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Gene expression analysis of “complex” tissues

Gene expression analysis of “complex” tissues. What is gene expression? What is a “complex” tissue? How can gene expression analysis be done? How should the findings be validated? Can this be used for diagnosis or prediction? Some conditions suited for diagnosis or prediction.

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Gene expression analysis of “complex” tissues

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  1. Gene expression analysis of “complex” tissues • What is gene expression? • What is a “complex” tissue? • How can gene expression analysis be done? • How should the findings be validated? • Can this be used for diagnosis or prediction? • Some conditions suited for diagnosis or prediction.

  2. What is gene expression? Genome Transcription mRNA Translation Protein Secretion

  3. What is a “complex” tissue? Anatomical complexity (1)

  4. Normal gastric corpus Adenocarcinoma What is a “complex” tissue? Patho-anatomical complexity (2)

  5. What is a “complex” tissue? Physiological complexity (1) ECL-cell EC-cell D-cell D1-cell A-like cell X-cell mage

  6. What is a “complex” tissue? Physiological complexity (2)

  7. Laser-assisted microdissection Digital camera/computer Lid Cell smear on an object glass with a polyethylene naphthalate membrane Motorised stage Objective Laser

  8. Laser-assisted microdissection Video view Small (ECL?) cell Large (parietal?) cell

  9. Laser-assisted microdissection Single-cell nested PCR ECL cell Parietal cell Corpus Template- HDC H+K+ CCK2-r CCK2-r

  10. Application and ethics Biomedical problem Genome- or system- wide screening High throughput single gene testing Statistical and computational analysis Gene expression analysis - a systematic approach?

  11. Biomedical problem • A physiological experiment • A pharmacological intervention • A subspecies characteristic • A dysfunction or a disease

  12. Genome- or system- wide screening • Microarray with genome or dedicated sets • Differential display • Various proteomics techniques

  13. Statistical and computational analysis • Many genes-few replicates represent unique problems • Case-to-case assessment necessary at all levels of analysis • No method(s) seem routinely usable • I.e. - close collaboration with highly skilled and • scientifically competent statisticians and • computing scientists is mandatory. • All experiments are opportunities to work with • statistics and computational methods on a • scientific level.

  14. High throughput single gene testing • High throughput si-RNA testing • Tissue microarray methods Final verification by carefully designed experiments, transgenics or other more laborious methods

  15. Application and ethics Some applications: • Biological • Generation of hypotheses • Integrated regulatory mechanisms • Medical • Classification of disease • Early diagnosis • Treatment stratification Some ethical concerns: • Surplus information • Is presymptomatic diagnosis desirable • Familial susceptibility

  16. Diagnosis and prediction General • Differentiation between malignant and • non-malignant growth • Differentiation between localised and • disseminated malignant tumour (surgical • decision-making) • Differentiation between similar but • non-malignant conditions • Differentiation between medically • responsive and non-responsive conditions • (medical decision-making)

  17. Diagnosis and treatment of gastrointestinal premalignancies Polypectomy FAP

  18. Diagnosis and prediction Examples from gastroenterology (1) Diagnosis and therapy response in Ulcerative colitis Crohn’s disease

  19. Diagnosis and prediction Examples from gastroenterology (2) Development of malignancy in ulcerative colitis DALM Adenocarcinoma

  20. Diagnosis and prediction Examples from gastroenterology (3) Gastric stump cancer

  21. Diagnosis and prediction Examples from gastroenterology (4) • Predicting: • Penetration • Haematogenic metastasis • Lymphatic metastasis • Intracavitary metastasis

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