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Printable Handouts
Navigable Slide Index
- Introduction
- Structure of the talk
- The human genome versus the human proteome
- Why bother about the proteins?
- The promise of proteomics
- How can we unlock the promise of proteomics?
- Concentrations can span 12 orders of magnitude
- Proteins are multifunctional
- Protein function can be context dependent
- Protein interactions and protein complexes
- Proteins have multiple PTMs
- An example: subpopulations of Histone H4
- Protein localisation is compartmentalised
- Concentrations and modifications change rapidly
- Technologies
- Protein identification
- How does mass spectrometry work?
- Principles of mass spectrometry
- Types of mass spectrometers: Time of Flight
- Types of mass spectrometers: Quadrupoles
- Types of mass spectrometers: Orbitrap
- Modern mass spectrometers: more than one MS
- Peptide sequencing by MS (1)
- Peptide sequencing by MS (2)
- Peptide sequencing by MS (3)
- Peptide sequencing by MS: some footnotes
- A typical workflow for MS analysis
- Pre-separation methods: 2D-gel electrophoresis
- Pre-separation methods: Gel-LC MS
- Pre-separation methods: 2D Chromatography
- Pre-separation: Capillary electrophoresis
- Pre-separation: The principle
- Selected & multiple reaction monitoring
- Mass cytometry
- Mass spectrometry and clinical use
- Protein arrays
- Types of protein arrays
- Protein target arrays – a twist
- Reverse phase protein arrays
- Protein capture arrays
- Immunohistochemistry (IHC)
- Tissue microarrays
- Fluorescence resonance energy transfer
- Matrix assisted laser desorption ionisation
- What samples are analysed?
- Data analysis
- The main applications of clinical proteomics
- Biomarkers – the basics (1)
- Biomarkers – the basics (2)
- Biomarkers – the basics (3)
- Life is a relationship among molecules
- Study design and statistical analysis
- More data on patients than ever before
- But how efficiently are we using these data?
- The –omics dilemma
- System biology model analysis
- Data integration
- A vision for systems medicine
- What will systems medicine deliver?
- From systems biology to systems medicine
- Thank you for listening!
Topics Covered
- Introduction
- The relationship between genome and proteome
- Why should we analyse the proteome?
- Challenges in proteome analysis
- Dynamic range of the proteome
- Complexity of the proteome
- Spatiotemporal organisation
- Dynamic changes in protein concentrations and modifications
- Technologies for analysing the proteome
- Mass spectrometry
- Protein arrays
- Imaging
- Data analysis
- The applications of clinical proteomics
- Biomarkers
- Systems biology approaches
Talk Citation
Kolch, W. (2013, November 5). Proteomic technologies and targets: an overview [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved November 21, 2024, from https://doi.org/10.69645/FBTS5775.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Walter Kolch has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
A selection of talks on Methods
Transcript
Please wait while the transcript is being prepared...
0:00
Hello, my name is Walter Kolch,
I'm the Director of Systems Biology Ireland in
the Conway Institute for Biomolecular and Biomedical
Research at University College Dublin in Ireland.
My personal interest is in
signal transduction pathways and we use a lot of proteomics tool to map these pathways.
I'd like to welcome you to this overview talk on proteomic technologies and targets.
0:24
The talk is in four parts.
I'll start with an introduction stating and laying out the problem.
Then I'll talk about some challenges associated with it and some of the technologies,
how we can address these problems and at the end is a short part on data analysis.
0:40
So let's start with a brief look at the human genome versus the human proteome.
Humans have 24 chromosomes which contains three billion base pairs worth of DNA.
These contain about 23,000 genes which can encode for about 100,000 messenger RNAs,
and this increase is generated through
alternative splicing and alternative use of promoters.
This 100,000 messenger RNAs can generate an equal number of proteins,
so we're dealing with about a 100,000 proteins.
However, if we take into account the protein modifications like
phosphorylation and other post-translational modifications
which occur once the proteins are made,
we actually deal with between half a million and
1.5 million different functional entities at the protein level.
So we have a huge increase in complexity as we move from the genome to the proteome.
1:34
So why then should we even bother about the proteins if they are so complicated?
The answer actually is fairly simple.
If you look at these two organisms,
a caterpillar and a butterfly,
they share the same genome but they have a different proteome.
So proteins are important because they can actually determine the phenotype.