• 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.

• 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.44 no.3
• /
• pp.127-135
• /
• 2021

Since the introduction of RNA sequencing (RNA-seq) as a high-throughput mRNA expression analysis tool, this procedure has been increasingly implemented to identify cell-level transcriptome changes in a myriad of model systems. However, early methods processed cell samples in bulk, and therefore the unique transcriptomic patterns of individual cells would be lost due to data averaging. Nonetheless, the recent and continuous development of new single-cell RNA sequencing (scRNA-seq) toolkits has enabled researchers to compare transcriptomes at a single-cell resolution, thus facilitating the analysis of individual cellular features and a deeper understanding of cellular functions. Nonetheless, the rapid evolution of high throughput single-cell "omics" tools has created the need for effective hypothesis verification strategies. Particularly, this issue could be addressed by coupling cell engineering techniques with single-cell sequencing. This approach has been successfully employed to gain further insights into disease pathogenesis and the dynamics of differentiation trajectories. Therefore, this review will discuss the current status of cell engineering toolkits and their contributions to single-cell and genome-wide data collection and analyses.

• 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.

• Interdisciplinary Bio Central
• /
• v.2 no.2
• /
• pp.1.1-1.8
• /
• 2010

Microdevices have been used as effective experimental tools for the rapid and multiplexed analysis of individual cells in single-cell assays. Technological advances for miniaturizing such systems and the optimization of delicate controls in micron-sized space homing cells have motivated many researchers from diverse fields (e.g., cancer research, stem cell research, therapeutic agent development, etc.) to employ microtools in their scientific research. Microtools allow high-throughput, multiplexed analysis of single cells, and they are not limited by the lack of large samples. These characteristics may significantly benefit the study of immune cells, where the number of cells available for testing is usually limited. In this review, I present an overview of several microtools that are currently available for single-cell analyses in two popular formats: microarrays and microfluidic microdevices. Then, I discuss the potential to study human immunology on the single-cell level, and I highlight several recent examples of immunoassays performed with single-cell microdevice assays. Finally, I discuss the outlook for the development of optimized assay platforms to study human immune cells. The development and application of microdevices for studies on single immune cells presents novel opportunities for the qualitative and quantitative characterization of immune cells and may lead to a comprehensive understanding of fundamental aspects of human immunology.

• 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.

• BMB Reports
• /
• v.50 no.6
• /
• pp.283-284
• /
• 2017

Single cell transcriptome analysis is a powerful tool for defining cell types or sub-populations within a heterogeneous bulk population. Tumor-associated microenvironment is a complex ecosystem consisting of numerous cell types that support tumor growth, angiogenesis, immune evasion, and metastasis. With the success of checkpoint inhibitors targeting the immune cell compartment, tumor microenvironment is emerging as a potential anti-cancer target, and understanding it has become an imminent subject in cancer biology.

• Proceedings of the KSME Conference
• /
• 2007.05b
• /
• pp.2222-2227
• /
• 2007

Newly structured metal-supported solid oxide fuel cell was fabricated and characterized by impedance analysis and galvanodynamic experiment. Using a cermet adhesive, thin ceramic layer composed of anode(Ni/YSZ) and electrolyte(YSZ) was joined with STS430 metal support of which flow channel was fabricated. $La_{0.8}Sr_{0.2}Co_{0.4}Mn_{0.6}O_3$ perovskite oxide was used as cathode material. Single cell performance was increased and saturated at operating time to 300hours at 800$^{\circ}C$ because of cathode sintering effect. The sintering effect was reinvestigated by half cell test and exchange current density was measured as 0.005A/$cm^2$. Maximum power density of the cell was 0.09W/$cm^2$ at 800$^{\circ}C$. Numerical analysis was carried out to classify main factors influencing the single cell performances. Compared to experimental IV curve, simulated curve based on experimental parameters such as exchange current density was in good agreement.

• 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.