Biology subjects

Kim, K. T.

Publications and source records attributed to Kim, K. T..

3 recordsLinked to original sources

bioRxiv2025

Single-nucleus RNA sequencing reveals the cellular diversity of cerebrospinal fluid in the context of intraventricular hemorrhage

BackgroundIntraventricular hemorrhage (IVH) is a common and severe complication of hemorrhagic brain injury. Current treatments offer limited improvement in long-term neurological outcomes. Inflammatory responses in the cerebrospinal fluid (CSF) after IVH are thought to drive secondary injury, but the cellular mechanisms underlying this inflammation remain poorly defined. MethodsWe performed single-nucleus RNA sequencing of leukocytes isolated from CSF collected through external ventricular drains in subjects with intracerebral (n = 6) or subarachnoid (n = 1) hemorrhage. We characterized transcriptionally distinct subpopulations of neutrophils, monocytes, and lymphocytes by comparison to reference datasets. Cell-cell signaling networks were analyzed to infer cytokine-mediated communication, and a flow cytometry panel was developed to validate transcriptomic findings in independent CSF samples. ResultsWe obtained 11,191 high-quality nuclei comprising neutrophils (53.8%), monocytes (26.1%), lymphocytes (17.8%), and non-immune cells (2.4%). Neutrophils segregated into Nascent, Quiescent, and Interferon-Activated states. Monocytes exhibited classical phenotypes that include interferon-activated states (characterized by expression of VCAN or PROK2) and CXC-chemokine expressing states (characterized by expression of CXCL5 or CXCL8). Lymphocytes were mainly naive and central memory CD4 T cells. Cell-cell signaling analysis predicted strong CXC chemokine signaling from monocytes to neutrophil subsets and IL-1 family-driven inflammatory responses across multiple populations. Type I and III interferon signaling defined a neutrophil population not previously described in the central nervous system. ConclusionThis study delineates the diverse cellular immune landscape of CSF after IVH. Transcriptomic profiles reveal interferon, IL-1, and CXC chemokine signaling networks as potential therapeutic targets to mitigate secondary injury.

genomics↗

bioRxiv2024

Motor Hotspot Localization Based on Electroencephalography Using Convolutional Neural Network in Patients with Stroke

BackgroundAlthough transcranial magnetic stimulation (TMS) is the optimal tool for identifying individual motor hotspots for transcranial electrical stimulation (tES), it requires a cumbersome procedure in which patients must visit the hospital each time and rely on expert judgment to determine the motor hotspot. Therefore, in previous study, we proposed electroencephalography (EEG)-based machine learning approach to automatically identify individual motor hotspots. In this study, we proposed an advanced EEG-based motor hotspot identification algorithm using a deep learning model and assessed its clinical feasibility and benefits by applying it to stroke patient EEGs. MethodsEEG data were measured from thirty subjects as they performed a simple hand movement task. We utilized the five types of input data depending on the processing levels to assess the signal processing capability of our proposed deep learning model. The motor hotspot locations were estimated using a two-dimensional convolutional neural network (CNN) model. The error distance between the 3D coordinate information of the individual motor hotspots identified by the TMS (ground truth) and EEGs was calculated using the Euclidean distance. Additionally, we confirmed the clinical benefits of our proposed deep-learning algorithm by applying the EEG of stroke patients. ResultsA mean error distance between the motor hotspot locations identified by TMS and our approach was 2.34 {+/-} 0.19 mm when using raw data from only 9 channels around the motor area. When it was tested on stroke patients, the mean error distance was 1.77 {+/-} 0.15 mm using only 5 channels around the motor area. ConclusionWe have demonstrated that an EEG-based deep learning approach can effectively identify the individual motor hotspots. Moreover, we validated the clinical benefits of our algorithm by successfully implementing it in stroke patients. Our algorithm can be used as an alternative to TMS for identifying motor hotspots and maximizing rehabilitation effectiveness.

bioengineering↗

bioRxiv2023

Multi-omics Identification and Route-Specific Characterization of Metastasis-specific EMT Genes and Their Microenvironmental Interactions

BackgroundGastric cancer (GC) constitute a significant cause of cancer-related mortality worldwide, with metastatic patterns including hematogenous, peritoneal, and ovarian routes. Although GC gene expression patterns have been extensively researched, the metastasis-specific gene expression landscape remains largely unexplored. MethodsWe undertook a whole transcriptome sequencing analysis of 66 paired primary and metastatic (hematogenous, peritoneal, or ovarian) GC tumors from 14 patients. Public databases including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) was used for validation. Single-cell RNA sequencing (scRNA-seq) of four ascites from serosa positive GC patients and five primary tumors by layer (superficial and deep) were analyzed. ResultsThrough differential expression analysis between paired primary and metastatic tumors by routes identified 122 unique metastasis-specific epithelial-mesenchymal transition (msEMT) genes. These genes demonstrated varying expression patterns depending on the metastatic route, suggesting route-specific molecular mechanisms in GC metastasis. High expression of msEMT genes in primary tumors was associated with more frequent CDH1 mutations, the genomically stable subtype, and poor prognosis in TCGA GC cohort. This association was further corroborated by poor prognosis and high predictive performance for peritoneal/ovarian recurrence in two independent cohorts (GSE66229; n=300, GSE84437; n=433). scRNA-seq analysis of five primary tumors (GSE167297) and four independent ascites samples from GC patients revealed that msEMT genes were predominantly expressed in diverse fibroblast sub-populations, rather than cancer cells. ConclusionsThis study illuminates the route-specific mechanisms and underlines the significance of msEMT genes and cancer-associated fibroblasts in peritoneal metastasis of GC.

genomics↗