Biology subjects

Jang, D.

Publications and source records attributed to Jang, D..

11 recordsLinked to original sources

bioRxiv2026

PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets

Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications. Article HighlightsO_LICurrent scRNA-seq datasets of pancreatic islet tissue are limited in size and scattered across donors, laboratories, and experimental conditions, underscoring the need for a consolidated resource. C_LIO_LIWe harmonized datasets from multiple sources to build a comprehensive single-cell map of isolated human pancreatic islets. C_LIO_LIOur atlas captures 448,935 cells from 191 high-quality samples across 140 donors and multiple phenotypic groups, identifying 13 distinct cell populations. C_LIO_LIAvailable at www.pankbase.org, the atlas provides a scalable, rigorously curated platform to support hypothesis-driven diabetes research and can enable a broad range of downstream computational applications. C_LI

genomics↗

bioRxiv2026

The Common Fund Data Ecosystem (CFDE)

The NIH Common Fund Data Ecosystem (CFDE) integrates data resources from 18 NIH Common Fund programs for discovery and integrative analysis. These programs generate valuable but heterogeneous datasets that can be difficult to discover, access, and reuse. CFDE aims to provide a collaborative, community-built infrastructure that links and enriches Common Fund programs. We describe the evolution, structure, and core technologies of CFDE, including practical approaches that support submission, integration, visualization, and public release of multimodal data. Training programs and workforce initiatives lower barriers to adoption. CFDE has devised solutions to critical issues facing cross-program initiatives, including data scale and heterogeneity, dataset integration, and long-term sustainability. We demonstrate the utility of linking Common Fund resources through integrative tools and cross-dataset queries to yield insights that would otherwise be infeasible. Collectively, CFDE shows that a standards-driven, federated approach enhances and unifies cross-disciplinary resources, fostering collaboration and data-driven discovery.

scientific communication and education↗

bioRxiv2025

Regularized Single-cell Imaging Enables Generalizable AI models for Stain-free Cell Viability Screening

Cell viability assays are essential tools in biomedical research and drug development. Artificial intelligence (AI) offers the potential to simplify these assays by predicting cell viability directly from brightfield microscopy images, but current models lack generalizability across diverse cell types and treatments. Here, we introduce a strategy called "regularized imaging", where single cells are isolated in nanowells to generate standardized image patches that simplify segmentation and improve training data quality. We trained our model using example images of live and dead cells from a single cell line exposed to four cytotoxic conditions (ethanol, andrographolide, daunorubicin, and serum starvation). Despite this narrow training dataset, the resulting model accurately identified live and dead cells after treatment by previously unseen compounds, successfully replicating dose-response curves comparable to fluorescence live/dead assays. Importantly, this model effectively generalized across diverse cell types, including both adherent and suspension cells. Additionally, microscopy-based cell viability analysis is non-destructive, enabling repeated measurements to perform kinetic studies to distinguish between fast- and slow-acting compounds. Our findings highlight how regularized single cell imaging enables the training of broadly generalizable AI models to recognize biologically relevant cell features for label-free cell screening workflows. One-sentence SummaryRegularized single-cell imaging in nanowells enables training of generalizable AI models for accurate, stain-free viability screening across previously unseen cell types and contexts.

bioengineering↗

bioRxiv2025

High-throughput Single Cell Motility Analysis using Nanowell-in-Microwells

Cell motility is important to many biological processes including cancer, immune response, and tissue repair. Conventional assays measure bulk cell motility, potentially overlooking important heterogeneity and missing important high motility subpopulations. Here, we introduce a high-throughput single-cell motility assay using nanowell-in-microwell plates to precisely track single cell position and analyze their migratory trajectories. By physically confining individual cells in nanowells, we eliminate cell-cell interactions and simplify cell segmentation and tracking. Using this platform, we characterized the motility of single cells across different culture conditions to identify distinct motility phenotypes. Single-cell trajectory analysis revealed pronounced directional persistence, with cells predominantly maintaining their direction of travel and trajectory along nanowell boundaries. Additionally, our approach facilitates the generation of labeled image datasets suitable for AI models to rapidly identify cell motility phenotypes from single-cell images. Together, our platform provides a robust, scalable method to analyze cell motility phenotypes and migration behavior at single-cell resolution. Significance StatementA novel single-cell motility assay that uses confinement in nanowell-in-microwell to enable high-throughput profiling of single cell motility phenotypes.

bioengineering↗

bioRxiv2025

Differential DNA damage response to WRN inhibition identifies a targetable vulnerability in ARID1A-mutated cancers

