Biology subjects

Park, W.-Y.

Publications and source records attributed to Park, W.-Y..

17 recordsLinked to original sources

bioRxiv2026

A Community-Driven Single-Cell PBMC Reference Integrating Landmark Datasets Spanning Health and Disease

Blood provides an accessible window into human health, yet the absence of a peripheral blood mononuclear cells (PBMCs) reference with standardized immune cell annotations has constrained comparisons between single-cell RNA sequencing (scRNAseq) studies. Here, we present HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million PBMCs from >2,600 donors across 15 studies spanning four continents, encompassing diverse populations, neonates to 97 years, across health and diverse immune-related diseases. We developed optimized integration workflows, including novel label-free metrics to assess integration quality, and generated community-driven consensus annotations for 192 immune cell subsets, identifying rare populations representing as few as 0.004% of PBMCs. HARP reveals coordinated cellular modules associated with age, sex, and disease, identifies female-biased interferon and inflammatory gene programs and their perturbation by COVID-19, and uncovers a novel sexual dimorphism in prostaglandin signaling. Finally, we introduce scTiger, a hierarchical label-transfer framework that accurately projects these 192 cell annotations onto ~18 million additional PBMCs, providing a robust and transferable reference for harmonizing future PBMC studies. HARP provides a high-resolution community-driven reference atlas for PBMC annotation and analysis, as a basis for emerging clinical applications of single-cell genomics.

immunology↗

bioRxiv2026

AI-Informed neoantigen prioritization enables a multi-epitope mRNA/LNP vaccine with antigen-specific immunogenicity and antitumor activity

Personalized neoantigen vaccines are an emerging strategy for cancer immunotherapy, but their effectiveness depends on selecting tumor-specific antigens capable of inducing functional T-cell responses. The VACINUS AI-informed neoantigen prioritization framework previously identified and peptide-validated three immunogenic Tier 1 neoantigens in the B16F10 melanoma model. In this study, we extended that framework by translating these validated neoantigens into a multi-epitope messenger RNA vaccine formulated with lipid nanoparticles and evaluating its preclinical immunogenicity and antitumor activity. The three VACINUS-prioritized B16F10 neoantigens were encoded within a single multi-epitope construct, BF-V1_27-Ser, and formulated to generate BF-RNA-P. In B16F10 tumor-bearing mice, BF-RNA-P induced neoantigen-specific CD8+T-cell responses, with the strongest response directed against the B16F10-1-4 epitope. Combination with anti-PD-1 further enhanced vaccine-induced CD44+IFN-{gamma}+ CD8+ T-cell activation, whereas anti-PD-1 alone did not induce detectable peptide-specific responses. BF-RNA-P also suppressed tumor growth in vivo, and combination treatment produced the strongest antitumor effect, reflected by reduced tumor volume and lower endpoint tumor burden. Together, these findings demonstrate that VACINUS-prioritized and peptide-validated neoantigens can be reformatted into a multi-epitope messenger RNA/lipid nanoparticle vaccine while retaining antigen-specific immunogenicity and antitumor activity. This study provides preclinical proof-of-concept for integrating AI-informed, TCR-aware neoantigen prioritization with messenger RNA/lipid nanoparticle delivery as a translational strategy for personalized cancer vaccine development.

immunology↗

bioRxiv2026

Cellular Complexity and Systemic Immune Profiles across Ancestral Diversity in Thailand and Mainland Southeast Asia

Mainland Southeast Asia (MSEA) remains under-represented in global immunogenomic references despite its extensive genetic heterogeneity. We present the first single-cell immune atlas of an MSEA population, utilizing Thai individuals from the Asian Immune Diversity Atlas (AIDA) as a representative cohort. We demonstrate that the Thai population is highly genetically diverse, reflecting its history as a geographic nexus for Asian admixture. By integrating single-cell transcriptomics with high-resolution genotyping, we show that genetic ancestry significantly shapes innate immune profiles, specifically CD14+ monocytes, highlighting potential evolutionary adaptations to regional pathogens. We identify specific sex-by-ancestry interactions that may drive the baseline activation of pro-inflammatory pathways in females, providing a long-sought cellular rationale for the high prevalence of autoimmune disorders observed in Southeast Asian populations. Ultimately, our study reveals that population-specific genetic architecture dictates immune heterogeneity often missed by self-reported ethnicity or country of origin, providing a critical immunogenomic reference for precision medicine in MSEA regions.

