An open-source solution for advanced imaging flow cytometry data analysis using machine learning

•Imaging flow cytometry enables potentially powerful, multiplexed single-cell analysis.•Data analysis techniques for imaging flow cytometry are largely manual and subjective.•Our machine learning workflow identifies phenotypes in imaging flow cytometry.•The workflow uses open-source software and doe...

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Bibliographic Details
Published in:Methods (San Diego, Calif.) Calif.), 2017-01, Vol.112, p.201-210
Main Authors: Hennig, Holger, Rees, Paul, Blasi, Thomas, Kamentsky, Lee, Hung, Jane, Dao, David, Carpenter, Anne E., Filby, Andrew
Format: Article
Language:English
Subjects:
Cell Count
Feature selection
Flow Cytometry - methods
High-throughput
Humans
Image Cytometry - methods
Image Processing, Computer-Assisted - statistics & numerical data
Imaging flow cytometry
Interphase - genetics
Jurkat Cells
Machine Learning
Mitosis
Open-source software
Profiling
Reproducibility of Results
Software
Workflow
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Summary:•Imaging flow cytometry enables potentially powerful, multiplexed single-cell analysis.•Data analysis techniques for imaging flow cytometry are largely manual and subjective.•Our machine learning workflow identifies phenotypes in imaging flow cytometry.•The workflow uses open-source software and does not require computational expertise. 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. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) 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 analysed using 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. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery.
ISSN:1046-2023
1095-9130
DOI:10.1016/j.ymeth.2016.08.018