Aphids are the leading pests in agricultural crops. A large-scale sequencing of 40,904 ESTs from the pea aphid Acyrthosiphon pisum was carried out to define a catalog of 12,082 unique transcripts. A strong AT bias was found, indicating a compositional shift between Drosophila melanogaster and A. pisum. An in silico profiling analysis characterized 135 transcripts specific to pea-aphid tissues (relating to bacteriocytes and parthenogenetic embryos). This project is the first to address the genetics of the Hemiptera and of a hemimetabolous insect.

High-density silicon probes have transformed neuroscience by enabling large-scale neural recordings at single-cell resolution. However, existing technologies have provided limited functionality in nonhuman primates (NHPs) such as macaques. In the present report, we describe the design, fabrication and performance of Neuropixels 1.0 NHP, a high-channel electrode array designed to enable large-scale acute recording throughout large animal brains. The probe features 4,416 recording sites distributed along a 45-mm shank. Experimenters can programmably select 384 recording channels, enabling simultaneous multi-area recording from thousands of neurons with single or multiple probes. This technology substantially increases scalability and recording access relative to existing technologies and enables new classes of experiments that involve electrophysiological mapping of brain areas at single-neuron and single-spike resolution, measurement of spike-spike correlations between cells and simultaneous brain-wide recordings at scale.

Preprint: https://doi.org/10.1101/2023.02.01.526664 

The dense connectivity in the brain means that one neuron's activity can influence many others. To observe this interconnected system comprehensively, an aspiration within neuroscience is to record from as many neurons as possible at the same time. There are two useful routes toward this goal: one is to expand the spatial extent of functional imaging techniques, and the second is to use animals with small brains. Here we review recent progress toward imaging many neurons and complete populations of identified neurons in small vertebrates and invertebrates.

Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding neuronal computation. Here, we describe a system that allows high-channel-count recordings from a small volume of neuronal tissue using a lightweight signal multiplexing headstage that permits free behavior of small rodents. The system integrates multishank, high-density recording silicon probes, ultraflexible interconnects, and a miniaturized microdrive. These improvements allowed for simultaneous recordings of local field potentials and unit activity from hundreds of sites without confining free movements of the animal. The advantages of large-scale recordings are illustrated by determining the electroanatomic boundaries of layers and regions in the hippocampus and neocortex and constructing a circuit diagram of functional connections among neurons in real anatomic space. These methods will allow the investigation of circuit operations and behavior-dependent interregional interactions for testing hypotheses of neural networks and brain function.

Matriglycan is a linear glycan (xylose-β1,3-glucuronate), which binds proteins in the extracellular matrix that contain laminin-globular domains and Lassa Fever Virus. It is indispensable for neuromuscular function. Matriglycan of insufficient length can cause muscular dystrophy with abnormal brain and eye development. LARGE1 (Like-acetylglucosaminyltransferase-1) uniquely synthesizes matriglycan on dystroglycan. The mechanism of matriglycan synthesis is not obvious from cryo-EM reconstructions of LARGE1. However, by reconstituting activity in vitro on recombinant prodystroglycan we show that the presence of the dystroglycan N-terminal domain (DGN), phosphorylated core M3, and a xylose-glucuronate primer are necessary for matriglycan polymerization by LARGE1. By introducing active site mutations, we demonstrate that LARGE1 processively polymerizes matriglycan on prodystroglycan, with its length regulated by the dystroglycan prodomain, DGN. Our enzymatic analysis of LARGE1 uncovers the mechanism of matriglycan synthesis on dystroglycan, which can form the basis for therapeutic strategies to treat matriglycan-deficient neuromuscular disorders and arenaviral infections.

The dramatic transformation from a larva to an adult must be accompanied by a coordinated activity of genes and hormones that enable an orchestrated transformation from larval to pupal/adult tissues. The maintenance of larval appendages and their subsequent transformation to appendages in holometabolous insects remains elusive at the developmental genetic level. Here the role of a key appendage patterning gene Distal-less (Dll) was examined in mid- to late-larval stages of the flour beetle, Tribolium castaneum. During late larval development, Dll was expressed in appendages in a similar manner as previously reported for the tobacco hornworm, Manduca sexta. Removal of this late Dll expression resulted in disruption of adult appendage patterning. Intriguingly, earlier removal resulted in dramatic loss of structural integrity and identity of larval appendages. A large amount of variability in appendage morphology was observed following Dll dsRNA injection, unlike larvae injected with dachshund dsRNA. These Dll dsRNA-injected larvae underwent numerous supernumerary molts, which could be terminated with injection of either JH methyltransferase or Methoprene-tolerant dsRNA. Apparently, the partial dedifferentiation of the appendages in these larvae acts to maintain high JH and, hence, prevents metamorphosis.

The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template. We provide a manually annotated gold standard that serves for evaluation of the registration framework involved in template generation and mapping. A method for registration quality assessment enables the automatic detection of registration errors, and a semi-automatic registration method allows one to correct registrations, which is a prerequisite for a high-quality, curated database of expression patterns. All computational methods are available within the larvalign software package: https://github.com/larvalign/larvalign/releases/tag/v1.0.

MOTIVATION: As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data.We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth.

RESULTS: We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data.

AVAILABILITY: All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.

SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. The core idea is to use data augmentations that preserve semantic meaning to generate synthetic examples of elements whose feature representations should be close to one another.
We demonstrate the utility of our method applied to nano-scale electron microscopy data, where even relatively small portions of animal brains can require terabytes of image data. Although supervised methods can be used to predict and identify known patterns of interest, the scale of the data makes it difficult to mine and analyze patterns that are not known a priori. We show the ability of our learned representation to enable query by example, so that if a scientist notices an interesting pattern in the data, they can be presented with other locations with matching patterns. We also demonstrate that clustering of data in the learned space correlates with biologically-meaningful distinctions. Finally, we introduce a visualization tool and software ecosystem to facilitate user-friendly interactive analysis and uncover interesting biological patterns. In short, our work opens possible new avenues in understanding of and discovery in large data sets, arising in domains such as EM analysis.