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Lipophagy is a form of autophagy by which lipid droplets (LDs) become digested to provide nutrients as a cellular response to starvation. Lipophagy is often studied in yeast,Saccharomyces cerevisiae, in which LDs become internalized into the vacuole. There is a lack of tools to quantitatively assess lipophagy in intact cells with high resolution and throughput. Here, we combine soft X-ray tomography (SXT) with fluorescence microscopy and use a deep learning computational approach to visualize and quantify lipophagy in yeast. We focus on yeast homologs of mammalian Niemann Pick type C proteins, whose dysfunction leads to Niemann Pick type C disease in humans, i.e., NPC1 (named NCR1 in yeast) and NPC2. We developed a convolutional neural network (CNN) model which classifies ring-shaped versus lipid-filled or fragmented vacuoles containing ingested LDs in fluorescence images from wild-type yeast and from cells lacking NCR1 (Δncr1cells) or NPC2 (Δnpc2cells). Using a second CNN model, which performs automated segmentation of LDs and vacuoles from high-resolution reconstructions of X-ray tomograms, we can obtain 3D renderings of LDs inside and outside of the vacuole in a fully automated manner and additionally measure droplet volume, number, and distribution. We find that cells lacking functional NPC proteins can ingest LDs into vacuoles normally but show compromised degradation of LDs and accumulation of lipid vesicles inside vacuoles. This phenotype is most severe inΔnpc2cells. Our new method is versatile and allows for automated high-throughput 3D visualization and quantification of lipophagy in intact cells.

The incorporation of Pt into an iron-nitrogen-carbon (FeNC) catalyst for the oxygen reduction reaction (ORR) was recently shown to enhance catalyst stability without Pt directly contributing to the ORR activity. However, the mechanistic origin of this stabilisation remained obscure. It is established herein with rotating ring disc experiments that the side product, H2O2, which is known to damage FeNC catalysts, is suppressed by the presence of Pt. The formation of reactive oxygen species is additionally inhibited, independent of intrinsic H2O2 formation, as determined by electron paramagnetic resonance. Transmission electron microscopy identifies an oxidised Fe-rich layer covering the Pt particles, thus explaining the inactivity of the latter towards the ORR. These insights develop understanding of FeNC degradation mechanisms during ORR catalysis, and crucially establish the required properties of a precious metal free protective catalyst to improve FeNC stability in acidic media.

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.

Structural analysis of biological membranes is important for understanding cell and sub-cellular organelle function as well as their interaction with the surrounding environment. Imaging of whole cells in three dimension at high spatial resolution remains a significant challenge, particularly for thick cells. Cryo-transmission soft X-ray microscopy (cryo-TXM) has recently gained popularity to image, in 3D, intact thick cells (∼10μm) with details of sub-cellular architecture and organization in near-native state. This paper reports a new tool to segment and quantify structural changes of biological membranes in 3D from cryo-TXM images by tracking an initial 2D contour along the third axis of the microscope, through a multi-scale ridge detection followed by an active contours-based model, with a subsequent refinement along the other two axes. A quantitative metric that assesses the grayscale profiles perpendicular to the membrane surfaces is introduced and shown to be linearly related to the membrane thickness. Our methodology has been validated on synthetic phantoms using realistic microscope properties and structure dimensions, as well as on real cryo-TXM data. Results demonstrate the validity of our algorithms for cryo-TXM data analysis. R.C. was partially funded by the Instituto de Salud Carlos III, Spain (FIS- PI11/01709), and BE-DGR 2012 BE1 00308 from AGAUR, Catalonia, Spain. C.Z. acknowledges the financial support from the Spanish Ministry of Economy and Competitiveness, through the Maria de Maeztu Programme for Centres/Units of Excellence in R&D (MDM-2015-0502). O.K. was supported by the Seventh Framework Program of the European Commission, grant agreement 278486: DEVELAGE. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under BioStruct-X (grant agreement N283570), the Spanish Ministry of Economy and Competitiveness (grant TIN2014-52923-R) and FEDER.

The transmission X-ray microscope’s 3D PSF exhibits an axial asymmetry that cannot be explained by an incoherent imaging model for either a lens or zone plate, with or without spherical aberration.

Fresnel zone plates produced by electron beam lithography and planar etching techniques provide a resolving power of about 10 nm. An alternative zone plate fabrication approach is based on alternately coating a micro-wire with two different materials. With this process, very thin zone layers with very high aspect ratios can be deposited. However, depending on the fabrication method, random zone positioning errors may introduce strong aberrations. We simulate the effect of positioning errors using different random fluctuations and study their influence on zone plate resolution. We find that random errors significantly decrease the contrast transfer of X-ray microscopes. Additionally, we derive an upper bound for the mean acceptable variance of the deposition rate.