Cookies Disclaimer

Our site saves small pieces of text information (cookies) on your device in order to deliver better content and for statistical purposes. You can disable the usage of cookies by changing the settings of your browser. By browsing our website without changing the browser settings you grant us permission to store that information on your device.

PepTracker®: Encyclopedia of Proteome Dynamics


Proteome Dynamics

Thanks to advances in mass spectrometry instrumentation, improved sample preparation methods and new data processing software, proteomics technology has now become a very versatile tool for studying cell biology. It provides a flexible set of assay formats for making quantitative measurements of a wide range of protein properties, corresponding to multiple dimensions of the cell proteome. Thus, our aims in modern proteomics has now extended beyond the simpler goal of protein identification to the more ambitious task of quantifying dynamic changes in an array of protein properties. These include protein abundance and turnover rates, subcellular protein localization, patterns of post-translational modifications (PTMs), formation of protein complexes and protein-protein interactions. In cell biology our aims also extend to documenting dynamic variations in all of these properties at different stages of the cell cycle and in response to physiological and environmental stimuli, etc. This provides a multidimensional description of the dynamic cell proteome that we believe is key to understanding cellular regulation at a system-wide level.

The aim of the EPD is to provide a convenient and user-friendly format for presenting and sharing these large and complex multidimensional sets of data derived from high throughput proteomics studies of dynamic biological responses in both human cells and model organisms. The main data types and experimental designs currently incorporated into the EPD are described briefly below. For further information, refer to the publications section and to the Lamond Lab website.

Abundance & Localisation

Using mass spectrometry-based proteomics we have studied the abundance levels and in some cases also the subcellular localization of the proteomes of multiple human cell lines and in the model organisms mice, nematodes and trypanosome. This includes analyses of the human cell lines; HeLa, HCT116, U2OS and NB4. We have analysed separate nuclear, nucleolar, chromatin, cytosolic, membrane and cytoskeleton fractions, in some of these cell lines. Much of these data are currently available in the public sections of the EPD and further data will continue to be released in future.
The graphs and visualisations provided in the EPD document the proportions of each identified protein in each subcellular compartment, classify them according to general abundance levels and where available, show the variance in measurements between biological and/or technical replicates.
We also focus on the analysis of the proteomes of a large variety of murine immune cells. All these immune cell populations have been purified to at least 98% purity and are derived from a common genetic background with standardised housing conditions to maximise comparability.

Protein Degradation, Synthesis, Turnover & Half-Life

We have studied the rates of protein degradation and synthesis in human HeLa and U2OS cell lines and estimated protein half-lives and turnover rates. These data were collected using experiments involving either translation inhibition following cycloheximide treatment and subsequent measurement of decreases in protein abundance levels (U2OS cells), or using a pulse-SILAC technique (HeLa cells). In the latter case cells grown in ‘medium’ isotope SILAC label were pulsed with ‘heavy’ isotope SILAC labeled media and these pulsed cells mixed with an equivalent number of normal, unlabeled media (ie ‘light’) as a spike-in control providing a reference to improve the data analysis. This allowed calculation of both synthesis and degradation rates for each protein and an estimation of half-life and turnover rate for many of the proteins. In each study we combined the measurements of protein degradation rates with measurements of subcellular localization, allowing discovery and analysis of separate pools of the same protein that showed differential rates of degradation in separate subcellular compartments.

Systematic detection of protein pools, isoforms and modifications

We have taken advantage of our large protein datasets and collection of multidimensional data spanning subcellular localization to identify protein pools and protein isoforms that vary in these properties. We have reported systematic approaches for detection of either distinct isoforms, or separate pools of the same isoform, with differential biological properties, such as turnover rate and subcellular localisation. For example, we have used the following data analysis strategies to identify isoforms de novo, based upon the measured proteomic data, by evaluating differences between the values for groups of peptides assigned to the same protein:

  • Candidate Approach: compares SILAC isotope ratios for predicted isoform-specific peptides, with ratio values for all peptides.

