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 signaling
The method is based on Mann?s earlier work with stable isotope-labeled amino acids in cell culture (SILAC),
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."
Data derived from the Science Watch/Hot Papers database and the Web of Science (Thomson ISI) show that Hot Papers are cited 50 to 100 times more often than the average paper of the same type and age.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)
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.
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.
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.