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Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images
Chapters
Summary July 15th, 2020
We present a freely available workflow built for rapid exploration and accurate analysis of cellular bodies in specific cell compartments in fluorescence microscopy images. This user-friendly workflow is designed on the open-source software Icy and also uses ImageJ functionalities. The pipeline is affordable without knowledge in image analysis.
Transcript
Substructure analyzer is a user friendly workflow that performs automatic analysis of multiple process microscopy metrics. It doesn't mean zero tons of open source software Icy and also using machine functionalities. Importantly, this workflow is affordable resort produce knowledge and images analysis.
Multi channel images are loaded within the workflow and pre processed in order to improve the signal to noise ratio and to remove imaging or defects. Then, image segmentation isolates regions of interest, also known as ROIs from the background. Several methods of segmentation are available depending on the level of clustering and the nature of the objects of interest.
Segmented objects are saved with a specific descriptor in a specific folder. Force and signals are then analyzed within the rows and multiple features such as location, size, shape in density textures but number and size are exported into an automatically created spreadsheet. Download Icy from the Icy website.
Then download Substructure Analyzer Protocol from Icy library of protocols. Open Icy, and click on tools in the ribbon menu. Click on protocols to open the protocols editor interface.
Click on load and open the protocol substructure analyzer. Protocol loading can take few seconds. The workflow is composed of 13 General blocks each block working as a pipeline composed of several boxes performing specific subtasks.
Each block or box is numbered and has a specific rank within the workflow. By clicking on this number, assign the closest possible position to the first to the selected block. Positions of the other blocks are reorganized.
By clicking on the upper left corner icon, the block can be collapsed expanded. It can also be enlarged, narrowed or removed. Each pipeline of the workflow is correct arose by a network of boxes connected together by other input and output.
To create a connection, click on an output and maintain until the cursor pertains any input. Connections can be removed by clicking on the output tag. If necessary rename the files so that sequences to be merged, have the same names prefix followed by distinct separator.
For example, sequences of individual channels from an image A are named image A underscore red, image A underscore blue, and so on. In the same folder, create a new folder per channel to merge. For example, to merge red, green and blue channels, create three folders and store the corresponding sequences within these folders.
Use only the block merge channels, remove the other blocks and save the protocol as merge channels. Access to the boxes in order to set parameters. In the box, channel number x, choose which channel to extract.
In classical RGB images, zero is red, one is green and two is blue. In the box, folder channel number x, Backslash the name of the folder containing images of channel x. In the box, separate channel number x.
The separator use for image name. In the box, color map channel number x, indicate with a number which column of model to use to visualize the corresponding channel in Icy. In the box, format of merged images, write the extension to save merged images.
On the upper left corner of the merge channels block, click on the link directly to the right of folder. In the open dialog box which appears, double click on the folder containing sequences of the first channel that has been defined in box folder channel number one. Then click on open, ran the protocol.
Merged images are saved in a merged folder in the same directory as the folders of individual channels. Object segmentation, is the most challenging step in image analysis. It's efficiency determines the accuracy of the resulting set measurements.
So, structure analyzer integrates both simple and more complex algorithms to propose different alternatives adapted to image complexity and user needs. If objects do not touch each other, other user does not need to differentiate clustered objects individually use the block segmentation A.When objects do not touch each other, but some of them are in close proximity, use the block segmentation B.For objects with a high clustering level and a convex shape, use the block segmentation C.If objects present a high clustering level and have irregular shapes, use the block segmentation D.Use the block segmentation in clusters cytoplasm to segment touching cytoplasm individually using segmented nuclei as markers. Block adaptive for primary object segmentation can process independently so that several blocks, can be used in the same run to compare their efficiency for a particular substructure, or to segment different types of substructures.
To illustrate segmentation, the block segmentation B, that will fit a larger number of issues has been chosen. To use this blog. First, link it to select folder.
Then, invokes channel signal set the channel of the object to segment. For example, to correspond to the B, in box HK means, set the intensity classes parameter and the approximate minimum and maximum sizes in pixels of objects to be detected. For intensity classes, a value of two will classify pixels in two classes, background and foreground.
It is thus adapted when contrast between the objects and the background is high. If foreground objects have its origins intensities, or contrast with the background is low, increase the number of classes. In box active contours optimize the detection of objects borders.
