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Characterizing Mutational Load and Clonal Composition of Human Blood
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Lineage reconstruction from clonal correlations.

Caleb Weinreb1, Allon M Klein2

  • 1Department of Systems Biology, Harvard Medical School, Boston, MA 02115.

Proceedings of the National Academy of Sciences of the United States of America
|July 8, 2020
PubMed
Summary
This summary is machine-generated.

This study uses DNA barcoding and branching process theory to reveal cell lineage relationships during development. It reconstructs cell differentiation pathways, uncovering new insights into hematopoiesis.

Keywords:
branching processesclonal barcodeslineage tracing

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Area of Science:

  • Developmental Biology
  • Systems Biology
  • Computational Biology

Background:

  • Understanding cell fate decisions is crucial in developmental biology.
  • Clonal labeling with DNA barcodes identifies cells with shared ancestry.
  • Stochasticity in cell fate choice can potentially reveal lineage relationships.

Purpose of the Study:

  • To explore harnessing cell fate stochasticity for lineage tracing using clonal barcoding.
  • To develop a method for inferring the order of cell fate decisions.
  • To identify conditions where barcode distribution reflects true lineage relationships.

Main Methods:

  • Utilizing a generalized multitype branching process model.
  • Analyzing barcode distribution across cell types.
  • Developing computational tools for lineage inference.
  • Examining barcode covariance symmetries for method validation.

Main Results:

  • Determined conditions for barcode distribution to encode lineage relationships.
  • Proposed a method for inferring the sequence of cell fate decisions.
  • Identified barcode covariance symmetries as a consistency check.
  • Demonstrated that broken symmetries indicate multiple differentiation pathways.
  • Reconstructed the hematopoietic hierarchy and identified novel lineage couplings.

Conclusions:

  • Stochasticity in cell fate choice can be leveraged for lineage reconstruction via DNA barcoding.
  • The proposed method accurately infers cell differentiation orders and identifies alternative pathways.
  • The findings provide valuable computational tools and insights into complex developmental processes like hematopoiesis.