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Course Catalog 2011-2012
SGN-6126 Cell culturing, microscopy and cell image analysis, 3 cr |
Additional information
This course is lectured every year.
Course webpage: http://www.cs.tut.fi/~sanchesr/SGN-6126/index.htm
Suitable for postgraduate studies
Person responsible
Andre Sanches Ribeiro, Olli Yli-Harja
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Final project work (40% of the final grade). Final reports on the experimental work and the results obtained from the image analysis (60%). To pass the course, the student is required to: a) Execute the final project and deliver three reports on the experimental works. b) Attend at least 80% of the lessons.
Learning outcomes
From this course the student will know how to prepare standard bacterial cultures and will have basic knowledge of how to operate microscopes. The student will also be introduced to state of the art software for cell image analysis and profiling. Finally, the students will gain insight on the interpretation of the results and will obtain knowledge on the efficiency of the algorithms when applied to real data. After the course, the student will be able to: 1) Identify and define experimental techniques related to bacterial culturing and cell imaging. Demonstrate the ability to apply these methods to extract information from biological systems. 2) Interpret data generated from the microscope, classify strengths and weaknesses of the bright field and fluorescence measurements, summarize results of the measurements and explain the connection between measurements and underlying biological processes. 3) Implement experimental techniques and apply them to extract data from biological systems. Calculate statistical properties of the features measured at the single cell level. Apply knowledge of existing software to extract the relevant information. 4) Analyze results of the measurements. Compare the various methodologies used for measuring a variable such as cell fluorescence levels. 5) Compare and appraise different algorithms for extracting quantifiable features from the images, and interpret the results from the measurements. 6) Create and develop new features that would improve the algorithms for specific goals.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Principles of cell culturing of bacteria and microscopy | ||
| 2. | Experimental cell culturing of bacteria | ||
| 3. | Imaging fluorescent bacteria under the microscope | ||
| 4. | State of the art tools for cell images analysis | ||
| 5. | Segmentation and extraction of information about the cells using CellProfiler |
Evaluation criteria for the course
Project work (40% of the final grade). Final reports on the experimental work and the results obtained from the image analysis (60%). To pass the course, the student is required to: a) Execute the project work and deliver three reports on the experimental works. b) Attend at least 80% of the lessons.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Molecular Biology of the Cell, 4th edition, Bruce Alberts, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, and Peter Walter. | English | |||||
| Journal | Fermino, A. M., Fay, F. S., Fogarty, K. and Singer, R. H., Visualization of single RNA transcripts in situ, Science, 280, 1998, 585-9. | English | |||||
| Journal | Reue, K., mRNA quantitation techniques: considerations for experimental design and application, J. Nutr. 128, 1998, 2038-44. | English | |||||
| Lecture slides | Lecture slides | English | |||||
| Other online content | CellProfiler | Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology 7:R100. | English | ||||
| Other online content | ImageJ | Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://rsb.info.nih.gov/ij/, 1997-2009 | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-6106 Computational Systems Biology | Advisable |
Additional information about prerequisites
Basic knowledge of biology/systems biology. Introduction to Computational Systems Biology (SGN-6056) or equivalent. Also, SGN-1157 Signal Processing Basics or SGN-1201 Signaalinkäsittelyn menetelmät.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
There is no equivalence with any other courses
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Signal Processing is becoming a key specialty in biology related efforts, both in research and in industry. However, signal processing students, in general, currently lack any experience in the experimental techniques and methods they will be required to understand to analyze and interpret the data that arises from them. In this course, students will be introduced to current techniques of cell culturing and imaging. Students will also be provided with knowledge of state-of-the-art algorithms for analyzing cell images. This course will be taught using a hands-on approach. Students will first be provided with one introductory lecture on the biological topics, such as cell culturing and cell imaging using microscopy. The following lesson will be experimental, in which the students will culture their cells. This is followed by a lesson where the cultured cells are imaged under the microscope. Next, there is a lecture on state of the art tools for cell image analysis, such as CellProfiler and ImageJ, which will then be used in the following three lectures to segment, track, and extract information about the cells previously imaged by the students. In the last lecture, as a final project, the student will use signal processing tools to analyze the data they have produced. The aim of the analysis will be chosen by the student from a set of proposed goals or proposed by the student. |