This tutorial shows how to segment and extract features from cell nuclei Galaxy for image analysis. As example use case, this tutorial shows you how to compare the phenotypes of PLK1 threated cells in comparison to a control. The data used in this tutorial is available at Zenodo.

RNA interference (RNAi) is used in the example use case for silencing genes by way of mRNA degradation. Gene knockdown by this method is achieved by introducing small double-stranded interfering RNAs (siRNA) into the cytoplasm. Small interfering RNAs can originate from inside the cell or can be exogenously introduced into the cell. Once introduced into the cell, exogenous siRNAs are processed by the RNA-induced silencing complex (RISC).The siRNA is complementary to the target mRNA to be silenced, and the RISC uses the siRNA as a template for locating the target mRNA. After the RISC localizes to the target mRNA, the RNA is cleaved by a ribonuclease. RNAi is widely used as a laboratory technique for genetic functional analysis. RNAi in organisms such as C. elegans and Drosophila melanogaster provides a quick and inexpensive means of investigating gene function. Insights gained from experimental RNAi use may be useful in identifying potential therapeutic targets, drug development, or other applications. RNA interference is a very useful research tool, allowing investigators to carry out large genetic screens in an effort to identify targets for further research related to a particular pathway, drug, or phenotype.

The example used in this tutorial deals with PLK1 knocked down cells. PLK1 is an early trigger for G2/M transition. PLK1 supports the functional maturation of the centrosome in late G2/early prophase and establishment of the bipolar spindle. PLK1 is being studied as a target for cancer drugs. Many colon and lung cancers are caused by K-RAS mutations. These cancers are dependent on PLK1.

Agenda

In this tutorial, we will deal with:

1. Getting data
2. Create feature extraction workflow
3. Apply workflow to screen
4. Plot feature extraction results
5. Conclusion

Getting data

The dataset required for this tutorial contains a screen of DAPI stained HeLa nuclei (more information). We will use a sample image from this dataset for training basic image processing skills in Galaxy.

Hands-on: Data upload

1. If you are logged in, create a new history for this tutorial

   Tip: Creating a new history

   To create a new history simply click the new-history icon at the top of the history panel:

   

2. Import galaxy-upload the following dataset from Zenodo or from the data library (ask your instructor).
   • Important: Choose the type of data as zip.

   https://zenodo.org/record/3362976/files/B2.zip
   

   Tip: Importing via links

   • Copy the link location

   • Click galaxy-upload Upload Data at the top of the tool panel

   • Select galaxy-wf-edit Paste/Fetch Data

   • Paste the link(s) into the text field

   • Press Start

   • Close the window

   Tip: Importing data from a data library

   As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

   1. Go into Data (top panel) then Data libraries
   2. Navigate to the correct folder as indicated by your instructor.
      • On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
   3. Select the desired files
   4. Click on Add to History galaxy-dropdown near the top and select as Datasets from the dropdown menu

   5. In the pop-up window, choose

      • “Select history”: the history you want to import the data to (or create a new one)
   6. Click on Import

3. Unzip ( Galaxy version 6.0+galaxy0) with the following parameters:
   • param-file “input_file”: Zipped input file
   • “Extract single file”: Single file
   • “Filepath”: B2--W00026--P00001--Z00000--T00000--dapi.tif

4. Rename galaxy-pencil the dataset to testinput.tif

   Tip: Renaming a dataset

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, change the Name field
   • Click the Save button

5. Unzip ( Galaxy version 6.0+galaxy0) with the following parameters:
   • param-file “input_file”: Zipped input file
   • “Extract single file”: All files

6. Rename galaxy-pencil the resulting collection to control

   Tip: Renaming a collection

   1. Click on the collection
   2. Click on the name of the collection at the top
   3. Change the name
   4. Press Enter

7. Import galaxy-upload the following dataset from Zenodo or from the data library (ask your instructor).
   • Important: Choose the type of data as zip.

     https://zenodo.org/record/3362976/files/B3.zip
     

   Tip: Importing via links

   • Copy the link location

   • Click galaxy-upload Upload Data at the top of the tool panel

   • Select galaxy-wf-edit Paste/Fetch Data

   • Paste the link(s) into the text field

   • Press Start

   • Close the window

   Tip: Importing data from a data library

   As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

   1. Go into Data (top panel) then Data libraries
   2. Navigate to the correct folder as indicated by your instructor.
      • On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
   3. Select the desired files
   4. Click on Add to History galaxy-dropdown near the top and select as Datasets from the dropdown menu

