In addition, PECAn features an incorporated statistical analysis and graph generation application, allowing users to visualize and evaluate their results in an automated fashion and at scale. This software readily performs myriad clonal analyses, including identifying the number, size, position, and fluorescence properties of single cells and cell territories by region and genotype in 3D space. In this work, we present a comprehensive, high-throughput image and data processing pipeline for automating analysis of mosaic experiments: the Pipeline for Enhanced Clonal Analysis (PECAn). In the field of cell competition, in particular, there is a pressing need for automated image and single cell analytic techniques 8. Minute cells are therefore said to act as ‘losers’ relative to wildtype ‘winners.’ Such experiments require careful localization of dying cells and categorization by genotype. This bottleneck therefore impairs both the speed, quality, and scope of data generation.Īn example of this challenge is demonstrated by imaging experiments used for the study of Minute cell competition in Drosophila imaginal wing discs – a process whereby cells carrying heterozygous mutations in ribosomal protein genes (Rp/+), known as Minutes, are eliminated from mosaic tissues when proximal to wildtype cells 7 (Supplementary Fig. Performing such analysis by hand is time-consuming, inconsistent, and error prone, with large datasets often requiring days of work. Analysis of such confocal images is therefore typically performed in a manual or semi-automated fashion. However, there are no image analysis tools designed to analyse complex, multi-genotype 3D image stacks and to measure, in the whole image or in specifically marked subregions, diverse parameters such as levels of apoptosis, fluorescence and speckle intensity, cell number and density. In recent years, a number of powerful image analysis tools have been developed, including image analysis environments enabling construction of custom algorithms, such as CellProfiler 2, and more targeted software, such as tools for analysis of morphogenesis and cell shape in situ 3, 4, 5 and machine-learning enabled segmentation of twin-spot clones 6. Extracting this information, however, is time consuming and complicated. The resulting images, typically acquired by 3D confocal microscopy, are information rich and provide insights on phenotypes, such as signal intensity of reporters, clone numbers, clone size, shape and apoptosis. While this technique presents unique avenues for investigating tissue biology, extracting information from mosaic tissues by microscopic analyses is often a bottleneck. These tools come in many forms but achieve a common goal: a subset of cells within a tissue acquire a genetic alteration absent in the surrounding tissue, creating genetically distinct territories of cells. With the advent of safe, non-invasive tools for generating and marking genetically distinct subpopulations of cells within intact organisms, clonal analysis has become a common tool for the study of heterogeneous cell populations and mosaic tissues (Reviewed in 1). Furthermore, using PECAn, we identify several genes with a role in cell competition by conducting an RNAi-based screen. We find an unappreciated sexual dimorphism in Minute cell growth in competing wing discs and identify, by statistical regression analysis, tissue parameters that model and correlate with competitive death. We demonstrate the power of this pipeline by applying it to the study of Minute cell competition. This enables researchers to perform rigorous analyses rapidly and at scale, without requiring programming skills. PECAn includes data handling, machine-learning-enabled segmentation, multivariant statistical analysis, and graph generation. Here we develop PECAn, a pipeline for image processing and statistical data analysis of complex multi-genotype 3D images. However, the 3D nature of clones makes sample image analysis challenging and slow, limiting the amount of information that can be extracted manually. Investigating organ biology often requires methodologies to induce genetically distinct clones within a living tissue.
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