Biology subjects

Jung, K. J.

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

7 recordsLinked to original sources

bioRxiv2026

Multi-Scale Tri-Modal Histology Dataset Integrating Tumor Morphology, Immune Patterns, and Clinical Outcomes

Accurate prognostic assessment of prostate cancer (PCa) requires an integrated understanding of tissue morphology-encompassing cell structure, glandular architecture, and tissue organization-and the immune environment. We present Prostate-TriMod, a novel tri-modal histology dataset designed to integrate high-resolution visual morphology with spatial tissue maps, immune infiltration patterns, and clinical outcomes. This dataset, generated from the Cell DIVE multiplexed imaging platform, consists of three synchronized modalities: (1) multiscale virtual H&E tiles (224px, 256px, 512px, and 2040px) providing visual morphological context, (2) spatial tissue maps identifying cancerous/non-cancerous epithelial cells, stroma and immune cell populations (via TOPAZ and CAT models), and (3) text captions generated from single-cell data and patterns. The dataset includes comprehensive clinical annotations, including Grade Groups and biochemical recurrence (BCR) status. By providing high-fidelity alignment between visual features, spatial tissue maps, and textual descriptions, Prostate-TriMod empowers the development of advanced multimodal AI frameworks. We expect this resource to support reuse in multimodal representation learning, spatial analysis, and benchmarking studies that link histology morphology and immune context to clinical outcomes in prostate cancer.

bioinformatics↗

bioRxiv2025

Annotation-Free Prediction of Cancer Cells and Glands and Spatial Analysis of Immune Cells.

Prostate cancer is classified as "immune-cold" due to limited infiltration of immune cells and no clear correlation between immune cells and clinical outcomes. However, immune cells are found in prostate cancers and the spatial relationships between these immune cells and cancer cells/glands have not been investigated, partly due to a lack of automated tools that classify both cancerous cells/glands. In this paper, we have developed an end-to-end tool (TOPAZ: Tissue Organization identification using sPAtial proteomics) that combines multiplexed single-cell protein data with histology images to: 1) predict cancerous versus non-cancerous epithelial cells using a Gaussian-mixture model; 2) predict cancerous/non-cancerous gland type using a principal curve estimation. Using TOPAZ to assign cancer and non-cancerous labels to cells and glands, we extracted multiscale spatial features from the classification results--including immune dense-region geometrical features and cell-to-gland distances-- and correlated the features with risk of biochemical recurrence and cancer grade. Tissue-microarrays containing 753 cores from 217 prostate cancer patients underwent multiplexed immunofluorescent imaging (Cell DIVE, Leica) for epithelial cell markers (panCK26, S6, NaKATPase), basal cell markers (p63, CK5), a cancer cell marker (AMACR), and T cell markers (CD3, CD4, CD8, FOXP3, CD68). Cancerous/non-cancerous cell classification from TOPAZ achieved 82% sensitivity and 99% specificity against expert annotation, and the pipeline further predicted cancerous/non-cancerous glands without manual threshold tuning. Regulatory-T-cell and helper-T-cell percentages decreased, and macrophage percentage increased with grade increase (P < 0.05). When the median distance from cancerous gland centroids to the nearest regulatory or helper T-cell exceeded approximately 50 {micro}m, the hazard of biochemical recurrence doubled (log-rank P < 0.01). The open-source Shiny app TOPAZ (https://chunglab.bmi.osumc.edu/TOPAZ) packages the workflow, predicting individual cell types and gland shapes. By combining probabilistic cell typing with gland-shape modeling, TOPAZ yields interpretable multiscale spatial features linked to prognosis and is released as an open web app for unrestricted use. Author SummarySpatial distribution of cancerous cells/glands and immune cell distribution has not been considered as a prognosticator in prostate cancer. In addition, automated tools that can quantify and integrate these distributions are lacking. We combined high-dimensional single-cell protein measurements with histology images to map gland structure in prostate cancer tissue. Our web-based tool, TOPAZ (https://chunglab.bmi.osumc.edu/TOPAZ) predicts whether each epithelial cell and gland in virtual H&E image is cancerous or not. Once the predictions are made, a spatial analysis workflow helps quantify spatial features of immune cells relative to the glands and correlate with recurrence risk and grades. Across 753 tissue cores from 217 prostate cancer patients, helper and regulatory-T-cells located more than about 50 {micro}m away from cancerous epithelial glands were associated with a higher risk of biochemical recurrence. The pipeline provides new insights for researchers and pathologists into prostate cancer progression and biochemical recurrence through integration of spatial location of cancer glands and immune cells.

cancer biology↗

bioRxiv2025

A Gentle Introduction to Spatial Transcriptomic Analysis with 10X Visium Data

Spatial transcriptomics (ST) combines single-cell RNA-seq gene expression data with spatial coordinates to provide an accurate, 2D picture of gene expression across a tissue sample. With this technology, we can discover detailed RNA localization, study development, investigate the tumor microenvironment, and create a tissue atlas. Full ST analysis requires several steps, however, as this protocol aims to be a simple introduction to the analysis process, only the foundational steps of clustering, spatially variable gene discovery, and cell-cell communication are presented, focusing on data obtained from the 10X Genomics Visium platform. An expanded protocol with full code is available at https://github.com/j-gillespie-dna/STanalysis.git

bioinformatics↗

bioRxiv2023

At-home, cell-free synthetic biology education modules for transcriptional regulation and environmental water quality monitoring

