Biology subjects

Lee, S. H. T.

Publications and source records attributed to Lee, S. H. T..

3 recordsLinked to original sources

bioRxiv2025

Integrated ambient modeling and genetic demultiplexing of single-cell RNA+ATAC multiome experiments with Ambimux

Single cell technologies have advanced at a rapid pace, providing assays for various molecular phenotypes. Droplet-based single cell technologies, particularly those based on nuclei isolation, such as simultaneous RNA+ATAC single-cell multiome, are susceptible to exogenous ambient molecule contamination, which can increase noise in cell type-level associations. We reasoned that genotype-based sample multiplexing can provide an opportunity to infer this ambient contamination by leveraging DNA variation in sequenced reads. Thus, we developed ambimux, a likelihood-based method to estimate ambient fractions and demultiplex single-cell multiome experiments using genotype-level data. Ambimux models the ambient or nuclear probability at the read level and thus can classify empty droplets and estimate droplet-specific ambient molecule fractions in each modality. We first evaluated our method using simulated data sets across a range of parameters. We found that ambimux closely estimated the ground truth droplet contamination fractions in the RNA (MAE=0.048) and ATAC (MAE=0.042) modalities. As a result, ambimux maintained high specificity (>95%) and was able to correctly assign singlets at considerably high ambient fractions (up to 60%) for both RNA and ATAC modalities. In comparison with models that do not consider ambient contamination, these only maintained similar sensitivity levels at considerably lower ambient fractions (up to 25%). We then generated a real data set of seven visceral adipose tissue biopsies run on a single 10x Multiome channel. We ran ambimux and detected 4,986 singlets, capturing similar numbers as other methods. Then, we sought to evaluate the fidelity of the ambient fraction estimates from ambimux. We split singlets into ambient-enriched (>5% contamination in both modalities) or nuclear-enriched (<5% in both) droplets and performed gene-peak linkage analysis. Low ambient droplets resulted in more significant hits with gene-peak links enriched at the transcription start site relative to high ambient droplets, suggesting that the ambient droplets identified by ambimux hamper the identification of biologically meaningful signals. In summary, we developed a joint single-cell multiome demultiplexing method, ambimux, that accurately models and estimates ambient molecule contamination in each modality.

bioinformatics↗

bioRxiv2024

Single-cell DNA methylome and 3D genome atlas of the human subcutaneous adipose tissue

Human subcutaneous adipose tissue (SAT) contains a diverse array of cell-types; however, the epigenomic landscape among the SAT cell-types has remained elusive. Our integrative analysis of single-cell resolution DNA methylation and chromatin conformation profiles (snm3C-seq), coupled with matching RNA expression (snRNA-seq), systematically cataloged the epigenomic, 3D topology, and transcriptomic dynamics across the SAT cell-types. We discovered that the SAT CG methylation (mCG) landscape is characterized by pronounced hyper-methylation in myeloid cells and hypo-methylation in adipocytes and adipose stem and progenitor cells (ASPCs), driving nearly half of the 705,063 detected differentially methylated regions (DMRs). In addition to the enriched cell-type-specific transcription factor binding motifs, we identified TET1 and DNMT3A as plausible candidates for regulating cell-type level mCG profiles. Furthermore, we observed that global mCG profiles closely correspond to SAT lineage, which is also reflected in cell-type-specific chromosome compartmentalization. Adipocytes, in particular, display significantly more short-range chromosomal interactions, facilitating the formation of complex local 3D genomic structures that regulate downstream transcriptomic activity, including those associated with adipogenesis. Finally, we discovered that variants in cell-type level DMRs and A compartments significantly predict and are enriched for variance explained in abdominal obesity. Together, our multimodal study characterizes human SAT epigenomic landscape at the cell-type resolution and links partitioned polygenic risk of abdominal obesity to SAT epigenome.

genomics↗

bioRxiv2022

Mutational Changes and Enhanced Cell Migration during Transformation of Breast Non-tumorigenic Cells to Tumorigenic Cells

Although cancer stem cells (CSCs) play a major role in tumorigenesis and metastasis, the role of genetic alterations in invasiveness of CSCs is still unclear. Tumor microenvironment signals, such as extracellular matrix (ECM) composition, significantly influence cell behaviors. Unfortunately, these signals are often lost in in vitro cell culture. This study determines putative CSC populations, examines genetic changes during tumorigenesis of human breast epithelial stem cells, and investigates single-cell migration properties on ECM-mimetic platforms. Whole exome sequencing data indicate that tumorigenic cells have a higher somatic mutation burden than non-tumorigenic cells, and that mutations exclusive to tumorigenic cells exhibit higher predictive deleterious scores. Tumorigenic cells exhibit distinct somatic copy number variations (CNVs) including gain of duplications in chromosomes 5 and 8. ECM-mimetic topography selectively enhances migration speed of tumorigenic cells, but not of non-tumorigenic cells, and results in a wide distribution of tumorigenic single-cell migration speeds, suggesting heterogeneity in cellular sensing of contact guidance cues. This study identifies mutations and CNVs acquired during breast tumorigenesis, which can be associated with enhanced migration of breast tumorigenic cells, and demonstrates that a nanotopographically-defined platform can be applied to recapitulate an ECM structure for investigating cellular migration in the simulated tumor microenvironment.

cancer biology↗