Gapr for large-scale collaborative single-neuron reconstruction.

Whole-brain analysis of single-neuron morphology is crucial for unraveling the complex structure of the brain. However, large-scale neuron reconstruction from terabyte and even petabyte data of mammalian brains generated by state-of-the-art light microscopy is a daunting task. Here, we developed 'Gapr' (Gapr accelerates projectome reconstruction) that streamlines deep learning-based automatic reconstruction, 'automatic proofreading' that reduces human workloads at high-confidence sites, and high-throughput collaborative proofreading by crowd users through the Internet. Furthermore, Gapr offers a seamless user interface that ensures high proofreading speed per annotator, on-demand conversion for handling large datasets, flexible workflows tailored to diverse datasets and rigorous error tracking for quality control. Finally, we demonstrated Gapr's efficacy by reconstructing over 4,000 neurons in mouse brains, revealing the morphological diversity in cortical interneurons and hypothalamic neurons. Here, we present Gapr as a solution for large-scale single-neuron reconstruction projects.

Single-neuron projectomes of mouse paraventricular hypothalamic nucleus oxytocin neurons reveal mutually exclusive projection patterns.

Oxytocin (OXT) plays important roles in autonomic control and behavioral modulation. However, it is unknown how the projection patterns of OXT neurons align with underlying physiological functions. Here, we present the reconstructed single-neuron, whole-brain projectomes of 264 OXT neurons of the mouse paraventricular hypothalamic nucleus (PVH) at submicron resolution. These neurons hierarchically clustered into two groups, with distinct morphological and transcriptional characteristics and mutually exclusive projection patterns. Cluster 1 (177 neurons) axons terminated exclusively in the median eminence (ME) and have few collaterals terminating within hypothalamic regions. By contrast, cluster 2 (87 neurons) sent wide-spread axons to multiple brain regions, but excluding ME. Dendritic arbors of OXT neurons also extended outside of the PVH, suggesting capability to sense signals and modulate target regions. These single-neuron resolution observations reveal distinct OXT subpopulations, provide comprehensive analysis of their morphology, and lay the structural foundation for better understanding the functional heterogeneity of OXT neurons.

Single-neuron analysis of dendrites and axons reveals the network organization in mouse prefrontal cortex.

The structures of dendrites and axons form the basis for the connectivity of neural network, but their precise relationship at single-neuron level remains unclear. Here we report the complete dendrite and axon morphology of nearly 2,000 neurons in mouse prefrontal cortex (PFC). We identified morphological variations of somata, dendrites and axons across laminar layers and PFC subregions and the general rules of somatodendritic scaling with cytoarchitecture. We uncovered 24 morphologically distinguishable dendrite subtypes in 1,515 pyramidal projection neurons and 405 atypical pyramidal projection neurons and spiny stellate neurons with unique axon projection patterns. Furthermore, correspondence analysis among dendrites, local axons and long-range axons revealed coherent morphological changes associated with electrophysiological phenotypes. Finally, integrative dendrite-axon analysis uncovered the organization of potential intra-column, inter-hemispheric and inter-column connectivity among projection neuron types in PFC. Together, our study provides a comprehensive structural repertoire for the reconstruction and analysis of PFC neural network.

Single-neuron projectome of mouse prefrontal cortex.

Prefrontal cortex (PFC) is the cognitive center that integrates and regulates global brain activity. However, the whole-brain organization of PFC axon projections remains poorly understood. Using single-neuron reconstruction of 6,357 mouse PFC projection neurons, we identified 64 projectome-defined subtypes. Each of four previously known major cortico-cortical subnetworks was targeted by a distinct group of PFC subtypes defined by their first-order axon collaterals. Further analysis unraveled topographic rules of soma distribution within PFC, first-order collateral branch point-dependent target selection and terminal arbor distribution-dependent target subdivision. Furthermore, we obtained a high-precision hierarchical map within PFC and three distinct functionally related PFC modules, each enriched with internal recurrent connectivity. Finally, we showed that each transcriptome subtype corresponds to multiple projectome subtypes found in different PFC subregions. Thus, whole-brain single-neuron projectome analysis reveals organization principles of axon projections within and outside PFC and provides the essential basis for elucidating neuronal connectivity underlying diverse PFC functions.

