An open-source solution for advanced imaging flow cytometry data analysis using machine learning.
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.
Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA; Dept. of Systems Biology & Bioinformatics, University of Rostock, 18051 Rostock, Germany; College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
Holger
H
Hennig
Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA; College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
Paul
P
Rees
Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764 Neuherberg, Germany.
Thomas
T
Blasi
Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA.
Lee
L
Kamentsky
Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA.
Jane
J
Hung
Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA.
David
D
Dao
Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA.
Anne E
AE
Carpenter
Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: Andrew.Filby@newcastle.ac.uk.
Andrew
A
Filby
D002452:Cell Count; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D007399:Interphase / Q000235:genetics; D019169:Jurkat Cells; D000069550:Machine Learning; D008938:Mitosis; D015203:Reproducibility of Results; D012984:Software; D057188:Workflow
Feature selection; High-throughput; Imaging flow cytometry; Machine learning; Open-source software; Profiling