Literature DB >> 34505265

Analysis of Clonal Composition in Human iPSC and ESC and Derived 2D and 3D Differentiated Cultures.

Bernat Del Olmo1, Daria Merkurjev2,3, Likun Yao2, Mel Lina Pinsach-Abuin1, Ivan Garcia-Bassets4, Angels Almenar-Queralt5.   

Abstract

Human induced pluripotent and embryonic stem cell cultures (hiPSC/hESC) are phenotypically heterogeneous and prone to clonal deviations during subculturing and differentiation. Clonal deviations often emerge unnoticed, but they can change the biology of the cell culture with a negative impact on experimental reproducibility. Here, we describe a computational workflow to profile the bulk clonal composition in a hiPSC/hESC culture that can also be used to infer clonal deviations. This workflow processes data obtained with two versions of the same method. The two versions-epigenetic and transcriptomic-rely on a mechanism of stochastic H3K4me3 deposition during hiPSC/hESC derivation. This mechanism generates a signature of ten or more H3K4me3-enriched clustered protocadherin (PCDH) promoters distinct in every single cell. The aggregate of single-cell signatures provides an identificatory feature in every hiPSC/hESC line. This feature is stably transmitted to the cell progeny of the culture even after differentiation unless there is a clonal deviation event that changes the internal balance of single-cell signatures. H3K4me3 signatures can be profiled by chromatin immunoprecipitation and next-generation sequencing (ChIP-seq). Alternatively, an equivalent PCDH-expression version can be profiled by RNA-seq in PCDH-expressing hiPSC/hESC-derived cells (such as neurons, astrocytes, and cardiomyocytes; and, in long-term cultures, such as cerebral organoids). Notably, our workflow can also distinguish genetically identical hiPSC/hESC lines derived from the same patient or generated in the same editing process. Together, we propose a method to improve data sharing and reproducibility in the hiPSC and hESC fields.
© 2021. Springer Science+Business Media, LLC.

Entities:  

Keywords:  CRISPR-Cas9; Cell authentication; Clonal composition; Data sharing; Drug screening; High-throughput; Organoids; Regenerative medicine; Reproducibility; hiPSC

Mesh:

Year:  2022        PMID: 34505265     DOI: 10.1007/7651_2021_414

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  7 in total

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Authors:  Alexander Dobin; Carrie A Davis; Felix Schlesinger; Jorg Drenkow; Chris Zaleski; Sonali Jha; Philippe Batut; Mark Chaisson; Thomas R Gingeras
Journal:  Bioinformatics       Date:  2012-10-25       Impact factor: 6.937

2.  Fast gapped-read alignment with Bowtie 2.

Authors:  Ben Langmead; Steven L Salzberg
Journal:  Nat Methods       Date:  2012-03-04       Impact factor: 28.547

3.  The Sequence Alignment/Map format and SAMtools.

Authors:  Heng Li; Bob Handsaker; Alec Wysoker; Tim Fennell; Jue Ruan; Nils Homer; Gabor Marth; Goncalo Abecasis; Richard Durbin
Journal:  Bioinformatics       Date:  2009-06-08       Impact factor: 6.937

4.  BEDTools: a flexible suite of utilities for comparing genomic features.

Authors:  Aaron R Quinlan; Ira M Hall
Journal:  Bioinformatics       Date:  2010-01-28       Impact factor: 6.937

5.  Tumor immunological phenotype signature-based high-throughput screening for the discovery of combination immunotherapy compounds.

Authors:  Haiyan Wang; Shasha Li; Qianyu Wang; Zhengshuo Jin; Wei Shao; Yan Gao; Lu Li; Kequan Lin; Lin Zhu; Huili Wang; Xuebin Liao; Dong Wang
Journal:  Sci Adv       Date:  2021-01-22       Impact factor: 14.136

6.  Mosdepth: quick coverage calculation for genomes and exomes.

Authors:  Brent S Pedersen; Aaron R Quinlan
Journal:  Bioinformatics       Date:  2018-03-01       Impact factor: 6.937

7.  Fast and accurate short read alignment with Burrows-Wheeler transform.

Authors:  Heng Li; Richard Durbin
Journal:  Bioinformatics       Date:  2009-05-18       Impact factor: 6.937

  7 in total

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