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ZEISS Microscopy Knowledge Base

Bio Applications

This toolkit offers the functionality to set up image analysis for very specific analysis scenarios, for example to count the number of cells in an image. Each individual analysis scenario is a module and has its own application.

Available Bio Applications

The following Bio Applications are currently available in ZEN:

Cell Counting
This module provides a simple automated image analysis workflow customized for counting of fluorescently labeled cell nuclei in biological samples and a tailored result presentation with interactive measurement tables, heatmap and plots. This module allows automatic monitoring of cell numbers and thus proliferation, i.e. under the influence of compound effects. The measurement features include the number and the density of the cell nuclei as well as the mean intensity and mean area of cells in a well.
Confluency
This module provides a simple automated image analysis workflow customized for quantifying the cell confluency using variance-based segmentation and a tailored result presentation with interactive measurement tables, heatmap and plots. Applications addressed by this module include cell confluency assays as a measure of quality control in cell-based assays and wound healing assays to follow cell migration and cell-cell interaction. The measurement features include the covered area and the area percentage.
Gene- and Protein Expression
This module provides a simple automated image analysis workflow customized to quantify gene expression and a tailored result presentation with interactive measurement tables, heatmap and plots. Applications addressed by this module include:

Automatic evaluation and quantification of the transfection efficiency to, for example, optimize the transfection protocol or to pick positive clones for targeted assays
Measurement of the expression level to quantify the abundance and distribution of labeled molecules within a cell population
Quantification of viral or bacterial infection

The measurement features include the total number of cells and number of positive cells, the percentage of positive cells and the mean intensity of the transfection channel.
Automated Spot Detection
This module provides a simple automated image analysis workflow customized to quantify spots in the cell nuclei (e.g. for FISH, telomeres, centromeres, foci counting,...) and a tailored result presentation with interactive measurement tables, heatmap and plots. The measurement features include total number of spots, average number of spots per cell, mean intensity of spots, mean area of nuclei, mean intensity of nuclei (nuclear stain).
Translocation
This module provides a simple automated image analysis workflow customized to automatically measure the translocation ratio between the cytoplasm and the nucleus and a tailored result presentation with interactive measurement tables, heatmap and plots. The measurement features include the total number of cells, the translocation ratio, as well as the mean intensity values of the nucleus and the ring.

General Workflow

The following workflow is generic for the use of a Bio Application.

Create a new setting for the respective Bio Application, see Creating a General Bio Application Setting and the respective descriptions for each individual application. This step has to be done only once per Bio Application, or if you want or need another setting for a particular application.
The created setting can be used for analysis of all suitable images.
Use the respective setting to run your Bio Application for images you want to analyze, see Running Bio Applications or Running Bio Applications in Batch Mode.

The images are analyzed and the results can be displayed in a specific results view.

Creating a General Bio Application Setting

Before you can use a Bio Application, you have to create a setting for it.

You have opened an image which is typical for the analysis scenario of the respective Bio Application.

On the Analysis tab, in the Bio Applications tool, click on the application.
Parameters are displayed.
For a new setting, click and select New. Alternatively, if you have a setting you want to use it as a template, click and select New from Template.
Enter a name for the new setting and click . Alternatively, enter a name and press Enter.
A new analysis setting file is created.

You have created a general setting which can now be opened in the Bio Applications wizard to set up the analysis for the respective Bio Application. For more information, refer to the descriptions of the individual Bio Applications, see Available Bio Applications.

Running Bio Applications

Analyzing in Batch Mode

You can also setup the analysis of multiple images with Bio Applications and their settings in Batch Mode, see Running Bio Applications in Batch Mode.

When you run a Bio Application, the image analysis defined in the specific setting is applied to the image.

You have opened the image you want to analyze.
You have created a setting for the Bio Application you want to use, see Creating a General Bio Application Setting.

On the Analysis tab, in the Bio Applications tool, click on the application.
For Setting, select the setting you have created for this application.
Click Run Analysis.
The analysis runs with the selected application and a result screen opens. On this result screen you can see the analyzed picture, charts, and a table.
Click Finish to close the result screen.

You have successfully run your Bio Application. The analysis results are saved in the image file and can be displayed and exported with the Bio Applications view, see Bio Applications View.

Creating a Setting for Cell Counting

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 which 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 Cell Counting.
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 objects you want to segment (e.g. Nuclei).
In the channel control, select the channel which contains the necessary information for the analysis (the channel in which the cell nuclei have been stained). For multi-channel images, which contain one of the following channels (DAPI, Hoechst, To-pro-3, HCS Nuclear Mask Deep Red), this channel is automatically preselected.
Select 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.
Click Finish.