ARID1A, a key subunit of the SWI/SNF chromatin remodeling complex, is frequently mutated in cancers. However, effective clinical treatments for patients with this mutation are limited, highlighting a demand for new therapeutic strategies. Here, we establish Werner syndrome ATP-dependent helicase (WRN) as a critical vulnerability target in ARID1A-mutated cancers. Upon genetic and pharmacological inhibition of WRN, ARID1A-mutated cells had defective Chk1-mediated DNA damage signaling, resulting in compensatory Chk2 activation, leading to G1-phase arrest and apoptosis, whereas ARID1A-proficient cells underwent Chk1-dependent G2/M arrest. Additional p21 inhibition, combined with WRN suppression, drove G1-arrested cells to re-enter the cell cycle, triggering mitotic catastrophe. The anti-tumor efficacy of WRN inhibition was validated using in vivo cell lines and patient-derived xenograft mouse models. Our findings define WRN as a selective therapeutic target in ARID1A-mutated cancers and suggest a combinatorial strategy of WRN and p21 inhibition as a therapeutic approach.

cancer biology↗

bioRxiv2024

SLIRP mediates nuclear-mitochondrial communication during antiviral signaling to promote robust interferon response

The abnormal innate immune response is a prominent feature underlying autoimmune diseases. One emerging factor that can trigger dysregulated immune activation is cytosolic mitochondrial double-stranded RNAs (mt-dsRNAs). However, the mechanism by which mt-dsRNAs stimulate immune responses remains poorly understood. Here, we discover SRA stem-loop interacting RNA binding protein (SLIRP) as a key amplifier of mt-dsRNA-triggered antiviral signals. In autoimmune diseases, SLIRP is commonly upregulated, and targeted knockdown of SLIRP dampens the interferon response. We find that the activation of melanoma differentiation-associated gene 5 (MDA5) by exogenous dsRNAs upregulates SLIRP, which then stabilizes mt-dsRNAs and promotes their cytosolic release to activate MDA5 further, augmenting the interferon response. Furthermore, the downregulation of SLIRP partially rescues the abnormal interferon-stimulated gene expression in autoimmune patients primary cells and makes cells vulnerable to certain viral infections. Our study unveils SLIRP as a pivotal mediator of interferon response through positive feedback amplification of antiviral signaling.

molecular biology↗

bioRxiv2024

Ribonanza: deep learning of RNA structure through dual crowdsourcing

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.

biophysics↗

bioRxiv2023

Leveraging type 1 diabetes human genetic and genomic data in the T1D Knowledge Portal

Translating genetic discoveries for type 1 diabetes (T1D) into mechanistic insight can reveal novel biology and therapeutic targets but remains a major challenge. We developed the T1D Knowledge Portal (T1DKP), a disease-specific resource of genetic and functional annotation data that enables users to develop hypotheses for T1D-based research and target discovery. The T1DKP can be used to query genes and genomic regions for genetic associations, identify epigenomic features, access results of bioinformatic analyses, and obtain expert-curated resources. The T1DKP is available at http://t1d.hugeamp.org.

genetics↗

bioRxiv2022

Deconstructing olfactory epithelium developmental pathways in esthesioneuroblastoma

ImportanceEsthesioneuroblastoma (ENB) is a rare tumor arising from the olfactory cleft region of the nasal cavity. Due to the low incidence of this tumor, as well as an absence of established reagents such as cell lines or murine models, understanding the mechanisms driving ENB pathobiology has been challenging. ObjectiveHere, we sought to apply advances from research on the human olfactory epithelial neurogenic niche, along with new biocomputational approaches, to better understand the cellular and molecular factors in low and high grade ENB and how specific transcriptomic markers may predict prognosis. Design, Setting, and ParticipantsThis was a retrospective biocomputational analysis utilizing human ENB and normal olfactory mucosal tissue and clinical outcomes data. The setting was a large academic medical center. Participants were selected based on available datasets, and included ENB patients across all four tumor grades, and patients with normal olfactory mucosa. Main Outcomes and MeasuresOutcomes include deconvolution analysis, differential gene expression analysis, overall survival, and immunohistochemistry. A machine learning model was used to predict cell-type proportion signatures in ENB, and Kaplan-Meier curves with associated log-rank tests were used to estimate differences in overall survival. ResultsWe analyzed a total of 19 ENB samples (9 low grade I/II, 10 high grade III/IV) with available bulk RNA-Sequencing and survival data, along with 10 samples from normal olfactory mucosa (3 bulk RNA-Sequencing, 7 single cell RNA-Sequencing). The bulk RNA-Sequencing deconvolution model identified a significant increase in globose basal cell (GBC) and CD8 T cell identities in high grade tumors (GBC from approximately 0% to 8%, CD8 T cell from 0.7% to 2.2%), and significant decreases in mature neuronal, Bowmans gland, and olfactory ensheathing programs, in high grade tumors (mature neuronal from 3.7% to approximately 0%, Bowmans gland from 18.6% to 10.5%, olfactory ensheathing from 3.4% to 1.1%). Trajectory analysis identified potential regulatory pathways in proliferative ENB cells, including PRC2. Survival analysis guided by gene expression in bulk RNA-Sequencing data identified favorable prognostic markers such as SOX9, S100B, and PLP1 expression. Conclusions and RelevanceOur analyses provide a basis for additional translational research on ENB management, as well as identification of potential new prognostic markers.

