Abstract
Phenotypic profiling of large three-dimensional microscopy data sets has not been widely adopted due to the challenges posed by cell segmentation and feature selection. The computational demands of automated processing further limit analysis of hard-to-segment images such as of neurons and organoids. Here we describe a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and advanced data visualization. We demonstrate the analysis potential on complex 3D images by investigating the phenotypic alterations of: neurons in response to apoptosis-inducing treatments and morphogenesis for oncogene-expressing human mammary gland acinar organoids. Our novel implementation of image analysis algorithms called Phindr3D allowed rapid implementation of data-driven voxel-based feature learning into 3D high content analysis (HCA) operations and constitutes a major practical advance as the computed assignments represent the biology while preserving the heterogeneity of the underlying data. Phindr3D is provided as Matlab code and as a stand-alone program (https://github.com/DWALab/Phindr3D).
Highlights
High content analysis (HCA) and phenotypic profiling together with genetic pathway analysis or compound screening have helped elucidate fundamental biological processes [1] and aid the discovery of novel therapeutic compounds [2]
Unbiased quantitative phenotypic analysis of microscopy images of cells grown in 3D organoids or in dense culture conditions in large enough numbers to reach statistical clarity remains a fundamental challenge
We report that using data-driven voxel-based features and machine learning it is possible to analyze complex 3D image data without compressing them to 2D, identifying individual cells or using computationally intensive deep learning
Summary
High content analysis (HCA) and phenotypic profiling together with genetic pathway analysis or compound screening have helped elucidate fundamental biological processes [1] and aid the discovery of novel therapeutic compounds [2]. While segmentation can be achieved for defined structures such as nuclei in spheroids [7] and volumetric tracing of single neuronal arbors in drosophila brains [8], large-scale analysis of complex, hard-to-segment 3D microscopy data is very hard to do with current tools. An attractive way to circumvent the problem of cell segmentation involves using whole image features [9,10]. These methods have not been applied to 3D images and standard implementations are not appropriate for micrographs of organoids as much of the 3D image volume is devoid of cells. A comprehensive architecture for high content analysis in 3D has not yet been established
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