   5. In the pop-up window, choose

      • “Select history”: the history you want to import the data to (or create a new one)
   6. Click on Import

8. Unzip ( Galaxy version 6.0+galaxy0) to extract the zipped screen:
   • param-file “input_file”: Zipped input file
   • “Extract single file”: All files
9. Rename galaxy-pencil the collection to PLK1
10. Upload galaxy-upload the following segmentation filter rules as a new pasted file (format: tabular):

    	area	eccentricity
    min	500	0.
    max	100000	0.5
    

    Tip: Creating a new file

    • Click galaxy-upload Upload Data at the top of the tool panel
    • Select galaxy-wf-edit Paste/Fetch Data at the bottom
    • Paste the file contents into the text field
    • Change Type from “Auto-detect” to tabular* Press Start and Close the window

11. Rename galaxy-pencil dataset to rules

    Tip: Renaming a dataset

    • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
    • In the central panel, change the Name field
    • Click the Save button

Create feature extraction workflow

First, we will create and test a workflow which extracts mean DAPI intensity, area, and major axis length of cell nuclei from an image.

Hands-on: Create feature extraction workflow

1. Filter 2D image ( Galaxy version 0.0.3-3) with the following parameters to smooth the image:
   • “Filter type”: Gaussian Blur
   • “Radius/Sigma”: 3
   • param-file “Source file”: testinput.tif file
2. Threshold image ( Galaxy version 0.0.5-2) with the following parameters to segment the image:
   • param-file “Source file”: output of Filter 2D image ( Galaxy version 0.0.3-3)
   • “Threshold Algorithm”: Otsu
   • “Dark Background”: Yes
3. Split binary image using watershed transformation ( Galaxy version 0.0.1-2) with the following parameters to split touching objects:
   • param-file “Source file”: output of Threshold image ( Galaxy version 0.0.5-2)
   • “Minimum distance between two objects.”: 20
4. Extract 2D features ( Galaxy version 0.1.1-2) with the following parameters to extract features from the segmented objects:
   • param-file “Label file”: output of Split binary image using watershed transformation ( Galaxy version 0.0.1-2)
   • “Use original image to compute additional features.”: No original image
   • “Select features to compute”: Select features
   • “Available features”:
     • param-check Add label id of label image
     • param-check Area
     • param-check Eccentricity
     • param-check Major Axis Length
5. Filter label map by rules ( Galaxy version 0.0.1) with the following parameters to filter the label map from 3. with the extracted features and a set of rules:
   • param-file “Source file”: output of Split binary image using watershed transformation ( Galaxy version 0.0.1-2)
   • param-file “Feature file”: output of Extract 2D features ( Galaxy version 0.1.1-2)
   • param-file “Rules file”: rules file
6. Extract 2D features ( Galaxy version 0.1.1-2) with the following parameters to extract features the final readout from the segmented objects:
   • param-file “Label file”: output of Filter label map by rules ( Galaxy version 0.0.1)
   • “Use original image to compute additional features.”: Use original image
   • param-file “Original image file”: testinput.tif file
   • “Select features to compute”: Select features
   • “Available features”:
     • param-check Mean Intensity
     • param-check Area
     • param-check Major Axis Length
7. Now we can extract the workflow for batch processing
   • Name it “feature_extraction”.
   • Remember to exclude Unzip ( Galaxy version 6.0+galaxy0) by unchecking the tool.
   • Don’t treat B2.zip and B3.zip as inputs (the workflow is supposed to be applied to the images directly).

   Tip: Extracting a workflow from your history

   1. Clean up your history: remove any failed (red) jobs from your history by clicking on the galaxy-delete button.

      This will make the creation of the workflow easier.

   2. Click on galaxy-gear (History options) at the top of your history panel and select Extract workflow.

      

      The central panel will show the content of the history in reverse order (oldest on top), and you will be able to choose which steps to include in the workflow.

   3. Replace the Workflow name to something more descriptive.

   4. Rename each workflow input in the boxes at the top of the second column.

   5. If there are any steps that shouldn’t be included in the workflow, you can uncheck them in the first column of boxes.

   6. Click on the Create Workflow button near the top.

      You will get a message that the workflow was created.

8. Edit the workflow you just created
   • Add the tool Input dataset and name it input image.
   • Name the input for the rules file filter rules.
   • Mark the results of steps 5 and 6 as outputs (by clicking on the asterisk next to the output name).

The resulting workflow should look something like this:

Open image in new tab

Apply workflow to screen

Now we want to apply our extracted workflow to original data and merge the results. For this purpose, we create a workflow which uses the previously created workflow as subworkflow.

