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OPEN ACCESS DOCUMENT
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Information of the Journal in which the present paper is
published:
 Elsevier, Microchemical Journal, 2014 117 255–261
 DOI http://dx.doi.org/10.1016/j.microc.2014.07.
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1
A non-target chemometric strategy applied to UPLC-MS
sphingolipid analysis of a cell line exposed to chlorpyrifos
pesticide: a feasibility study
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Kássio M. G. Limaa,b*, Carmen Bediab, Romá Taulerb
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a
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b
UFRN-IQ, Biological Chemistry and Chemometrics, 59072-970, Natal, Brazil
IDAEA-CSIC, Jordi Girona 18, 08028 Barcelona, Spain
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* Corresponding author: Kássio M. G. Lima, UFRN-IQ, Biological Chemistry
and Chemometrics, 59072-970 Natal, Brazil. Tel.:+55 84 3342 2323; fax: +55
83 3211 9224.
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A non-target chemometric strategy applied to UPLC-MS
sphingolipid analysis of a cell line exposed to chlorpyrifos
pesticide: a feasibility study
40
41
42
Kássio M. G. Limaa,b*, Carmen Bediab, Romá Taulerb
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a
46
b
UFRN-IQ, Biological Chemistry and Chemometrics, 59072-970, Natal, Brazil
IDAEA-CSIC, Jordi Girona 18, 08028 Barcelona, Spain
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Abstract: A non-target chemometrics study based on the application of Multivariate
49
Curve Resolution Alternating Least Squares (MCR-ALS) method to a data set obtained
50
by ultra-performance liquid chromatographic coupled to mass spectrometry (UPLC-
51
MS) has been applied to the study of human prostate cancer (DU145) cell line samples
52
treated with the organophosphate pesticide chlorpyrifos (CPF). Full scan UPLC-MS
53
data sets were segmented in 17 different chromatographic windows and submitted to a
54
non-target detailed study. Every one of these chromatographic windows of the different
55
analyzed samples (treated and non-treated with CPF) was column-wise augmented in a
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new data matrix with their m/z values in the common column mode to preserve the
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fulfillment of the assumed spectral bilinear model. MCR-ALS was used to recover the
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elution and mass spectral profiles of the pure components present in each of the
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analyzed chromatographic windows. ANOVA (p  0.05) was then applied to compare
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the areas under the concentration profiles of the MCR-ALS resolved components in the
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CPS treated and control samples. This analysis allowed the detection of those
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sphingolipids having their concentration in cells modified by the presence of CPS
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compared to control samples where this contaminant was absent. Positively identified
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sphingolipids included sphingomyelins, dihydrosphingomyelin and C16 ceramide. The
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strategy described in this work is proposed for a general non-target UPLC-MS MCR-
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ALS analysis of the effect of environmental contaminants in cells in lipidomic and
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metabonomic studies.
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Keywords: lipidomics; Chlorpyriphos; sphingolipids; cancer cells; MCR-ALS; UPLCMS
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* Corresponding author. Tel.: +55 84 3342 2323; fax: +55 83 3211 9224.
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1. Introduction
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Sphingolipids are a highly diverse family of lipids that serve not only as critical
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components of biological membranes but also as regulators of a vast number of cellular
75
processes such as regulation of cell cycle, apoptosis, migration, inflammation,
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proliferation and recognition among others[1]. Two of the most studied sphingolipids,
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ceramide and sphingosine-1-phosphate (S1P), which are metabolically interconnected
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by two enzymatic steps, have opposite functions in cell signaling. Whereas ceramide
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mediates many cell-stress responses, like apoptosis and cell senescence, S1P has crucial
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roles in cell survival, migration and inflammation[2]. Some other bioactive
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sphingolipids
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glucosylceramide or dihydroceramide. Many of the bioactive sphingolipids in biological
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systems are often closely related structurally and metabolically forming an
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interconnected network of bioactive mediators whose relevance in homeostasis and
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disease is gaining scientific appreciation.
include the sphingoid
base
sphingosine, ceramide-1-phosphate,
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Different analytical strategies for sample preparation, ionization modes and
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instrumental designs have been proposed for the analysis of sphingolipids by mass
88
spectrometry technology[3]. Design for this methodology has been provided structure