cancer biology↗

bioRxiv2021

Aging-related olfactory loss is associated with olfactory stem cell transcriptional alterations in humans

Presbyosmia, or aging related olfactory loss, occurs in a majority of humans over age 65 years, yet remains poorly understood, with no specific treatment options. The olfactory epithelium (OE) in the nasal fossa is the peripheral organ for olfaction, and is subject to acquired damage, suggesting a likely site of pathology in aging. OE basal stem cells reconstitute the neuroepithelium in response to cell loss under normal conditions. In aged OE, patches of respiratory-like metaplasia have been observed histologically, consistent with a failure in normal neuroepithelial homeostasis or repair. Accordingly, we have focused on identifying cellular and molecular changes in presbyosmic OE. Combining psychophysical testing with olfactory mucosa biopsy analysis, single cell RNA-sequencing (scRNA-seq), and human olfactory culture studies, we identified evidence for inflammation-associated changes in the OE stem cells of presbyosmic patients. The presbyosmic basal stem cells exhibited increased expression of genes involved in response to cytokines or stress, or the regulation of proliferation and differentiation. To facilitate further study of human OE stem cells, we developed an adult human basal cell culture model. Characterization of cultures using scRNA-seq confirmed maintenance of a reserve stem cell-like phenotype, and brief cytokine exposure in basal cell cultures resulted in increased expression of TP63, a transcription factor acting to prevent OE stem cell differentiation. Our data are consistent with a process by which aging-related inflammatory changes in OE stem cells may contribute to presbyosmia, via the disruption of normal epithelial homeostasis, suggesting that OE stem cells may represent a rational therapeutic target for restoration of olfaction. One Sentence SummarySingle cell profiling suggests that inflammatory-associated olfactory epithelial stem cell dysfunction is associated with presbyosmia in humans.

neuroscience↗

bioRxiv2019

GV1001 prevents ovarian follicle loss triggered by an anti-VEGF monoclonal antibody targeting non-small cell lung carcinoma xenografts in mice

Premature ovarian failure (POF) that could result from chemotherapy applied to young female cancer patients is a significant challenge in reproductive biology. It is widely believed that the hyperactivation of dormant primordial follicles following chemotherapy is a leading cause of POF, but it remains unclear how therapeutic cues are generated and transduced into follicular activation. Here, we provide evidence that supports that GV1001, an immunotherapeutic peptide targeting telomerase, plays a role in the deterrence of POF in mice. In vivo non-small cell lung carcinoma (NSCLC) tumor xenografts were produced by inoculating NSCLC cells into the flank of BALB/c female athymic mice and then subjected to cancer chemotherapy with GV1001 and bevacizumab, an anti-cancer antibody drug, humanized anti-VEGF monoclonal antibody. Bevacizumab when administered at the dosage of 5 mg/kg for three weeks was effective in inhibiting growth of NSCLC tumor xenografts, and its anti-cancer efficacy was not interfered by the presence of GV1001. As expected, bevacizumab induced follicular loss by accelerating primordial follicle growth into primary or secondary follicles concomitant with a decline of serum antimullerian hormone (AMH) level and deactivation of Foxo3 signaling as evidenced by immunohistochemical and immunofluorescent assessment. However, bevacizumab-induced follicle stimulating effects were mitigated by GV1001 co-administration as evidenced by the analysis of follicular count, serum AMH level, and Foxo3a expression. From this study, we propose that a combinatorial administration of GV1001 and bevacizumab could deter POF of young female cancer patients without hampering the anti-cancer effectiveness of bevacizumab.

pharmacology and toxicology↗