Proteomics, the large-scale study of proteins, plays a crucial role in understanding biological systems and disease mechanisms, making it a valuable tool for translational science and drug discovery. Whether you're in biotech or pharma, understanding proteomics can help you unlock insights into disease biomarkers, identify novel drug targets, and guide clinical decision-making. In this blog post, we'll give you a quick overview of popular proteomics assays, how they work, and how to analyze the resulting data, with a special focus on downstream analysis – where tools like Pluto Bio can help streamline the process by providing powerful tools for proteomics data interpretation and insights.
What is proteomics?
Proteins are the workhorses of the cell, carrying out virtually every biological function. Unlike genomics, which focuses on DNA, proteomics is concerned with understanding the structure, function, and interactions of proteins. While our genes may be static, proteins are dynamic and reflect the true biological activity in cells, tissues, and organs.
Why use proteomics?
For biotech and pharma professionals, proteomics can be a critical tool in target discovery, drug development, and biomarker identification. By measuring the abundance, modification, and interactions of proteins, you can gain insights into disease processes and find new therapeutic targets. Proteomics in particular is important because gene expression (e.g. mRNA levels measured by RNA-seq) and protein levels (proteomics) aren’t always correlated. This is because there are different, complex networks that regulate mRNA and protein. In some cases, a gene may be highly expressed at the transcript level, but the protein is expressed at a very low level or vice versa. Some underlying biological reasons why gene expression and protein abundance may differ include:
- Post-transcriptional regulation: Even if the mRNA levels are low, the efficiency of mRNA translation into protein can be enhanced by mechanisms like increased mRNA stability, efficient ribosome binding, or favorable codon usage, leading to higher protein expression.
- Protein stability: A protein may be particularly stable in certain cells, with a slow degradation rate. This stability could maintain high protein levels even when mRNA transcripts are not abundantly produced.
- Cellular localization and trafficking: Some proteins are involved in cell surface functions. Efficient trafficking and membrane localization can result in the accumulation of the protein at the cell surface, increasing its functional availability and apparent expression level despite lower mRNA quantities.
- Regulation by microRNAs: Specific microRNAs may regulate gene expression by targeting mRNA for degradation or inhibiting its translation, thus lowering its apparent expression without affecting the protein levels significantly.
- Feedback mechanisms: There might be feedback loops in place that regulate the transcription of a gene based on the protein levels or signaling state of the cell, ensuring that protein levels are maintained appropriate to the cell’s functional state. Because of this, measuring protein abundance with proteomics (especially when performed in conjunction with gene expression measurements) provides another valuable layer of information helping you form a more complete picture of a biological system or disease.
Popular proteomics assays
There are several different approaches to proteomics, each offering its strengths depending on your specific needs. Let’s take a look at some of the most popular assays used in the field:
- Untargeted mass spectrometry What it is: Untargeted mass spectrometry, sometimes referred to as shotgun proteomics, is one of the most comprehensive techniques for proteomic analysis. It measures as many proteins as possible in a sample without prior knowledge of which proteins are present. How it works: The sample is digested into peptides, which are then ionized and fragmented in the mass spectrometer. The resulting data is used to identify and quantify the proteins present. Since this is a discovery-based approach, it’s particularly useful when you don't know what proteins to look for but want a broad, unbiased overview of the proteome. Strengths: High throughput, comprehensive, and unbiased; ideal for discovery of new biomarkers and understanding disease mechanisms.
- Targeted mass spectrometry What it is: With targeted mass spectrometry, you focus on a predefined set of proteins rather than trying to analyze everything in the sample like with untargeted mass spectrometry. How it works: Using techniques like Selected Reaction Monitoring (SRM) or Parallel Reaction Monitoring (PRM), targeted mass spectrometry measures the abundance of specific proteins by monitoring specific ion transitions corresponding to those proteins. This method is highly sensitive and accurate, making it ideal for validating specific biomarkers or monitoring changes in protein levels across experimental conditions. Strengths: High sensitivity and specificity; great for quantifying known proteins or validating findings from untargeted mass spectrometry.
- Olink (Proximity Extension Assay - PEA) What it is: Olink is a multiplexed immunoassay technology designed to measure a broad array of proteins in a sample with minimal sample input. How it works: Olink’s Proximity Extension Assay (PEA) uses pairs of antibodies that are complementary to the target proteins. When both antibodies bind to a target protein, they bring together oligonucleotides, which are then extended and detected using a PCR-based method. This allows for high-throughput, sensitive, and quantitative protein measurements. Strengths: High sensitivity, large panels, and low sample volume; ideal for measuring hundreds of proteins in plasma or serum.
