A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.

Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience. VIDEO ABSTRACT.

Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy.

Motivation: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order. Results: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems. Availability and Implementation: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldlab/section-sort, http://github.com/saalfeldlab/z-spacing and http://github.com/saalfeldlab/z-spacing-spark. Contact: saalfelds@janelia.hhmi.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Graphical model for joint segmentation and tracking of multiple dividing cells.

MOTIVATION: To gain fundamental insight into the development of embryos, biologists seek to understand the fate of each and every embryonic cell. For the generation of cell tracks in embryogenesis, so-called tracking-by-assignment methods are flexible approaches. However, as every two-stage approach, they suffer from irrevocable errors propagated from the first stage to the second stage, here from segmentation to tracking. It is therefore desirable to model segmentation and tracking in a joint holistic assignment framework allowing the two stages to maximally benefit from each other. RESULTS: We propose a probabilistic graphical model, which both automatically selects the best segments from a time series of oversegmented images/volumes and links them across time. This is realized by introducing intra-frame and inter-frame constraints between conflicting segmentation and tracking hypotheses while at the same time allowing for cell division. We show the efficiency of our algorithm on a challenging 3D+t cell tracking dataset from Drosophila embryogenesis and on a 2D+t dataset of proliferating cells in a dense population with frequent overlaps. On the latter, we achieve results significantly better than state-of-the-art tracking methods. AVAILABILITY AND IMPLEMENTATION: Source code and the 3D+t Drosophila dataset along with our manual annotations will be freely available on http://hci.iwr.uni-heidelberg.de/MIP/Research/tracking/