PCM1 conveys centrosome asymmetry to polarized endosome dynamics in regulating daughter cell fate.

Vertebrate radial glia progenitors (RGPs), the principal neural stem cells, balance self-renewal and differentiation through asymmetric cell division (ACD), during which unequal inheritance of centrosomes is observed. Mechanistically, how centrosome asymmetry leads to distinct daughter cell fate remains largely unknown. Here we find that the centrosome protein Pericentriolar Material 1 (Pcm1), asymmetrically distributed at the centrosomes, regulates polarized endosome dynamics and RGP fate. In vivo time-lapse imaging and nanoscale-resolution expansion microscopy of zebrafish embryonic RGPs detect Pcm1 on Notch ligand-containing endosomes, in a complex with the polarity regulator Par-3 and dynein motor. Loss of pcm1 disrupts endosome dynamics, with clonal analysis uncovering increased neuronal production at the expense of progenitors. Pcm1 facilitates an exchange of Rab5b (early) for Rab11a (recycling) endosome markers and promotes the formation of Par-3 and dynein macromolecular complexes on recycling endosomes. Finally, in human-induced pluripotent stem cell-derived brain organoids, PCM1 shows asymmetry and co-localization with PARD3 and RAB11A in mitotic neural progenitors. Our data reveal a new mechanism by which centrosome asymmetry is conveyed by Pcm1 to polarize endosome dynamics and Notch signaling in regulating ACD and progenitor fate.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

DaXi-high-resolution, large imaging volume and multi-view single-objective light-sheet microscopy.

The promise of single-objective light-sheet microscopy is to combine the convenience of standard single-objective microscopes with the speed, coverage, resolution and gentleness of light-sheet microscopes. We present DaXi, a single-objective light-sheet microscope design based on oblique plane illumination that achieves: (1) a wider field of view and high-resolution imaging via a custom remote focusing objective; (2) fast volumetric imaging over larger volumes without compromising image quality or necessitating tiled acquisition; (3) fuller image coverage for large samples via multi-view imaging and (4) higher throughput multi-well imaging via remote coverslip placement. Our instrument achieves a resolution of 450 nm laterally and 2 µm axially over an imaging volume of 3,000 × 800 × 300 µm. We demonstrate the speed, field of view, resolution and versatility of our instrument by imaging various systems, including Drosophila egg chamber development, zebrafish whole-brain activity and zebrafish embryonic development - up to nine embryos at a time.

Democratising deep learning for microscopy with ZeroCostDL4Mic.

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.