SGN-53206 Cell Culturing, Microscopy and Cell Image Analysis, 3 cr

Additional information

This course is lectured every year.

Course webpage: https://www.cs.tut.fi/~sanchesr/SGN-53206/index.htm

Only 18 students can attend the course every year, due to space limitations in the laboratory. The following criteria will be used to select which students will attend the course. From those attending the first lecture, we will select according to the order of enrolment in the course. If you do not attend the first lecture, the next person in the list will be given priority. The order of enrolment is visible in POP.
Suitable for postgraduate studies.

Person responsible

Andre Sanches Ribeiro

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

Instructions for students on how to achieve the learning outcomes

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 (0-5)

Study material

Correspondence of content