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allo-isoleucine by electrospray ionization-tandem mass spectrometry and partial least square regression: Application to saliva samples
Journal Pre-proof Determination of leucine and isoleucine/allo-isoleucine by electrospray ionizationtandem mass spectrometry and partial least square regression: Application to saliva samples Ana María Casas-Ferreira, Miguel del Nogal-Sánchez, Encarnación RodríguezGonzalo, Bernardo Moreno-Cordero, José Luis Pérez-Pavón PII:
S0039-9140(20)30102-8
DOI:
https://doi.org/10.1016/j.talanta.2020.120811
Reference:
TAL 120811
To appear in:
Talanta
Received Date: 3 December 2019 Revised Date:
3 February 2020
Accepted Date: 5 February 2020
Please cite this article as: Ana.Marí. Casas-Ferreira, M.d. Nogal-Sánchez, Encarnació. RodríguezGonzalo, B. Moreno-Cordero, José.Luis. Pérez-Pavón, Determination of leucine and isoleucine/alloisoleucine by electrospray ionization-tandem mass spectrometry and partial least square regression: Application to saliva samples, Talanta (2020), doi: https://doi.org/10.1016/j.talanta.2020.120811. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
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Determination of leucine and isoleucine/allo-isoleucine by
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electrospray ionization-tandem mass spectrometry and
3
partial least square regression: application to saliva samples
4
Ana María Casas-Ferreira, Miguel del Nogal-Sánchez*, Encarnación Rodríguez-
5
Gonzalo, Bernardo Moreno-Cordero, José Luis Pérez-Pavón
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Departamento de Química Analítica, Nutrición y Bromatología. Facultad de Ciencias
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Químicas, Universidad de Salamanca. 37008 Salamanca, SPAIN
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* Corresponding author: (fax) +34-923-294483; (e-mail) [email protected]
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1
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Abstract
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Herein we propose, for the first time, a rapid method based on flow injection analysis,
13
electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) and multivariate
14
calibration for the determination of L-leucine, L-isoleucine and L-allo-isoleucine in
15
saliva. As far as we know, multivariate calibration has never been applied to the data
16
from this non-separative approach. The possibilities of its use were explored and the
17
results obtained were compared with the corresponding ones when using univariate
18
calibration.
19
Partial least square regression (PLS1) multivariate calibration models were built for
20
each analyte by analyzing different saliva samples, and were subsequently applied to the
21
analysis of another set of samples which had not been used in any calibration step. For
22
Leu, the model worked satisfactorily with root mean square errors in the prediction step
23
of 17 %. This error can be considered acceptable and is common in methodologies that
24
do not include a separation step. Results were compared with those obtained when
25
univariate calibration was used, using the m/z transition 132.1→43.0 as the quantitation
26
variable. In this case, the obtained results were not acceptable, with RMSEP of 236 %,
27
due to the fact that saliva samples contained another compound, different to the target
28
analytes, which also shared the same transition.
29
Ile and aIle have the same fragmentation patterns, so quantification of the sum of both
30
compounds was performed, with RMSEP of 14 % using a PLS1 model. Similar results
31
were obtained when a univariate calibration model using the m/z transition 132.1→69.0
32
was employed. However, the use of this transition should be carefully examined when
33
other compounds present in the matrix contribute to the analytical signal.
2
34
The method increases sample throughput more than one order of magnitude compared
35
to the corresponding LC-ESI-MS/MS method and is especially suitable as screening.
36
When abnormally high or low concentrations of the analytes studied are obtained, the
37
use of the method that includes separation is recommended to confirm the results.
38
Keywords: saliva; electrospray ionization tandem mass spectrometry; multivariate
39
calibration; non-separative method; leucines
40
3
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1. Introduction
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The use of non-separative analytical techniques based on mass spectrometry (MS)
43
presents an interesting alternative for targeted analysis, mainly due to their speed of
44
analysis. The analytical signals generated are characteristic of the compounds present in
45
the samples and can be considered the fingerprint of the sample. In some cases, suitable
46
treatment of the signals by chemometric techniques is required in order to extract the
47
quantitative and qualitative information contained in the signal profile [1]. Multivariate
48
calibration is a technique used when quantitative information has to be obtained from
49
profile signals. It refers to the process of relating the analyte concentration to a
50
measured response of multicomponent mixtures [2-4]. The analyte concentration is
51
obtained as a function of many measured responses (non-specific predictors), generally
52
physical information from spectra, such as near infrared spectra, Raman or mass
53
spectra, among others [5]. Partial least squares (PLS) calibration has become one of the
54
most used techniques for multivariate calibration because of the quality of the
55
calibration models produced and their implementation due to the availability of
56
software.
57
Determination of isomeric compounds using non-separative methods based on mass
58
spectrometry represents a remarkable challenge because of their identical chemical
59
composition [6, 7]. Furthermore, the analysis of the aforementioned compounds in
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complex biological samples could be even more complicated because of the presence of
61
other isobaric compounds. The use of electron ionization (EI) sources or electrospray
62
ionization (ESI) and tandem mass spectrometric methods (MS/MS) can alleviate these
63
challenges in cases where the compounds either differ in bond dissociation energies or
64
possess constitutional arrangements that produce unique fragmentation spectra. 4
65
However, in many cases, fragment ions are often shared by the precursor ions and hence
66
MS is not sufficient to confidently identify these components in a complex mixture [8].
67
The use of multivariate calibration techniques can add a new perspective for analysis
68
because many variables can be used simultaneously for quantification purposes.
69
In this work, we evaluate the possibilities of flow injection analysis-electrospray
70
ionization-tandem mass spectrometry (FIA-ESI-MS/MS) and multivariate calibration
71
for the determination of three isomeric compounds, L-leucine (Leu), L-isoleucine (Ile)
72
and L-allo-isoleucine (aIle), in saliva samples. Leu and Ile are naturally occurring
73
compounds found in the low µg L-1 to the medium mg L-1 concentration range [9,10].
74
Regarding aIle, it has not been previously detected in saliva. However, we decided to
75
include it in the present study to evaluate the potential of the proposed methodology to
76
distinguish two diastereomers (Ile and aIle).
77
The determination of the selected analytes in biological samples is an important issue
78
because they have been long associated with several diseases, such as inborn errors of
79
metabolism [11, 12] and cancer [13-16]. Moreover, the use of samples involving a non-
80
invasive method of sample collection, such as saliva, is an interesting option to plasma
81
analysis. Saliva can reflect the physiological state of the body and it has been shown
82
that most of the compounds present in blood are also present in this biological fluid [17,
83
18].
84
The work presented here represents a fast, simple and cheap alternative to those found
85
in the literature for the determination of these compounds. Several non-separative
86
methodologies have already been proposed, such as the formation of analyte derivatives
87
[11, 19, 20] or different mass spectrometry based strategies, such as photodissociation
88
of cold gas-phase noncovalent complexes [21], ion mobility mass spectrometry [8, 22] 5
89
or using specific transitions by means of ion source fragmentation [23]. Each of these
90
strategies present their own advantages and disadvantages, although most of them imply
91
extensive sample manipulation [11] or the use of several separation steps [22, 24].
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2. Materials and methods
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2.1. Chemicals and standard solutions
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L-leucine
95
standard, IS) were supplied by Sigma-Aldrich (Steinheim, Germany). Methanol and
96
heptafluorobutyric acid (HFBA) were also supplied by Sigma-Aldrich. UHQ water was
97
obtained with a Wasserlab Ultramatic water purification system (Noain, Spain). A set of
98
stock solutions (1000 mg L-1) of these compounds in UHQ water were prepared and
99
stored at 4 ºC. These solutions were used to spike the water and saliva samples at the
(Leu), L-isoleucine (Ile), L-allo-isoleucine (aIle) and L-leucine-1-13C (internal
100
different concentrations analyzed.
101
2.2. Saliva samples
102
Saliva samples (unstimulated) were collected from nine apparently healthy subjects of
103
both sexes (5 females and 4 males). They were collected into a 10-mL glass vial and
104
stored at -20ºC in the dark until use. The subjects did not ingest any food or beverages
105
and did not brush their teeth within 1 h before sample collection. After thawing,
106
samples were centrifuged at 1811 x g during 10 min to precipitate the denatured mucins.
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Then, the supernatant was filtered using a Nylon filter (0.45 µm pore size, 17 mm i.d.)
