TITLE: Co-culture leukemia high-content image analysis using supervised machine learning
AUTHORS: Hayden L Bell

[DESCRIPTION]
This protocol describes a non-informatics based approach to high-content image analysis of acute leukemia cells in co-culture with mesenchymal stromal cells (MSCs) using supervised machine learning. The analysis pipeline leverages two powerful, open-source software applications - Cell Profiler and Ilastik. The aim of this protocol is to provide a basic skeleton pipeline for image analysis to, at minimum, determine absolute cell numbers for each cell class from a fluorescence microscopy image of cells stained with a DNA dye.

This protocol is a detailed companion walkthrough for the Github repository available at https://github.com/hayden-bell/Image_Analysis.

[BEFORE_START]
Two open-source applications are required for this image analysis pipeline: Ilastik (https://github.com/ilastik/ilastik) and Cell Profiler (https://github.com/CellProfiler).

This protocol uses the project files from the Github repository available at https://github.com/hayden-bell/Image_Analysis. Download the BaseProject.ilp and BaseProject.cppipe files before starting.

Use high-quality fluorescence microscopy images in a lossless high-resolution file format such as TIF.

[GUIDELINES]
This example pipeline uses images from patient-derived xenograft (PDX) acute leukemia cells (AML/ALL) in co-culture with human bone marrow-derived mesenchymal stem cells (MSCs). The images are raw grayscale TIF images of cells assayed across a wide range of experimental conditions and live cells stained with the nucleic acid dye CyQUANTTM.

In principle, any fluorescence microscope with a stable illumunation source that can acquire images of DNA stained cells can be used for this analysis pipeline. Appropriate high-content imaging systems include the Zeiss CellDiscoverer 7 and the PerkinElmer Opera.

As default, quantitative data will be exported as an SQLite database and will require software to handle this file format. If working with low numbers of images at any one time, data can be alternatively exported as a CSV file format using the ExportToSpreadsheet module which is compatible with Microsoft Excel software.

[STEPS]
SECTION: Training a supervised machine learning model (semantic segmentation)
1. Open the Ilastik software and load the BaseProject.ilp project.
SECTION: Training a supervised machine learning model (semantic segmentation)
2. In the Input Data tab, load several different images (up to ~10) for training which are representative of different experimental conditions.

For example, images from positive and negative controls whereby cell number is maximised/minimised.
SECTION: Training a supervised machine learning model (semantic segmentation)
3. In the Feature Selection tab, click Select Features... and ensure all 37 features are selected.
SECTION: Training a supervised machine learning model (semantic segmentation)
4. In the Training tab, ensure there are three separate Labels/classes in order as:

PDX
MSC
Bg

SECTION: Training a supervised machine learning model (semantic segmentation)
4.1. Using the Brush Cursor, manually annotate within several nuclei of each class using the respective Label class. Use the zoom to view the image large to ensure precision in annotation.

Errors can be corrected using the Eraser Cursor and the image contrast can be changed using the Window Leveling tool to better visualise dimmer nuclei.

  

SECTION: Training a supervised machine learning model (semantic segmentation)
4.2. Use the Live Update feature to view a real-time overlay of the probability map for each class over the original training images.
SECTION: Training a supervised machine learning model (semantic segmentation)
4.3. Iteratively refine the annotations across the training dataset images until performance is good.


 

SECTION: Training a supervised machine learning model (semantic segmentation)
5. In the Prediction Export panel, select Source: Probabilities.
SECTION: Training a supervised machine learning model (semantic segmentation)
6. In the Batch Processing tab, click 'Select Raw Data Files...'  to import all of the test image data files.
SECTION: Training a supervised machine learning model (semantic segmentation)
5.1. Click 'Choose Export Image Settings...' and ensure the output file is tif format with the axis order yxc.
SECTION: Training a supervised machine learning model (semantic segmentation)
5.2. Choose the Output File destination as {dataset_dir}/probabilities/{nickname}_{result_type}.tif
SECTION: Training a supervised machine learning model (semantic segmentation)
7. Click 'Process all files'.
SECTION: Quantifying individual cell nuclei (instance segmentation)
8. Open the Cell Profiler software and import the BaseProject.cppipe pipeline (File > Import > Pipeline from File...).
SECTION: Quantifying individual cell nuclei (instance segmentation)
9. In the Images module, load the probability map images generated from the Ilastik project.

Note: do not load the original images at this step.
SECTION: Quantifying individual cell nuclei (instance segmentation)
11. Optional: Outlines of how well the pipeline identifies individual PDX or MSC nuclei can be visualised using the OverlayOutlines module. Select the checkpoint to enable this module and save the output by using the SaveImages module.


SECTION: Quantifying individual cell nuclei (instance segmentation)
12. In the ExportToDatabase module, modify the Experiment name and SQLite database filename to better identify the experimental data output.

 

SECTION: Quantifying individual cell nuclei (instance segmentation)
13. Click Analyze Images to process the imported dataset and export data as SQLite database format.
SECTION: Reading the data output
14. Data can be read using any database software application which can open SQLite file format.

Data can be retrieved from the [Experiment name]_Per_Image data table.

Recorded data include:
Predicted PDX nuclei counts (Image_Count_LeukaemicNuclei)
Predicted MSC nuclei counts (Image_Count_MSCNuclei)
Image file name (Image_FileName_CyQ)
Image well location (Image_Metadata_Well)
Plus any additional data exported from separate modules or metadata extractions.
SECTION: Quantifying individual cell nuclei (instance segmentation)
10. Optional: In the Metadata tab, regular expressions (regex) can be used to extract meaningful data from each image filename such as plate id, well id, etc.

By default, the pipeline will attempt to extract the well id of each image in the format A1 or A01.