PiTMaP: A New Analytical Platform for High-Throughput Direct Metabolome Analysis by Probe Electrospray Ionization/Tandem Mass Spectrometry Using an R Software-Based Data Pipeline
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https://figshare.com/articles/dataset/PiTMaP_A_New_Analytical_Platform_for_High-Throughput_Direct_Metabolome_Analysis_by_Probe_Electrospray_Ionization_Tandem_Mass_Spectrometry_Using_an_R_Software-Based_Data_Pipeline/12302645
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A new
analytical platform called PiTMaP was developed for high-throughput
direct metabolome analysis by probe electrospray ionization/tandem
mass spectrometry (PESI/MS/MS) using an R software-based data pipeline.
PESI/MS/MS was used as the data acquisition technique, applying a
scheduled-selected reaction monitoring method to expand the targeted
metabolites. Seventy-two metabolites mainly related to the central
energy metabolism were selected; data acquisition time was optimized
using mouse liver and brain samples, indicating that the 2.4 min data
acquisition method had a higher repeatability than the 1.2 and 4.8
min methods. A data pipeline was constructed using the R software,
and it was proven that it can (i) automatically generate box-and-whisker
plots for all metabolites, (ii) perform multivariate analyses such
as principal component analysis (PCA) and projection to latent structures-discriminant
analysis (PLS-DA), (iii) generate score and loading plots of PCA and
PLS-DA, (iv) calculate variable importance of projection (VIP) values,
(v) determine a statistical family by VIP value criterion, (vi) perform
tests of significance with the false discovery rate (FDR) correction
method, and (vii) draw box-and-whisker plots only for significantly
changed metabolites. These tasks could be completed within ca. 1 min.
Finally, PiTMaP was applied to two cases: (1) an acetaminophen-induced
acute liver injury model and control mice and (2) human meningioma
samples with different grades (G1–G3), demonstrating the feasibility
of PiTMaP. PiTMaP was found to perform data acquisition without tedious
sample preparation and a posthoc data analysis within ca. 1 min. Thus,
it would be a universal platform to perform rapid metabolic profiling
of biological samples.
创建时间:
2020-05-07



