Biology subjects

Moriyasu, D.

Publications and source records attributed to Moriyasu, D..

3 recordsLinked to original sources

bioRxiv2026

Multiplexed mapping of long-range projections with FluoCode, a combinatorial fluorescent barcoding system

Comprehensive mapping of long-range projections remains a major challenge in neuroscience. Conventional virus tracing labels only one or a few pathways per animal, and inter-individual comparison requires registering whole-brain images to a standard atlas. However, inter-individual variability in morphology and wiring limits fine-scale mapping. Here we report "FluoCode", a combinatorial fluorescent barcoding system for multiplexed projection mapping within single animals. We developed adeno-associated virus (AAV) vectors that express two of seven different fluorescent proteins, yielding 21 distinct color codes, and a "FluoCode Reader" that automatically classifies neurite signals by color combinations and morphology. Using FluoCode, we mapped projections from up to 21 cortical areas to subcortical regions in individual mice, revealing fine-scale topographic maps in small subcortical nuclei. In the marmoset, mapping from the primary visual cortex demonstrated topographic projections in higher visual areas and thalamic nuclei. FluoCode thus enables fine-scale, within-animal comparison of long-range projections in rodents and primates.

neuroscience↗

bioRxiv2026

Visualization of neuronal morphology of the suprachiasmatic nucleus vasopressin neurons by Cre/FLPe-based genetic two-step sparse labelling - GT-SPARCL

Resolving the morphology of individual neurons in densely packed brain regions remains challenging. Sparse labeling is essential for delineating cellular architecture, yet achieving reproducible low-density labeling e.g. < 1% has been a persistent technical hurdle/problem. We present a genetically encoded two-step strategy, GT-SPARCL (Genetic Two-Step Sparse Cre/FLPe Combination Labeling), which leverages two independent stochastic recombination events to deliver stable, tunable low-density labeling in mice. As a proof of concept, we applied this method to visualizing arginine vasopressin (AVP) neurons in the suprachiasmatic nucleus (SCN), the master circadian pacemaker composed of [~]10,000 neurons per side in mice. Using two-photon imaging of whole-mount SCN blocks, we reconstructed individual AVP neurons and uncovered previously under-resolved morphological heterogeneity. Based on axonal trajectories, we distinguished five structural types, including subclasses with commissural projections to the contralateral SCN and others extending projections beyond the nucleus. The majority ([~]70%) exhibited projections both within and outside the ipsilateral SCN, whereas the second most dominant subset ([~]20%) projected exclusively outside the SCN, representing an output-biased type. In contrast, neurons projecting exclusively within the ipsilateral SCN were exceptionally rare, suggesting that "dedicated" local-circuit AVP neurons do not form a major structural subtype. Collectively, our data indicate that AVP neurons are not structurally uniform but instead comprise diverse projection-defined subtypes, implying subtype-depen-dent contributions to intra-SCN communication, bilateral coupling, and circadian output. Beyond the SCN, our GT-SPARCL method may be applicable for achieving low-density labelling of neurons that can be defined by other specific Cre mouse lines.

neuroscience↗

bioRxiv2022

Automated neuronal reconstruction with super-multicolour fluorescence imaging

Fluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we established stochastic "super-multicolour" labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron was labelled with a unique combination of fluorescent proteins, which were then imaged and separated by linear unmixing. We also established an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder). To classify colour combinations, we used a newly developed unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space were clustered based on a given threshold distance. Our new strategy allows for the reconstruction of neurites for up to hundreds of neurons at a millimetre scale without manual tracing.

neuroscience↗