Biology subjects

Toyoshima, Y.

Publications and source records attributed to Toyoshima, Y..

7 recordsLinked to original sources

bioRxiv2026

Image-scanning light-sheet microscopy for high-speed volumetric imaging of complex biological dynamics

Volumetric fluorescence microscopy is a powerful method for studying complex biological systems because it enables comprehensive observation of structural and physiological dynamics. In particular, light-sheet microscopy (LSM) is a leading option for real-time volumetric fluorescence imaging as it combines high imaging speed, low phototoxicity, minimal photobleaching, high spatiotemporal resolution, and low computational burden. To capture fast biological events, various efforts have been made to improve the imaging speed of volumetric fluorescence microscopy, including LSM. However, existing approaches entail significant trade-offs that make routine volumetric imaging at and beyond video rates challenging under practical conditions. Here, we introduce image-scanning LSM, a method that substantially increases the volumetric imaging speed achievable with LSM while preserving key performance metrics, such as spatial resolution and photon efficiency, as well as accessibility. Our implementation, termed image-scanning oblique plane (ISOP) microscopy, enables volumetric fluorescence imaging at up to 1,000 volumes per second with submicrometer lateral spatial resolution. We demonstrate the broad utility of ISOP microscopy by recording and analyzing the dynamics of behaving and rapidly moving organisms.

bioengineering↗

bioRxiv2024

CGC1, a new reference genome for Caenorhabditis elegans

The original 100.3 Mb reference genome for Caenorhabditis elegans, generated from the wild-type laboratory strain N2, has been crucial for analysis of C. elegans since 1998 and has been considered complete since 2005. Unexpectedly, this long-standing reference was shown to be incomplete in 2019 by a genome assembly from the N2-derived strain VC2010. Moreover, genetically divergent versions of N2 have arisen over decades of research and hindered reproducibility of C. elegans genetics and genomics. Here we provide a 106.4 Mb gap-free, telomere-to-telomere genome assembly of C. elegans, generated from CGC1, an isogenic derivative of the N2 strain. We used improved long-read sequencing and manual assembly of 43 recalcitrant genomic regions to overcome deficiencies of prior N2 and VC2010 assemblies, and to assemble tandem repeat loci including a 772-kb sequence for the 45S rRNA genes. While many differences from earlier assemblies came from repeat regions, unique additions to the genome were also found. Of 19,972 protein-coding genes in the N2 assembly, 19,790 (99.1%) encode products that are unchanged in the CGC1 assembly. The CGC1 assembly also may encode 183 new protein-coding and 163 new ncRNA genes. CGC1 thus provides both a completely defined reference genome and corresponding isogenic wild-type strain for C. elegans, allowing unique opportunities for model and systems biology.

genomics↗

bioRxiv2023

WormTracer: A precise method for worm posture analysis using temporal continuity

This study introduces WormTracer, a novel algorithm designed to accurately quantify temporal evolution of worm postures. Unlike conventional methods that analyze individual images separately, WormTracer estimates worm centerlines within a sequence of images concurrently. This process enables the resolution of complex postures that are difficult to assess when treated as isolated images. The centerlines obtained through WormTracer exhibit higher accuracy compared to those acquired using conventional methods. By applying principal component analysis to the centerlines obtained by WormTracer, we successfully generated new eigenworms, a basic set of postures, that enables a more precise representation of worm postures than existing eigenworms. Author summaryC. elegans is a valuable model organism for comprehensive understanding of genes, neurons and behavior. Quantification of behavior is essential for clarifying these relationships, and posture information plays a crucial role in the analyses. However, accurately quantifying the posture of C. elegans from video images of worms is challenging, and while various methods have been developed to date, they have their own limitations. In this study, we developed an analytical tool called WormTracer, which can obtain worm centerlines more accurately than conventional methods, even when worms assume complex postures. Using this tool, we successfully obtained new eigenworms, basis postures of a worm, that can more accurately reproduce various postures than conventional eigenworms. WormTracer and the new eigenworms will be valuable assets for future quantitative studies on worm locomotion and sensorimotor behaviors.

bioinformatics↗

bioRxiv2022

Deducing ensemble dynamics and information flow from the whole-brain imaging data