As the field of synthetic biology expands, the need to grow and train science, technology, engineering, and math (STEM) practitioners is essential. However, the lack of access to hands-on demonstrations has led to inequalities of opportunity and practice. In addition, there is a gap in providing content that enables students to make their own bioengineered systems. To address these challenges, we develop four shelf-stable cell-free biosensing educational modules that work by just-adding-water and DNA to freeze-dried crude extracts of Escherichia coli. We introduce activities and supporting curricula to teach the structure and function of the lac operon, dose-responsive behavior, considerations for biosensor outputs, and a build-your-own activity for monitoring environmental contaminants in water. We piloted these modules with K-12 teachers and 130 high school students in their classrooms - and at home - without professional laboratory equipment or researcher oversight. This work promises to catalyze access to interactive synthetic biology education opportunities.

synthetic biology↗

bioRxiv2022

Analysis of community connectivity in spatial transcriptomics data

The advent of high throughput spatial transcriptomics (HST) has allowed for unprecedented characterization of spatially distinct cell communities within a tissue sample. While a wide range of computational tools exist for detecting cell communities in HST data, none allow for characterization of community connectivity, i.e., the relative similarity of cells within and between found communities - an analysis task that can elucidate cellular dynamics in important settings such as the tumor microenvironment. To address this gap, we introduce the concept of analysis of community connectivity (ACC), which entails not only labeling distinct cell communities within a tissue sample, but understanding the relative similarity of cells within and between communities. We develop a Bayesian multi-layer network model called BANYAN for integration of spatial and gene expression information to achieve ACC. We use BANYAN to implement ACC in invasive ductal carcinoma, and uncover distinct community structure relevant to the interaction of cell types within the tumor microenvironment. Next, we show how ACC can help clarify ambiguous annotations in a human white adipose tissue sample. Finally, we demonstrate BANYANs ability to recover community connectivity structure via a simulation study based on real sagittal mouse brain HST data. AvailabilityAn R package banyan is available at https://github.com/carter-allen/banyan. Contactchung.911@osu.edu Supplementary informationSupplementary data are available online. Author SummaryThe proliferation of spatial transcriptomics technologies have prompted the development of numerous statistical models for characterizing the makeup of a tissue sample in terms of distinct cell sub-populations. However, existing methods regard inferred sub-populations as static entities and do not offer any ability to discover the relative similarity of cells within and between communities, thereby obfuscating the true interactive nature of cells in a tissue sample. We develop BANYAN: a statistical model for implementing analysis of community connectivity (ACC), i.e., the process of inferring the similarity of cells within and between cell sub-populations. We demonstrate the utility of ACC through the analysis of a publicly available breast cancer data set, which revealed distinct community structure between tumor suppressive and invasive cancer cell sub-populations. We then showed how ACC may help elucidate ambiguous sub-population annotations in a publicly available human white adipose tissue data set. Finally, we implement a simulation study to validate BANYANs ability to recover true community connectivity structure in HST data.

bioinformatics↗

bioRxiv2021

Redox regulation by TXNRD3 during epididymal maturation underlies capacitation-associated mitochondrial activation and sperm motility in mice

During epididymal transit, redox remodeling protects mammalian spermatozoa, preparing them for survival in the subsequent journey to fertilization. However, molecular mechanisms of redox regulation in sperm development and maturation remain largely elusive. In this study, we report that TXNRD3, a thioredoxin reductase family member particularly abundant in elongating spermatids at the site of mitochondrial sheath formation, regulates redox homeostasis to support male reproduction. Using Txnrd3-/- mice, our biochemical, ultrastructural, and live cell imaging analyses revealed impairments in sperm morphology and motility under conditions of TXNRD3 deficiency. We reveal that mitochondria develop more defined cristae during capacitation in wild type sperm. Absence of TXNRD3 alters redox status in both the head and tail during sperm maturation and capacitation, resulting in defective mitochondrial ultrastructure and activity under capacitating conditions. These findings provide insights into molecular mechanisms of redox homeostasis and bioenergetics during sperm maturation, capacitation, and fertilization.

physiology↗

bioRxiv2018

Onset, Time to Recurrence, and Recurrence Risk Factors of Myocardial Infarction and Ischemic Stroke: 10-Year Nationwide one-Million Population Database

Background and PurposeWe aimed to determine the differences in the pathophysiology of myocardial infarction (MI) and ischemic stroke (IS) and present their basic epidemiologic data for public health policy.\n\nMethodsWe included patients with a history of admission with diagnostic codes of MI and IS in the National Health Information Database (NHID) of the National Health Insurance Service (NHIS) Sample Cohort 2002-2013. We investigated the time to primary and secondary events, difference in incidence based on sex, time interval for the secondary event following the primary event in MI and IS, and the relative risk of recurrent events compared with that of primary events.\n\nResultsThe mean age of onset of IS was significantly higher than that of MI (65.67{+/-}12.25 vs. 60.4{+/-}13.07 years; P<0.05). The mean period from primary IS to secondary IS was significantly shorter than that from primary IS to secondary MI (2.09{+/-}2.80 vs. 5.35{+/-}2.90 years; P=0.016). The mean period from primary MI to secondary MI was significantly shorter than that from primary MI to secondary IS (2.30{+/-}2.75 vs. 5.16{+/-}3.26 years; P<0.001). The incidence of IS in men was significantly higher than that in women. The relative risk of recurrent IS and MI was greater than that of the primary event. In patients younger than 70 years, the incidence of MI was higher than that of IS, while in those above 70 years it was lower (P<0.05).\n\nConclusionsThe age at onset, time to recurrence, sex, and relative risks are significantly different between ischemic stroke and myocardial infarction, suggesting that the underlying pathophysiologic mechanisms might be different between the two.

epidemiology↗