- Seer (Proteograph) What it is: Seer’s Proteograph is a platform that provides deep proteome profiling by isolating proteins directly from complex biological samples, such as blood or tissue, without the need for prior protein fractionation or labeling. How it works: The technology uses a proprietary approach that leverages a combination of nanomaterials and surface chemistry to isolate and capture proteins directly from complex biological matrices. This enables the measurement of proteins across a wide dynamic range, with minimal sample preparation. Strengths: Offers deep proteomic coverage with high sensitivity and the ability to analyze challenging samples like blood or serum.
- Somalogic (SOMAscan) What it is: SOMAscan is a platform that uses synthetic protein-binding reagents called SOMAmer probes to measure the abundance of hundreds to thousands of proteins in a sample. How it works: These SOMAmer probes specifically bind to target proteins, allowing for their detection and quantification. The assay combines high-throughput processing with the specificity of antibody-based capture, allowing for a deep and reliable look at the proteome. Strengths: Measures a large number of proteins (up to 1,300); highly reproducible and sensitive for clinical applications and biomarker discovery.
How to analyze proteomics data
Once you’ve obtained your proteomics data, the next step is analysis. This typically involves two main stages: primary analysis (which is performed on the mass spectrometer or specialized software) and downstream analysis (which can be done using platforms like Pluto Bio).
Primary analysis: Peak picking and identification
Primary analysis is where the mass spectrometer software comes in. This process involves peak picking, where the instrument identifies and quantifies the peptides or proteins based on their mass-to-charge ratios (m/z). Most mass spectrometers come with their own analysis software, which performs tasks like:
- Peak detection: Identifying peaks corresponding to peptides in a mass spectrum.
- Protein identification: Using databases to match detected peptides with known protein sequences.
- Quantification: Estimating the abundance of proteins based on peptide signals. Although primary analysis is critical, it typically requires domain-specific software and expertise, which is often out of scope for general-purpose platforms like Pluto Bio.
Downstream analysis: Beyond the mass spectrometer
Once you’ve got your processed data (usually in the form of protein abundance or peptide intensity), you can move on to downstream analysis. This is where platforms like Pluto Bio really shine. Here are some common analyses performed on proteomics data:
- Differential abundance analysis: This involves comparing protein levels across different conditions (e.g., disease vs. control). By identifying proteins that are significantly upregulated or downregulated, you can uncover potential biomarkers or therapeutic targets.
- Pathway analysis: Proteomics data is often used to map out changes in biological pathways. By examining which proteins are involved in specific pathways, you can identify biological processes that are affected by disease or drug treatment, and pinpoint key regulatory mechanisms.
- Protein-protein interaction analysis: Proteins rarely act alone. Instead, they interact with one another to form networks that regulate cellular functions. Analyzing these interactions helps you understand how proteins collaborate within cellular pathways, revealing crucial signaling networks or new therapeutic targets.
Why Pluto Bio for proteomics analysis?
As proteomics data grows increasingly complex, researchers need a platform that simplifies analysis while enabling deeper insights. Pluto Bio is designed to streamline this process, allowing you to focus on actionable findings rather than getting bogged down by data wrangling. With Pluto Bio, you can easily perform essential downstream analyses, including differential abundance analysis, pathway analysis, protein-protein interaction analysis, and more. But Pluto Bio goes even further with its multi-omics integration capabilities. One of our platform’s unique strengths is the ability to seamlessly integrate proteomics data with other omics data, such as gene expression data (transcriptomics), to build a more holistic view of biological processes. By connecting the dots between gene expression changes and the proteins they encode, you gain a more comprehensive understanding of disease biology and drug responses. Beyond analysis, Pluto Bio also supports team collaboration in ways that make it easy to share insights and manage projects. You can create and maintain target and biomarker lists, manage teams with tailored access to different projects and experiments, and use commenting features to discuss findings with team members. Whether your team is working remotely or in-person, these features ensure seamless communication, keeping everyone on the same page and accelerating your research.
Conclusion
Proteomics is an essential tool for uncovering disease mechanisms, identifying drug targets, and discovering biomarkers for clinical applications. Whether you're using untargeted or targeted mass spectrometry, or multiplexed platforms like Olink, Seer, or Somalogic, robust data analysis is key to unlocking these insights. While primary analysis is typically handled with instrument-specific software, the real value comes in the downstream analysis – where platforms like Pluto Bio truly excel. For translational scientists, Pluto Bio offers a powerful, user-friendly solution for proteomics analysis. Our platform supports differential abundance analysis, pathway analysis, and more, as well as seamless multi-omics integration, enabling you to connect proteomics data with gene expression and other omics layers. With built-in team collaboration tools, Pluto Bio helps accelerate drug target discovery, biomarker identification, and other critical research, ensuring you stay aligned and focused on the insights that matter most. Interested in learning more or trying out Pluto Bio? Get started here.