LCMS_Raw_Data_NPPM_Radiosensitization_Study

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Authors: 王亮, 尹亮亮, 苟巧, 吉艳琴
Cited by: 0

Abstract

This dataset contains raw untargeted metabolomics data derived from human glioblastoma U87 MG cells, generated to investigate the radiosensitizing mechanism of biomimetic Fe3O4 nanoparticles (NPPM).Experimental Design and Sample Preparation:U87 MG cells were divided into four experimental groups: Negative Control (NC), NPPM Only (NP), Irradiation Only (IR), and NPPM combined with Irradiation (NP_IR). Cells in the NP and NP_IR groups were treated with NPPM (100 μmol/L). After 24 hours of drug incubation, the medium was replaced, and the IR and NP_IR groups received a single dose of 5 Gy radiation (6 MV, 600 cGy/min). Cells were harvested 24 hours post-irradiation. Metabolites were extracted using a cold methanol/water (4:1, v/v) solution, followed by grinding, ultrasonication, and centrifugation to obtain the supernatant.Data Acquisition:The metabolic profiles were acquired using a Thermo Scientific Vanquish Horizon UHPLC system coupled with a Thermo Scientific Exploris 240 mass spectrometer. Chromatographic separation was performed on a Waters ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 µm) at 40 °C. The mass spectrometer operated in both positive and negative electrospray ionization (ESI) modes, with a scan range of m/z 70–1050.Data Structure and Format:The dataset consists of raw mass spectrometry files in .raw format (Thermo Scientific proprietary format). The data are organized into two main categories based on the ionization mode: Positive Mode (POS) and Negative Mode (NEG). Quality Control (QC) samples are included to monitor instrument stability.Potential Usage:This dataset can be processed using metabolomics software (e.g., Progenesis QI) for peak alignment, normalization, metabolite identification.

Fields of Study & Subjects

Radiation Therapy and Dosimetry Metabolomics and Mass Spectrometry Studies Glioma Diagnosis and Treatment

Publication details

Journal / source
ScienceDB
Publication date
4 February 2026
Language
EN
Document type
Dataset