108
and 200 µL were added to a vial, diluted up to 1 mL with UHQ water and injected. For
109
spiked samples, 200 µL of saliva were added to a vial and diluted up to 1 mL with UHQ
110
water spiked with the studied analytes. The IS was added to all the samples at a
111
concentration of 300 µg L-1. 6
112
2.3. LC-ESI-MS/MS analysis
113
Aliquots of 10 µL of the aqueous samples were injected in a LC-MS/MS system
114
consisting of a 1200 series LC chromatograph equipped with a binary pump, a
115
membrane degasser, an autosampler, two six-port valves and a 6410 LC/MS triple
116
quadrupole (QqQ) mass spectrometer, all from Agilent Technologies (Waldbronn,
117
Germany). The triple quadrupole mass spectrometer was equipped with an electrospray
118
ionization (ESI) source. The QqQ nebulizer pressure and voltage were set at 50 psi and
119
+4000 V, respectively. Nitrogen was used as the drying (12 L min-1, 350 ºC) and
120
collision gas.
121
For the LC-ESI-MS/MS analysis, a reversed-phase analytical column Cortecs C18 (2.1
122
x 50 mm, 2.7 µm) from Waters (Milford, MA, USA) was used. The mobile phase
123
consisted of a UHQ water (solvent A)/methanol (solvent B) mixture, both with 0.1 %
124
HFBA v/v. The solvent gradient used was as follows: 5 % B for 0.5 min, then a gradient
125
from 5 % to 20 % B from 0.5 to 13 min, continuing with a gradient from 20 % to 70 %
126
B from 13 to 15 min (holding isocratic conditions during 1 min) and finally returning to
127
5 % B from 15 min to 19 min and holding conditions during 9 min in order to re-
128
equilibrate the column. The flow rate was set to 0.25 mL min-1 and the column was
129
thermostated at 25 ºC. The total chromatographic run time was 28 min. Although the
130
elution time for the latter analyte was 10.7 min, an additional time of 17 min was
131
required to re-equilibrate the column for further analysis.
132
2.4. FIA-ESI-MS/MS analysis
133
The instrumental setup used to perform FIA-ESI-MS/MS analysis was the same as
134
previously described. A six-port valve was used to switch from the non-separative to the
135
chromatographic analysis without any instrumental modification. A peek tube was used 7
136
to connect the valve to the mass spectrometer. Methanol was used as carrier phase (0.1
137
% HFBA, v/v) and the flow rate was maintained at 1.0 mL min-1. The analysis time was
138
1.0 min.
139
2.5. Fragmentation studies and optimization of the MRM conditions
140
Analyte fragmentation studies were performed using product ion scan analysis mode.
141
Precursor ions were fixed at m/z 132.1 and scans were performed in the m/z 20.0 to
142
135.0 range. Different collision energies (from 1 to 40 eV) and cell acceleration
143
voltages (4 and 7 V) were evaluated.
144
For calibration modeling, the multiple reaction monitoring (MRM) analysis mode was
145
used. Transitions were selected based on the results obtained from the fragmentation
146
evaluation. Optimum conditions implied that for univariate studies, specific transitions
147
for Leu and the aIle and Ile pair were used (Table 1). For multivariate calibration, all the
148
m/z ratios with abundance intensities higher than 5 % were selected. A total of 62 MRM
149
transitions were used for multivariate modeling (Table 2). All the measurements were
150
performed at unit mass resolution and dwell time was fixed at 10 ms.
151
2.6. Data acquisition and model construction
152
Data acquisition was performed using the MassHunter software (version B.07.01) from
153
Agilent Technologies. An in‐house script was used to obtain the signal intensity for
154
each MRM transition monitored. Multivariate analysis was performed by partial least
155
square calibration (PLS) using The Unscrambler® statistical package (CAMO
156
Software) [25]. The NIPALS (Nonlinear Iterative Partial Least Squares) algorithm was
157
used. Independent variables in the PLS1 were the intensities of all the MRM transitions
158
(62) detected during data acquisition divided by the intensity of the 133.1→86.1 8
159
transition (IS). Dependent variables were the concentrations obtained with the LC-ESI-
160
MS/MS method.
161
3. Results and discussion
162
3.1. Selection of MRM transitions for analysis
163
In order to select suitable MRM transitions for the isomers quantification, spectra
164
obtained using product ion scan acquisition mode were recorded using standard aqueous
165
solutions of the compounds at a concentration of 10 mg L-1. In all cases, the precursor
166
ion selected was the m/z 132.1 that corresponds to the quasi-molecular ion [M+H]+ of
167
all the molecules. Different collision energies (CE, 1, 2, 3, 4, 5, 10, 15, 20, 30 and 40
168
eV) and cell acceleration voltages (CAV, 4 and 7 V) were evaluated under the
169
assumption that the analyzed compounds would show different fragmentation patterns
170
for a given collision energy.
171
Fig. 1 shows the spectra obtained for the Leu, Ile and aIle at three of the CE evaluated
172
(CAV 7). When the results obtained for Leu and Ile or Leu and aIle were compared,
173
specific fragment ions were found for each one of the compounds. Those were, for
174
example, m/z 43 at 40 eV for Leu and m/z 69 at 20 eV or m/z 56 and 57 at 40 eV for aIle
175
and Ile. Moreover, m/z abundance differences between analytes were also observed (m/z
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86 or 30 at 10 and 20 eV, 39 at 40 eV, among others).
177
It should be noted that some of these fragmentation differences have already been
178
described [24]. Bishop et al. have also proposed a method for the quantification of Leu
179
and Ile where they proposed scanning the immonium ion at m/z 86 (directly produced in
180
the ion source) as the precursor ion, to generate unique fragment ions for Leu and Ile, at
181
m/z 43 and 57, respectively [23]. This possibility has also been evaluated in this work 9
182
and MS conditions were optimized using the m/z 86 as precursor ion. However, this
183
option was rapidly discarded due to the loss of sensitivity observed when the immonium
184
ion was selected as the precursor ion (data not shown).
185
With regard to Ile and aIle, the situation is more complex because Ile and aIle are
186
diastereoisomers and few or no fragmentation pattern differences were expected in
187
advance. Unique fragments were not found to distinguish these diastereoisomers (Fig.
188
1). However, slight intensity differences at several m/z values were observed. These
189
differences were mainly observed at m/z 132 and 86 values and low CE values.
190
3.2. Targeted analysis: quantification of Leu, Ile and aIle in saliva samples
191
As Leu and Ile are naturally present in the matrix of analysis, their prior quantification
192
is required in order to construct the multivariate calibration models and to check their
193
prediction capabilities. Although aIle has not been previously detected in saliva, its
194
absence in the analyzed samples required confirmation. Thus, the first step was the
195
determination of the analytes in the saliva samples using the LC-ESI-MS/MS method
196
(these values were considered reference ones). Beyond here, the chromatographic
197
analysis is no longer required.
198
Matrix effects were evaluated. Three calibration curves were obtained for five
199
calibration levels in three different matrices, UHQ water and two different saliva
200
samples. The concentration ranges were between 350 and 1400 µg L-1 for Leu, 150 and
201
600 µg L-1 for Ile and between 15 and 60 µg L-1 for aIle. The slopes of the three
202
calibration curves were compared and significant differences were observed. Thus,
203
matrix effects were confirmed, so the standard addition method was used for
204
quantification. Each saliva sample was spiked at three concentration levels and were
10
205
analyzed in triplicate. The quantitation MRM transition used was 132.1→86.1 for all
206
the compounds (Table 1).
207
3.2.1. Leucine
208
3.2.1.1. LC-ESI-MS/MS analysis
209
Leucine concentration levels in the analyzed saliva samples were found to be in the 0.21
210
to 12 mg L-1 range. These results were in good agreement with previous published
211
results [9]. Table 3 shows the concentrations found for the diluted samples using the
212
LC-ESI-MS/MS method and the concentration ranges used for the standard addition.
213
Fig. 2a shows the total ion current (TIC) chromatogram of a saliva sample injected at
214
four concentration levels. This was at the endogenous concentrations of the compounds
215
(green solid line) and at the three different concentration levels used for the standard
216
addition. As can be seen, in addition to the signals obtained for the target compounds,
217
there was a chromatographic peak corresponding to an unknown isobaric compound
218
(tR=1.5 min, putative identified as creatine) [9, 26] with an important contribution to the
219
analytical signal. The abundance of this analyte was different among samples.
220
Keeping in mind that the objective of the present study is the development of a
221
methodology that does not include a separation step for the determination of the
222
selected isobars, the presence of this unknown compound should not be a problem as
223
long as it shows a different fragmentation pattern compared to the target compounds.
224
However, it was observed that it shared several MRM transitions with the target
225
analytes. Specifically, transition 132.1→44.0 with Leu, Ile and aIle and transition
226
132.1→43.0 with Leu (Fig. 2b). This second overlapping was critical because it was the
11
227
only MRM transition that could be used to distinguish Leu from Ile and aIle when no
228
chromatographic separation was used (Table 1).
