Biology subjects

Jeong, Y.

Publications and source records attributed to Jeong, Y..

28 records · Page 2Linked to original sources

bioRxiv2023

Decoding Single-Cell Multiomics: scMaui - A Deep Learning Framework for Uncovering Cellular Heterogeneity in Presence of Batch Effects and Missing Data

The recent advances in high-throughput single-cell sequencing has significantly required computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome the sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on stacked variational encoders and adversarial learning. scMaui reduces the dimensionality of integrated data modalities to a latent space which outlines cellular heterogeneity. It can handle multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover various assays and preprocessing pipelines. We show that scMaui accomplishes superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

bioinformatics↗

bioRxiv2022

Dynamic switching between distinct oscillatory rhythms in prefrontal-amygdala circuits for dimorphic defensive behaviors under natural threats

The medial prefrontal cortex (mPFC) and basolateral amygdala (BLA) are involved in the regulation of defensive behavior under threat, but their engagement in flexible behavior shifts remains unclear. Here, we report the oscillatory activities of mPFC-BLA circuit in reaction to a naturalistic threat, created by a predatory robot in mice. Specifically, we found dynamic frequency tuning among two different theta rhythms ([~]5 or [~]10 Hz) was accompanied by agile changes of two different defensive behaviors (freeze or flight). By analyzing flight trajectories, we also found that high beta ([~]30 Hz) is engaged in the top-down process for goal-directed flights and accompanied by a reduction in fast gamma (60-120 Hz, peak near 70 Hz). The elevated beta nested the fast gamma activity by its phase more strongly. Our results suggest that the mPFC-BLA circuit has a potential role in oscillatory gear shifting allowing flexible information routing for behavior switches. HighlightsO_LIWhen threatened, mice take quick defensive behaviors such as freeze or flight. C_LIO_LImPFC-BLA theta tunes its frequency at 5 or 10 Hz for freeze or flight, respectively. C_LIO_LILow and high theta rhythms in mPFC-BLA emerge in a mutually exclusive way. C_LIO_LImPFC-driven beta emerges during goal-directed flights, coordinating fast gamma in BLA. C_LI eTOC BlurbHan et al. presents neural dynamics of mPFC-BLA network for freeze-or-flight defensive behaviors under naturalistic threats. Tuning the theta frequency in the mPFC-BLA network is for fast and agile actions under a naturalistic threat, and mPFC-driven beta oscillatory burst is for strategic action.

neuroscience↗

bioRxiv2021

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair resolution. It has tremendous potential for cell-type heterogeneity analysis with intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, the systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection (BED), DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With these sophisticated evaluation, we demonstrate the method achieving the highest performance in different types of samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and limitations which need to be handled.

bioinformatics↗

bioRxiv2021

Intravital imaging of cerebral microinfarct reveals an astrocyte reaction led to glial scar

Cerebral microinfarct increases the risk of dementia. But how microscopic cerebrovascular disruption affects the brain tissue in cellular-level are mostly unknown. Herein, with a longitudinal intravital imaging, we serially visualized in vivo dynamic cellular-level changes in astrocyte, pericyte and neuron as well as microvascular integrity after the induction of cerebral microinfarction for 1 month in mice. At day 2-3, it revealed a localized edema with acute astrocyte loss, neuronal death, impaired pericyte-vessel coverage and extravascular leakage indicating blood-brain barrier (BBB) dysfunction. At day 5, edema disappeared with recovery of pericyte-vessel coverage and BBB integrity. But brain tissue continued to shrink with persisted loss of astrocyte and neuron in microinfarct until 30 days, resulting in a collagen-rich fibrous scar surrounding the microinfarct. Notably, reactive astrocytes appeared at the peri-infarct area early at day 2 and thereafter accumulated in the peri-infarct. Oral administration of a reversible monoamine oxidase B inhibitor significantly decreased the astrocyte reactivity and fibrous scar formation. Our result suggests that astrocyte reactivity may be a key target to alleviate the impact of microinfarction.

neuroscience↗

bioRxiv2021

Graph neural network based heterogeneous propagation scheme for classifying Alzheimer's disease

Deep learning frameworks for disease classification using neuroimaging and non-imaging information require the capability of capturing individual features as well as associative information among subjects. Graphs represent the interactions among nodes, which contain the individual features, through the edges in order to incorporate the inter-relatedness among heterogeneous data. Previous graph-based approaches for disease classification have focused on the similarities among subjects by establishing customized functions or solely based on imaging features. The purpose of this paper is to propose a novel graph-based deep learning architecture for classifying Alzheimers disease (AD) by combining the resting-state functional magnetic resonance imaging and demographic measures without defining any study-specific function. We used the neuroimaging data from the ADNI and OASIS databases to test the robustness of our proposed model. We combined imaging-based and non-imaging information of individuals by categorizing them into distinctive nodes to construct a subject-demographic bipartite graph. The approximate personalized propagation of neural predictions, a recently developed graph neural network model, was used to classify the AD continuum from cognitively unimpaired individuals. The results showed that our model successfully captures the heterogeneous relations among subjects and improves the quality of classification when compared with other classical and deep learning models, thus outperforming the other models.

