Biology subjects

Kwon, S. H.

Publications and source records attributed to Kwon, S. H..

18 recordsLinked to original sources

bioRxiv2026

Overlapping yet distinct spatiomolecular profiles across mood disorders and biological sex in the human prefrontal cortex

Major depressive disorder (MDD) and bipolar disorder (BD) exhibit both overlapping and distinct clinical features, including sex differences in prevalence and symptomatology, but how these similarities and differences are reflected at the molecular and cellular level in the human brain remains poorly understood. Here, we applied spatial transcriptomics to the human dorsolateral prefrontal cortex from individuals with MDD, BD, and neurotypical controls to resolve molecular programs associated with diagnosis and biological sex. Our analysis revealed sex- and diagnosis-specific differences across white matter, the neurovascular unit (NVU), and neuronal compartments. Within white matter, we identified female-specific survival-response programs shared in both MDD and BD, and divergent myelin-related gene expression patterns across disorders (CLDN11, CNDP1). Within the NVU, transcriptional evidence of impaired vascular integrity was accompanied by astrocytic response profiles in females with MDD and males with BD, with inflammatory responses further evident in females with MDD (IFITM3, CEBPD, IL1R1). In donors with BD of both sexes and in females with MDD, inhibitory neurons exhibited impaired neuropeptide expression (SST, CORT, CRH). Integration with donor genotype information highlighted that genetic risk for mood disorders influences MAPK3 expression, implicating altered ERK signaling, which was further supported by disorder-associated changes in Ras/MEK/ERK pathway components in neurons (MAPK3, DUSP6, RASD1, ELK1). Together, these findings reveal coordinated, cell type- and spatial domain-specific molecular programs that distinguish shared and disorder-specific transcriptomic changes across mood disorders in a sex-dependent manner.

neuroscience↗

bioRxiv2026

Perturbation of genes linked to common schizophrenia risk variants identifies cilia programs

Schizophrenia (SCZ) is a common psychiatric disorder characterized by psychosis, emotional withdrawal, and cognitive deficits. Most SCZ risk variants reside in non-coding regions of the genome and are thought to influence disease risk by modulating gene regulation. However, the target genes, biological pathways, and cell types through which these variants exert their effects remain poorly understood. To address this gap, we employed in vivo CRISPR droplet sequencing (CROP-seq) in the postnatal mouse neocortex. We perturbed 12 SCZ risk genes previously linked to functionally validated risk variants, followed by single-cell RNA sequencing. We identified 3,031 differentially expressed genes (DEGs) that recapitulate transcriptional alterations observed in postmortem SCZ brains. Integrative analysis using DEG clustering, factor analysis, and gene regulatory network inference uncovered convergent gene programs with distinct biological functions and cell type specificity. Notably, ciliary transcriptional programs consistently emerged across analytical frameworks. The primary cilium is a neurocircuit modulating signaling organelle in neurons and glia that remains understudied in SCZ. Perturbation of key contributors to the ciliary transcriptional programs led to significant alterations in ciliary structure, suggesting that SCZ genetic risk factors may influence how brain cells sense and transduce extracellular signals through synapse-independent mechanisms. Together, this study provides the first in vivo characterization of the functional consequence of common variant architecture in SCZ and implicates ciliary dysfunction as a convergent downstream mechanism.

neuroscience↗

bioRxiv2026

LEO1 loss promotes ER stress-adapted migration and cholesterol dependency in colorectal cancer

The RNA polymerase-associated factor 1 complex (PAF1C) is an evolutionarily conserved transcription elongation complex that regulates RNA polymerase II-mediated transcription and chromatin modification. LEO1, a core subunit of PAF1C, has been implicated in developmental gene regulation, WNT signaling, and leukemogenesis; however, its role in solid tumor progression remains poorly understood. In this study, we found that although LEO1 expression is generally elevated in colorectal cancer (CRC), its expression is reduced in stage IV tumors and is associated with poor clinical outcomes. To investigate its function, we established LEO1 -deficient HCT116 cell line and performed transcriptomic analyses. Loss of LEO1 suppressed epithelial differentiation and developmental gene programs while inducing cell cycle delay. Despite these changes, LEO1-deficient cells exhibited aggressive phenotypes, including enlarged nuclei and increased expression of migration-associated genes, which were further enhanced under glucose deprivation. Motif analysis identified FOXM1 as a key regulator of these migration-related genes. Mechanistically, LEO1 deficiency promoted accelerated transcriptional activation of GRP78, a central regulator of endoplasmic reticulum (ER) stress adaptation. GRP78 was required for survival under ER stress conditions, and its inhibition suppressed both migration and migration-associated gene expression. In addition, transcriptomic analyses revealed upregulation of cholesterol metabolism-related genes in LEO1-deficient cells. Consistently, treatment with the HMG-CoA reductase inhibitor atorvastatin selectively impaired their survival, indicating cholesterol metabolic dependency. Collectively, these findings demonstrate that LEO1 loss promotes ER stress-adapted migration and cholesterol metabolic dependency in CRC, suggesting that these pathways may represent therapeutic vulnerabilities in metastatic LEO1-low CRC.

