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Updated: Jun 13, 2026

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
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TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

Functional proteomic pattern identification under low dose ionizing radiation.

Young Bun Kim1, Chin-Rang Yang, Jean Gao

  • 1The Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.

Artificial Intelligence in Medicine
|May 18, 2010
PubMed
Summary
This summary is machine-generated.

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This study reveals low dose radiation effects on cell signaling pathways using advanced proteomic analysis. New methods detect subtle protein changes, improving understanding of radiation biology.

Area of Science:

  • Cellular and Molecular Biology
  • Radiation Biology
  • Proteomics

Background:

  • High-dose radiation is a known carcinogen, but low-dose radiation effects on cellular signaling remain unclear.
  • Low-dose radiation impacts DNA repair, cell survival, cell cycle, growth, and death pathways.

Purpose of the Study:

  • To elucidate proteomic patterns influenced by low-dose radiation.
  • To identify regulatory proteins and kinases involved in radiation-induced cellular responses.

Main Methods:

  • Utilized reverse-phase protein microarray (RPPM) with quantum dots for quantitative detection.
  • Developed a discriminative feature pattern identification system (DFPIS) to analyze protein dependencies and network motifs.
  • Investigated dynamic responses across various radiation doses (0cGy to 5Gy) and time points (1-72h).

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Published on: November 15, 2017

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Last Updated: Jun 13, 2026

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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Main Results:

  • Presented computational results from DFPIS analysis of ataxia-telangiectasia mutated (ATM) cells.
  • Identified distinct responsive proteins and kinases at different radiation doses and time points.
  • Compared strong jumping emerging patterns (SJEPs) in ATM-proficient and ATM-deficient cells.

Conclusions:

  • The combined RPPM and DFPIS approach successfully detects signaling pattern alterations at low radiation doses.
  • This methodology overcomes limitations of conventional technologies in low-dose radiation research.