The identification of synaptic partners is challenging in dense nerve bundles, where many processes occupy regions beneath the resolution of conventional light microscopy. To address this difficulty, we have developed GRASP, a system to label membrane contacts and synapses between two cells in living animals. Two complementary fragments of GFP are expressed on different cells, tethered to extracellular domains of transmembrane carrier proteins. When the complementary GFP fragments are fused to ubiquitous transmembrane proteins, GFP fluorescence appears uniformly along membrane contacts between the two cells. When one or both GFP fragments are fused to synaptic transmembrane proteins, GFP fluorescence is tightly localized to synapses. GRASP marks known synaptic contacts in C. elegans, correctly identifies changes in mutants with altered synaptic specificity, and can uncover new information about synaptic locations as confirmed by electron microscopy. GRASP may prove particularly useful for defining connectivity in complex nervous systems.

Precise control of sister chromatid separation is essential for the accurate transmission of genetic information. Sister chromatids must remain linked to each other from the time of DNA replication until the onset of chromosome segregation, when the linkage must be promptly dissolved. Recent studies suggest that the machinery that is responsible for the destruction of mitotic cyclins also degrades proteins that play a role in maintaining sister chromatid linkage, and that this machinery is regulated by the spindle-assembly checkpoint. Studies on these problems in budding yeast are hampered by the inability to resolve its chromosomes by light or electron microscopy.

Proper functioning of organelles such as the ER or the Golgi apparatus requires luminal accumulation of Ca(2+) at high concentrations. Here we describe a ratiometric low-affinity Ca(2+) sensor of the GFP-aequorin protein (GAP) family optimized for measurements in high-Ca(2+) concentration environments. Transgenic animals expressing the ER-targeted sensor allowed monitoring of Ca(2+) signals inside the organelle. The use of the sensor was demonstrated under three experimental paradigms: (1) ER Ca(2+) oscillations in cultured astrocytes, (2) ex vivo functional mapping of cholinergic receptors triggering ER Ca(2+) release in acute hippocampal slices from transgenic mice, and (3) in vivo sarcoplasmic reticulum Ca(2+) dynamics in the muscle of transgenic flies. Our results provide proof of the suitability of the new biosensors to monitor Ca(2+) dynamics inside intracellular organelles under physiological conditions and open an avenue to explore complex Ca(2+) signaling in animal models of health and disease.

When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states.

We found that glia secrete myoglianin, a TGF-β ligand, to instruct developmental neural remodeling in Drosophila. Glial myoglianin upregulated neuronal expression of an ecdysone nuclear receptor that triggered neurite remodeling following the late-larval ecdysone peak. Thus glia orchestrate developmental neural remodeling not only by engulfment of unwanted neurites but also by enabling neuron remodeling.

We provide evidence for a prodegenerative, glial-derived signaling framework in the Drosophila neuromuscular system that includes caspase and mitochondria-dependent signaling. We demonstrate that Drosophila TNF-α (eiger) is expressed in a subset of peripheral glia, and the TNF-α receptor (TNFR), Wengen, is expressed in motoneurons. NMJ degeneration caused by disruption of the spectrin/ankyrin skeleton is suppressed by an eiger mutation or by eiger knockdown within a subset of peripheral glia. Loss of wengen in motoneurons causes a similar suppression providing evidence for glial-derived prodegenerative TNF-α signaling. Neither JNK nor NFκβ is required for prodegenerative signaling. However, we provide evidence for the involvement of both an initiator and effector caspase, Dronc and Dcp-1, and mitochondrial-dependent signaling. Mutations that deplete the axon and nerve terminal of mitochondria suppress degeneration as do mutations in Drosophila Bcl-2 (debcl), a mitochondria-associated protein, and Apaf-1 (dark), which links mitochondrial signaling with caspase activity in other systems.

BACKGROUND: In many animals, the first few hours of life proceed with little or no transcription, and developmental regulation at these early stages is dependent on maternal cytoplasm rather than the zygotic nucleus. Translational control is critical for early Drosophila embryogenesis and is exerted mainly at the gene level. To understand post-transcriptional regulation during Drosophila early embryonic development, we used sucrose polysomal gradient analyses and GeneChip analysis to illustrate the translation profile of individual mRNAs. RESULTS: We determined ribosomal density and ribosomal occupancy of over 10,000 transcripts during the first ten hours after egg laying. CONCLUSION: We report the extent and general nature of gene regulation at the translational level during early Drosophila embryogenesis on a genome-wide basis. The diversity of the translation profiles indicates multiple mechanisms modulating transcript-specific translation. Cluster analyses suggest that the genes involved in some biological processes are co-regulated at the translational level at certain developmental stages.

Cell and tissue specific gene expression is a defining feature of embryonic development in multi-cellular organisms. However, the range of gene expression patterns, the extent of the correlation of expression with function, and the classes of genes whose spatial expression are tightly regulated have been unclear due to the lack of an unbiased, genome-wide survey of gene expression patterns.

Neuroscience research is becoming increasingly more collaborative and interdisciplinary with partnerships between industry and academia and insights from fields beyond neuroscience. In the age of institutional initiatives and multi-investigator collaborations, scientists from around the world shared their perspectives on the effectiveness of large-scale collaborations versus single-lab, hypothesis-driven science.

Connectomics is a subfield of neuroscience that aims to map the brain’s intricate wiring diagram. Accurate neuron segmentation from microscopy volumes is essential for automating connectome reconstruction. However, current state-of-the-art algorithms use image-based convolutional neural networks that are limited to local neuron shape context. Thus, we introduce a new framework that reasons over global neuron shape with a novel point affinity transformer. Our framework embeds a (multi-)neuron point cloud into a fixed-length feature set from which we can decode any point pair affinities, enabling clustering neuron point clouds for automatic proofreading. We also show that the learned feature set can easily be mapped to a contrastive embedding space that enables neuron type classification using a simple KNN classifier. Our approach excels in two demanding connectomics tasks: proofreading segmentation errors and classifying neuron types. Evaluated on three benchmark datasets derived from state-of-the-art connectomes, our method outperforms point transformers, graph neural networks, and unsupervised clustering baselines.