Single_Cell_RNAseq_at_various_stages_of_HiPSCs_differentiating_toward_definitive_endoderm_and_endoderm_derived_lineages

When comparing the differentiation capacities of pluripotent stem cell lines that have different genetic backgrounds, batch to batch experimental variablility poses a significant challenge, especially when trying to identify smaller effects. One way to address this issue is to differentiate several different lines in the same culture dish, thereby elimating experimental variation. In addition, it allows researchers to analyze many more lines with less experiments. Parallel single cell RNA-Seq exploits that individual cells are tagged and hence each cell can be reliably assigned to the donor of origin based on the genetic variants it contains. In addition, analyzing the genetic signature of single cells within a differentiating population can reveal differentation stages that are not easily detected in bulk RNAseq data. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Type: Transcriptome Analysis
Archiver: European Genome-Phenome Archive (EGA)

1 Dataset 5 Publications

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001005741	When comparing the differentiation capacities of pluripotent stem cell lines that have different genetic backgrounds, batch to batch experimental variablility poses a significant challenge, especially when trying to identify smaller effects. One way to address this issue is to differentiate several different lines in the same culture dish, thereby elimating experimental variation. In addition, it allows researchers to analyze many more lines with less experiments. Parallel single cell RNA-Seq exploits that individual cells are tagged and hence each cell can be reliably assigned to the donor of origin based on the genetic variants it contains. In addition, analyzing the genetic signature of single cells within a differentiating population can reveal differentation stages that are not easily detected in bulk RNAseq data. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/	Illumina HiSeq 2500	13433

Publications	Citations
Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Cuomo ASE, Seaton DD, McCarthy DJ, Martinez I, Bonder MJ, Garcia-Bernardo J, Amatya S, Madrigal P, Isaacson A, Buettner F, Knights A, Natarajan KN, HipSci Consortium, Vallier L, Marioni JC, Chhatriwala M, Stegle O. Nat Commun 11: 2020 810	126
Optimizing expression quantitative trait locus mapping workflows for single-cell studies. Cuomo ASE, Alvari G, Azodi CB, single-cell eQTLGen consortium, McCarthy DJ, Bonder MJ. Genome Biol 22: 2021 188	32
Cell reprogramming shapes the mitochondrial DNA landscape. Wei W, Gaffney DJ, Chinnery PF. Nat Commun 12: 2021 5241	16
splatPop: simulating population scale single-cell RNA sequencing data. Azodi CB, Zappia L, Oshlack A, McCarthy DJ. Genome Biol 22: 2021 341	8
Somatic mutations alter the differentiation outcomes of iPSC-derived neurons. Puigdevall P, Jerber J, Danecek P, Castellano S, Kilpinen H. Cell Genom 3: 2023 100280	3