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Proteomics Market: Opportunities and Challenges

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Proteomics Market: Opportunities and Challenges

November 05
18:04 2019
Proteomics Market: Opportunities and Challenges

Prominent players in global the proteomics market are Thermo Fisher Scientific, Inc. (US), Agilent Technologies, Inc. (US), GE Healthcare (US), Bio-Rad Laboratories, Inc. (US), Danaher Corporation (US), Luminex Corporation (US), Bruker Corporation (US), Merck KGaA (Germany), Waters Corporation (US), PerkinElmer, Inc. (US), Creative Proteomics (US), and Promega Corporation (US).
An increase in the research activity, especially in the area of personalized medicine and drug discovery is the primary growth driver for this market.

The global proteomics market is projected to reach USD 38.74 billion by 2024 from USD 19.49 billion in 2019, at a CAGR of 14.7% during the forecast period. An increase in the research activity, especially in the area of personalized medicine and drug discovery is the primary growth driver for this market. In addition, the increasing R&D expenditure by pharmaceutical and biotechnology companies, increasing government funding, and technological advancements are also propelling the growth of the proteomics market.

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Opportunities

Growing prominence of nanoproteomics—the convergence of nanotechnology and proteomics

The rapidly growing field of proteomics has excelled in various life disciplines but not without challenges such as the analysis of vast PTMs, dynamic range of molecules, and biological complexities. These challenges are being overcome with the use of nanotechnology.

The field of nanotechnology has been associated with several proteomics applications such as phosphoproteomics/metal oxide nanoparticles, nanostructure surfaces for protein separation, and analytical detection of biomarker proteins using array techniques. This has led to the emergence of nanoproteomics, a science involving the application of proteomics techniques aided by nanotechnology. This technique is utilized as a complementary component to revolutionize proteomics through different kinds of nanotechnology applications, including nanoporous structures, functionalized nanoparticles, quantum dots, and polymeric nanostructures.

The advances in this field have begun to allow researchers to identify low-abundance proteins in samples through techniques that rely on both nanoparticles and nanoscale devices. The application of nanotechnology in clinical proteomics has enhanced the concentration sensitivity levels (CSLs) and throughput of analytical measurement systems while reducing system costs. The development of biosensor-based nanodiagnostics is an applicable nanotechnology proteomics tool for medical research. For instance, the magneto-nano sensor protein chip and a multiplex magnetic sorter based on magnetic nanoparticles are being developed to allow rapid conversion of discrete biomolecules binding events into electrical signals. The entire system can detect target molecules in less than an hour.

Currently, a number of research programs are focusing on increasing the applications of nanotechnology to improve the productivity, efficiency, accuracy, and precision of the proteomics technologies being used. Some of the most promising candidates for nanoproteomics (carbon nanotubes and nanowires, quantum dots, and nanoscopic gold particles) are being studied for various applications in the research field. They offer advantages such as high sensitivity, real-time measurements, and improved reproducibility. These applications, although still in their infancy, have led to several highly sensitive new methods of drug delivery and targeted therapy for clinical use. Refinement of these applications to improve outcomes provides significant growth opportunities for players in the proteomics market.

Challenges

Data management in proteomics research

Proteomics is a rapidly expanding field, aided by improvements in instrumentation accuracy and sensitivity, size, and affordability. This offers many possibilities for personalized and precision medicine, which could improve diagnosis, treatment, and disease management in the coming years. However, with the increase in proteome analysis, there is an inevitable increase in the volume of data generated. There are various challenges associated with large data volumes; these include issues related to:

Data Storage: While technological advancements have enabled automation in the data storage process by creating databases, there is a struggle to understand the full potential of such datasets and to keep pace with data output. Cloud storage offers a solution to this challenge but does not overcome the hurdle of storing sensitive and confidential data. Thus, even though data generation has become a cheaper process, the cost of maintaining this data for long durations needs to be factored in.

Data Integration: One major challenge is the integration of new and existing proteome data with other valuable omics and metadata. Currently, there is no optimal or standardized approach to enable data integration due to inconsistencies in annotation, reporting of datasets, and outputs of analysis pipelines. Moreover, proteome coverage tends to be poorer than other omics data types.

Unifying Data Repositories: While the amount of proteomic data generated is growing, the amount cataloged in publicly available repositories does not reflect this influx. The lack of a unified database for data sharing increases the complications for researchers aiming to undertake data mining on existing studies and place new data in a broader context.

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