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Updated: Aug 8, 2026
05:58
Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing
Published on: August 16, 2024
Reconstruction of Cell Diversity and Cell Lineages from Somatic Mutations in Single-Cell Transcriptomic Data
Satoshi Oota1, Kuniya Abe2, Cheng-Tsung Pan3,4
1Center for Advanced Photonics, The National Institute of Physical and Chemical Research, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
Summary
Real-Time Course Analysis (RTCA) reconstructs cell lineages using somatic variants from single-cell RNA sequencing (scRNA-seq). This scalable method accurately models developmental processes in complex tissues.
Area of Science:
- Genomics
- Developmental Biology
- Computational Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) provides high-resolution cellular data.
- Inferring temporal relationships and cell lineages from scRNA-seq data is challenging.
Purpose of the Study:
- Introduce Real-Time Course Analysis (RTCA), a novel framework for reconstructing cell lineages.
- Evaluate RTCA's scalability and robustness compared to existing methods.
- Apply RTCA to analyze developmental pathways in human placental tissues.
Main Methods:
- RTCA reconstructs cell lineages using phylogenetic signal from somatic variants in nuclear-encoded transcripts.
- Simulations assessed RTCA's accuracy with varying cell numbers and mutation data sparsity.
- Comparative analysis with PhylinSic, a genotype-mediated inference framework.
Main Results:
- RTCA demonstrates scalability, maintaining accurate tree reconstruction with increased cell numbers.
- RTCA is more robust than PhylinSic against sparse mutation signals and data dropout.
- Application to placental samples revealed bifurcating phylogenetic trees consistent with known differentiation pathways.
Conclusions:
- RTCA provides a scalable and cost-effective solution for reconstructing developmental trajectories.
- The method integrates lineage and expression information for biologically meaningful models.
- RTCA enhances understanding of cellular development in complex normal tissues.
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