Biology subjects

Na, K. J.

Publications and source records attributed to Na, K. J..

12 recordsLinked to original sources

bioRxiv2026

Spatial Transcriptomics Reveals a Conserved Border Niche and Etiology-Associated Immune Rewiring in Hepatocellular Carcinoma

Background and AimsThe tumor-stroma interface in hepatocellular carcinoma (HCC) harbors critical intercellular interactions that shape immune evasion and treatment response, yet its spatial architecture remains poorly characterized across etiologies. Whether hepatitis B virus (HBV)-related and non-B non-C (NBNC) HCC share conserved border niche features or exhibit etiology-specific microenvironment programs is unknown. We aimed to spatially resolve the tumor boundary ecosystem and identify etiology-associated signaling networks with translational relevance. Approach and ResultsWe performed 10x Visium spatial transcriptomics on 11 HCC specimens (7 HBV, 4 NBNC) and applied a machine-learning pipeline integrating CancerFinder and SpaceFlow to define tumor, boundary, and stromal domains. Across etiologies, the boundary zone showed a recurrent desmoplastic niche characterized by cancer-associated fibroblast, tumor-associated macrophage, and tumor endothelial cell accumulation with collagen-integrin extracellular matrix remodeling, including COL1A1-ITGA11 and COL4A1-ITGAV. Etiology-associated differences were observed in the organization of border-zone signaling programs. In representative HBV-related sections, CCL19-CCR7 signaling showed a comparatively restricted, endothelial-skewed topology, whereas representative NBNC sections showed broader inflammatory ligand-receptor networks with elevated NF-kB-associated pathway activity. ConclusionsThe HCC tumor-stroma border harbors a recurrent desmoplastic niche upon which etiology-associated immune regulatory programs may be superimposed. These findings generate spatial hypotheses relevant to etiology-informed biomarker development and future therapeutic stratification.

cancer biology↗

bioRxiv2026

Spatial tumor microenvironmental architecture of chemoradiotherapy-resistant residual esophageal squamous cell carcinoma

PurposeResidual ESCC after neoadjuvant CCRT reflects incomplete treatment response and carries a high risk of recurrence. We sought to define the spatial tumor microenvironmental features of CCRT-resistant residual ESCC -- using tumor regression grade (TRG) 2-3 tumors as a model of poor pathologic response -- with mechanistic refinement at single-cell resolution and outcome assessment in internal and external cohorts. Experimental DesignWe profiled post-CCRT ESCC tissue microarray (TMA) cores by Visium FFPE whole-transcriptome spatial transcriptomics and orthogonal Xenium single-cell-resolution in situ profiling on serial sections from the same TMA blocks. Spot-level cell-type composition was inferred by CellDART deconvolution against a published ESCC single-cell RNA sequencing (scRNAseq). Local malignant-cell-enriched regions were defined by CancerFinder. We additionally re-analyzed the scRNAseq dataset to characterize SPP1 and CXCL5 macrophage states and their ligand-receptor signaling using CellChat. Outcome associations were evaluated in the SNUH cohort and externally tested in the TCGA ESCC cohort. ResultsTRG 2-3 residual tumors exhibited effector immune-cell exclusion from malignant-cell-enriched regions, SPP1/CXCL5 macrophage accumulation, microvascular rarefaction, and proliferative-metabolic reprogramming. Single-cell re-analysis confirmed that CXCL5 macrophages constitute a transcriptional subset of the broader SPP1 macrophage population. CellChat showed that SPP1 macrophages dominantly signal through SPP1-CD44 and SPP1-integrin axes to stromal and epithelial targets, and additionally engage immunosuppressive NECTIN2-TIGIT, CD86-CTLA4, and LGALS9-HAVCR2 programs with CD8 T cells, providing a mechanistic context for local immune exclusion. Visium LIANA and Xenium distance-gradient profiling localized SPP1-associated signaling preferentially to tumor cells and CAFs. Higher SPP1 expression and lower endothelial abundance were associated with shorter disease-free survival in the SNUH cohort. Higher SPP1 expression was also associated with shorter disease-free survival in the independent TCGA ESCC cohort. ConclusionsCCRT-resistant residual ESCC is characterized by a spatially organized tumor microenvironmental niche centered on SPP1-associated macrophage programs and microvascular rarefaction. These spatially resolved findings identify candidate macrophage- and vasculature-targeted axes for overcoming treatment resistance. Translational RelevancePatients with esophageal squamous cell carcinoma (ESCC) who harbor residual disease after neoadjuvant chemoradiotherapy (CCRT) remain at high risk of recurrence, yet the spatial biology of this resistant residual state has been poorly defined. Here, we combine Visium whole-transcriptome and Xenium single-cell-resolution spatial transcriptomics with single-cell RNA-seq re-analysis and external TCGA context to define the tumor microenvironmental architecture of CCRT-resistant residual ESCC. The resistant niche is characterized by effector immune-cell exclusion from malignant-cell-enriched regions, accumulation of SPP1/CXCL5 macrophage programs that signal to tumor cells and cancer-associated fibroblasts through SPP1-CD44 and SPP1-integrin axes, microvascular rarefaction, and proliferative-metabolic reprogramming. These findings define a coordinated spatial tumor microenvironmental signature of CCRT-resistant residual ESCC and nominate macrophage-tumor/stromal interactions and vascular injury as biologic axes that may help guide future strategies to overcome treatment resistance.

