Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy.

Image analysis techniques provide objective and reproducible statistics for interpreting microscopy data. At higher dimensions, three-dimensional (3D) volumetric and spatiotemporal data highlight additional properties and behaviors beyond the static 2D focal plane. However, increased dimensionality carries increased complexity, and existing techniques for general segmentation of 3D data are either primitive, or highly specialized to specific biological structures. Borrowing from the principles of 2D topological data analysis (TDA), we formulate a 3D segmentation algorithm that implements persistent homology to identify variations in image intensity. From this, we derive two separate variants applicable to spatial and spatiotemporal data, respectively. We demonstrate that this analysis yields both sensitive and specific results on simulated data and can distinguish prominent biological structures in fluorescence microscopy images, regardless of their shape. Furthermore, we highlight the efficacy of temporal TDA in tracking cell lineage and the frequency of cell and organelle replication.

Cluster analysis for localisation-based data sets: dos and don'ts when quantifying protein aggregates.

Many proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution limit of conventional optical microscopy, single molecule localisation microscopy (SMLM) can map molecule locations with nanometre precision. The data from SMLM is not a conventional pixelated image and instead takes the form of a point-pattern-a list of the x, y coordinates of the localised molecules. To extract the biological insights that researchers require cluster analysis is often performed on these data sets, quantifying such parameters as the size of clusters, the percentage of monomers and so on. Here, we provide some guidance on how SMLM clustering should best be performed.

Phospholipid tail asymmetry allows cellular adaptation to anoxic environments.

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. Using advanced microscopy, molecular dynamics simulations, and lipidomics by mass spectrometry we demonstrated 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, Schizosaccharomyces japonicus, which can grow in aerobic and anaerobic conditions, is capable of utilizing this strategy, whereas its sister species, the well-known model organism Schizosaccharomyces pombe, cannot. The incorporation of asymmetric-tailed phospholipids might be a general adaptation to hypoxic environmental niches.

Topology-based fluorescence image analysis for automated cell identification and segmentation.

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.