229
3.2.1.2. FIA-ESI-MS/MS analysis
230
The set of samples previously analyzed were injected using the FIA-ESI-MS/MS
231
method. Fig. 2c shows the profile signal obtained for the same saliva sample (Fig. 2a) at
232
the different concentration levels previously described.
233
First, a univariate calibration model by the standard addition protocol was built for the
234
quantification of Leu. Based on previous results, the MRM transition used for this
235
purpose was 132.1→43.0, specific for this compound. An isotopically labeled internal
236
standard (13C-Leu) was added to all the samples at a concentration of 300 µg L-1 and
237
area values were normalized to the ones obtained for the 133.1→86.1 transition (IS).
238
Fig. 3a represents the concentration of Leu in the saliva samples obtained with the LC-
239
ESI-MS/MS method (reference values, x axis) versus the ones obtained with the FIA-
240
ESI-MS/MS method (predicted concentrations, y axis). As is shown in the figure, the
241
concentration values obtained with the method that does not include a separation step
242
were considerably higher than the values obtained with the LC-ESI-MS/MS method.
243
The difference between the reference values and the predicted ones was 236%. This
244
error is expressed as (1): ∑ ( − ̂ ) I (%) = 100
̅
245
where ci is the reference value, ĉi is the predicted concentration, I is the number of
246
samples and ̅ is the average of the Leu concentrations (reference values).
12
247
As stated before, the unknown isobaric compound present in the saliva matrix
248
contributes to the signal obtained for the transition 132.1→43.0, which hinders the
249
quantification of Leu in saliva samples using univariate calibration models. No other
250
MRM transition can be used for Leu quantification due to the spectra overlapping with
251
Ile and aIle. Signals obtained with the transition 86.1→43.0 were also evaluated but,
252
again, the loss of sensitivity observed made this transition unusable.
253
In order to overcome this situation, multivariate calibration based on partial least square
254
regression (PLS1) was used. Five saliva samples (saliva 1-5) and all their standard
255
additions (15 samples: 5 saliva samples spiked at three concentration levels) were used
256
to construct the calibration models (20 samples in total). Each sample was analyzed in
257
duplicate. The use of different saliva samples and the addition of an IS allowed the
258
construction of one model valid for the quantification of any sample.
259
A PLS1 model was built for Leu using a maximum of 20 factors. Cross-validation
260
(leave one out) was used to select the optimum number of PLS components
261
corresponding to the minimum root mean standard error of validation (RMSEV). Under
262
these conditions, the optimum number of PLS components was 5 and the E (%) value
263
was 11 % in the cross-validation step. All the MRM transitions used to build the model
264
were significant. As an example, Fig. 4 shows the contribution of each transition to the
265
first two PLS factors. As shown in Fig. 4, all the variables in the first PLS factor had a
266
positive contribution to the model. However, transitions corresponding to product ion
267
44.0 and some corresponding to 43.0 had a negative contribution in the second one
268
subtracting the contribution of the interfering compound to the model.
269
The model was applied to the analysis of 4 saliva samples (saliva 6-9) which had not
270
been included in the calibration step. Standard additions of the aforementioned samples 13
271
(12 samples: 4 saliva samples spiked at three concentration levels) were also analyzed.
272
The total number of samples used in the external validation was 16. Fig. 3b shows the
273
results obtained. The predicted concentrations using the PLS1 model were similar to the
274
ones obtained with the LC-ESI-MS/MS method. The error was reduced from 236% to
275
17%. Values similar to that reported here can be considered acceptable and are common
276
in methodologies that do not include a separation step [27, 28].
277
These results highlight that the use of multivariate models allows the quantification of
278
Leu in saliva samples, even with the presence of unidentified interfering compounds
279
and the lack of specific transitions. Moreover, this methodology allows the
280
determination of 24 samples per hour (1 min required for data acquisition and 1.5 min
281
for injection), while it is only possible to analyze 2 samples per hour when the LC-ESI-
282
MS/MS method is used. This demonstrates the high throughput of the proposed
283
methodology, with an increase of one order of magnitude of the number of samples
284
analyzed per hour (see Fig. 2).
285
3.2.2. Isoleucine and allo-isoleucine
286
3.2.2.1. LC-ESI-MS/MS analysis
287
Ile and aIle were quantified in the saliva samples using the standard addition protocol.
288
Analysis confirmed that aIle was not present in the samples. Ile concentrations were
289
found to be between 0.04 and 7 mg L-1. Table 3 shows the concentrations found for the
290
diluted samples and the concentrations added for the standard addition quantification.
291
3.2.2.2. FIA-ESI-MS/MS analysis
14
292
The construction of univariate calibration models for the individual quantification of Ile
293
and aIle was not considered due to the absence of specific MRM transitions for these
294
analytes.
295
Thus, a PLS1 model was built for each analyte. The calibration set used for model
296
construction was the same saliva samples used for Leu. The optimum number of PLS
297
factors (cross-validation leave one out) was 2 and 3 for Ile and aIle, respectively. For
298
Ile, all the MRM transitions were significant, while for aIle only 5 of the 62 measured
299
MRM transitions were so. The rest of the variables (57) had regression coefficients with
300
uncertainty values higher than the absolute value from the model [23]. The E (%) was
301
22 % and 79 % for Ile and aIle, respectively. Both values were higher than the E (%)
302
found for Leu (11 %). Both analytes presented complete overlapping in all the selected
303
transitions and the slight intensity differences previously observed were not enough to
304
distinguish between them.
305
These models were applied to the analysis of a set of saliva samples which had not been
306
included in the calibration step (same samples used for Leu analysis). With regard to Ile,
307
the predicted concentrations were not satisfactory. Fig. 5a shows the Ile predicted
308
concentrations obtained with the LC-ESI-MS/MS method versus the concentrations
309
obtained with the PLS1 calibration model. The relative error was 66 %. The samples
310
with the highest deviations from the reference values corresponded to those with the
311
highest aIle concentration levels (Fig. 5b).
312
In the case of aIle, the E (%) of the PLS1 model was so elevated (79 %) that it was not
313
possible to use for the quantification of aIle in an external validation set.
314
Based on these results, it was concluded that it was not possible to quantify Ile and aIle
315
individually. However, a new PLS1 calibration model was built considering the sum of 15
316
concentrations of both analytes. The saliva samples considered for the calibration set
317
were maintained. The optimum number of PLS factors (cross-validation leave one out)
318
was 4 and the E (%) was 15 %. All the MRM transitions (62) were significant.
319
When this model was applied to an external validation set, the E (%) obtained was 14
320
%. Fig. 6 shows the predicted values using the PLS1 model versus the ones obtained
321
with the LC-ESI-MS/MS method (reference values) for the external validation set. The
322
model was able to assign correct concentrations to all the samples, independently of the
323
Ile/aIle concentrations ratios.
324
Finally, a univariate calibration model using the quantitation transition 132.1→69.0 was
325
built for the sum of both compounds. The E (%) obtained for the external calibration
326
was 17 %. This result was similar to the one obtained when the PLS1 model was used
327
(14 %). The slight difference observed could be attributed to the small contribution of
328
Leu to the quantitation transition [29]. When the multivariate model was used, factors 2,
329
3 and 4 showed a negative contribution of the specific transitions described for Leu.
330
However, univariate models were not able to distinguish this contribution.
331
Although satisfactory results were also obtained with the univariate calibration models,
332
the use of multivariate calibration models increases the reliability of the results as they
333
take into account possible interferences of other compounds present in the sample that
334
could contribute to the signal, as has been observed for Leu. Moreover, quantification is
335
performed with multiple MRM transitions, not only one, increasing the reliability of the
336
results.
337
4. Conclusions
16
338
A rapid method based on FIA-ESI-MS/MS has been proposed for the determination of
339
L-leucine and the sum of L-isoleucine and L-allo-isoleucine in saliva samples. The
340
method allows the analysis of 24 samples per hour against the 2 that can be analyzed
341
with the corresponding LC-ESI-MS/MS method. The use of multivariate calibration
342
based on partial least squares regression (PLS1) allowed to extract the useful
343
information from the profile signal and to obtain satisfactory results in the
344
quantification of these analytes. In addition, this chemometric technique took into
345
account the presence of other interfering compounds with the same transitions as the
346
analytes of interest.
347
When abnormally high or low concentrations of the analytes studied are obtained [30],
348
the use of the LC-ESI-MS/MS method is recommended to confirm the results. In this
349
way, it is beneficial to analyze the vast majority of samples in the laboratory with this
350
method, which is time and cost effective.
