Dataset Information

Deconstructing the dynamic transcriptional program of pluripotent stem cells.

ABSTRACT: Pluripotent stem cells (PSCs) are capable of dynamic interconversion between distinct substates, but the regulatory circuits specifying these states and enabling transitions between them are not well understood. We set out to address this issue and map the landscape of gene expression variability in PSCs by single-cell expression profiling of PSCs under different chemical and genetic perturbations. We find that signaling factors and developmental regulators show highly variable expression in PSCs, with expression states for some variable genes heritable through multiple cell divisions. Expression variability and population heterogeneity can be influenced by perturbation of signaling pathways and chromatin regulators. Strikingly, either removal of mature miRNAs or pharmacologic blockage of external signaling pathways drives PSCs into a low-noise ground state characterized by a reconfigured pluripotency regulatory network, increased self-renewal efficiency, and a distinct chromatin state, an effect mediated by the action of opposing miRNA families on the c-myc / Lin28 / let-7 axis. These findings illuminate the causes of transcriptional heterogeneity in PSCs and their consequences for cellular decision-making.

ORGANISM(S): Mus musculus

PROVIDER: GSE60749 | GEO | 2014/12/04

SECONDARY ACCESSION(S): PRJNA259420

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Yao2016_Calcium_Signaling This model is described in the article: Distinct cellular states determine calcium signaling response Jason Yao, Anna Pilko, Roy Wollman Molecular Systems Biology Abstract: The heterogeneity in mammalian cells signaling response is largely a result of preexisting cell to cell variability. It is unknown whether cell to cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single cell level. This model is hosted on BioModels Database and identified by: MODEL1611150001. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Intra-tumor heterogeneity (ITH) has been studied at the morphologic, genomic, and transcriptomic level, but not proteomic level. Recent advances in mass spectrometric (MS) proteome quantification techniques, exemplified by SWATH-MS, a massively parallel targeting method, now also support precise quantitative proteomic comparisons across multiple samples, thus identifying molecular and implied functional differences. Here we used SWATH-MS to analyze the proteome profiles of a set of fresh-frozen prostate tissue samples derived from radical prostatectomy specimens. A high confidence set of 1,906 proteins were consistently quantified across 60 biopsy-level tissue samples from three prostatectomy patients, each consisting of 1.0 mm punch biopsies from histologically malignant (acinar and ductal adenocarcinoma) and matching benign prostatic hyperplasia tissues. The quantitative protein profiles allowed independent quantification of the degree of intra-tumor heterogeneity for each protein in benign and malignant tissues. We found that while majority of the proteins showed comparably low intra-tumor variability, 122 proteins were highly variable in malignant and/or matching benign tissues. We observed that proteins that varied between patients or tissue types also tended to be highly variable within prostate tissues, suggesting that these variability patterns will be a critical selection criterion in future protein biomarker studies. The data also permitted investigation of the heterogeneity of multiple biochemical pathways. The high variability of several of the pathways, including Glypican-1 network, alpha-linolenic acid metabolism and celecoxib pathway, explained contradictory results regarding them in the literature. In conclusion, we demonstrated a methodology for investigating proteomic intra-tumor heterogeneity from biopsy-level tissue samples, and quantified the degree of intra-tumor heterogeneity of 1,906 proteins in prostate tumors. The method and data presented here have advanced our understanding of tumor biology and offered critical insights for future biomarker development.

Dataset Information

Deconstructing the dynamic transcriptional program of pluripotent stem cells.

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets