• Molecules and Cells
• /
• v.46 no.2
• /
• pp.120-129
• /
• 2023

Recent technical advances have enabled unbiased transcriptomic and epigenetic analysis of each cell, known as "single-cell analysis". Single-cell analysis has a variety of technical approaches to investigate the state of each cell, including mRNA levels (transcriptome), the immune repertoire (immune repertoire analysis), cell surface proteins (surface proteome analysis), chromatin accessibility (epigenome), and accordance with genome variants (eQTLs; expression quantitative trait loci). As an effective tool for investigating robust immune responses in coronavirus disease 2019 (COVID-19), many researchers performed single-cell analysis to capture the diverse, unbiased immune cell activation and differentiation. Despite challenges elucidating the complicated immune microenvironments of chronic inflammatory diseases using existing experimental methods, it is now possible to capture the simultaneous immune features of different cell types across inflamed tissues using various single-cell tools. In this review, we introduce patient-based and experimental mouse model research utilizing single-cell analyses in the field of chronic inflammatory diseases, as well as multi-organ atlas targeting immune cells.

• Genomics & Informatics
• /
• v.17 no.1
• /
• pp.3.1-3.4
• /
• 2019

Intratumor heterogeneity within a single tumor mass is one of the hallmarks of malignancy and has been reported in various tumor types. The molecular characterization of intratumor heterogeneity in breast cancer is a significant challenge for effective treatment. Using single-cell RNA sequencing (RNA-seq) data from a public resource, an ERBB pathway activated triple-negative cell population was identified. The differential expression of three subtyping marker genes (ERBB2, ESR1, and PGR) was not changed in the bulk RNA-seq data, but the single-cell transcriptomes showed intratumor heterogeneity. This result shows that ERBB signaling is activated using an indirect route and that the molecular subtype is changed on a single-cell level. Our data propose a different view on breast cancer subtypes, clarifying much confusion in this field and contributing to precision medicine.

• Genomics & Informatics
• /
• v.16 no.4
• /
• pp.17.1-17.6
• /
• 2018

Tumor heterogeneity, the cellular mosaic of multiple lineages arising from the process of clonal evolution, has continued to thwart multi-omics analyses using traditional bulk sequencing methods. The application of single-cell sequencing, in concert with existing genomics methods, has enabled high-resolution interrogation of the genome, transcriptome, epigenome, and proteome. Applied to cancers, these single-cell multi-omics methods bypass previous limitations on data resolution and have enabled a more nuanced understanding of the evolutionary dynamics of tumor progression, immune evasion, metastasis, and treatment resistance. This review details the growing number of novel single-cell multi-omics methods applied to tumors and further discusses recent discoveries emerging from these approaches, especially in regard to immunotherapy.

• Molecules and Cells
• /
• v.47 no.2
• /
• pp.100031.1-100031.13
• /
• 2024

It is now well-accepted that obesity-induced inflammation plays an important role in the development of insulin resistance and type 2 diabetes. A key source of the inflammation is the murine epididymal and human visceral adipose tissue. The current paradigm is that obesity activates multiple proinflammatory immune cell types in adipose tissue, including adipose-tissue macrophages (ATMs), T Helper 1 (Th1) T cells, and natural killer (NK) cells, while concomitantly suppressing anti-inflammatory immune cells such as T Helper 2 (Th2) T cells and regulatory T cells (Tregs). A key feature of the current paradigm is that obesity induces the anti-inflammatory M2 ATMs in lean adipose tissue to polarize into proinflammatory M1 ATMs. However, recent single-cell transcriptomics studies suggest that the story is much more complex. Here we describe the single-cell genomics technologies that have been developed recently and the emerging results from studies using these technologies. While further studies are needed, it is clear that ATMs are highly heterogeneous. Moreover, while a variety of ATM clusters with quite distinct features have been found to be expanded by obesity, none truly resemble classical M1 ATMs. It is likely that single-cell transcriptomics technology will further revolutionize the field, thereby promoting our understanding of ATMs, adipose-tissue inflammation, and insulin resistance and accelerating the development of therapies for type 2 diabetes.

• Mass Spectrometry Letters
• /
• v.14 no.4
• /
• pp.121-140
• /
• 2023

It is becoming more and more clear that each cell, even those of the same type, has a unique identity. This sophistication and the diversity of cell types in tissue are what are pushing the necessity for spatially distributed omics at the single-cell (SC) level. Single-cell chemical assessment, which also provides considerable insight into biological, clinical, pharmacodynamic, pathological, and toxicity studies, is crucial to the investigation of cellular omics (genomics, metabolomics, etc.). Mass spectrometry (MS) as a tool to image and profile single cells and subcellular organelles facilitates novel technical expertise for biochemical and biomedical research, such as assessing the intracellular distribution of drugs and the biochemical diversity of cellular populations. It has been illustrated that ambient mass spectrometry (AMS) is a valuable tool for the rapid, straightforward, and simple analysis of cellular and sub-cellular constituents and metabolites in their native state. This short review examines the advances in ambient mass spectrometry (AMS) and ambient mass spectrometry imaging (AMSI) on single-cell analysis that have been authored in recent years. The discussion also touches on typical single-cell AMS assessments and implementations.

• Molecules and Cells
• /
• v.42 no.3
• /
• pp.189-199
• /
• 2019

Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.

• Genomics & Informatics
• /
• v.20 no.1
• /
• pp.2.1-2.11
• /
• 2022

The method of single-cell RNA sequencing has been rapidly developed, and numerous experiments have been conducted over the past decade. Their results allow us to recognize various subpopulations and rare cell states in tissues, tumors, and immune systems that are previously unidentified, and guide us to understand fundamental biological processes that determine cell identity based on single-cell gene expression profiles. However, it is still challenging to understand the principle of comprehensive gene regulation that determines the cell fate only with transcriptome, a consequential output of the gene expression program. To elucidate the mechanisms related to the origin and maintenance of comprehensive single-cell transcriptome, we require a corresponding single-cell epigenome, which is a differentiated information of each cell with an identical genome. This review deals with the current development of single-cell epigenomic library construction methods, including multi-omics tools with crucial factors and additional requirements in the future focusing on DNA methylation, chromatin accessibility, and histone post-translational modifications. The study of cellular differentiation and the disease occurrence at a single-cell level has taken the first step with single-cell transcriptome and is now taking the next step with single-cell epigenome.

• Genomics & Informatics
• /
• v.8 no.4
• /
• pp.201-205
• /
• 2010

The H1 histone family, member N, testis-specific (H1FNT) is exclusively expressed in the testis, and had its possible role for sperm chromatin formation. The purpose of this study is to investigate any genetic association of H1FNT gene with male infertility, especially at the promoter region. We examined the promoter single nucleotide polymorphisms (SNP) of H1FNT gene which is located within transcription factor binding site for its association with male infertility. The statistical analysis showed that the -1129A>T polymorphism was present at a statistically significance in male infertility (p=0.0059 and 0.0349 for hetero and risk type, respectively). The dual-luciferase promoter assay was performed to examine the polymorphic effect of this promoter SNP by the cloning of promoter region (1700bp fragment) into pGL3-basic vector. In our plasmid based reporter system, there is no big difference between wild and risk type. In conclusion, H1FNT -1129A>T promoter SNP is statistically significant with male infertility, especially with subfertile (non-azoospermia) group. Further analysis of its functional polymorphic effect in vivo may provide the biological significance of testis-specific histone with spermatogenesis.

• Journal of Animal Science and Technology
• /
• v.53 no.6
• /
• pp.511-516
• /
• 2011

Fas gene known to associate with intramuscular fat content in Korean cattle was selected for DNA marker development. Fas (APO-1, CD95), a member of the tumor necrosis factor (TNF) receptor superfamily, is a cell membrane protein that mediates apoptosis (programmed cell death). We discovered single nucleotide polymorphisms (SNPs) within Fas gene in order to develop novel DNA markers at genomic level. Of this gene to search for SNP, sequences of whole exon and 1kb range of both front and back of the gene using 24 cattle were determined by direct-sequencing methods. As a result, 16 SNPs in exon, 37 SNPs in intron and 2 SNPs in promoter region, a total of 55 SNPs were discovered. In these SNPs, thirty-one common polymorphic sites were selected considering their allele frequencies, haplotype-tagging status and Linkage Disequilibrium (LD) for genotyping in larger-scale subjects. Selected SNPs were confirmed genotype through SNaPshot method (n=274) and were examined for possible genetic association of Fas polymorphisms with carcass weight (CWT), eye muscle area (EMA), and backfat thickness (BF). So, the SNP have been identified significant g.-12T>G, g.1112T>G and g.32548T>C. These results suggest that polymorphism of Fas gene was associated with meat quality traits in Hanwoo.

• Genomics & Informatics
• /
• v.18 no.3
• /
• pp.26.1-26.6
• /
• 2020

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type-specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.