351
Acknowledgements
352
The authors wish to thank the Spanish Ministry of Economy, Industry and
353
Competitiveness for funding the project CTQ2017-87886-P/BQU; the Junta de Castilla
354
y León for project SA055P17; and the Samuel Solórzano Foundation (FS/19-2017). The
355
authors are grateful to Dr. P.G. Jambrina for his help in the data analysis.
17
356
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Ionization
Tandem
Mass
Spectrometry
with
Collision-Induced
457
22
458
Figure captions
459 460
Fig. 1. Product ion spectra (precursor ion, m/z 132.1, scan range, m/z 20.0-135.0) of the
461
three target compounds at three different collision energies: 1, 20 and 40 eV.
462
Fig. 2. Chromatograms obtained for a saliva sample. (a) Total ion current (TIC)
463
chromatograms obtained with the LC-ESI-MS/MS method for the standard addition of a
464
saliva sample. (b) Multiple Reaction Monitoring (MRM) chromatograms of the saliva
465
sample for the two transitions shared by the interfering compound and Leu and Ile/aIle.
466
(c) TIC chromatograms obtained with the FIA-ESI-MS/MS method for the standard
467
addition of the same saliva sample as (a).
468
Fig. 3. Correlation plots of the predicted concentration (FIA-ESI-MS/MS) versus
469
reference concentration by the LC method using (a) univariate calibration model and
470
standard addition protocol (b) partial least square regression (PLS1) model for the
471
external validation step.
472
Fig. 4. Plot of the PLS1 loadings of each of the 62 variables for the first two factors for
473
Leu model.
474
Fig. 5. (a) Correlation plot of the predicted concentration versus reference concentration
475
(LC-ESI-MS/MS) for isoleucine in presence of different concentrations of allo-
476
isoleucine. (b) Concentrations of Ile and aIle of the samples which deviate most from
477
the reference values (marked). Samples are identified as the number of the saliva sample
478
analyzed (S) and the level of the standard addition (L), being 0 the endogenous
479
concentration and 3 the highest level of the standard addition.
480
Fig. 6. Correlation plot of the predicted concentrations versus reference ones obtained
481
for the sum of Ile and aIle using PLS1 model for the external validation step.
482 483 484 485 486 23
487
Table 1. Experimental parameters of the target analytes for univariate analysis.
488
Compound Ile
Leu
9.2
9.8
10.7
---
132.1
132.1
132.1
---
86.1/69.1
86.1/69.1
86.1/43.1
---
41
41
81
---
Collision energy (eV)
5/17
5/17
5/21
---
Precursor ion (m/z)
132.1
132.1
132.1
133.1
Product ion (m/z)
69.1
69.1
43.1
86.1
Fragmentor (V)
41
41
81
41
Collision energy (eV)
17
17
21
5
LC-ESI-MS/MS FIA-ESI-MS/MS
Univariate
tR (min)
Univariate
13
aIle
Precursor ion (m/z) Product ion (m/z) Fragmentor (V)
C-Leu
489
490
24
Table 2. MS/MS transitions selected for multivariate calibration.
491
CAV
4
7
Precursor ion (m/z)
132.1
132.1
Fragmentor (V)
41
41
CE (eV)
Product ion (m/z)
3
86.0
4
132.0
5
132.0
15
86.0
20
86.0, 41.0
30
44.0
1
132.1, 86.1
2
132.1, 86.1
3
132.1, 86.1, 30.0
4
132.1, 86.1, 30.0
5
132.1, 86.1, 30.0
10
132.1, 86.1, 69.0, 30.0
15
86.1, 69.0, 44.0, 43.0, 41.0, 30.0
20
30
40
86.1, 69.0, 58.0, 57.0, 45.0, 44.0, 43.0, 41.0, 30.0 86.1, 69.0, 58.0, 57.0, 56.0, 45.0, 44.0, 43.0, 41.0, 39.0, 27.0 58.0, 57.0, 56.0, 45.0, 44.0, 43.0, 42.0, 41.0, 39.0, 30.0, 29.0, 27.0
492 493
25
494
Table 3. Concentrations of the target analytes obtained with the LC-ESI-MS/MS
495
method in the analyzed diluted saliva samples and concentrations added for the standard
496
addition quantification.
Sample Saliva 1 Saliva 2 Saliva 3 Saliva 4 Saliva 5 Saliva 6 Saliva 7 Saliva 8 Saliva 9 497 498
Concentration (µg L-1) Leucine Isoleucine Aloisoleucine Added Prediction* Added Prediction* Added Prediction* 0-500 (24±2) x 10 0-140 61±6 0-40
26
Figure 1
O
12
Leucine
Abundance (104)
H3C OH
10 CH3
NH2
8
CE = 40 eV 6 4
CE = 20 eV
2
CE = 1 eV
0 20
40
60
80
100
120
140
m/z 18 CH3
16
OH
O
14
Abundance (104)
Isoleucine
H3C
NH2
12 10
CE = 40 eV
8 6
CE = 20 eV
4 2
CE = 1 eV
0 20
40
60
80
100
120
140
m/z 18 CH3
16
OH
O
14
Abundance (104)
Allo-isoleucine
H3C
NH2
12 10
CE = 40 eV
8 6
CE = 20 eV
4 2
CE = 1 eV
0 20
40
60
80
m/z
100
120
140
Figure 2
a Endogenous concentration Level 1 standard addition (L1) Level 2 standard addition (L2) Level 3 standard addition (L3)
Abundance (104)
2.8 2.4 2.0
TIC
Isoleucine Leucine
Aloisoleucine 1.6
Interfering compound 1.2 0.8 0.4 0
0
2
1
3
4
5
6
7
8
9
10
11
12
13
27
28
Time (min) b
MRM (132.143.0) MRM (132.144.0)
Abundance (103)
5.0
Interfering compound 4.0 3.0 2.0 1.0
Isoleucine
Leucine
0.0 0
1
2
3
4
5
6
7
8
9
10
11
12
13
27
28
Time (min)
3.2
3.2
2.8
2.8
Abundance (104)
Abundance (104)
c 2.4 2.0 1.6 1.2 0.8 0.4
Endogenous concentration Level 1 standard addition (L1) Level 2 standard addition (L2) Level 3 standard addition (L3)
TIC
2.4 2.0 1.6 1.2 0.8 0.4
0
0 0
1
Time (min)
0
0.2
0.4
0.6
0.8
1.0
Time (min)
Figure 3
Leucine b
Univariate calibration 30
25
20
15
10
MRM (132.143.0) 5
E (%) = 236 0 0
5
10
15
20
LC-ESI-MS/MS (reference, mg
25
L-1)
30
FIA-ESI-MS/MS (predicted, mg L-1)
FIA-ESI-MS/MS (predicted, mg L-1)
a
Multivariate calibration 30 25 20 15 10 5
E (%) = 17 0 0
5
10
15
20
25
LC-ESI-MS/MS (reference, mg
30
L-1)
Figure 4
1st PLS factor 132.1
86.1
58.0, 57.0, 56.0 and 45.0
60
40
Regression coefficients
30
69.1
50
44.0 and 43.0 42.0, 41.0, 39.0, 30.0 and 27.0
20 10 0
2nd PLS factor 70 60 50 40 30 20 10 0 -10
Figure 5
FIA-ESI-MS/MS (predicted, mg L-1)
a
b
Multivariate calibration 15
Isoleucine
S8_L3
12.5 S8_L2
S5_L3
10 S8_L1
S5_L2
7.5 S8_L3
S5_L3
S5_L1
5 S8_L2
S5_L2 2.5
S8_L1
E (%) = 66
S5_L1
0 0
2.5
5
7.5
10
12.5
LC-ESI-MS/MS (reference, mg L-1)
15
aIle 8
Ile 6
4
2
0
2
4
Reference concentrations (mg
6 L-1)
8
Figure 6
FIA-ESI-MS/MS (predicted, mg L-1)
Multivariate calibration 15
Ile+aIle 12.5
9.4 mg L-1 Ile 0.2 mg L-1 aIle
10
7.5
5
0.6 mg L-1 Ile 2.5 mg L-1 aIle
2.5
E (%) = 14 0 0
2.5
5
7.5
10
12.5
LC-ESI-MS/MS (reference, mg L-1)
15
• • • •
A rapid method based on FIA-ESI-MS/MS and multivariate calibration is proposed Determination of L-leucine was achieved when interfering compounds were present Determination of the sum of L-isoleucine and L-allo-isoleucine was achieved The method allows the analysis of 24 saliva samples per hour
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S0039-9140(20)30102-8
DOI:
https://doi.org/10.1016/j.talanta.2020.120811
Reference:
TAL 120811
To appear in:
Talanta
Received Date: 3 December 2019 Revised Date:
3 February 2020
Accepted Date: 5 February 2020
Please cite this article as: Ana.Marí. Casas-Ferreira, M.d. Nogal-Sánchez, Encarnació. RodríguezGonzalo, B. Moreno-Cordero, José.Luis. Pérez-Pavón, Determination of leucine and isoleucine/alloisoleucine by electrospray ionization-tandem mass spectrometry and partial least square regression: Application to saliva samples, Talanta (2020), doi: https://doi.org/10.1016/j.talanta.2020.120811. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
1
Determination of leucine and isoleucine/allo-isoleucine by
2
electrospray ionization-tandem mass spectrometry and
3
partial least square regression: application to saliva samples
4
Ana María Casas-Ferreira, Miguel del Nogal-Sánchez*, Encarnación Rodríguez-
5
Gonzalo, Bernardo Moreno-Cordero, José Luis Pérez-Pavón
6
Departamento de Química Analítica, Nutrición y Bromatología. Facultad de Ciencias
7
Químicas, Universidad de Salamanca. 37008 Salamanca, SPAIN
8 9
* Corresponding author: (fax) +34-923-294483; (e-mail) [email protected]
10
1
11
Abstract
12
Herein we propose, for the first time, a rapid method based on flow injection analysis,
13
electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) and multivariate
14
calibration for the determination of L-leucine, L-isoleucine and L-allo-isoleucine in
15
saliva. As far as we know, multivariate calibration has never been applied to the data
16
from this non-separative approach. The possibilities of its use were explored and the
17
results obtained were compared with the corresponding ones when using univariate
18
calibration.
