The Critical Role of Proteomics in Precision Medicine

Advancements in genetic science have enabled researchers to study the entire genomes of both individuals and populations. Many of the complex processes that influence health and disease have been illuminated by genomics, making it possible to analyze individual genetic responses to specific medications. While these are notable strides on the path toward precision medicine, it’s increasingly clear that genomics reveals only part of the clinical picture.

Proteomics, the study of the structure and function of expressed proteins in an organism, can help fill in important gaps. Since an organism's genes encode the instructions for constructing each protein, proteomics and genomics are inextricably linked. By combining these two exciting fields of study, researchers can get closer to achieving true precision medicine and ensuring the right treatments are delivered to the right patients at the right time.

Addressing complexity in proteomics and biomarker discovery

While the genome contains all the information for an individual to develop, genes are static. Importantly, the proteome reflects the real-time status of a patient’s health and therefore may be better suited to diagnose a patient’s current state and predict future development of a disease. But proteomics is significantly more challenging than genomics, largely because of the complex chemistry involved.   

One of the greatest obstacles in proteomics is the complexity of protein samples, which may contain thousands of different proteins at varying concentrations across a high dynamic range. To overcome this obstacle, researchers analyze and interpret massive amounts of data using advanced technologies such as mass spectrometry (MS) which help in the discovery of novel protein biomarkers.

Notably, in proteomics experiments, proteins must first be digested into peptides, which are then separated by chromatography, and finally analyzed by mass spectrometry. With proteomic samples that generate between 10,000 and 100,000 peptides per sample, chromatography separation is needed to reduce complexity and does so by gradually introducing peptides into the mass spectrometer for analysis, which may take minutes to hours, depending on experimental conditions and the speed of the mass spectrometer. Fortunately, new advancements in MS are making it possible to dramatically increase throughput, empowering researchers to accelerate analysis and process up to 180 samples per day.  

The mass spectrometer acquires data in two ways: data-dependent acquisition (DDA) and data-independent acquisition (DIA) – in other words, these are the manners in which it fragments the peptides that are introduced to the mass spectrometer. The DDA method involves acquiring data sequentially, one peptide at a time, beginning with the most intense and progressing to the least intense. As the chromatographic separation progresses, not all peptides are fragmented because of the sequential nature of the process. The DIA approach involves fragmenting all peptides within a given m/z range at the same time, then using software to deconvolute the results. This approach allows for far more peptides to be fragmented thereby providing higher reproducibility, better quantitative accuracy and precision for proteomics experiments. DIA approaches can characterize thousands of proteins and produce fewer missing values, making it ideal for large cohorts of more than 100 samples.

The identification of protein biomarkers is an important aspect of translational proteomics research. Over the past two decades, MS technology has accelerated due to these advancements in sample preparation techniques, instrumentation and data analysis pipelines. This has enabled researchers to acquire data consistently and overcome common challenges like inappropriate interpretation, additional testing and prolonged clinical trials.

Ensuring accuracy and overcoming false positives

In the search for novel protein biomarkers in precision medicine research, scientists must confirm that a given spectral pattern is not a false positive which can stem from sample contamination or detection of existing protein. Fortunately, in the MS-based proteomics data analysis, scientists have developed and applied different approaches to estimate false discovery rate (FDR) in the spectral matching processes. Estimating the FDR in an experiment is important to assess and maintain the quality of protein identification. The well-accepted approach is target-decoy strategy, and new strategies have been proposed and applied in the field as well.

Promising technology advances

Researchers conducting quantitative proteomics in the study of precision medicine are now benefiting from important advances in technology that are fueling new discoveries that hold potential for millions of patients around the world.

Orbitrap analyzer-based mass spectrometers, for example, not only enable a more accurate FDR estimation, but also higher confidence in protein and peptide identification and more accurate and precise quantification than other approaches through higher resolving power and better mass accuracy. In fact, some MS instruments can achieve up to 480,000 resolution at m/z 200 with < 3 ppm RMS mass accuracy.

There are a broad range of pre-built and optimized method templates for diverse applications and workflows available as well. Examples include a Tandem Mass Tag (TMT) workflow that can multiplex up to 18 single cells in one proteomics analysis, boosting throughput dramatically and post-translational modification profiling such as phosphoproteomics with a high throughput of 60 SPD which can reveal new insights into the mechanism of drug resistance.

As a result of the growing number of technological advancements and accelerated workflows in proteomics, it is now possible to expand the focus to encompass new territories. Single-cell proteomics is now a viable field of study due to dramatic improvements in the sensitivity of MS workflows and enables direct insight into intercellular dynamics. This will result in a greater comprehension of cell signaling pathways and cellular heterogeneity, which is essential for the development of precision medicine. Moreover, MS-based single-cell proteomics has the potential to transform disease diagnosis and treatment by enabling the identification of disease-specific protein biomarkers at the single-cell level.

In just a short time, technological advances will generate a complete digital fingerprint of patient samples, which will be able to link phenotype, diagnosis, treatment and outcome at the molecular level. In addition, innovations in data processing and software will enable medical professionals to administer the appropriate treatment to the appropriate individual at the appropriate time – the ultimate goal of precision medicine.