systems biology↗

bioRxiv2026

Federated single-cell QTL meta-analysis reveals novel disease mechanisms

Genetic effects on gene expression are often cell type-specific and obscured in bulk analyses. To resolve this context-dependent regulation, we performed a federated cis-eQTL meta-analysis across 12 PBMC datasets (2,032 individuals, 2.5 million cells). Across six immune cell types, we identified cis-eQTLs for 6,592 genes and fine-mapped 14,985 independent loci. Notably, the 42% of eQTLs that were undetected in a bulk eQTL study on 43,301 whole blood samples also showed stronger enrichment for disease GWAS loci. We further identified three genome-wide significant and 65 suggestive loci affecting the abundance of (rare) immune cell types and validated these using previously reported hematological GWAS and bulk-derived trans-eQTLs. Integrating single-cell cis-eQTLs with bulk trans-eQTLs enabled us to anchor 6,382 trans-eGenes (37.2% novel) to upstream regulators and reconstruct directed gene regulatory relationships. For example, a hemorrhoidal disease-associated variant showed a CD4+ T cell-specific cis-eQTL on BACH1 that colocalized with 45 immune and metabolic trans-eGenes. These results demonstrate the power of single-cell QTL meta-analysis in interpreting complex trait genetics.

genetics↗

bioRxiv2025

STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features

The adaptive immune system monitors cellular integrity by recognizing short peptides from intracellular proteins presented on Major Histocompatibility Complex class I (MHC-I) molecules, collectively termed peptide-MHC complexes (pMHC), enabling detection of foreign or mutated proteins. With the rising importance of immunotherapies targeting neoantigens in cancers, the ability to accurately predict which peptides will bind to the diverse population of MHC alleles is critically important. Current computational methods for pMHC-I prediction fall broadly into sequence-based methods, which rely heavily on large training datasets, and structure-based methods that leverage structural modeling and energetics of pMHC binding. While sequence-based methods have been popularly used, their performance is dependent on the size and quality of training data. On the other hands, while structure-based approaches can generalize better across diverse MHC alleles, they traditionally depend on identifying a single global minimum energy conformation, an assumption that often fails due to the inherent binding promiscuity of MHC-I molecules. To address these limitations, we developed a STRUMP-I (STRUcture-based pMHC Prediction (for class I)), a novel pMHC binding prediction tool that directly leverages a broad set of force-field-derived energy terms as machine-learning features. STRUMP-I achieves performance comparable to state-of-the-art sequence-based models while significantly outperforming them on MHC alleles with limited representation in training data. Furthermore, STRUMP-I demonstrates strong synergy when integrated with sequence-based methods, notably enhancing prediction precision. The robustness and generalizability of STRUMP-I were confirmed by evaluating its predictive performance on independent, previously unseen datasets, including an experimentally validated cancer neoantigen dataset. This combined approach advances our capability to reliably identify clinically relevant neoantigen targets. The source code and trained models are available at https://github.com/yoonjoolab/STRUMP-I

bioinformatics↗

bioRxiv2024

Integrated analysis of COVID-19 multi-omics data for eQTLs reveals genetic mechanisms underlying disease severity

The global pandemic caused by the SARS-CoV-2 virus provided an unprecedented opportunity to investigate genetic factors influencing the disease severity of the viral infection. Despite a plethora of recent research on both SARS-CoV-2 and COVID-19, few have taken a systems biology approach to address individual-level variation, especially based on non-European populations. Accordingly, we analyzed multi-omics data generated at three timepoints from 193 Korean COVID-19 patients with mild or severe symptoms, composed of whole genome sequencing, blood-based single-cell RNA-sequencing (2.15M cells), 195 cytokine profiles, and human leukocyte antigen (HLA) allele data. We identified expression quantitative trait loci (eQTLs), disease severity interacting eQTLs (n = 388), and disease progression interacting eQTLs (n = 945) for various cell types. We elucidated a complex regulatory mechanism involving HLA genes and their targets, and identified genetic determinants of cytokine levels. Finally, we show how regulation of ieQTLs is established by upstream transcription factors (TFs), illustrating complex regulation of the IGFBP7 ieQTL by a combined action of two TFs, which is potentially important in conferring differential severity. This study illuminates an efficient molecular interrogation framework that can be applied toward understanding infectious disease progression in individuals of different genotypes.

genetics↗

bioRxiv2024

Systemic CD8+ T cell effector signature predicts prognosis of lung cancer immunotherapy