cancer biology↗

bioRxiv2026

Distinct Spatial Programs of Response versus Resistance in Non-Small Cell Lung Cancer after Neoadjuvant Chemoimmunotherapy

BackgroundNeoadjuvant chemoimmunotherapy (nCIT) has become a standard treatment for locally advanced resectable non-small cell lung cancer (NSCLC), yet the spatial biology underlying treatment resistance remains poorly understood. We used spatial transcriptomics to define the microenvironmental architecture of residual cancers in patients who did not achieve major pathologic response (non-MPR) compared with those who did (MPR). MethodsSpatial transcriptomics was performed on 10 formalin-fixed paraffin-embedded (FFPE) tumor blocks (5 MPR, 5 non-MPR) obtained from 8 patients treated with nCIT. A deep learning algorithm was applied to detect viable residual cancer spots from treatment-induced fibrosis and necrosis. Spatial deconvolution, distance modeling, ligand-receptor analysis, and functional pathway scoring were integrated to characterize niche-specific programs. ResultsMPR cancer core displayed an immune-permissive remodeling environment with deep infiltration of cytotoxic CD8+ T cells, mature dendritic cells (LAMP3+, CCR7+), and active efferocytosis signaling (APOE-TREM2), alongside robust MHC class II expression. Non-MPR cancer core, by contrast, exhibited spatial immune exclusion: a dense fibroblast barrier reinforced by TIMP1-CD63 signaling and Treg-enriched boundaries physically restricted effector T cell access to the cancer core. Residual cancer cells in non-MPR samples maintained active cell cycling and independently upregulated cytochrome P450-mediated drug detoxification and DNA damage response pathways without inducing MHC class II expression -- effectively decoupling intrinsic survival from immune recognition. The non-MPR core also showed a hyper-metabolic profile, including elevated glutathione metabolism consistent with antioxidant buffering against chemotherapy-induced oxidative stress. TROP2 was broadly expressed across the non-MPR cancer core and co-localized with DNA damage response and nuclear factor erythroid 2-related factor 2 resistance signatures. ConclusionsResidual cancer cores in non-MPR tumors appear to represent evolved resistant niches sustained by structural immune exclusion, metabolic rewiring, and DNA repair proficiency. These findings highlight the spatial co-localization of epithelial anchors, such as TROP2, with intrinsic resistance pathways, providing a structural rationale for developing novel precision therapeutic strategies to bypass stromal barriers and overcome the cancer cores intrinsic repair capacity.

cancer biology↗

bioRxiv2024

Spatial transcriptomics unveils landscape of resistance to concurrent chemo-radiotherapy in hypopharyngeal squamous cell carcinoma: the role of SPP1+ macrophages

Hypopharyngeal squamous cell carcinoma (SCC) is a highly aggressive cancer with a poor prognosis, particularly in advanced stages where concurrent chemoradiotherapy (CCRT) is used for treatment. However, resistance to CCRT poses a significant challenge, often leading to treatment failure and disease progression. This study explores the tumor microenvironment (TME) of hypopharyngeal SCC to understand the molecular mechanisms underlying CCRT resistance. Using spatial transcriptomics (ST), we analyzed tissue samples from patients with locally advanced hypopharyngeal SCC, distinguishing between those who were CCRT-resistant and those who were CCRT-naive. The analysis revealed six distinct cellular clusters within the TME, including a prominent epithelio-immune cellular area in CCRT-resistant tissues. SPP1 was identified as a key gene with significantly higher expression in CCRT-resistant samples, specifically within macrophages. Further investigation showed that SPP1+ macrophages interacted with malignant epithelial cells through SPP1-CD44 and SPP1-ITGB1 ligand-receptor pairs. These interactions were primarily localized in the peri-tumoral and intra-tumoral regions, highlighting their potential role in driving CCRT resistance. Our findings suggest that SPP1+ macrophages contribute to the resistant phenotype in hypopharyngeal SCC by modulating the TME and interacting with cancer cells. Understanding these interactions offers valuable insights into the mechanisms of CCRT resistance and may inform the development of targeted therapies to improve patient outcomes.

