3D reconstruction of cell nuclei in a full Drosophila brain

We reconstructed all cell nuclei in a 3D image of a Drosophila brain acquired by serial section electron microscopy (EM). The total number of nuclei is approximately 133,000, at least 87% of which belong to neurons. Neuronal nuclei vary from several hundred down to roughly 5 cubic micrometers. Glial nuclei can be even smaller. The optic lobes contain more than two times the number of cells than the central brain. Our nuclear reconstruction serves as a spatial map and index to the cells in a Drosophila brain.

A three-organelle complex made by wrappER contacts with peroxisome and mitochondria responds to liver lipid flux changes

Hepatic lipid homeostasis depends on intracellular pathways that respire fatty acid (FA) in peroxisomes and mitochondria and on systemic pathways that secrete FA into the bloodstream, either free or condensed in very-low-density lipoprotein (VLDL) triglycerides. These systemic and intracellular pathways are interdependent, but it is unclear whether and how they integrate into a single cellular circuit. Here, we report that mouse liver wrappER, a distinct ER compartment with apparent FA- and VLDL-secretion functions, connects peroxisomes and mitochondria. Correlative light electron microscopy, quantitative serial section electron tomography, and 3D organelle reconstruction analysis show that the number of peroxisome-wrappER-mitochondria complexes changes throughout fasting-to-feeding transitions and doubles when VLDL synthesis stops following acute genetic ablation of Mttp in the liver. Quantitative proteomic analysis of peroxisome-wrappER-mitochondria complex-enriched fractions indicates that the loss of Mttp upregulates global FA β-oxidation, thereby integrating the dynamics of this three-organelle association into hepatic FA flux responses. Therefore, liver lipid homeostasis occurs through the convergence of systemic and intracellular FA-elimination pathways in the peroxisome-wrappER-mitochondria complex.

The location of InsP3 receptors in Purkinje cells of murine cerebellum does not supports a direct interaction in the transfer of calcium ions between ER and mitochondria

The inositol-3-phosphate receptors (IP3Rs) of cerebellar Purkinje cells are located in abundant, large stacks of endoplasmic reticulum (ER) cisternae. Using thin section electron microscopy, we identify very frequent associations of the ER stacks with mitochondria. The associations have two components: a single, close ER-mitochondria contact on one side to the stack, and multiple layers of ER cisternae decorated by IP3Rs receptors on the side away from the mitochondria. Due to their location in the stacks, IP3Rs are never in contact with the mitochondria, although they are in their vicinity. We conclude that transfer of Ca2+ between ER and mitochondria is not directly mediated by IP3Rs, but is based on mitochondrial Ca2+ uptake from the local cytoplasmic spikes during IP3Rs’ activity.

Synaptic and mitochondrial plasticity associated with fear memory revealed by deep learning-based 3D reconstruction

Reconstruction of serial section electron microscopy (ssEM) data greatly facilitates neuroscience research, but such reconstruction is computationally expensive. Informative data about physiological functions can in theory be obtained from ssEM datasets by extracting distinct cellular structures without large-scale reconstruction, but an efficient method is needed to accomplish this. Here, we developed a Region-CNN (R-CNN) based deep learning method to identify, segment, and reconstruct synapses and mitochondria from ssEM data. We applied this method to explore the changes in synaptic and mitochondrial configuration in the auditory cortex of mice subjected to auditory fear conditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we found that fear conditioning significantly increases the number while decreasing the size of mitochondria, and also noted that it promoted the formation of multi-contact synapses comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Combinatorial modeling indicated that such multi-dendritic synapses increased information storage capacity of new synapses by over 50%, representing a synaptic memory engram. Our method achieved high accuracy and speed in synapse and mitochondrion extraction, and its application revealed structural and functional insights about cellular engrams associated with fear conditioning.

Distinct Synaptic Transfer Functions in Same-Type Photoreceptors

Many sensory systems use ribbon-type synapses to transmit their signals to downstream circuits. The properties of this synaptic transfer fundamentally dictate which aspects in the original stimulus will be accentuated or suppressed, thereby partially defining the detection limits of the circuit. Accordingly, sensory neurons have evolved a wide variety of ribbon geometries and vesicle pool properties to best support their diverse functional requirements. However, the need for diverse synaptic functions does not only arise across neuron types, but also within. Here we show that UV-cones, a single type of photoreceptor of the larval zebrafish eye, exhibit striking differences in their synaptic ultrastructure and consequent calcium to glutamate transfer function depending on their location in the eye. We arrive at this conclusion by combining serial section electron microscopy and simultaneous “dual-colour” 2-photon imaging of calcium and glutamate signals from the same synapse in vivo. We further use the functional dataset to fit a cascade-like model of the ribbon synapse with different vesicle pool sizes, transfer rates and other synaptic properties. Exploiting recent developments in simulation-based inference, we obtain full posterior estimates for the parameters and compare these across different retinal regions. The model enables us to extrapolate to new stimuli and to systematically investigate different response behaviours of various ribbon configurations. We also provide an interactive, easy-to-use version of this model as an online tool. Overall, we show that already on the synaptic level of single neuron types there exist highly specialized mechanisms which are advantageous for the encoding of different visual features.

Reducing Manual Operation Time to Obtain a Segmentation Learning Model for Volume Electron Microscopy Using Stepwise Deep Learning With Manual Correction

Three-dimensional (3D) observation of a biological sample using serial-section electron microscopy is widely used. However, organelle segmentation requires a significant amount of manual time. Therefore, several studies have been conducted to improve their efficiency. One such promising method is 3D deep learning (DL), which is highly accurate. However, the creation of training data for 3D DL still requires manual time and effort. In this study, we developed a highly efficient integrated image segmentation tool that includes stepwise DL with manual correction. The tool has four functions: efficient tracers for annotation, model training/inference for organelle segmentation using a lightweight convolutional neural network, efficient proofreading, and model refinement. We applied this tool to increase the training data step by step (stepwise annotation method) to segment the mitochondria in the cells of the cerebral cortex. We found that the stepwise annotation method reduced the manual operation time by one-third compared with that of the fully manual method, where all the training data were created manually. Moreover, we demonstrated that the F1 score, the metric of segmentation accuracy, was 0.9 by training the 3D DL model with these training data. The stepwise annotation method using this tool and the 3D DL model improved the segmentation efficiency for various organelles.

The organization of the gravity-sensing system in zebrafish

Motor circuits develop in sequence from those governing fast movements to those governing slow. Here we examine whether upstream sensory circuits are organized by similar principles. Using serial-section electron microscopy in larval zebrafish, we generated a complete map of the gravity-sensing (utricular) system from the inner ear to the brainstem. We find that both sensory tuning and developmental sequence are organizing principles of vestibular topography. Patterned rostrocaudal innervation from hair cells to afferents creates a directional tuning map in the utricular ganglion, forming segregated pathways for rostral and caudal tilt. Furthermore, the mediolateral axis of the ganglion is linked to both developmental sequence and temporal kinetics. Early-born pathways carrying phasic information preferentially excite fast escape circuits, whereas later-born pathways carrying tonic signals excite slower postural and oculomotor circuits. These results demonstrate that vestibular circuits are organized by tuning direction and kinetics, aligning them with downstream motor circuits and behaviors.

Eye-Region Specific Ribbon Tuning Supports Distinct Modes of Synaptic Transmission in Same-Type Cone-Photoreceptors

SummaryMany sensory systems use ribbon-type synapses to transmit their signals to downstream circuits. The properties of this synaptic transfer fundamentally dictate which aspects in the original stimulus will be accentuated or suppressed, thereby partially defining the detection limits of the circuit. Accordingly, sensory neurons have evolved a wide variety of ribbon geometries and vesicle pool properties to best support their diverse functional requirements. However, the need for diverse synaptic functions does not only arise across neuron types, but also within. Here we show that UV-cones, a single type of photoreceptor of the larval zebrafish eye, exhibit striking differences in their synaptic ultrastructure and consequent calcium to glutamate transfer function depending on their location in the eye. We arrive at this conclusion by combining serial section electron microscopy and simultaneous “dual-colour” 2-photon imaging of calcium and glutamate signals from the same synapse in vivo. We further use the functional dataset to fit a cascade-like model of the ribbon synapse with different vesicle pool sizes, transfer rates and other synaptic properties. Exploiting recent developments in simulation-based inference, we obtain full posterior estimates for the parameters and compare these across different retinal regions. The model enables us to extrapolate to new stimuli and to systematically investigate different response behaviours of various ribbon configurations. We also provide an interactive, easy-to-use version of this model as an online tool. Overall, we show that already on the synaptic level of single neuron types there exist highly specialized mechanisms which are advantageous for the encoding of different visual features.