Link between embryonic stem cell pluripotency and homologous allelic pairing of <i>Oct4</i> loci

Asaka, Mitsuhiro; Uranishi, Kousuke; Suzuki, Ayumu; Hirasaki, Masataka; Nishimoto, Masazumi; Okuda, Akihiko

doi:10.1111/dgd.12403

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…We can see that some genes such as POU5F1, L1TD1 and PCGF1 have been validated to play a key role in cell differentiation and pluripotency. POU5F1 encodes a transcription factor that plays an important role in embryonic development and stem cell pluripotency (28). LITD1 is related to the post-transcriptional regulation in human pluripotency (29).…”

Section: Resultsmentioning

confidence: 99%

Cell-specific network constructed by single-cell RNA sequencing data

Dai

Zeng

et al. 2019

Nucleic Acids Research

140

175

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Single-cell RNA sequencing (scRNA-seq) is able to give an insight into the gene–gene associations or transcriptional networks among cell populations based on the sequencing of a large number of cells. However, traditional network methods are limited to the grouped cells instead of each single cell, and thus the heterogeneity of single cells will be erased. We present a new method to construct a cell-specific network (CSN) for each single cell from scRNA-seq data (i.e. one network for one cell), which transforms the data from ‘unstable’ gene expression form to ‘stable’ gene association form on a single-cell basis. In particular, it is for the first time that we can identify the gene associations/network at a single-cell resolution level. By CSN method, scRNA-seq data can be analyzed for clustering and pseudo-trajectory from network perspective by any existing method, which opens a new way to scRNA-seq data analyses. In addition, CSN is able to find differential gene associations for each single cell, and even ‘dark’ genes that play important roles at the network level but are generally ignored by traditional differential gene expression analyses. In addition, CSN can be applied to construct individual network of each sample bulk RNA-seq data. Experiments on various scRNA-seq datasets validated the effectiveness of CSN in terms of accuracy and robustness.

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Section: Resultsmentioning

confidence: 99%