• Molecules and Cells
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• 제42권3호
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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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• 제44권3호
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• pp.127-135
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• 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.

• 한국정보통신학회논문지
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• 제25권11호
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• pp.1512-1518
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• 2021

단일세포 RNA-Seq 은 개별 세포의 유전자 발현을 제공하므로 세포마다 차등적인 고해상도 정보를 준다. 단일세포 RNA-Seq 자료에 대하여 군집화는 세포의 유형과 고수준의 생물 과정을 이해하기 위하여 수행된다. 매우 고차원이고 대용량인 단일세포 RNA-Seq을 효과적으로 처리하기 위하여, 본 논문은 변이 자동인코더를 사용하여 고차원의 자료공간을 저차원의 잠재공간으로 변환하여, 보다 정확한 군집화를 수행할 수 있는 특징공간을 만든다. 차원이 축소된 잠재공간에 다양한 군집화 방법을 적용하는 접근을 다양한 전통적인 단일세포 RNA-Seq 군집화 방법과 성능을 비교하였다. 군집화 실험을 통하여, 제안한 방법은 기존 방법들보다 다양한 군집화 성능기준에서 성능이 개선되었다.

• Genomics & Informatics
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• 제18권3호
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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.

• Korean Circulation Journal
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• 제53권1호
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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.

• Molecules and Cells
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• 제46권2호
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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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• 제17권1호
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• pp.3.1-3.4
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• 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.

• BMB Reports
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• 제53권8호
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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.

• 응용통계연구
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• 제33권4호
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• pp.511-526
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• 2020

단세포 RNA 시퀀싱 데이터(single-cell RNA-sequencing data, 이하 단세포 RNA 데이터)는 세포 조직으로부터 추출한 각 단세포 별 유전자의 신호를 기록한 데이터로, 세포 간의 이질성을 파악하는 것을 주요 목적으로 한다. 그러나 단세포 RNA 데이터는 샘플링 및 기술적인 한계로 인해 결측비율이 높고, 노이즈가 크다. 이러한 이유 때문에 기존의 군집화 방법을 적용하는 데에 한계가 존재한다. 본 논문에서는 단세포 RNA 데이터 분석에서 모티브를 얻어 스펙트럼 군집화(spectral clustering) 기반의 방법을 제안한다. 특히 유사도 행렬(similarity matrix) 계산에서 유전자 별로 가중치를 부여하여 기존의 단세포 데이터 분석 방법과 차별화하였다. 제안하는 군집화 방법은 유전자별 가중치를 부여함과 동시에 세포를 군집화한다. 군집화는 반복 알고리즘을 통해 제안하는 비볼록식(non-convex optimization)을 풀어 진행한다. 또한 실데이터 적용과 시뮬레이션을 통해 제안하는 군집화 방법이 기존의 방법보다 군집을 잘 구분하는 것을 보인다.

• Molecules and Cells
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• 제39권12호
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• pp.841-846
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• 2016

Local protein synthesis mediates precise spatio-temporal regulation of gene expression for neuronal functions such as long-term plasticity, axon guidance and regeneration. To reveal the underlying mechanisms of local translation, it is crucial to understand mRNA transport, localization and translation in live neurons. Among various techniques for mRNA analysis, fluorescence microscopy has been widely used as the most direct method to study localization of mRNA. Live-cell imaging of single RNA molecules is particularly advantageous to dissect the highly heterogeneous and dynamic nature of messenger ribonucleoprotein (mRNP) complexes in neurons. Here, we review recent advances in the study of mRNA localization and translation in live neurons using novel techniques for single-RNA imaging.