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Cell Specific Gene Expression01:58

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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SCDevDB: A Database for Insights Into Single-Cell Gene Expression Profiles During Human Developmental Processes.

Zishuai Wang1, Xikang Feng1, Shuai Cheng Li1

  • 1Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.

Frontiers in Genetics
|October 16, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces SCDevDB, a novel database for single-cell gene expression profiling across human developmental pathways. It enables comprehensive analysis of gene expression dynamics during development, offering insights into cell group relationships.

Keywords:
cell typedatabasedevelopmentdifferential expressiongene expressionsingle cell

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Area of Science:

  • Developmental Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) generates high-resolution data on cellular processes.
  • Existing human single-cell expression atlases lack biological context and relational analysis of cell groups.
  • Current databases primarily offer gene-specific searches and visualizations without developmental pathway integration.

Purpose of the Study:

  • To develop a comprehensive database (SCDevDB) for investigating single-cell gene expression profiling across diverse human developmental pathways.
  • To integrate multiple scRNA-seq datasets and establish a framework for analyzing developmental cell group relationships.
  • To provide advanced analytical tools for exploring gene expression dynamics during human development.

Main Methods:

  • Collected and curated 10 human single-cell RNA-seq datasets.
  • Categorized cells into 176 distinct developmental cell groups.
  • Constructed 24 human developmental pathways for integrated analysis.

Main Results:

  • SCDevDB allows users to query gene expression profiles across various developmental pathways.
  • Identified differentially expressed genes within each developmental pathway.
  • Generated T-distributed stochastic neighbor embedding (t-SNE) maps illustrating developmental stage relationships based on gene expression, alongside Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses.

Conclusions:

  • SCDevDB offers a valuable resource for exploring human developmental biology through single-cell gene expression data.
  • The database facilitates the study of gene expression dynamics and cell-cell relationships during development.
  • SCDevDB provides a platform for comprehensive analysis of developmental pathways and associated gene functions.