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Related Experiment Video

Updated: Jan 9, 2026

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Published on: December 16, 2017

Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.

Zhenyi Zhang¹, Zihan Wang², Yuhao Sun³

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

NPJ Systems Biology and Applications

|December 4, 2025

Summary

This summary is machine-generated.

This review explores computational modeling for dynamic cellular processes using single-cell and spatial omics data. Advanced techniques reveal how gene expression changes over time and space, enhancing our understanding of cell biology.

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Last Updated: Jan 9, 2026

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

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Published on: December 16, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Single Cell Fate Mapping in Zebrafish

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Published on: October 5, 2011

Area of Science:

Computational Biology
Genomics
Cell Biology

Background:

Cellular processes are dynamic, changing across time and space.
Single-cell and spatial omics technologies offer high-resolution gene expression data.
Characterizing dynamic cellular states requires advanced analytical methods.

Purpose of the Study:

To review recent modeling strategies for time-series and spatiotemporal transcriptomic data.
To highlight the connections between dynamical systems, generative modeling, and biological insights.
To demonstrate how computational tools can deepen the understanding of single-cell dynamics.

Main Methods:

Review of computational modeling approaches.
Analysis of time-series transcriptomic data.
Analysis of spatiotemporal transcriptomic data.

Main Results:

Identification of key modeling strategies for dynamic transcriptomic data.
Emphasis on the integration of dynamical systems and generative modeling.
Demonstration of computational tools' utility in uncovering biological insights.

Conclusions:

Computational modeling is crucial for understanding dynamic cellular processes.
Time-series and spatiotemporal transcriptomic data analysis is advancing rapidly.
These methods provide deeper insights into the dynamic nature of single cells.