Proteinquantifizierung Standardisierung Bioinformatik

1. Proteinquantifizierung Standardisierung Bioinformatik Proteinquantifizierung:  U. Korf, Deutsches Krebsforschungszentrum (DKFZ)  T. Nann, Freiburger Materialforschungszentrum (FMF) Bioinformatik:  W. Huber, DKFZ W. Huber, Div. Molecular Genome Analysis, DKFZ

2. spin offs mathematical modeling Standardized procedures: experiments data management modeling statistical analysis biological system quantification technology

3. This talk Protein quantification the parallelized Western Blot Protein labeling accuracy in detection Bioinformatics standardization, statistical analysis data exchange W. Huber, Div. Molecular Genome Analysis, DKFZ

4. Protein quantification methods Method Sample Pro Contra Western-Blot protein Well-established Simple protocol Time-consuming Small number of samples Quantification is hard to standardize 2D-Gel complex protein mixture High resolution Time-consuming Limited pI/MW-range only Identification: mass spectrometry Mass Spec (ICAT) protein no need for antibodies relative concentration Expensive - hardware  - personnel ELISA protein measure protein activity and interaction high sample consumption slow Antibody Array 10 proteins relative or absolute (with internal standards) detection of PTMs (phophorylation) method in development phase Economist Mar 15, 2003 „Studying proteins has long be a slow, arduous process. Protein chips ... [promise to do for biology] ... what microprocessors did for personal computing.“

5. Protein Array Technology Sample Preparation Antibodies Immobilization of Proteins Blocking Procedure Assay Scanning Spotting (384 well-plate) (25 Arrays/Run) Data Analysis Recombinant Proteins

6. Protein quantification 1. Fluorescent labeling (2 colors) 2. Relative quantification: abundance ratios lysate 1 / lysate 2 3. Absolute quantification: lysate / dilution series of known reference 4. Quantification of protein phoshporylation: phosphospecific antibodies

7. Data analysis Subarray 1 Subarray 2 250 ng/ml Cy3-PKA Cy3-BSA Cy3-PKA Cy5-BSA Cy3-BSA Cy3-PKA establish: dynamic range sensitivity specificity reproducibility Cy3-BSA Cy3-PKA

8. Nanoparticle fluorescence labeling Bandgap Nanoparticle con- ducting band LUMO Energy HOMO valence band Molecule Bulk crystal Size

9. Photoluminescence of CdSe nanorods with different width.

10. Tunable fluorescence spectra CdTe CdSe

11. + brigthness - bleaching Rhodamine Nanorods Rhodamine Nanorods Rhodamine Nanorods

12. Nanoparticles vs. organic fluorophores “In comparison with organic dyes such as rhodamine, this class of luminescent labels is 20 times as bright, 100 times as stable against photobleaching, and one-third as wide in spectral linewidth.” From: W. C. W. Chang, S. Nie, Science, 1998, 281, 2016-2018.

13. Standardization Pubmed: gene expression profiling [MeSH] gene expression profiling / (methods OR standards) [MeSH] number ratio

14. Five aspects of standardization publication control systematic errors (calibration) exchange of models control stochastic errors (statistics) exchange of data W. Huber, Div. Molecular Genome Analysis, DKFZ

15. Calibration: control systematic errors regression of correction transformations (label incorporation rate, absolute amount of sample, probe sensitivity) standardized experimental conditions (cell lysis, labeling, desalting; array coating spotting buffer, scanner settings) W. Huber, Div. Molecular Genome Analysis, DKFZ

16. Calibration: control systematic errors quantity of interest (transcript abundance) measured quantity (fluorescence intensity) Calibration: - establish a useful calibration model - estimate (enough about) aik, bik - determine error bars W. Huber, Div. Molecular Genome Analysis, DKFZ

17. "A program that only runs at one site is not software, it’s a piece of hardware" R. Gentleman Standardization of data analysis and modeling efforts Software development in biosciences research: trade-off between o performance, specialisation o platform-independence, common standards, modular architecture  fragmentation of languages, data formats, analysis and modeling platforms  inflexible monoliths, becoming harder to maintain than to abandon - a tendency for empire-building…

18. Standardization of data analysis and modeling efforts Approach the writing of academic software like a scientific publication: o peer-reviewed o easily available and widely exchanged between scientists o adherent to standards (formal and informal) o augmental, modular Examples: R (Statistics) Bioconductor (Functional Genomics) Bioperl (Human Genome Project)

19. SBML: systems biology markup language Standardized exchange of systems biology model descriptions between different groups / software W. Huber, Div. Molecular Genome Analysis, DKFZ

20. Public Database MAGE-ML: Microarray and Gene Expression Markup Language Standardized exchange of experimental data on transcript or protein abundances Project Database LIMS Institutional database W. Huber, Div. Molecular Genome Analysis, DKFZ

21. I III III III I N T M T N N T N T T C Publication of systems biology research In traditional biology, results can be published as still image, static diagram, or text: In systems biology, results will be published as dynamic documents, mixing data, program code, graphical visualisation, descriptive text. … WWW "supplements" … Sweave … several research projects are under way (eg “Reproducible Research Project”, Bell Labs / Harvard)

22. Thank you