10.1021/ac401400b.s001
Yanhua Chen
Yanhua
Chen
Guoqing Shen
Guoqing
Shen
Ruiping Zhang
Ruiping
Zhang
Jiuming He
Jiuming
He
Yi Zhang
Yi
Zhang
Jing Xu
Jing
Xu
Wei Yang
Wei
Yang
Xiaoguang Chen
Xiaoguang
Chen
Yongmei Song
Yongmei
Song
Zeper Abliz
Zeper
Abliz
Combination
of Injection Volume Calibration by Creatinine
and MS Signals’ Normalization to Overcome Urine Variability
in LC-MS-Based Metabolomics Studies
American Chemical Society
2013
Walker 256 carcinoma cells
injection volumes
signal
peak area normalization
creatinine values
MS
urine samples
strategy
Injection Volume Calibration
concentration
Overcome Urine Variability
normalization methods
rat urine samples
2013-08-20 00:00:00
Journal contribution
https://acs.figshare.com/articles/journal_contribution/Combination_of_Injection_Volume_Calibration_by_Creatinine_and_MS_Signals_Normalization_to_Overcome_Urine_Variability_in_LC_MS_Based_Metabolomics_Studies/2384860
It is essential to choose one preprocessing
method for liquid chromatography–mass
spectrometry (LC-MS)-based metabolomics studies of urine samples in
order to overcome their variability. However, the commonly used normalization
methods do not substantially reduce the high variabilities arising
from differences in urine concentration, especially for signal saturation
(abundant metabolites exceed the dynamic range of the instrumentation)
or missing values. Herein, a simple preacquisition strategy based
on differential injection volumes calibrated by creatinine (to reduce
the concentration differences between the samples), combined with
normalization to “total useful MS signals” or “all
MS signals”, is proposed to overcome urine variabilities. This
strategy was first systematically compared with other popular normalization
methods by application to serially diluted urine samples. Then, the
method has been verified using rat urine samples of pre- and postinoculation
of Walker 256 carcinoma cells. The results showed that the calibration
of injection volumes based on creatinine values could effectively
eliminate intragroup differences caused by variations in the concentrations
of urinary metabolites, thus giving better parallelism and clustering
effects. In addition, peak area normalization could further eliminate
intraclass differences. Therefore, the strategy of combining peak
area normalization with calibration of injection volumes of urine
samples based on their creatinine values is effective for solving
problems associated with urinary metabolomics.