Luiza Handschuh Karol Marcinkowski University of Medical Sciences, Department of Haematology

1. VIRTUAL LABORATORY AND ITS APPLICATION IN GENOMICS Luiza Handschuh Karol Marcinkowski University of Medical Sciences, Department of Haematology Institute of Bioorganic Chemistry PAS, Center of Excellence for Nucleic Acid-based Technologies INGRID 2008, Lacco Ameno, 11.04.2008

2. Virtual Laboratory - definition and advanteges „The Virtual Laboratory is a distributed workgroup environment, with the main task of providing a remote access to the various kind of rare and expensive scientific laboratory equipment and computational resources”(http://vlab.psnc.pl/) • A specific representative of the RIS (Remote Instrumentation Systems) • Based on grid environment, already implemented in the VLab System by Poznan Supercomputing and Networking Center (PSNC) • Independent on physical location of the instruments • Designed to cooperate with many other grid systems • Devoted to experimental and computational tasks – supporting the postprocessing phase of experiment • Experiments made in other laboratories and their results can be shared enabling the workgroup

3. Modular architecture of the Virtual Laboratory system

4. Preparing a sample and/or input data (e.g. parameters) Measurement/computation Data processing and visualization Data storage and management Experiment execution in the Virtual Laboratory

5. Workflow management Dynamic Measurement Scenario (DMS) design: • Analysis of application • Connection diagram construction • Description of additional dependecies • Generation of applications and links description • Generation of the measurement scenario description an example workflow In Scenario Submission Application in NMR studies

6. Case diagram nodes – experimental/computational tasks; edges - paths of measurement execution follow Data storage and management Digital Library – a crucial component in most typical RIS & VL systems, a module responsible for data storage and management (DSM) • unique digital collection • possibility of software extention • cooperation with the library integrated systems, e.g. catalogue databases • possiblity of searching and browsing • widespread access (via Internet)

7. Mitochondria Cell nucleus Endoplasmatic reticulum DNA Organism Tissues Organs Golgi Apparatus Plasma membrane Biological introduction

8. protein expression G E N E nucleus Functional genomics – how the genom works? PROTEIN RNA DNA

9. genotype phenotype Genomics answers the fundamental biological questions

10. Microarray experiment

11. „Application of functional genomics tools forestablishing complex model of tumor transformation.Studies on molecular mechanisms of acute myeloid leukemia pathogenesis” as a part of a huge project announced by Polish Ministry of Science and Informatization in 2005: „Application of functional genomics and bioinformatics for creation and characterisation of models describing biological processes ofgreat importance in medicine and agriculture” (PBZ-MNiI-2/1/2005) Institute of Bioorganic Chemistry PAS, Poznań Karol Marcinkowski University of Medical Sciences, Department of Haematology Poznań Supercomputing and Networking Center Poznań University of Technology

12. AML M1 maturation blockade GRANULOCYTIC LINEAGE CFU-G Myeloblast Myelocyte Band BONE MARROW Promyelocyte Metamyelocyte Segment CFU- CFU- CFU- STEM blast GEMM GM CELL MONOCYTIC LINEAGE CFU-M Monoblast Monocyte ERYTHROCYTIC LINEAGE Polichromatophilic erythroblast CFU-E Proerythroblast Reticulocyte BFU-E basophilic ortochromatic Erythrocyte erythroblast erythroblast PLATELET LINEAGE CFU-mega Megakaryoblast Megakaryocyte Platelets LYMPHOCYTIC LINEAGE bone marrow T and B lymphocytes blood/bone marrow blood Research model – acute myeloid leukemia AML M1 FAB type Haematopoesis scheme AML M1 is almost homogenous cell population (myeloblasts consist 90% of the whole bone marrow cell pool) Molecular determinants of this AML type are still not well described. Blasts from patient with FAB M1 AML (Cancer Medicine, 5th edition)

13. Bioinformatic analysis Bioinformatic analysis Bioinformatic analysis Bioinformatic analysis of obtained data of obtained data of obtained data of obtained data Schematic description of research IMPLICATED INSTITUTES: UMS - Karol Marcinkowski Universityof Medical Sciences IBCH - Institute of Bioorganic AML patients / healthy bone marrow donors Chemistry PAS UMS PCNS - Poznań Supercomputing CD 34 cells isolation from + and Networking Center blood and bone marrow samples UT • Poznań University • of Technology + CD 34 miRNA analysis Transcriptome analysis Standard clinical IBCH IBCH IBCH using DNA Proteome analysis UMS using DNA microarrays diagnosis UT UT UT microarrays - morphology –based blood and bone marrow cell analysis - microarray probe selection - DNA microarray printing - total protein extraction - microarray printing (commercial probes) - 2-dimensional electrophoresis -RNA isolation and labeling - miRNA isolation - gel scanning and analysis • immunophenotyping, • molecular biology tests - hybridisation - miRNA i labeling - protein identification - cytogenetics - scanning and analysis - hybridisation using mass spectrometry - scanning and analysis UMS IBCH IBCH IBCH UT UT UT UT Normalisation PCNS and bioinformatic analysis Elaboration of new AMLdiagnostic Genomics virtual UT PCNS of obtained data IBCH tools based on DNA microarrays laboratoryestablishement UT UMS & protein 2DE analysis results Elaboration of a Hospitals Biological model of leukemic transformation PCNS IBCH country-wide data base Research institutes UT UT

14. Microarray construction Microarray of our own design – 924 oligonucleotide probes (DNA fragments, 50-70 nt) complementary to the genes involved in AML pathogenesis and control ones AROS (70 nt)

15. Example of preprocessed microarray images HL60 19sz The same slide No.19

16. First step of computational work – data collection • Grid adjustment • Quantitative analysis - pixels counting for each spot and background (mean and median) Signal intensity: 1- 216 (65535)

17. Fragment of gpr file with microarray raw data, generated by Scanarray Express

19. Virtual Genomics Laboratory – automation of microarray data analysis I. Raw data normalization II. Normalized data analysis

21. High level analysis of microarray data - examples Left: 55 genes at least 4-fold overexpressed in the tested samples comparing to the healthy control Samples No. 20, 22 & 27 represent patients after treatment Wright: Genes differentiating samples with various types of leukemia

22. Experiment execution in the Virtual Laboratory of Genomics In future equipement should be directly available for scientists/doctors who work in other laboratories/institutes in Poland via Internet Now: only the multistep analysis of the microarray data can be automated: the same universal strategy will be applied in every case in order to obtain satisfactory gene expression results

23. Outlook of Virtual Laboratory of Genomics

25. The authors of publication Marcin Lawenda, Norbert Meyer, Maciej Stroinski,Jan Weglarz Poznan Supercomputing and Networking Center Luiza Handschuh, Piotr Stepniak, Marek Figlerowicz (director of the project) Institute of Bioorganic Chemistry PAS Others participants of the project: Maciej Kaźmierczak,Mieczysław Komarnicki, Krzysztof Lewandowski Karol Marcinkowski University of Medical Sciences, Departament of Haematology Piotr Formanowicz,Jacek Błazewicz Poznan University of Technology, Institute of Informatics