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Related Concept Videos

Induced Pluripotent Stem Cells01:13

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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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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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Related Experiment Video

Updated: Jan 3, 2026

Stencil Micropatterning of Human Pluripotent Stem Cells for Probing Spatial Organization of Differentiation Fates
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Automated Design of Pluripotent Stem Cell Self-Organization.

Ashley R G Libby1, Demarcus Briers2, Iman Haghighi3

  • 1Developmental and Stem Cell Biology PhD Program, University of California, San Francisco, San Francisco, CA, USA; Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA, USA.

Cell Systems
|November 25, 2019
PubMed
Summary

This study uses computational modeling and machine learning to control human pluripotent stem cell (hPSC) self-organization, enabling precise engineering of organoids and tissues by predicting morphogenic patterns.

Keywords:
bioengineeringcontrol theorymachine learningmathematical optimizationmulticellular patterningstem cell biology

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Area of Science:

  • Stem cell biology
  • Computational biology
  • Bioengineering

Background:

  • Human pluripotent stem cells (hPSCs) self-organize into organoids, but controlling their development (morphogenesis) is challenging.
  • Novel methods are needed to precisely direct pattern formation in hPSC-derived organoids.

Purpose of the Study:

  • To develop a data-driven approach for controlling hPSC self-organization and morphogenesis.
  • To enable precise spatial control of multicellular patterning for tissue engineering.

Main Methods:

  • Combined genetic engineering (CDH1 and ROCK1 knockdown) with computational modeling (extended cellular Potts model).
  • Utilized machine learning and mathematical pattern optimization for in silico parameter optimization.
  • Validated computational predictions through in vitro experiments.

Main Results:

  • Successfully controlled hPSC self-organization by modulating gene expression.
  • Machine learning optimized computational model parameters to predict emergent patterns.
  • In vitro experiments quantitatively recapitulated the predicted in silico patterns.

Conclusions:

  • Morphogenic dynamics of hPSCs can be accurately predicted using model-driven exploration and machine learning.
  • This approach enables spatial control of multicellular patterning for engineering human organoids and tissues.