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Cell Specific Gene Expression01:58

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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During embryogenesis, cells become progressively committed to different fates through a two-step process: specification followed by determination. Specification is demonstrated by removing a segment of an early embryo, “neutrally” culturing the tissue in vitro—for example, in a petri dish with simple medium—and then observing the derivatives. If the cultured region gives rise to cell types that it would normally generate in the embryo, this means that it is specified. In...
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  1. Home
  3. Fatecode Enables Cell Fate Regulator Prediction Using Classification-supervised Autoencoder Perturbation.
  1. Home
  3. Fatecode Enables Cell Fate Regulator Prediction Using Classification-supervised Autoencoder Perturbation.

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Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation.

Mehrshad Sadria1, Anita Layton2, Sidhartha Goyal3

  • 1Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.

Cell Reports Methods
|July 10, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

We developed Fatecode, a computational tool using single-cell RNA sequencing data to identify cell fate regulators. This method aids in understanding cell reprogramming for tissue repair and disease recovery.

Keywords:
CP: Developmental biologyCP: Systems biology

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Area of Science:

  • Computational biology
  • Genomics
  • Developmental biology

Background:

  • Cell reprogramming is crucial for regenerative medicine, enabling tissue repair and disease recovery.
  • Identifying regulators of cell fate is essential for controlling reprogramming processes.
  • Single-cell RNA sequencing (scRNA-seq) provides high-resolution data for studying cellular heterogeneity.

Purpose of the Study:

  • To introduce Fatecode, a novel computational method for predicting cell fate regulators from scRNA-seq data.
  • To enable in silico prediction of genes that can modulate cell type populations.
  • To advance the understanding and application of cell reprogramming technologies.

Main Methods:

  • Utilizing a deep learning-based classification-supervised autoencoder to learn latent representations of scRNA-seq data.
  • Performing in silico perturbation experiments on latent representations to predict regulatory genes.
  • Validating Fatecode using simulations from a gene-regulatory network model and real scRNA-seq datasets.

    • Main Results:

    • Fatecode successfully predicts cell fate regulators from scRNA-seq data.
    • The method demonstrated effectiveness in simulations and biological datasets of blood and brain development.
    • Identified genes capable of altering cell type distribution in silico.

    Conclusions:

    • Fatecode offers a powerful computational approach to discover cell fate regulators.
    • This tool can accelerate the identification of targets for cell reprogramming strategies.
    • The findings support the potential of Fatecode in advancing regenerative medicine and developmental biology research.