Although CMOS fabrication has enabled a quick evolution in the design of high-density neural probes and neural-recording chips, the scaling and miniaturization of the complete data-acquisition systems has happened at a slower pace. This is mainly due to the complexity and the many requirements that change depending on the specific experimental settings. In essence, the fundamental challenge of a neural-recording system is getting the signals describing the largest possible set of neurons out of the brain and down to data storage for analysis. This requires a complete system optimization that considers the physical, electrical, thermal and signal-processing requirements, while accounting for available technology, manufacturing constraints and budget. Here we present a scalable and open-standards-based open-source data-acquisition system capable of recording from over 10,000 channels of raw neural data simultaneously. The components and their interfaces have been optimized to ensure robustness and minimum invasiveness in small-rodent electrophysiology.

High-resolution extracellular electrophysiology is the gold standard for recording spikes from distributed neural populations, and is especially powerful when combined with optogenetics for manipulation of specific cell types with high temporal resolution. We integrated these approaches into prototype Neuropixels Opto probes, which combine electronic and photonic circuits. These devices pack 960 electrical recording sites and two sets of 14 light emitters onto a 1 cm shank, allowing spatially addressable optogenetic stimulation with blue and red light. In mouse cortex, Neuropixels Opto probes delivered high-quality recordings together with spatially addressable optogenetics, differentially activating or silencing neurons at distinct cortical depths. In mouse striatum and other deep structures, Neuropixels Opto probes delivered efficient optotagging, facilitating the identification of two cell types in parallel. Neuropixels Opto probes represent an unprecedented tool for recording, identifying, and manipulating neuronal populations.

Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capacities is hampered by technical challenges. The key barriers to accelerating scientific discovery correspond to the FAIR data principles: findability, global access to data, software interoperability, and reproducibility/re-usability. We conducted a hackathon dedicated to making strides in those steps. This manuscript is a technical report summarizing these achievements, and we hope serves as an example of the effectiveness of focused, deliberate hackathons towards the advancement of our quickly-evolving field.

The accumulation of amyloid-beta (Abeta) into plaques is a hallmark feature of Alzheimer’s disease (AD). While amyloid precursor protein (APP)-related proteins are found in most organisms, only Abeta fragments from human APP have been shown to induce amyloid deposits and progressive neurodegeneration. Therefore, it was suggested that neurotoxic effects are a specific property of human Abeta. Here we show that Abeta fragments derived from the Drosophila orthologue APPL aggregate into intracellular fibrils, amyloid deposits, and cause age-dependent behavioral deficits and neurodegeneration. We also show that APPL can be cleaved by a novel fly beta-secretase-like enzyme. This suggests that Abeta-induced neurotoxicity is a conserved function of APP proteins whereby the lack of conservation in the primary sequence indicates that secondary structural aspects determine their pathogenesis. In addition, we found that the behavioral phenotypes precede extracellular amyloid deposit formation, supporting results that intracellular Abeta plays a key role in AD.

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

The vast majority of the adult fly ventral nerve cord is composed of 34 hemilineages, which are clusters of lineally related neurons. Neurons in these hemilineages use one of the three fast-acting neurotransmitters (acetylcholine, GABA, or glutamate) for communication. We generated a comprehensive neurotransmitter usage map for the entire ventral nerve cord. We did not find any cases of neurons using more than one neurotransmitter, but found that the acetylcholine specific gene ChAT is transcribed in many glutamatergic and GABAergic neurons, but these transcripts typically do not leave the nucleus and are not translated. Importantly, our work uncovered a simple rule: All neurons within a hemilineage use the same neurotransmitter. Thus, neurotransmitter identity is acquired at the stem cell level. Our detailed transmitter- usage/lineage identity map will be a great resource for studying the developmental basis of behavior and deciphering how neuronal circuits function to regulate behavior.

Mbd3, a member of nucleosome remodeling and deacetylase (NuRD) co-repressor complex, was previously identified as an inhibitor for deterministic induced pluripotent stem cell (iPSC) reprogramming, where up to 100% of donor cells successfully complete the process. NuRD can assume multiple mutually exclusive conformations, and it remains unclear whether this deterministic phenotype can be attributed to a specific Mbd3/NuRD subcomplex. Moreover, since complete ablation of Mbd3 blocks somatic cell proliferation, we aimed to explore functionally relevant alternative ways to neutralize Mbd3-dependent NuRD activity. We identify Gatad2a, a NuRD-specific subunit, whose complete deletion specifically disrupts Mbd3/NuRD repressive activity on the pluripotency circuitry during iPSC differentiation and reprogramming without ablating somatic cell proliferation. Inhibition of Gatad2a facilitates deterministic murine iPSC reprogramming within 8 days. We validate a distinct molecular axis, Gatad2a-Chd4-Mbd3, within Mbd3/NuRD as being critical for blocking reestablishment of naive pluripotency and further highlight signaling-dependent and post-translational modifications of Mbd3/NuRD that influence its interactions and assembly.

Reconstructing a connectome from an EM dataset often requires a large effort of proofreading automatically generated segmentations. While many tools exist to enable tracing or proofreading, recent advances in EM imaging and segmentation quality suggest new strategies and pose unique challenges for tool design to accelerate proofreading. Namely, we now have access to very large multi-TB EM datasets where (1) many segments are largely correct, (2) segments can be very large (several GigaVoxels), and where (3) several proofreaders and scientists are expected to collaborate simultaneously. In this paper, we introduce NeuTu as a solution to efficiently proofread large, high-quality segmentation in a collaborative setting. NeuTu is a client program of our high-performance, scalable image database called DVID so that it can easily be scaled up. Besides common features of typical proofreading software, NeuTu tames unprecedentedly large data with its distinguishing functions, including: (1) low-latency 3D visualization of large mutable segmentations; (2) interactive splitting of very large false merges with highly optimized semi-automatic segmentation; (3) intuitive user operations for investigating or marking interesting points in 3D visualization; (4) visualizing proofreading history of a segmentation; and (5) real-time collaborative proofreading with lock-based concurrency control. These unique features have allowed us to manage the workflow of proofreading a large dataset smoothly without dividing them into subsets as in other segmentation-based tools. Most importantly, NeuTu has enabled some of the largest connectome reconstructions as well as interesting discoveries in the fly brain.