cancer biology↗

bioRxiv2024

Analysis of Unmapped RNA-seq Data from Cancer Spatial Transcriptome to Decipher Cancer Microbiome

Recent research increasingly emphasizes the importance of the microbiome in the development and progression of cancer. Thus, exploring the microbiome modulation in tumor microenvironment and understanding its composition and function are becoming important. The introduction of spatial transcriptomics has provided new insights into microbiome research, as it enables how microbiome affects tumor microenvironment. In this study, we analyzed the spatial distribution of microbial RNA observed through PathSeq and our extended method for unmapped RNA reads in oral squamous cell carcinoma, head and neck cancer, and colorectal cancer samples. Our novel method is designed to identify and classify microbial RNA, using a custom reference that includes species-specific microbial 16S rRNA sequences to enhance the accuracy of species-level microbiome analysis. The results of this study showed a potential for a deeper understanding of the microbial distribution and their functional roles within cancer tissues. These findings will reevaluate the role of the microbiome in cancer research, providing an insight for the development of microbiome-based therapies in the future.

bioinformatics↗

bioRxiv2024

Spatial Transcriptomics Reveals Spatially Diverse Cancer-Associated Fibroblast in Lung Squamous Cell Carcinoma Linked to Tumor Progression

While cancer-associated fibroblasts (CAFs) are crucial in influencing tumor growth and immune responses in lung cancer, we still lack a comprehensive understanding of their spatial organization associated with tumor progression and clinical outcomes. This gap highlights the need to elucidate how the intricate spatial arrangement of CAFs affects their interactions within the tumor microenvironment, ultimately shaping cancer progression and patient prognosis. Here, we unveil the spatial diversity of CAFs in lung squamous cell carcinoma (LUSC), a prevalent and aggressive lung cancer type, elucidating their impact on tumor progression and patient outcomes using spatial transcriptomics (ST). Image-based ST data from 33 LUSC patients demonstrated a significant association of spatial interactions of tumor epithelium and CAFs with tumor size and metabolic activity measured by [18F]fluorodeoxyglucose PET. Furthermore, the proximity of fibroblasts to tumor epithelial cells was linked to recurrence-free survival in LUSC patients. By characterizing CAFs based on their spatial relationship, we identified distinct molecular signatures related to spatially distinct fibroblast subpopulations. In addition, barcode-based ST data from 8 LUSC patients revealed spatially overlapping fibroblast regions characterized by upregulated glycolysis pathways. Our study underscores the importance of the complex spatial dynamics of the tumor microenvironment revealed by ST and its implications for patient outcomes in LUSC.

cancer biology↗

bioRxiv2023

Mapping cell types in the tumor microenvironment from tissue images via deep learning trained by spatial transcriptomics of lung adenocarcinoma

Profiling heterogeneous cell types in the tumor microenvironment (TME) is important for cancer immunotherapy. Here, we propose a method and validate in independent samples for mapping cell types in the TME from only hematoxylin and eosin (H&E)-stained tumor tissue images using spatial transcriptomic data of lung adenocarcinoma. We obtained spatial transcriptomic data of lung adenocarcinoma from 22 samples. The cell types of each spot were estimated using cell type inference based on a domain adaptation algorithm with single-cell RNA-sequencing data. They were used to train a convolutional neural network with a corresponding H&E image patch as an input. Consequently, the five predicted cell types estimated from the H&E images were significantly correlated with those derived from the RNA-sequencing data. We validated our model using immunohistochemical staining results with marker proteins from independent lung adenocarcinoma samples. Our resource of spatial transcriptomics of lung adenocarcinoma and proposed method with independent validation can provide an annotation-free and precise profiling method of tumor microenvironment using H&E images.