BackgroundWhile immune checkpoint inhibitors (ICIs) are adopted as standard therapy in non-small cell lung cancer (NSCLC) patients, factors that influence variable prognosis still remain elusive. Therefore, a deeper understanding is needed of how germline variants regulate the transcriptomes of circulating immune cells in metastasis, and ultimately influence immunotherapy outcomes. MethodsWe collected peripheral blood mononuclear cells (PBMCs) from 73 ICI-treated NSCLC patients, conducted single-cell RNA sequencing, and called germline variants via SNP microarray. Determination of expression quantitative trait loci (eQTL) allows elucidating genetic interactions between germline variants and gene expression. Utilizing aggregation-based eQTL mapping and network analysis across eight blood cell types, we sought cell-type-specific and ICI-prognosis-dependent gene regulatory signatures. ResultsOur sc-eQTL analysis identified 3,616 blood- and 702 lung-cancer-specific eGenes across eight major clusters and treatment conditions, highlighting involvement of immune-related pathways. Network analysis revealed TBX21-EOMES regulons activity in CD8+ T cells and the enrichment of eQTLs in higher-centrality genes as predictive factors of ICI response. ConclusionsOur findings suggest that in the circulating immune cells of NSCLC patients, transcriptomic regulation differs in a cell type- and treatment-specific manner. They further highlight the role of eQTL loci as broad controllers of ICI-prognosis-predicting gene networks. The predictive networks and identification of eQTL contributions can lead to deeper understanding and personalized ICI therapy response prediction based on germline variants.

genomics↗

bioRxiv2024

Hypothalamus-specific NSCs derived from hPSCs ameliorate age-associated dysfunction upon transplantation into aged mouse hypothalamus

The hypothalamus is the brain region that regulates systemic body metabolism and multiple brain functions. The adult hypothalamus harbors neural stem/precursor cell (NSC)-like cells. Along with age-related body changes, the hypothalamic NSC (htNSC) population declines, indicating the potential of htNSC replacement as an anti-aging strategy. Here, we developed protocol to generate htNSCs from human pluripotent stem cells (hPSCs). Implanting the hPSC-derived htNSCs into the hypothalamus of aged mice ameliorated age-related declines in metabolic fitness, physical capacity, and cognitive function. Mechanistically, these anti-aging effects were mediated by inter-tissue communication: enhanced neuronal activity in the htNSC-transplanted hypothalamus stimulated adipose tissues to produce and release the anti-aging molecule eNAMPT into systemic circulation via the sympathetic nervous system. Concurrently, the aged inflammatory environment in the hypothalamus was alleviated by peripheral anti-aging signals. Collectively, our findings support the potential of anti- or healthy aging therapies by targeting hypothalamus.

cell biology↗

bioRxiv2024

Increased inflammatory signature in myeloid cells of non-small cell lung cancer patients with high clonal hematopoiesis burden

Clonal hematopoiesis of indeterminate potential (CHIP) allows estimation of clonal dynamics and documentation of somatic mutations in the hematopoietic system. Recent studies utilizing large cohorts of the general population and patients have revealed significant associations of CHIP burden with age and disease status, including in cancer and chronic diseases. An increasing number of cancer patients are treated with immune checkpoint inhibitors (ICI), but the association of ICI response in non-small cell lung cancer (NSCLC) patients with CHIP burden remains to be determined. We collected blood samples from 100 metastatic NSCLC patients before and after ICI for high-depth sequencing of the CHIP panel and 63 samples for blood single-cell RNA sequencing(scRNA-seq). Whole exome sequencing (WES) was performed in an independent replication cohort of 180 patients. The impact of CHIP status on the immunotherapy response was not significant. However, metastatic lung cancer patients showed higher CHIP prevalence (44/100 for patients vs 5/42 for controls; P = 0.01). In addition, lung squamous cell carcinoma patients showed increased burden of larger clones compared to lung adenocarcinoma patients (8/43 for LUSC vs 2/50 for LUAD; P = 0.04). Furthermore, single cell RNA-seq analysis of the matched patients showed significant enrichment of inflammatory pathways mediated by NF-B in myeloid clusters of the severe CHIP group. Our findings suggest minimal involvement of CHIP mutation and clonal dynamics during immunotherapy but a possible role of CHIP as an indicator of immunologic response in NSCLC patients.

genomics↗

bioRxiv2023

TSpred: a robust prediction framework for TCR-epitope interactions based on an ensemble deep learning approach using paired chain TCR sequence data

Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications such as cancer immunotherapy. However, due to the scarcity of available data, it is known to be a challenging task particularly for novel epitopes. Here, we propose TSpred, a new ensemble deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. This method combines the predictive power of CNN and the attention mechanism to capture the patterns underlying TCR-epitope interactions. In particular, we design a reciprocal attention mechanism which contributes to higher model generalizability to unseen epitopes. We perform a comprehensive evaluation of our model and observe that TSpred achieves state-of-the-art performances in both seen and unseen epitope specificity prediction tasks. Our model performs consistently well across both of the two widely used negative sampling strategies, while avoiding the potential bias associated with each strategy. Also, compared to other predictors, it is more robust to bias related to peptide imbalance in the dataset. In addition, the reciprocal attention component of our model allows for model interpretability by capturing structurally important binding regions. Results indicate that TSpred is a robust and reliable method for the task of TCR-epitope binding prediction.