bioinformatics↗

bioRxiv2020

SLAMF7 engagement super-activates macrophages in acute and chronic inflammation

Macrophages regulate protective immune responses to infectious microbes, but aberrant macrophage activation frequently drives pathological inflammation. To identify regulators of vigorous macrophage activation, we analyzed RNA-seq data from synovial macrophages and identified SLAMF7 as a receptor associated with a super-activated macrophage state in rheumatoid arthritis. We implicated IFN-{gamma} as a key regulator of SLAMF7 expression. Engaging this receptor drove an exuberant wave of inflammatory cytokine expression, and induction of TNF- following SLAMF7 engagement amplified inflammation through an autocrine signaling loop. We observed SLAMF7-induced gene programs not only in macrophages from rheumatoid arthritis patients, but in gut macrophages from active Crohns disease patients and lung macrophages from severe COVID-19 patients. This suggests a central role for SLAMF7 in macrophage super-activation with broad implications in pathology.

immunology↗

bioRxiv2020

Increased capillary stalling is associated with endothelial glycocalyx loss in subcortical vascular dementia

Proper regulation and patency of cerebral microcirculation is crucial for maintaining a healthy brain. Capillary stalling, i.e., the brief interruption of microcirculation mainly by leukocytes, has been observed in several diseases and contributes to disease pathogenesis or progression. However, the underpinning mechanism for leukocyte capillary plugging remains elusive. Therefore, we investigated the mechanism of capillary stalling in mice during the development of subcortical vascular dementia (SVaD), the most common type of vascular dementia characterized by impaired microcirculation and associated pathological features. Longitudinal optical coherence tomography angiography showed increased number of stalled segments as the disease progressed, while two-photon microscopy indicated a less extensive endothelial glycocalyx (EG) in the stalled segments. We also found that increased gliosis and blood-brain barrier leakage were correlated with the increased number of stalled segments. Based on the above, we conclude that EG potentially mediates capillary stalling and can be a therapeutic target of SVaD.

neuroscience↗

bioRxiv2020

Is FAM19A5 an adipokine? Peripheral FAM19A5 in wild-type, FAM19A5 knock-out, and LacZ knock-in mice.

FAM19A5 is a novel secretory protein primarily expressed in the brain. However, a recent study reported that FAM19A5 is an adipocyte-derived adipokine that regulates vascular smooth muscle function through sphingosine-1-phosphate receptor 2 (S1PR2). In our study, we investigated FAM19A5 transcript and protein levels in peripheral tissues, including adipose tissues, from wild-type, FAM19A5 knockout, and FAM19A5 LacZ knockin mice. We found that FAM19A5 transcript levels in the central nervous system were much greater than those in any of the peripheral tissues, including adipose tissues. Furthermore, the FAM19A5 protein levels in adipose and reproductive tissues were below detectable limits for Western blot analysis. Additionally, we found that the FAM19A5 protein did not interact with S1PR2 in terms of G protein-mediated signal transduction, {beta}-arrestin recruitment, or ligand-mediated internalization. Taken together, our findings revealed basal levels of FAM19A5 transcripts and proteins in peripheral tissues, confirming its primary expression in the central nervous system and lack of significant interaction with S1PR2.

genetics↗

bioRxiv2019

Clinically-relevant cell type cross-talk identified from a human lung tumor microenvironment interactome

Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway. To develop a deeper understanding of the interactions between cells within human lung tumors we performed RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We mapped the cell-specific differential expression of prognostically-associated secreted factors and cell surface genes, and computationally reconstructed cross-talk between these cell types to generate a novel resource we call the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identified and validated a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also found a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior. These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.\n\nSummaryRNA-seq profiling of sorted populations from primary lung cancer samples identifies prognostically relevant cross-talk between cell types in the tumor microenvironment.

cancer biology↗

bioRxiv2019

Dilated Saliency U-Net for White Matter Hyperintensities Segmentation using Irregularity Age Map

White matter hyperintensities(WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image(MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for Alzheimer s disease and brain aging processes. However, the automatic WMH segmentation is challenging because of the variable intensity range, size and shape. U-Net tackled this problem through the dense prediction and showed competitive performances on not only WMH segmentation/detection but also on varied image segmentation tasks, but it still accompanies a high complexity of the network architecture. In this study, we propose to use Saliency U-Net architecture and irregularity age map(IAM) to decrease the U-Net complexity without a performance loss. We trained Saliency U-Net using both T2-FLAIR MRI sequence and IAM. Since IAM guides where irregularities, in which WMH is possibly included, exist on the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The better performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net to recognise the shape of large WMH more accurately by learning multi-context on MRI slices. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which is the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest train time and the least number of parameters. In conclusion, based on the experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation.

neuroscience↗