Polyamine metabolites provide pathophysiological information on disease or therapeutic efficacy, yet rapid screening methods for these biomarkers are lacking. Here, we developed high-throughput polyamine metabolite profiling based on multisegment injection capillary electrophoresis triple quadrupole tandem mass spectrometry (MSI-CE-MS/MS).
Trang 1Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/chroma
Kaori Igarashi a , Sana Ota a , Miku Kaneko a , Akiyoshi Hirayama a , Masanobu Enomoto b ,
Kenji Katumata b , Masahiro Sugimoto a , c , Tomoyoshi Soga a , d , ∗
a Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka 997-0052, Japan
b Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, 6-7-1, Nishijinjuku, Shinjuku, Tokyo 160-0023, Japan
c Research and Development Center for Minimally Invasive Therapies, Medical Research Institute, Tokyo Medical University, 6-1-1, Sinjuku, Tokyo 160-0022,
Japan
d Faculty of Environmental Information Studies, Keio University, 5322 Endo, Fujisawa 252-0882, Japan
a r t i c l e i n f o
Article history:
Received 13 April 2021
Revised 3 June 2021
Accepted 15 June 2021
Available online 20 June 2021
Keywords:
Multisegment injection
capillary electrophoresis
Mass spectrometry
Saliva
Polyamine
Biomarker
Colorectal cancer
a b s t r a c t
Polyamine metabolites provide pathophysiological information on disease or therapeutic efficacy, yet rapid screening methods for these biomarkers are lacking Here, we developed high-throughput polyamine metabolite profiling based on multisegment injection capillary electrophoresis triple quadrupole tandem mass spectrometry (MSI-CE-MS/MS), which allows sequential 40-sample injection followed by electrophoretic separation and specific mass detection To achieve consecutive analysis of polyamine samples, 1 M formic acid was used as the background electrolyte (BGE) The BGE spacer vol- ume had an apparent effect on peak resolution among samples, and 20 nL was selected as the optimal volume The use of polyamine isotopomers as the internal standard enabled the correction of matrix effects in MS detection This method is sensitive, selective and quantitative, and its utility was demon- strated by screening polyamines in 359 salivary samples within 360 min, resulting in discrimination of colorectal cancer patients from noncancer controls
© 2021 The Authors Published by Elsevier B.V This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
1 Introduction
Recent analysis of the entire set of low-molecular-weight
bi-ological compounds (metabolome) has revealed new biomarkers
that correlate with disease severity and that respond to
therapeu-tic efficacy or toxicity [1–4] However, few metabolite biomarkers
have been implemented in clinical practice because unlike proteins
and peptides, it is difficult to produce monoclonal or polyclonal
antibodies for low-molecular-weight molecules [5] ; therefore,
de-veloping rapid screening methods such as the enzyme-linked
im-munosorbent assay (ELISA) commonly used in clinical laboratories
is challenging.
∗Corresponding author at: Institute for Advanced Biosciences, Keio University,
246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052 Japan
E-mail addresses: sugawara@ttck.keio.ac.jp (K Igarashi), sana.ota@ttck.keio.ac.jp
(S Ota), KKK-miku@ttck.keio.ac.jp (M Kaneko), hirayama@ttck.keio.ac.jp (A Hi-
rayama), enomoto@tokyo-med.ac.jp (M Enomoto), k-katsu@tokyo-med.ac.jp (K Ka-
tumata), msugi@sfc.keio.ac.jp (M Sugimoto), soga@sfc.keio.ac.jp (T Soga)
GC/MS and LC-MS are commonly used for the analysis of low-molecular-weight compounds However, these approaches require more than 10 min per sample due to the low sample throughput associated with solute elution and column preconditioning; thus, the costs associated with these techniques preclude their clinical application.
Capillary electrophoresis mass spectrometry (CE-MS) is a pow-erful tool for the comprehensive analysis of charged metabolites [ 6 , 7 ] In this marriage of techniques, CE confers rapid analysis and efficient resolution, and MS provides high selectivity and sensitiv-ity [6] ; thus, CE-MS metabolomics has been widely applied in a variety of fields [ 4 , 8–10 ] Recently, Britz-McKibbin et al [11–16] re-ported the multisegment injection (MSI)-CE-MS method, which al-lows sequential multisample injection in series within a capillary tube using the sample stacking technique and enables many sam-ple measurements within a single run.
