Biology subjects

Jang, I.

Publications and source records attributed to Jang, I..

7 recordsLinked to original sources

bioRxiv2025

Designing smart spatial omics experiments with S2Omics

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

genomics↗

bioRxiv2025

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while retaining the crucial spatial context within tissues. However, existing ST platforms suffer from high costs, long turnaround times, low resolution, limited gene coverage, and small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that predicts super-resolution gene expression and automatically annotates cellular-level tissue architecture for large-sized tissues that exceed the capture areas of standard ST platforms. The accuracy of iSCALE were validated by comprehensive evaluations, involving benchmarking experiments, immunohistochemistry staining, and manual annotation by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion associated cellular characteristics that were undetectable by conventional ST experiments. Our results demonstrate iSCALEs utility in analyzing large-sized tissues with automatic and unbiased tissue annotation, inferring cell type composition, and pinpointing regions of interest for features not discernible through human visual assessment.

genomics↗

bioRxiv2024

Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology

Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune check-point inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction, which was by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEERs tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

bioinformatics↗

bioRxiv2023

Integrative Spatial Analysis of H&E and IHC Images Identifies Prognostic Immune Subtypes Correlated with Progression-Free Survival in HPV-Related Oropharyngeal Squamous Cell Carcinoma

PurposeDeep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images. MethodsA retrospective study was conducted on patients with HPV(+) oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a DL pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes. ResultsAnalysis of 88 primary tumors and 70 involved lymph node tissue images revealed an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirmed a significantly better prognosis for these subtypes. ConclusionsIntegrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV(+) OPSCC.

cancer biology↗

bioRxiv2023

KoNA: Korean Nucleotide Archive as a New Data Repository for Nucleotide Sequence Data

During the last decade, generation and accumulation of petabase-scale high-throughput sequencing data have resulted in ethical and technical challenges, including access to human data, and transfer, storage, and sharing of enormous amount of data. To promote data-driven research in biology, the Korean government announced that all the biological data generated from government-funded research projects should be deposited in the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. We introduce the Korean Nucleotide Archive (KoNA), a repository for nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next generation sequencing data from several national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure (SOP) was adopted, which is similar to the International Nucleotide Sequence Database Collaboration. The SOP includes quality control processes for submitted data and metadata using an automated pipeline followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed in a cloud-computing service for genomic data analysis called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances data experience including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea, but also provides datasets to researchers globally and contribute to advances in genomics. KoNA is available at https://www.kobic.re.kr/kona/.

genomics↗

bioRxiv2019

A reference map of the human proinsulin biosynthetic interaction network

The beta-cell protein synthetic machinery is dedicated to the production of insulin, which plays a critical role in organismal homeostasis. Insulin synthesis requires the proper folding and trafficking of its precursor, proinsulin, yet the precise network of proinsulin protein interactions in the secretory pathway remains poorly defined. In the present study we conducted unbiased profiling of the proinsulin interactome in human islets, utilizing a human proinsulin-specific monoclonal antibody for affinity purification and mass spectrometry. Stringent analysis identified a central node of interactions between human proinsulin and sequential secretory pathway proteins that is remarkably conserved across 3 ethnicities and both genders. Among the most prominent proinsulin interactions was with ER-localized peroxiredoxin-4 (PRDX4). A functional role for PRDX4 in beta-cells was demonstrated by gene silencing that rendered proinsulin susceptible to misfolding, particularly in response to oxidative stress. Conversely, exogenous PRDX4 improved proinsulin folding. Notably, oxidative stress and even high glucose treatment alone induced proinsulin misfolding in human islets and MIN6 cells, and this was accompanied by sulfonylation of PRDX4, a modification known to inactivate peroxiredoxins. This finding prompted PRDX4 analysis in a panel of human islet samples that revealed significantly higher levels of sulfonylated (inactive) PRDX4 in islets from patients with T2D compared to that of healthy individuals. Taken together, these data highlight the importance of elucidating the complete proinsulin interactome in human islets in order to understand critical steps controlling insulin biosynthesis, beta cell function, and T2D.

biochemistry↗

bioRxiv2017

Prometheus: omics portals for interkingdom comparative genomic analyses

Functional analyses of genes are crucial for unveiling biological responses, for genetic engineering, and for developing new medicines. However, functional analyses have largely been restricted to model organisms, representing a major hurdle for functional studies and industrial applications. To resolve this, comparative genome analyses can be used to provide clues to gene functions as well as their evolutionary history. To this end, we present Prometheus (http://prometheus.kobic.re.kr),web-based omics portal that contains more than 17,215 sequences from prokaryotic and eukaryotic genomes. This portal supports interkingdom comparative analyses via a domain architecture-based gene identification system, Gene Search, and users can easily and rapidly identify single or entire gene sets in specific pathways. Bioinformatics tools for further analyses are provided in Prometheus or through BioExpress, a cloud-based bioinformatics analysis platform. Prometheus suggests a new paradigm for comparative analyses with large amounts of genomic information.

bioinformatics↗