Clinical Proteomics Course

Lesson 1 • Protein Structure and Function Basics

Reviews protein structural hierarchy and functional classes relevant to disease. Provides the biochemical vocabulary needed for downstream proteomics methods.

Lesson 2 • Overview of Clinical Proteomics Applications

Surveys biomarker discovery, disease mechanism studies, and therapeutic targeting. Motivates the full course by linking proteomics to real diagnostic and treatment decisions.

Lesson 3 • The Proteome and Its Complexity

Defines the proteome and contrasts it with the genome and transcriptome. Grounds students in why protein-level analysis is essential for clinical insight.

Lesson 1 • Protein Extraction and Quantification

Teaches lysis buffers, extraction protocols, and protein assay methods for diverse matrices. Directly prepares students for sample preparation in subsequent chapters.

Lesson 2 • Pre-Analytical Variables and Bias

Identifies how collection time, anticoagulants, and handling temperature alter protein profiles. Teaches systematic control of confounders before analysis begins.

Lesson 3 • Clinical Sample Types and Sources

Catalogues plasma, serum, urine, tissue, CSF, and other matrices used in clinical proteomics. Connects sample choice to the biological question being asked.

Lesson 1 • Data Acquisition Modes

Contrasts data-dependent, data-independent, and targeted acquisition strategies. Prepares students to match acquisition mode to clinical study design.

Lesson 2 • Mass Analysers and Their Performance

Compares quadrupole, ion trap, Orbitrap, and TOF analysers on resolution and speed. Guides instrument selection based on clinical sensitivity requirements.

Lesson 3 • Liquid Chromatography Coupled to MS

Describes nano-LC and UHPLC separation strategies that reduce sample complexity before MS. Explains how chromatographic parameters affect proteome coverage.

Lesson 4 • Tandem Mass Spectrometry and Fragmentation

Covers MS/MS fragmentation modes and ion series used for peptide sequencing. Connects fragmentation patterns to protein identification workflows.

Lesson 1 • Reduction, Alkylation, and Denaturation

Explains chemical steps that unfold proteins and block cysteines before digestion. Ensures complete and reproducible proteolytic cleavage in downstream steps.

Lesson 2 • Enzymatic Digestion Strategies

Covers trypsin, Lys-C, Glu-C, and other proteases and their cleavage specificity. Guides selection of digestion strategy to maximise sequence coverage.

Lesson 3 • Peptide Cleanup and Enrichment

Teaches solid-phase extraction, StageTip, and phosphopeptide enrichment methods. Directly improves signal quality and PTM detection in MS analysis.

Lesson 4 • Depletion of High-Abundance Proteins

Addresses how albumin and immunoglobulins mask low-abundance biomarkers in plasma. Teaches depletion strategies and their trade-offs for clinical proteomics.

Lesson 1 • Raw Data Processing and Peak Picking

Covers spectral deconvolution, peak detection, and file format standards. Establishes the data quality foundation before any database search.

Lesson 2 • Statistical Analysis of Proteomics Data

Applies t-tests, ANOVA, and multivariate methods to differential protein expression. Addresses multiple testing correction critical for clinical biomarker studies.

Lesson 3 • Pathway and Network Analysis

Translates protein lists into biological pathways and interaction networks. Enables mechanistic interpretation of clinical proteomics findings.

Lesson 4 • Protein Quantification Methods

Covers intensity-based, spectral counting, and reporter ion quantification. Connects quantification method to the labelling strategy chosen in sample prep.

Lesson 1 • Glycoproteomics in Clinical Research

Explains N- and O-linked glycosylation and their roles in cancer and infection biomarkers. Teaches lectin enrichment and glycan characterisation by MS.

Lesson 2 • Phosphoproteomics Principles and Methods

Covers phosphorylation biology, enrichment strategies, and site localisation scoring. Connects kinase signalling dysregulation to cancer and metabolic disease.

Lesson 3 • Acetylation, Methylation, and Other PTMs

Surveys histone acetylation, protein methylation, and SUMOylation in epigenetic regulation. Expands PTM coverage beyond phosphorylation for comprehensive disease analysis.

Lesson 4 • Ubiquitination and Protein Degradation

Addresses ubiquitin-mediated proteasomal degradation and its clinical relevance. Covers diGly remnant enrichment for ubiquitination site mapping.

Lesson 1 • Targeted MS Assay Development

Teaches MRM and PRM assay design for quantifying specific proteins in clinical matrices. Bridges discovery proteomics to reproducible, high-throughput clinical measurement.

Lesson 2 • Clinical Validation and Qualification

Addresses independent cohort validation, clinical utility assessment, and qualification frameworks. Completes the biomarker pipeline from discovery to clinical decision support.

Lesson 3 • Analytical Validation of Proteomics Assays

Covers accuracy, precision, linearity, and matrix effects per bioanalytical guidelines. Prepares students to generate data packages accepted by regulatory bodies.

Lesson 4 • Biomarker Discovery Study Design

Covers cohort selection, power calculations, and confounding variable control. Prevents common design flaws that invalidate clinical proteomics biomarker studies.

Lesson 1 • Infectious Disease and Host Response

Uses proteomics to characterise host-pathogen interactions and identify sepsis and viral infection biomarkers. Connects immune proteome changes to clinical severity.

Lesson 2 • Cancer Proteomics and Tumour Biomarkers

Covers tumour tissue proteomics, plasma biomarker discovery, and proteogenomics integration. Demonstrates how proteomics refines cancer subtyping beyond genomics alone.

Lesson 3 • Neurological Disease and CSF Proteomics

Focuses on CSF and brain tissue proteomics for Alzheimer's, Parkinson's, and ALS. Addresses the unique challenges of low-abundance neurological biomarkers.

Lesson 4 • Metabolic and Inflammatory Disease Proteomics

Applies proteomics to diabetes, obesity, and autoimmune diseases to identify mechanistic and diagnostic proteins. Integrates proteomics with metabolomics for systems-level insight.