Traditional protein analysis methods: Mass spectrometry

Mapping a cell’s proteome, the collection of proteins and proteoforms it contains, can provide detailed information on biological activities and make it possible to study the mechanisms behind these activities as well as diseases associated with them. Knowledge of the proteome may lead to a broad range of discoveries and applications including everything from new therapeutics to new ways to protect plants from drought.

Given the essential importance of the proteome, scientists have been studying it for many years using various protein analysis methods like mass spectrometry, protein sequencing and more. In this “Traditional protein analysis” series, we cover how researchers typically study the proteins that make up the proteome.

These protein analysis methods are not necessarily “ omics” scale technologies, which generally aim to measure all or a large amount of a particular type of biological molecule in a sample. Nonetheless, these methods form the foundations upon which proteomic analysis methods have been built. In a separate series, we dive into the technical concepts behind emerging omics technologies, which aim to enable scientists to study the full proteome.

Mass spectrometry for protein analysis

Mass spectrometry, also called mass spec, is one of the primary tools researchers turn to for proteomic analysis. Mass spectrometers can identify specific proteins with high resolution, and they’re a fundamental tool for many protein researchers.

How does a mass spectrometer work?

While there are many versions of mass spectrometry, they generally involve:

• Extracting proteins from a biological sample
• Breaking the proteins into peptides
• Giving the peptides an electric charge
• Passing the charged peptides through a device inside a mass spectrometer where they travel at a certain rate based on their mass and charge
• Breaking separated peptides apart into small fragments
• Passing the fragments through a device that separates them according to mass and charge again
• Allowing the separated fragments to hit a detector and generating spectra, which reveal the number of fragments of different masses and charges that hit the detector
• Computational matching of the generated spectra to known or predicted spectra for specific proteins

Using mass spectrometry for proteomics research 

Using mass spectrometry, a researcher can get a list of proteins in their sample. Proteomics researchers can also use mass spectra to measure the relative abundance of the identified proteins.

As you might imagine, some proteins can generate very similar spectra and/or may not be separated well because they have similar masses and charges. In addition, protein spectra signals from high abundance proteins can often drown out signals from low abundance proteins. Thus, even with extensive optimization, mass spectrometry can only measure a fraction of the proteome.

Researchers can complement mass spectrometry with a variety of techniques that make the protein spectra easier to analyze. For instance, before putting them through the mass spectrometer, researchers can pass their proteins or digested proteins (peptides) through separation columns. These columns are tubes containing porous materials that separate proteins or peptides based on properties like size or hydrophobicity. Separations can be accomplished off-line, where a single sample is separated into multiple fractions and each fraction is analyzed separately.

Alternatively, with in-line separation, a researcher adds a single sample to the separation column and the proteins or peptides exiting the column can directly enter a mass spectrometer. Protein or peptide analytes are sequentially analyzed by the mass spectrometer based on how they interact with the column. As only a fraction of the proteins or peptides in the sample exit the column at any point in time, the mass spectrometer can focus analysis on a smaller number of analytes increasing detection and quantitation resolution.

While there are many mass spectrometry techniques for proteomics that can ultimately make the resulting spectra easier to analyze, they introduce biases into the protein detection process. For instance, some proteins may be incompatible with certain columns. Thus, these time-consuming processes may limit the amount of data researchers get from mass spectrometry in one experiment.

Advantages of man spectrometry

• Omics-scale – While mass spectrometry cannot provide measurements of the full proteome, it can provide data on thousands of proteins at once. This provides more comprehensive information than the other techniques discussed in this series and makes mass spectrometry a better option for proteomics research.

• Can detect peptide modifications – Although the data analysis can be difficult, mass spectrometry can be used to identify proteins that have been modified through biological processes such as phosphorylation and glycosylation. These modified proteins are known as proteoforms.

Disadvantages of mass spectrometry

• Low dynamic range – Signals from high abundance proteins can drown out those from low abundance proteins, making potentially very important proteins difficult to analyze.

• Peptide-based inference of protein identity – This technique infers protein identity by compiling the sequences of multiple component peptides. Such inferences can be inaccurate if multiple protein species in a sample are composed of similar peptides.

• Cost – Mass spectrometers are very expensive.

• Low ease-of-use – Protein preparation for mass spectrometry may need to be highly customized depending upon the sample type and what fraction of the proteome researchers wish to study. Mass spectrometers themselves also have a high learning curve and the spectra they generate must be analyzed by specialized software.

Despite its limitations for proteomics, mass spectrometry has been, for many years, the main tool available to researchers wishing to analyze more and more of the proteome. The technique has been used to make many biological discoveries and develop many therapeutics. It is continually being improved through instrumentation developments, innovative methods, and improved software for data analysis. To overcome some of the drawbacks of mass spectrometry for proteomics, many researchers are developing new technologies that will make it easier to comprehensively analyze the proteome. We discuss the concepts behind some of these up-and-coming proteomics technologies, including our own platform, in other “Proteomics” posts.

Learn about protein sequencing as well as antibodies and other affinity reagents in the previous two posts in this series.

Notes on comparing different protein analysis methods

In the graphics portraying the technologies throughout this series, we provide qualitative assessments of proteome coverage and ease-of-use for each. Technologies with low proteome coverage, like western blotting and flow cytometry, are generally used for targeted experiments analyzing a small number of proteins at once. Medium coverage technologies like affinity arrays can look at 100’s to 1000’s of proteins in a single experiment. Truly comprehensive, high proteome coverage technologies can analyze tens of thousands of proteins at once.

Some protein analysis methods are easier to use than others. Low ease-of-use protein analysis technologies generally require complicated, difficult, or customized sample preparation involving a lot of hands-on experimenter time, and their data may be difficult to analyze or require bioinformatics support. High ease-of-use protein analysis technologies employ simple, standardized sample preparation, include more automation, and provide simple, data-rich outputs including protein abundance. Medium ease-of-use technologies have some mix of these attributes.

In addition to assessing standard metrics like these, we also discuss some of the advantages and disadvantages of each protein analysis technique. Many protein profiling technologies have specific pros and cons that make them ideal for certain applications, but not others.

Importantly, the technology assessments here should not be viewed as definitive. Rather, our goal is to help you think about how you can best leverage these technologies for your specific experimental goals.