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Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data.

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  • 1Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India.

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|June 18, 2020
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Summary
This summary is machine-generated.

We developed LinTIMaT, a new statistical method for reconstructing cell lineages using both mutation and expression data. This approach overcomes computational challenges, enabling more accurate and integrated developmental lineage trees.

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Area of Science:

  • Developmental Biology
  • Computational Biology
  • Genomics

Background:

  • Single-cell RNA sequencing and CRISPR-Cas9 barcode editing are used to study developmental lineages.
  • Existing methods face computational challenges due to noisy mutation data and inability to combine experiments.

Purpose of the Study:

  • To develop a novel statistical method for reconstructing cell lineages.
  • To integrate mutation and expression data for improved lineage inference.
  • To enable the reconstruction of species-invariant lineage trees.

Main Methods:

  • Developed LinTIMaT, a maximum-likelihood statistical framework.
  • Integrated single-cell RNA sequencing (expression) data with CRISPR-Cas9 barcode editing (mutation) data.
  • Applied the method to reconstruct cell lineages at the whole organism level.

Main Results:

  • LinTIMaT resolves ambiguities present in mutation-only lineage inference using expression data.
  • Expression data enables the integration of multiple experimental lineages into a single, invariant lineage tree.
  • Reconstructed lineages show improved cell type coherence and functional gene set significance.

Conclusions:

  • LinTIMaT offers a robust method for accurate cell lineage reconstruction.
  • The method provides new insights into progenitor populations and differentiation pathways.
  • Integrating mutation and expression data is crucial for advancing developmental lineage studies.