A scalable, reproducible and open-source pipeline for morphologically profiling image cytometry data

Abstract

Due to high resolution and throughput of modern image cytometry platforms, morphologically profiling generated datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), an image processing software aimed at running on distributed high performance computing infrastructure. SCIP is scalable, flexible, open-source and enables reproducible image processing. It performs projection, illumination correction, segmentation, background masking and extensive morphological profiling on various imaging types. We showcase SCIPs capabilities on three large-scale image cytometry datasets. First, we process an imaging flow cytometry (IFC) dataset of human white blood cells and show how the obtained features are used to classify cells into 8 cell types based on bright- and darkfield imagery. Secondly, we process an automated microscopy dataset of human white blood cells to divide them into cell types in an unsupervised manner. Finally, a high-content screening dataset of breast cancer cells is processed to predict the mechanism-of-action of a large set of compound treatments. The software can be installed from the PyPi repository. Its source code is available at https://github.com/ScalableCytometryImageProcessing/SCIP under the GNU General Public License version 3. It has been tested on Unix operating systems. Issues with the software can be submitted at https://github.com/ScalableCytometryImageProcessing/SCIP/issues. 1 Author SummaryCytometry is a field of biology that studies cells by measuring their characteristics. In image cytometry, this is done by acquiring images of cells. In order to gain biological insight from a set of images, an extensive amount of measurements are derived from them describing the cells they contain. These measurements include, for instance, a cells area, diameter, or the average brightness of the cell image. These measurements can then be analyzed using automated software tools to understand, for example, how cells respond to drug treatments, or how cells differ between a healthy and a diseased person. In this work, we present a novel software tool that is able to efficiently compute image measurements on large datasets of images. We do this by harnessing the power of high performance computing infrastructure. By enabling image cytometry researchers to make use of more computational power, they can more efficiently process complex and large datasets, paving the way to novel, fascinating biological discoveries.