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specific, quantitative analysis of the “signaling” backbone species, Cer and Cer 1-
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phosphates, sphingoid base, sphingoid base 1-phosphates, N-acyl chaims, polar
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headgroups and others[4–8]. In these approaches for sphingolipids, some of the
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advantages that mass spectrometry provide are: (a) an in-depth profile of small samples
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(e.g., 106 cells or even fewer); (b) a signal response which can be correlated to analyte
94
concentration provided there are suitably matched internal standards to normalize for
95
differences in ionization and fragmentation of individual molecular species; (c) a broad
4
96
dynamic range which enables analysis of most of the compounds presents in biological
97
samples.
98
Liquid chromatography – electrospray ionization – tandem mass spectroscopy
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(LC-ESI – MS/MS) is often employed for sphingolipid studies because it can lead to the
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development of fast and sensitive analytical protocols with high-throughput potential
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[9,10]. Nowadays, emerging development in analytical technologies such as fast high-
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resolution separation systems (e.g., ultraperformance liquid chromatography, UPLC)
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coupled with high-mass accuracy such as time-of-flight (TOF)[11], quadrupole-time-of-
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flight (Q-TOF)[12] also can provide more information from the sphingolipid
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experimental data generated.
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In addition to the development of analytical technologies for sphingolipids,
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another key contributing factor to the rise of this field are the advances in data
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processing and bioinformatics[13–15]. The analytical platform in lipidomic experiments
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generates large amounts of data from a single sample of two-dimensional nature
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(chromatogram/mass spectra). For example, a typical data set obtained from a
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quadrupole instrument, scanning in the mass range of 100–1000 m/z, with 0.5 amu
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resolution sampled at 2.5 Hz for 30 minutes results in approximately 8 million data
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values. Some shortcomings can be usually overcome by chemometrics approaches such
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as denoising[16], compression of the data matrices[17] and models using the second
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order advantage[18]. Multivariate Curve Resolution (MCR) methods can be applied to
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the complete resolution of elution/concentration and mass spectra profiles for the
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different components present in very complex samples, such as those coming from
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metabonomic and lipidomic studies, analyzed by chromatographic methods,. Among
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multivariate curve resolution methods, the Multivariate Curve Resolution-Alternating
5
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Least Squares (MCR-ALS) method has become a very popular chemometric tool which
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has been applied successfully to resolve multiple component responses from unknown
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unresolved mixtures[19–21].
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Chlorpyrifos (CPF) is an important organophosphate endocrine disruptor
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pesticide, which has raised considerable concern in recent decades because it damages
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epithelial cells and acts mainly against the central nervous system [22,23]. In this work,
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a chemometric strategy based on MCR-ALS is applied to UPLC-MS three-way data
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arrays to perform a sphingolipid study in prostate cancer cell line samples (DU145)
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following treatment with chlorpyrifos. Informative UPLC-MS fingerprint sphingolipids
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data sets were segmented in 17 chromatographic windows for their non-target study.
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MCR-ALS was then applied on the augmented data matrices obtained from treated and
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non-treated samples. To test for statistically significative differences on resolved
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component areas upon CPS treatment, ANOVA was applied at every chromatographic
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window of different cell samples (control and CPF treated).
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Results of the analysis of the sphingolipidome from cell extracts can contribute to
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a better understanding of the role of sphingolipids in the investigated context[24], in this
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case, in their involvement in the cytotoxicity of chlorpyrifos, on the prostate cancer cell
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line DU145. The goal of this study is therefore to increase the understanding of the