19
Partial least square regression (PLS1) multivariate calibration models were built for
20
each analyte by analyzing different saliva samples, and were subsequently applied to the
21
analysis of another set of samples which had not been used in any calibration step. For
22
Leu, the model worked satisfactorily with root mean square errors in the prediction step
23
of 17 %. This error can be considered acceptable and is common in methodologies that
24
do not include a separation step. Results were compared with those obtained when
25
univariate calibration was used, using the m/z transition 132.1→43.0 as the quantitation
26
variable. In this case, the obtained results were not acceptable, with RMSEP of 236 %,
27
due to the fact that saliva samples contained another compound, different to the target
28
analytes, which also shared the same transition.
29
Ile and aIle have the same fragmentation patterns, so quantification of the sum of both
30
compounds was performed, with RMSEP of 14 % using a PLS1 model. Similar results
31
were obtained when a univariate calibration model using the m/z transition 132.1→69.0
32
was employed. However, the use of this transition should be carefully examined when
33
other compounds present in the matrix contribute to the analytical signal.
2
34
The method increases sample throughput more than one order of magnitude compared
35
to the corresponding LC-ESI-MS/MS method and is especially suitable as screening.
36
When abnormally high or low concentrations of the analytes studied are obtained, the
37
use of the method that includes separation is recommended to confirm the results.
38
Keywords: saliva; electrospray ionization tandem mass spectrometry; multivariate
39
calibration; non-separative method; leucines
40
3
41
1. Introduction
42
The use of non-separative analytical techniques based on mass spectrometry (MS)
43
presents an interesting alternative for targeted analysis, mainly due to their speed of
44
analysis. The analytical signals generated are characteristic of the compounds present in
45
the samples and can be considered the fingerprint of the sample. In some cases, suitable
46
treatment of the signals by chemometric techniques is required in order to extract the
47
quantitative and qualitative information contained in the signal profile [1]. Multivariate
48
calibration is a technique used when quantitative information has to be obtained from
49
profile signals. It refers to the process of relating the analyte concentration to a
50
measured response of multicomponent mixtures [2-4]. The analyte concentration is
51
obtained as a function of many measured responses (non-specific predictors), generally
52
physical information from spectra, such as near infrared spectra, Raman or mass
53
spectra, among others [5]. Partial least squares (PLS) calibration has become one of the
54
most used techniques for multivariate calibration because of the quality of the
55
calibration models produced and their implementation due to the availability of
56
software.
57
Determination of isomeric compounds using non-separative methods based on mass
58
spectrometry represents a remarkable challenge because of their identical chemical
59
composition [6, 7]. Furthermore, the analysis of the aforementioned compounds in
60
complex biological samples could be even more complicated because of the presence of
61
other isobaric compounds. The use of electron ionization (EI) sources or electrospray
62
ionization (ESI) and tandem mass spectrometric methods (MS/MS) can alleviate these
63
challenges in cases where the compounds either differ in bond dissociation energies or
64
possess constitutional arrangements that produce unique fragmentation spectra. 4
65
However, in many cases, fragment ions are often shared by the precursor ions and hence
66
MS is not sufficient to confidently identify these components in a complex mixture [8].
67
The use of multivariate calibration techniques can add a new perspective for analysis
68
because many variables can be used simultaneously for quantification purposes.
69
In this work, we evaluate the possibilities of flow injection analysis-electrospray
70
ionization-tandem mass spectrometry (FIA-ESI-MS/MS) and multivariate calibration
71
for the determination of three isomeric compounds, L-leucine (Leu), L-isoleucine (Ile)
72
and L-allo-isoleucine (aIle), in saliva samples. Leu and Ile are naturally occurring
73
compounds found in the low µg L-1 to the medium mg L-1 concentration range [9,10].
74
Regarding aIle, it has not been previously detected in saliva. However, we decided to
75
include it in the present study to evaluate the potential of the proposed methodology to
76
distinguish two diastereomers (Ile and aIle).
77
The determination of the selected analytes in biological samples is an important issue
78
because they have been long associated with several diseases, such as inborn errors of
79
metabolism [11, 12] and cancer [13-16]. Moreover, the use of samples involving a non-
80
invasive method of sample collection, such as saliva, is an interesting option to plasma
81
analysis. Saliva can reflect the physiological state of the body and it has been shown
82
that most of the compounds present in blood are also present in this biological fluid [17,
83
18].
84
The work presented here represents a fast, simple and cheap alternative to those found
85
in the literature for the determination of these compounds. Several non-separative
86
methodologies have already been proposed, such as the formation of analyte derivatives
87
[11, 19, 20] or different mass spectrometry based strategies, such as photodissociation
88
of cold gas-phase noncovalent complexes [21], ion mobility mass spectrometry [8, 22] 5
89
or using specific transitions by means of ion source fragmentation [23]. Each of these
90
strategies present their own advantages and disadvantages, although most of them imply
91
extensive sample manipulation [11] or the use of several separation steps [22, 24].
92
2. Materials and methods
93
2.1. Chemicals and standard solutions
94
L-leucine
95
standard, IS) were supplied by Sigma-Aldrich (Steinheim, Germany). Methanol and
96
heptafluorobutyric acid (HFBA) were also supplied by Sigma-Aldrich. UHQ water was
97
obtained with a Wasserlab Ultramatic water purification system (Noain, Spain). A set of
98
stock solutions (1000 mg L-1) of these compounds in UHQ water were prepared and
99
stored at 4 ºC. These solutions were used to spike the water and saliva samples at the
(Leu), L-isoleucine (Ile), L-allo-isoleucine (aIle) and L-leucine-1-13C (internal
100
different concentrations analyzed.
101
2.2. Saliva samples
102
Saliva samples (unstimulated) were collected from nine apparently healthy subjects of
103
both sexes (5 females and 4 males). They were collected into a 10-mL glass vial and
104
stored at -20ºC in the dark until use. The subjects did not ingest any food or beverages
105
and did not brush their teeth within 1 h before sample collection. After thawing,
106
samples were centrifuged at 1811 x g during 10 min to precipitate the denatured mucins.
107
Then, the supernatant was filtered using a Nylon filter (0.45 µm pore size, 17 mm i.d.)
108
and 200 µL were added to a vial, diluted up to 1 mL with UHQ water and injected. For
109
spiked samples, 200 µL of saliva were added to a vial and diluted up to 1 mL with UHQ
110
water spiked with the studied analytes. The IS was added to all the samples at a
111
concentration of 300 µg L-1. 6
112
2.3. LC-ESI-MS/MS analysis
113
Aliquots of 10 µL of the aqueous samples were injected in a LC-MS/MS system
114
consisting of a 1200 series LC chromatograph equipped with a binary pump, a
115
membrane degasser, an autosampler, two six-port valves and a 6410 LC/MS triple
116
quadrupole (QqQ) mass spectrometer, all from Agilent Technologies (Waldbronn,
117
Germany). The triple quadrupole mass spectrometer was equipped with an electrospray
118
ionization (ESI) source. The QqQ nebulizer pressure and voltage were set at 50 psi and
119
+4000 V, respectively. Nitrogen was used as the drying (12 L min-1, 350 ºC) and
120
collision gas.