Polyamines such as spermine, spermidine and their N-acetylated forms are low-molecular-weight cations Because of their positive charges, polyamines bind to DNA and RNA and are involved in a variety of biological processes, including gene
expres-https://doi.org/10.1016/j.chroma.2021.462355
0021-9673/© 2021 The Authors Published by Elsevier B.V This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Trang 2sion and translation, cell proliferation, membrane stabilization, and
organ development [17–19] In cancer, polyamine metabolism is
frequently dysregulated, indicating that elevated polyamine levels
are necessary for transformation and tumor progression [20]
Re-cently, polyamines have attracted much attention not only as
tar-gets for anticancer strategies but also as diagnostic tools [ 19 , 21 , 22 ].
Thus, a high-throughput screening method for these biomarkers is
urgently needed.
Here, we developed a high-throughput MSI-CE-tandem mass
spectrometry (MS/MS) method that allowed 40 consecutive
analy-ses of salivary polyamines within 40 min, resulting in the
discrimi-nation of colorectal cancer (CRC) patients from noncancer controls.
2 Materials and methods
2.1 Chemicals
N1-Acetylspermidine (N1-Ac-Spd), N8-acetylspermidine
(N8-Ac-Spd)-d3 and spermine-d20 were purchased from Toronto
Re-search Chemicals (Toronto, Canada); N1-Ac-Spd-d6,
N1,N8-diacetyl-spermidine (N1,N8-DiAc-Spd)-d6, N1-acetylspermine
(N1-Ac-Spm)-d3 and N1,N12-diacetylspermine (N1,N12-DiAc-Spm)-d6 were
pur-chased from Santa Cruz Biotechnology (Dallas, TX);
creatinine-d3 was purchased from Taiyo Nippon Sanso (Tokyo, Japan); and
spermidine-d3 was purchased from IsoSciences (Ambler, PA) All
other reagents were obtained from Sigma-Aldrich (St Louis, MO)
or Wako (Osaka, Japan) Water was purified with a Milli-Q
purifi-cation system (Millipore, Bedford, MA).
2.2 Clinical samples
This study was conducted according to the Declaration of
Helsinki principles The study protocol was approved by the Ethics
Committee of Tokyo Medical University (No 2346) Written
in-formed consent was obtained from each subject before
participat-ing in the study The 359 collected saliva samples, including 57
healthy controls (HCs), 26 patients with benign colorectal tumors
(BCTs) and 276 patients with CRC (Table S1), were divided into
nine batches, and each batch comprising 39 or 40 saliva filtrates
was applied to the MSI-CE-MS/MS system.
2.3 Saliva collection
Saliva providers were not allowed to take any food except
wa-ter intake after 9:00 p.m on the previous day They were required
to brush their teeth without toothpaste on the day of saliva
col-lection and had to refrain from drinking water, smoking,
tooth-brushing, and intense exercise 1h before saliva collection Before
saliva collection, they were required to gargle with water and then
all saliva samples were collected from 8:00 to 11:00 a.m
Approx-imately 400 μL of unstimulated saliva was collected in a 50 mL
polypropylene tube using a polypropylene straw 1.1 cm in diameter
to assist the saliva collection After collection, saliva samples were
immediately stored at -80 °C Creatinine normalization was used to
correct for differences in salivary rate/hydration status that
con-tributes to greater biological variability.
2.4 Sample preparation
Saliva (90 μL) samples were transferred to 1.5 mL polypropylene
reaction tubes (Greiner Bio-One International, Tokyo, Japan), and
10 μL Milli-Q water containing 100 μM creatinine-d3 and 10 μM
each polyamine isotopomer was added to the tube The solution
was vortexed for 10 and then centrifugally filtered through a
Pall-Nanosep-3-kDa Omega cutoff filter (Pall Corporation, Japan) to
re-move proteins and other macromolecules at 9100 × g for 3 h at
4 °C Then, the filtrate was injected into CE-MS/MS system.
