Automated 3D cytoplasm segmentation in soft X-ray tomography.

Cells' structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune T cells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.

Environmental exposure to metallic pollution impairs honey bee brain development and cognition.

Laboratory studies show detrimental effects of metallic pollutants on invertebrate behaviour and cognition, even at low levels. Here we report a field study on Western honey bees exposed to metal and metalloid pollution through dusts, food and water at a historic mining site. We analysed more than 1000 bees from five apiaries along a gradient of contamination within 11 km of a former gold mine in Southern France. Bees collected close to the mine exhibited olfactory learning performances lower by 36% and heads smaller by 4%. Three-dimensional scans of bee brains showed that the olfactory centres of insects sampled close to the mine were also 4% smaller, indicating neurodevelopmental issues. Our study raises serious concerns about the health of honey bee populations in areas polluted with potentially harmful elements, particularly with arsenic, and illustrates how standard cognitive tests can be used for risk assessment.

Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning.

Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology that provide critical insights into animal behaviour, ecology and evolution. So far, however, such analyses have required extensive manual effort, which considerably limits the scope for comparative research. Here we used micro-CT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and may underpin behavioural variability central to complex social organisations. In addition, the bumblebee dataset showed a significant level of lateralization in optic and antennal lobes, providing a potential explanation for reported variations in visual and olfactory learning. Our fast, robust and user-friendly approach holds considerable promises for carrying out large-scale quantitative neuroanatomical comparisons across a wider range of animals. Ultimately, this will help address fundamental unresolved questions related to the evolution of animal brains and cognition.

Introducing Biomedisa as an open-source online platform for biomedical image segmentation.

We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.

Parasitoid biology preserved in mineralized fossils.

About 50% of all animal species are considered parasites. The linkage of species diversity to a parasitic lifestyle is especially evident in the insect order Hymenoptera. However, fossil evidence for host-parasitoid interactions is extremely rare, rendering hypotheses on the evolution of parasitism assumptive. Here, using high-throughput synchrotron X-ray microtomography, we examine 1510 phosphatized fly pupae from the Paleogene of France and identify 55 parasitation events by four wasp species, providing morphological and ecological data. All species developed as solitary endoparasitoids inside their hosts and exhibit different morphological adaptations for exploiting the same hosts in one habitat. Our results allow systematic and ecological placement of four distinct endoparasitoids in the Paleogene and highlight the need to investigate ecological data preserved in the fossil record.