About

Professor Junhyong Kim is the Chair and Patricia M. Williams Professor in the Department of Biology at the University of Pennsylvania. He is also the Co-Director of the Penn Program in Single Cell Biology. His research focuses on the evolutionary and molecular biology of the cell, contributing to understanding cellular processes at a molecular level. Dr. Kim's laboratory is located in Lynch Laboratory, where he leads a team that includes senior scientists, research staff, postdoctoral fellows, graduate students, visiting scholars, and affiliates. His work involves both wet lab and dry lab spaces, supporting a broad range of research activities in cellular and molecular biology.

Research topics

• Computational biology
• Biology
• Cell biology
• Bioinformatics
• Data Mining
• Computer Science
• Genetics
• Artificial Intelligence
• Data science
• Anatomy

Selected publications

• Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-21

  articleOpen access

  Abstract Spatial transcriptomics enables the study of how cells coordinate their molecular states within tissue, providing insight into both normal function and disease processes. A key challenge is to identify gene expression programs that vary continuously across space and are coordinated between cell types. We present CoPro , a computational framework for detecting the spatially coordinated progression of cellular states. CoPro can operate in both supervised and unsupervised modes to identify gene programs that co-vary within or between cell types, and to disentangle multiple overlapping spatial patterns. CoPro can be applied to single-cell-level spatial transcriptomics datasets, including MERFISH, SeqFISH+, Xenium, and histology-imputed transcriptomic data. We demonstrate the utility of CoPro with data collected from colon, brain, liver, and kidney tissues. In the colon, CoPro separates epithelial differentiation along the crypt axis from spatially localized inflammatory signals. In the aging liver, it identifies multiple aging-associated cellular programs superimposed on anatomical zonation. In the brain, the flexible kernel design enables the decoupling of the gene expression gradient along the dorsal-ventral and medial-lateral axes. In the kidney, CoPro identifies tubule-vasculature coordination that is essential in nephron function. These results demonstrate CoPro’s utility for analyzing spatial coordination of gene expression in complex tissues and disentangling overlapping biological processes, such as anatomical organization and disease-associated variation.

  PublisherDOI

• Leaves in Transition: Single nuclei RNA sequencing provides insights into sorghum’s juvenile and adult phases

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-25

  article

  ABSTRACT The transition from juvenile to adult phase (JA) is a key developmental process in plants, driven by conserved pathways that shape growth and stress responses. In monocots such as sorghum, this transition influences traits linked to environmental resilience, yet the regulation of micro-anatomical features such as trichomes and bulliform cells remains poorly understood. Here, we use single-nucleus RNA sequencing (snRNA-seq) to generate high-resolution gene expression maps of juvenile and adult sorghum leaves. Contrary to the traditional developmental model, we find that trichomes are present in both juvenile and adult leaves, with stage-dependent differences in gene expression. We also observe that bulliform cells, typically considered adult-specific, are present in juvenile leaves. At the transcriptomic level, cell populations cluster more strongly by developmental stage than by cell type, with juvenile cells grouping with other juvenile cells and adult cells with adult counterparts, indicating a dominant effect of developmental state on gene expression. In addition, we identify enrichment of dhurrin biosynthetic genes in trichome-associated cells as well as elevated expression of aquaporin genes in these cells, pointing to a potential role for trichomes in coordinating defense and water-related processes. Together, these findings refine current models of sorghum development and suggest that key epidermal cells and their associated functions may be established earlier than previously known. Collectively, this study provides a single nucleus resolved framework for understanding how developmental stage shapes gene expression in sorghum leaves.

  PublisherDOI

• Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS

  Nature Communications · 2025-01-05 · 5 citations

  articleOpen accessSenior author

  Single cell ATAC-seq (scATAC-seq) experimental designs have become increasingly complex, with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current scATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that allows complex hypothesis testing of accessibility-modulating factors while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves a 17% to 122% higher power on average than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks, including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to datasets from various tissues and show its ability to reveal previously undiscovered insights in scATAC-seq data. scATAC-seq data pose statistical challenges due to sparsity and cell-specific sequence capture. Here, the authors present PACS, a zero-adjusted statistical model that enables complex hypothesis testing of accessibility-modulating factors while addressing sparse and incomplete data.

