AbstractMigratory macrophages (Mφ) play critical roles in tissue development, homeostasis and disease, so it is important to understand how their migration machinery is regulated. We have employed 3D-SIM and 3D-STORM super-resolution techniques to map the subcellular localisation of adhesion and motility related proteins in bone marrow derived mouse Mφ, and to study how their distribution changes under different conditions. napari (https://napari.org) is a new n-dimensional array viewer for Python. napari was designed to be n-dimensional first, blazing fast, and play well with the existing scientific Python ecosystem. napari’s main architectural choices are: – a 2D *or* 3D canvas that is powered by VisPy and OpenGL for buttery-smooth interaction with the current view, – slicing sliders for the axes not currently being displayed: as many are generated as needed for the current dataset, allowing exploration of higher-dimensional data, such as multi-channel, volumetric 3D time series, – a *layer list*, to enable overlays of different views of the same data space (such as segmentations of images or point detections) – simple data models, enabling two-way data flow between display, annotations, and analysis results. I will demonstrate how these principles enable diverse workflows, such as: – viewing n-dimensional images (NumPy arrays) and larger-than-RAM datasets using dask and zarr – adding interactive annotation steps within custom computational workflows – creating a quick viewer for custom images using plugins – viewing neural network results during training on the fly napari was developed as an open source project from the beginning, and over 60 people have contributed to its development thus far.
Juan Nunez Iglesias
Bio available soon