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Team:ETHZ Basel/Modeling/Imaging
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| + | The cells were placed in a 50 μm (?) high flow chamber (details on the setup later). | ||
| + | The movies were made out of bright field images. The excitation was 60ms, the period something around 1/7s, pretty much the fps rate of the movie (we can go heigher, but yet we already produced like 2 GB of raw images for every movie). Every image has the size of 1344 x 1024 pixels, 12 or 16 bit grayscale (I forgot what I have chosen, probably we will reduce it anyway later to speed up the imaging pipeline). For both movies the images were made out-of-focus to simplify cell detection. | ||
Revision as of 18:20, 11 August 2010
Setup of the Microscope
A fluorescence microscope with motorized x, y and z control, a motorized shutter and a 60× lens is used with appropriate fluorescence filters for the fluorescence signals. Light-emitting diode arrays are installed as light sources for red light (660 nm) and far-red light (748 nm) pulses.
Control of the Microscope
The microscope is connected to a workstation using the core drivers and interfaces of μManager (see Stuurman et al. (2007) or [1]). To provide a mechanism to change the cell's input signal depending on its fluorescence signal, we developed the microscope software μPlateImager, which enables for parallel acquisition of images and the modification of light input signals. μPlateImager uses the Java interface of the μManager core to control the microscope and can be configured by a separate platform-independent visual user interface. μPlateImager uses the undocumented Java MATLAB Interface (JMI) to connect to Matlab (The MathWorks, Natick, MA) based on the open source project matlabcontrol (see [2]). The microscope thus can be closely controlled by standard Matlab scripts.
Image Analysis
Regularly, a Matlab script is executed by μPlateImager, which triggers the microscope to take an out-of-focus image. Afterwards, the script invokes the cell detection and tracking algorithm. By comparing the change in the position of the E. lemming over several consecutive images, the direction of the E. lemming is estimated. Based on the difference between the estimated direction and the reference direction set by the user, the Matlab script triggers either a red light (660nm), a far-red light (748nm) or no pulse (see description of the control algorithm) to induce or repress tumbling.
First Imaging
To test the setup of the microscope we imaged a ΔCheR strain. The videos of the imaging can be found below. Please remark that the quality of the movie was decreased and that the setup will be enhanced so that the final movies will look nicer (yet just a quick & dirty trial).
The cells were placed in a 50 μm (?) high flow chamber (details on the setup later). The movies were made out of bright field images. The excitation was 60ms, the period something around 1/7s, pretty much the fps rate of the movie (we can go heigher, but yet we already produced like 2 GB of raw images for every movie). Every image has the size of 1344 x 1024 pixels, 12 or 16 bit grayscale (I forgot what I have chosen, probably we will reduce it anyway later to speed up the imaging pipeline). For both movies the images were made out-of-focus to simplify cell detection.
