Biology subjects

Cho, R. J.

Publications and source records attributed to Cho, R. J..

6 recordsLinked to original sources

bioRxiv2024

Genetic evolution of keratinocytes to cutaneous squamous cell carcinoma

We performed multi-omic profiling of epidermal keratinocytes, precancerous actinic keratoses, and squamous cell carcinomas to understand the molecular transitions during skin carcinogenesis. Single-cell mutational analyses of normal skin cells showed that most keratinocytes have remarkably low mutation burdens, despite decades of sun exposure, however keratinocytes with TP53 or NOTCH1 mutations had substantially higher mutation burdens. These observations suggest that wild-type keratinocytes (i.e. without pathogenic mutations) are able to withstand high dosages of cumulative UV radiation, but certain pathogenic mutations break these adaptive mechanisms, priming keratinocytes for transformation by increasing their mutation rate. Mutational profiling of squamous cell carcinomas adjacent to actinic keratoses revealed TERT promoter and CDKN2A mutations emerging in actinic keratoses, whereas additional mutations inactivating ARID2 and activating the MAPK-pathway delineated the transition to squamous cell carcinomas. Surprisingly, actinic keratoses were often not related to their neighboring squamous cell carcinoma, indicating that collisions of unrelated neoplasms are common in the skin. Spatial variation in gene expression patterns was common in both tumor and immune cells, with high expression of checkpoint molecules at the invasive front of tumors. In conclusion, this study catalogues the key events during the evolution of cutaneous squamous cell carcinoma.

genomics↗

bioRxiv2023

Characterizing tissue structures from spatial omics with spatial cellular graph partition

Spatial transcriptomic and proteomic measurements enable high-dimensional characterization of tissues. However, understanding organizations of cells at different spatial scales and extracting tissue structures of interest remain challenging tasks that require extensive human annotations. To address this need for consistent identification of tissue structures, in this work, we present a novel annotation method Spatial Cellular Graph Partitioning (SCGP) that allows unsupervised identification of tissue structures that reflect the anatomical and functional units of human tissues. We further present a reference-query extension pipeline SCGP-Extension that enables the generalization of existing reference tissue structures to previously unseen samples. Our experiments demonstrate reliable and robust partitionings of both spatial transcriptomics and proteomics datasets encompassing different tissue types and profiling techniques. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease and skin disorder, underscoring its potential in facilitating spatial analysis and driving new discoveries.

bioinformatics↗

bioRxiv2022

Nuclear architecture protein Distal antenna balances genome-binding and phase-separation properties to regulate neuroblast competence

Neural progenitors transit through multiple competence states that restrict production of each neural cell type. In Drosophila neuroblasts, a timed genome reorganization relocates the cell fate gene, hunchback, to the nuclear periphery, terminating competence to produce early-born neurons. Distal antenna (Dan), a pipsqueak (Psq) superfamily protein, is transiently downregulated at mid-embryogenesis, which is required for this relocation. Here we find that Dan is a highly intrinsically disordered protein, and when its Psq DNA-binding domain is increasingly disrupted, Dan coalesces into steadily larger, interconnected hubs of rapid protein exchange. Consistent with these phase-separation properties, Dan has a predicted LARKS domain, a structural motif that forms reversible interactions associated with phase-separation. In the embryo, loss of either the Psq motif or the LARKS domain abrogates Dans ability to maintain neuroblast early competence upon misexpression, suggesting that Dan requires both DNA-binding and phase-separation to regulate neuroblast competence. Finally, we found that Dan strongly interacts with proteins of the nuclear pore complex (NPC), and Elys, a core NPC scaffold protein known to regulate genome architecture, binds the hb intron and is required for competence termination. Together, the results support a model for how Dans phase-separation properties can mediate dynamic restructuring by balancing genome-binding, self-association, and interaction among nuclear architecture regulators.

developmental biology↗

bioRxiv2022

Spectral clustering of single-cell multi-omics data on multilayer graphs

Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem. We introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs (SCML) and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis. The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum

bioinformatics↗

bioRxiv2020

Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell surface protein count data from CITE-seq

Recent advances in next generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. Count data of surface proteins from the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology pose new computational challenges, and there is currently a dearth of rigorous mathematical tools for analyzing the data. This work utilizes concepts and ideas from Riemannian geometry to remove batch effects between samples and develops a statistical framework for distinguishing positive signals from background noise. The strengths of these approaches are demonstrated on two independent CITE-seq data sets in mouse and human. Python source code implementing the algorithms is available at https://github.com/jssong-lab/SAGACITE.

bioinformatics↗

bioRxiv2019

Multi-region exome sequencing of ovarian teratomas reveals 2N near-diploid genomes, paucity of somatic mutations, and extensive allelic imbalances shared across mature, immature, and disseminated components

Immature teratoma is a subtype of malignant germ cell tumor of the ovary that occurs most commonly in the first three decades of life, frequently with bilateral ovarian disease. Despite being the second most common malignant germ cell tumor of the ovary, little is known about its genetic underpinnings. Here we performed multi-region whole exome sequencing to interrogate the genetic zygosity, clonal relationship, DNA copy number, and mutational status of 52 pathologically distinct tumor components from 10 females with ovarian immature teratomas, with bilateral tumors present in 5 cases and peritoneal dissemination in 7 cases. We found that ovarian immature teratomas are genetically characterized by 2N near-diploid genomes with extensive loss of heterozygosity and an absence of genes harboring recurrent somatic mutations or known oncogenic variants. All components within a single ovarian tumor (immature teratoma, mature teratoma with different histologic patterns of differentiation, and yolk sac tumor) were found to harbor an identical pattern of loss of heterozygosity across the genome, indicating a shared clonal origin. In contrast, the 4 analyzed bilateral teratomas showed distinct patterns of zygosity changes in the right versus left sided tumors, indicating independent clonal origins. All disseminated teratoma components within the peritoneum (including gliomatosis peritonei) shared a clonal pattern of loss of heterozygosity with either the right or left primary ovarian tumor. The observed genomic loss of heterozygosity patterns indicate that diverse meiotic errors contribute to the formation of ovarian immature teratomas, with 11 out of the 15 genetically distinct clones determined to result from the failure of meiosis I or II. Overall, these findings suggest that copy-neutral loss of heterozygosity resulting from meiotic abnormalities may be sufficient to generate ovarian immature teratomas from germ cells.

cancer biology↗