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Related Experiment Video

Updated: May 20, 2026

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers (MADM)
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Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers (MADM)

Published on: May 8, 2020

Computational approaches for multimodal lineage tracing.

Kun Wang1,2, Xionglei He3,4,5, Zheng Hu6

  • 1State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China.

Nature Reviews. Genetics
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

Multimodal lineage tracing, combining heritable information with single-cell multi-omics, reveals cellular dynamics. New computational tools enhance analysis of complex data for insights into cell fate and disease.

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Last Updated: May 20, 2026

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers (MADM)
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Published on: May 8, 2020

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Cell Lineage Analyses and Gene Function Studies Using Twin-spot MARCM

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Area of Science:

  • Cellular and Molecular Biology
  • Developmental Biology
  • Computational Biology

Background:

  • Understanding cell fate decisions is crucial for development, regeneration, and disease.
  • Multimodal lineage tracing integrates heritable information with single-cell multi-omics for high-resolution cellular dynamics.
  • Analyzing complex, heterogeneous lineage-resolved single-cell multi-omic data requires advanced computational methods.

Purpose of the Study:

  • To survey recent advances in computational frameworks for analyzing lineage-resolved, single-cell multi-omic data.
  • To highlight methods enabling accurate lineage reconstruction, trajectory inference, and ancestral state estimation.
  • To identify molecular programs driving cell-state transitions.

Main Methods:

  • Comprehensive survey of computational methodologies for multimodal lineage tracing.
  • Review of techniques for lineage reconstruction and trajectory inference.
  • Discussion of deep learning-based analytical models.

Main Results:

  • Methodological advances significantly expand the computational toolkit for lineage-resolved, single-cell multi-omic data analysis.
  • New tools enable more accurate lineage reconstruction, trajectory inference, and ancestral state estimation.
  • Identification of molecular programs driving cell-state transitions is improved.

Conclusions:

  • Sophisticated computational frameworks are essential for extracting biological insights from complex multimodal lineage tracing data.
  • Emerging high-resolution lineage-tracing technologies and deep learning models promise a more complete view of cellular evolution.
  • These advancements offer a quantitative understanding of cellular dynamics in health and disease.