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

Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.