cancer biology↗

bioRxiv2022

STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data

Unraveling the spatial configuration of the tumor microenvironment (TME) is key to understanding tumor-immune interactions to translate them into immuno-oncology. With the advent of spatially resolved transcriptomics (SRT), the TME could be dissected for whole cell types across numerous RNAs. We suggest a novel approach, STopover, which performs topological analysis to compute the colocalization patterns between cell types and map the location of cell{square}cell interactions. While gradually lowering the threshold for the feature, the connected components (CCs) were extracted based on the spatial distance between the unit tissue region and the persistence of the CCs. Local and global Jaccard indices were calculated between the CCs of a feature pair to measure the extent of spatial overlap. The STopover was applied to various lung cancer data obtained from SRT platforms, both barcode and image-based SRT, and could explain the infiltration patterns of immune and stromal cells in the TME. Moreover, the method predicted the top cell{square}cell communication based on the ligand{square}receptor database and highlighted the main region of the interaction. STopover is a tool to decipher spatial interaction in the tissue and shed light on the pathophysiology underlying the microenvironment.

genomics↗

bioRxiv2022

Relative infectivity of the SARS-CoV-2 Omicron variant in human alveolar cells

With the emergence of multiple highly transmissible SARS-CoV-2 variants during the recent pandemic, the comparison of their infectivity has become a substantially critical issue for public health. However, a direct assessment of these viral characteristics has been challenging due to the lack of appropriate experimental models and efficient methods. Here, we integrated human alveolar organoids and single-cell transcriptome sequencing techniques to facilitate the evaluation. In a proof-of-concept study using the assay with four highly transmissible SARS-CoV-2 variants, including GR (B.1.1.119), Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (BA.1), a rapid evaluation of the relative infectivity was possible. Our results demonstrate that the Omicron (BA.1) variant is 3-5-fold more infectious to human alveolar cells than the other SARS-CoV-2 variants at the early phase of infection. To our knowledge, this study provides the first direct measurement of the infectivity of the Omicron variant and new experimental procedures that can be applied for monitoring newly emerging viral variants.

microbiology↗

bioRxiv2021

CellDART: Cell type inference by domain adaptation of single-cell and spatial transcriptomic data

Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to mouse brain and human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the suggested approach was competent to the other computational methods in predicting the spatial localization of excitatory neurons. Besides, CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.

genomics↗

bioRxiv2020

Discovery of potential imaging and therapeutic targets for severe inflammation in COVID-19 patients

The COVID-19 pandemic has caused more than 540,000 deaths globally. Hyperinflammation mediated by dysregulated monocyte/macrophage function is considered to be the key factor that triggers severe illness in COVID-19. However, no specific targeting molecule has been identified for detecting or treating hyperinflammation related to dysregulated macrophages in severe COVID-19. Herein, we suggest candidate targets for imaging and therapy in severe COVID-19 by analyzing single-cell RNA-sequencing data based on bronchoalveolar lavage fluid of COVID-19 patients. We found that expression of SLC2A3, which can be imaged by [18F]fluorodeoxyglucose, was higher in macrophages from severe COVID-19 patients. Furthermore, by integrating the surface target database and drug-target binding database with RNA-sequencing data of severe COVID-19, we identified CCR1 and FPR1 as surface and druggable targets for drug delivery as well as molecular imaging. Our results provide a resource for candidate targets in the development of specific imaging and therapy for COVID-19-related hyperinflammation.

bioinformatics↗

bioRxiv2017

A risk stratification model for lung cancer based on gene coexpression network

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of the previously reported models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray datasets obtained from lung adenocarcinoma, gene coexpression network analysis was performed to identify survival-related network modules. Representative genes of these network modules were selected and then, risk stratification model was constructed exploiting deep learning algorithm. The model was validated in two independent test cohorts. Survival analysis using univariate and multivariate Cox regression was performed using the output of the model to evaluate whether the model could predict patients overall survival independent of clinicopathological variables. Five network modules were significantly associated with patients survival. Considering prognostic significance and representativeness, genes of the two survival-related modules were selected for input data of the risk stratification model. The output of the model was significantly associated with patients overall survival in the two independent test sets as well as training set (p < 0.00001, p < 0.0001 and p = 0.02 for training set, test set 1 and 2, respectively). In multivariate analyses, the model was associated with patients prognosis independent of other clinical and pathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature, and the clinical application of deep learning in genomic data science for prognosis prediction.

bioinformatics↗