing EGFR dynamics, he and Lamond applied the time-lapse technique to study the behavior of HeLa cell nucleoli when rRNA transcription is inhibited. If nucleoli are transient structures designed solely for making ribosome subunits, as was previously believed, they should collapse when rRNA transcription is inhibited. Lamond and Mann?s results, presented in 2005, show a different picture.2 While many proteins in the nucleolus, including RNA-processing factors and exosome components, did indeed decrease in response to the inhibition, others, such as small nuclear ribonucleoproteins (snRNPs), increased. Indeed, some proteins, such as coilin, increased up to 10-fold. "It clearly indicated that the nucleolus is not simply a transient structure," Lamond says.

Lamond and Mann have extended their approach to the study of protein turnover within the nucleolus. In not-yet-published work they?ve found that more ribosomal proteins are imported into the nucleus than what would typically be needed for ribosome subunit production. "The nucleolus is doing a lot more than making ribosome subunits," says Lamond. "There is a huge amount of regulation and sensory input cycling in and out of it in a much more dramatic way than is realized." Last year, other researchers used Lamond and Mann?s data to publish an interaction map of nucleolar proteins.4

Lamond and Mann?s work on the nucleolar proteome using SILAC has matched well with small-scale measurements using traditional fluorescence microscopy. Lamond says that such a "dual strategy" is going to be key for systems biological studies of cells and organelles. "The combination of these two approaches is going to be very powerful," agrees Misteli. "It can be applied to any cell biological problem."

Having extended the time-lapse method to serine and threonine phosphorylation, Mann and his team in 2006 mapped the in vivo phosphoproteome in HeLa cells.3 They reported 6,600 phosphorylation sites on 2,244 proteins (more than all previous studies combined) and characterized their temporal dynamics in response to growth factor stimulation. Now available online in the Phosida database, their data set has been cross-referenced to the SwissProt and International Protein Index databases. "Mann?s approach can be extended to any type of protein signaling," says Orna Resnekov of the Molecular Sciences Institute in Berkeley, Calif., who studies yeast pheromone signal transduction. "His work sets a clear path for everybody who wants to do these types of studies."

References

1. B. Blagoev et al., "Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics," Nat Biotechnol, 22:1139-45, 2004. (cited in 113 papers) 2. J.S. Andersen et al., "Nucleolar proteome dynamics," Nature, 433:77-83, 2005. (Cited in 113 papers) 3. J.V. Olsen et al., "Global, in vivo, and site-specific phosphorylation dynamics in signaling networks," Cell, 127:635-48, November 2006. 4. A. Hinsby et al., "A wiring of the human nucleolus," Mol Cell, 22:285-95, 2006. 5. M. Mann, "Organellar proteomics," The Scientist, 18(7):32, April 12, 2004.