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biological toxic effects of CPF as endocrine disruptor pesticide on a prostate cancer cell
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line DU145. This study is based on a non-target chemometric analysis of the data sets
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obtained from UPLC-MS analysis of sphingolipid extracts of CPS treated and no-
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treated prostate cancer cell samples.
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2. Experimental
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2.1 Materials
6
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Chlorpyrifos, cell culture media and reagents were obtained from Sigma.
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Analytical grade methanol and chloroform were purchased from Merck and Carlo Erba
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respectively. HPLC Gradient Grade acetonitrile was from Fischer Chemicals.
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Sphingolipid standards were obtained from Avanti Polar Lipids.
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2.2 Cell Culture
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DU145 prostate cancer cells were obtained from the American Type Culture
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Collection. This cell line was cultured in RPMI 1640 medium supplemented with 10%
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heat inactivated fetal bovine serum, 100U/mL penicillin and 100 g mL-1 streptomycin,
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at 37ºC in a humidified atmosphere containing 5% of CO2. The experiments were
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carried out at low passage of cells.
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2.3 Treatment of cells
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Two million of DU145 cells were seeded in 10 cm diameter Petri dishes in 10 mL
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of RPMI media. After 24 hours, cells were treated with 25 mol L-1 of chlorpyrifos or
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vehicle (DMSO) in triplicate. The DMSO concentration was 0.008% (v/v) and was
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without effect on cell viability (data not shown). After 24 hours of treatment, cells were
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harvested using a rubber scrapper into 2 mL of ice-cold PBS and counted. Cells were
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centrifuged at 1300 rpm for 3 minutes at 4ºC and cell pellets were washed twice with
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cold PBS.
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2.4 Extraction procedure for sphingolipid analysis by UPLC-TOF
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Sphingolipid extracts were prepared as described[10]. Briefly, 100 L of
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deionized water were added to the cell pellets and the suspension was transferred to
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borosilicate glass test tubes with Teflon caps. Then, 500 L of methanol and 250 L
7
166
chloroform were subsequently added. This mixture was fortified with internal standards
167
of sphingolipids (N-dodecanoylsphingosine, N-dodecanoylglucosyl-sphingosine, D-
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erythro-dihydrosphingosine and N-dodecanoylsphingosylphosphorylcholine), 200 pmol
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each. Samples were sonicated until they appeared dispersed, then incubated overnight at
170
48ºC in a heating water bath. The tubes were then cooled and 75 L of 1 mol L-1 KOH
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in methanol were added. After 2h incubation at 37ºC, KOH was neutralized with 75 L
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of 1 mol L-1 acetic acid. The samples were then evaporated under N2 stream and
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transferred to 1.5 mL eppendorf tubes after addition of 500 L of methanol. Samples
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were evaporated again and resuspended in 150 L of methanol. The tubes were
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centrifugated at 10000 rpm for 3 minutes and 130 L of the supernatants were
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transferred to UPLC vials for injection.
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2.5 Liquid chromatography and mass spectrometry
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The LC/MS analysis consisted of a Waters Aquity UPLC system connected to a
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Waters LCT Premier orthogonal accelerated time of flight mass spectrometer (Waters),
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operated in positive electrospray ionization mode. Full scan spectra from 50 to 1500 Da
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were acquired, and individual spectra were summed to produce data points each of 0.2s.
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Mass accuracy and reproducibility were maintained by using an independent reference
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spray via the LockSpray interference. The analytical column was a 100 X 2.1-mm inner
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diameter, 1.7 mm C8 Acquity UPLC bridged ethylene hybrid (Waters). The two mobile
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phases were phase A: MeOH/H2O/HCOOH (74:25:1, v/v) and phase B: MeOH/
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HCOOH (99:1, v/v); both contained 5 mmol L-1 ammonium formate. The column was
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held at 30 °C.
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2.6 Peak assignment and identification of sphingolipids
8
189
Positive identification of compounds was based on the accurate mass
