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.

Haploid larvae in non-mammalian vertebrates are lethal, with characteristic organ growth retardation collectively called 'haploid syndrome'. In contrast to mammals, whose haploid intolerance is attributed to imprinting misregulation, the cellular principle of haploidy-linked defects in non-mammalian vertebrates remains unknown. Here, we investigated cellular defects that disrupt the ontogeny of gynogenetic haploid zebrafish larvae. Unlike diploid control larvae, haploid larvae manifested unscheduled cell death at the organogenesis stage, attributed to haploidy-linked p53 upregulation. Moreover, we found that haploid larvae specifically suffered the gradual aggravation of mitotic spindle monopolarization during 1-3 days post-fertilization, causing spindle assembly checkpoint-mediated mitotic arrest throughout the entire body. High-resolution imaging revealed that this mitotic defect accompanied the haploidy-linked centrosome loss occurring concomitantly with the gradual decrease in larval cell size. Either resolution of mitotic arrest or depletion of p53 partially improved organ growth in haploid larvae. Based on these results, we propose that haploidy-linked mitotic defects and cell death are parts of critical cellular causes shared among vertebrates that limit the larval growth in the haploid state, contributing to an evolutionary constraint on allowable ploidy status in the vertebrate life cycle.

l-Lactate is a monocarboxylate produced during the process of cellular glycolysis and has long generally been considered a waste product. However, studies in recent decades have provided new perspectives on the physiological roles of l-lactate as a major energy substrate and a signaling molecule. To enable further investigations of the physiological roles of l-lactate, we have developed a series of high-performance (Δ/ = 15 to 30 ), intensiometric, genetically encoded green fluorescent protein (GFP)-based intracellular l-lactate biosensors with a range of affinities. We evaluated these biosensors in cultured cells and demonstrated their application in an preparation of brain tissue. Using these biosensors, we were able to detect glycolytic oscillations, which we analyzed and mathematically modeled.

Cholecystokinin-expressing interneurons (CCKIs) are hypothesized to shape pyramidal cell-firing patterns and regulate network oscillations and related network state transitions. To directly probe their role in the CA1 region, we silenced their activity using optogenetic and chemogenetic tools in mice. Opto-tagged CCKIs revealed a heterogeneous population, and their optogenetic silencing triggered wide disinhibitory network changes affecting both pyramidal cells and other interneurons. CCKI silencing enhanced pyramidal cell burst firing and altered the temporal coding of place cells: theta phase precession was disrupted, whereas sequence reactivation was enhanced. Chemogenetic CCKI silencing did not alter the acquisition of spatial reference memories on the Morris water maze but enhanced the recall of contextual fear memories and enabled selective recall when similar environments were tested. This work suggests the key involvement of CCKIs in the control of place-cell temporal coding and the formation of contextual memories.

The brain generates diverse neuron types which express unique homeodomain transcription factors (TFs) and assemble into precise neural circuits. Yet a mechanistic framework is lacking for how homeodomain TFs specify both neuronal fate and synaptic connectivity. We use Drosophila lamina neurons (L1-L5) to show the homeodomain TF Brain-specific homeobox (Bsh) is initiated in lamina precursor cells (LPCs) where it specifies L4/L5 fate and suppresses homeodomain TF Zfh1 to prevent L1/L3 fate. Subsequently, Bsh activates the homeodomain TF Apterous (Ap) in L4 in a feedforward loop to express the synapse recognition molecule DIP-β, in part by Bsh direct binding a DIP-β intron. Thus, homeodomain TFs function hierarchically: primary homeodomain TF (Bsh) first specifies neuronal fate, and subsequently acts with secondary homeodomain TF (Ap) to activate DIP-β, thereby generating precise synaptic connectivity. We speculate that hierarchical homeodomain TF function may represent a general principle for coordinating neuronal fate specification and circuit assembly.

Most mammalian cells prevent viral infection and proliferation by expressing various restriction factors and sensors that activate the immune system. While anti-human immunodeficiency virus type 1 (HIV-1) host restriction factors have been identified, most of them are antagonized by viral proteins. This has severely hindered their development in anti-HIV-1 therapy. Here, we describe CCHC-type zinc-finger-containing protein 3 (ZCCHC3) as a novel anti-HIV-1 factor that is not antagonized by viral proteins. ZCCHC3 suppresses production of HIV-1 and other retroviruses. We show that ZCCHC3 acts by binding to Gag nucleocapsid protein via zinc-finger motifs. This prevents interaction between the Gag nucleocapsid protein and viral genome and results in production of genome-deficient virions. ZCCHC3 also binds to the long terminal repeat on the viral genome via the middle-folded domain, sequestering the viral genome to P-bodies, which leads to decreased viral replication and production. Such a dual antiviral mechanism is distinct from that of any other known host restriction factors. Therefore, ZCCHC3 is a novel potential target in anti-HIV-1 therapy.

Machine learning models are only as good as the data to which they are fit. As such, it is always preferable to use as much data as possible in training models. What data can be used for fitting a model depends a lot on the formulation of the task. We introduce Hot-Distance, a novel segmentation target that incorporates the strength of signed boundary distance prediction with the flexibility of one-hot encoding, to increase the amount of usable training data for segmentation of subcellular structures in focused ion beam scanning electron microscopy (FIB-SEM).

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population’s mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of an evolving population subject to a standard serial dilution protocol. We find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but has a negligible effect when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape.

In the perception of color, wavelengths of light reflected off objects are transformed into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. We report the discovery of neurons in the Drosophila optic lobe with hue-selective properties, which enables circuit-level analysis of color processing. From our analysis of an electron microscopy volume of a whole Drosophila brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Our model predicts that recurrent connections in the circuit are critical for generating hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirm this prediction. Our findings reveal a circuit basis for hue selectivity in color vision.

We developed a significantly improved genetically encoded quantitative adenosine triphosphate (ATP) sensor to provide real-time dynamics of ATP levels in subcellular compartments. iATPSnFR2 is a variant of iATPSnFR1, a previously developed sensor that has circularly permuted super-folder GFP inserted between the ATP-binding helices of the ε-subunit of a bacterial F0-F1 ATPase. Optimizing the linkers joining the two domains resulted in a ∼ 5-6 fold improvement in the dynamic range compared to the previous generation sensor, with excellent discrimination against other analytes and affinity variants varying from 4 μM to 500 μM. A chimeric version of this sensor fused to either the HaloTag protein or a suitably spectrally separated fluorescent protein, provides a ratiometric readout allowing comparisons of ATP across cellular regions. Subcellular targeting of the sensor to nerve terminals reveals previously uncharacterized single synapse metabolic signatures, while targeting to the mitochondrial matrix allowed direct quantitative probing of oxidative phosphorylation dynamics.