This guide explains how to create a density distribution map, also called ”Heat-Map”. The script generates contiguous boxes in X,Y and Z directions that can be used as ROIs for further analysis (Compartmentalization, gradient distribution, etc.)
This guide explains how to create a density distribution map, also called ”Heat-Map”. The script generates contiguous boxes in X,Y and Z directions that can be used as ROIs for further analysis (Compartmentalization, gradient distribution, etc.)
Python script code usage rights:The user has the permission to use, modify and distribute this code, as long as this copyright notice remains part of the code itself: Copyright(c) 2021 arivis AG, Germany. All Rights Reserved.
In order to define the contigues sub-regions (sampling volume) features, some parameters of the script should be adjusted to match your analysis needs. These parameters are located in the code area labeled as USER SETTING.
SIZE_BOX_X : Set the X sub-volume size.
SIZE_BOX_Y : Set the Y sub-volume size.
SIZE_BOX_Z : Set the Z sub-volume size.
All the values are expressed in metric units (µm).
If one of the box dimensions is bigger than the correspondent volume size, the script will not be executed and an error message is issued.
Only the parameters located in the USER SETTING area can be modified. Don’t change any other number, definition or text in the code outside this dedicated area.
Run the DivideScope RevE (3_4) Python Script by pressing the Run Script button or pressing the F5 key.
Note: Activate the Output Panel, if not already displayed. The status of the script execution (errors including) will be visualized here.
This is the script result, a 3D matrix of sub-volumes:
The sub-volumes segments are shown in the objects table using the TAG Script.
Once the script is executed and the sub-volumes have been created, several density distribution analyses can be performed on the sample. The simplest one is to get the single/multiple channel(s) intensity features inside the boxes. More sophisticated approaches can include a compartment analysis.
The COMPARTMENTALIZATION concept is strictly related to the studies of the interactions and of the relationship between the structure compartments.
Complex hierarchies between the structures can be established and evaluated using this operator. Objects inside a parent structure can be selected. Their position inside the main structure, as well as their distribution (clustering), and other features, can be evaluated. A child object can be a parent for other objects. The number of the available nested levels are, theoretically, unlimited.
In the example above, The COMPARTMENTALIZATION is extended on 3 levels. The result is a hierarchical link between the Cell (Reference) and its nucleus (subject) . The nucleus is, in turn, related to the vesicles it contains. Finally, the vesicles count per cell is obtained.
The COMPARTMENTALIZATION analysis is not limited to the biological samples, even if this is the most common situation. Any structure located inside of a defined surrounding volume can be evaluated.
It is not mandatory that the parent object is a defined structure (e.g. Cell or Nucleus). It can be an anatomical region or, generally speaking, a sub-region of interest of the sample volume. These regions can be drawn both manually or using the interactive method.
The COMPARTMENTALIZATION approach is the base on which more complex and sophisticated evaluations can be performed.
On the next pages the compartment concept will be detailed.
Refer to the Application Note #8 for detailed information about «How to Draw objects interactively».
The Simple Compartments' schema is organized on two levels of hierarchy, the Reference (Parent) and the Subject (Child). The child objects belonging to the parent spaces are labelled TAG as compartmentalized.
These Reference structure can also be drawn both manually or using the interactive method. Refer to the Application Note #8 for detailed information about «How to Draw objects interactively».
Compartments setup:
The left most item is a reference. The items below the left most item is the subject. It must be shifted one position on right compared to the reference.
The commands on top of the dialog are used to set the hierarchy.
The Multiple Compartments’ schema uses 3 levels or more of hierarchy. Starting from the first child on the top, each level is checked with the previous one. The child objects belonging to its parent spaces are labelled TAG as compartmentalized.
Compartments setup:
The left most item is a reference. The items below the left most item is the subject. It must be shifted one position to right compared to the reference.
The 3rd level is set by shifting the TAG one position to right compared to the subject on top of it.
The commands on top of the dialog are used to set the hierarchy.
The Simple Compartments' schema (multiple subjects) is organized on two levels of hierarchy, but the child level can have more entries. The child objects of each entry, belonging to the parent spaces, are labelled TAG as compartmentalized.
Compartments setup:
The left most item is a reference. The items below the left most item is the subject. It must be shifted one position to the right compared to the reference.
The 2nd subject is set to the same level of the
The Compartments results are shown in the data table. The TAG Compartments is used to label the classified objects.
The commands on top of the dialog are used to set the hierarchy.
Simple Compartments (2 levels)
Multiple Compartments (multiple levels).
Single Compartments (multiple subjects).
The purpose of the density distribution Heat-Map, is to evaluate, through a colored map (heat-map), the objects population concentration per volume’ unit inside the whole sample.
These boxes are used as Parent (compartment reference) to establish the compartmentalization of the counted objects (child) in the sampled volume. The different colors show the density (or concentration) in the specific sub space. Usually, the cold colors (e.g. blue hues) show a low concentration, while the hot colors (e.g. red hues) show high concentration values.
Using different scripts a special boxes layout can be created. Here below are quick examples of the Gradient profile and the Spiral distribution map:
The Object gradient profile task computes the concentration’ gradient of the child objects along a specific direction.
Regular sized boxes are created to cover the selected part of the volume' sample with contiguous subspaces.
These boxes are used as Parent to establish the compartmentalization of the counted objects (child) in the sampled volume. The boxes can be created following a linear progression, or covering a more complex path (e.g. spiral path).
The pipeline has to be created according to the user analysis requirements as well as the sample typology.
The sample labeling, the imaging technique (Fluorescence, EM, Tomography, bright-field ...) and the image characteristics are important to drive the pipeline setup.
The knowledge of the biological structures under evaluation, it’s behavior and the expected features' trend are also important as well. All the above information should be used to build a target driven pipeline.
To achieve the application note goals, only a couple of operators are mandatory, as described below:
Please refer to arivis Pro Help for more details.
Results (segments and measurements) will be stored in the dataset only if the Store Objects operator has been correctly set. Tick appropriately the option as shown below before complete the pipeline execution.
Features can be added or removed from the data table using the Feature Column command.