Dataset Information

Single-cell transcriptomics unveils gene regulatory network plasticity.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.

Results

We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases.

Conclusions

Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.

SUBMITTER: Iacono G

PROVIDER: S-EPMC6547541 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Single-cell transcriptomics unveils gene regulatory network plasticity.

Iacono Giovanni G Massoni-Badosa Ramon R Heyn Holger H

Genome biology 20190604 1

<h4>Background</h4>Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.<h4>Results</h4>We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics tha ...[more]

PMID: 31159854

Similar Datasets

Project description:Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, and its prognosis is still poor due to therapy resistance, metastasis, and recurrence. In recent years, increasing evidence has shown that the existence of lung cancer stem cells is responsible for the propagation, metastasis, therapy resistance, and recurrence of the tumor. During their transition to cancer stem cells, tumor cells need to inhibit cell differentiation and acquire invasive characteristics. However, our understanding of the property and role of such lung cancer stem cells is still limited. In this study, lung adenocarcinoma cancer stem cells (LCSCs) were enriched from the PC-9 cell line in a serum-free condition. PC-9 cells grew into spheres and showed higher survival rates when exposed to gefitinib: the drug used for the treatment of LUAD. Additionally, we found that the canonical stemness marker protein CD44 was significantly increased in the enriched LCSCs. Then, LCSCs were inoculated into the groin of nude mice for 1.5 months, and tumors were detected in the animals, indicating the strong stemness of the cells. After that, we performed single-cell RNA sequencing (scRNA-seq) on 7320 LCSCs and explored the changes in their transcriptomic signatures. We identified cell populations with a heterogeneous expression of cancer stem marker genes in LCSCs and subsets with different degrees of differentiation. Further analyses revealed that the activation of the FOXM1 (oncoprotein) transcription factor is a key factor in cell dedifferentiation, which enables tumor cells to acquire an epithelial-mesenchymal transition phenotype and increases the LCSC surface marker CD44. Moreover, we found that the combination of CD44, ABCG2, and ALCAM was a specific marker for LCSCs. In summary, this study identified the potential factors and molecular mechanisms underlying the stemness properties of LUAD cancer cells; it could also provide insight into developing novel and effective therapeutic approaches.

Dataset Information

Single-cell transcriptomics unveils gene regulatory network plasticity.

Background

Results

Conclusions

Publications

Single-cell transcriptomics unveils gene regulatory network plasticity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets