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Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nanometer range and strong contrast for membranous structures without requiring labeling or chemical fixation. The short acquisition time and the relatively large field of view leads to fast acquisition of large amounts of tomographic image data. Segmentation of these data into accessible features is a necessary step in gaining biologically relevant information from cryo-soft X-ray tomograms. However, manual image segmentation still requires several orders of magnitude more time than data acquisition. To address this challenge, we have here developed an end-to-end automated 3D segmentation pipeline based on semisupervised deep learning. Our approach is suitable for high-throughput analysis of large amounts of tomographic data, while being robust when faced with limited manual annotations and variations in the tomographic conditions. We validate our approach by extracting three-dimensional information on cellular ultrastructure and by quantifying nanoscopic morphological parameters of filopodia in mammalian cells.

Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nm range and strong contrast for membranous structures without requirement for labeling or chemical fixation. The short acquisition time and the relatively large volumes acquired allow for fast acquisition of large amounts of tomographic image data. Segmentation of these data into accessible features is a necessary step in gaining biologically relevant information from cryo-soft X-ray tomograms. However, manual image segmentation still requires several orders of magnitude more time than data acquisition. To address this challenge, we have here developed an end-to-end automated 3D-segmentation pipeline based on semi-supervised deep learning. Our approach is suitable for high-throughput analysis of large amounts of tomographic data, while being robust when faced with limited manual annotations and variations in the tomographic conditions. We validate our approach by extracting three-dimensional information on cellular ultrastructure and by quantifying nanoscopic morphological parameters of filopodia in mammalian cells. Competing Interest Statement The authors have declared no competing interest.

Mutations in the human desmin gene (DES) cause autosomal-dominant and -recessive cardiomyopathies, leading to heart failure, arrhythmias, and AV blocks. We analyzed the effects of vascular pressure overload in a patient-mimicking p.R349P desmin knock-in mouse model that harbors the orthologue of the frequent human DES missense mutation p.R350P. Transverse aortic constriction (TAC) was performed on heterozygous (HET) DES-p.R349P mice and wild-type (WT) littermates. Echocardiography demonstrated reduced left ventricular ejection fraction in HET-TAC (WT-sham: 69.5 ± 2.9%, HET-sham: 64.5 ± 4.7%, WT-TAC: 63.5 ± 4.9%, HET-TAC: 55.7 ± 5.4%; p<0.01). Cardiac output was significantly reduced in HET-TAC (WT sham: 13088 ± 2385 μl/min, HET sham: 10391 ± 1349μl/min, WT-TAC: 8097 ± 1903μl/min, HET-TAC: 5793 ± 2517μl/min; p<0.01). Incidence and duration of AV blocks as well as the probability to induce ventricular tachycardias was highest in HET-TAC. We observed reduced mtDNA copy numbers in HET-TAC (WT-sham: 12546 ± 406, HET-sham: 13526 ± 781, WT-TAC: 11155 ± 3315, HET-TAC: 8649 ± 1582; p = 0.025), but no mtDNA deletions. The activity of respiratory chain complexes I and IV showed the greatest reductions in HET-TAC. Pressure overload in HET mice aggravated the clinical phenotype of cardiomyopathy and resulted in mitochondrial dysfunction. Preventive avoidance of pressure overload/arterial hypertension in desminopathy patients might represent a crucial therapeutic measure.