Local labs where proteomics services are available.
There are thousands of proteins expressed in cells, tissues and present in biological fluids. Traditionally they were analysed individually. Antibodies binding to a single protein enables individual protein detection. Using immunohistochemistry a particular protein's localisation and abundance can be visualised within cells and tissues. Western Blot and ELISA allow fairly accurate relative quantitation of individual proteins.
In proteomics determining the relative abundance of many proteins simultaneously provides information on the interactions and functions of those proteins within the biological system.
Two-dimensional difference gel electrophoresis (2D DIGE), separates multiple proteins by charge and size. The size of the spots on the gel indicated the quantity of protein present. The spots of interest are then excised and identified by mass spectrometry. While offering protein-centric quantitation, relatively few proteins are visualised and identified.
The most common technique in proteomics is liquid chromatography coupled tandem mass spectrometry (LC-MSMS). It is possible to detect whole proteins by mass spectrometry (Top-down proteomics). It is, however, more common to digest the proteins into peptides and then use mass spectrometry to detect the peptides, later inferring the identity and abundance of the proteins from the detected peptides, referred to as bottom-up proteomics.
In targetted proteomics, peptides fragments from selected proteins are isolated and detected by LC-MSMS, providing accurate and specific quantification of a few known proteins. In discovery proteomics, the most abundant proteins are indiscriminately detected and quantitated by LC-MSMS. The quantitation is not as robust as the above techniques, but the large number of proteins identified makes it most valuable.
Proteomics datasets uploaded to public repositories can be downloaded and mined for additional data. The databases entries need to contain a minimum amount of information for submission. This information describes the experiment, methods of data generation, and data processing. Uploaded data comprises raw data files and results.
Data Analysis Toolkits
Mass Spectrometry data requires multiple data analysis steps. First, the spectra need to be matched to theoretical peptides. False discovery rates for peptide spectral matches need to be calculated. The protein identifications need to be interpolated. Protein quantitation then needs to be determined. These tools have been compiled into pipelines, but can also be run separately.
Bioinformaticians are available to assist you with your project.
The earlier you contact them the more assistance they will be able to offer. In particular, the experimental design is critical in ensuring the success of any project. Contacting a statistician and ensuring your experiment has enough statistical power will go a long way to ensuring its success. Selecting the best technology for your project will ensure you get the best results for the your project. Omics research is costly, choosing the most appropriate technology for you experiment and budget is therefor critical.
Runing a pilot study and having an expert check the quality of the results before continuing with the bulk of the analysis is also important. The pilot project will also allow you to familiaries youself with the sample analysis process and the data generated and the means of analysis, before embarking on the whole project.
Once you have produced the data, you will realise omics technologies produce mountains on data. It often requires some expertise in handelling big data, to deal with the amounts of data produced. Fortunatly we have tools and resources to store and process your data making it easy for you to understand. Contact our team of expert bioninformaticians for assistance on all levels of your project.