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measurement with an error of <5 ppm and its LC retention time, compared to the data of
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a previously elaborated homemade database of sphingolipid standards injected under the
192
same chromatographic and spectrometric conditions[25].
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2.7 Data analysis
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Figures 1 and 2 show a detailed scheme of the different steps involved in this
study.
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[Insert Figure 1 here]
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After the extraction procedure for sphingolipid analysis, each UPLC-LCMS
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chromatographic run (see Experimental section) recorded for every sample (three
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treated and three nontreated samples) give a data set which was stored in ASCII format
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by the Databridge function of MassLynxTM V 4.1 software. This data set obtained in the
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analysis of every sample was then imported in MATLAB 7.10.0 (R2010a)
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computational environment using an in-house program specially designed for this
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purpose. The size of the two-way data matrix generated by this program for every
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sample was 1275 x 9001 (retention time and m/z values, respectively) and 0.1
206
resolution. Data from every chromatogram were normalized by the added internal
207
sphingolipid standard area and by the number of cells present in each sample (see
208
Experimental section)
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Daubechies simpler wavelet[26], without losing relevant chemical information and
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filtering noise. Every reduced data matrix
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chromatographic time windows (as shown in Table 1 and Figure 1) to simplify the
212
overall complexity of the data sets.
m/z data values were binned to 4501 using
level one
was then subdivided in seventeen
9
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[Insert Table 1 here]
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As with any other instrumental signal, chromatograms contain three major
215
components: signal, noise and background baseline. Elimination of the chromatogram
216
baseline is a critical step for reducing the complexity of the measured chromatograms
217
and facilitates their analysis. With this goal in mind, the methodology of asymmetric
218
least-squares[27], was applied to the different chromatographic windows of every
219
analyzed sample.
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A short description of the Multivariate Curve Resolution – Alternating Least
221
Squares (MCR-ALS) method used in this work is given here. For a more detailed
222
description of the method, see refs [21,28]. This algorithm is based on a bilinear model
223
(Equation 1) that decomposes the data matrix D, containing the raw information about
224
all the components present in a data set, into the product of two matrices C and ST,
225
containing the pure response profiles associated with the variation of each contribution
226
in the row (matrix C) and the column directions (matrix ST).
227
D = CST + E
(1)
228
In the case of hyphenated chromatographic spectroscopic detection data, every
229
analyzed sample gives a data matrix D of dimensions (I,J) which contains the MS
230
spectra (rows) at all retention times (i=1,…I) in its rows, and the chromatograms at all
231
spectra m/z channels (j=1…J) in its columns. C matrix of dimensions (I,N) has the
232
elution or concentration profiles of each resolved component (n=1,N) and matrix ST of
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dimensions (N,J) has the pure spectra of these components, respectively. E is the error
234
matrix of dimensions (I,J), i.e., the residual variation of the data set that is not related to
235
any of the resolved components. Decomposition of data matrix D is achieved by an
10
236
alternating iterative least-squares minimization of E under suitable constraining
237
conditions such as nonnegativity in spectral and concentration profiles. The matrix C
238
contains the N elution profiles (column-wise) and the matrix ST contains the MS spectra
239
(row-wise) of the N resolved components.
240
In this work, Multivariate Curve Resolution Alternating Least Squares (MCR-
241
ALS) was applied to the analysis of the whole set of MS chromatographic runs obtained
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in the analysis of the investigated prostate cancer cell line (CPS treated and control)
243
samples. Although MCR-ALS could have been applied individually to every
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chromatographic window of the data matrix obtained in the analysis of every cell
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sample, simultaneous analysis of data matrices obtained in the analysis of a particular
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chromatographic window for the six different analyzed samples, three control ones and
247
three CPS treated ones, was preferred, both to improve the resolution of the different
248
coeluted components and to allow their relative quantitative estimation and comparison.
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[Insert Figure 2 here]
250
In Equation 2 and Figure 2, MCR-ALS bilinear data decomposition analysis is
251
shown for the simultaneous analysis of the data matrices from the same
252
chromatographic window for both treated and control samples, setting one on top of the
253
other and keeping their column vector space in common (augmented data matrices) as it
254
is shown in next equation:
255
D k ,aug
D k ,c1   C k ,c1 
E k ,c1 