Recent development of large-scale activity imaging of neuronal ensembles provides opportunities for understanding how activity patterns are generated in the brain and how information is transmitted between neurons or neuronal ensembles. However, methodologies for extracting the component properties that generate overall dynamics are still limited. In this study, the results of time-lapse 3D imaging (4D imaging) of head neurons of the nematode C. elegans were analyzed by hitherto unemployed methodologies. By combining time-delay embedding with independent component analysis, the whole-brain activities were decomposed to a small number of component dynamics. Results from multiple samples, where different subsets of neurons were observed, were further combined by matrix factorization, revealing common dynamics from neuronal activities that are apparently divergent across sampled animals. By this analysis, we could identify components that show common relationships across different samples and those that show relationships distinct between individual samples. We also constructed a network model building on time-lagged prediction models of synaptic communications. This was achieved by dimension reduction of 4D imaging data using the general framework gKDR (gradient kernel dimension reduction). The model is able to decompose basal dynamics of the network. We further extended the model by incorporating probabilistic distribution, resulting in models that we call gKDR-GMM and gKDR-GP. The models capture the overall relationships of neural activities and reproduce the stochastic but coordinated dynamics in the neural network simulation. By virtual manipulation of individual neurons and synaptic contacts in this model, information flow could be estimated from whole-brain imaging results.

neuroscience↗

bioRxiv2021

Small Vessel Diseases: 3D Characteristics of the Vasculature and White Matter

Cerebral small vessel disease (SVD) is associated with white matter hyperintensities (WMHs), thereby contributing to vascular dementia and movement disorder. However, the pathomechanisms responsible for WMH-related small vessel degeneration remain poorly understood due to the technical limitations of current methods. The aim of this study was to clarify the 2- and 3-dimensional (2D and 3D) pathological features of small vessels and white matter (WM) in the brains of patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), HTRA1-autosomal dominant disease (HTRA1-AD) and sporadic SVD (sSVD). From a cohort of 86 consecutive autopsied patients with SVDs, we retrieved those with genetically confirmed CADASIL and HTRA1-AD (three and three, respectively), and four with sSVD. We quantitatively evaluated WM and vascular changes in the frontal portion of the centrum semiovale and temporal lobe using conventional 2D and chemically cleared 3D analytical methods with light-sheet fluorescence microscopy. Quantitatively, the WM pathology, including the density of myelin, axons and gliosis, was most severe in CADASIL, but unexpectedly sSVD was second in order of severity, followed by HTRA1-AD. The density of clasmatodendrocytes, known to be irreversibly injured astrocytes, was considerably highest in HTRA1-AD. The vascular pathology, including arteriole and capillary sclerosis and the extent of the perivascular space, was most severe in CADASIL, whereas the density of smooth muscle actin (SMA) positivity was most decreased in HTRA1-AD. 3D immunohistochemistry for SMA demonstrated two distinct patterns of SMA loss within the vessels: (1) CADASIL and sSVD: diffuse loss, being prominent in small branches, (2) HTRA1-AD: selective loss in main branches. Overall, the extent of WM and vascular degeneration is most severe in CADASIL, whereas SMA loss is most evident in HTRA1-AD. These differences in the size and distribution of affected vessels may be related to the heterogeneous WM pathology and underlying pathomechanisms of SVD.

pathology↗

bioRxiv2019

An annotation dataset facilitates automatic annotation of whole-brain activity imaging of C. elegans

Annotation of cell identity is an essential process in neuroscience that allows for comparing neural activities across different animals. In C. elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal is limited in practice and comprehensive methods for cell annotation are required. Here we propose an efficient annotation method that can be integrated with the whole-brain imaging technique. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. The large positional variations illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters to tackle this problem. We also developed an automatic annotation method with human interaction functionality that facilitates annotation for whole-brain imaging.

neuroscience↗

bioRxiv2017

An ensemble learning approach to auto-annotation for whole-brain C. elegans imaging

Shifting from individual neuron analysis to whole-brain neural network analysis opens up new research opportunities for Caenorhabditis elegans (C. elegans). An automated data processing pipeline, including neuron detection, segmentation, tracking and annotation, will significantly improve the efficiency of analyzing whole-brain C. elegans imaging. The resulting large data sets may motivate new scientific discovery by exploiting many promising analysis tools for big data. In this study, we focus on the development of an automated annotation procedure. With only around 180 neurons in the central nervous system of a C. elegans, the annotation of each individual neuron still remains a major challenge because of the high density in space, similarity in neuron shape, unpredictable distortion of the worms head during motion, intrinsic variations during worm development, etc. We use an ensemble learning approach to achieve around 25% error for a test based on real experimental data. Also, we demonstrate the importance of exploring extra source of information for annotation other than the neuron positions.

bioinformatics↗