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A generic, cost-effective, and scalable cell lineage analysis platform.

Tamir Biezuner1,2, Adam Spiro1,2, Ofir Raz1,2

  • 1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 761001, Israel.

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|August 26, 2016
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This study introduces a new biochemical-computational platform for scalable and cost-effective single-cell lineage analysis. The method generates lineage trees from targeted sequencing data, enabling large-scale human cell discovery.

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Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • Single-cell genomics advances necessitate improved lineage tracing methods.
  • Current methods are often low-resolution, not scalable, or introduce bias.
  • Functional dependencies can limit existing single-cell sequencing approaches.

Purpose of the Study:

  • To develop an integrated biochemical-computational platform for generic single-cell lineage analysis.
  • To create a retrospective, cost-effective, and scalable solution for lineage tracing.
  • To facilitate large-scale human cell lineage discovery.

Main Methods:

  • Developed a biochemical-computational pipeline for single-cell lineage analysis.
  • Input individual cells and produce targeted single-cell sequencing data.
  • Generated lineage trees from the sequencing data.

Main Results:

  • Validated the platform using cells from an ex vivo grown tree.
  • Analyzed the platform's feasibility landscape via computer simulations.
  • Demonstrated a scalable and cost-effective approach to lineage tracing.

Conclusions:

  • The platform serves as a generic tool for cell lineage analysis.
  • Enables retrospective and scalable lineage tracing.
  • Paves the way for large-scale human cell lineage discovery.