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Somatic to iPS Cell Reprogramming01:29

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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...
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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Related Experiment Video

Updated: Sep 7, 2025

Direct Lineage Reprogramming of Adult Mouse Fibroblast to Erythroid Progenitors
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Ranking reprogramming factors for cell differentiation.

Jennifer Hammelman1,2, Tulsi Patel3,4,5, Michael Closser3,4,5

  • 1Computational and Systems Biology, MIT, Cambridge, MA, USA.

Nature Methods
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

Identifying transcription factors for cell reprogramming is key for regenerative medicine. This study evaluates computational methods, finding that those using chromatin accessibility data, like DeepAccess and diffTF, perform best for discovering reprogramming factors.

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Last Updated: Sep 7, 2025

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Cell Surface Marker Mediated Purification of iPS Cell Intermediates from a Reprogrammable Mouse Model
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Area of Science:

  • Computational biology
  • Molecular biology
  • Regenerative medicine

Background:

  • Transcription factor overexpression is a validated strategy for cell reprogramming.
  • Developing a universal method to identify factors for arbitrary cell type generation remains a challenge.

Purpose of the Study:

  • To assess the efficacy of nine computational methods in identifying and ranking transcription factors for cell reprogramming.
  • To compare methods utilizing gene expression, biological networks, and chromatin accessibility data.

Main Methods:

  • Evaluated nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF, DeepAccess).
  • Tested methods on eight target cell types with known reprogramming solutions.
  • Compared performance based on data types (gene expression, networks, chromatin accessibility) and optimized parameters.

Main Results:

  • The best methods identified 50-60% of reprogramming factors within the top ten candidates.
  • Methods employing chromatin accessibility data demonstrated superior performance.
  • DeepAccess and diffTF showed higher correlation with transcription factor significance in differentiation protocols.

Conclusions:

  • Chromatin accessibility-based methods are most effective for identifying cell reprogramming factors.
  • AME and diffTF are recommended for systematic prioritization of transcription factor candidates.
  • This work aids in designing improved cell reprogramming protocols for therapeutic applications.