MSCProfiler: An image processing workflow to investigate Mesenchymal Stem Cell heterogeneity using imaging flow cytometry data.

Abstract

Single-cell immuno-heterogeneity has always been the forerunner of any change in homeostasis of cellular functions in the body. Mesenchymal stem cells represent a viable source for the development of cell-based therapies. Multiple conditions giving rise to inter, and intra-population variations result in heterogeneity and multipotent differentiation ability of these cells of stromal origin. Cell surface markers which are important members of membrane proteins, ion channels, transporter, adhesion, and signaling molecules generally differentiate between stromal cells of different origin. However, existing analytical tools cannot always model a pattern of their surface distribution in successive generations of growth and proliferation. In this study, we have developed a post-acquisition image analysis pipeline for human mesenchymal stromal cells obtained from exfoliated deciduous teeth (hSHEDs). Using the open-source image processing software CellProfiler, a pipeline has been developed to extract cellular features from 50,000-100,000 single-cell images. We made use of the image flow cytometry technology to explore the morphometric properties of hSHEDs, along with their surface marker distribution. This unbiased pipeline can extract cellular, geometrical and texture features such as shape, size, eccentricity, entropy, intensities as a measure of cellular heterogeneity. For the first time, we have described an automated, unbiased image assessment protocol implemented in a validated open-source software, leveraging the suite of image-based measurements to develop the prototype named as MSCProfiler. The hallmark of this screening workflow has been the identification and removal of image-based aberrations to identify the single-cell bright field and fluorescent images of mesenchymal stem cells.