cancer biology↗

bioRxiv2026

Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex

The dorsolateral prefrontal cortex (dlPFC) is central to cognitive dysfunction in schizophrenia (SCZ), yet how molecular changes are organized across cortex remains unclear. Here, we applied complementary spatial transcriptomic approaches spanning laminar domains, microenvironments, and cell types in postmortem human dlPFC. At the laminar level, SCZ-associated transcriptional changes were strongest in glia-enriched domains (layer 1/meninges and white matter), including down-regulation of microglia-associated genes, whereas genetic risk localized to neuronal-rich gray matter. We next analyzed SCZ-linked microenvironments including neuropil, neuronal, perineuronal net, and vascular compartments. Within these, neuronal and synaptic transcriptional changes were most prominent in neuropil, showing down-regulation of activity-dependent synaptic genes and inhibitory neuron markers. At the cellular level, these signals reflected intrinsic alterations localized to cell types. Across analyses, patterns converged on altered BDNF-TrkB signaling and inhibitory circuit dysfunction. Together, our findings highlight spatial scale as a key determinant in resolving neuronal and non-neuronal aspects of SCZ-associated biology.

neuroscience↗

bioRxiv2025

3D Hybrid Bioprinting for Complex Multi-Tissue Engineering

3D bioprinting has revolutionized tissue engineering, enabling intricate, physiologically relevant constructs unattainable with conventional techniques, yet it remains limited in integrating soft and rigid multifunctional components for complex multi-tissue applications. In this study, we introduce a 3D hybrid bioprinting approach implementing the Hybprinter platform, which integrates multiple 3D printing modules under optimized conditions for a continuous bioprinting process with multiple soft and hard biomaterials. This approach demonstrates robust biocompatibility and broad tissue engineering potential for modeling and therapeutic applications. The capacity to fabricate multi-hydrogel hybrid constructs is illustrated by representative examples highlighting vascularization, multifunctionality, mechanical robustness, and implant suturability. Notably, compared with commonly fabricated hydrogel-only constructs, the resulting hybrid constructs achieve over a 1000-fold increase in mechanical strength, and demonstrated enhanced osteogenic differentiation, underscoring their suitability for load-bearing musculoskeletal and orthopedic tissue engineering. Additionally, cell-laden hydrogel constructs demonstrated robust chondrogenic differentiation, highlighting the capacity for lineage-specific tissue development in vitro. Beyond these outcomes, the presented hybrid bioprinting approach integrates essential tissue engineering attributes that unites mechanical robustness and suturable capacity with multi-material integration, gradient property design, incorporation of bioactive agents, and support for multi-cell loading. This versatile platform advances complex tissue engineering and holds promise for patient specific, organ-on-demand applications.

bioengineering↗

bioRxiv2025

Impact of Alzheimer's disease risk factors and local neuromelanin content on the transcriptomic landscape of the human locus coeruleus

The locus coeruleus (LC) is a small noradrenergic nucleus in the dorsal pons that sends projections across the brain regulating sleep, arousal, attention, stress responses, and some forms of cognition. LC neurons show pathology in the earliest stages of Alzheimers disease (AD), including age-related accumulation of hyperphosphorylated tau (pTau) and accelerated loss of neuromelanin (NM) pigmentation. NM-sensitive neuroimaging of the LC predicts previous cognitive decline, clinical severity, and future AD progression. While these findings suggest that the LC plays an etiologic role in AD, the molecular landscape of the LC prior to clinical manifestation of sporadic AD remains largely uncharacterized. This information is critical for developing interventions that preserve LC integrity and function. We performed spatially-resolved transcriptomics on 85 sections of human postmortem LC from N=33 neurotypical middle-aged donors, balanced for epidemiologic AD risk factors including sex, African or European ancestry, and APOE genotype (carriers of the E4/risk or E2/protective alleles). Comparing across APOE genotypes, we find astrocytic gene expression differences proximal to LC neurons. Associating NM content with local gene expression, we show that higher overall APOE gene expression correlates with reduced NM content and an enrichment of NM-associated genes in aging pathways. Unexpectedly, we find enriched LC expression of cholesterol synthesis genes, alongside evidence for lipid synthesis gene regulatory network activity in NM-containing LC specifically, revealing a potential intersection between intrinsic lipid metabolism in LC neurons, NM, and the role of APOE-mediated lipid biology in AD. Together, these data illuminate the molecular features of the human LC at spatial resolution with unprecedented sampling depth, revealing how AD risk factors and NM content influence resilience and susceptibility of this critical brain nucleus to pathology accumulation and degeneration.