bioinformatics↗

bioRxiv2023

Decoding spatial organization maps and context-specific landscapes of breast cancer and its microenvironment via high-resolution spatial transcriptomic analysis

Single-cell RNA transcriptomics has revealed the intricate heterogeneity of both tumors and their microenvironment. However, a notable limitation is its inability to retain spatial context, a crucial aspect of understanding cell identity and function. In this study, we employed imaging-based single-cell spatial transcriptomics to elucidate the tumor and immunological landscapes of two breast cancer samples. By resolving over 400 000 cells per slide, we demonstrated that transcriptional differences lead to structural disparities within and between tumors. Additionally, we observed that the composition of the tumor microenvironment varies depending on its spatial location. Notably, we detected immune cell gradients transitioning from the tumor periphery to its core regions and from tertiary lymphoid structure to immune inflamed regions, in alignment with the specific function of each cell type. This finding facilitated a more precise classification of the tumor immune microenvironment. This study provides a comprehensive dataset for breast cancer researchers and underscores the significance of spatial context in understanding the multifaceted heterogeneity of cancer and its environment.

cancer biology↗

bioRxiv2023

Pan-cancer analysis reveals multifaceted roles of retrotransposon-fusion RNAs

Transposon-derived transcripts are abundant in RNA sequences, yet their landscape and function, especially for fusion transcripts derived from unannotated or somatically acquired transposons, remains underexplored. Here, we developed a new bioinformatic tool to detect transposon-fusion transcripts in RNA-sequencing data and performed a pan-cancer analysis of 10,257 cancer samples across 34 cancer types as well as 3,088 normal tissue samples. We identified 52,277 cancer-specific fusions with [~]30 events per cancer and hotspot loci within transposons vulnerable to fusion formation. Exonization of intronic transposons was the most prevalent genic fusions, while somatic L1 insertions constituted a small fraction of cancer-specific fusions. Source L1s and HERVs, but not Alus showed decreased DNA methylation in cancer upon fusion formation. Overall cancer-specific L1 fusions were enriched in tumor suppressors while Alu fusions were enriched in oncogenes, including recurrent Alu fusions in EZH2 predictive of patient survival. We also demonstrated that transposon-derived peptides triggered CD8+ T-cell activation to the extent comparable to EBV viruses. Our findings reveal distinct epigenetic and tumorigenic mechanisms underlying transposon fusions across different families and highlight transposons as novel therapeutic targets and the source of potent neoantigens.

cancer biology↗

bioRxiv2023

Quantification of the escape from X chromosome inactivation with the million cell-scale human single-cell omics datasets reveals heterogeneity of escape across cell types and tissues

One of the two X chromosomes of females is silenced through X chromosome inactivation (XCI) to compensate for the difference in the dosage between sexes. Among the X-linked genes, several genes escape from XCI, which could contribute to the differential gene expression between the sexes. However, the differences in the escape across cell types and tissues are still poorly characterized because no methods could directly evaluate the escape under a physiological condition at the cell-cluster resolution with versatile technology. Here, we developed a method, single-cell Level inactivated X chromosome mapping (scLinaX), which directly quantifies relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA-sequencing (scRNA-seq) data. The scLinaX and differentially expressed genes analyses with the scRNA-seq datasets of [~]1,000,000 blood cells consistently identified the relatively strong degree of escape in lymphocytes compared to myeloid cells. An extension of scLinaX for multi-modal datasets, scLinaX-multi, suggested a stronger degree of escape in lymphocytes than myeloid cells at the chromatin-accessibility level with a 10X multiome dataset. The scLinaX analysis with the human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes identified the larger effect sizes of the PRKX gene locus-lymphocyte counts association in females than males. This could suggest evidence of the underlying impact of escape on the genotype-phenotype association in humans. Overall, scLinaX and the quantified catalog of escape identified the heterogeneity of escape across cell types and tissues and would contribute to expanding the current understanding of the XCI, escape, and sex differences in gene regulation.

genomics↗

bioRxiv2022

Genetic architecture of creativity and extensive genetic overlap with psychiatric disorders revealed from genome-wide association analyses of 241,736 individuals