 



D k ,c 2  C k ,c 2 
E k ,c 2 
D  C 
E 
k ,c 3
k ,c 3
k ,c 3
T




  Ck , augSTk  Eaug


Sk  
 D k ,t1   C k ,t1 
 E k ,t1 

 



D k ,t 2   C k ,t 2 
E k ,t 2 
D   C 
E 
 k ,t 3   k ,t 3 
 k ,t 3 
 
(2)
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256
for k = 1,…17 windows
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This new column-wise augmented data matrix D k , aug (for chromatographic
258
window k) contains now six individual data matrices corresponding to the same window
259
for the three control and the three CPS treated individual data matrices. The number of
260
rows of these column-wise augmented data matrices is equal to the total number of
261
recorded elution times in the six different chromatographic runs, and the number of
262
columns is the same number of columns present in any of them, since they have exactly
263
the same number of m/z values after data binning (4501). Figure 2 illustrates how this
264
augmented data matrix Dk,aug corresponding to chromatographic window k (taken as
265
example) is decomposed by MCR-ALS. Once C k , aug has been finally estimated by the
266
alternating least squares (ALS) optimization algorithm, the recovery of the
267
sphingolipids full non-binned MS spectra can be achieved by a post-processing
268
application of a non-negative least squares step to the original non-compressed data
269
matrix which facilitated their identification in appropriate data bases (see below).
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The quality of MCR-ALS models was measured using the lack of fit (lof,
271
Equation 3), and the explained variance (R2, Equation 4), for the particular bilinear
272
model used in this analysis. These two figures of merit are calculated as:
273
lack of fit (%) =
2
d
(%)  
d
*2
ij
2
ij
 (d  d ) x100
d
*
ij
ij
2
ij
 100
(3)
274
R
275
where i= 1,…,I and j = 1,…, J, d ij is an element of the experimental matrix D¸ d *ij is the
276
element of the MCR-ALS reproduced matrix D* and IxJ is the total number of elements
277
in the data set.
(4)
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278
Effects produced by CPS treatment on sphingolipids concentrations were
279
statistically assessed by one-way ANOVA[29] on the calculated areas of the resolved
280
elution/concentration profiles (Caug, k) for the different resolved components (n=1,…N)
281
in every chromatographic window for the three samples submitted to CPS treatment
282
compared to the three control samples (non-treated samples). Variations in lipid
283
concentrations (measured by the areas of their resolved elution profiles) under CPS
284
treatment compared to the same lipid concentration responses in control samples were
285
statistically evaluated. A p-level of 0.05 was used as an “acceptable limit” level for
286
these differences to conclude about their significance. Once these effects were assessed,
287
MS spectra of the corresponding lipids showing statistically significative changes in
288
their concentration areas were analyzed in detail and searched in data bases for their
289
identification (see below in the results section). Possible candidates were further
290
confirmed from raw data analysis at full resolution power of the MS-TOF Waters
291
equipment with Masslynx software.
292
3. Results and discussion
293
In any “omics” methodology (such as metabolomics or lipidomics), the
294
hypothesis is that the response pattern of numerous analytes, both known and unknown,
295
is reflective of a situation and that the comprehensive nature of the data set enables
296
evaluation of systemic responses. In this way, the concentration of chlorpyrifos (CPF)
297
chosen in the present study (25 mol L-1) reduced cell viability to 75%, according to
298
preliminary
299
concentrations of chlorpyrifos from 0.8  mol L-1 to 100 mol L-1 for 24 hours. The
