A Cell Adhesion-Based Reconstitution Method for Studying Cell Polarity

Cell polarity is an evolutionarily conserved process of asymmetric spatial organization within cells and is essential to tissue structure, signal transduction, cell migration, and cell division. The establishment and maintenance of polarity typically involves extensive protein-protein interactions that can be made further intricate by cell cycle-dependent regulation. These aspects can make interpreting phenotypes within traditional in vivo genetic systems challenging due to pleiotropic effects in loss-of-function experiments. Minimal reconstitution methods offer investigators the advantage of stricter control of otherwise complex systems and allow for more direct assessment of the role of individual components to the process of interest. Here I provide a detailed protocol for a cell adhesion-based method of inducing cell polarity within non-polarized Drosophila S2 cells. This technique is simple, cost effective, moderate throughput, and amenable to RNAi-based loss-of-function studies. The ability to “plug-and-play” genes of interest allows investigators to easily assess the contribution of individual protein domains and post-translational modifications to their function. The system is ideally suited to test not only the requirement of individual components but also their sufficiency, and can provide important insight into the epistatic relationship among multiple components in a protein complex. Although designed for use within Drosophila cells, the general premise and protocol should be easily adapted to mammalian cell culture or other systems that may better suit the interests of potential users

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity.We developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells.MatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

Availability of data and materials: The images and MatQuantify code are freely available for non-commercial use from http://matquantify.sourceforge.net.Background: Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity. Results: We developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells. Conclusion: MatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis.Children's Medical Reserach Institute. MK and ETT were supported by Kids Cancer Alliance (KCA). IMD received funding from the University of Sydney, Sydney Medical School Summer Research Scholarship program