Spatiotemporal single-cell analysis of gene expression in the mouse suprachiasmatic nucleus.

Mammalian circadian behaviors are orchestrated by the suprachiasmatic nucleus (SCN) in the ventral hypothalamus, but the number of SCN cell types and their functional roles remain unclear. We have used single-cell RNA-sequencing to identify the basic cell types in the mouse SCN and to characterize their circadian and light-induced gene expression patterns. We identified eight major cell types, with each type displaying a specific pattern of circadian gene expression. Five SCN neuronal subtypes, each with specific combinations of markers, differ in their spatial distribution, circadian rhythmicity and light responsiveness. Through a complete three-dimensional reconstruction of the mouse SCN at single-cell resolution, we obtained a standardized SCN atlas containing the spatial distribution of these subtypes and gene expression. Furthermore, we observed heterogeneous circadian gene expression between SCN neuron subtypes. Such a spatiotemporal pattern of gene regulation within the SCN may have an important function in the circadian pacemaker.

Construction of microRNA- and microRNA*-mediated regulatory networks in plants.

The critical biological roles of microRNAs (miRNAs) have been well recognized. However, knowledge on the regulatory activities of miRNA*s is limited. Although several studies pointed to the capacity of this small RNA species to repress target genes in animals, few related analyses were performed in plants. Here, we set out to uncover the repressive effects of miRNA*s on their targets in both Arabidopsis and rice. Systemic identification of miRNA*s was performed through secondary structure-based predictions and expression level-based verification. The targets of the miRNA*s were predicted and further filtered based on degradome sequencing data, resulting in comprehensive miRNA*--target lists with high reliability. Besides, comprehensive miRNA--target lists were also obtained. The phenomenon that one transcript was targeted by two or more miRNA(*)s was observed, which was defined as co-regulation. Finally, comprehensive miRNA- and miRNA*-mediated regulatory networks were constructed. Further investigation of some specific subnetworks implied the utility of these networks for biologists. This study could broaden the current understanding of miRNA gene-mediated regulation in plants.

PmiRKB: a plant microRNA knowledge base.

MicroRNAs (miRNAs), one type of small RNAs (sRNAs) in plants, play an essential role in gene regulation. Several miRNA databases were established; however, successively generated new datasets need to be collected, organized and analyzed. To this end, we have constructed a plant miRNA knowledge base (PmiRKB) that provides four major functional modules. In the 'SNP' module, single nucleotide polymorphism (SNP) data of seven Arabidopsis (Arabidopsis thaliana) accessions and 21 rice (Oryza sativa) subspecies were collected to inspect the SNPs within pre-miRNAs (precursor microRNAs) and miRNA-target RNA duplexes. Depending on their locations, SNPs can affect the secondary structures of pre-miRNAs, or interactions between miRNAs and their targets. A second module, 'Pri-miR', can be used to investigate the tissue-specific, transcriptional contexts of pre- and pri-miRNAs (primary microRNAs), based on massively parallel signature sequencing data. The third module, 'MiR-Tar', was designed to validate thousands of miRNA-target pairs by using parallel analysis of RNA end (PARE) data. Correspondingly, the fourth module, 'Self-reg', also used PARE data to investigate the metabolism of miRNA precursors, including precursor processing and miRNA- or miRNA*-mediated self-regulation effects on their host precursors. PmiRKB can be freely accessed at http://bis.zju.edu.cn/pmirkb/.

High-throughput degradome sequencing can be used to gain insights into microRNA precursor metabolism.

The approximately 21 nucleotide microRNAs (miRNAs) are one type of well-defined small RNA species, and they play critical roles in various biological processes in organisms. In plants, most miRNAs exert repressive regulation on their targets through cleavage, and a number of miRNA-target pairs have been validated either by modified 5' RACE (rapid amplification of cDNA ends), or by newly developed high-throughput strategies. All these data have greatly advanced our understanding of the regulatory roles of plant miRNAs. On the other hand, deep insights into miRNA precursor processing, and miRNA- or miRNA*-mediated self-regulation of their host precursors could be gained from high-throughput degradome sequencing data, based on the general framework of miRNA generation in plants. Here, the focus is on the recent research progress on this issue, and several interesting points were raised.