300
introduction of a sphingolipidomics strategy may broaden our understanding of the
results of repeated MTT viability assays performed at gradient
13
301
biological toxic effects of CPF as an endocrine disruptor pesticide on the prostate cancer
302
cell line DU145.
303
The complete resolution of a particular lipidomic sample by the combined use of
304
LC-MS and MCR-ALS and using the data matrix augmentation strategy explained in
305
the method section depended on some factors such as spectral selectivity, peak overlap
306
and signal/noise ratio. As it can be seen in Table 1, some chromatographic windows
307
(specially, 1, 2 and 17) presented a larger lack of fit than others and this result was
308
related to their lower signal/noise ratio. In this way, the number of components to be
309
resolved for every data matrix Dk,aug k=1,…,17, corresponding to each of the 17
310
chromatographic windows was initially estimated by singular value decomposition
311
(SVD) and tested and updated according to the data fit and to morphological acceptance
312
of the shapes of the recovered chromatographic and spectra profiles for the proposed
313
number of components. Initial estimates for mass spectra profiles were obtained from
314
the purest elution time variables[30]. MCR-ALS was then applied to the column-wise
315
augmented matrices corresponding to each chromatographic window and the elution
316
and spectral profiles of the components were resolved using non-negativity (elution and
317
spectra profiles) and normalization (spectra profiles) constraints. Once ALS
318
optimization reached its optimum, the resulting elution (concentration) profiles were
319
integrated and thus the areas for the resolved peaks were obtained. In this way, the area
320
of all resolved components present in both type of samples (control and treated) were
321
estimated for each chromatographic window. The whole set of calculated areas for
322
control and treated samples were submitted to ANOVA statistical analysis
323
This approach was applied to all investigated chromatographic windows and it
324
was finally possible to detect 12 peaks with their areas showing significant differences
325
(p0.05) between CPS treated and control samples, as shown in Table 2. For each of
14
326
these peaks, mass and retention time values were used to investigate potential chemical
327
compounds. From pure spectra at 0.1 mass resolution, a preliminary identification of the
328
possible lipids was performed using some databases, such as Lipid Maps[31,32], Lipid
329
Bank[33], CyberLipids[34], and LIPIDAT[35]. Additionally, this identification was
330
then further confirmed using MassLynx software with original raw LC-MS data (with
331
0.0001 mass resolution) by searching retention times of the resolved lipids and looking
332
for their mass spectra in detail. Sphingolipids finally identified in this study are given in
333
Table 2. As example of this approach, Figures 3a and 3b show the comparison between
334
the elution profiles retrieved by MCR-ALS analysis and ANOVA results obtained for
335
window 7. As can be seen in Figure 3a, the profile 1 shows an increase of the area under
336
CPS treatment in agreement to ANOVA results (Figure 3b, p = 0.0116), and the MS
337
spectrum corresponding to this compound is identified and confirmed as C16 ceramide
338
(Table 2, [MH]+ = 538.5232 m/z). Finally, as also shown in Table 2, a general decrease
339
of the areas under CPS treatment of the most important sphingomyelin species
340
(including the saturated form dihydrosphingomyelin) was observed, in contrast to a
341
significative increase of the C16 ceramide compound.
342
[Insert Figure 3a and b here]
343
All these findings suggest an activation of sphingomyelinases (SMases), the
344
