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Updated: Oct 19, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.

Peijie Zhou^1,2, Shuxiong Wang², Tiejun Li³

¹LMAM and School of Mathematical Sciences, Peking University, Beijing, China.

Nature Communications

|September 24, 2021

Summary

This summary is machine-generated.

MuTrans identifies cell-state transitions from single-cell data using multiscale reduction. This method reveals cell fate dynamics and transition trajectories, advancing our understanding of complex biological systems.

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Last Updated: Oct 19, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Area of Science:

Single-cell transcriptomics
Computational biology
Systems biology

Background:

Single-cell technologies enable detailed analysis of cellular heterogeneity.
Detecting dynamic cell-state transitions from static single-cell data is a significant challenge.

Purpose of the Study:

To develop a computational method for identifying cell-state transitions and underlying dynamics from single-cell transcriptome data.
To characterize transient cell states and quantify cell fate trajectories.

Main Methods:

MuTrans, a method employing a multiscale reduction technique.
Iterative unification of transition dynamics across multiple scales to construct a cell-fate dynamical manifold.
Application of coarse-grained transition path theory to quantify transition trajectory likelihoods.

Main Results:

MuTrans successfully distinguishes stable and transition cells.
Identifies key genes associated with transient states and transition drivers.
Demonstrates robustness and scalability across five different single-cell experimental platforms.
Validates findings in diverse biological systems including tumor EMT, iPSC differentiation, and blood cell differentiation.

Conclusions:

MuTrans effectively bridges data-driven and model-based approaches for analyzing cell-fate transitions at single-cell resolution.
Provides a scalable and robust framework for unraveling complex cell fate dynamics.