Biology subjects

Jo, Y.

Publications and source records attributed to Jo, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Computational conjugate adaptive optics for longitudinal through-skull imaging of cortical myelin

We present a 1.3-m reflection matrix microscope and computational conjugate adaptive optics algorithm for label-free longitudinal imaging of cortical myelin through an intact mouse skull. The myelination processes of the same mice were observed from 3 to 10 postnatal weeks down to cortical layer 4 with a near-diffraction-limited resolution of 0.79 {micro}m. Our system will expedite the investigations on the role of myelination in learning, memory, and brain disorders.

neuroscience↗

bioRxiv2022

L-threonine mediated DAF-16/HSF-1 activation inhibits ferroptosis and increases healthspan

The pathways that impact longevity in the wake of dietary restriction (DR) pathway remain still ill-defined. Most studies have focused on nutrient limitation and perturbations of energy metabolism to explain the beneficial effects of DR. We showed that the essential amino acid L-threonine was elevated in Caenorhabditis elegans under DR, and that L-threonine supplementation increased its healthspan. To elucidate the underlying mechanism, we conducted metabolic and transcriptomic profiling using LC-MS/MS and RNA-seq analyses in worms that were fed with RNAi to induce loss of function of key candidate mediators and evaluated healthspan. L-threonine supplementation and loss-of-threonine dehydrogenase, which govern L-threonine metabolism, increased the healthspan of C. elegans by attenuating ferroptosis in a ferritin-dependent manner. Tran-scriptomic analysis of C. elegans supplemented with L-threonine showed that FTN-1 encoding ferritin was elevated, implying FTN-1 is an essential mediator of longevity promotion through L-threonine. Organismal ferritin levels were positively correlated with chronological aging and L-thre-onine supplementation further increased ferritin levels, which protected against age-associated ferroptosis through the DAF-16 and HSF-1 pathways. Our investigation uncovered the role of a distinct and universal metabolite, L-threonine, in DR-mediated improvement in organismal healthspan, suggesting it could be an effective intervention for preventing senescence progression and age-induced ferroptosis.

cell biology↗

bioRxiv2021

Restoring single-molecule localizations with wavefront sensing adaptive optics for deep-tissue super-resolution imaging

The specimen-induced aberration has been a major factor limiting the imaging depth of single-molecule localization microscopy (SMLM). Here, we report the application of label-free wavefront sensing adaptive optics to SMLM for deep-tissue super-resolution imaging. The proposed system measures complex tissue aberrations from intrinsic reflectance rather than fluorescence emission and physically corrects the wavefront distortion more than three-fold stronger than the previous limit. This enables us to resolve sub-diffraction morphologies of cilia and oligodendrocytes in whole intact zebrafish as well as dendritic spines in thick mouse brain tissues at the depth of up to 102 m with localization number enhancement by up to 37 times and localization precision comparable to aberration-free samples. The proposed approach can expand the application range of SMLM to intact animals that cause the loss of localization points owing to severe tissue aberrations.

cancer biology↗

bioRxiv2021

Cortico-Subcortical Interactions in Overlapping Communities of Edge Functional Connectivity

Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.

neuroscience↗

bioRxiv2021

Binary outcomes of enhancer activity underlie stable random monoallelic expression

Mitotically stable random monoallelic gene expression (RME) is documented for a small percentage of autosomal genes. Here we investigated the role of enhancers in the RME of natural killer (NK) cell receptor genes. Enhancers were accessible and enriched in H3K27ac on silent and active alleles alike, decoupling enhancer activation and expression. Enhancers controlled gene expression frequency, as predicted by the binary model of enhancer action, and enhancer deletion converted the broadly expressed Nkg2d into an RME gene, recapitulating natural variegation. The results suggested that RME is a consequence of general enhancer properties and therefore many genes may be subject to some degree of RME, which was borne out by analysis of a panel of genes previously thought to be universally expressed within defined hematopoietic lineages: Nkg2d, Cd45, Cd8a and Thy1. We propose that previously documented RME is an extreme on a continuum of intrinsically probabilistic gene expression.

