Label-free cell cycle analysis for high-throughput imaging flow cytometry

Abstract

Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. Here we demonstrate label-free prediction of DNA content and quantification of the mitotic cell cycle phases by applying supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad application to other cell types.

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

“…In ([6]), we reported the label-free classification of the cell cycle phases using supervised machine learning techniques on bright-field and dark-field images only. Such high-throughput analyses of IFC data can now be streamlined in a smooth and user-friendly way, making machine learning techniques more accessible.…”

“…We previously developed a prototype workflow in our effort to develop a label-free assay for cell cycle analysis, using machine learning on the bright-field and dark-field images image setts from an imaging flow cytometer [6]. That analysis consisted of several steps (see Methods section for details) that required the use of commercial software (MATLAB) both before and after using the open source software CellProfiler.…”

Current status and potential of morphometric sperm analysis

“…This type of analysis will couple the collection of high-throughput data with streamlined image analysis. Sperm features such as size and shape, granularity, intensity, radial distribution, and texture could be obtained145 in a large sperm population. In addition, the main advantage of this technique, which makes it unique, is the ability simultaneously to evaluate the morphometric and physiological parameters in the same sperm cell.…”

Image-Based Histological Evaluation of Scaffold-Free 3D Osteoblast Cultures

“…Since high-quality images as well as hardware with high-throughput capabilities are available for imaging solutions, the bioimage informatics community is also strongly developing more sophisticated software solutions capable of obtaining information-rich measurements from acquired image data in an accurate and reproducible manner [12,13]. Besides a range of commercial software solutions [14], open-source software packages-such as Fiji/ImageJ [15], Tango [16], Icy [17], and CellProfiler [18]-have been reported over the last years.…”

“…For a comprehensive review on biological imaging software tools and data-analysis strategies for image-based cell profiling, the authors refer to the work of Eliceiri et al [19] and Caicedo et al [20]. Although sophisticated software tools with deep learning [21] and machine learning capabilities [22] have been developed for cell cycle analysis for imaging flow cytometry [13], morphological profiling using multiplexed fluorescent dyes [23], and microvascular network characteristics [24], only very few computer-assisted methods are reported for histomorphometry. Recently reported image analysis work is still relying on commercial software solutions which are costly, not freely available, and do not use an open source software approach.…”