Creativity is heritable and exhibits familial aggregation with psychiatric disorders, but its genomic basis and genetic relationship with psychiatric disorders remain largely unknown. Here, we conducted a genome-wide association study (GWAS) using an expanded, machine learning-based definition of creativity in individuals of European ancestry from the UK Biobank (n = 241,736) and identified 25 creativity-associated loci. Extensive genetic overlap with psychiatric disorders, including schizophrenia, major depression, bipolar I disorder, attention deficit/hyperactivity disorder, and anorexia nervosa, was demonstrated by the genetic correlation, polygenic risk score, and MiXeR analyses. The condFDR and conjFDR analyses identified additional loci for creativity and psychiatric disorders, as well as shared genetic loci between creativity and psychiatric disorders. This GWAS showed significant correlations with GWASs using traditional definitions of creativity and GWASs adjusted for educational attainment. Our findings contribute to the understanding of the genetic architecture of creativity and reveal its polygenic relationships with psychiatric disorders.

genomics↗

bioRxiv2021

Symmetry breaking of hPSCs in micropattern generates a polarized spinal cord-like organoid (pSCO) with dorsoventral organization

Brain organoid research is advancing, but generation of organoids with proper axis formation, which could lead to spatially ordered structures for complex brain structure and function, still remains a challenge. Axis formation and related spatial cell organization in the CNS are initiated by the symmetry breaking during the early embryo development. It has been demonstrated that the geometrically confined culture of human pluripotent stem cells (hPSCs) can be used to induce symmetry breaking and regionalized cell differentiation. In this study, we generated a polarized spinal cord organoid with a self-organized dorsoventral (DV) organization, using 2D cell patterning by geometric confinement. Initially, the application of caudalization signals to hPSCs promoted the regionalized cell differentiation along the radial axis and sprouting-like protrusion morphogenesis in cell colonies confined to ECM protein micropatterns. Detachment of colonies turned them into extended spinal cord-like organoids which maintained center- and edge-derived two poles. Further analyses including single cell RNA sequencing and spatial transcriptome analysis unveiled that these organoids contained rich repertoire of developing spinal cord cells and exhibited the spatially ordered DV domain formation along the long axis without external organizing signals. Modulation of BMP and Shh signaling can control the extent of DV coverage in organoids following the principles of embryo patterning. Our study provides a simple, and precisely controllable method to generate spatially-ordered organoids for understanding of biological principles of cell patterning and axis formation during neural development.

developmental biology↗

bioRxiv2019

JuLI: accurate detection of DNA fusions in clinical sequencing for precision oncology

Accurate detection of genomic fusions by high-throughput sequencing in clinical samples with inadequate tumor purity and formalin-fixed paraffin embedded (FFPE) tissue is an essential task in precise oncology. We developed the fusion detection algorithm Junction Location Identifier (JuLI) for optimization of high-depth clinical sequencing. We implemented novel filtering steps to minimize false positives and a joint calling function to increase sensitivity in clinical setting. We comprehensively validated the algorithm using high-depth sequencing data from cancer cell lines and clinical samples and whole genome sequencing data from NA12878. We showed that JuLI outperformed state-of-the-art fusion callers in cases with high-depth clinical sequencing and rescued a driver fusion from false negative in plasma cell-free DNA. JuLI is freely available via GitHub (https://github.com/sgilab/JuLI).

bioinformatics↗

bioRxiv2018

Constructing and visualizing cancer genomic maps in 3D spatial context by phenotype-based high-throughput laser-aided isolation and sequencing (PHLI-seq)

A spatially resolved analysis of the heterogeneous cancer genome, in which the data are connected to the three-dimensional space of a tumour, is crucial to understand cancer biology and the clinical impact of cancer heterogeneity on patients. However, despite recent progress in spatially resolved transcriptomics, spatial mapping of genomic data in a high-throughput and high-resolution manner has been challenging due to current technical limitations. Here, we describe a novel approach, phenotype-based high-throughput laser-aided isolation and sequencing (PHLI-seq), which enables high-throughput isolation of a single-cell or a small number of cells and their genome-wide sequence analysis to construct genomic maps within cancer tissue in relation to the phenotypes of the cells. By applying PHLI-seq, we reveal the heterogeneity of breast cancer tissues at a high resolution and map the genomic landscape of the cells to their corresponding spatial locations and phenotypes in the tumour mass. Additionally, with different staining modalities, the genotypes of the cells can be connected to corresponding phenotypic information of the tissue. Together with the spatially resolved genomic analysis, we can infer the histories of heterogeneous cancer cells in two or three dimensions, providing significant insight into cancer biology and precision medicine.

genomics↗