An Interpretable Framework to Characterize Compound Treatments on Filamentous Fungi using Cell Painting and Deep Metric Learning

Abstract

The cell painting microscopy imaging protocol has recently gained traction in the biology community as it allows, through the addition of fluorescent dyes, to acquire images that highlight intra-cellular components that are not visible through traditional whole-cell microscopy. While previous works have successfully applied cell painting to mammalian cells, we devise a staining protocol applicable to a filamentous fungus model. Following a principled visual inspection and annotation protocol of phenotypes by domain-experts, we devise an efficient, robust, and conceptually simple image analysis strategy based on the Deep Cosine Metric Learning paradigm that allows to estimate phenotypical similarities across different imaging modalities. We experimentally demonstrate the benefits of our pipeline in the tasks of estimating dose-response curves over a wide range of subtle phenotypical variations. Last, we showcase how our learned metrics can group image samples according to different modes of action and biological targets in an interpretable manner.