Advances in


Written by

Molly Campbell

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Proteins – the biological “workhorses” of cells – are integral to life, providing regulatory, signaling, structural, metabolic, transport and immune functions, among other roles.

A term that was first coined in 1994 by Professor Marc Wilkins, proteome refers to the entire collection of proteins that can be expressed in a cell, tissue or organism.

Proteomics refers to the study of the proteome. Examples of proteins that might be studied include:


Click on the individual protein examples to learn more.

Proteomics research can offer a comprehensive understanding of the molecular processes that underpin different biological states, such as healthy or diseased processes, in different cells, tissues or organisms.

Proteomics workflows and technologies

Proteomics research methods are often categorized as either “top down” or “bottom up”, which reflects whether the sample is separated prior to the peptides being analyzed. Choosing which approach to take typically depends on the research query.  

Several different analytical techniques can be adopted in proteomics research, broadly divided into two categories: low-throughput and high-throughput.


Antibody-based methods

Enzyme-linked immunosorbent assay (ELISA) and western blotting utilize antibodies targeted to specific proteins – or epitopes – to identify and quantify proteins in a sample.

Gel-based methods

A “mature” method for screening protein expression at a large-scale.

2D gel electrophoresis separates proteins according to their isoelectric point, and SDS-PAGE separates them further still, according to their molecular mass.

Chromatography-based methods

Used to separate and purify proteins from complex biological mixtures. Different types of chromatography  can be used to separate proteins based on characteristics such as:       

  • Charge
  • Molecular size
  • Binding affinity with ligands

Chromatography is often used to prepare proteins for downstream high-throughput methods.



Apply small quantities of sample to a chip. Antibodies can be immobilized to the chip, capturing target proteins from a sample. Divided into different categories:

Analytical – used to measure levels of protein expression and their binding affinities.

Functional – used to characterize protein functions, e.g., interactions and enzyme-substrate turnover.  

– proteins from sample of interest are bound to the chip, which is then treated with specific antibodies to target these proteins.

The most comprehensive approach for quantifying and profiling proteins, their interactions and any modifications.

Many variations of workflows are adopted, but the general principle is as follows:

  1. Proteins/peptides are ionized by the ion source of the mass spectrometer.
  2. The ions are then separated according to their mass to charge ratio by the mass analyzer.
  3. Ions are detected.

The level of sophistication and sensitivity of high-throughput techniques underpins many research advancements made in proteomics over recent years, and the field shows no signs of slowing down.

Advances in technologies that are likely to impact the utility of proteomics data can be categorized as advances in sample quality, preparation, data collection and data analysis:

Applications of proteomics

The applications of proteomics research are numerous and continue to grow. Click on the key examples to learn more.


“Everyone in proteomics stands on each other's shoulders […] One of the things that has been fantastic to watch is the way that so many people with different approaches have come together to try and make this all happen,”

Professor Marc Wilkins said.

To obtain accurate quantification information for proteins across sample sets, the proteins present in the sample must first be identified. The traditional workflow for protein identification is a data dependent workflow (DDA), but over the last 10 years, data independent acquisition (DIA or SWATH DIA) has emerged as a powerful workflow for quantitative proteomics. Recent advances in algorithms have increased the ease of using DIA for protein identification workflows by removing the need to generate an experimental spectral library in advance.

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