Junhyong Kim

The paper introduces Live D-seq, a method for longitudinal, non-destructive single-cell RNA sequencing, and demonstrates its ability to track cellular dynamics over time.

This paper introduces Live-ST, a method for longitudinal spatial transcriptomics, and demonstrates its use in tracking drug response in melanoma tissues.

This study uses snRNA-seq to analyze sorghum leaf development, revealing that trichomes and bulliform cells exist in both juvenile and adult stages with stage-specific gene expression patterns.

CoPro identifies spatially coordinated gene expression programs across tissues, revealing distinct cellular states and their interactions in colon, brain, liver, and kidney.

The paper examines evolutionary divergence in the dendritic transcriptome of mammalian neurons, finding greater divergence in dendrites than soma while core functions remain conserved.

The paper introduces ORPHEUS, a method to quantify circadian desynchrony in single-cell data, finding reduced synchrony in AD-affected human brain excitatory neurons.

The paper introduces a subspace clustering method for single-cell RNA-seq data to identify cell types based on transcriptome constraints and demonstrates its effectiveness on C. elegans data.

SCO-pH enables high-throughput single-cell acidification analysis by compartmentalizing cells in microfluidic droplets and measuring extracellular pH changes to distinguish metabolic states.

PACS is a statistical model for analyzing snATAC-seq data that improves power and controls false positives in differential accessibility analysis.

The paper analyzes mtDNA SNVs in single mitochondria from mouse neurons and astrocytes, revealing cell-type-specific mutation patterns and purifying selection.

This study uses smFISH to characterize the spatial distribution of six dendritic mRNAs at sub-micrometer resolution, revealing gene-specific and morphology-dependent localization patterns and evidence of co-transport and clustering.

This paper develops a method to generate proximal tubule precursor cells in kidney organoids using transient Notch signaling activation, showing functional maturity and injury response.

This study compares gene expression patterns across embryonic development in C. elegans and C. briggsae to identify evolutionary constraints and divergence.

The paper shows quantitative counts in single nucleus ATAC-seq are informative for estimating regulatory states and proposes PIC as a uniform method for feature characterization.

The paper identifies androgen receptor-mediated regulation of sexually dimorphic gene expression in the mouse kidney and finds limited conservation in human kidney.

The paper maps the regulatory landscape of the mouse kidney at single cell resolution, identifying key transcription factors and regulatory circuits, and linking variants to kidney disease.

The paper uses single-cell RNA sequencing and lineage tracing in mouse kidneys to reveal sex differences, cell diversity, and lineage convergence in nephron development.

CHEX-seq identifies open-chromatin regions in fixed single cells, enabling spatial chromatin analysis in complex environments.

The paper maps lineage and transcriptome relationships in C. elegans embryogenesis using single-cell transcriptomics, revealing lineage-transcriptome correlation breakdown in late divisions.

The paper identifies dendritically localized mRNA isoforms in mouse neurons using sub-cellular RNA sequencing and provides a resource of known localized RNAs.

The paper introduces ORNASEQ, an ontology for capturing data provenance in RNA sequencing studies, and provides a sample dataset for developing provenance tools.

This paper compares single cell RNA sequencing and single molecule RNA FISH for rare cell analysis, finding that higher transcriptome complexity per cell improves accuracy for small gene sets.

PIVOT provides an R-based application with a graphical interface to simplify transcriptomics data analysis for non-programmers by integrating multiple tools and enabling interactive exploration.

The paper introduces TASC, a statistical framework to account for technical noise in scRNA-seq by modeling dropout rates and amplification bias, improving differential expression analysis.

This paper discusses challenges and future directions in single-cell analysis, highlighting advances in technologies and computational methods for studying cellular heterogeneity.

The paper introduces a machine learning method for accurate protein fold recognition, achieving over 96% accuracy and predicting folds for 34,330 human protein domains.