One very powerful image-based cytometric technology is imaging flow cytometry (IFC, Fig. While cytometric technologies such as fluorescence-based flow and mass cytometry can currently measure 30–40 parameters per cell, the parameter output from image-based cytometry systems can be almost infinite and often continuous (non-discrete) in nature. While extremely powerful, it is a significant challenge to derive meaningful, objective conclusions from the high parameter output inherent to nearly all cytometric approaches. “Cytometry” translates in literal terms to mean “cell measurement” and can best be described as the derivation of numbers from the measurement of large populations of single cells. At the methodological level, there is currently a massive paradigm shift away from so called “bulk” analysis techniques toward single cell-focused approaches that are able to cope far better with the challenges posed by heterogeneity. Our ability to appreciate biological heterogeneity is limited by the resolving power of the analytical approaches at our disposal. This creates a complex set of challenges for understanding how individual cells within heterogeneous communities interact with one another in order to determine the phenotype and function of higher organisms with respect to both healthy and disease states. It is now widely accepted that cellular and molecular heterogeneity pervades all biological systems. 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. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. 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 high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. 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. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. 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. 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 high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells.
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