1. 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 VIRTUAL LABORATORY AND ITS APPLICATION IN GENOMICS

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/)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. Experiment execution in the Virtual Laboratory 1.Preparing a sample and/or input data (e.g. parameters) 1.Measurement/computation 2.Data processing and visualization 3.Data storage and management

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. 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) Case diagram nodes – experimental/computational tasks; edges - paths of measurement execution follow

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

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

9. Genomics answers the fundamental biological questions genotype phenotype

10. Microarray experiment

11. „Application of functional genomics tools for establishing 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 of great 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. Research model – acute myeloid leukemia AML M1 FAB type 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. Haematopoesis scheme BONE MARROW STEM CELL CFU- blast CFU- GEMM CFU- GM CFU-GMyeloblast Promyelocyte Myelocyte Metamyelocyte Band Segment CFU-MMonoblastMonocyte CFU-EProerythroblast basophilic erythroblast Polichromatophilic erythroblast ortochromatic erythroblast Reticulocyte Erythrocyte BFU-E CFU-megaMegakaryoblast T and B lymphocytes bone marrow blood/bone marrow blood Megakaryocyte Platelets GRANULOCYTIC LINEAGE AML M1 maturation blockade MONOCYTIC LINEAGE ERYTHROCYTIC LINEAGE PLATELET LINEAGE LYMPHOCYTIC LINEAGE Blasts from patient with FAB M1 AML (Cancer Medicine, 5th edition)

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

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 The same slide No.19 HL6019sz

16. First step of computational work – data collection 1.Grid adjustment 2.Quantitative analysis - pixels counting for each spot and background (mean and median) Signal intensity: 1- 2 16 (65535)

17. BlockColumnRowNameIDXYDia. F1 MedianF1 MeanF1 SD B1 MedianB1 MeanB1 SD 111AATK962526831279530526733647015717489 121AATK962531741279330026130618216017573 131AATK962536581281930026831618615417176 141ADRA2C1524149128273001601777715717483 151ADRA2C1524645128243001621799315317183 161ADRA2C1525135128313001561727715116980 171ANGTP12845619128623001171355914616577 181ANGTP12846105128593001171366115016872 112ANGTP12842674133613001251446815317280 122ATF346731661330230016619611115717581 132ATF346736621329730015718610615917783 142ATF34674144133313001371577615517279 152BCL2L15984647133103559721707161315317180 162BCL2L15985134132983507971751180216017780 172BCL2L15985629132993358031666170715317077 182 C1QTNF 61149046104133393001121318215617479 113 C1QTNF 61149042672138403001191376316217778 123 C1QTNF 611490431661383930011814214115917783 133CBL867366013807300247305187157210701 143CBL86741591379229524128216115317277 153CBL86746501379929021025816115317077 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