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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Updated: Jul 13, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
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scITDG: a tool for identifying time-dependent genes in single-cell transcriptome sequencing data.

Yandong Zheng1,2,3, Chengyu Liu1,2,3, Weiqi Zhang4,3,5

  • 1State Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China.

Marine Life Science & Technology
|December 1, 2025
PubMed
Summary

We developed scITDG, a new tool for analyzing time-dependent gene expression in single-cell data. This method reveals dynamic gene patterns crucial for understanding aging and regeneration processes.

Keywords:
AgingRegenerationSingle-cell sequencingTime-dependent genesscITDG R package

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Area of Science:

  • Single-cell transcriptomics
  • Computational biology
  • Systems biology

Background:

  • Analyzing time-dependent gene expression at single-cell resolution is crucial for understanding dynamic biological processes.
  • Existing tools lack comprehensive capabilities for capturing temporal gene expression dynamics in single cells.

Purpose of the Study:

  • Introduce scITDG, a novel computational tool for analyzing time-dependent gene expression in single-cell RNA sequencing data.
  • Enhance existing single-cell analysis platforms (Seurat, Scanpy) with advanced temporal analysis features.

Main Methods:

  • Integration of natural cubic splines regression with bootstrapping resampling.
  • Development of scITDG for compatibility with Seurat and Scanpy workflows.
  • Application of scITDG to analyze gene expression dynamics in aging and regeneration models.

Main Results:

  • scITDG effectively identifies dynamic gene expression patterns at single-cell resolution across multiple time points.
  • Revealed intricate gene expression modules associated with mice aging.
  • Uncovered gene expression dynamics during axolotl limb regeneration.

Conclusions:

  • scITDG addresses a critical gap in single-cell temporal data analysis.
  • The tool provides valuable insights into cellular function and response mechanisms in aging and regeneration.
  • scITDG is versatile and applicable to diverse biological contexts like development, disease, and therapy response.