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Lineage Commitment

Lineage Commitment

Commitment is the process whereby stem cells:

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Updated: Jan 16, 2026

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

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Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Nancy Zhang¹, Yuntian Fu^2,3,4, Divij Mathew⁵

¹Graduate Program in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Research Square

|October 3, 2025

Summary

This summary is machine-generated.

Clonotrace integrates gene expression and lineage barcodes to improve cell fate inference. This computational framework enhances understanding of cell differentiation dynamics, particularly for T cells in clinical settings.

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Last Updated: Jan 16, 2026

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Published on: May 8, 2020

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

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

Published on: March 2, 2017

Area of Science:

Single-cell biology
Computational biology
Immunology

Background:

Single-cell technologies enable joint measurement of molecular states and clonal identities.
Current computational methods for cell differentiation often overlook lineage information from barcodes.
T cell analysis, crucial in clinical settings, can benefit from integrating transcriptional and clonal data.

Purpose of the Study:

To develop a computational framework, Clonotrace, that jointly models gene expression and clonotype information.
To improve the fidelity of inferring cell state transitions and fate biases.
To provide a broadly applicable tool for lineage-barcoded single-cell datasets.

Main Methods:

Developed Clonotrace, a computational framework integrating gene expression and clonotype data.
Applied Clonotrace to diverse systems including T cells, hematopoietic differentiation, and cancer models.
Validated the framework's ability to reveal differentiation hierarchies and identify fate-determining genes.

Main Results:

Clonotrace infers cell state transitions and fate biases with higher fidelity than methods relying solely on transcriptional similarity.
The framework successfully revealed differentiation hierarchies in various biological systems.
Identified candidate fate-determining genes driving lineage commitment across different cell types.

Conclusions:

Clonotrace offers a powerful approach to leverage lineage barcode information for enhanced cell fate inference.
The framework is broadly applicable to various lineage-barcoded single-cell datasets, including T cells in immunotherapy and tumor microenvironments.
This method advances the study of cell dynamics, differentiation, and lineage commitment.