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Related Experiment Video

Updated: May 20, 2025

A Practical Approach to Genetic Inducible Fate Mapping: A Visual Guide to Mark and Track Cells In Vivo

A Practical Approach to Genetic Inducible Fate Mapping: A Visual Guide to Mark and Track Cells In Vivo

Published on: December 30, 2009

Machine-guided cell-fate engineering.

Evan Appleton¹, Jenhan Tao², Songlei Liu¹

¹Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.

|May 18, 2025

Summary

This summary is machine-generated.

This study introduces CellCartographer, a machine learning tool that designs transcription factor combinations for efficient cell differentiation. It rapidly converts induced pluripotent stem cells (iPSCs) into various specialized cell types with high accuracy.

Keywords:

CP: Stem cell research cell-fate engineering computer-aided design machine learning stem cell biology

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Last Updated: May 20, 2025

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Published on: January 26, 2013

Area of Science:

Stem cell biology
Computational biology
Genomics

Background:

Induced pluripotent stem cells (iPSCs) are crucial for studying cell function and differentiation.
Transcription factor (TF) over-expression is an efficient method for cell differentiation, but identifying optimal TF combinations is challenging.

Purpose of the Study:

To develop a machine learning (ML) pipeline, CellCartographer, for designing multiplex TF pooled-screening experiments for efficient cell-type conversions.
To iteratively refine TF combinations for high-efficiency differentiation of iPSCs into specific cell types.

Main Methods:

Utilized a machine learning pipeline (CellCartographer) integrating chromatin accessibility and transcriptomics data.
Designed multiplex TF pooled-screening experiments for cell-type conversions.
Iteratively refined TF combinations based on screening results.

Main Results:

Successfully differentiated iPSCs into twelve cell types with low efficiency in initial screens.
Achieved high-efficiency differentiation for six cell types in under six days through iterative refinement.
Functionally validated iPSC-derived cytotoxic T cells, regulatory T cells, type II astrocytes, and hepatocytes.

Conclusions:

CellCartographer provides an effective ML-driven approach for designing TF combinations for efficient iPSC differentiation.
The method enables rapid and functionally accurate generation of various specialized cell types from iPSCs.