molecular biology↗

bioRxiv2020

Data-driven multiplexed microtomography of endogenous subcellular dynamics

Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales would be an invaluable tool in biomedicine. However, conventional imaging modalities have stark tradeoffs precluding the fulfilment of all functional requirements. Here we propose the refractive index (RI), an intrinsic quantity governing light-matter interaction, as a means for such measurement. We show that major endogenous subcellular structures, which are conventionally accessed via exogenous fluorescence labeling, are encoded in 3D RI tomograms. We decode this information in a data-driven manner, thereby achieving multiplexed microtomography. This approach inherits the advantages of both high-specificity fluorescence imaging and label-free RI imaging. The performance, reliability, and scalability of this technology have been extensively characterized, and its application within single-cell profiling at unprecedented scales has been demonstrated.

bioengineering↗

bioRxiv2020

Subject identification using edge-centric functional connectivity

Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed "fingerprinting" analyses on functional connectivity to identify subjects idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterized subjects identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

neuroscience↗

bioRxiv2020

The diversity and multiplexity of edge communities within and between brain systems

The human brain is composed of regions that can be grouped into functionally specialized systems. These systems transiently couple and decouple across time to support complex cognitive processes. Recently, we proposed an edge-centric model of brain networks whose elements can be clustered to reveal communities of connections whose co-fluctuations are correlated across time. It remains unclear, however, how these co-fluctuation patterns relate to traditionally-defined brain systems. Here, we address this question using data from the Midnight Scan Club. We show that edge communities transcend traditional definitions of brain systems, forming a multiplexed network in which all pairs of brain systems are linked to one another by at least two distinct edge communities. Mapping edge communities back to individual brain regions and deriving a novel distance metric to describe the similarity of regions "edge community profiles", we then demonstrate that the within-system similarity of profiles is heterogeneous across systems. Specifically, we find that heteromodal association areas exhibit significantly greater diversity of edge communities than primary sensory systems. Next, we cluster the entire cerebral cortex according to the similarity of regions edge community profiles, revealing systematic differences between traditionally-defined systems and the detected clusters. Specifically, we find that regions in heteromodal systems exhibit dissimilar edge community profiles and are more likely to form their own clusters. Finally, we show show that edge communities are highly personalized and can be used to identify individual subjects. Collectively, our work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brains system level architecture. Our work provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.

neuroscience↗

bioRxiv2020

Identification of viruses belonging to the family Partitiviridae from plant transcriptomes

Viruses in the family Partitiviridae consist of non-enveloped viruses with bisegmented double-stranded RNA genomes. Viruses in this family have been identified from plants and fungi. In this study, we identified several viruses belonging to the family Partitiviridae using plant transcriptomes. From 11 different plant species, we identified a total of 74 RNA segments representing 23 partitiviruses. Of 74 RNA segments, 28 RNA segments encode RNA-dependent RNA polymerases (RdRp) while 46 RNA segments encode coat proteins (CPs). According to ICTV demarcation for the family Partitiviridae, 25 RNAs encoding RdRp and 41 RNAs encoding CP were novel RNA segments. In addition, we identified eight RNA segments (three for RdRp and five for CP) belonging to the known partitivruses. Taken together, this study provides the largest number of partitiviruses from plant transcriptomes in a single study.

