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. 2011 Mar;11(6):1153-9.
doi: 10.1002/pmic.201000548. Epub 2011 Feb 16.

Mass spectrometry-based immuno-precipitation proteomics - the user's guide

Sara ten Have  1 , Séverine Boulon, Yasmeen Ahmad, Angus I Lamond
Affiliations

Mass spectrometry-based immuno-precipitation proteomics - the user's guide

Sara ten Have et al. Proteomics. 2011 Mar.

Abstract

Immuno-precipitation (IP) experiments using MS provide a sensitive and accurate way of characterising protein complexes and their response to regulatory mechanisms. Differences in stoichiometry can be determined as well as the reliable identification of specific binding partners. The quality control of IP and protein interaction studies has its basis in the biology that is being observed. Is that unusual protein identification a genuine novelty, or an experimental irregularity? Antibodies and the solid matrices used in these techniques isolate not only the target protein and its specific interaction partners but also many non-specific 'contaminants' requiring a structured analysis strategy. These methodological developments and the speed and accuracy of MS machines, which has been increasing consistently in the last 5 years, have expanded the number of proteins identified and complexity of analysis. The European Science Foundation's Frontiers in Functional Genomics programme 'Quality Control in Proteomics' Workshop provided a forum for disseminating knowledge and experience on this subject. Our aim in this technical brief is to outline clearly, for the scientists wanting to carry out this kind of experiment, and recommend what, in our experience, are the best potential ways to design an IP experiment, to help identify possible pitfalls, discuss important controls and outline how to manage and analyse the large amount of data generated. Detailed experimental methodologies have been referenced but not described in the form of protocols.

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Figures

Figure 1
Figure 1
(A) The above diagram characterises the relative changes (percentage of protein identified in IP results (right) and total percentage of protein as a fraction of cell extract (left)) in terms of the abundance of the proteins identified in response to different experimental procedures. Whether comparing intensities directly in a label-free experiment, or utilising a SILAC approach to quantify proteins, these changes should be taken into consideration. It also indicates the importance of having a bead control (non-specific proteins which bind to beads) characterised for every experiment – because the non-specific proteins identified in bead controls vary for different cell lines, antibodies, beads, etc. (B) The immuno-precipitation workflow. Protein–protein interactions analysis utilising IP techniques can be approached in many different ways, using complex samples such as tissue biopsies, or single cell-type samples, and with labelled or label-free scenarios, illustrated by the flow chart.
Figure 2
Figure 2
The graph depicts the normalised distribution of average (log) protein intensities detected in all protein identifications, showing the normalised distribution of the population. The three graphs derived from the main graph describe the frequency of occurrence of the proteins in each protein intensity region. It is interesting to note that the number of proteins in the highest intensity range is 100-fold less than the numbers seen in the low and mid-intensity ranges. This indicates that the very high intensity proteins are only a small percentage of the proteins seen. Secondly, the graphs show a positive correlation between protein intensity and frequency of occurrence, which suggests that high-intensity proteins have a higher likelihood of being contaminants. Therefore, using a tool such as the Protein Frequency Library to tease apart significance of these protein identifications is helpful. The data shown above consists of 21 682 independent protein identifications, from 140 IP experiments performed in two different laboratories. These IP experiments included GFP-tagged protein pull downs, endogenous protein pull downs and included the use of agarose, sepharose and dynabeads. The peak of ~600 proteins at 0 is due to the ability of MaxQuant to identify proteins/peptides from the MS/MS spectra, with insufficient information from the MS spectra to determine intensities.
Figure 3
Figure 3
An example of protein ratio frequency graph showing the normalised distribution and the median value of the data. The ‘normalised bell-shaped curve’ is centred over a log ratio of 0; this means the mixing of SILAC samples was done accurately (i.e. exactly equal protein levels from each extract mixed). If the ratios deviate significantly from this, then likely an error was made when mixing, and ratio values will need to be adjusted accordingly (see Determining significance section). The green and red vertical lines indicate the (arbitrary) borders of significance. In general, proteins with high SILAC ratios usually correspond to specific interaction partners. Ambiguity appears largely in the pink zone, where proteins have log ratios close to 0 and can correspond either to contaminants, or to specific interaction partners with low affinity and/or low abundance. To discriminate, the PFL can be helpful.
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