catabolic enzymes that convert sphingomyelin to ceramide, which is a key bioactive
345
sphingolipid that mediates programmed cell death signaling. This SMase activation is a
346
well-characterized feature observed for some other stimuli such us chemotherapeutic
347
drugs, ionizing radiation, UVA light, heat, CD95 as well as infection with some
348
pathogenic bacteria and viruses, revealing SMases as biochemical mediators in cell
349
response to stress factors. The activation of acid SMase and neutral SMase, the two
15
350
types of enzyme that contribute to ceramide-triggered apoptosis, occurs early after
351
direct stimulation of death receptors located in plasma membrane[36–41], as shown in
352
Figure 4. Therefore our findings suggest that chlorpyrifos exerted an activation of
353
SMases leading to an increase of ceramide, which in turn could be responsible for the
354
cell death observed in our experimental conditions.
355
356
[Insert Figure 4 here]
4. Conclusion
357
The application of MCR-ALS coupled to non-target UPLC-MS analysis of
358
sphingolipids in cells after CPF exposure has been shown to be a powerful strategy for
359
lipidomic studies. Following this strategy, the area of the MCR-ALS resolved
360
chromatographic elution profiles of sphingolipids extracted from CPF-treated cells can
361
be statistically compared to those extracted from control cells, and those showing a
362
significant change of concentration identified from their MS spectra. From these results
363
the study of the effects of this contaminant in cell and the understanding of
364
sphingolipids role under CPF exposure can be investigated. The chemometrics approach
365
presented in this work extends the potential of traditional target analytical approaches in
366
lipidomic studies and it is proposed for the general analysis of the effects of
367
contaminants in lipidomic and metabonomic studies in different contexts.
368
Acknowledgments
369
The research leading to these results has received funding from the European
370
Research Council under the European Union's Seventh Framework Programme
371
(FP/2007-2013) / ERC Grant Agreement n. 32073. K.M.G. Lima thanks the CNPq for
372
his Post-Doctoral Fellowship Ref. 246742/2012-7.
16
373
References
374
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488
20
489
490
Figure 1: Flowchart of the strategy used in this work for the analysis of the effects of
chlorpyrifos (CPS) on the sphingolipids extracted from cell samples
Cell Sample
with/without
CPS Treatment
4501 (m/z)
Window 1
62
53
40
Window 3
21
32
95
105
35
54
103
93
33
27
53
Window 6
311
491
Window 2
Window 4
Window 5
Window 7
Window 8
Window 9
Window 10
Window 11
Window 12
Window 13
Window 14
Window 15
Window 16
Window 17
87
Data
62
53
pretreatment
40Normalization
21 Binning
32 Windowing
95
AsLS
105
35
54
103
93
33
27
53
311
9001 (m/z)
One sample
Raw Data matrix
Sphingolipids
Extracion
MATLAB
Data
Transfer
D
1275 (t)
71
87
LC-MS sample
analysis
RC-UPLC-ESI-MS
system
Raw matrix
492
493
494
495
496
497
498
499
500
21
501
Figure 2. Example of column-wise augmented data matrix for chromatographic
502
window k (Dk,aug) built from individual data matrices obtained in the LC-MS analysis of
503
the three replicate control cell samples and three replicate CPS treated cell samples
504
respectively. Bilinear decomposition by MCR-ALS of this augmented data matrix
505
using Equation 2. Example of results of this analysis are given for one component of
506
chromatographic window 17.
Window 17 (t)
Control1
Control2
Control3
Treated1
Treated
Teated3
4501(m/z)
N components
...
MCR-ALS
N components
4501(m/z)
....................
...
S Tk
Window 17
...
C k , aug
D k , aug
6000
X 105
ONE-WAY ANOVA (p < 0.05)
4000
3000
2000
1.5
0
15 8
x 10
0.5
8
0
1
17
Time (min)
18
19
2
sphingolipid
MS spectrum
