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Quantification of cell identity from single-cell gene expression profiles.

Idan Efroni1, Pui-Leng Ip2, Tal Nawy3

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Area of Science:

  • Molecular Biology
  • Developmental Biology
  • Genomics

Background:

  • Defining cell identity is crucial in biology, particularly during development.
  • Single-cell RNA sequencing (scRNA-seq) offers insights into cell states but requires improved methods for identity mapping.
  • Existing techniques struggle to accurately classify cell identities during dynamic transitions.

Purpose of the Study:

  • To develop and validate a quantitative method for determining cell identity from scRNA-seq data.
  • To apply this method to classify cells in plant and human samples, including during regeneration.
  • To investigate cell identity dynamics in response to tissue injury.

Main Methods:

  • Utilized repositories of cell type-specific transcriptomes for quantitative identity assessment.
  • Applied single-cell RNA sequencing (scRNA-seq) to profile cells from Arabidopsis root tips and human glioblastoma tumors.
  • Analyzed scRNA-seq data from regenerating plant roots following tip excision.

Main Results:

  • Accurately classified cell identities in both plant (Arabidopsis root tips) and human (glioblastoma) samples.
  • Identified a previously uncharacterized transient collapse of cell identity in regenerating plant roots.
  • Observed this identity collapse at a distance from the site of injury, indicating a systemic response.

Conclusions:

  • The developed method provides a robust way to quantify cell identity using scRNA-seq.
  • The findings highlight the dynamic nature of cell identity, even in response to localized stimuli.
  • A quantitative cell identity index is biologically relevant and can reveal novel developmental phenomena.