2.5 Instrumentation
All CE-MS experiments were performed using an Agilent G1600
CE system, an Agilent 6410 triple-quadrupole MS/MS system, an Agilent 6210 time-of-flight mass spectrometry (TOFMS) system, an Agilent 1100 series isocratic HPLC pump, a G1603A Agilent CE-MS adapter kit and a G1607A Agilent CE-ESI-MS sprayer kit (all Agilent Technologies, Santa Clara, CA) The CE-MS adapter kit included a capillary cassette that facilitates thermostating of the capillary, and the CE-ESI-MS sprayer kit, which simplifies coupling the CE sys-tem with MS systems, was equipped with an electrospray source LC-MS/MS was performed using an Agilent 1290 Infinity LC sys-tem comprised a HiP autosampler, quaternary pump and column compartment, and an Agilent 6460 triple-quadrupole MS/MS sys-tem Electrical conductivities were measured by a Yokogawa 73,301 Digital Multi Meter.
2.6 MSI-CE-MS/MS conditions for polyamine analysis
Fused-silica capillaries with 50 μm i.d x 110 cm total length were used as the separation capillary The background electrolyte (BGE) for MSI-CE separation was a 1 M formic acid solution Prior
to first use, a new capillary was pretreated with BGE for 20 min Before MSI injection, the capillary was preconditioned for 4 min
by flushing with BGE Samples were injected with a pressure injec-tion of 50 mbar, alternating between 1 (~1 nL) for each sample plug and 20 (~20 nL) for the BGE spacer plug for a total of 40 discrete samples analyzed within a single run The applied voltage was set at 30 kV, the capillary temperature was thermostated to
20 °C, and the sample tray was cooled below 5 °C An Agilent 1100 series pump equipped with a 1:100 splitter was used to deliver
10 μL/min of 5 mM ammonium acetate in 50% (v/v) methanol-water to the CE interface, where it was used as a sheath liquid around the outside of the CE capillary to provide a stable electri-cal connection between the tip of the capillary and the grounded electrospray needle The mass spectrometer was operated in mul-tireaction monitoring (MRM) mode using positive ionization, and a
40 0 0 V ion spray voltage was applied The flow rate of nebulizer nitrogen gas and drying nitrogen gas (heater temperature 300 °C) was maintained at 10 psig and 10 L/min, respectively The Q1 (pro-tonated precursor ion), Q3 (production), fragmentor and collision energy for each polyamine and creatinine are listed in Table 1
2.7 CE-TOFMS and CE-MS/MS conditions for single-sample analysis
Fused-silica capillaries with 50 μm i.d x 110 cm total length were used as the separation capillary Samples were injected at
50 mbar for 5 (~5 nL) For CE-TOFMS, the fragmentor, skimmer, and Oct RFV voltages were set at 75 V, 50 V, and 125 V, respec-tively A flow rate of drying nitrogen gas was maintained at 7 L/min Methanol-water (50% v/v) containing 0.1 μM hexakis(2,2-difluoroethoxy)pho-sphazene was delivered as the sheath liquid
at 10 μL/min Automatic recalibration of each acquired spectrum was performed using reference masses of reference standards ([13C isotopic ion of protonated methanol dimer (2CH3OH + H)] + , m/z
6 6.0 630 6) and protonated hexakis + , m/z 622.02896) Exact mass data were acquired at a rate of 1.5 spectra/s over a 50–10 0 0 m/z range For CE-MS/MS, methanol-water (50% v/v) was delivered as the sheath liquid at 10 μL/min Others were identical to those used
in MSI-CE-MS/MS conditions.
2.8 LC-MS/MS conditions for single-sample analysis
The analytical conditions were identical to those of LC-triple quadrupole MS described by Tomita et al [22]
Trang 3Table 1
Optimized MRM parameters for each polyamine and its isotopomer
Compound Q1( m/z ) Q3( m/z ) Fragmentor (V) Collision Energy (V)
2.9 Data analysis
We developed a data analysis tool called MSI-MasterHands that
can enable peak detection and integration of MSI-CE-MS/MS data.
