Proteomics Mass Spectrometry

Mass spectrometry tells you what proteins are in your sample and how much of each, without having to decide in advance what to look for. Unlike antibody-based methods, mass spectrometry needs no target list, so a single unbiased measurement can identify and quantify thousands of proteins at once. Mass spectrometry collects detailed amino acid sequence data enabling measurement of isoforms, mutations and modifications. That makes it a method of choice when you want to know what is actually changing at the protein level between different conditions.

With modern mass spectrometry-based proteomics, researchers can:

• Identify the proteins in a complex mixture, from whole-cell lysates to tissue and biofluids. If you can extract the proteins, you can identify them
• Quantify how protein abundance shifts between samples, treatments, or timepoints, with high reproducibility
• Distinguish and quantify protein isoforms and mutations and measure cellular protein turnover rates
• Characterize post-translational modifications (PTMs) such as phosphorylation, glycosylation, and more down to the specific site on the protein
• Map protein–protein interactions, complexes, and intact proteoforms
• Identify and quantify proteins from challenging sources such as single cells and plasma
• Throughput has advanced dramatically: modern mass spectrometers can run hundreds of samples per day, or few thousand samples in a matter of days to a couple of weeks of instrument time

Whether hunting disease biomarkers, dissecting a mechanism, or characterizing a biologic, mass spectrometry turns sample into a quantitative readout of its entire proteome.

Chemoproteomics provides a direct, proteome-wide view of drug–protein interactions in cells, tissues, or other biologically relevant samples. Rather than focusing on a single suspected target, mass spectrometry–based chemoproteomics enables researchers to measure where a compound binds across thousands of proteins simultaneously, helping scientists understand both its intended mechanism and its broader biological effects.

With modern mass spectrometry–based chemoproteomics workflows, researchers can:

• Identify direct drug targets by measuring which proteins are bound or occupied by your compound in a native biological context
• Discover off-target interactions that may contribute to efficacy, toxicity, or unexpected phenotypes
• Quantify target engagement across doses, timepoints, and treatment conditions to establish pharmacological activity
• Assess compound selectivity across the proteome to compare lead molecules and support candidate prioritization
• Map binding sites to specific amino acid residues, including cysteines and other druggable hotspots
• Profile covalent and reversible interactions using competitive and affinity-based strategies
• Study target engagement in complex systems, from cultured cells to tissues and in vivo samples
• Assess the role modifications, isoforms, fusion proteins and mutations have in drug specificity
• Screen drug fragment libraries to map new protein binding pockets for training AI models to improve drug development

Chemoproteomics is particularly valuable for target deconvolution, mechanism-of-action studies, and drug discovery programs where understanding selectivity is critical. By combining selective chemical probes with high-resolution mass spectrometry, it transforms a treated sample into a comprehensive map of the proteins and sites engaged by your compound, revealing not only whether your drug reaches its intended target, but also what else it may be affecting across the proteome.

Immunopeptidomics provides a direct view of the peptides displayed by HLA molecules on the surface of cells, revealing the antigens that are actually available for recognition by T cells. Unlike genomic or computational approaches that predict which peptides might be presented, immunopeptidomics use mass spectrometry to identify and characterize the peptides that are truly presented in a biological sample.

With a modern mass spectrometry–based immunopeptidomics workflow, you can:

• Identify naturally presented HLA peptides from both Class I and Class II molecules
• Discover tumor-associated antigens and neoantigens for cancer immunotherapy research
• Verify predicted epitopes by confirming their presentation on the cell surface
• Characterize immune responses to infection, vaccination, autoimmune disease, or therapeutic intervention
• Compare antigen presentation across cell types, disease states, treatments, or timepoints
• Study how drugs, cytokines, or genetic modifications reshape the immunopeptidome
• Support T cell therapy and vaccine development by identifying clinically relevant antigen targets
• Profile thousands of presented peptides from complex biological samples with increasing sensitivity and depth
• Reveal the "dark immunopeptidome" by discovering novel HLA-presented peptides derived from non-canonical sequences that are often missed by prediction-based approaches, expanding the landscape of potential immune targets

Immunopeptidomics has become an essential tool for cancer immunology, infectious disease research, autoimmune disease studies, and therapeutic development. By directly measuring the peptides presented by HLA molecules, it provides a highly biologically relevant picture of what the immune system can actually "see," helping researchers identify actionable antigens and understand immune recognition with confidence.

Single-cell proteomics reveals the protein composition of individual cells, allowing you to measure cellular heterogeneity that is often hidden in bulk analyses. Rather than averaging signals across millions of cells, it enables you to identify distinct cell populations, characterize cell states, and understand how individual cells respond differently within the same sample.

With a modern mass spectrometry based single-cell proteomics workflow, you can:

• Quantify thousands of proteins in individual cells in an unbiased manner
• Identify distinct cell populations and states within complex tissues or heterogeneous samples
• Discover rare cell types that may be masked in bulk measurements
• Track cellular responses to drugs, genetic perturbations, environmental changes, or disease progression
• Characterize functional heterogeneity by measuring proteins directly rather than inferring biology from RNA alone
• Map developmental, differentiation, and activation trajectories across cell populations
• Integrate with transcriptomic and spatial datasets to build a more complete picture of cellular biology
• Profile hundreds to thousands of single cells with increasing depth, sensitivity, and throughput

Single-cell proteomics is transforming research in immunology, oncology, neuroscience, developmental biology, and drug discovery. By measuring proteins directly in individual cells, it provides a functional view of cellular diversity, helping researchers uncover the cell populations, pathways, and mechanisms driving biological outcomes.

Spatial proteomics combines protein measurements with spatial or proximity context, enabling you to map where proteins, cell types, and biological processes are located within intact tissue. Rather than dissociating cells and losing spatial information, spatial proteomics preserve tissue architecture, revealing how protein dynamics or cells interact with their local environment and with one another.

With modern spatial proteomics workflows, you can:

• Map protein expression directly within tissue sections while preserving spatial context
• Identify distinct cell types and states and determine where they are located within the tissue
• Characterize cellular neighborhoods and microenvironments that influence disease progression and therapeutic response
• Study cell–cell interactions involved in immunity, inflammation, development, and cancer biology
• Compare protein expression across tissue regions, including healthy and diseased areas
• Discover spatial biomarkers associated with prognosis, treatment response, or disease mechanisms
• Investigate tissue heterogeneity that may be obscured in bulk or dissociated-cell analyses
• Integrate spatial, proteomic, genomic, and histological data for a more comprehensive understanding of biology

Spatial proteomics is increasingly used in oncology, immunology, neuroscience, and translational research to uncover how tissue organization influences biological function. By revealing not only which proteins are present, but also where they are expressed, spatial proteomics offers critical insight into the cellular interactions and microenvironments that drive health and disease.