scholarly journals Comparative transcriptome analysis of hESC- and iPSC-derived lentoid bodies

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Muhammad Ali ◽  
Firoz Kabir ◽  
Jason J. Thomson ◽  
Yinghong Ma ◽  
Caihong Qiu ◽  
...  
Keyword(s):  
Stem Cells ◽  
Rna Sequencing ◽  
Transcriptome Analysis ◽  
Pluripotent Stem Cells ◽  
Embryonic Stem ◽  
Comparative Transcriptome ◽  
Rna Seq ◽  
Peripheral Blood Mononuclear ◽  
Report Generation ◽  
Comparative Transcriptome Analysis

AbstractThe ocular lens serves as an excellent system to investigate the intricate details of development and differentiation. Generation of lentoid bodies or lens-like structures using pluripotent stem cells is important for understanding the processes critical for lens morphogenesis and the mechanism of cataractogenesis. We previously reported the generation of peripheral blood mononuclear cell (PBMC)-originated, induced pluripotent stem cells (iPSCs). Here, we report generation of lentoid bodies from human embryonic stem cells (hESCs) and (PBMC)-originated, iPSCs employing the “fried egg” method with brief modifications. The ultrastructure analysis of hESC- and iPSC-derived lentoid bodies identified closely packed lens epithelial- and differentiating fiber-like cells. In addition, we performed RNA sequencing (RNA-Seq) based transcriptome profiling of hESC- and iPSC-derived lentoid bodies at differentiation day 25. Next-generation RNA sequencing (RNA-Seq) of hESC- and iPSC-derived lentoid bodies detected expression (≥0.659 RPKM) of 13,975 and 14,003 genes, respectively. Comparative transcriptome analysis of hESC- and iPSC-derived lentoid bodies revealed 13,563 (>96%) genes common in both datasets. Among the genes common in both transcriptome datasets, 12,856 (~95%) exhibited a quantitatively similar expression profile. Next, we compared the mouse lens epithelial and fiber cell transcriptomes with hESC- and iPSC-derived lentoid bodies transcriptomes and identified > 96% overlap with lentoid body transcriptomes. In conclusion, we report first-time comparative transcriptome analysis of hESC- and iPSC-derived lentoid bodies at differentiation day 25.

scholarly journals Identification of New Transcription Factors that Can Promote Pluripotent Reprogramming

2021 ◽  
Author(s):  
Ping Huang ◽  
Jieying Zhu ◽  
Yu Liu ◽  
Guihuan Liu ◽  
Ran Zhang ◽  
...  
Keyword(s):  
Stem Cells ◽  
Transcription Factors ◽  
Embryonic Stem Cells ◽  
Rna Sequencing ◽  
Human Urine ◽  
Embryonic Stem ◽  
Rna Seq ◽  
Reprogramming Efficiency ◽  
Yamanaka Factors ◽  
Urine Cells

Abstract Background Four transcription factors, Oct4, Sox2, Klf4, and c-Myc (the Yamanka factors), can reprogram somatic cells to induced pluripotent stem cells (iPSCs). Many studies have provided a number of alternative combinations to the non-Yamanaka factors. However, it is clear that many additional transcription factors that can generate iPSCs remain to be discovered. Methods The chromatin accessibility and transcriptional level of human embryonic stem cells and human urine cells were compared by Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) and RNA sequencing (RNA-seq) to identify potential reprogramming factors. Selected transcription factors were employed to reprogram urine cells, and the reprogramming efficiency was measured. Urine-derived iPSCs were detected for pluripotency by Immunofluorescence, quantitative polymerase chain reaction, RNA sequencing and teratoma formation test. Finally, we assessed the differentiation potential of the new iPSCs to cardiomyocytes in vitro. Results ATAC-seq and RNA-seq datasets predicted TEAD2, TEAD4 and ZIC3 as potential factors involved in urine cell reprogramming. Transfection of TEAD2, TEAD4 and ZIC3 (in the presence of Yamanaka factors) significantly improved the reprogramming efficiency of urine cells. We confirmed that the newly generated iPSCs possessed pluripotency characteristics similar to normal H1 embryonic stem cells. We also confirmed that the new iPSCs could differentiate to functional cardiomyocytes. Conclusions In conclusion, TEAD2, TEAD4 and ZIC3 can increase the efficiency of reprogramming human urine cells into iPSCs, and provides a new stem cell sources for the clinical application and modeling of cardiovascular disease. Graphical abstract

scholarly journals Comparative transcriptome analysis of flower heterosis in two soybean F1 hybrids by RNA-seq