121
For the LC-ESI-MS/MS analysis, a reversed-phase analytical column Cortecs C18 (2.1
122
x 50 mm, 2.7 µm) from Waters (Milford, MA, USA) was used. The mobile phase
123
consisted of a UHQ water (solvent A)/methanol (solvent B) mixture, both with 0.1 %
124
HFBA v/v. The solvent gradient used was as follows: 5 % B for 0.5 min, then a gradient
125
from 5 % to 20 % B from 0.5 to 13 min, continuing with a gradient from 20 % to 70 %
126
B from 13 to 15 min (holding isocratic conditions during 1 min) and finally returning to
127
5 % B from 15 min to 19 min and holding conditions during 9 min in order to re-
128
equilibrate the column. The flow rate was set to 0.25 mL min-1 and the column was
129
thermostated at 25 ºC. The total chromatographic run time was 28 min. Although the
130
elution time for the latter analyte was 10.7 min, an additional time of 17 min was
131
required to re-equilibrate the column for further analysis.
132
2.4. FIA-ESI-MS/MS analysis
133
The instrumental setup used to perform FIA-ESI-MS/MS analysis was the same as
134
previously described. A six-port valve was used to switch from the non-separative to the
135
chromatographic analysis without any instrumental modification. A peek tube was used 7
136
to connect the valve to the mass spectrometer. Methanol was used as carrier phase (0.1
137
% HFBA, v/v) and the flow rate was maintained at 1.0 mL min-1. The analysis time was
138
1.0 min.
139
2.5. Fragmentation studies and optimization of the MRM conditions
140
Analyte fragmentation studies were performed using product ion scan analysis mode.
141
Precursor ions were fixed at m/z 132.1 and scans were performed in the m/z 20.0 to
142
135.0 range. Different collision energies (from 1 to 40 eV) and cell acceleration
143
voltages (4 and 7 V) were evaluated.
144
For calibration modeling, the multiple reaction monitoring (MRM) analysis mode was
145
used. Transitions were selected based on the results obtained from the fragmentation
146
evaluation. Optimum conditions implied that for univariate studies, specific transitions
147
for Leu and the aIle and Ile pair were used (Table 1). For multivariate calibration, all the
148
m/z ratios with abundance intensities higher than 5 % were selected. A total of 62 MRM
149
transitions were used for multivariate modeling (Table 2). All the measurements were
150
performed at unit mass resolution and dwell time was fixed at 10 ms.
151
2.6. Data acquisition and model construction
152
Data acquisition was performed using the MassHunter software (version B.07.01) from
153
Agilent Technologies. An in‐house script was used to obtain the signal intensity for
154
each MRM transition monitored. Multivariate analysis was performed by partial least
155
square calibration (PLS) using The Unscrambler® statistical package (CAMO
156
Software) [25]. The NIPALS (Nonlinear Iterative Partial Least Squares) algorithm was
157
used. Independent variables in the PLS1 were the intensities of all the MRM transitions
158
(62) detected during data acquisition divided by the intensity of the 133.1→86.1 8
159
transition (IS). Dependent variables were the concentrations obtained with the LC-ESI-
160
MS/MS method.
161
3. Results and discussion
162
3.1. Selection of MRM transitions for analysis
163
In order to select suitable MRM transitions for the isomers quantification, spectra
164
obtained using product ion scan acquisition mode were recorded using standard aqueous
165
solutions of the compounds at a concentration of 10 mg L-1. In all cases, the precursor
166
ion selected was the m/z 132.1 that corresponds to the quasi-molecular ion [M+H]+ of
167
all the molecules. Different collision energies (CE, 1, 2, 3, 4, 5, 10, 15, 20, 30 and 40
168
eV) and cell acceleration voltages (CAV, 4 and 7 V) were evaluated under the
169
assumption that the analyzed compounds would show different fragmentation patterns
170
for a given collision energy.
171
Fig. 1 shows the spectra obtained for the Leu, Ile and aIle at three of the CE evaluated
172
(CAV 7). When the results obtained for Leu and Ile or Leu and aIle were compared,
173
specific fragment ions were found for each one of the compounds. Those were, for
174
example, m/z 43 at 40 eV for Leu and m/z 69 at 20 eV or m/z 56 and 57 at 40 eV for aIle
175
and Ile. Moreover, m/z abundance differences between analytes were also observed (m/z
176
86 or 30 at 10 and 20 eV, 39 at 40 eV, among others).
177
It should be noted that some of these fragmentation differences have already been
178
described [24]. Bishop et al. have also proposed a method for the quantification of Leu
179
and Ile where they proposed scanning the immonium ion at m/z 86 (directly produced in
180
the ion source) as the precursor ion, to generate unique fragment ions for Leu and Ile, at
181
m/z 43 and 57, respectively [23]. This possibility has also been evaluated in this work 9
182
and MS conditions were optimized using the m/z 86 as precursor ion. However, this
183
option was rapidly discarded due to the loss of sensitivity observed when the immonium
184
ion was selected as the precursor ion (data not shown).
185
With regard to Ile and aIle, the situation is more complex because Ile and aIle are
186
diastereoisomers and few or no fragmentation pattern differences were expected in
187
advance. Unique fragments were not found to distinguish these diastereoisomers (Fig.
188
1). However, slight intensity differences at several m/z values were observed. These
189
differences were mainly observed at m/z 132 and 86 values and low CE values.
190
3.2. Targeted analysis: quantification of Leu, Ile and aIle in saliva samples
191
As Leu and Ile are naturally present in the matrix of analysis, their prior quantification
192
is required in order to construct the multivariate calibration models and to check their
193
prediction capabilities. Although aIle has not been previously detected in saliva, its
194
absence in the analyzed samples required confirmation. Thus, the first step was the
195
determination of the analytes in the saliva samples using the LC-ESI-MS/MS method
196
(these values were considered reference ones). Beyond here, the chromatographic
197
analysis is no longer required.
198
Matrix effects were evaluated. Three calibration curves were obtained for five
199
calibration levels in three different matrices, UHQ water and two different saliva
200
samples. The concentration ranges were between 350 and 1400 µg L-1 for Leu, 150 and
201
600 µg L-1 for Ile and between 15 and 60 µg L-1 for aIle. The slopes of the three
202
calibration curves were compared and significant differences were observed. Thus,
203
matrix effects were confirmed, so the standard addition method was used for
204
quantification. Each saliva sample was spiked at three concentration levels and were
10
205
analyzed in triplicate. The quantitation MRM transition used was 132.1→86.1 for all
206
the compounds (Table 1).
207
3.2.1. Leucine
208
3.2.1.1. LC-ESI-MS/MS analysis
209
Leucine concentration levels in the analyzed saliva samples were found to be in the 0.21
210
to 12 mg L-1 range. These results were in good agreement with previous published
211
results [9]. Table 3 shows the concentrations found for the diluted samples using the
212
LC-ESI-MS/MS method and the concentration ranges used for the standard addition.
213
Fig. 2a shows the total ion current (TIC) chromatogram of a saliva sample injected at
214
four concentration levels. This was at the endogenous concentrations of the compounds
215
(green solid line) and at the three different concentration levels used for the standard
216
addition. As can be seen, in addition to the signals obtained for the target compounds,
217
there was a chromatographic peak corresponding to an unknown isobaric compound
218
(tR=1.5 min, putative identified as creatine) [9, 26] with an important contribution to the
219
analytical signal. The abundance of this analyte was different among samples.
220
Keeping in mind that the objective of the present study is the development of a
221
methodology that does not include a separation step for the determination of the
222
selected isobars, the presence of this unknown compound should not be a problem as
223
long as it shows a different fragmentation pattern compared to the target compounds.
224
However, it was observed that it shared several MRM transitions with the target
225
analytes. Specifically, transition 132.1→44.0 with Leu, Ile and aIle and transition
226
132.1→43.0 with Leu (Fig. 2b). This second overlapping was critical because it was the
11
227
only MRM transition that could be used to distinguish Leu from Ile and aIle when no
228
chromatographic separation was used (Table 1).
229
3.2.1.2. FIA-ESI-MS/MS analysis
230
The set of samples previously analyzed were injected using the FIA-ESI-MS/MS
231
method. Fig. 2c shows the profile signal obtained for the same saliva sample (Fig. 2a) at
232
the different concentration levels previously described.
