Create Gene & Protein Expression Setting

Creating a Setting for Gene- and Protein Expression

File Types

Bio Applications can only be applied to suitable image files. If you try to use them with an unsuitable image type, a message is displayed. The following types of images are not supported for this application:

Z-Stacks
Unprocessed Airyscan data
Unprocessed Apotome data
Multi-phase images
Multi-block images
PSF images

You have opened an image with at least two channels that is typical for the analysis scenario.
You have set up a general setting for your application, see Creating a General Bio Application Setting.

On the Analysis tab, in the Bio Applications tool, click Gene- and Protein Expression.
The parameters are displayed.
Select your created setting as well as your image and click Create Setting.
The Bio Applications wizard opens.
Enter a Name for the segmented objects (the nuclei), select the Channel in which the nuclei are stained as well as a Color for the resulting masks.
Select Manual if you want to define the objects manually by clicking in the image. Otherwise, select Automatic. In the Manual mode, you can click on the objects you want to segment and the lower and higher threshold values will be adapted automatically. Alternatively you can directly enter a Threshold (lowest and highest value) or use the Histogram for the pixel values used by the segmentation. For color images, you can set the threshold for each color channel.
If you want to use machine learning for segmentation, click Semantic or Instance for semantic or instance segmentation. The prerequisite for semantic segmentation is that you have installed the 3rd party Python Tools during the installation of ZEN. For instance segmentation, you need to have the Docker Desktop software running.
A dropdown to select your model is displayed. It contains your own trained and imported networks if they have been trained on one channel. For semantic segmentation, a default neural network for the segmentation of fluorescently labeled nuclei is provided.
In the AI Model dropdown, select the model you want to use for segmentation.
Select the Model Class that should be used for segmentation and set a Min. Confidence. For instance segmentation you also have to select the AI Model Version.
If you want to perform a rolling ball background subtraction, click On.
Set the lowest and highest value for the Area and Circularity measurement to filter out unwanted objects.
A result preview is displayed by the Image View. The unwanted objects are displayed in white.
If you manually want to include certain objects, for Pick to Include click + and then click on the object in the image.
The values employed for the filters Area and Circularity are updated accordingly to include the selected object and any other objects that fulfil the newly adapted filter criteria.
Set the distance between the boundary of the masks of the segmented nuclei and the rings where the transfection is measured. A negative value means that the ring already begins inside of the nucleus. A positive value creates a ring that starts in a distance from the boundary of the nuclei masks.
Set the width of the rings displayed in the image.
Click Next.
The Gene Expression step opens.
Enter a Name for the transfected cells, select the Channel in which you want to measure the transfection as well as a Color for the resulting masks.
Set the lowest and highest value for the mean intensity of the transfection channel, for which the cells are counted as "positive".
If you manually want to include certain intensities, for Pick to Include click + and then click on the area in the image.
The values employed for Intensity Mean are updated accordingly.
Click Finish.

You have created a setting for Gene Expression which can now be used to analyze images by clicking Run Analysis, see Running Bio Applications. As a result, this settings calculates the transfection efficiency, which can then be displayed in Bio Applications view after the run of the analysis.