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Related Concept Videos

Somatic to iPS Cell Reprogramming01:29

Somatic to iPS Cell Reprogramming

Reprogramming alters the gene expression in somatic cells, transforming them into induced pluripotent stem (iPS) cells over several generations. Scientists can reprogram cells by introducing genes for four transcription factors—Oct4, Sox2, Klf4, and c-Myc (OSKM) by viral or non-viral methods. These factors are also known as Yamanaka factors after Shinya Yamanaka, who first generated iPS cells using mouse skin cells. Yamanaka was awarded the Nobel Prize in Physiology or Medicine in 2012 for this...

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Related Experiment Video

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Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR
09:03

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Published on: May 29, 2014

Estimating the quality of reprogrammed cells using ES cell differentiation expression patterns.

Bo Zhang1, Beibei Chen, Tao Wu

  • 1National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People's Republic of China.

Plos One
|February 2, 2011
PubMed
Summary
This summary is machine-generated.

Assessing induced pluripotent stem cell (iPSC) quality is challenging. This study introduces a novel "Differentiation-index coordinate" method using gene expression to quantify iPSC pluripotency and developmental potential.

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Area of Science:

  • Stem cell biology
  • Epigenetics and gene regulation
  • Bioinformatics and computational biology

Background:

  • Somatic cell reprogramming yields induced pluripotent stem cells (iPSCs), but quality assessment remains difficult, especially in human cells.
  • Pluripotency is traditionally confirmed via assays like tetraploid complementation, but direct quality metrics from gene expression are needed.
  • Existing methods struggle to accurately estimate the developmental potential and differentiation state of iPSCs.

Purpose of the Study:

  • To develop a novel supervised method for assessing induced pluripotent stem cell (iPSC) quality using gene expression profiles.
  • To create a 2-D 'Differentiation-index coordinate' that visualizes cell state trajectories during differentiation.
  • To establish a quantitative 'Distance index' for evaluating iPSC pluripotency and developmental capacity.

Main Methods:

  • Developed a novel linear model to define 'developing lines' representing differentiation trajectories from time-course expression data.
  • Constructed a 2-D 'Differentiation-index coordinate' for mouse and human cells using these 'developing lines'.
  • Quantified iPSC quality using a 'Distance index' based on the projection of iPSC expression profiles relative to ESCs and fibroblasts.

Main Results:

  • The 'Differentiation-index coordinate' successfully distinguished between different cell types and their differentiation states.
  • The 'Distance index' correlated with iPSC pluripotency, as validated by analysis of tetraploid complementation assay data.
  • Identified significantly changed gene sets along the 'developing lines', providing insights into differentiation dynamics.

Conclusions:

  • The proposed 'Differentiation-index coordinate' method is effective for estimating iPSC quality from gene expression profiles.
  • This approach offers a new way to analyze time-resolved experimental data and understand cell state transitions.
  • The 'Distance index' provides a quantitative measure of pluripotency, aiding stem cell research and therapeutic development.