Advancing Proteomics: Techniques, Applications, and Future Directions
Proteomics is a rapidly advancing field that focuses on the large-scale study of proteins, their structure, function, and interactions within biological systems. This course provides an in-depth exploration of proteomics, covering its foundational concepts, cutting-edge technologies, applications in disease research, and its integration with other 'omics' fields. Students will gain knowledge of both experimental techniques and computational tools that drive the proteomics field forward.
| Responsible | Lieven Gevaert |
|---|---|
| Last Update | 21/01/2025 |
| Completion Time | 4 hours 45 minutes |
| Members | 6 |
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Intermediate
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Introduction:4Lessons · 55 mins
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Proteomics is the study of the entire complement of proteins expressed in a cell, tissue, or organism at any given time. It differs from genomics in that genomics focuses on the static sequence of
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Historical Context
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The proteome vs genome
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Protein function and interaction
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Module 2: Key Techniques in Proteomics5Lessons · 1 hr 5 mins
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Mass Spectrometry (MS) Principles of MS Mass spectrometry identifies proteins by measuring the mass-to-charge ratio of peptide ions. The peptides are ionized, separated by mass, and analyzed to de
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Two-Dimensional Gel Electrophoresis (2D-GE) Principle and Application 2D-GE separates proteins first based on their isoelectric point (charge) and then based on molecular weight. It creates a gel
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Protein Microarrays Overview Protein microarrays involve a solid surface where proteins are spotted, and their interactions with antibodies, ligands, or other proteins are analyzed. Applications T
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Chromatography and Electrophoresis Protein Separation Techniques Chromatography Techniques like liquid chromatography (LC) separate proteins based on properties like size, charge, and hydrophobici
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ProteomicsFig1
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Module 3: Computational Tools in Proteomics2Lessons · 30 mins
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Bioinformatics for Proteomics Tools for Protein Identification Bioinformatics tools like Mascot and Sequest help match the peptides detected by mass spectrometry to known proteins in databases. Th
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Quantitative Proteomics and Data Integration Quantitative Methods Label-free quantification methods compare the relative abundance of peptides in different samples by their ion intensities. Labeli
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Module 4: Applications of Proteomics in Disease Research4Lessons · 1 hr
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Proteomics in Cancer Research Proteomic Biomarkers Proteomics is extensively used in cancer research to identify biomarkers for early detection, prognosis, and treatment response. (The HER2 protei
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Neurodegenerative Diseases Protein Misfolding in Diseases In diseases like Alzheimer's and Parkinson’s, proteins like amyloid beta (in Alzheimer's) and alpha-synuclein (in Parkinson's) misfold and
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Cardiovascular Diseases Proteomic Markers for Cardiovascular Disease Proteomics is being used to discover biomarkers for early diagnosis and risk assessment in cardiovascular diseases, such as myo
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Metabolic Disorders Proteomics provides insights into the molecular mechanisms underlying diseases like obesity, diabetes, and metabolic syndrome by identifying biomarkers and studying metabolic s
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Module 5: Emerging Frontiers in Proteomics3Lessons · 45 mins
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Single-Cell Proteomics Concept and Techniques Single-cell proteomics allows us to study proteins in individual cells, providing insights into cellular heterogeneity that can be masked in bulk tiss
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Proteogenomics Integration of Proteomics and Genomics Proteogenomics combines proteomics with genomic data to identify new biomarkers and better understand diseases like cancer at a molecular leve
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AI in Proteomics Applications of AI Artificial intelligence and machine learning are increasingly being used to analyze large proteomics datasets. AI can help identify patterns in protein expressi
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Module 6: Case Studies and Practical Applications1Lessons · 15 mins
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Module 6 Case Studies and Practical Applications
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Module 7: Future Directions and Conclusion1Lessons · 15 mins
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Challenges in Proteomics Technical and Biological Challenges Proteomics faces challenges like incomplete protein databases, difficulty analyzing low-abundance proteins, and ensuring reproducibilit
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