Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples.
In this tutorial, we demonstrate a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms . Compensated image files (.cif) from an imaging flow cytometer are generated with the software IDEAS from Millipore. The .cif files are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analyzed with cutting-edge machine learning and clustering approaches using ‘‘user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets.
1.Generating image tiles from .cif files and importing the images tiles into CellProfiler
2.CellProfiler pipeline for object segmentation and extraction of hundreds of morphological features per cell
3.Machine learning using CellProfiler Analyst
4.Machine learning using custom scripts (R, python)