microbiology↗

bioRxiv2020

Identification of viruses from fungal transcriptomes

Viruses infecting fungi are referred to as mycoviruses. Here, we carried out in silico mycovirome studies using public fungal transcriptomes. We identified 468 virus-associated contigs assigned to five orders, 21 families, 26 genera, and 88 species. We assembled 120 viral genomes with diverse RNA and DNA genomes. The phylogenetic tree and genome organization unveiled the possible host origin of mycovirus species and diversity of their genome structures. Most identified mycoviruses were originated from fungi; however, some mycoviruses had strong phylogenetic relationships with those from insects and plants. The viral abundance and mutation frequency of mycoviruses were very low; however, the compositions and populations of mycoviruses were very complex. Although coinfection of diverse mycoviruses in the fungi was common in our study, most mycoviromes had a dominant virus species. The compositions and populations of mycoviruses were more complex than we expected. Monilinia viromes revealed that there were strong deviations in the composition of viruses and viral abundance among samples. Gigaspora viromes showed that the chemical strigolactone might promote virus replication and mutations, while symbiosis with endobacteria might suppress virus replication and mutations. This study revealed the diversity and host distribution of mycoviruses.

microbiology↗

bioRxiv2020

Although host-related factors are important for the formation of gut microbiota, environmental factors cannot be ignored

The gut microbiome is essential to human health. However, little is known about the influence of the environment versus host-related factors (e.g. genetic background, sex, age, and body mass) in the formation of human intestinal microflora. Here, we present evidence in support of the importance of host-related factors in the establishment and maintenance of individual gut assemblages. We collected fecal samples (n = 249) from 44 Korean naval trainees and 39 healthy people living in Korea over eight weeks and sequenced the bacterial 16S rRNA genes. The following hypotheses were tested: 1) microbiome function is linked to its diversity, community structure, and genetic host-related factors, and 2) preexisting host-related factors have a more significant effect on gut microbiome formation and composition than environmental factors. For each individual, the difference between the initial gut microbiota and that after eight weeks was negligible even though the 44 naval trainees lived in the same area and received the same diet, the same amount of exercise, and the same amount of physical stress during the study. This suggests that host-related factors, rather than environmental factors, is a key determinant of individual gut microflora. Moreover, eight weeks of physical training and experiencing the same environmental conditions resulted in an increase in the species Bifidobacterium, Faecalibacterium, and Roseburia in most trainees, suggesting a healthier intestinal environment. IMPORTANCEIn order to understand the role of human gut microbiome, it is important to know how individuals gut microbiota are formed. In this study, we tested the host-related factors versus environmental factors to affect gut microbiome and found that the former have a more association. However, we also found that the controlled environment give an effect on the gut microflora as well. This study provides preliminary evidence that differences in the formation and diversity of gut microbiota within a population could be determined by host-related factors rather than environmental factors.

microbiology↗

bioRxiv2019

Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Network neuroscience has relied on a node-centric network model in which cells, populations, and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. Here, we develop an edge-centric network model, which generates the novel constructs of \"edge time series\" and \"edge functional connectivity\" (eFC). Using network analysis, we show that at rest eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We go on to show that eFC is systematically and consistently modulated by variation in sensory input. In future work, the edge-centric approach could be used to map the connectional architecture of brain circuits and for the development of brain-based biomarkers of disease and development.

neuroscience↗

bioRxiv2019

Rapid and label-free identification of individual bacterial pathogens exploiting three-dimensional quantitative phase imaging and deep learning

The healthcare industry is in dire need for rapid microbial identification techniques. Microbial infection is a major healthcare issue with significant prevalence and mortality, which can be treated effectively during the early stages using appropriate antibiotics. However, determining the appropriate antibiotics for the treatment of the early stages of infection remains a challenge, mainly due to the lack of rapid microbial identification techniques. Conventional culture-based identification and matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy are the gold standard methods, but the sample amplification process is extremely time-consuming. Here, we propose an identification framework that can be used to measure minute quantities of microbes by incorporating artificial neural networks with three-dimensional quantitative phase imaging. We aimed to accurately identify the species of bacterial bloodstream infection pathogens based on a single colony-forming unit of the bacteria. The successful distinction between a total of 19 species, with the accuracy of 99.9% when ten bacteria were measured, suggests that our framework can serve as an effective advisory tool for clinicians during the initial antibiotic prescription. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/596486v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@786668org.highwire.dtl.DTLVardef@8b4eb4org.highwire.dtl.DTLVardef@1dc2452org.highwire.dtl.DTLVardef@1d4888c_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2019

Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of chimeric antigen receptor T cells

We propose and experimentally validate a label-free, volumetric, and automated assessment method of immunological synapse dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed approach enables automatic and quantitative spatiotemporal analyses of immunological synapse kinetics regarding morphological and biochemical parameters related to the total protein densities of immune cells, thus providing a new perspective for studies in immunology.

immunology↗

bioRxiv2018

The draft genome of an octocoral, Dendronephthya gigantea

BackgroundCoral reefs composed of stony corals are threatened by global marine environmental changes. However, soft coral communities composed of octocorallian species, appear more resilient. The genomes of several species of cnidarians have been published, including stony corals, sea anemones, and hydra, but as of yet no octocoral species. To fill this phylogenetic gap within the cnidarian, we sequenced the octocoral, Dendronephthya gigantea, a non-symbiotic soft coral, commonly known as the carnation coral.\n\nFindingsThe D. gigantea genome size is approximately 276 Mb. A high-quality genome assembly was constructed using 29.85Gb (108x coverage) of PacBio long reads and 35.54Gb (128x coverage) of Illumina short paired-end reads resulting in the largest N50 value reported among cnidarian of 1.4 Mb. About 12 % of the genome consisted of repetitive elements. We found 28,879 protein-coding genes. This gene set contained about 94% metazoan single-copy orthologs, indicating the gene models were predicted with high quality compared to other cnidarians. Based on molecular phylogenetic analysis, octocoral and hexacoral divergence occurred approximately 544 million years ago. Moreover, there is a clear difference in Hox gene composition: unlike in hexacorals, Antp superclass member Evx gene was absent in D. gigantea.\n\nConclusionsWe present the first genome assembly of a non-symbiotic octocoral, D. gigantea to aid in the comparative genomic analysis of cnidarians, including comparisons of stony and soft corals and symbiotic and non-symbiotic corals. In addition, the genome of this species may provide clues about differential genetic coping mechanisms between soft and stony coral regarding the global warming.

genomics↗

bioRxiv2018

Deep-learning-based label-free segmentation of cell nuclei in time-lapse refractive index tomograms

In order to identify cell nuclei, fluorescent proteins or staining agents has been widely used. However, use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis, and even interferes with intrinsic physiological conditions. In this work, we proposed a method of label-free segmentation of cell nuclei in optical diffraction tomography images by exploiting a deep learning framework. The proposed method was applied for precise cell nucleus segmentation in two, three, and four-dimensional label-free imaging. A novel architecture with optimised training strategies was validated through cross-modality and cross-laboratory experiments. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available.

biophysics↗

bioRxiv2018

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells

We present a rapid and label-free method for hematologic screening for diseases and syndromes, utilizing quantitative phase imaging (QPI) and machine learning. We aim to establish an efficient blood examination framework that does not suffer from the drawbacks of conventional blood assays, which are incapable of profiling single cells or using labeling procedures. Our method involves the synergistic employment of QPI and machine learning. The high-dimensional refractive index information arising from the QPI-based profiling of single red blood cells is processed to screen for diseases and syndromes using machine learning, which can utilize high-dimensional data beyond the human level. Accurate screening for iron-deficiency anemia, reticulocytosis, hereditary spherocytosis, and diabetes mellitus is demonstrated (>99% accuracy) using the proposed method. Furthermore, we highlight the synergy between QPI and machine learning in the proposed method by analyzing the performance of the method.

biophysics↗

bioRxiv2017

Holographic deep learning for rapid optical screening of anthrax spores

Establishing early warning systems for anthrax attacks is crucial in biodefense. Here we present an optical method for rapid screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and sub-genus specificity. The unique representation learning capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate diagnosis of pathogens, and facilitate exciting new applications.

microbiology↗