for comp. n
6
Data base
sphingolipid
identification
4
2
0
0
507
Elution profile
For comp. n
848.7761
10
CONTROL
16
12
p = 0.1336
1
-0.5
C1
C2
C3
S1
S2
S3
1000
2
MS Intensity
AREA MCR-ALS
2.5
MS Intensity
5000
200
400
600
800
1000
m/z
508
509
510
511
512
513
22
514
Figure 3 (a) MCR-ALS resolved time elution profiles corresponding to the analysis of
515
cells for window number 7. Control peaks (black solid lines) and CPS treated peaks (red
516
solid lines). (b) Results of one way statistical (ANOVA) comparison between CPS
517
treated sphingolipid profile mean peak areas and control sphingolipid profile mean peak
518
areas obtained for window 7, p is the significance level associated to the obtained mean
519
differences using 3 replicates.
12000
(a)
Control
Treated
10000
Intensity
8000
Comp3
6000
Comp1
4000
Comp2
Comp4
2000
0
7.6
7.7
7.8
7.9
8
Retention Time (min)
520
2000
8000
7000
Area opt
Area opt
8.2
9000
(b)
1800
1600
8.1
p =0.0116
1400
6000
p =0.0477
5000
4000
1200
3000
1000
2000
1000
800
1
Control
0
2
Comp
1
1
Control
2
Comp
2
9000
3000
Area opt
Area opt
8000
2500
p =0.0035
2000
p =0.1357
7000
6000
5000
4000
1500
3000
2000
1000
1
Control
2
Comp
3
1
Control
2
Comp 4
521
522
23
523
524
525
526
527
528
529
530
Figure 4. Schema of the hypothetic interaction of chlorpyrifos with sphingomyelin
metabolism and cell death signaling. SM, sphingomyelin; Cer, ceramide; FAN, factor
associated with neutral sphingomyelinase activation. The activation of
sphingomyelinase coupled receptors (SMCRs) in the plasma membrane by cytotoxic
stimuli leads to the recruitment of proteins that activate acid and neutral SMases. Acid
SMase is translocated to the outer leaflet of plasma membrane from the lysosome upon
activation. Neutral SMase is activated through the protein adaptor FAN. Generation of
ceramide in plasma membrane initiates the signaling pathway to programmed cell death.
531
532
533
534
24
535
Table 1. MCR-ALS data fitting results in the analysis of the k=1,…17 different
536
chromatographic windows augmented data matrices Dk,aug, each one of them with the
537
individual data matrices coming from the three control and the three CPS treated
538
samples (see Equation 2)
Dk,aug (window)
539
540
541
RT (min)
Nr. of
components
3
3
4
3
3
3
4
6
5
3
4
3
4
4
3
3
6
1
0.60 – 1.40
2
1.55 – 2.05
3
2.21 – 2.90
4
2.91 – 3.50
5
3.60 – 4.05
6
4.04 – 4.29
7
4.30 – 4.65
8
4.70 – 5.80
9
5.90 – 7.10
10
7.15 – 7.55
11
7.57 – 8.20
12
8.60 – 9.80
13
10.20 – 11.27
14
11.30 – 11.69
15
11.70 – 12.00
16
12.20 – 12.80
17
15.00 – 18.60
a
Lack of fit according Equation 3.
b 2
R explained data variance according Equation 4.
Lack of fita (%)
R2 b(%)
29.35
40.04
16.05
12.07
7.13
16.07
12.49
17.70
3.20
2.14
5.61
5.31
4.74
2.86
4.51
4.06
46.77
91.38
83.96
97.42
98.54
99.49
97.41
98.43
96.86
99.89
99.95
99.68
99.71
99.77
99.91
99.79
99.83
78.12
25
542
543
544
Table 2: Changes and identification of sphingolipid species profiles (Low-Dose Group
vs. Control Group, at the significance level p < 0.05) resolved by MCR-ALS. SM,
sphingomyelin; DHSM, dihydrosphingomyelin; Cer, ceramide.
545
RT
p
[MH]+ m/z
trenda
Sphingolipid
(min)
1
2
3
4
5
6
7
8
6.108
0.0438
675.5422
SM
C14:0

9
6.411
0.0015
701.5581
SM C16:1

10
7.353
0.0137
703.5668
SM C16:0

11
7.650
0.0116
538.5232
Cer C16:0

7.817
0.0035
729.5971
SM C18:1

7.870
0.0190
705.5914
DHSM
C16:0

12
8.754
0.025
731.6063
SM C18:0

13
10.613
0.004
785.6571
SM C22:1

10.734
0.0021
811.6752
SM C24:2

14
11.392
0.0159
787.6769
SM C22:0

11.494
0.006
813.6963
SM C24:1

15
16
12.547
0.0016
815.7039
SM C24:0

17
a
Change trend compared with control. (): up-regulated. (): down-regulated.
Window
Molecular
formula
C37H76N2O6P
C39H78N2O6P
C39H80N2O6P
C34H68NO3
C41H82N2O6P
C39H82N2O6P
C41H84N2O6P
C45H90N2O6P
C47H92N2O6P
C45H92N2O6P
C47H94N2O6P
C47H96N2O6P
-
546
547
26