Peroxisomes are ubiquitous and dynamic organelles that house many important pathways of cellular metabolism. In recent years it has been demonstrated that mitochondria are tightly connected with peroxisomes and are defective in several peroxisomal diseases. Indeed, these two organelles share metabolic routes as well as resident proteins and, at least in mammals, are connected via a vesicular transport pathway. However the exact extent of cross-talk between peroxisomes and mitochondria remains unclear. Here we used a combination of high throughput genetic manipulations of yeast libraries alongside high content screens to systematically unravel proteins that affect the transport of peroxisomal proteins and peroxisome biogenesis. Follow up work on the effector proteins that were identified revealed that peroxisomes are not randomly distributed in cells but are rather localized to specific mitochondrial subdomains such as mitochondria-ER junctions and sites of acetyl-CoA synthesis. Our approach highlights the intricate geography of the cell and suggests an additional layer of organization as a possible way to enable efficient metabolism. Our findings pave the way for further studying the machinery aligning mitochondria and peroxisomes, the role of the juxtaposition, as well as its regulation during various metabolic conditions. More broadly, the approaches used here can be easily applied to study any organelle of choice, facilitating the discovery of new aspects in cell biology.

Peroxisomes are cellular organelles with vital functions in lipid, amino acid and redox metabolism. The cellular formation and dynamics of peroxisomes are governed by PEX genes; however, the regulation of peroxisome abundance is still poorly understood. Here, we use a high-content microscopy screen in Saccharomyces cerevisiae to identify new regulators of peroxisome size and abundance. Our screen led to the identification of a previously uncharacterized gene, which we term PEX35, which affects peroxisome abundance. PEX35 encodes a peroxisomal membrane protein, a remote homolog to several curvature-generating human proteins. We systematically characterized the genetic and physical interactome as well as the metabolome of mutants in PEX35, and we found that Pex35 functionally interacts with the vesicle-budding-inducer Arf1. Our results highlight the functional interaction between peroxisomes and the secretory pathway.

Peroxisomes are ubiquitous cell organelles involved in many metabolic and signaling functions. Their assembly requires peroxins, encoded by PEX genes. Mutations in PEX genes are the cause of Zellweger Syndrome spectrum (ZSS), a heterogeneous group of peroxisomal biogenesis disorders (PBD). The size and morphological features of peroxisomes are below the diffraction limit of light, which makes them attractive for super-resolution imaging. We applied Stimulated Emission Depletion (STED) microscopy to study the morphology of human peroxisomes and peroxisomal protein localization in human controls and ZSS patients. We defined the peroxisome morphology in healthy skin fibroblasts and the sub-diffraction phenotype of residual peroxisomal structures (‘ghosts’) in ZSS patients that revealed a relation between mutation severity and clinical phenotype. Further, we investigated the 70 kDa peroxisomal membrane protein (PMP70) abundance in relationship to the ZSS sub-diffraction phenotype. This work improves the morphological definition of peroxisomes. It expands current knowledge about peroxisome biogenesis and ZSS pathoethiology to the sub-diffraction phenotype including key peroxins and the characteristics of ghost peroxisomes.

BackgroundSuper resolution (SR) microscopy enabled cell biologists to visualize subcellular details up to 20 nm in resolution. This breakthrough in spatial resolution made image analysis a challenging procedure. Direct and automated segmentation of SR images remains largely unsolved, especially when it comes to providing meaningful biological interpretations.ResultsHere, we introduce a novel automated imaging analysis routine, based on Gaussian, followed by a segmentation procedure using CellProfiler software (www.cellprofiler.org). We tested this method and succeeded to segment individual nuclear pore complexes stained with gp210 and pan-FG proteins and captured by two-color STED microscopy. Test results confirmed accuracy and robustness of the method even in noisy STED images of gp210.ConclusionsOur pipeline and novel segmentation procedure may benefit end-users of SR microscopy to analyze their images and extract biologically significant quantitative data about them in user-friendly and fully-automated settings.

Light microscopy has become an indispensable tool for the life sciences, as it enables the rapid acquisition of three-dimensional images from the interior of living cells/tissues. Over the last decades, super-resolution light microscopy techniques have been developed, which allow a resolution up to an order of magnitude higher than that of conventional light microscopy. Those techniques require labelling of cellular structures with fluorescent probes exhibiting specific properties, which are supplied from outside and therefore have to surpass cell membranes. Currently, major efforts are undertaken to develop probes which can surpass cell membranes and exhibit the photophysical properties required for super-resolution imaging. However, the process of probe development is still based on a tedious and time consuming manual screening. An accurate computer based model that enables the prediction of the cell permeability based on their chemical structure would therefore be an invaluable asset for the development of fluorescent probes. Unfortunately, current models, which are based on multiple molecular descriptors, are not well suited for this task as they require high effort in the usage and exhibit moderate accuracy in their prediction. Here, we present a novel fragment based lipophilicity descriptor DeepFL-LogP, which was developed on the basis of a deep neural network. DeepFL-LogP exhibits excellent correlation with the experimental partition coefficient reference data (R2 = 0.892 and MSE = 0.359) of drug-like substances. Further a simple threshold permeability model on the basis of this descriptor allows to categorize the permeability of fluorescent probes with 96% accuracy. This novel descriptor is expected to largely simplify and speed up the development process for novel cell permeable fluorophores.

Revealing the subcellular phenotypes at the molecular scale is critical to understand the mechanisms by which the cells function and respond to chemical treatments. Super-resolution microscopy and robust analysis tools enabled biologists to reveal and quantify phenotypes at unprecedented resolution. Developing automated imaging analysis solutions for super-resolution imaging will make high-content-screening (HCS) applicable for super-resolution microscopy, which will give access to new complex information. Here, I provide an instant automated analysis procedure for analyzing super-resolution images via CellProfiler ( www.cellprofiler.org ) platform.

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