233
First, a univariate calibration model by the standard addition protocol was built for the
234
quantification of Leu. Based on previous results, the MRM transition used for this
235
purpose was 132.1→43.0, specific for this compound. An isotopically labeled internal
236
standard (13C-Leu) was added to all the samples at a concentration of 300 µg L-1 and
237
area values were normalized to the ones obtained for the 133.1→86.1 transition (IS).
238
Fig. 3a represents the concentration of Leu in the saliva samples obtained with the LC-
239
ESI-MS/MS method (reference values, x axis) versus the ones obtained with the FIA-
240
ESI-MS/MS method (predicted concentrations, y axis). As is shown in the figure, the
241
concentration values obtained with the method that does not include a separation step
242
were considerably higher than the values obtained with the LC-ESI-MS/MS method.
243
The difference between the reference values and the predicted ones was 236%. This
244
error is expressed as (1): ∑ ( − ̂ ) I (%) = 100
̅
245
where ci is the reference value, ĉi is the predicted concentration, I is the number of
246
samples and ̅ is the average of the Leu concentrations (reference values).
12
247
As stated before, the unknown isobaric compound present in the saliva matrix
248
contributes to the signal obtained for the transition 132.1→43.0, which hinders the
249
quantification of Leu in saliva samples using univariate calibration models. No other
250
MRM transition can be used for Leu quantification due to the spectra overlapping with
251
Ile and aIle. Signals obtained with the transition 86.1→43.0 were also evaluated but,
252
again, the loss of sensitivity observed made this transition unusable.
253
In order to overcome this situation, multivariate calibration based on partial least square
254
regression (PLS1) was used. Five saliva samples (saliva 1-5) and all their standard
255
additions (15 samples: 5 saliva samples spiked at three concentration levels) were used
256
to construct the calibration models (20 samples in total). Each sample was analyzed in
257
duplicate. The use of different saliva samples and the addition of an IS allowed the
258
construction of one model valid for the quantification of any sample.
259
A PLS1 model was built for Leu using a maximum of 20 factors. Cross-validation
260
(leave one out) was used to select the optimum number of PLS components
261
corresponding to the minimum root mean standard error of validation (RMSEV). Under
262
these conditions, the optimum number of PLS components was 5 and the E (%) value
263
was 11 % in the cross-validation step. All the MRM transitions used to build the model
264
were significant. As an example, Fig. 4 shows the contribution of each transition to the
265
first two PLS factors. As shown in Fig. 4, all the variables in the first PLS factor had a
266
positive contribution to the model. However, transitions corresponding to product ion
267
44.0 and some corresponding to 43.0 had a negative contribution in the second one
268
subtracting the contribution of the interfering compound to the model.
269
The model was applied to the analysis of 4 saliva samples (saliva 6-9) which had not
270
been included in the calibration step. Standard additions of the aforementioned samples 13
271
(12 samples: 4 saliva samples spiked at three concentration levels) were also analyzed.
272
The total number of samples used in the external validation was 16. Fig. 3b shows the
273
results obtained. The predicted concentrations using the PLS1 model were similar to the
274
ones obtained with the LC-ESI-MS/MS method. The error was reduced from 236% to
275
17%. Values similar to that reported here can be considered acceptable and are common
276
in methodologies that do not include a separation step [27, 28].
277
These results highlight that the use of multivariate models allows the quantification of
278
Leu in saliva samples, even with the presence of unidentified interfering compounds
279
and the lack of specific transitions. Moreover, this methodology allows the
280
determination of 24 samples per hour (1 min required for data acquisition and 1.5 min
281
for injection), while it is only possible to analyze 2 samples per hour when the LC-ESI-
282
MS/MS method is used. This demonstrates the high throughput of the proposed
283
methodology, with an increase of one order of magnitude of the number of samples
284
analyzed per hour (see Fig. 2).
285
3.2.2. Isoleucine and allo-isoleucine
286
3.2.2.1. LC-ESI-MS/MS analysis
287
Ile and aIle were quantified in the saliva samples using the standard addition protocol.
288
Analysis confirmed that aIle was not present in the samples. Ile concentrations were
289
found to be between 0.04 and 7 mg L-1. Table 3 shows the concentrations found for the
290
diluted samples and the concentrations added for the standard addition quantification.
291
3.2.2.2. FIA-ESI-MS/MS analysis
14
292
The construction of univariate calibration models for the individual quantification of Ile
293
and aIle was not considered due to the absence of specific MRM transitions for these
294
analytes.
295
Thus, a PLS1 model was built for each analyte. The calibration set used for model
296
construction was the same saliva samples used for Leu. The optimum number of PLS
297
factors (cross-validation leave one out) was 2 and 3 for Ile and aIle, respectively. For
298
Ile, all the MRM transitions were significant, while for aIle only 5 of the 62 measured
299
MRM transitions were so. The rest of the variables (57) had regression coefficients with
300
uncertainty values higher than the absolute value from the model [23]. The E (%) was
301
22 % and 79 % for Ile and aIle, respectively. Both values were higher than the E (%)
302
found for Leu (11 %). Both analytes presented complete overlapping in all the selected
303
transitions and the slight intensity differences previously observed were not enough to
304
distinguish between them.
305
These models were applied to the analysis of a set of saliva samples which had not been
306
included in the calibration step (same samples used for Leu analysis). With regard to Ile,
307
the predicted concentrations were not satisfactory. Fig. 5a shows the Ile predicted
308
concentrations obtained with the LC-ESI-MS/MS method versus the concentrations
309
obtained with the PLS1 calibration model. The relative error was 66 %. The samples
310
with the highest deviations from the reference values corresponded to those with the
311
highest aIle concentration levels (Fig. 5b).
312
In the case of aIle, the E (%) of the PLS1 model was so elevated (79 %) that it was not
313
possible to use for the quantification of aIle in an external validation set.
314
Based on these results, it was concluded that it was not possible to quantify Ile and aIle
315
individually. However, a new PLS1 calibration model was built considering the sum of 15
316
concentrations of both analytes. The saliva samples considered for the calibration set
317
were maintained. The optimum number of PLS factors (cross-validation leave one out)
318
was 4 and the E (%) was 15 %. All the MRM transitions (62) were significant.
319
When this model was applied to an external validation set, the E (%) obtained was 14
320
%. Fig. 6 shows the predicted values using the PLS1 model versus the ones obtained
321
with the LC-ESI-MS/MS method (reference values) for the external validation set. The
322
model was able to assign correct concentrations to all the samples, independently of the
323
Ile/aIle concentrations ratios.
324
Finally, a univariate calibration model using the quantitation transition 132.1→69.0 was
325
built for the sum of both compounds. The E (%) obtained for the external calibration
326
was 17 %. This result was similar to the one obtained when the PLS1 model was used
327
(14 %). The slight difference observed could be attributed to the small contribution of
328
Leu to the quantitation transition [29]. When the multivariate model was used, factors 2,
329
3 and 4 showed a negative contribution of the specific transitions described for Leu.
330
However, univariate models were not able to distinguish this contribution.
331
Although satisfactory results were also obtained with the univariate calibration models,
332
the use of multivariate calibration models increases the reliability of the results as they
333
take into account possible interferences of other compounds present in the sample that
334
could contribute to the signal, as has been observed for Leu. Moreover, quantification is
335
performed with multiple MRM transitions, not only one, increasing the reliability of the
336
results.
337
4. Conclusions
16
338
A rapid method based on FIA-ESI-MS/MS has been proposed for the determination of
339
L-leucine and the sum of L-isoleucine and L-allo-isoleucine in saliva samples. The
340
method allows the analysis of 24 samples per hour against the 2 that can be analyzed
341
with the corresponding LC-ESI-MS/MS method. The use of multivariate calibration
342
based on partial least squares regression (PLS1) allowed to extract the useful
343
information from the profile signal and to obtain satisfactory results in the
344
quantification of these analytes. In addition, this chemometric technique took into
345
account the presence of other interfering compounds with the same transitions as the
346
analytes of interest.
347
When abnormally high or low concentrations of the analytes studied are obtained [30],
348
the use of the LC-ESI-MS/MS method is recommended to confirm the results. In this
349
way, it is beneficial to analyze the vast majority of samples in the laboratory with this
350
method, which is time and cost effective.
351
Acknowledgements
352
The authors wish to thank the Spanish Ministry of Economy, Industry and
353
Competitiveness for funding the project CTQ2017-87886-P/BQU; the Junta de Castilla
354
y León for project SA055P17; and the Samuel Solórzano Foundation (FS/19-2017). The
355
authors are grateful to Dr. P.G. Jambrina for his help in the data analysis.
