Yonsei University · Medicine
Professor Junha Cha's research lab specializes in single-cell systems biology, focusing on reconstructing cell type-specific gene regulatory networks (CGNs) to decode cellular heterogeneity in human health and disease. The lab develops computational platforms such as scHumanNet and HCNetlas to integrate single-cell transcriptomics with functional genomics, enabling the identification of context-specific gene functions and disease mechanisms. By leveraging imputation methods and reference interactomes, the lab advances precision medicine through systems-level understanding of gene networks in primary and metastatic cancers, as well as complex diseases. Their work bridges single-cell omics with network biology to uncover novel drug targets and disease-associated cell types.
Figures are computed from collected data and may differ slightly.
Understanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profi
NCT03737968.
A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell
Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast
Gene network models provide a foundation for graph theory approaches, aiding in the novel discovery of drug targets, disease genes, and genetic mechanisms for various biological functions. Disease genetics must be interpreted within the cellular context of disease-associated cell types, which cannot be achieved with datasets consisting solely of organism-level samples. Single-cell RNA sequencing (scRNA-seq) technology allows computational distinction of cell states which provides a unique opport
Cell type-specific actions of disease genes add a significant layer of complexity to the genetic architecture underlying diseases, obscuring our understanding of disease mechanisms. Single-cell omics have revealed the functional roles of genes at the cellular level, identifying cell types critical for disease progression. Often, a gene impact on disease through its altered network within specific cell types, rather than mere changes in expression levels. To explore the cell type-specific roles o
Predictors of immune checkpoint inhibitor response in cancer remain elusive. From a previous phase 2 neoadjuvant immunotherapy window-of-opportunity study, we present the single-cell RNA and T cell receptor (TCR) sequencing analysis of 57 pre- and post-treatment tumor biopsies from head and neck cancer patients treated with durvalumab (anti-PD-L1) alone or with tremelimumab (anti-CTLA-4), identifying key cellular and molecular predictors of immune checkpoint inhibitor (ICI) response. Malignant c
Abstract A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet ( https://github.com/netbiolab/scHumanNet ), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Ba
Abstract Oropharyngeal squamous cell carcinoma (OPSCC) is a prevalent subtype of head and neck cancer representing one of the largest diagnosed malignancies worldwide. A distinct subtype of OPSCC induced by Human Papilloma Virus (HPV) is well known to favor beneficial clinical outcomes in cancer treatment compared to HPV-negative OPSCCs. However, HPV-positive OPSCCs in the context of immunotherapy remains unclear, as no distinct benefits are observed in clinical trials. In this study, we perform
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