The tool detected the peak top of each peak through the use of
Python’s PeakUtils library, and determined the peak boundary by
the minimum intensity between each peak top Then the peak area
of every peak was calculated by integrating the intensity between
the two boundaries Subsequently, the concentration in each
sam-ple was calculated by comparison of peak area of analyte with that
of its corresponding isotope-labeled polyamine standard To
evalu-ate the difference of the metabolite concentrations among three
gropus, Kruskal-Wallis and Dunn’s test was used To evaluate the
consistency among the three analytical methods, simple linear
re-gressions were conducted These analyses were conducted using
GraphPad (v8.4.3, GraphPad Software, San Diego, CA, USA) The
re-ceiver operating characteristic (ROC) curve analysis was conducted
using MetaboAnalyst (ver 5.0, https://www.metaboanalyst.ca/ ).
3 Results and discussion
3.1 Development of a high-throughput MSI-CE-MS/MS method for
polyamine analysis
Biological samples contain many isomers that are detected at
the same m/z value; thus, in the case of the MSI-CE-MS
sys-tem, these isomers are expected to overlap with others from
dif-ferent samples To confirm the presence of isomers, we analyzed
polyamines in a saliva sample with normal single injection
CE-TOFMS and CE-MS/MS (Fig 1S) As expected, several isomers,
in-cluding N1- and N8-acetylspermidine, were detected at the same
m/z value by CE-TOFMS However, CE-MS/MS with multireaction
monitoring (MRM) provided sufficient selectivity, resulting in the
detection of every polyamine at different m/z values Therefore, we
selected CE-MS/MS for further experiments.
Then, we developed an MSI-CE-MS/MS method to achieve
high-throughput screening of polyamine biomarkers One of the keys to
success in expanding MSI to large-scale consecutive sample
analy-sis was to amplify the sample stacking effect Theoretically, sample
stacking effectively occurs when the sample plug exhibits lower
electrical conductivity than the BGE spacer because this
condi-tion exhibits greater voltage in the sample zone ( Fig 1 a,b) Thus,
polyamines migrate fast in the sample zone but slowly in the BGE
spacer, which results in the concentration of polyamines at the
sample and BGE boundary ( Fig 1 b) Afterwards, when the
sam-ple zone and the BGE are mixed, the voltage becomes constant,
and electrophoresis occurs ( Fig 1 c) This phenomenon allows se-quential multiple sample injection in series within a capillary, fol-lowed by electrophoretic separation and mass detection ( Fig 1 d).
As we reported previously, formic acid showed excellent resolution capability for various cationic species [ 6 , 7 , 23 ] and was selected as the BGE The effect of its concentration on spermidine resolution among samples was examined, and the highest formic acid con-centration (1 M) provided better resolution in 40 consecutive sper-midine standard analyses (Fig 2S).
Another key was the BGE spacer volume, which significantly af-fected spermidine resolution among the samples When the BGE spacer volume was less than 10 nL, poor resolution occurred ( Fig 2 ) On the other hand, although better resolution was ob-tained, spermidine peaks from the 1st to 8th samples in the electrophero-gram were eluted from the separation capillary at
40 nL The use of 20 nL provided good resolution for spermidine peaks in all 40 samples ( Fig 2 ); thus, we selected 20 nL as the BGE spacer volume.
Using the MSI-CE-MS/MS method, we demonstrated 40 consec-utive analyses of a polyamine standard mixture containing sper-middine, N1-Acetylspermidine (N1-Ac-Spd), N8-Acetylspermidine (N8-Ac-Spd), spermine, N1,N8-diacetylspermidine (N1,N8-DiAc-Spd), N1-acetylspermine (N1-Ac-Spm) and N1,N12-diacetylspermine (N1,N12-DiAc-Spm) Unexpectedly, although their concentrations were identical, the peak area and height of each polyamine fluctuated ( Fig 3 in purple) We presumed that this phenomenon might be due to matrix effects, which are caused
by the alteration of ionization efficiency of target analytes in the presence of coeluting compounds in MS.
