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The endomembrane system is essential for healthy cell function, with the various compartments carrying out a large number of specific biochemical reactions. To date, almost all of our understanding of the endomembrane system has come from the study of cultured cells growing as monolayers. However, monolayer-grown cells only poorly represent the environment encountered by cells in the human body. As a first step to address this disparity, we have developed a platform that allows us to investigate and quantify changes to the endomembrane system in three-dimensional (3D) cell models, in an automated and highly systematic manner. HeLa Kyoto cells were grown on custom-designed micropatterned 96-well plates to facilitate spheroid assembly in the form of highly uniform arrays. Fully automated high-content confocal imaging and analysis were then carried out, allowing us to measure various spheroid-, cellular- and subcellular-level parameters relating to size and morphology. Using two drugs known to perturb endomembrane function, we demonstrate that cell-based assays can be carried out in these spheroids, and that changes to the Golgi apparatus and endosomes can be quantified from individual cells within the spheroids. We also show that image texture measurements are useful tools to discriminate cellular phenotypes. The automated platform that we show here has the potential to be scaled up, thereby allowing large-scale robust screening to be carried out in 3D cell models. [ABSTRACT FROM AUTHOR]

Proteins are biological units whose essence is defined by their functional relationships with other proteins or biomolecules such as RNA, DNA, lipids, or carbohydrates. These functions encompass enzymatic, structural, regulatory, or physical interaction roles. The STRING database (Nucleic Acids Research, 8 Jan 2021;49(D1): D605-12) provides an index that defines the functional interaction networks between proteins in model organisms. To facilitate the identification, visualization, and evaluation of potential functional networks across organisms from different phylogenetic lineages, we have developed PhyloString (https://biocomputo.ibt.unam.mx/phylostring/), a web server that utilizes the indices of the STRING database. PhyloString decomposes these functional networks into modules, representing cohesive units of proteins grouped based on their similarity of STRING values and the phylogenetic origins of their respective organisms. This study presents and thoroughly discusses examples of such functional networks and their modules identified using PhyloString. [ABSTRACT FROM AUTHOR]