Article series: Technologies and techniques

Multidimensional proteomics for cell biology

Journal name:
Nature Reviews Molecular Cell Biology
Volume:
16,
Pages:
269–280
Year published:
DOI:
doi:10.1038/nrm3970
Published online

Abstract

The proteome is a dynamic system in which each protein has interconnected properties — dimensions — that together contribute to the phenotype of a cell. Measuring these properties has proved challenging owing to their diversity and dynamic nature. Advances in mass spectrometry-based proteomics now enable the measurement of multiple properties for thousands of proteins, including their abundance, isoform expression, turnover rate, subcellular localization, post-translational modifications and interactions. Complementing these experimental developments are new data analysis, integration and visualization tools as well as data-sharing resources. Together, these advances in the multidimensional analysis of the proteome are transforming our understanding of various cellular and physiological processes.

At a glance

Figures

  1. Multidimensional proteome analysis of cells and tissues.
    Figure 1: Multidimensional proteome analysis of cells and tissues.

    a | Proteins can have many different properties (dimensions) that are either largely physically (yellow shaded area), chemically (orange shaded area) or biologically (beige shaded area) relevant. Shown in this figure are some of the properties that we think are most important for cell biology research and those that need to be taken into consideration when developing new separation methods for multidimensional analysis. b | A series of stacked cubes is shown, each of which contains a discrete pool of proteomics data that correspond to the value of each dimension (localization, cell cycle phase and turnover rate). For each cube (see expanded cubes) we can analyse other dimensions such as protein activity, total protein abundance and phosphorylation stoichiometry. Together these visually represent an approach for the multidimensional analysis of protein data. The spheres inside the expanded cubes A and B represent a specific protein of interest that in the G1 phase of the cell cycle may exist in either the cytosol or in the nucleus, with fast and slow turnover rates, respectively. These different pools (cubes) of the same protein have different properties, including increased protein abundance, phosphorylation and activity in the nuclear pool (cube B) compared with the cytosolic pool (cube A).

  2. Methods for protein turnover analysis.
    Figure 2: Methods for protein turnover analysis.

    Proteome-wide turnover is typically measured using one of the two approaches shown in this figure. Method 1 involves pulse-labelling of amino acids using either stable isotope labelling by amino acids in cell culture (SILAC) or 15N-labelling. The cells start with proteins (stars) containing 'light' stable isotopes and for various periods of time are switched into media with a 'heavy' isotope that is stably incorporated into specific amino acids and thus labels newly synthesize proteins. Proteins with rapid turnover rates (for example, cytosolic proteins (part a)) will rapidly incorporate high levels of the heavy isotope, whereas protein pools that have slower turnover rates (from the membrane (part b) and the nucleus (part c) in this example) will show slower rates of heavy isotope incorporation. The ratio between light- and heavy-labelled peptides, which can be extracted using mass spectrometry-based analysis, is a measure of the rate of turnover for each peptide detected and thereby for each protein. Method 2 involves the use of cycloheximide to block protein synthesis in live cells for various periods of time. The comparison of protein abundance between untreated and treated cells enables a calculation of the depletion rate of a protein in the cells, which may occur as a result of its degradation, its secretion or both. The comparison of protein abundances may use any quantitative mass spectrometry technique, such as label-free analysis and isobaric-tag labelling. For methods 1 or 2, the use of cellular fractionation (into subcellular compartments in this case) can greatly increase the information gained compared with the analysis of total cell lysates (part d). The protein depicted in this figure is effectively stable in the nuclear compartment (part c), has a slow turnover rate in the membrane-associated pool (part b) and a fast turnover rate in the cytosol (part a). When total cell lysates are examined, such turnover differences can be masked by the pools of protein that are most abundant. In this example, the fast turnover of the cytosolic pool masks the stable nuclear fraction when total cell lysates are examined. m/z, the mass to charge ratio of each peptide ion as measured by mass spectrometers.

  3. Approaches for the analysis of protein interactions.
    Figure 3: Approaches for the analysis of protein interactions.

    There are three main approaches for unbiased analysis of protein–protein interactions. a | The first approach, based on affinity pull-down and isolation, uses either specific antibodies to an endogenous protein or to a tagged version of a protein to specifically isolate the protein of interest and its interacting partners. Protein complexes are eluted for subsequent analysis by digestion and liquid chromatography followed by tandem mass spectrometry (LC-MS/MS), before statistical approaches are applied to identify specific- (purple and blue circles) from nonspecific- (green circles) binding partners present in the eluted mixture. b | The second approach is based on proximity labelling, in which cell lines are constructed that ectopically express a protein of interest (blue circle) fused to either a promiscuous biotin ligase or a peroxidase enzyme. These enzymes can then covalently transfer biotin labels (orange star) to proteins in close proximity (purple circle), which are potential interacting proteins. The cells can then be lysed and the biotinylated proteins specifically isolated using streptavidin-conjugated beads. Similar to the procedure described in part a, the isolated proteins are digested and analysed by LC-MS/MS and statistical tests are applied to identify specific (blue and purple circles) versus nonspecific (green circles) label transfer or pull-down. c | The third approach uses protein correlation profiling with various biochemical methods, such as chromatography and density gradient centrifugation, to separate endogenous protein complexes according to size, density, charge or hydrophobicity, assuming that interacting proteins will co-elute. This analysis may involve a single type of separation or may involve multiple forms of separation, either sequentially or in parallel. Following the collection of fractions from each separation, the digestion of proteins and their identification by LC-MS/MS analysis, an elution profile for each detected protein is generated and compared with that of other proteins. Clustering algorithms can then identify co-eluting proteins and infer the protein complexes in the lysate. Additional information can also be obtained such as the size or the density of each identified complex. HIC, hydrophobic interaction chromatography; IEX, ion-exchange chromatography; SEC, size-exclusion chromatography.

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Author information

Affiliations

  1. Laboratory for Quantitative Proteomics, Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK.

    • Mark Larance &
    • Angus I. Lamond

Competing interests statement

The authors declare no competing interests.

Corresponding authors

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Author details

  • Mark Larance

    Mark Larance is a Royal Society of Edinburgh Scottish Government Personal Research Fellow at the University of Dundee, UK. He studies metabolism using a combination of cell biology, biochemistry and mass spectrometry-based proteome analysis.

  • Angus I. Lamond

    Angus I. Lamond is Professor of Biochemistry and a Wellcome Trust Principal Research Fellow at the University of Dundee, UK. His group study gene expression and the functional organization of mammalian cell nuclei. Their approach combines quantitative mass spectrometry-based proteomics with fluorescence imaging, cell biology and computational methods.

Supplementary information

PDF files

  1. Supplementary information S1 (box) (285 KB)

    Stable isotope labelling techniques for proteome analysis.

  2. Supplementary information S2 (figure) (193 KB)

    Web-based data sharing of multi-dimensional proteomics datasets.

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