#CELLPROFILER IDENTIFY POSTIVE CELLS SOFTWARE#
#CELLPROFILER IDENTIFY POSTIVE CELLS DOWNLOAD#

Set ‘Default Output folder’ to the same folder as the input folder. Double-click on a few of the images to see what they look like. The names of your images should now appear in the bottom left square of the CellProfiler interface. Start CellProfiler and set the ‘Default Input Folder’ to the folder where you placed your images.

Once you have extracted information from the images and stored them in a database you will use Cellprofiler Analyst to visualize your data and to train a classifier to distinguish between treated and untreated cells.ĬellProfiler Select Input and Output folder Unzip TranslocationData.zip. You will thereafter run it automatically on all the images in the experiment. The first task is to set up a CellProfiler ‘pipeline’ consisting of a number of ‘modules’ and then test the pipeline on a few images. You have a total of 26 images and approximately 200 cells per image, so you want to automate the process. Methods Using Cellprofiler and CellProfiler Analyst, your job is to outline (segment) each cell and cytoplasm and extract a number of measurements from each cell. You will also use a simple text file called ‘Translocation_doses_n_controls.csv’ containing information about where on the 96-well plate the wells were located and how the cells were treated. 10 wells were used to create a gradient with increasing concentration of the drug. Material Your images originate from a ’96-well plate’, but you will work with a subset of 26 images with the following setup: 8 wells untreated (negative control) and 8 wells were treated with the maximum dose of 150nM (positive control). In this case how a protein is translocated from one subcellular compartment to another. The optimal goal of developing this type of image based screens is to use it to search for other, previously unknown drugs that have the same effect on the cell level, and may be of use for treatment of patients (possibly with less side effects than the known drug).Ĭhallenge Analyze how cells respond to treatment with a drug. We also want to optimize the image analysis to separate positive (treated with 150nM drug) and negative controls (untreated) as well as possible. We know that 150nM of a positive control drug causes the cells to transport the protein from the cytoplasm to the nucleus, but we do not know the lowest possible dose to see this effect. Scenario In this experiment we have human osteosarcoma cells where a ‘forkhead’-protein has been labeled with GFP (a green fluorescing protein).

#CELLPROFILER IDENTIFY POSTIVE CELLS DOWNLOAD#

Download sample images and a description of experimental parameters from However, for this exercise the programs have already been installed on your computer, and can be found on ‘Gemensamt on 'IT-Pclab' (G:) → Program’.

#CELLPROFILER IDENTIFY POSTIVE CELLS SOFTWARE#

Starting software and download images CellProfiler and CellProfilerAnalyst can be downloaded from This webpage also provides tutorials, examples and forum where you can find answers questions you may have and get help. Computer exercise in CellProfiler & CellProfiler Analyst, 111129 Carolina Wählby, & Martin Simonsson,

Cellprofiler identify postive cells