Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors

Kazutoshi Takahashi, Shinya Yamanaka

Published 25 August 2006 · Cell · Journal article

Summary

Takahashi and Yamanaka demonstrated that pluripotent stem cells can be generated directly from mouse fibroblast cultures by introducing a defined set of transcription factors. Screening candidate genes associated with pluripotency, they identified four factors—Oct3/4, Sox2, c-Myc, and Klf4—that were sufficient to reprogram both embryonic and adult fibroblasts into cells they termed induced pluripotent stem (iPS) cells. These iPS cells resembled embryonic stem cells in morphology, growth, and marker expression and could form teratomas containing tissues of all three germ layers.

Key findings

Identified four transcription factors (Oct3/4, Sox2, c-Myc, and Klf4) that are together sufficient to reprogram differentiated mouse fibroblasts into pluripotent stem cells.
Showed that the resulting iPS cells resemble embryonic stem cells in morphology, proliferation, gene expression, and the ability to form teratomas with derivatives of all three germ layers.
Found that Nanog was not required among the reprogramming factors, narrowing the minimal factor set needed to induce pluripotency.

Subjects & keywords

Biology & Genetics induced pluripotent stem cells ips cells reprogramming stem cells yamanaka factors

Cite this paper

APA

Kazutoshi Takahashi, & Shinya Yamanaka (2006). Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell. https://doi.org/10.1016/j.cell.2006.07.024

BibTeX

@article{takahashi2006induction,
  author    = {Kazutoshi Takahashi and Shinya Yamanaka},
  title     = {Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors},
  journal   = {Cell},
  year      = {2006},
  doi       = {10.1016/j.cell.2006.07.024},
  url       = {https://doi.org/10.1016/j.cell.2006.07.024}
}

Related in Biology & Genetics

Biology2024

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Josh Abramson, Jonas Adler and John M. Jumper

This paper introduced AlphaFold 3, a unified deep learning model that predicts the joint structure of complexes containing proteins, nucleic acids, small-molecule ligands, ions, and modified residues. It replaces much of the prior architecture with a diffusion-based module that directly generates atomic coordinates. The model achieved substantially improved accuracy over specialized tools across many interaction types, including protein-ligand and protein-nucleic acid complexes.

Nature Open access

Biology2023

De novo design of protein structure and function with RFdiffusion

Joseph L. Watson, David Juergens and David Baker

This paper introduced RFdiffusion, a generative diffusion model built on the RoseTTAFold network for de novo protein design. It enables a range of design tasks, including unconditional generation, symmetric oligomer design, functional motif scaffolding, and binder design. Many designs were experimentally validated, with solved structures closely matching the intended models.

Nature Open access

Biology2023

A draft human pangenome reference

Wen-Wei Liao, Mobin Asri and Jana Ebler

The Human Pangenome Reference Consortium presents a first draft human pangenome built from 47 phased, diploid genome assemblies of genetically diverse individuals. The assemblies cover more than 99% of the expected sequence per genome at over 99% base-level and structural accuracy, and are combined into a graph-based reference. Relative to GRCh38, the pangenome adds about 119 million base pairs of euchromatic polymorphic sequence and 1,115 gene duplications, improving representation of variation at structurally complex loci.

Nature Open access