17
356
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https://doi.org/10.1016/S1044-0305(99)00098-7
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electrospray ionization tandem mass spectrometry, Rapid Commun. Mass Spectrom. 21
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Isoleucine by Low Energy ESI-TRAP MSn, J. Am. Soc. Mass Spectrom. 18 (2007) 57-
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63 https://doi.org/10.1016/j.jasms.2006.08.011.
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reversed-phase liquid chromatography with tandem mass spectrometry and the
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(2016) 92-99 https://doi.org/10.1016/j.aca.2016.01.032.
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449
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450
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451
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452
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453
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454
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455
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456
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Ionization
Tandem
Mass
Spectrometry
with
Collision-Induced
457
22
458
Figure captions
459 460
Fig. 1. Product ion spectra (precursor ion, m/z 132.1, scan range, m/z 20.0-135.0) of the
461
three target compounds at three different collision energies: 1, 20 and 40 eV.
462
Fig. 2. Chromatograms obtained for a saliva sample. (a) Total ion current (TIC)
463
chromatograms obtained with the LC-ESI-MS/MS method for the standard addition of a
464
saliva sample. (b) Multiple Reaction Monitoring (MRM) chromatograms of the saliva
465
sample for the two transitions shared by the interfering compound and Leu and Ile/aIle.
466
(c) TIC chromatograms obtained with the FIA-ESI-MS/MS method for the standard
467
addition of the same saliva sample as (a).
468
Fig. 3. Correlation plots of the predicted concentration (FIA-ESI-MS/MS) versus
469
reference concentration by the LC method using (a) univariate calibration model and
470
standard addition protocol (b) partial least square regression (PLS1) model for the
471
external validation step.
472
Fig. 4. Plot of the PLS1 loadings of each of the 62 variables for the first two factors for
473
Leu model.
474
Fig. 5. (a) Correlation plot of the predicted concentration versus reference concentration
475
(LC-ESI-MS/MS) for isoleucine in presence of different concentrations of allo-
476
isoleucine. (b) Concentrations of Ile and aIle of the samples which deviate most from
477
the reference values (marked). Samples are identified as the number of the saliva sample
478
analyzed (S) and the level of the standard addition (L), being 0 the endogenous
479
concentration and 3 the highest level of the standard addition.
480
Fig. 6. Correlation plot of the predicted concentrations versus reference ones obtained
481
for the sum of Ile and aIle using PLS1 model for the external validation step.
482 483 484 485 486 23
487
Table 1. Experimental parameters of the target analytes for univariate analysis.
488
Compound Ile
Leu
9.2
9.8
10.7
---
132.1
132.1
132.1
---
86.1/69.1
86.1/69.1
86.1/43.1
---
41
41
81
---
Collision energy (eV)
5/17
5/17
5/21
---
Precursor ion (m/z)
132.1
132.1
132.1
133.1
Product ion (m/z)
69.1
69.1
43.1
86.1
Fragmentor (V)
41
41
81
41
Collision energy (eV)
17
17
21
5
LC-ESI-MS/MS FIA-ESI-MS/MS
Univariate
tR (min)
Univariate
13
aIle
Precursor ion (m/z) Product ion (m/z) Fragmentor (V)
C-Leu
489
490
24
Table 2. MS/MS transitions selected for multivariate calibration.
491
CAV
4
7
Precursor ion (m/z)
132.1
132.1
Fragmentor (V)
41
41
CE (eV)
Product ion (m/z)
3
86.0
4
132.0
5
132.0
15
86.0
20
86.0, 41.0
30
44.0
1
132.1, 86.1
2
132.1, 86.1
3
132.1, 86.1, 30.0
4
132.1, 86.1, 30.0
5
132.1, 86.1, 30.0
10
132.1, 86.1, 69.0, 30.0
15
86.1, 69.0, 44.0, 43.0, 41.0, 30.0
20
30
40
86.1, 69.0, 58.0, 57.0, 45.0, 44.0, 43.0, 41.0, 30.0 86.1, 69.0, 58.0, 57.0, 56.0, 45.0, 44.0, 43.0, 41.0, 39.0, 27.0 58.0, 57.0, 56.0, 45.0, 44.0, 43.0, 42.0, 41.0, 39.0, 30.0, 29.0, 27.0
492 493
25
494
Table 3. Concentrations of the target analytes obtained with the LC-ESI-MS/MS
495
method in the analyzed diluted saliva samples and concentrations added for the standard
496
addition quantification.
Sample Saliva 1 Saliva 2 Saliva 3 Saliva 4 Saliva 5 Saliva 6 Saliva 7 Saliva 8 Saliva 9 497 498
Concentration (µg L-1) Leucine Isoleucine Aloisoleucine Added Prediction* Added Prediction* Added Prediction* 0-500 (24±2) x 10 0-140 61±6 0-40
26
Figure 1
O
12
Leucine
Abundance (104)
H3C OH
10 CH3
NH2
8
CE = 40 eV 6 4
CE = 20 eV
2
CE = 1 eV
0 20
40
60
80
100
120
140
m/z 18 CH3
16
OH
O
14
Abundance (104)
Isoleucine
H3C
NH2
12 10
CE = 40 eV
8 6
CE = 20 eV
4 2
CE = 1 eV
0 20
40
60
80
100
120
140
m/z 18 CH3
16
OH
O
14
Abundance (104)
Allo-isoleucine
H3C
NH2
12 10
CE = 40 eV
8 6
CE = 20 eV
4 2
CE = 1 eV
0 20
40
60
80
m/z
100
120
140
Figure 2
a Endogenous concentration Level 1 standard addition (L1) Level 2 standard addition (L2) Level 3 standard addition (L3)
Abundance (104)
2.8 2.4 2.0
TIC
Isoleucine Leucine
Aloisoleucine 1.6
Interfering compound 1.2 0.8 0.4 0
0
2
1
3
4
5
6
7
8
9
10
11
12
13
27
28
Time (min) b
MRM (132.143.0) MRM (132.144.0)
Abundance (103)
5.0
Interfering compound 4.0 3.0 2.0 1.0
Isoleucine
Leucine
0.0 0
1
2
3
4
5
6
7
8
9
10
11
12
13
27
28
Time (min)
3.2
3.2
2.8
2.8
Abundance (104)
Abundance (104)
c 2.4 2.0 1.6 1.2 0.8 0.4
Endogenous concentration Level 1 standard addition (L1) Level 2 standard addition (L2) Level 3 standard addition (L3)
TIC
2.4 2.0 1.6 1.2 0.8 0.4
0
0 0
1
Time (min)
0
0.2
0.4
0.6
0.8
1.0
Time (min)
Figure 3
Leucine b
Univariate calibration 30
25
20
15
10
MRM (132.143.0) 5
E (%) = 236 0 0
5
10
15
20
LC-ESI-MS/MS (reference, mg
25
L-1)
30
FIA-ESI-MS/MS (predicted, mg L-1)
FIA-ESI-MS/MS (predicted, mg L-1)
a
Multivariate calibration 30 25 20 15 10 5
E (%) = 17 0 0
5
10
15
20
25
LC-ESI-MS/MS (reference, mg
30
L-1)
Figure 4
1st PLS factor 132.1
86.1
58.0, 57.0, 56.0 and 45.0
60
40
Regression coefficients
30
69.1
50
44.0 and 43.0 42.0, 41.0, 39.0, 30.0 and 27.0
20 10 0
2nd PLS factor 70 60 50 40 30 20 10 0 -10
Figure 5
FIA-ESI-MS/MS (predicted, mg L-1)
a
b
Multivariate calibration 15
Isoleucine
S8_L3
12.5 S8_L2
S5_L3
10 S8_L1
S5_L2
7.5 S8_L3
S5_L3
S5_L1
5 S8_L2
S5_L2 2.5
S8_L1
E (%) = 66
S5_L1
0 0
2.5
5
7.5
10
12.5
LC-ESI-MS/MS (reference, mg L-1)
15
aIle 8
Ile 6
4
2
0
2
4
Reference concentrations (mg
6 L-1)
8
Figure 6
FIA-ESI-MS/MS (predicted, mg L-1)
Multivariate calibration 15
Ile+aIle 12.5
9.4 mg L-1 Ile 0.2 mg L-1 aIle
10
7.5
5
0.6 mg L-1 Ile 2.5 mg L-1 aIle
2.5
E (%) = 14 0 0
2.5
5
7.5
10
12.5
LC-ESI-MS/MS (reference, mg L-1)
15
• • • •
A rapid method based on FIA-ESI-MS/MS and multivariate calibration is proposed Determination of L-leucine was achieved when interfering compounds were present Determination of the sum of L-isoleucine and L-allo-isoleucine was achieved The method allows the analysis of 24 saliva samples per hour
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: