petiteFinder: An automated computer vision tool to compute Petite colony frequencies in baker’s yeast

Abstract

Mitochondrial respiration is central to cellular and organismal health in eukaryotes. In baker’s yeast, however, respiration is dispensable under fermentation conditions. Because yeast are tolerant of this mitochondrial dysfunction, yeast are widely used by biologists as a model organism to ask a variety of questions about the integrity of mitochondrial respiration. Fortunately, baker’s yeast also display a visually identifiable Petite colony phenotype that indicates when cells are incapable of respiration. Petite colonies are smaller than their Grande (wild-type) counterparts, and their frequency can be used to infer the integrity of mitochondrial respiration in populations of cells. In this study, we introduce a deep learning enabled tool, petiteFinder, to leverage the Petite colony phenotype and increase the throughput of the Petite frequency assay. This automated computer vision tool detects Grande and Petite colonies and computes Petite colony frequencies from scanned images of Petri dishes. It addresses issues in scalability and reproducibility of the Petite colony assay which currently relies on laborious manual colony counting methods. Combined with the detailed experimental protocols we provide, we believe this study can serve as a foundation to standardize this assay. Finally, we comment on how Petite colony detection as a computer vision problem highlights ongoing difficulties with small object detection in existing object detection architectures.