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A multi-omics dataset of human transcriptome and proteome stable reference.

Shaohua Lu1,2, Hong Lu3, Tingkai Zheng3

  • 1Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou, China. 356511612@qq.com.

Scientific Data
|July 13, 2023
PubMed
Summary

Researchers developed a stable human hepatocellular cell line, MHCC97H, as a standard reference material for omics technologies. This cell line ensures reproducible transcriptome and proteome analysis, advancing precision medicine.

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Area of Science:

  • Biomedical Sciences
  • Genomics
  • Proteomics

Background:

  • High-throughput omics technologies are crucial for biomedical advancements.
  • Poor reproducibility of omics techniques hinders their widespread application.
  • Standard reference materials are needed to validate omics workflows.

Purpose of the Study:

  • To identify and characterize a stable cell line for omics standard reference material.
  • To assess the long-term transcriptomic and proteomic stability of the MHCC97H cell line.
  • To provide reliable reference datasets for transcriptomic and proteomics research.

Main Methods:

  • Culturing and subculturing the human hepatocellular cell line MHCC97H for 9 generations.
  • Performing transcriptomic analysis to assess gene expression stability.
  • Conducting proteomic analysis using both data-dependent and data-independent acquisition methods.

Main Results:

  • The MHCC97H cell line exhibited high transcriptomic stability (r = 0.983–0.997) across subculturing generations.
  • Proteomic analysis demonstrated excellent reproducibility (r = 0.966–0.994) across different acquisition methods.
  • The stability of the MHCC97H cell line was validated across multiple laboratories and platforms.

Conclusions:

  • The MHCC97H cell line serves as a robust and reproducible standard reference material for omics studies.
  • This standardized material and associated datasets will enhance the accuracy and reliability of omics workflows.
  • The findings support the advancement of omics technology applications in precision medicine.