To confirm this, we analyzed the standard mixture spiked with their isotope-labeled standards and found that the fluctuation pat-terns between each polyamine and its isotope-labeled standard were closely matched ( Fig 3 in grey), which suggested that matrix effects could be normalized by isotope-labeled standards Further-more, the use of polyamine isotopomers as the internal standard solved another important peak identification problem in CE-MS data where migration time variations between samples are signif-icant [7] In the MSI-CE-MS/MS method, polyamine identification was performed based on its accurate mass (m/z) and comigration with a matching isotope-labeled standard as reported [16] Validation of this MSI-CE-MS/MS method was performed, and the results are listed in Table 2 Reproducibility corrected by isotope-labeled internal standards was practical; the relative peak areas for all polyamines with relative standard deviation (RSD) values ( n = 40) were between 3.1 and 9.1% The calibration curves for all polyamines were linear in the range between 0.1
Trang 4Fig 1 Explanatory diagram of the MSI-CE-MS/MS method for polyamine analysis (a) The samples and BGE are alternately injected into a single capillary (b) Sample stacking
of polyamines occurs when the sample plug exhibits lower electrical conductivity than the BGE (c) Electrophoresis starts when the voltage becomes constant by mixing the sample and BGE solutions (d) Polyamines in each sample are consecutively detected by tandem mass spectrometry at their specific m/z values E and the red line indicate the electromotive force and the voltage applied, respectively (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig 2 Effect of BGE spacer volume on spermidine resolution among samples The spermidine standard (20 μM each) was injected 40 times consecutively and simultaneously
analyzed by MSI-CE-MS/MS
and 10 0 0 μmol/L with correlation coefficients above 0.9898 This
method was considerably sensitive, and the concentration
detec-tion limits for the polyamines were between 2.9 and 21 nmol/L
with a pressure injection of 50 mbar for 1 (1 nL); i.e., mass
de-tection limits ranged from 2.9 to 21 amol at a signal-to-noise ratio
of 3.
3.2 High-throughput analysis of salivary polyamines from colorectal cancer patients
Recently, salivary and urinary polyamines have been reported as promising biomarkers in various cancers, such as oral, breast, col-orectal and pancreatic cancers [ 19 , 24–26 ] In many types of
Trang 5can-Fig 3 Fluctuation of peak heights and areas in 40 consecutive analyses of polyamine standards (1 μM each, purple) and their deuterium-labeled isotopomers (1 μM each,
grey) with MSI-CE-MS/MS Abbreviations: DiAc, diacetyl; Spm, spermine; Ac, acetyl; and Spd, spermidine (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 2
Reproducibility, linearity, and sensitivity for polyamine standard analysis
Compound RSD a( n = 40) (%) Linearity bCorrelation Detection Limit (nmol/L)
a RSD was calculated by the relative peak area, in which the peak area was divided by the peak area of its isotopomer
b Calibration curves for all compounds were plotted at 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200,
500 and 1000 μmol/L
cer, the MYC oncogene is amplified or overexpressed due to
sev-eral factors [ 9 , 27 ] MYC drives the transcription of the gene
en-coding ornithine decarboxylase (ODC), the rate-limiting enzyme in
poly-amine biosynthesis, resulting in elevation of polyamine levels
[ 20 , 24 ].
To explore the possibilities of MSI-CE-MS/MS for
high-throughput salivary polyamine analysis, we measured the
electri-cal conductivities for the salivary samples and the BGE The
con-ductivities of the saliva filtrates and the BGE (1 M formic acid)
were 2.3 and 20.8 × 10−4/ Ω m, respectively, which was expected
to allow effective sample stacking Then, we applied our
MSI-CE-MS/MS method for 40 consecutive analyses of a saliva sample
ob-tained from patients with CRC Forty well-defined peaks of each
polyamine were observed (Fig S3), and electric current was stable
and constant during all the run (Fig S4), the amount of polyamines
quantified using their isotopomers as internal standards and their
%RSD values are listed in Table 3 Overall, acceptable reproducibil-ities (4.4% 14%, n = 40) were obtained except for N8-Ac-Spd, the less abundant polyamine (Fig S3).
