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

• Molecules and Cells
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• v.44 no.3
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• pp.136-145
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• 2021

Senescent cells that gradually accumulate during aging are one of the leading causes of aging. While senolytics can improve aging in humans as well as mice by specifically eliminating senescent cells, the effect of the senolytics varies in different cell types, suggesting variations in senescence. Various factors can induce cellular senescence, and the rate of accumulation of senescent cells differ depending on the organ. In addition, since the heterogeneity is due to the spatiotemporal context of senescent cells, in vivo studies are needed to increase the understanding of senescent cells. Since current methods are often unable to distinguish senescent cells from other cells, efforts are being made to find markers commonly expressed in senescent cells using bulk RNA-sequencing. Moreover, single-cell RNA (scRNA) sequencing, which analyzes the transcripts of each cell, has been utilized to understand the in vivo characteristics of the rare senescent cells. Recently, transcriptomic cell atlases for each organ using this technology have been published in various species. Novel senescent cells that do not express previously established marker genes have been discovered in some organs. However, there is still insufficient information on senescent cells due to the limited throughput of the scRNA sequencing technology. Therefore, it is necessary to improve the throughput of the scRNA sequencing technology or develop a way to enrich the rare senescent cells. The in vivo senescent cell atlas that is established using rapidly developing single-cell technologies will contribute to the precise rejuvenation by specifically removing senescent cells in each tissue and individual.

• Tuberculosis and Respiratory Diseases
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• v.87 no.3
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• pp.252-260
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• 2024

Chronic obstructive pulmonary disease (COPD) affects close to 400 million people worldwide and is the 3rd leading cause of mortality. It is a heterogeneous disorder with multiple endophenotypes, each driven by specific molecular networks and processes. Therapeutic discovery in COPD has lagged behind other disease areas owing to a lack of understanding of its pathobiology and scarcity of biomarkers to guide therapies. Single cell RNA sequencing (scRNA-seq) is a powerful new tool to identify important cellular and molecular networks that play a crucial role in disease pathogenesis. This paper provides an overview of the scRNA-seq technology and its application in COPD and the lessons learned to date from scRNA-seq experiments in COPD.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.511-526
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• 2020

Single-cell RNA-sequencing (scRNA-seq) data consists of each cell's RNA expression extracted from large populations of cells. One main purpose of using scRNA-seq data is to identify inter-cellular heterogeneity. However, scRNA-seq data pose statistical challenges when applying traditional clustering methods because they have many missing values and high level of noise due to technical and sampling issues. In this paper, motivated by analyzing scRNA-seq data, we propose a novel spectral-based clustering method by imposing different weights on genes when computing a similarity between cells. Assigning weights on genes and clustering cells are performed simultaneously in the proposed clustering framework. We solve the proposed non-convex optimization using an iterative algorithm. Both real data application and simulation study suggest that the proposed clustering method better identifies underlying clusters compared with existing clustering methods.

• Korean Circulation Journal
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• v.53 no.1
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• pp.1-16
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• 2023

Recently, single cell RNA sequencing (scRNA-seq) technology has enabled the discovery of novel or rare subtypes of cells and their characteristics. This technique has advanced unprecedented biomedical research by enabling the profiling and analysis of the transcriptomes of single cells at high resolution and throughput. Thus, scRNA-seq has contributed to recent advances in cardiovascular research by the generation of cell atlases of heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and diseases. This review summarizes the overall workflow of the scRNA-seq technique itself and key findings in the cardiovascular development and diseases based on the previous studies. In particular, we focused on how the single-cell sequencing technology can be utilized in clinical field and precision medicine to treat specific diseases.

• The Korean Journal of Physiology and Pharmacology
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• v.28 no.5
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• pp.403-411
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• 2024

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools. RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNA-seq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

• Molecules and Cells
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• v.46 no.2
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• pp.106-119
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• 2023

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.

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

• Molecules and Cells
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• v.45 no.9
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• pp.610-619
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• 2022

Cellular senescence plays a paradoxical role in tumorigenesis through the expression of diverse senescence-associated (SA) secretory phenotypes (SASPs). The heterogeneity of SA gene expression in cancer cells not only promotes cancer stemness but also protects these cells from chemotherapy. Despite the potential correlation between cancer and SA biomarkers, many transcriptional changes across distinct cell populations remain largely unknown. During the past decade, single-cell RNA sequencing (scRNA-seq) technologies have emerged as powerful experimental and analytical tools to dissect such diverse senescence-derived transcriptional changes. Here, we review the recent sequencing efforts that successfully characterized scRNA-seq data obtained from diverse cancer cells and elucidated the role of senescent cells in tumor malignancy. We further highlight the functional implications of SA genes expressed specifically in cancer and stromal cell populations in the tumor microenvironment. Translational research leveraging scRNA-seq profiling of SA genes will facilitate the identification of novel expression patterns underlying cancer susceptibility, providing new therapeutic opportunities in the era of precision medicine.

• BMB Reports
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• v.53 no.8
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• pp.393-399
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• 2020

Recent advancements in the resolution and throughput of single-cell analyses, including single-cell RNA sequencing (scRNA-seq), have achieved significant progress in biomedical research in the last decade. These techniques have been used to understand cellular heterogeneity by identifying many rare and novel cell types and characterizing subpopulations of cells that make up organs and tissues. Analysis across various datasets can elucidate temporal patterning in gene expression and developmental cues and is also employed to examine the response of cells to acute injury, damage, or disruption. Specifically, scRNA-seq and spatially resolved transcriptomics have been used to describe the identity of novel or rare cell subpopulations and transcriptional variations that are related to normal and pathological conditions in mammalian models and human tissues. These applications have critically contributed to advance basic cardiovascular research in the past decade by identifying novel cell types implicated in development and disease. In this review, we describe current scRNA-seq technologies and how current scRNA-seq and spatial transcriptomic (ST) techniques have advanced our understanding of cardiovascular development and disease.