Proteomics' new order

Lost in Translation (LiT): IUPHAR Review 6

Translational medicine is a roller coaster with occasional brilliant successes and a large majority of failures. Lost in Translation 1 ('LiT1'), beginning in the 1950s, was a golden era built upon earlier advances in experimental physiology, biochemistry and pharmacology, with a dash of serendipity, that led to the discovery of many new drugs for serious illnesses. LiT2 saw the large-scale industrialization of drug discovery using high-throughput screens and assays based on affinity for the target molecule. The links between drug development and university sciences and medicine weakened, but there were still some brilliant successes. In LiT3, the coverage of translational medicine expanded from molecular biology to drug budgets, with much greater emphasis on safety and official regulation. Compared with R&D expenditure, the number of breakthrough discoveries in LiT3 was disappointing, but monoclonal antibodies for immunity and inflammation brought in a new golden era and kinase inhibitors such as imatinib were breakthroughs in cancer. The pharmaceutical industry is trying to revive the LiT1 approach by using phenotypic assays and closer links with academia. LiT4 faces a data explosion generated by the genome project, GWAS, ENCODE and the 'omics' that is in danger of leaving LiT4 in a computerized cloud. Industrial laboratories are filled with masses of automated machinery while the scientists sit in a separate room viewing the results on their computers. Big Data will need Big Thinking in LiT4 but with so many unmet medical needs and so many new opportunities being revealed there are high hopes that the roller coaster will ride high again.

Cell biology through proteomics--ad astra per alia porci

Isolated subcellular fractions have been instrumental in elucidating cell function. The use of such fractions for the identification and biochemical characterization of subcellular organelles, combined with cell- free systems, has provided key insights into the function and machineries of organelles, including those involved in vesicle transport, quality control and protein sorting. Despite their obvious utility, popular cell biology has come to regard in vitro-based approaches as inferior to in vivo-based approaches. Usual criticisms are contamination, non-representative processes and an inability to recreate the dynamic processes seen in vivo. In a similar way, proteomics has been viewed with reservation. Despite this, and building on the tradition of in vitro-based approaches, organelle proteomics based on liquid chromatography and tandem mass-spectrometry has recently made significant contributions to cell biology, and now allows the molecular machineries of organelles to be defined with high precision.

A HUPO test sample study reveals common problems in mass spectrometry-based proteomics

We performed a test sample study to try to identify errors leading to irreproducibility, including incompleteness of peptide sampling, in liquid chromatography-mass spectrometry-based proteomics. We distributed an equimolar test sample, comprising 20 highly purified recombinant human proteins, to 27 laboratories. Each protein contained one or more unique tryptic peptides of 1,250 Da to test for ion selection and sampling in the mass spectrometer. Of the 27 labs, members of only 7 labs initially reported all 20 proteins correctly, and members of only 1 lab reported all tryptic peptides of 1,250 Da. Centralized analysis of the raw data, however, revealed that all 20 proteins and most of the 1,250 Da peptides had been detected in all 27 labs. Our centralized analysis determined missed identifications (false negatives), environmental contamination, database matching and curation of protein identifications as sources of problems. Improved search engines and databases are needed for mass spectrometry-based proteomics.

Cardiovascular Proteomics

Proteomics is the new systems biological approach to the study of proteins and protein variation on a large scale as a result of biological processes and perturbations. The field is undergoing a dramatic transformation, owing to the completion and annotation of the human genome as well as technological advances to study proteins on a large scale. The new science of proteomics can potentially yield novel biomarkers reflecting cardiovascular disease, establish earlier detection strategies, and monitor responses to therapy. Technological advances permit the unprecedented large-scale identification of peptide sequences in a biological sample with mass spectrometry, whereas gel-based techniques provide further refinement on the status of post-translational modification. The application of high throughput protein evaluation with a subset of predefined targets, identified through proteomics, microarray profiling, and pathway analysis in animal models and human tissues, is gaining momentum in research and clinical applications. Proteomic analysis has provided important insights into ischemic heart disease, heart failure, and cardiovascular pathophysiology. The combination of proteomic biomarkers with clinical phenotypes and genetic haplotype information can lead to a more precise diagnosis and therapy on an individual basis--the fundamental premise of "personalized medicine."

Data Standards: A Call to Action

Access to data is something that every molecular biologist takes for granted nowadays, but data alone is of little use unless it is made available in a useable form through the development and global uptake of data standards. The challenge of standards development has been taken up by grass-roots movements working within several different branches of the biomedical research community. Many of these initiatives are proving extremely successful; for example, the Gene Ontology, which provides a controlled vocabulary for describing the properties of gene products, the Microarray Gene Expression Data Society's standards for describing microarray experiments, and the emerging standards developed by the Proteomics Standards Initiative are gaining broad acceptance. Standards development now faces its greatest ever challenge--the integration of diverse data types to fulfill the goals of systems biology. Now is the time for the communities that are developing these standards, the funding bodies that have invested so heavily in high-throughput data generation, and the publishers of biomedical research papers to cooperate fully to make the goals of integrated data analysis a reality.