• 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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• 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.

• 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.

• 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.

• 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.

• 대한임상검사과학회지
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• 제56권1호
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• pp.10-20
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• 2024

RNA-시퀀싱은 표본에 대한 전사체 전체의 패턴을 제공하는 기법이다. 그러나 RNA-시퀀싱은 표본 내 전체 세포에 대한 평균 유전자 발현만 제공할 수 있으며, 표본 내의 이질성(heterogeneity)에 대한 정보는 제공하지 못한다. 단일 세포 RNA-시퀀싱 기술의 발전을 통해 우리는 표본의 단일 세포 수준에서 이질성과 유전자 발현의 동역학(dynamics)에 대한 이해를 할 수 있게 되었다. 예를 들어, 우리는 단일 세포 RNA-시퀀싱을 통해 복잡한 조직을 구성하는 다양한 세포 유형을 식별할 수 있으며, 특정 세포 유형의 유전자 발현 변화와 같은 정보를 알 수 있다. 단일 세포 RNA-시퀀싱은 처음 도입된 이후 많은 이들의 관심을 끌게 되었으며, 이를 활용하기 위한 대규모 생물정보학(bioinformatics) 도구가 개발되었다. 그러나 단일 세포 RNA-시퀀싱에서 생성된 빅데이터 분석에는 데이터 전처리에 대한 이해와 전처리 이후 다양한 분석 기술에 대한 이해가 필요하다. 본 종설에서는 단일 세포 RNA-시퀀싱 데이터분석과 관련된 작업과정의 개요를 제시한다. 먼저 데이터의 품질 관리, 정규화 및 차원 감소와 같은 데이터의 전 처리 과정에 대해 설명한다. 그 이후, 가장 일반적으로 사용되는 생물정보학 도구를 활용한 데이터의 후속 분석에 대해 설명한다. 본 종설은 이 분야에 관심이 있는 새로운 연구자를 위한 가이드라인을 제공하는 것을 목표로 한다.

• Molecules and Cells
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• 제45권9호
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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.

• 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.

• Genomics & Informatics
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• 제21권2호
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• pp.18.1-18.11
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• 2023

Immunologists have activated T cells in vitro using various stimulation methods, including phorbol myristate acetate (PMA)/ionomycin and αCD3/αCD28 agonistic antibodies. PMA stimulates protein kinase C, activating nuclear factor-κB, and ionomycin increases intracellular calcium levels, resulting in activation of nuclear factor of activated T cell. In contrast, αCD3/αCD28 agonistic antibodies activate T cells through ZAP-70, which phosphorylates linker for activation of T cell and SH2-domain-containing leukocyte protein of 76 kD. However, despite the use of these two different in vitro T cell activation methods for decades, the differential effects of chemical-based and antibody-based activation of primary human T cells have not yet been comprehensively described. Using single-cell RNA sequencing (scRNA-seq) technologies to analyze gene expression unbiasedly at the single-cell level, we compared the transcriptomic profiles of the non-physiological and physiological activation methods on human peripheral blood mononuclear cell-derived T cells from four independent donors. Remarkable transcriptomic differences in the expression of cytokines and their respective receptors were identified. We also identified activated CD4 T cell subsets (CD55⁺) enriched specifically by PMA/ionomycin activation. We believe this activated human T cell transcriptome atlas derived from two different activation methods will enhance our understanding, highlight the optimal use of these two in vitro T cell activation assays, and be applied as a reference standard when analyzing activated specific disease-originated T cells through scRNA-seq.

• IMMUNE NETWORK
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• 제20권4호
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• pp.34.1-34.8
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• 2020

Cryopreservation and thawing of PBMCs are inevitable processes in expanding the scale of experiments in human immunology. Here, we carried out a fundamental study to investigate the detailed effects of PBMC cryopreservation and thawing on transcriptomes. We sorted Tregs from fresh and cryopreserved/thawed PBMCs from an identical donor and performed single-cell RNA-sequencing (scRNA-seq). We found that the cryopreservation and thawing process minimally affects the key molecular features of Tregs, including FOXP3. However, the cryopreserved and thawed sample had a specific cluster with up-regulation of genes for heat shock proteins. Caution may be warranted in interpreting the character of any cluster of cells with heat shock-related properties when cryopreserved and thawed samples are used for scRNA-seq.