Systematic Replication Enables Normalization of High-throughput Imaging Assays

Abstract

MotivationHigh-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work we consider how an experimental design incorporating multiple levels of replication enables removal of technical artifacts from such image-based platforms. ResultsWe develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application on MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays. AvailabilityRaw data is on synapse (syn2862345), analysis code is on github (gjhunt/mema norm), a Docker image is available on dockerhub (gjhunt/memanorm). online.