Hands-on: Create screen analysis workflow

1. Create a new workflow in the workflow editor.

   Tip: Creating a new workflow

   1. Click Workflow on the top bar
   2. Click the new workflow galaxy-wf-new button
   3. Give it a clear and memorable name
   4. Clicking Save will take you directly into the workflow editor for that workflow
   5. Need more help? Please see the How to make a workflow subsection here

2. Add a Input dataset collection node and name it input images
3. Add a Input dataset node and name it rules
4. Add the feature_extraction workflow as node.
   • param-file “input image”: input images output of Input dataset collection
   • param-file “filter rules”: rules output of Input dataset
5. Add a Collapse Collection node.
   • param-file “Collection of files to collapse into single dataset”: output of feature_extraction workflow
   • “Keep one header line”: Yes
   • “Append File name”: No
   • Mark the tool output as workflow output
6. Save your workflow and name it analyze_screen

The resulting workflow should look something like this:

Open image in new tab

Hands-on: Run screen analysis workflow

1. Run the screen analysis workflow workflow on the control screen and the rules file

   Tip: Running a workflow

   • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
   • Click on the workflow-run (Run workflow) button next to your workflow
   • Configure the workflow as needed
   • Click the Run Workflow button at the top-right of the screen
   • You may have to refresh your history to see the queued jobs

2. Run the screen analysis workflow workflow on the PLK1 screen and the rules file

Plot feature extraction results

Finally, we want to plot the results for better interpretation.

Hands-on: Plot feature extraction results

1. Click on the Visualize this data galaxy-barchart icon of the Collapse Collection results.
2. Run Box plot with the following parameters:
   • “Provide a title”: Screen features
   • “X-Axis label”:
   • “Y-Axis label”:
   • “1: Data series”:
     • “Provide a label”: Mean intensity
     • “Observations”: Column 1
   • “2: Data series”:
     • “Provide a label”: Area
     • “Observations”: Column 2
   • “3: Data series”:
     • “Provide a label”: Major axis length
     • “Observations”: Column 3

   Question

   Plot the feature distribution of PLK1 and control. What differences do you observe between the screens?

   Solution

   The phenotype of PLK1 threated cells show a higher mean intensity and a shorter major axis in comparison to the control.

One of the resulting plots should look something like this:

Conclusion

In this exercise you imported images into Galaxy, segmented cell nuclei, filtered segmentations by morphological features, extracted features from segmentations, scaled your workflow to a whole screen, and plotted the feature extraction results using Galaxy.

You've Finished the Tutorial

Please also consider filling out the Feedback Form as well!

Key points

• Galaxy workflows can be used to scale image analysis pipelines to whole screens.

• Segmented objects can be filtered using the Filter label map by rules tool.

• Galaxy charts can be used to compare features extracted from screens showing cells with different treatments.

Frequently Asked Questions

Useful literature

Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.

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Citing this Tutorial

1. Thomas Wollmann, Leonid Kostrykin, Analyse HeLa fluorescence siRNA screen (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/imaging/tutorials/hela-screen-analysis/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{imaging-hela-screen-analysis,
author = "Thomas Wollmann and Leonid Kostrykin",
	title = "Analyse HeLa fluorescence siRNA screen (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/imaging/tutorials/hela-screen-analysis/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Funding

These individuals or organisations provided funding support for the development of this resource

Go Further

Do you want to extend your knowledge? Follow one of our recommended follow-up trainings:

• tutorial Hands-on: Basics of machine learning

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/imaging/tutorials/hela-screen-analysis/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: 2d_auto_threshold
  owner: imgteam
  revisions: 0c777d708acc
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: 2d_auto_threshold
  owner: imgteam
  revisions: 3df9f0a4bf34
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: 2d_feature_extraction
  owner: imgteam
  revisions: 5791a7f65275
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: 2d_feature_extraction
  owner: imgteam
  revisions: 0a53256b48c6
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: 2d_filter_segmentation_by_features
  owner: imgteam
  revisions: e576b73a2e2f
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: 2d_simple_filter
  owner: imgteam
  revisions: dba87c4b32d3
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: 2d_simple_filter
  owner: imgteam
  revisions: f70b215d155c
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: 2d_split_binaryimage_by_watershed
  owner: imgteam
  revisions: f8f7987586b7
  tool_panel_section_label: Imaging
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: unzip
  owner: imgteam
  revisions: 57f0914ddb7b
  tool_panel_section_label: Collection Operations
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: collapse_collections
  owner: nml
  revisions: 90981f86000f
  tool_panel_section_label: Collection Operations
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

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