Protein Identification and Characterization
Protein characterization including molecular weight determination, identification, and sequence confirmation.
Discovery & Quantitative Analysis
Proteomics
LC-MS/MS-based protein identification, characterization, and quantitative profiling for biological research. We support studies ranging from protein-level characterization to comparative analysis across biological and experimental conditions.
Overview
Measure protein-level changes across biological systems.
We offer a variety of proteomics services including deep proteome coverage for detection and differential analysis, intact protein and peptide level characterization, and post-translational modification analysis.
Comparative Profiling
Measure relative protein abundance across conditions to identify patterns associated with biological or experimental differences.
Post-translational Modification Analysis
Characterize post-translational modifications associated with protein function and regulation.
Statistical and Pathway Analysis
Apply statistical and pathway analyses to assist in the detection and interpretation of biologically meaningful differences in protein abundance.
Workflow
From study design to interpretable protein data.
Proteomics performance depends on thoughtful experimental design, appropriate sample preparation, robust LC-MS/MS acquisition, and careful data processing.
1
Study Design
Define biological comparisons, sample numbers, and analytical goals.
2
Sample Preparation
Prepare proteins and peptides using a workflow appropriate for the sample matrix.
3
LC-MS/MS
Acquire high-resolution tandem mass spectrometry data using appropriate separation and acquisition conditions.
4
Data Processing
Identify proteins and generate quantitative measurements with quality-control assessment.
5
Interpretation
Evaluate differential patterns and interpret results in biological context.
Study Design
Reliable proteomics results begin with sound experimental design.
Plan the experiment around the biological question.
Sample type, expected biological variability, group size, sample handling, and the desired level of quantitative comparison all influence the analytical strategy. Early consultation can help align the proteomics workflow with the study objective.
Biological replication
Appropriate replication is essential for distinguishing biological differences from technical variation.
Sample consistency
Collection, storage, and preparation should be as consistent as possible across experimental groups.
Analytical objective
Discovery, comparative profiling, and more focused quantitative questions can require different experimental strategies.
Results
Results are organized for both analytical review and biological interpretation.
Proteomics projects can generate thousands of peptide and protein measurements. Our goal is to return results in a form that makes quality assessment, statistical comparison, and interpretation practical.
Protein identification results
Tables of identified proteins and associated quantitative measurements, organized for downstream analysis.
Quality assessment
Review of data quality and consistency to support interpretation of quantitative differences.
Differential Analysis
Protein abundance differences across experimental groups are evaluated using study-appropriate statistical methods.
Figures and biological interpretation
Results are summarized with clear visualizations and interpreted in the context of the study question.
Protein identification is the starting point, not the endpoint.
The most useful proteomics studies connect protein-level measurements back to the biological question. Experimental context, statistical evidence, effect size, and functional relationships should all be considered when deciding which findings warrant follow-up.
Planning a proteomics study?
Tell us about your samples and research question and we can help determine the appropriate analytical approach.