You have created a setting for Cell Counting which can now be used to analyze images by clicking Run Analysis, see Running Bio Applications.

Creating a Setting for Confluency

File Types

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

You have opened an image which 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 Confluency.
The parameters are displayed.
Select your created setting and click Create Setting.
The Bio Applications wizard opens.
Enter a Name for the class you want to segment,
In the channel control, select the channel which contains the necessary information for the analysis. For multi-channel images, which contain a channel out of the list (Bright, Oblique, DIC and PGC), this channel is automatically preselected.
Select a Color for the resulting masks.
Per default, the Segmentation Type is set to Manual to use a manual variance-based segmentation.
Select if you want to segment the Structure in your image or the Background (this inverts the applied threshold values).
Define the Threshold for the variance calculated from one pixel with the neighboring pixels and adjust the Kernel Size for this calculation.
If you want to use machine learning for segmentation, click AI-based (the prerequisite is that you have installed the 3rd party Python Tools during the installation of ZEN).
A dropdown to select your model is displayed. It contains your own trained and imported networks if they have been trained on one channel.
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.
Set the Min. Object Size to define the minimum area in pixel that an object must have to be segmented.
Activate Fill all holes if you want to close the holes in the segmented masks. Otherwise leave Fill all holes deactivated.
Set the Min. Hole Size to define minimum area in pixel of the holes in the detected objects.
Click Finish.

You have created a setting for Confluency which can now be used to analyze images by clicking Run Analysis, see Running Bio Applications.

Creating a Setting for Gene- and Protein Expression

File Types

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.

Creating a Setting for Automated Spot Detection

File Types

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

You have opened an image 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 Automated Spot Detection.
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 nuclei you want to segment, select the Channel which contains the necessary information as well as a Color for the resulting masks.
For the segmentation of the nuclei, 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 the 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 spots should be detected. 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 second step of the wizard opens.
Enter a Name for the spots, select the Channel with which the spots are stained as well as a Color for the resulting masks.
For the segmentation of the nuclei, 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 (Low and High) 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.
A dropdown to select your model is displayed.
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.
A result preview is displayed by the Image View.
For the Spots step, a rolling ball background subtraction is automatically enabled.
If you do not want to perform a rolling ball background subtraction, click Off.
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.
Click Finish.

You have created a setting for Spot Detection which can now be used to analyze images by clicking Run Analysis, see Running Bio Applications.

Creating a Setting for Translocation

File Types

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

You have opened a multichannel image which 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 on Translocation.
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.
Select the Translocation Channel.
Set the distance between the boundary of the masks of the segmented nuclei and the rings where the translocation is measured. A positive value creates a ring that starts in a distance from the boundary of the nuclei masks.
Set the width of the rings.
Click Finish.

You have created a setting for translocation which can now be used to analyze images by clicking Run Analysis, see Running Bio Applications.

Running Bio Applications in Batch Mode

You are on the Processing tab.
You have created settings for all Bio Applications you want to use.

Click Batch.
The Batch Processing view is displayed in the Center Screen Area.
Click +Add.
A file browser opens.
Select an image you want to analyze and click Open.
The image is added to the list in the Batch Processing view.
Repeat the previous steps until all images you want to analyze are opened in the Batch Processing view. You can also select multiple images by pressing the Ctrl button while selecting.
All images are displayed in the list.
In the list of the Batch Processing view, select an image.
In the Batch Method tool, select Bio Applications.
Parameters are available for the image.
In the Parameters tool, select the Bio Application and the Setting you want to use for the image.
You have set up the analysis for the selected image.
Repeat the previous steps until you set the analysis for all opened images. If you want to analyze multiple images with the same Bio Application and setting, you can also select multiple images by pressing the Ctrl button, or all images by pressing Ctrl + A.
You have set up the analysis for all images.
Click Apply. Alternatively, if you want to run the analysis for only some of the images, select the images in the list and click Run Selected.
The images are analyzed with the respective Bio Application and settings.
The analyzed images are saved in the defined output folder. The results can be displayed and exported if you open the resulting images in ZEN and use the Bio Applications view, see Exporting Results of the Bio Applications Analysis.

Exporting Results of the Bio Applications Analysis

You have analyzed an image with a Bio Application, see Running Bio Applications or Running Bio Applications in Batch Mode.
You have opened the results of the analysis in the Bio Applications view.

Set the displayed information (chart, chart axis, table, etc.) in the Bio Applications view according to your needs. The export uses the currently displayed information.
In the Export tab, activate the checkboxes for all the information you want to export and select the corresponding format for each with the dropdown lists.
Click Export.
A file browser opens.
Name the file for export, navigate to the folder where the results should be exported to and click Save.