11 research outputs found
Liquid ChromatographyâMass Spectrometry Calibration Transfer and Metabolomics Data Fusion
Metabolic profiling is routinely performed on multiple
analytical
platforms to increase the coverage of detected metabolites, and it
is often necessary to distribute biological and clinical samples from
a study between instruments of the same type to share the workload
between different laboratories. The ability to combine metabolomics
data arising from different sources is therefore of great interest,
particularly for large-scale or long-term studies, where samples must
be analyzed in separate blocks. This is not a trivial task, however,
due to differing data structures, temporal variability, and instrumental
drift. In this study, we employed blood serum and plasma samples collected
from 29 subjects diagnosed with small cell lung cancer and analyzed
each sample on two liquid chromatographyâmass spectrometry
(LC-MS) platforms. We describe a method for mapping retention times
and matching metabolite features between platforms and approaches
for fusing data acquired from both instruments. Calibration transfer
models were developed and shown to be successful at mapping the response
of one LC-MS instrument to another (Procrustes dissimilarity = 0.04;
Mantel correlation = 0.95), allowing us to merge the data from different
samples analyzed on different instruments. Data fusion was assessed
in a clinical context by comparing the correlation of each metabolite
with subject survival time in both the original and fused data sets:
a simple autoscaling procedure (Pearsonâs <i>R</i> = 0.99) was found to improve upon a calibration transfer method
based on partial least-squares regression (<i>R</i> = 0.94)
Liquid ChromatographyâMass Spectrometry Calibration Transfer and Metabolomics Data Fusion
Metabolic profiling is routinely performed on multiple
analytical
platforms to increase the coverage of detected metabolites, and it
is often necessary to distribute biological and clinical samples from
a study between instruments of the same type to share the workload
between different laboratories. The ability to combine metabolomics
data arising from different sources is therefore of great interest,
particularly for large-scale or long-term studies, where samples must
be analyzed in separate blocks. This is not a trivial task, however,
due to differing data structures, temporal variability, and instrumental
drift. In this study, we employed blood serum and plasma samples collected
from 29 subjects diagnosed with small cell lung cancer and analyzed
each sample on two liquid chromatographyâmass spectrometry
(LC-MS) platforms. We describe a method for mapping retention times
and matching metabolite features between platforms and approaches
for fusing data acquired from both instruments. Calibration transfer
models were developed and shown to be successful at mapping the response
of one LC-MS instrument to another (Procrustes dissimilarity = 0.04;
Mantel correlation = 0.95), allowing us to merge the data from different
samples analyzed on different instruments. Data fusion was assessed
in a clinical context by comparing the correlation of each metabolite
with subject survival time in both the original and fused data sets:
a simple autoscaling procedure (Pearsonâs <i>R</i> = 0.99) was found to improve upon a calibration transfer method
based on partial least-squares regression (<i>R</i> = 0.94)
Liquid ChromatographyâMass Spectrometry Calibration Transfer and Metabolomics Data Fusion
Metabolic profiling is routinely performed on multiple
analytical
platforms to increase the coverage of detected metabolites, and it
is often necessary to distribute biological and clinical samples from
a study between instruments of the same type to share the workload
between different laboratories. The ability to combine metabolomics
data arising from different sources is therefore of great interest,
particularly for large-scale or long-term studies, where samples must
be analyzed in separate blocks. This is not a trivial task, however,
due to differing data structures, temporal variability, and instrumental
drift. In this study, we employed blood serum and plasma samples collected
from 29 subjects diagnosed with small cell lung cancer and analyzed
each sample on two liquid chromatographyâmass spectrometry
(LC-MS) platforms. We describe a method for mapping retention times
and matching metabolite features between platforms and approaches
for fusing data acquired from both instruments. Calibration transfer
models were developed and shown to be successful at mapping the response
of one LC-MS instrument to another (Procrustes dissimilarity = 0.04;
Mantel correlation = 0.95), allowing us to merge the data from different
samples analyzed on different instruments. Data fusion was assessed
in a clinical context by comparing the correlation of each metabolite
with subject survival time in both the original and fused data sets:
a simple autoscaling procedure (Pearsonâs <i>R</i> = 0.99) was found to improve upon a calibration transfer method
based on partial least-squares regression (<i>R</i> = 0.94)
Metabolites differing between UQ and GDM groups at 2-y follow-up (<i>p</i><0.05).
