Dataset Information

Using Cell Type-Specific Genes to Identify Cell-Type Transitions Between Different in vitro Culture Conditions.

ABSTRACT: In vitro differentiation or expansion of stem and progenitor cells under chemical stimulation or genetic manipulation is used for understanding the molecular mechanisms of cell differentiation and self-renewal. However, concerns around the cell identity of in vitro-cultured cells exist. Bioinformatics methods, which rely heavily on signatures of cell types, have been developed to estimate cell types in bulk samples. The Tabula Muris Senis project provides an important basis for the comprehensive identification of signatures for different cell types. Here, we identified 46 cell type-specific (CTS) gene clusters for 83 mouse cell types. We conducted Gene Ontology term enrichment analysis on the gene clusters and revealed the specific functions of the relevant cell types. Next, we proposed a simple method, named CTSFinder, to identify different cell types between bulk RNA-Seq samples using the 46 CTS gene clusters. We applied CTSFinder on bulk RNA-Seq data from 17 organs and from developing mouse liver over different stages. We successfully identified the specific cell types between organs and captured the dynamics of different cell types during liver development. We applied CTSFinder with bulk RNA-Seq data from a growth factor-induced neural progenitor cell culture system and identified the dynamics of brain immune cells and nonimmune cells during the long-time cell culture. We also applied CTSFinder with bulk RNA-Seq data from reprogramming induced pluripotent stem cells and identified the stage when those cells were massively induced. Finally, we applied CTSFinder with bulk RNA-Seq data from in vivo and in vitro developing mouse retina and captured the dynamics of different cell types in the two development systems. The CTS gene clusters and CTSFinder method could thus serve as promising toolkits for assessing the cell identity of in vitro culture systems.

SUBMITTER: He X

PROVIDER: S-EPMC8267371 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Estrogens produce biological effects by interacting with two estrogen receptors, ERalpha and ERbeta. Drugs that selectively target ERalpha or ERbeta might be safer for conditions that have been traditionally treated with non-selective estrogens. Several synthetic and natural ERbeta-selective compounds have been identified. One class of ERbeta-selective agonists is represented by ERB-041 (WAY-202041) which binds to ERbeta much greater than ERalpha. A second class of ERbeta-selective agonists derived from plants include MF101, nyasol and liquiritigenin that bind similarly to both ERs, but only activate transcription with ERbeta. Diarylpropionitrile represents a third class of ERbeta-selective compounds because its selectivity is due to a combination of greater binding to ERbeta and transcriptional activity. However, it is unclear if these three classes of ERbeta-selective compounds produce similar biological activities. The goals of these studies were to determine the relative ERbeta selectivity and pattern of gene expression of these three classes of ERbeta-selective compounds compared to estradiol (E(2)), which is a non-selective ER agonist. U2OS cells stably transfected with ERalpha or ERbeta were treated with E(2) or the ERbeta-selective compounds for 6 h. Microarray data demonstrated that ERB-041, MF101 and liquiritigenin were the most ERbeta-selective agonists compared to estradiol, followed by nyasol and then diarylpropionitrile. FRET analysis showed that all compounds induced a similar conformation of ERbeta, which is consistent with the finding that most genes regulated by the ERbeta-selective compounds were similar to each other and E(2). However, there were some classes of genes differentially regulated by the ERbeta agonists and E(2). Two ERbeta-selective compounds, MF101 and liquiritigenin had cell type-specific effects as they regulated different genes in HeLa, Caco-2 and Ishikawa cell lines expressing ERbeta. Our gene profiling studies demonstrate that while most of the genes were commonly regulated by ERbeta-selective agonists and E(2), there were some genes regulated that were distinct from each other and E(2), suggesting that different ERbeta-selective agonists might produce distinct biological and clinical effects.

Dataset Information

Using Cell Type-Specific Genes to Identify Cell-Type Transitions Between Different in vitro Culture Conditions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets