The Proteomics Facility at the University of Bradford is equipped with the latest instrumentation and provides research support in 3 main areas:
- identification of new protein targets for drug development and new biomarkers for disease detection and interpretation,
- functional and biochemical characterisation of established targets to assist refinement of next generation drug candidates,
- pharmacoproteomics and toxicoproteomics of drugs tested in cell lines, preclinical models and Phase I clinical trials, to determine their effects on protein expression, in liver, tumours and other organs.
Please contact us if you wish to discuss your proteomics needs or collaborative research opportunities with our team.
The Proteomics Facility is housed in dedicated laboratories for handling biological samples (including ethically approved use of human tissues) and preparing protein samples for analysis and includes:
- Orbitrap Fusion mass spectrometer,
- Ultraflex MALDI MS/MS mass spectrometer,
- On-line and off-line Ultimate 3000 nanoHPLCs,
- Proteineer FC LC MALDI fraction collector,
- Preparative electrophoretic systems,
- Computer network for database management (Bruker Proteinscape, Thermo Proteome Discoverer) searching (Mascot, Sequest, XTandem!)
Proteomics Contract Research
The proteomics team offer a range of bespoke contract research services to industrial clients and university partners focused in the following application areas:
- Identification and validation of new protein targets for drug development
- Identification and validation of novel biomarkers
- Pharmacoproteomics and toxicoproteomics
- Exploring cellular and molecular mechanisms
- Biopharmaceuticals characterisation
We handle a diverse range of biological, archaeological and forensic samples (human, animal, microbe, plant, tissue, biofluids, cell cultures) for protein preparation using:
- Dounce and disperse homogenisation
- Chaotropic agents and detergents (e.g. 2D and 3D cell cultures)
- Concentrating (e.g. secretomes)
- Desalting (e.g. urine)
- Depletion of abundant protein (e.g. plasma)
- Proteases e.g. trypsin digestion
We are happy to discuss bespoke strategies for your specific sample preparation
The proteomics team offer a number of options for quantitive startegies depending on your requirements
- Isobaric tags (iTRAQ, TMT)
- Metabolic labelling (SILAC),
- Label-free quantification
- Multiple and Parallel reaction monitoring (MRM/PRM)
iTRAQ - Isobaric tags for relative and absolute quantitation
Labelling with isobaric tags enables the incorporation of a stable isotope chemical modification onto trypsin digested peptides. We have extensive experience of isobaric tag projects investigating cell culture monolayers and 3D spheroid models, preclinical models, tissue and liquid biopsies. Human biopsies can be investigated individually comparing matched healthy and disease material, or for large cohorts (with extracts from patients in the same group pooled to identify common expression changes).
Our Proteomics Service includes full consultation on the design of 4-, 6-, 8- and 10-plex isobaric label-based experimental strategies.
SILAC - Stable isotope labelling with amino acids in cell culture
A SILAC strategy requires incorporation of stable isotope labelled lysine and arginine into proteins while the cells are growing. Typically a control cell line is grown in “heavy” medium and experimental cell lines grown in “light” or normal medium for 6 to 8 generations. The SILAC approach is also appropriate for secretome studies. Multiple parallel SILAC experiments (multi-SILAC) can be performed each comparing and experimental variable cell lines (e.g. time course, phenotype variants) to the ”heavy” labelled control.
Our Proteomics Service includes full consultation on the design of SILAC experimental strategies. We have extensive experience of SILAC and multiSILAC strategies working with extracts and secretomes from cell lines and primary cells.
Multiple reaction monitoring (MRM)/parallel reaction monitoring (PRM)
Multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) provides a targeted proteomics approach incorporating relative or absolute quantification. By using LC MS to focus on the specific properties (LC retention time, parent mass and daughter ion masses) of peptides that are unique to the protein of interest. Unique peptides for the target protein are identified from in silico proteolytic digest of the sequence and from databases of experimental data. Synthetic forms of the peptides are prepared and used to establish the LC and mass spectrometric parameters (limit of detection, limit of quantitation, dynamic range) for quantification of the protein in biological samples.
Using the inherent quantitative peak areas of mass signals from peptides analysed by mass spectrometry provides a convenient approach comparing protein expression in a large number of samples (>10). The average of the peak areas of the three best peptides identifying a protein is used.
With the Orbitrap Fusion, we also have the capability of use a data independent analysis (DIA) approach. For this, we generate a bespoke spectral library of MS/MS for the biological system under study, against which data from experimental samples can be searched for protein identification and quantification. DIA is particularly useful for high throughput analysis of large sample cohorts.
Protein groups of interest are investigated by bioinformatics analysis:
- Gene ontology profiling (DAVID, UniProt)
- Protein-protein interaction (STRING)
- Metabolic pathway analysis (KEGG)
- Functional and mechanistic significance (FunRich)
- Correlation with internet-based proteomics databases (Human Plasma Project, Human Protein Atlas)
Mass spectrometry data is processed through a search engine (Mascot or XTandem!) to provide a list of identified proteins, and where required, relative or absolute quantification. The proteins are then subject to statistical analysis to identify significantly changed proteins:
- Volcano plots for significant expression changes between multiple datasets (R Studio, Limma t-statistics)
- Correlation coefficients for comparison of paired datasets (PRISM, Student t-test)
- Hierarchical clustering for population studies (R Studio)
- Principal component analysis for cohort comparisons