To investigate the quantification accuracy of this system, we analyzed saliva samples spiked with 10 nmol of each polyamine standard and calculated the recovery ( Table 3 ) The recovery rates for spermine (61%) and spemidine (69%) were lower than others, which may be caused by partial peak overlap in the fast migrating peaks ( Fig 3 ) Those for other polyamines were ranged from 73
to 89% These results indicate that most of the polyamines can be approximately precisely quantified by the proposed MSI-CE-MS/MS method.
Using the MSI-CE-MS/MS system, we performed polyamine analysis of a total of 359 saliva samples from 276 patients with colorectal cancer (CRC), 26 patients with benign colorectal tumors (BCTs) and 57 healthy controls (HCs) (Table S1) The 359 saliva
Trang 6Table 3
Polyamine amount, reproducibility, and recovery in CRC patient saliva analysis
Compound Amount a(nmol/L) RSD ( n = 40) (%) Recovery Rate (%)
a The amount of polyamines was quantified using their isotopomers as internal stan- dards
Fig. 4 MSI-CE-MS/MS analysis of polyamines in salivary samples obtained from HCs ( n = 20, blue) and CRC patients ( n = 20, magenta) For interpretation of the references
to color in this figure legend, the reader is referred to the web version of this article
samples were divided into nine batches, and each batch containing
39 or 40 samples was successively determined within 360 min.
For saliva analysis, normalization is an important issue
be-cause differences in salivary rate/hydration status contributes to
greater biological variability To overcome this defect, we
per-formed metabolome analysis of the 359 saliva samples with a
single CE-TOFMS [7] and confirmed the positive correlation
be-tween overall metabolite concentrations and creatinine
concentra-tions (Fig S5) Consequently, we used creatinine to correct for
dif-ferences in salivary rate/hydration status in this study.
Fig 4 shows representative MSI-CE-MS/MS electropherograms
of salivary polyamines These seven compounds in 20 HCs were
ei-ther slightly detected or not detected, whereas they were markedly
increased in most CRC samples Overall, the polyamine levels in
most of the HCs were low, whereas those in the patients with CRC
or BCTs were significantly higher Among polyamines, N1-Ac-Spd,
N1-Ac-Spm and N1,N12-DiAc-Spm showed a high ability to
dis-Table 4
Median concentration (nmol/L) of polyamines
in salivary samples from HCs ( n = 57), BCTs ( n = 26) and CRCs ( n = 276)
N1,N12-DiAc-Spm 86.8 163 315
criminate CRC patients from non-CRC controls ( Fig 5 and Table 4 ) and the lower limits of quantification (at a signal-to-noise ratio of 10) for N1-Ac-Spd, N1-Ac-Spm and N1,N12-DiAc-Spm were 330, 12 and 79 nmol/L in saliva, respectively After normalization of sali-vary rate/hydration status by creatinine level, these results were compared with those obtained by a single-sample analysis method with CE-TOFMS and LC-MS/MS The Pearson correlation coefficients
Trang 7Fig. 5 MSI-CE-MS/MS analysis of N1-acetylspermidine, N1-acetyl-spermine and N1, N12-diacetylspermine in salivary samples from HCs ( n = 57), BCTs ( n = 26) and CRC
( n = 276) To visualize individual polyamine levels, box and whiskers plots were used The horizontal bars represent the medians, quartiles, and 10% of both ends Outside data are depicted in plots The Kruskal-Wallis test and Dunn’s test as post-test were used to determine statistical significance ∗∗∗p < 0.001, ∗∗p < 0.01 and ∗p < 0.05
Fig 6 Linear regression of of polyamine levels in 359 salivary samples (A) between MSI-CE-MS/MS and normal single CE-TOFMS method, and (B) between MSI-CE-MS/MS
and normal single LC-MS/MS method Both x and y-axis indicate log 10 of the polyamine concentration/creatinine concentration (no limit) The regressed lines with their coefficients and intercepts are shown Correlation coefficients ( r ) and p values are calculated by Pearson correlation Not detected peaks were excluded from this analysis
demonstrated statistically significant relationships among the three
methods ( Fig 6 ), which indicates that the proposed MSI-CE-MS/MS
method provides almost the same quantification accuracy as
CE-TOFMS and LC-MS/MS.
