Bringing forward the next generation of medicines

Mark Rogers, global scientific director, life sciences at SGS examines how proteomics studies are helping bolster the next generation of medicines.  

Proteomics defines the methodologies used to characterise the proteome - the entire protein complement of a cell, tissue, or organism under a specific set of conditions. The foundation for this area of study comprises fractionation of a protein mixture, identification of the individual proteins and bioinformatics to analyse and compile the data.

The first protein studies that may be considered under this definition began in the mid 1970’s with the introduction of two-dimensional gel electrophoresis. While protein identification was not possible, this advance in separation technology led to the first attempts to catalog all human proteins. Further advances in this field however, were only possible after the development of sensitive and reliable methods for identification. The first techniques were based upon N-terminal, Edman sequencing and have been refined to allow microsequencing from trace amounts of electroblotted proteins. This technique has now largely been replaced as a result of developments in mass spectrometry. Since the early protein characterisation work by “FAB-mapping” in the mid 1980’s⁽¹⁾, the application of MS to protein analysis, including identification, has become ubiquitous. Significant improvements in sensitivity, accuracy, speed and automation of mass spectrometric techniques have been made this, the primary method of choice for protein identification.

Concomitant to the development of modern-day proteomics has also been the evolution of other disciplines such as glycomics, genomics, transcriptomics, lipodomics and metabolomics each with a particular focus on understanding specific cellular functions and processes ⁽²⁾. There has also been a movement towards data-driven diagnoses and personalised therapies based on data obtained from such omic studies. Data from both genomics and transcriptomics have demonstrated only limited ability as predictors of cellular phenotype while proteomics, although more challenging, appears to offer much greater relevance for precision medicine directed at a particular phenotype.

Today, a typical experiment in proteomics comprises three phases. Firstly, isolation of a protein fraction from a desired source which may range from whole cell / tissue extracts to single cell organelles. Secondly, the acquisition of structural information, primarily sequence data by MS-based technologies driven by automated software interpretation. Finally, database utilisation, where the application of bioinformatics has improved the identification and quantitation of proteome data.

The field of proteomics may be separated into three categories: Expression Proteomics, in which analyses are performed to identify and quantitatively determine the differential expression of protein(s) within the proteome in response to an event. This application has been facilitated by the development of techniques such as isotope-coded affinity tags (ICAT), stable isotopic labelling with amino acids in cell culture (SILAC), and isobaric tags for relative and absolute quantitation (iTRAQ). Structural Proteomics, in which data is used to identify and map the protein content within specific cellular locations. It is hoped that this will eventually lead to a greater understanding of cellular architecture and the influence that specific proteins provide to each cell’s distinctive characteristics. Functional Proteomics, a term broadly applied to proteome studies that do not fall within the previous two categories and includes studies into protein-ligand binding and protein signalling.

Since its inception, the field of proteomics has continued to improve and expand both in terms of technology and application. To date, approximately 10,000 proteins have been studied from human tissue alone, which has expanded our understanding of signalling, regulatory, and metabolic pathways. However, the majority of these studies have been performed with large cell populations such as cell lysates, negating the ability to provide cell type specific data. The term “nanoproteomics” was introduced almost 10 years ago but it has only been recently that advances in technology have allowed this idiom to become a reality ⁽³⁾. Today, very low number cell populations and even single cell proteomics are possible and can reveal critical information related to rare cell populations, hard-to-obtain clinical specimens, and the cellular heterogeneity of pathological tissues.

The last two decades has seen the growth of proteomics-based technologies for the molecular characterisation of pathogen-host relationships and insights into the biological basis of infectious diseases ⁽⁴⁾. Specifically, proteomic studies have had a role in defining the physical interaction between host proteins, viral proteins and nucleic acids and have been influential in the monitoring of host cell responses, including changes in protein abundance upon viral entry and during the course of infection. MS-based proteomics have been used to help define the molecular structure and composition of viral and bacterial pathogens and have supported the development of diagnostics and therapies in the emerging field of multi-omics, which can provide a holistic view of pathogen-host relationships. For many years the identification of bacteria and yeast pathogens by MS-related technologies has been a widely accepted as a clinical diagnostic tool while more recently, PCR–electrospray ionization mass spectrometry ⁽⁵⁾, has emerged as an approach that is capable of identifying nearly all known human pathogens either from microbial isolates or directly from clinical specimens.

It seems inevitable that the future applications of proteomics will become part of broader studies involving data from other omics techniques. The integration of genomics and proteomics with disease phenotypes, for example, has the potential to establish disease or individual-specific proteotypes, which may aid in making therapies more specific to the disease or individual. As the multiplicity of omics studies increases, the challenge will not be in which system to apply these methods, but rather how to properly integrate, visualise, and interpret the complex outputs. Flexible yet comprehensive platforms for integrating large-scale data sets will become a vital component in such investigations.

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