  PublisherOA PDFDOI

• Supplementary Figures S1-S13 from The FBXO45–GEF-H1 Axis Controls Germinal Center Formation and B-cell Lymphomagenesis

  2025-04-02

  preprintOpen access

  <p>Supplementary Data file shows all the supplementary figures, figure legends and supplementary table legends.</p>

  PublisherDOI

• 글로벌 인플레이션의 국내파급효과와 경기안정화 정책 분석(The Domestic Spillover Effects of Global Inflation <span>and Related Economic Stabilization Policies)</span>

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Table S7 from The FBXO45–GEF-H1 Axis Controls Germinal Center Formation and B-cell Lymphomagenesis

  2025-04-02

  preprintOpen access

  <p>Supplementary Table S7, Table showing source data and statistics for non-high-throughput experiments such as flow cytometry, quantitative PCR, protein experiments and other molecular and cellular assays.</p>

  PublisherDOI

• Table S6 from The FBXO45–GEF-H1 Axis Controls Germinal Center Formation and B-cell Lymphomagenesis

  2025-04-02

  preprintOpen access

  <p>Supplementary Table S6, Details of the FISH probes used to identify copy number variations (CNV) affecting FBXO45 and ARHGEF2 in clinical samples.</p>

  PublisherDOI

• Controlling nephron precursor differentiation to generate proximal-biased kidney organoids with emerging maturity

  Nature Communications · 2025-08-30 · 8 citations

  articleOpen access

  The kidney maintains fluid homeostasis by reabsorbing essential compounds and excreting waste. Proximal tubule cells, crucial for reabsorbing sugars, ions, and amino acids, are highly susceptible to injury, often leading to pathologies necessitating dialysis or transplants. Human pluripotent stem cell-derived kidney organoids offer a platform to model renal development, function, and disease, but proximal nephron differentiation and maturation in these structures is incomplete. Here, we drive proximal tubule development in pluripotent stem cell-derived kidney organoids by mimicking in vivo proximal differentiation. Transient PI3K inhibition during early nephrogenesis activates Notch signaling, shifting nephron axial differentiation towards epithelial and proximal precursor states that mature to proximal convoluted tubule cells broadly expressing physiology-imparting solute carriers including organic cation and organic anion family members. The "proximal-biased" organoids thus acquire function, and on exposure to nephrotoxic injury, display tubular collapse and DNA damage, and upregulate injury response markers HAVCR1/KIM1 and SOX9 while downregulating proximal transcription factor HNF4A. Here, we show that proximally biased human-derived kidney organoids provide a robust model to study nephron development, injury responses, and a platform for therapeutic discovery.

  PublisherOA PDFDOI

• Subspace clustering identifies transcriptome constraints determining cell type identity

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-04

  articleOpen accessSenior author

  With the advent of single-cell RNA-sequencing, researchers now have the ability to define cell types from large amounts of transcriptome information. Currently, most clustering algorithms measure cell-to-cell similarities using distance metrics based on the assumption that each cluster is comprised of "nearby" neighbors. In effect, clusters are a collection of similar cells in the embedded metric. Here, we propose that biological clusters should be comprised of sets of cells that satisfy a set of stochiometric constraints, whose intersections define a cell type. We propose to model each cell population with a single affine subspace, where all cells of the same type share a common set of linear constraints. We present an algorithm that leverages this subspace structure and learns a cell-to-cell affinity matrix based on notions of subspace similarity. We simulate scRNA-seq data according to the subspace model and benchmark our algorithm against pre-existing methods. We further benchmark our algorithm on a C. elegans dataset and show recovery of information on both cell type and developmental time. Lastly, we find the subspaces that our algorithm recovers allow us to find biologically significant genes involved in an organism's development.

  PublisherDOI

• Table S5 from The FBXO45–GEF-H1 Axis Controls Germinal Center Formation and B-cell Lymphomagenesis

  2025-04-02

  preprintOpen access

  <p>Supplementary Table S5, FISH analysis to identify copy number variations (CNV) affecting genes of interest within the corresponding genomic loci.</p>

  PublisherDOI

Recent grants

• AToL: Collaborative Research: A Phylogenomic Toolbox for Assembling the Tree of Life

  NSF · $538k · 2003–2009

• NIH Grant U01MH09895304

  NIH · $2.3M · 2016

• Center for Sub-Cellular Genomics

  NIH · $9.0M · 2018–2024

• Training Program in Computational Genomics

  NIH · $9.0M · 1999–2030

• NIH Grant R01MH088849

  NIH · $1.2M · 2014

Frequent coauthors

• James Eberwine

  University of Pennsylvania

  57 shared

• Stephen Fisher

  38 shared

• Hannah Dueck

  National Cancer Institute

  28 shared

• Jennifer Spaethling

  Translational Therapeutics (United States)

  21 shared

• Robert R. Sokal

  20 shared

• David Issadore

  University of Pennsylvania

  20 shared

• Jai‐Yoon Sul

  Translational Therapeutics (United States)

  19 shared

• Young-Ji Na

  19 shared

Labs

• Kim LabPI

  Evolutionary and Molecular Biology of the Cell

Education

• Ph.D., Biology

  University of California, San Diego

  2000

• B.S., Biology

  University of California, San Diego

  1995