Membrane biophysical properties are critical to cell fitness and depend on unsaturated phospholipid acyl tails. These can only be produced in aerobic environments since eukaryotic desaturases require molecular oxygen. This raises the question of how cells maintain bilayer properties in anoxic environments. Here, we demonstrate the existence of an alternative pathway to regulate membrane fluidity that exploits phospholipid acyl-tail length asymmetry, replacing unsaturated species in the membrane lipidome. We show that the fission yeast, S. japonicus, which can grow in aerobic and anaerobic conditions, is capable of utilizing this strategy whereas its sister species, the well-known model organism S. pombe, cannot. The incorporation of asymmetric-tailed phospholipids might be a general adaptation to hypoxic environmental niches.One-Sentence SummaryIn anoxic environments, saturated asymmetric acyl-tailed phospholipids can replace unsaturated ones to maintain membrane physical properties.

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily‐shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally‐sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.

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