During the process, a folder will be created to save images of segmented objects. In box text, name this folder, for example, segmenting nuclei. To set the format for saving images of segmented objects, fill the box format of images of segmented objects, ran the protocol.
The folder is created in the folder containing merged images. Different blocks have been developed to adapt to the number of laws and channels and cell compartments to be analyzed in segmented objects. In the following example, for the analysis, choose the block fluorescence analysis P.Two channels in the same compartment and link it to select folder.
The segmentation block should have been processed before this analysis set the parameters inbox folder images ROI, write the name of the folder containing images of segmented objects preceded by a backslash. Inbox format of images of segmented objects, write the format's used to save images of segmented objects. Inbox kill borders to remove both objects.
Otherwise, right now, the installation of the more fully J collection of imaging is required to use this function. In boxes channel spot signal, set the channel where spots have to be detected. In classical RTP images, zero is red One is green and two is blue.
In boxes name of localized molecule, write the name of the molecule localizing into the spots. Number of fields to answer depends on the number of molecules. In boxes wavelengths per detector block, set spots detection parameters for each individual Channel.
In order to process different blocks in a room, keep connections of chosen blocks, with the block select folder. Make sure that their ranks below the good processing of the workflow. Before running the workflow, it is also recommended to remove unused blocks and save the new protocol with another name.
Click on run to start the workflow. When it opens eye look books appears. Double click on the folder containing the nurse images Then click on Open, the workflow will automatically run.
If the processing is completed the message, the workflow executed successfully will appear in the lower right hand corner, all the blocks will be flagged with the green sign. If not the block and the inside box presenting the arrow sign will indicate the elements correct. Importantly, several displays allow to visualize the intermediate results during each run in order to control the processing.
The rapidity, flexibility and functionality of this workflow will be restricted to various examples. In this first example, we analyze the nuclear translocation of NF kappa B.After simulation with increasing concentrations of TNF alpha. Nuclei and cytoplasm were delineated using segmentation C and E blocks.
The NF kappa B flourish in signal was analyzed using the global translocation analysis block. More than 40, 000 cells in 96 images were analyzed in 26 minutes. The generated data was used to establish this dose response curve showing the induction of NF Kappa B nuclear translocation by TNF alpha.
The workflow can also be used to detect file size and extract specific information about their features. Here, properties in individual cells were detected by localizing the EDC for protein. Nuclei in cytoplasm were delineated using segmentation A and E blocks.
EDC4 was analyzed using fluorescence analysis blocks C.In this example, cytoplasmic EDC4 sign have been detected in both analyzed cells. The size in pixels of each full side is given in the spreadsheet. In this example, we took advantage of the versatility of the workflow to study carbohydrates a longer kinetics of oxidative stress.
Nuclear force sign of coilin are the main structural components of carbohydrates Their number and size were analyzed according to size, stress status, assessed by double strand breaks localized with 53BP1. Nuclei were delineated using segmentation proxy, Nuclear force and signals of coillin and 53BP1, were simultaneously analyzed using fluorescence analysis Block B.Using data from 2300 individual cells, we evidenced a significant increase of the number of Greenfield site after stress induction associated with a decrease of their size. This data strongly suggests that oxidative stress changes the nucleation power of a carbohydrates in using a nuclear plasmic distribution into number of smaller nuclear for site to justify a change in coilin expression could alter its localization and change the nucleation of curve bodies.
An exogenous GFP coilin fusion protein was overexpressed. Features of our bodies Were analyzed according to GFP coilin overexpression level. Nuclei were delineated using segmentation block A.The fluorescent signals of coilin and GFP coilin, were simultaneously analyzed using fluorescence analysis block B.The overexpression level of GFP coilin was reflected by the meaning density of the GFP signal in individual nuclei.
Data generated by substructure analyzer show that GFP coilin in overexpression significantly increases the size and number of carbohydrates. Since oxidative stress increases the number of carbohydrates but reduces their size. This data might reflect that oxidative stress effect on the structure of carbohydrates is most probably induced by a change of their composition rather than by an effect on certain amount of coilin.
So structure analyzer is a highly modular workflow for bio image analysis. It can be adapted to several contexts from the simple merging of channels to the quantification of multiple floors and signals in thousands of cells. It also integrates simple and complex segmentation algorithms depending on image complexity and automates the extraction of fluorescent signal features.
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