Biology subjects

Joo, H.

Publications and source records attributed to Joo, H..

6 recordsLinked to original sources

bioRxiv2026

Transient Splicing-Associated Isoform Remodeling Defines Early MAPKi Resistance Adaptation in Melanoma

A long-standing question in targeted therapy resistance is how tumor cells rapidly enter drug-tolerant states before stable resistance emerges. In BRAF-mutant melanoma, early adaptation to MAPK inhibitor (MAPKi) therapy is accompanied by profound phenotypic plasticity, yet the molecular mechanisms that enable this transition remain poorly understood. Here, we show that MAPKi adaptation is characterized by a transient transcript isoform remodeling that is largely invisible to conventional gene-expression analysis. We integrated and analyzed multiple different datasets, including bulk and single-cell RNA-seq datasets from melanoma cell lines, murine models, patient-derived xenografts and patient tumors, spanning no-treatment, on-treatment/minimal residual disease, and acquired-resistant states. We find that differential transcript usage peaks during the adaptive on-treatment phase and then is markedly attenuated when resistance was established. These dynamics were conserved across experimental systems and supported by independent exon-usage analyses. Notably, most genes undergoing differential transcript usage showed no corresponding change in total gene expression, and genes with isoform-level changes more strongly highlighted MAPKi resistance-associated pathways, including PI3K/AKT/mTOR and receptor tyrosine kinase signaling. Further, classifiers trained on cell-line transcript-usage features identified the adaptive state more accurately than gene-expression-based models in independent PDX and patient datasets. Interestingly, MAPKi-associated isoform changes recapitulated melanoblast-associated developmental splicing programs and were linked to a few key splicing regulators. Together, these findings identify an underappreciated regulatory layer connecting drug tolerance, developmental plasticity, and acquired resistance. Beyond the current context, our results suggest that comprehensively tracking isoform and splicing alterations may potentially advance our understanding of the emergence of treatment resistance in additional indications and treatment regimes.

cancer biology↗

bioRxiv2026

An Itch Receptor Drives Melanoma

Mas-Related GPCR X4 (MRGPRX4) is a sensory neuron-restricted receptor for cholestatic itch. Here, we identify MRGPRX4 as an unexpected melanoma driver. MRGPRX4 is upregulated in melanoma and is confined to invasive, neural-crest-like and pre-EMT states associated with dedifferentiation and therapy resistance. Ectopic MRGPRX4 expression in mouse melanocytes drives 100% penetrant, highly metastatic melanoma, demonstrating oncogenic behavior. MRGPRX4 promotes melanoma cell proliferation and invasion through basal, ligand-independent, PI3K-AKT-MAPK activation. Multi-omics links MRGPRX4 expression to a mesenchymal/neural-crest-like program that defines a distinct invasive MRGPRX4? niche. Comparing transcptomics of MRGPRX4-driven tumors shows a broad overlap with BRAF/NRAS-driven tumors; however, the MRGPRX4 model also enriches an ECM-rich, invasive neural crest-like melanoma state. MRGPRX4 further remodels the tumor microenvironment toward an immunosuppressive, checkpoint-high state enriched in suppressive myeloid cells. Pharmacologic MRGPRX4 inhibition suppresses basal signaling and limits melanoma growth and invasion. In sum, melanoma exploits MRGPRX4 to acquire an invasive and immunosuppressive phenotype, nominating this somatosensory GPCR as a promising therapeutic target.

cancer biology↗

bioRxiv2025

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA (The Cancer Genome Atlas) melanoma data. First, we demonstrate that TIME_ACT scores accurately identify tumors with activated immune microenvironments across different cancer types. Further, analysis of spatial features reveals that tumor microenvironment regions with dense lymphocyte infiltration near tumor cells have high TIME_ACT scores, successfully marking localized immune activation. Second, across 25 transcriptomic ICB cohorts encompassing nine cancer types, TIME_ACT achieves a mean AUC of 0.76 and a mean odds ratio of 5.77, significantly outperforming 30 established transcriptomic signatures and prediction methods for ICB response, including a recently developed foundation model for immunotherapy response prediction. Third, we show that TIME_ACT scores can be accurately inferred from routine tumor histopathology slides and that slide-inferred TIME_ACT scores predict ICB response across nine new independent patient cohorts spanning eight cancer types, achieving a mean AUC of 0.72 and a mean odds ratio of 4.99. These findings establish TIME_ACT as a robust, pan-cancer biomarker that enables accurate, low-cost, and clinically scalable prediction of ICB response from routine histopathology.

cancer biology↗

bioRxiv2024

Dual-mode action of scalable, high-quality engineered stem cell-derived SIRP alpha extracellular vesicles for treating acute liver failure

Acute liver failure (ALF) is a critical inflammatory condition characterized by rapid hepatocyte death, impaired liver regeneration due to the delayed removal of necroptotic cells, and high mortality rates. This study introduces a novel dual-mode action therapeutic approach using extracellular vesicles expressing Signal Regulatory Protein Alpha (SIRP-EVs) derived from genetically engineered mesenchymal stem cells (MSCs). These SIRP-EVs are designed to concurrently resolve necroptosis and promote liver regeneration. Our studies identified CD47 and SIRP as promising therapeutic targets for ALF. We developed a scalable 3D bioreactor-based process that produces high-purity SIRP-EVs, which preserve MSC properties and achieve significant production levels. SIRP-EVs target both macrophages and necroptotic hepatocytes in ALF models, enhancing macrophage phagocytic activity against necroptotic cells via CD47 blockade and promoting liver regeneration by reprogramming macrophages with MSC-derived cargo. Comprehensive in vitro and in vivo studies demonstrate that SIRP-EVs decrease CD47+ necroptotic cells and promote liver regeneration in ALF models, leading to reduced liver damage markers and enhanced survival rates. These findings highlight the potential of SIRP-EVs as a dual-mode action therapeutic for ALF, offering promising prospects for their application in other inflammatory diseases. Moreover, these results pave the way for advancing engineered EV-based therapies toward clinical implementation.

bioengineering↗

bioRxiv2020

Data-driven multiplexed microtomography of endogenous subcellular dynamics

Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales would be an invaluable tool in biomedicine. However, conventional imaging modalities have stark tradeoffs precluding the fulfilment of all functional requirements. Here we propose the refractive index (RI), an intrinsic quantity governing light-matter interaction, as a means for such measurement. We show that major endogenous subcellular structures, which are conventionally accessed via exogenous fluorescence labeling, are encoded in 3D RI tomograms. We decode this information in a data-driven manner, thereby achieving multiplexed microtomography. This approach inherits the advantages of both high-specificity fluorescence imaging and label-free RI imaging. The performance, reliability, and scalability of this technology have been extensively characterized, and its application within single-cell profiling at unprecedented scales has been demonstrated.

bioengineering↗

bioRxiv2017

Holographic deep learning for rapid optical screening of anthrax spores

Establishing early warning systems for anthrax attacks is crucial in biodefense. Here we present an optical method for rapid screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and sub-genus specificity. The unique representation learning capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate diagnosis of pathogens, and facilitate exciting new applications.

microbiology↗