<p>Metabolites have been classified according to their molecular structures or known metabolic functions/pathway participation. Within each class, data have been separated in to those with higher and lower ratios and are then presented in order from lowest to highest <i>p</i> value. The molecular weights, calculated as the monoisotopic mass, are included. Ratios with 95% confidence intervals in parentheses are shown. CE Cholesteryl ester; CEHC, 2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman; DG, diglyceride; HEPE, hydroxy-eicosapentaenoic acid; PC, phosphatidylcholine; PG, phosphatidylglycine; The values in parentheses (for example PC(34â¶0)) relate to the total fatty acid carbon chain length and number of carbon double bonds (unsaturation) in each metabolite. *Identification by matching of retention time and accurate mass to authentic chemical standard.</p><p>Metabolites differing between UQ and GDM groups at 2-y follow-up (<i>p</i><0.05).</p
msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics
Tandem mass spectrometry
(MS/MS or MS<sup>2</sup>) is a widely used
approach for structural annotation and identification of metabolites
in complex biological samples. The importance of assessing the contribution
of the precursor ion within an isolation window for MS<sup>2</sup> experiments has been previously detailed in proteomics, where precursor
ion purity influences the quality and accuracy of matching to mass
spectral libraries, but to date, there has been little attention to
this data-processing technique in metabolomics. Here, we present msPurity,
a vendor-independent R package for liquid chromatography (LC) and
direct infusion (DI) MS<sup>2</sup> that calculates a simple metric
to describe the contribution of the selected precursor. The precursor
purity metric is calculated as âintensity of a selected precursor
divided by the summed intensity of the isolation windowâ. The
metric is interpolated at the recorded point of MS<sup>2</sup> acquisition
using bordering full-scan spectra. Isotopic peaks of the selected
precursor can be removed, and low abundance peaks that are believed
to have limited contribution to the resulting MS<sup>2</sup> spectra
are removed. Additionally, the isolation efficiency of the mass spectrometer
can be taken into account. The package was applied to Data Dependent
Acquisition (DDA)-based MS<sup>2</sup> metabolomics data sets derived
from three metabolomics data repositories. For the 10 LC-MS<sup>2</sup> DDA data sets with > ±1 Da isolation windows, the median
precursor
purity score ranged from 0.67 to 0.96 (scale = 0 to +1). The R package
was also used to assess precursor purity of theoretical isolation
windows from LC-MS data sets of differing sample types. The theoretical
isolation windows being the same width used for an anticipated DDA
experiment (±0.5 Da). The most complex sample had a median precursor
purity score of 0.46 for the 64,498 XCMS determined features, in comparison
to the less spectrally complex sample that had a purity score of 0.66
for 5071 XCMS features. It has been previously reported in proteomics
that a purity score of <0.5 can produce unreliable spectra matching
results. With this assumption, we show that for complex samples there
will be a large number of metabolites where traditional DDA approaches
will struggle to provide reliable annotations or accurate matches
to mass spectral libraries
Metabolites differing between control and GDM groups at 2-y follow-up (<i>p</i><0.05).
<p>Metabolites have been classified according to their molecular structures or known metabolic functions/pathway participation. Within each class the data have been separated in to those with higher and lower ratios and are then presented in order from lowest to highest <i>p</i> value. The molecular weights, calculated as the monoisotopic mass, are included. Ratios with 95% confidence intervals in parentheses are shown. CE cholesteryl ester; DG, diglyceride; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycine; PGF, prostaglandin; PI, phosphatidylinositol; PS, phosphatidylserine; The values in parentheses (for example PC(34â¶0)) relate to the total fatty acid carbon chain length and number of carbon double bonds (unsaturation) in each metabolite. *Identification by matching of retention time and accurate mass to authentic chemical standard.</p><p>Metabolites differing between control and GDM groups at 2-y follow-up (<i>p</i><0.05).</p
Phospholipids that differed significantly between control and UQ groups at 2-y follow-up (<i>p</i><0.05).
<p>*Identification by matching of retention time and accurate mass to authentic chemical standard.</p
Long chain fatty acids that differed significantly between control and UQ groups at 2-y follow-up (<i>p</i><0.05).
<p>PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, glycerophosphoglycerol; PS, phosphatidylserine.</p
Clinical data for participants during pregnancy and at follow-up in the three study groups.
<p><i>p</i> values calculated applying ANOVA or **Chi-squared tests. #Data are geometric mean and 95% confidence intervals</p><p>Clinical data for participants during pregnancy and at follow-up in the three study groups.</p