In the brain, neurons that fail to assemble into functional circuits are eliminated. Their clearance depends on microglia, immune cells that colonize the CNS during embryogenesis. Despite the importance of these cells in development and disease, the mechanisms that target and position microglia within the brain are unclear. Here we show that, in zebrafish, attraction of microglia into the brain exploits differences in developmental neuronal apoptosis and that these provide a mechanism for microglial distribution. Reducing neuronal cell death results in fewer microglia, whereas increased apoptosis enhances brain colonization, resulting in more microglia at later stages. Interestingly, attraction into the brain depends on nucleotide signaling, the same signaling system used to guide microglia toward brain injuries. Finally, this work uncovers a cell-non-autonomous role for developmental apoptosis. Classically considered a wasteful process, programmed cell death is exploited here to configure the immune-neuronal interface of the brain.

Selective-plane illumination microscopy has proven to be a powerful imaging technique due to its unsurpassed acquisition speed and gentle optical sectioning. However, even in the case of multiview imaging techniques that illuminate and image the sample from multiple directions, light scattering inside tissues often severely impairs image contrast. Here we combine multiview light-sheet imaging with electronic confocal slit detection implemented on modern camera sensors. In addition to improved imaging quality, the electronic confocal slit detection doubles the acquisition speed in multiview setups with two opposing illumination directions allowing simultaneous dual-sided illumination. Confocal multiview light-sheet microscopy eliminates the need for specimen-specific data fusion algorithms, streamlines image post-processing, easing data handling and storage.

Amoeboid cells are fundamental to animal biology and broadly distributed across animal diversity, but their evolutionary origin is unclear. The closest living relatives of animals, the choanoflagellates, display a polarized cell architecture (with an apical flagellum encircled by microvilli) that closely resembles that of epithelial cells and suggests homology, but this architecture differs strikingly from the deformable phenotype of animal amoeboid cells. Here, we show that choanoflagellates subjected to confinement differentiate into an amoeboid form by retracting their flagella and activating myosin-based motility. This switch allows escape from confinement and is conserved across choanoflagellate diversity. The conservation of the amoeboid cell phenotype across animals and choanoflagellates, together with the conserved role of myosin, is consistent with the homology of amoeboid motility in both lineages. We hypothesize that the differentiation between animal epithelial and crawling cells might have evolved from a stress-induced phenotypic switch between flagellate and amoeboid forms in their single-celled ancestors.

Fluorescence imaging techniques such as single molecule localization microscopy, highcontent screening and light-sheet microscopy are producing ever-larger datasets, which poses increasing challenges in data handling and data sharing. Here, we introduce a realtime compression library that allows for very fast (beyond 1 GB/s) compression and decompression of microscopy datasets during acquisition. In addition to an efficient lossless mode, our algorithm also includes a lossy option, which limits pixel deviations to the intrinsic noise level of the image and yields compression ratio of up to 100-fold. We present a detailed performance analysis of the different compression modes for various biological samples and imaging modalities. opened new perspectives in biology by increasing the speed of imaging, the number of specimens or the resolution of the observed structures. Even though these methods bring undeniable advantages, the data production speed and experiment sizes (Fig. 1a, Supplementary Table 1) are increasing in such a fast pace that in many cases data handling quickly becomes a bottleneck for new discoveries [12]-[14]. A straightforward solution to this problem is to perform image compression. Nonetheless, this typically implies incompatibilities with certain software packages, slow compression speed, and only moderate file size reduction for lossless methods. Although the compression ratio (original size / compressed size) can be substantially increased with lossy compression algorithms, their use is often discouraged [15] as the degree of information loss heavily depends on the image content and cannot be explicitly controlled.To address these challenges, we developed a new compression library called B 3 D, which is capable of extremely fast compression and decompression of large microscopy datasets. Our library is built on the CUDA architecture [16] for GPU-based compression, which not only enables high processing speed, but also relieves load on the central processing unit, allowing compression directly during image acquisition. The algorithm has two main components. First, a prediction is made for each pixel based on the neighboring pixel values, and second, the prediction errors are run-length and Huffman encoded to effectively reduce the data size (Supplementary Note and Supplementary Fig. 1). We compared our algorithm's performance with TIFF (LZW), JPEG2000, and the speed-optimized KLB [17] by measuring compression speed, decompression speed and resulting file size (Fig. 1b). Only B³D is capable of handling the sustained high data rate of modern sCMOS cameras typically . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a

The evolution of different cell types was a key process of early animal evolution1–3. Two fundamental cell types, epithelial cells and amoeboid cells, are broadly distributed across the animal tree of life4,5 but their origin and early evolution are unclear. Epithelial cells are polarized, have a fixed shape and often bear an apical cilium and microvilli. These features are shared with choanoflagellates – the closest living relatives of animals – and are thought to have been inherited from their last common ancestor with animals1,6,7. The deformable amoeboid cells of animals, on the other hand, seem strikingly different from choanoflagellates and instead evoke more distantly related eukaryotes, such as diverse amoebae – but it has been unclear whether that similarity reflects common ancestry or convergence8. Here, we show that choanoflagellates subjected to spatial confinement differentiate into an amoeboid phenotype by retracting their flagella and microvilli, generating blebs, and activating myosin-based motility. Choanoflagellate cell crawling is polarized by geometrical features of the substrate and allows escape from confined microenvironments. The confinement-induced amoeboid switch is conserved across diverse choanoflagellate species and greatly expands the known phenotypic repertoire of choanoflagellates. The broad phylogenetic distribution of the amoeboid cell phenotype across animals9–14 and choanoflagellates, as well as the conserved role of myosin, suggests that myosin-mediated amoeboid motility was present in the life history of their last common ancestor. Thus, the duality between animal epithelial and crawling cells might have evolved from a temporal phenotypic switch between flagellate and amoeboid forms in their single-celled ancestors3,15,16.