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181061 ◽  
Author(s):  
Chunbao Zhang ◽  
Chunjing Lin ◽  
Fuyou Fu ◽  
Xiaofang Zhong ◽  
Bao Peng ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
F1 Hybrids ◽  
Comparative Transcriptome ◽  
Rna Seq ◽  
Comparative Transcriptome Analysis

scholarly journals A step-by-step workflow for low-level analysis of single-cell RNA-seq data

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2122 ◽  
Author(s):  
Aaron T.L. Lun ◽  
Davis J. McCarthy ◽  
John C. Marioni
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Marker Gene ◽  
Embryonic Stem ◽  
Cycle Phase ◽  
Data Sets ◽  
Rna Seq ◽  
Technical Noise ◽  
Low Level

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available data sets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

scholarly journals Comparative transcriptome analysis of rumen papillae in suckling and weaned Japanese Black calves using RNA sequencing

2018 ◽  
Vol 96 (6) ◽  
pp. 2226-2237 ◽  
Author(s):  
Koki Nishihara ◽  
Daichi Kato ◽  
Yutaka Suzuki ◽  
Dahye Kim ◽  
Misato Nakano ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Transcriptome Analysis ◽  
Comparative Transcriptome ◽  
Comparative Transcriptome Analysis

scholarly journals Comparative transcriptome analysis of a fan-shaped inflorescence in pineapple using RNA-seq

Genomics ◽  
2021 ◽  
Vol 113 (6) ◽  
pp. 3653-3665
Author(s):  
Tao Xie ◽  
Jing Zhang ◽  
Aiping Luan ◽  
Wei Zhang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
Comparative Transcriptome ◽  
Rna Seq ◽  
Comparative Transcriptome Analysis

scholarly journals Comparative Transcriptome Analysis Reveals the Potential Cardiovascular Protective Targets of the Thyroid Hormone Metabolite 3-Iodothyronamine (3-T1AM)

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhou Haiyan ◽  
Hu Bailong ◽  
Zhang Bei ◽  
Wang Yiming ◽  
Liu Xingde
Keyword(s):  
Cardiovascular Diseases ◽  
Thyroid Hormone ◽  
Transcriptome Analysis ◽  
H9c2 Cells ◽  
Comparative Transcriptome ◽  
Rna Seq ◽  
Hub Genes ◽  
Comparative Transcriptome Analysis ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

Background. The thyroid hormone metabolite 3-iodothyronamine (3-T1AM) is rapidly emerging as a promising compound in decreasing the heart rate and lowering the cardiac output. The aim of our study was to fully understand the molecular mechanism of 3-T1AM on cardiomyocytes and its potential targets in cardiovascular diseases. Materials and Methods. In our study, we utilized RNA-Seq to characterize the gene expression in H9C2 cells after 3-T1AM treatment. Comparative transcriptome analysis, including gene ontology, signaling pathways, disease connectivity analysis, and protein-protein interaction networks (PPI), was presented to find the critical gene function, hub genes, and related pathways. Results. A total of 1494 differently expressed genes (DEGs) were identified (192 upregulated and 1302 downregulated genes) in H9C2 cells for 3-T1AM treatment. Of these, 90 genes were associated with cardiovascular diseases. The PPI analysis indicated that 5 hub genes might be the targets of 3-T1AM. Subsequently, eight DEGs characterized using RNA-Seq were confirmed by RT-qPCR assays. Conclusions. Our study provides a comprehensive analysis of 3-T1AM on H9C2 cells and delineates a new insight into the therapeutic intervention of 3-T1AM for the cardiovascular diseases.

Sign in / Sign up

Export Citation Format

Share Document