The area under the receiver operating characteristic (ROC) curve
(AUC) is used to assess the discrimination ability of biomarkers.
Commonly used serum markers for the diagnosis of CRC are neu-ron –specific enolase (NSE), carcinoembryonic antigen (CEA), can-cer antigen (CA)19-9, CA125 and CA242 The AUC of NSE, CEA, CA19-9, CA 125 and CA242 are 0.766, 0.682, 0.560, 0.590 and 0.651, respectively [28] Among the salivary polyamines, N1-Ac-Spm showed the highest AUC of 0.834 ( Fig 7 ), allowing for
Trang 8dis-Fig 7 ROC curve analysis of the ability of N1-acetylspermidine, N1-acetylspermine, N1,N12-diacetylspermine and combined three polyamines to discriminate patients with
CRC from noncancer controls ROC curves are black curves and 95% confidential intervals are shown in light purple (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
crimination of CRC from noncancer samples, i.e., samples from the
patients with BCTs and the HCs, and the optimal cut off value
in salivary samples determined by ROC analysis was 39.5 nmol/L.
Although our previous study reported that urinary
N1,N12-DiAc-Spm was the most useful biomarker for the discrimination of
CRC [22] , regarding salivary samples, N1-Ac-Spm showed the
best discrimination ability between CRC patients and non-CRC
controls.
Previous papers reported that MSI-CE-MS has been
exten-sively validated to provide precise and accuracy metabolite
mea-surements as compared to other analytical platforms,
includ-ing colorimetric assays [29] , GC-MS [13] and LC-UV [30] Our
salivary polyamine analysis demonstrated that MSI-CE-MS/MS
achieved the same quantification results with conventional
CE-TOFMS and HPLC-MS/MS methods Taken together, this approach
can be promising tools for the high-throughput screening of low
molecular-weight species.
On the other hand, there is a limitation in this study As shown
in Table S1, the number of healthy controls and patient groups
in-cluding age, sex, was significantly different Moreover, salivary
N1-Ac-Spm might be elevated in other types of cancer Therefore,
fur-ther studies should be necessary to transfer this approach to
clini-cal applications.
4 Conclusions
We present a high-throughput, selective and sensitive MSI-CE-MS/MS method for screening salivary polyamines Compared with other techniques, this method has several advantages: (1) the method is rapid; 40 salivary samples can be analyzed within
40 min; (2) polyamines are selectively determined without other matrix interference; (3) sensitivity is considerably high; and (4) sample preparation is minimal The methodology provides practi-cal reproducibility, excellent linearity and quantification accuracy Its utility was demonstrated by analyzing polyamines in 359 sali-vary samples obtained from patients with CRC or benign polyps and HCs, and a couple polyamines (e.g., N1-acetylspermine) can potentially discriminate cancer patients from noncancer controls Therefore, it is expected that the proposed method can potentially
be applied to screening CRC in laboratory tests, and this approach could lead to the practical use of many types of low-molecular-weight biomarkers in a wide range of applications.
CRedit authorship contibution statement
T.S led the entire project and wrote the manuscript K.I and A.H performed the MSI-CE-MS/MS and all CE-MS experiments S.O.
Trang 9and M.K performed the LC-MS/MS experiments M.E., K.K and M.S.
collected and provided the human specimens T.S., K.I and M.S.
generated the figures.
Declaration of Competing Interest
The authors declare that they have no known competing
finan-cial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgements
We thank Toru Akiba, Ayame Enomoto and Kumi Suzuki
(Insti-tute for Advanced Biosciences, Keio University) for technical
assis-tance This work was supported by research funds from the
Yama-gata prefectural government and the city of Tsuruoka.
Supplementary materials
Supplementary material associated with this article can be
found, in the online version, at doi: 10.1016/j.chroma.2021.462355
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