Locations Everyone: Lights, Camera, Action!
I
n recent years, major advances have occurred in the methods that can be used to analyze proteome composition, especially in the application of MS to tissue and plasma samples. These advances have led to a dramatic increase in basic knowledge about protein expression and to the identification of many proteins that are likely to be important for diagnosis or treatment of disease. The bulk of this work has been based on comparisons between healthy and diseased samples. However, much less attention has been paid to the analysis of proteome variation within a sample over space and time. Parallel to these developments in MS-based proteomics technologies, major advances have occurred in microscopy methods that are suitable for addressing the distribution and dynamics of proteins at the cellular and subcellular levels. The development of highly automated fluorescence microscopes and the formulation of computational methods for the analysis of the resulting images have been especially important. These approaches have tremendous potential for determining not only which proteins are expressed in a particular cell or tissue but also where within those cells or tissues each protein is found. The different perspectives on spatial proteomics are illustrated by the terms commonly used to describe approaches to the problem of subcellular location. Approaches derived from the MS-based proteomics community are typically referred to as “organelle proteomics”, whereas those derived from microscopy are referred to as “location proteomics” or “toponomics”. The first term embodies a view that each organelle has a distinct and fixed proteome that can be identified, usually by fractionation. The latter terms focus on determining the locations of proteins within cells or tissues; this perspective allows for variation in protein location over time without a requirement that explicit boundaries dividing proteins into organelles be identified. The identification of organelle constituents can be an important starting point, but we believe that a much more detailed and dynamic description of subcellular organization will be necessary for a thorough understanding of cell and tissue behaviors.
10.1021/pr8009947
© 2009 American Chemical Society
This level of complexity comes at a cost for those researchers interested in proteomics: instead of studies of tens of organelle proteomes, we will need studies that detail the precise locations of tens of thousands of proteins within hundreds of cell types under hundreds of conditions over many timescales. Further, these studies must be grounded in an inherently probabilistic framework rather than simply being lists of components, because the distributions of proteins among compartments in different cells are not fixed but governed by rates of association, dissociation, and transport (all of which can change). Models that capture variation in spatial pattern from cell to cell are just beginning to be built. Ultimately, this structured information about spatial and temporal proteome variation will need to be incorporated into virtual cell models so that they can accurately simulate complex cellular behaviors such as cell motility, regulated secretion, cell division, and differentiation. This special issue contains articles that address proteome variation over space and time. The papers describe the use of automated microscopy to learn how components of complex subcellular structures are assembled and regulated, as well as the kinetics of proteome changes in various models of disease onset or differentiation. We would like to express our thanks to all of the authors and reviewers who have contributed to this special issue. The field of proteomics faces a major challenge in the coming years: to develop a comprehensive picture of protein differences not only across individuals but also within individuals. Spatial scales ranging from subcellular organelles to tissues and temporal scales from fractions of seconds to years must be taken into account. We hope that some of the approaches presented in this special issue will contribute to the development of this picture. ROBERT F. MURPHY Lane Center for Computational Biology Carnegie Mellon University JOSHUA LABAER Harvard Institute of Proteomics Harvard Medical School
Journal of Proteome Research • Vol. 8, No. 1, 2009 1