neuroscience↗

bioRxiv2025

Spatio-molecular gene expression reflects dorsal anterior cingulate cortex structure and function in the human brain

In the human brain, the dorsal anterior cingulate cortex (dACC) plays key roles in various components of cognitive control, and is particularly relevant for reward processing and conflict monitoring. The dACC regulates expression of fear and pain, and its dysfunction is implicated in a number of neuropsychiatric disorders. Compared to more recently specialized neocortical areas, such as the dorsolateral prefrontal cortex (dlPFC), the dACC is evolutionarily older. The regions agranular structure, and other evolutionary specializations, such as the presence of von Economo neurons (VENs), contribute to its specialized roles in cognitive and emotional processing. Here, we generated paired spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the dACC in ten adult neurotypical donors to define molecular profiles for dACC cell types and spatial domains. Using non-negative matrix factorization (NMF), we integrated these data by identifying gene expression patterns within the snRNA-seq data, which were projected onto the SRT data to infer the spatial localization. Combining these data with publicly available resources, we revealed insights about molecular profiles, spatial topography, enrichment of disease risk, and putative connectivity of spatially-localized dACC cell types, including VENs. Utilizing published dlPFC snRNA-seq and SRT data collected in the same neurotypical brain donors used here, we deployed cross-region comparison analyses between dACC and dlPFC to understand spatio-molecular specializations and laminar organization across human brain evolution. To make this comprehensive molecular resource accessible to the scientific community, we made both raw and processed data freely available, including through interactive web applications.

neuroscience↗

bioRxiv2024

An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus

The hippocampus contains many unique cell types, which serve the structures specialized functions, including learning, memory and cognition. These cells have distinct spatial organization, morphology, physiology, and connectivity, highlighting the importance of transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus in ten adult neurotypical donors to define molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization (NMF) and label transfer, we integrated these data by defining gene expression patterns within the snRNA-seq data and inferring their expression in the SRT data. We identified NMF patterns that captured transcriptional variation across neuronal cell types and indicated that the response of excitatory and inhibitory postsynaptic specializations were prioritized in different SRT spatial domains. We used the NMF and label transfer approach to leverage existing rodent datasets, identifying patterns of activity-dependent transcription and subpopulations of dentate gyrus granule cells in our SRT dataset that may be predisposed to participate in learning and memory ensembles. Finally, we characterized the spatial organization of NMF patterns corresponding to non-cornu ammonis pyramidal neurons and identified snRNA-seq clusters mapping to distinct regions of the retrohippocampus, to three subiculum layers, and to a population of presubiculum neurons. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.

neuroscience↗

bioRxiv2024

lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression

Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. Current computational tools estimate relative RNA abundances rather than cell type proportions in tissues with varying cell sizes, leading to biased estimates. We present lute, a computational tool to accurately deconvolute cell types with varying sizes. Our software wraps existing deconvolution algorithms in a standardized framework. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition. Software is available from https://bioconductor.org/packages/lute.

bioinformatics↗

bioRxiv2024

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

BackgroundCellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. ResultsA rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. ConclusionsBisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.

neuroscience↗

bioRxiv2023

Influence of Alzheimer's disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex

Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which measures spatial gene expression coupled with immunofluorescence protein co-detection, in post-mortem human brain tissue from individuals with late-stage Alzheimers disease (AD) to investigate changes in spatial gene expression with respect to amyloid-{beta} (A{beta}) and hyperphosphorylated tau (pTau) pathology. We identified A{beta}-associated transcriptomic signatures in the human inferior temporal cortex (ITC) during late-stage AD, which we further investigated at cellular resolution with combined immunofluorescence and single molecule fluorescent in situ hybridization (smFISH) co-detection technology. We present a workflow for analysis of Visium-SPG data and demonstrate the power of multi-omic profiling to identify spatially-localized changes in molecular dynamics that are linked to pathology in human brain disease. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes at https://research.libd.org/Visium_SPG_AD/.

neuroscience↗

bioRxiv2023

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/. SummaryGeneration of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease.

neuroscience↗

bioRxiv2022

The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimers and Parkinsons disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats.

neuroscience↗

bioRxiv2022

Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types

Next-generation sequencing technologies have facilitated data-driven identification of gene sets with different features including genes with stable expression, cell-type specific expression, or spatially variable expression. Here, we aimed to define and identify a new class of "control" genes called Total RNA Expression Genes (TREGs), which correlate with total RNA abundance in heterogeneous cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single cell RNA-sequencing (RNA-seq) data, available as an R/Bioconductor package at https://bioconductor.org/packages/TREG. We demonstrated the utility of our method in the postmortem human brain using multiplex single molecule fluorescent in situ hybridization (smFISH) and compared candidate TREGs against classic housekeeping genes. We identified AKT3 as a top TREG across five brain regions, especially in the dorsolateral prefrontal cortex.