  • Rule of Thirds Approach:compares the mean isotope ratio values for all peptides in each of three equal segments along the linear length of a protein, assessing differences between segment values.
  • Consecutive Peptide Approach: compares mean isotope ratio values for each sequential group of three adjacent peptides, assessing differences with the mean value for all peptides assigned to the protein.
  • Protein Fractionation Data: independently evaluates isotope ratio values for the same peptides isolated from different cell fractions.

Analysis of Cell Cycle

We have studied how gene expression varies at different phases of the cell cycle in the human myeloid leukemia NB4 cell line. We have used centrifugal elutriation to separate cells growing in asynchronous cultures into populations that are enriched for distinct phases of interphase, ie G1, S and G2/M. The elutriation method provides an analysis of a ‘minimally perturbed’ cell cycle, by avoiding the stress effects of drug arrest treatments that otherwise can be used to enrich cell populations at distinct cell cycle phases. Using the elutriated NB4 cells we have compared changes in the abundance levels of both protein and mRNA across the cell cycle.
We have also compared variations in the proteomes of NB4 cells at similar cell cycle stages generated by arresting cells by either serum starvation (G0/G1), or treatment with the drugs hydroxyurea (S phase), or RO-3306 (G2/M). These data indicate that there are very large effects on the proteomes of arrested cells that appear to be stress related and that do not represent changes in protein levels that occur under physiological conditions in a normal, unperturbed cell cycle.

Analysis of Protein Complexes

High resolution size exclusion chromatography (SEC) was used in these experiments to separate intact protein complexes by size and shape. These methods have been adapted for cells in culture, animal tissues and whole organisms. Thus they have been used to analyse human U2OS cells and to study protein complexes in extracts prepared from mice and nematodes. After the collection of fractions across the entire SEC elution profile, protein complexes were digested to peptides and analysed by LC-MS/MS.
The SEC elution profile of individual proteins was generated through various bioinformatics approaches. Each protein elution profile was clustered with all other protein profiles detected, to identify groups of profiles that are similar and hence proteins that are likely to be part of the same complex due to their co-elution.
These experiments were all performed with multiple biological replicates per condition so that our date are displayed as the mean elution profile for each protein across the biological replicates, with error bars showing the standard deviation. We have also shown the mean elution profile heatmap for all of those proteins that are co-clustered with the protein currently displayed.
More recent experiments in U2OS cells have now combined the SEC-MS proteomics approach with in vivo cross-linking, allowing more efficient recovery of total cellular protein complexes because strongly denaturing extraction buffers can be used. The cross-linked proteins within complexes cannot now dissociate during extraction and subsequent HPLC analysis.

Starvation Responses

Animals are adapted to adjust their metabolism in response to acute starvation. Regulatory pathways governing the starvation response and the regulation of life span functionally overlap and involve insulin/IGF-1 signalling, AMPK, autophagy and TOR. We have used quantitative mass spectrometry-based proteomics to identify the kinetics and magnitude of the proteome response to adult-onset starvation in the nematode C. elegans and in adult mouse liver tissue. Measuring changes in abundance of up to 7,000 proteins, we show that acute starvation rapidly alters the abundance of hundreds of proteins, many involved in metabolic processes that these organisms to shift from an anabolic to a catabolic state. We also detect specific changes in the abundance of linker histones, histone variants and post-translational histone modifications associated with the epigenetic control of gene expression.

Biological Response

The BioResponse section contains a variety of datasets which contain the proteomic response to a range of stimuli. Example for these stimuli are drug treatments (e.g. the mTORC1 inhibitor rapamycin), nutrient and cytokine starvation responses or the activation of oncogenes like SRC. In most of the cases a so-called volcano plot is used for the illustration of the data.
In this kind of plots the mean fold-change of a protein in the stimulus vs the control condition is plotted against the statistical significance of this change in expression. This allows for the easy identification of proteins which are affected by the stimuli by a large extent and also did so in a reproducible manner. The volcano plots are interactive and datapoints of interest can be highlighted (by clicking on them) to show more information about the proteins they represent.