Single-Cell RNA-seq Analysis of a Human Embryonic Stem Cell to Endothelial Cell System Based on Transcription Factor Overexpression

Xu, Xia; Chen, Junru; Zhao, Hao; Pi, Yuze; Lin, Ge; Hu, Liang

doi:10.1007/s12015-023-10598-y

Cited by 2 publications

(1 citation statement)

References 51 publications

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“…Numerous studies have employed scRNA-seq to examine variations in gene expression within both hESCs and their differentiated derivatives. A scRNA-seq study analysed a high-efficiency hESC-endothelial cell induction system and identified differentially expressed genes (DEGs) involved in endothelial cell differentiation [ 17 ]. A recent study used scRNA-seq to examine the developmental path of cardiomyocytes derived from human pluripotent stem cells in engineered tissues, uncovering DEGs related to cardiac differentiation [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs)

Alamin,

Humaira Sultana,

Babarinde

et al. 2024

Briefings in Bioinformatics

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The complicated process of neuronal development is initiated early in life, with the genetic mechanisms governing this process yet to be fully elucidated. Single-cell RNA sequencing (scRNA-seq) is a potent instrument for pinpointing biomarkers that exhibit differential expression across various cell types and developmental stages. By employing scRNA-seq on human embryonic stem cells, we aim to identify differentially expressed genes (DEGs) crucial for early-stage neuronal development. Our focus extends beyond simply identifying DEGs. We strive to investigate the functional roles of these genes through enrichment analysis and construct gene regulatory networks to understand their interactions. Ultimately, this comprehensive approach aspires to illuminate the molecular mechanisms and transcriptional dynamics governing early human brain development. By uncovering potential links between these DEGs and intelligence, mental disorders, and neurodevelopmental disorders, we hope to shed light on human neurological health and disease. In this study, we have used scRNA-seq to identify DEGs involved in early-stage neuronal development in hESCs. The scRNA-seq data, collected on days 26 (D26) and 54 (D54), of the in vitro differentiation of hESCs to neurons were analyzed. Our analysis identified 539 DEGs between D26 and D54. Functional enrichment of those DEG biomarkers indicated that the up-regulated DEGs participated in neurogenesis, while the down-regulated DEGs were linked to synapse regulation. The Reactome pathway analysis revealed that down-regulated DEGs were involved in the interactions between proteins located in synapse pathways. We also discovered interactions between DEGs and miRNA, transcriptional factors (TFs) and DEGs, and between TF and miRNA. Our study identified 20 significant transcription factors, shedding light on early brain development genetics. The identified DEGs and gene regulatory networks are valuable resources for future research into human brain development and neurodevelopmental disorders.

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Section: Introductionmentioning

confidence: 99%

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs)

Alamin,

Humaira Sultana,

Babarinde

et al. 2024

Briefings in Bioinformatics

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Beyond Differential Expression: Embracing Cell-to-Cell Variability in Single-Cell Gene Expression Data Analysis

Gatlin,

Gupta,

Romero

et al. 2024

Preprint

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Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular variability by capturing gene expression profiles of individual cells. The importance of cell-to-cell variability in determining and shaping cell function has been widely appreciated. Nevertheless, differential expression (DE) analysis remains a cornerstone method in analytic practice, by focusing exclusively on mean expressional differences, current computational analyses overlook the rich information encoded by variability within the single-cell gene expression data. Thus, there is a need for more relevant approaches to assess data variability rather than the mean in order to offer a deeper understanding of cellular systems. Here we present spline-DV, a novel statistical framework for differential variability (DV) analysis using scRNA-seq data. The spline-DV method identifies genes exhibiting significantly increased or decreased expression variability among cells derived from two experimental conditions. Case studies show that DV genes identified using spline-DV are representative, and functionally relevant in the tested cellular conditions including obesity, fibrosis, and cancer.

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Single-Cell RNA-seq Analysis of a Human Embryonic Stem Cell to Endothelial Cell System Based on Transcription Factor Overexpression

Cited by 2 publications

References 51 publications

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs)

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs)

Beyond Differential Expression: Embracing Cell-to-Cell Variability in Single-Cell Gene Expression Data Analysis

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