Bernd Bodenmiller: Catalogue data in Spring Semester 2021 |
| Name | Prof. Dr. Bernd Bodenmiller |
| Field | Quantitative Biomedicine |
| Address | Quantitative Biomedizin ETH Zürich, HPL H 22 Otto-Stern-Weg 7 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 93 93 |
| bernd.bodenmiller@biol.ethz.ch | |
| URL | http://www.bodenmillerlab.com |
| Department | Biology |
| Relationship | Associate Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 551-0338-00L | Current Approaches in Single Cell Analysis (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: BME327 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | 2 credits | 2V | B. Bodenmiller, University lecturers | |
| Abstract | In this lecture, we will discuss the most important single cell approaches, the questions they can address and current developments. We will cover single cell: genomics, transcriptomics, proteomics (CyTOF mass cytometry), metabolomics and highly multiplexed imaging. Finally, we will also discuss the latest approaches for the analysis of such generated highly multiplexed single cell data. | ||||
| Learning objective | On completion of this module the students should be able to: - explain the basic principles of single cell analysis techniques - identify and justify the limitations of the current single cell technologies and suggest reasonable improvements - know the basic challenges in data analysis imposed by the complex multi parameter data. Key skills: On completion of this module the students should be able to: - summarize and discuss the impact these technologies have on biology and medicine - design biological and biomedical experiments for which single cell analysis is essential | ||||
| Content | Currently single cell analysis approaches revolutionize the way we study and understand biological systems. In all biological and biomedical settings, cell populations and tissues are highly heterogeneous; this heterogeneity plays a critical role in basic biological processes such as cell cycle, development and organismic function, but is also a major player in disease, e.g. for cancer development, diagnosis and treatment. Currently, single cell analysis techniques are rapidly developing and find broad application, as the single cell measurements not only enable to study cell specific functions, but often reveal unexpected biological mechanisms in so far (assumed) well understood biological processes. In this lecture, we will discuss the most important single cell approaches, the questions they can address and current developments. We will cover single cell genomics, single cell transcriptomics, single cell proteomics (CyTOF mass cytometry), single cell metabolomics and highly multiplexed single cell imaging. Finally, we will also discuss the latest approaches for the analysis of such generated highly multiplexed single cell data. | ||||
| 551-0364-00L | Functional Genomics Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module BIO 254 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | 3 credits | 2V | C. von Mering, C. Beyer, B. Bodenmiller, M. Gstaiger, H. Rehrauer, R. Schlapbach, K. Shimizu, N. Zamboni, further lecturers | |
| Abstract | Functional genomics is key to understanding the dynamic aspects of genome function and regulation. Functional genomics approaches use the wealth of data produced by large-scale DNA sequencing, gene expression profiling, proteomics and metabolomics. Today functional genomics is becoming increasingly important for the generation and interpretation of quantitative biological data. | ||||
| Learning objective | Functional genomics is key to understanding the dynamic aspects of genome function and regulation. Functional genomics approaches use the wealth of data produced by large-scale DNA sequencing, gene expression profiling, proteomics and metabolomics. Today functional genomics is becoming increasingly important for the generation and interpretation of quantitative biological data. Such data provide the basis for systems biology efforts to elucidate the structure, dynamics and regulation of cellular networks. | ||||
| Content | The curriculum of the Functional Genomics course emphasizes an in depth understanding of new technology platforms for modern genomics and advanced genetics, including the application of functional genomics approaches such as advanced sequencing, proteomics, metabolomics, clustering and classification. Students will learn quality controls and standards (benchmarking) that apply to the generation of quantitative data and will be able to analyze and interpret these data. The training obtained in the Functional Genomics course will be immediately applicable to experimental research and design of systems biology projects. | ||||
| Prerequisites / Notice | The Functional Genomics course will be taught in English. | ||||