Animal bodies are composed of hundreds of cell types that differ in location, morphology, cytoarchitecture, and physiology. This is reflected by cell type-specific transcription factors and downstream effector genes implementing functional specialisation. Here, we establish and explore the link between cell type-specific gene expression and subcellular morphology for the entire body of the marine annelid Platynereis dumerilii. For this, we registered a whole-body cellular expression atlas to a high-resolution electron microscopy dataset, automatically segmented all cell somata and nuclei, and clustered the cells according to gene expression or morphological parameters. We show that collective gene expression most efficiently identifies spatially coherent groups of cells that match anatomical boundaries, which indicates that combinations of regionally expressed transcription factors specify tissue identity. We provide an integrated browser as a Fiji plugin to readily explore, analyse and visualise multimodal datasets with remote ondemand access to all available datasets. Figure 1: Ultrastructure of a whole Platynereis by serial block-face scanning electron microscopy.A: The 3D SBEM dataset can be observed in multiple orientations, either along the acquisition plane (transversal plane, top row) or as orthogonal projections (horizontal plane, bottom row; scale bar: 50 µm). B-E: fine ultrastructure is revealed when exploring the datasets at native resolution (10 nm pixel-size x/y; scale bars: 2 µm). B: epithelial cell, interfacing the cuticle and the underlying muscle. Bundles of cytoskeletal filaments (arrowhead) form part of the attachment complex (inset). C: the adult eye forms a pigment cup composed of pigment cells (PiC) and rhabdomeric photoreceptors (rPRC). The photoreceptors extend a distal segment made by microvillar projections (mi) for light detection. In the centre of the pigment cup is the vitreous body (vb). D: longitudinal muscle fibres are cut transversally displaying cross-sections of the sarcomere as well as of the sarcoplasmic reticulum that contacts the plasma membrane (inset). E: cross section of the distal part of the nephridia, highlighting the autocell junction (arrow) which forms a lumen. The lumen houses a bundle of motile cilia (identified by the 9+2 microtubule arrangement, inset), which are contributed by each cell of the nephridium, noted by the presence of basal bodies. Panels B to E are snapshots selected from the full volume and can be retrieved directly from the PlatyBrowser "Bookmark" function. Supplementary Figure 2A). Figure 2 gives a few examples of the achieved segmentation quality for epidermal cells (B), muscles (C) and nephridia (D). We measured nuclei sizes to range from 33.6 to 147.5 cubic microns, and cell sizes from 59.8 to 1224.6 cubic microns. Note that neurites in the neuropil have not been segmented, as they are not sufficiently preserved in the EM volume for automated segmentation. Figure 2: Segmentation of nuclei, cells, tissues and body parts.A. Cells and nuclei ar...

Highlights d A cellular atlas integrates gene expression and ultrastructure for an entire annelid d Morphometry of all segmented cells, nuclei, and chromatin categorizes cell classes d Molecular anatomy and projectome of head ganglionic nuclei and mushroom bodies d An open-source browser for multimodal big image data exploration and analysis

The intrinsic genetic program of a cell is not sufficient to explain all of the cell’s activities. External mechanical stimuli are increasingly recognized as determinants of cell behavior. In the epithelial folding event that constitutes the beginning of gastrulation in Drosophila, the genetic program of the future mesoderm leads to the establishment of a contractile actomyosin network that triggers apical constriction of cells and thereby tissue folding. However, some cells do not constrict but instead stretch, even though they share the same genetic program as their constricting neighbors. We show here that tissue-wide interactions force these cells to expand even when an otherwise sufficient amount of apical, active actomyosin is present. Models based on contractile forces and linear stress–strain responses do not reproduce experimental observations, but simulations in which cells behave as ductile materials with nonlinear mechanical properties do. Our models show that this behavior is a general emergent property of actomyosin networks in a supracellular context, in accordance with our experimental observations of actin reorganization within stretching cells.

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Facing the challenge of exploring and sharing multi-terabyte, multi-modal and multi-scale image data of heterogeneous dimensionality, we developed MoBIE, a Fiji plugin that provides rich visualization features to enable browsing data from numerous biomedical applications on a standard laptop computer. MoBIE also supports segmentations, associated measurements and annotations. Users can configure complex views of datasets, share them with collaborators, and use them for interactive figure panels. The MoBIE plugin also offers a convenient interface for converting data into compatible data formats; an additional Python library facilitates managing diverse MoBIE projects.

Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes are now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii, it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organisation of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources.

Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes is now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a convolutional neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii, it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organization of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources.

No abstract