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Related Concept Videos

EPS and iPS Cells in Disease Research01:21

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Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...
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Stem cells are undifferentiated cells that divide and produce different types of cells. Ordinarily, cells that have differentiated into a specific cell type are post-mitotic—that is, they no longer divide. However, scientists have found a way to reprogram these mature cells so that they “de-differentiate” and return to an unspecialized, proliferative state. These cells are also pluripotent like embryonic stem cells—able to produce all cell types—and are therefore...
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Disease modeling studies using induced pluripotent stem cells: are we using enough controls?

Adiv A Johnson1, Cynthia Andrews-Pfannkoch1, Timothy J Nelson2

  • 1Department of Ophthalmology, Mayo Clinic, Rochester, MN 55905, USA.

Regenerative Medicine
|December 16, 2017
PubMed
Summary

Induced pluripotent stem cells (iPSCs) are vital for disease modeling. However, current studies often lack sufficient, appropriately matched controls, potentially confounding results due to genetic variation.

Keywords:
differentiationdisease modelingdisease-in-a-dishembryonic stem cellsgenetic variabilityiPSCinduced pluripotent stem cellsreprogrammingstem cells

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Area of Science:

  • Stem cell biology
  • Genetics
  • Disease modeling

Background:

  • Differentiated induced pluripotent stem cells (iPSCs) from disease patients offer powerful disease modeling capabilities.
  • Comparison with iPSCs from healthy individuals is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To evaluate the adequacy of control groups in current iPSC-based disease modeling studies.
  • To identify limitations in existing methodologies regarding control subject matching.

Main Methods:

  • Informal retrospective survey of iPSC disease modeling studies.
  • Analysis of the number and characteristics of control groups in published high-impact research.

Main Results:

  • The median and average number of controls used were 1 and 1.6, respectively.
  • Most studies failed to control for crucial demographic factors like age, gender, and ethnicity.
  • Observed phenotypic differences may be attributed to insufficient controls and inherent genetic variation.

Conclusions:

  • Current iPSC disease modeling studies employ an insufficient number of controls.
  • Future research must incorporate more controls and ensure proper matching for age, gender, and ethnicity to ensure reliable results.