CoChat
Sign Up
Cited by 57

Molecular, phenotypic, and sample-associated data to describe pluripotent stem cell lines and derivatives

Daily, Kenneth, Ho Sui, Shannan J., Schriml, Lynn M., Dexheimer, Phillip J., Salomonis, Nathan, Schroll, Robin, Bush, Stacy, Keddache, Mehdi +15 more·2017·Scientific Data
DOI 10.1038/sdata.2017.30PMC PMC5369318
Open this paper in CoChat — free account

Abstract

The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples. Cell lines were differentiated and characterized at a central laboratory using standardized cell culture methodologies, protocols, and metadata descriptors. Stem cell and derived differentiated lines were characterized using RNA-seq, miRNA-seq, copy number arrays, DNA methylation arrays, flow cytometry, and molecular histology. All materials, including raw data, metadata, analysis and processing code, and methodological and provenance documentation are publicly available for re-use and interactive exploration at https://www.synapse.org/pcbc. The goal is to provide data that can improve our ability to robustly and reproducibly use human pluripotent stem cells to understand development and disease.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/sdata.2017.30

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Acceptable intervals for periodic testing to confirm the genetic and phenotypic stability of the cell system can be highly case-dependent.

    “It is recommended to develop acceptable intervals for periodic testing to confirm the genetic and phenotypic stability of the cell system; these, however, can be highly case-dependent (Blazquez-Prunera et al., 2017; Daily et al., 2017; Meza-Zepeda et al., 2008).”

    From Guidance document on Good cell and tissue culture practice 2.0 (GCCP 2.0) (2021) · cites this paper as Daily et al., 2017

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 31

  1. Synapse Omberg L. 2016 http://dx.doi.org/10.7303/syn1773109 · DOI
  2. Trounson A. & DeWitt N. D. Pluripotent stem cells progressing to the clinic. Nat. Rev. Mol. Cell Biol. 17, 194– 200 (2016). 26908143
  3. Kimbrel E. A. & Lanza R. Current status of pluripotent stem cells: moving the first therapies to the clinic. Nat. Rev. Drug Discov. 14, 681– 692 (2015). 26391880
  4. Jang Y.-Y. & Ye Z. Gene correction in patient-specific iPSCs for therapy development and disease modeling. Hum. Genet. 135, 1041– 1058 (2016). 27256364 PMC5881117
  5. Takahashi K. & Yamanaka S. Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell 126, 663– 676 (2006). 16904174
  6. Nagata N. & Yamanaka S. Perspectives for Induced Pluripotent Stem Cell Technology: New Insights Into Human Physiology Involved in Somatic Mosaicism. Circ. Res. 114, 505– 510 (2014). 24481841
  7. Thomson J. A. Embryonic Stem Cell Lines Derived from Human Blastocysts. Science 282, 1145– 1147 (1998). 9804556
  8. Salomonis N. et al. Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consortium. Stem Cell Reports 7, 110– 125 (2016). 27293150 PMC4944587
  9. Omberg L. et al. Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas. Nat. Genet. 45, 1121– 1126 (2013). 24071850 PMC3950337
  10. Smith B. et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotechnol. 25, 1251– 1255 (2007). 17989687 PMC2814061
  11. Whetzel P. L. & NCBO Team. NCBO Technology: Powering semantically aware applications. J. Biomed. Semantics 4, S8 (2013). 23734708 PMC3633000
  12. Zhang J. et al. Extracellular Matrix Promotes Highly Efficient Cardiac Differentiation of Human Pluripotent Stem Cells: The Matrix Sandwich Method. Circ. Res. 111, 1125– 1136 (2012). 22912385 PMC3482164
  13. D’Amour K. A. et al. Efficient differentiation of human embryonic stem cells to definitive endoderm. Nat. Biotechnol. 23, 1534– 1541 (2005). 16258519
  14. Karolchik D. et al. The UCSC Genome Browser database: 2014 update. Nucleic Acids Res. 42, D764– D770 (2014). 24270787 PMC3964947
  15. Kim D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013). 23618408 PMC4053844
  16. Trapnell C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511– 515 (2010). 20436464 PMC3146043
  17. Emig D. et al. AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Res. 38, W755– W762 (2010). 20513647 PMC2896198
  18. Quinlan A. R. & Hall I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841– 842 (2010). 20110278 PMC2832824
  19. Robinson J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24– 26 (2011). 21221095 PMC3346182
  20. Wang W.-C. et al. miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics 10, 328 (2009). 19821977 PMC2767369
  21. Kozomara A. & Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68– D73 (2014). 24275495 PMC3965103
  22. Pantano L., Estivill X. & Marti E. SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells. Nucleic Acids Res. 38, e34 (2010). 20008100 PMC2836562
  23. Aryee M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363– 1369 (2014). 24478339 PMC4016708
  24. Li H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078– 2079 (2009). 19505943 PMC2723002
  25. Danecek P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156– 2158 (2011). 21653522 PMC3137218
  26. Chen J., Bardes E. E., Aronow B. J. & Jegga A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305– W311 (2009). 19465376 PMC2703978
  27. Purcell S. et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am. J. Hum. Genet. 81, 559– 575 (2007). 17701901 PMC1950838
  28. Kanehisa M., Sato Y., Kawashima M., Furumichi M. & Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457– D462 (2016). 26476454 PMC4702792
  29. Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28, 27– 30 (2000). 10592173 PMC102409
  30. Fabregat A. et al. The Reactome pathway Knowledgebase. Nucleic Acids Res. 44, D481– D487 (2016). 26656494 PMC4702931
  31. Croft D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472– D477 (2014). 24243840 PMC3965010

Cited by

Sorted by citations

Authors
Daily, K., Ho Sui, S. J., Schriml, L. M., Dexheimer, P. J., Salomonis, N., Schroll, R., Bush, S., Keddache, M., Mayhew, C., Lotia, S., Perumal, T. M., Dang, K., Pantano, L., Pico, A. R., Grassman, E., Nordling, D., Hide, W., Hatzopoulos, A. K., Malik, P., Cancelas, J. A., Lutzko, C., Aronow, B. J., & Omberg, L.
ORCID
Daily, Kenneth
Venue
Scientific Data
Publisher
Springer Science and Business Media LLC
Published
2017-03-28
Volume
4
Issue
1
ISSN
2052-4463
DOI
10.1038/sdata.2017.30
PMC
PMC5369318
Cited by
57
References
3
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review