bioinformatics↗

bioRxiv2022

Wireless closed-loop smart bandage for chronic wound management and accelerated tissue regeneration

Chronic non-healing wounds represent a major source of morbidity for patients and a significant economic burden. Current wound care treatments are generally passive and are unable to adapt to changes in the wound environment in real time. By integrating multimodal sensors and adding stimulators in a bandage, real-time physiological monitoring is possible and provides an opportunity for active intervention into the complex wound environment. Here, we develop a battery-free flexible bioelectronic system consisting of wirelessly powered, closed-loop sensing and stimulation circuits with tissue-interfacing tough conducting hydrogel electrodes for robust signal transduction, on-demand adhesion, and detachment. Using multiple pre-clinical models, we demonstrate the capability of our wound care system to continuously monitor skin impedance and temperature, to trigger directional electrical stimulation. The accelerated wound closure was confirmed to be due to the activation of pro-regenerative genes linked to accelerated wound closure, increased neovascularization, and enhanced dermal recovery.

bioengineering↗

bioRxiv2022

Allometric tissue-scale forces activate mechanoresponsive immune cells to drive pathological foreign body response to biomedical implants

For decades, it has been assumed that the foreign body response (FBR) to biomedical implants is primarily a reaction to the chemical and mechanical properties of the implant. Here, we show for the first time that a third independent variable, allometric tissue-scale forces (which increase exponentially with body size), can drive the biology of FBR in humans. We first demonstrate that pathological FBR in humans is mediated by immune cell-specific Rac2 mechanotransduction signaling, independent of implant chemistry or mechanical properties. We then show that mice, which are typically poor models of human FBR, can be made to induce a strikingly human-like pathological FBR by altering these extrinsic tissue forces. Altering these extrinsic tissue forces alone activates Rac2 signaling in a unique subpopulation of immune cells and results in a human-like pathological FBR at the molecular, cellular, and local tissue levels. Finally, we demonstrate that blocking Rac2 signaling negates the effect of increased tissue forces, dramatically reducing FBR. These findings highlight a previously unsuspected mechanism for pathological FBR and may have profound implications for the design and safety of all implantable devices in humans. One-Sentence SummaryAllometric tissue-scale forces at the implant-tissue interface drive pathological foreign body response.

bioengineering↗

bioRxiv2021

SUFI: An automated approach to spectral unmixing of fluorescent biological images

Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that uses multiple fluorescent dyes - each measuring a specific biological signal - that are simultaneously measured and subsequently "unmixed" to provide a read-out for each individual signal. This strategy allows for measuring multiple signals in a single data capture session - for example, multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are not optimized for challenging biological specimens such as postmortem human brain tissue, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images (https://github.com/LieberInstitute/SUFI). This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using Vertex Component Analysis, and then performs one of three unmixing algorithms derived from remote sensing. We demonstrate these remote sensing algorithms performance on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot that is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis of smFISH data, and thereby provide a one-stop solution for multispectral fluorescence image analysis and quantification. In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.

bioinformatics↗

bioRxiv2020

Complement and coagulation cascades are potentially involved in dopaminergic neurodegeneration in alpha-synuclein-based mouse models of Parkinson's disease

Parkinsons disease (PD) is the second most common neurodegenerative disorder that results in motor dysfunction and eventually, cognitive impairment. -Synuclein protein has been known to be the most culprit protein, but the underlying pathological mechanism still remains to be elucidated. As an effort to clarify the pathogenesis mechanism by -synuclein, various PD mouse models with -synuclein overexpression have been developed. However, the systemic analysis of protein abundance change by the overexpressed -synuclein in the whole proteome level has been still lacking. To address this issue, we established two sophisticated mouse models of PD by injecting -synuclein preformed fibrils (PFF) or by inducing overexpression of human A53T -synuclein to discover overlapping pathways, which could be altered in the two different types of PD mouse model. For more accurate quantification of mouse brain proteome, stable isotope labeling with amino acid in mammal-based quantification was implemented. As a result, we have successfully identified a total of 8,355 proteins from both of the mouse models; [~]6,800 and [~]7,200 proteins from -synuclein PFF injected mice and human A53T -synuclein transgenic mice, respectively. From the pathway analysis of the differentially expressed proteins in common, the complement and coagulation cascade pathway were determined as the most enriched ones. This is the first study that highlights the significance of the complement and coagulation pathway in the pathogenesis of PD through proteome analyses with two sophisticated mouse models of PD.

neuroscience↗