Biology subjects

Sugawara, K.

Publications and source records attributed to Sugawara, K..

7 recordsLinked to original sources

bioRxiv2026

Automahout: an autonomous image analysis system for cell tracking

Cell tracking in time-lapse images is complex image analysis that involves detection, linking and proofreading, often requiring the algorithm to be tuned for every new dataset. We present Automahout, an autonomous agent for the Mastodon/ELEPHANT platform that carries out these operations. It looks at the data and tries existing models; where none fits, it annotates reference cells, trains a detector from those sparse annotations, reviews its output against them and feeds the corrections back until its own stopping criteria are met. It then links, checks the lineage and moves on. On three public datasets, a 2D fluorescence culture, a 2D phase-contrast culture and a 3D embryo, it produced lineages without human input and reached TRA scores of 0.95-0.96.

bioinformatics↗

bioRxiv2025

Mastodon: the Command Center for Large-Scale Lineage-Tracing Microscopy Datasets

Understanding development in living organisms requires following the divisions, movements, and fates of cells across developing systems. While advances in microscopy have enabled whole-embryo imaging at the cellular level, extracting and analyzing cell lineages from these massive datasets remains a significant computational challenge. We present Mastodon, a scalable, extensible software platform for manual, semi-automated, and automated cell tracking in large images. A purpose-built graph model supports responsive performance for datasets with millions of annotations, making Mastodon a future-proof platform for cell lineage analysis. Built as a Fiji plugin, Mastodon enables interactive visualization, editing, and analysis of complex lineage trees, seamlessly integrated with the raw image data. Comprehension of cell lineages in complex three-dimensional geometries is facilitated by interoperability with the powerful open-source render engine Blender. In three distinct developmental contexts, we demonstrate how Mastodon will accelerate biological insights by providing user-friendly navigation and explorative analysis in complex lineage datasets.

bioinformatics↗

bioRxiv2025

A skeletal muscle-sympathetic nerve-intestine network underlies muscle inflammation and atrophy induced by immobilization

Immobility is a common cause of muscle atrophy, but the underlying mechanisms have remained unclear. Here we show that limb immobilization in mice elicits inflammation and atrophy of skeletal muscle that are preventable by neutralizing antibodies to the chemokine CXCL10. Limb immobilization also induced changes to the gut microbiota and intestinal inflammation, and either sterilization of the intestine with antibiotics or administration of 10-hydroxy-cis-12-octadecenoic acid--a linoleic acid-derived gut microbial metabolite--prevented intestinal and muscle inflammation as well as muscle atrophy induced by immobilization, implicating intestinal inflammation in muscle inflammation and atrophy. Limb immobilization activated sympathetic nerves and increased {beta}2-adrenergic receptor gene (Adrb2) expression in the intestine. Single-cell RNA-sequencing analysis revealed that, among cells expressing Adrb2 in the intestine, immobilization increased only the population of macrophages. Pharmacological inhibition or macrophage-specific ablation of Adrb2 prevented immobilization-induced intestinal and muscle inflammation. Our results thus implicate a previously unrecognized muscle-nerve-intestine network in immobilization-induced muscle atrophy.

molecular biology↗

bioRxiv2024

Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs

High resolution live imaging of regeneration presents unique challenges, due to the nature of the specimens (large mobile animals), the duration of the process (spanning days or weeks), and the fact that cellular resolution must be achieved without damage caused by lengthy exposures to light. Building on previous work that allowed us to image different parts of the process of leg regeneration in the crustacean Parhyale hawaiensis, we present here a method for live imaging that captures the entire process of leg regeneration, spanning up to 10 days, at cellular resolution. Our method includes (1) mounting and long-term live imaging of regenerating legs under conditions that yield high spatial and temporal resolution but minimise photodamage, (2) fixing and in situ staining of the regenerated legs that were imaged, to identify cell fates, and (3) computer-assisted cell tracking to determine the cell lineages and progenitors of identified cells. The method is optimised to limit light exposure while maximising tracking efficiency. Combined with appropriate cell-type-specific markers, this method may allow the description of cell lineages for every regenerated cell type in the limb.

developmental biology↗

bioRxiv2023

Training deep learning models for cell image segmentation with sparse annotations

Deep learning is becoming more prominent in cell image analysis. However, collecting the annotated data required to train efficient deep-learning models remains a major obstacle. I demonstrate that functional performance can be achieved even with sparsely annotated data. Furthermore, I show that the selection of sparse cell annotations significantly impacts performance. I modified Cellpose and StarDist to enable training with sparsely annotated data and evaluated them in conjunction with ELE-PHANT, a cell tracking algorithm that internally uses U-Net based cell segmentation. These results illustrate that sparse annotation is a generally effective strategy in deep learning-based cell image segmentation. Finally, I demonstrate that with the help of the Segment Anything Model (SAM), it is feasible to build an effective deep learning model of cell image segmentation from scratch just in a few minutes.

bioinformatics↗

bioRxiv2023

Subjective sleep onset latency is influenced by sleep structure and body heat loss in human subjects

ObjectivesThe current study examined the relationship between subjective SOL, sleep structure, changes in skin and body temperature, and subjective evaluation in healthy young adults to elucidate the pathophysiological mechanisms of insomnia. MethodsTwenty-eight participants (mean age: 21.5 {+/-} 0.5 years) with no sleep problems participated in a 1-hour polysomnographic recording that obtained objective sleep parameters during the daytime while skin and body temperatures were recorded. The distal-proximal skin temperature gradient (DPG) was calculated. Subjective parameters, such as subjective SOL, sleep time, and restorative sleepiness, were evaluated before and after sleep. ResultsMost participants estimated their sleep latency as being longer than their actual SOL (13.7 min vs. 7.6 min). Objective SOL was significantly correlated with each sleep stage parameter whereas subjective SOL was negatively correlated with stage N2 sleep duration (Rho = -0.454, p = 0.020), slow-wave activity and delta power (Rho = -0.500, p = 0.011, Rho = -0.432, p = 0.031, respectively), and {Delta}DPG (the degree of reduction of heat loss before and after lights-off). Stepwise regression analysis showed that {Delta}DPG was the strongest predictive factor in explaining the length of subjective SOL. ConclusionsThe degree of heat dissipation before falling asleep contributed most to the sensation of falling asleep in healthy young adults. This finding may be helpful for elucidating the physiological mechanisms of insomnia and its treatment. STATEMENT OF SIGNIFICANCEThe time estimate ability is also activated during sleep. However, some insomniacs have abnormalities in this function and suffer from sleep state misperception, which is a discrepancy between subjective and objective sleep time. They often overestimate the sleep onset latency. We investigated the relationship between subjective sleep onset latency, sleep structure, EEG frequency components, and body temperature during the process of falling asleep in healthy adults. It was suggested that the Stage N2 duration, the amounts of slow wave activity around after lights-out, and the degree of heat dissipation before falling asleep may be related to the perception of falling asleep. These results are expected to contribute to the understanding of the pathophysiological mechanisms and to the treatment of insomnia.

physiology↗

bioRxiv2021

Tracking cell lineages in 3D by incremental deep learning

Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep learning models. Moreover, annotation, training, prediction and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the softwares performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort.

developmental biology↗