Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study [bulk]

ABSTRACT: Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study [bulk]

PROVIDER: PRJNA486654 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR7716800_1.fastq.gz	Fastqsanger.gz
	SRR7716800_2.fastq.gz	Fastqsanger.gz
	SRR7716810_1.fastq.gz	Fastqsanger.gz
	SRR7716810_2.fastq.gz	Fastqsanger.gz
	SRR7716820_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 912

Similar Datasets

Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study [bulk]

Project description:Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounds such as passaging effects and progenitor storage. Single cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.

2018-08-20 | GSE118733 | GEO

Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study

Project description:Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study

| PRJNA486653 | ENA

Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study [single-cell]

2018-08-20 | GSE118734 | GEO

Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study [single-cell]

Project description:Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study [single-cell]

| PRJNA486655 | ENA

Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study.

Project description:Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long-term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounders such as passaging effects and progenitor storage. Single-cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis-generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.

| S-EPMC6178242 | biostudies-literature

Reproducibility of molecular phenotypes after long-term differentiation to Human iPSC-Derived Neurons: a multi-site omics study

Project description:This SuperSeries is composed of the SubSeries listed below.

2018-08-20 | GSE118735 | GEO

Kidney organoid reproducibility across multiple human iPSC lines and diminished off target cells after transplantation revealed by single cell transcriptomics

Project description:Human iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we used single cell RNA-Seq (scRNA-Seq) to profile 450,118 cells to show that organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes were largely reproducible across iPSC lines, time points, protocols, and replicates, cell proportions were variable between different iPSC lines. Off-target cell proportions were the most variable. Prolonged in vitro culture did not alter cell types, but organoid transplantation under the mouse kidney capsule diminished off-target cells. Our work shows how scRNA-seq can help score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

2019-11-04 | GSE136314 | GEO

Personal Omics Profiling Reveals Dynamic Molecular Phenotypes and Actionable Medical Risks

Project description:We have determined the whole genome sequence of an individual at high accuracy and performed an integrated analysis of omics profiles over a 1.5 year period that included healthy and two virally infected states. Omics profiling of transcriptomes, proteomes, cytokines, metabolomes and autoantibodyomes from blood components have revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways that occurred during healthy and disease states. Many changes were associated with allele- and edit-specific expression at the RNA and protein levels, which may contribute to personalized responses. Importantly, genomic information was also used to predict medical risks, including Type II Diabetes (T2D), whose onset was observed during the course of our study using standard clinical tests and molecular profiles, and whose disease progression was monitored and subsequently partially managed. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states. Examination of blood component in 20 different time points over 1.5 years which includes 2 disease state and 18 healty state Related exome studies at: SRX083314 SRX083313 SRX083312 SRX083311

2012-03-16 | E-GEOD-32874 | biostudies-arrayexpress

Integrated analysis of omics profiles

Project description:This SuperSeries is composed of the following subset Series: GSE32691: Autoantibody profile timecourse of UNK GSE32874: Personal Omics Profiling Reveals Dynamic Molecular Phenotypes and Actionable Medical Risks Refer to individual Series

2012-03-16 | E-GEOD-33029 | biostudies-arrayexpress

Patient iPSC models reveal glia-intrinsic phenotypes in multiple sclerosis

Project description:Patient iPSC models reveal glia-intrinsic phenotypes in multiple sclerosis

| PRJNA998387 | ENA