Strategies for Using
Postcolumn Infusion of Standards
to Correct for Matrix Effect in LC-MS-Based Quantitative Metabolomics
The
field of metabolomics, which studies
small molecules in biological
matrices, is rapidly growing and the number of published studies reporting
on molecular biomarkers with e.g. diagnostic or prognostic value continues
to increase. While individual metabolomics
studies can provide valuable information, the potential of metabolomics
could be further exploited if data of different studies could be combined
and interpreted beyond their original goals. This is currently challenging
due to the usually semiquantitative nature of metabolomics data, hampering
its widespread translation to the clinic and integration with other
-omics data. The most widely applied
technique in metabolomics is liquid chromatography
coupled to mass spectrometry (LC-MS). This
technique has a superb sensitivity compared to e.g. NMR, but it is
not as quantitative. Absolute quantification
using LC-MS requires customized methods using calibration lines with
reference standards. Usually, internal standards (ISTDs) are added
to all samples, including calibration samples, to correct for (i)
variability in analyte recovery during sample preparation and (ii)
variability of the effect of other components in the sample matrix
on the ionization of the analyte-of-interest (matrix effect). Quantification
is then performed using the peak area ratio of the analyte and its
ISTD. The latter can be a structural analogue of the analyte-of-interest,
of which successful cases have been reported, but the ideal ISTD is a stable isotope-labeled (SIL) analogue because
of its near identical chemical and physical characteristics. However, these SIL standards are usually expensive
and their commercial availability is limited. The costs and poor availability of SIL standards, but also
the
hassle of adding individual ISTDs per analyte are some of the reasons
that increasing the metabolite coverage of a method is often at the
expense of the quantitative reliability. This, because in practice methods with high metabolic coverage often
only use one or two ISTDs to correct for the recovery and matrix effect
of a whole compound class. Although it
may be justified that this suffices to correct for variation in recovery,
limited ISTDs may not be adequate for the correction of matrix effect
during ionization. Matrix effect can be caused by compounds coeluting
with the analyte of interest by e.g. affecting the droplet formation,
changing the droplet viscosity or surface tension, competing for charge,
or inducing coprecipitation with nonvolatiles. These processes change with the composition of the eluent exiting
the LC column, and an ISTD not coeluting with the analyte of interest
can seriously compromise quantification. A solution to this
problem of noncoeluting ISTDs was already proposed
by Choi et al. in 1999. They demonstrated
that by continuously adding a SIL drug after the LC column and before
entering the MS, as a postcolumn infused standard (PCIS), its signal
could correct for ion suppression on both the drug and its earlier
eluting metabolite. This was not possible when the same compound was
added to the samples as a spiked ISTD, because the two analytes suffered
from ion suppression to a different extent. After this first publication, the innovative PCIS technique for
matrix effect correction has only sparsely been adopted, − even though it has great potential to overcome practical issues
in quantitation. The limited use of PCIS might be explained
by an underappreciation
of the problem of matrix effect, a reluctance to abandon the widely
recognized method for matrix effect correction (i.e., adding SIL-ISTDs
to all samples), an underestimation of the great potential of PCIS
or a lack of guidance on how to incorporate PCIS in existing or new
LC-MS methods. With the current paper, we aim to remove these possible
objections and demonstrate that PCIS is a solution to elevate metabolomics
from semiquantification to absolute quantification. Using an LC-MS/MS
method for measuring metabolites related to the endocannabinoid system
in plasma, for which SIL standards were available and which suffer
from ion suppression, we will provide
strategies to incorporate PCIS into LC-MS/MS methods. We will discuss
proposed characteristics of a good PCIS candidate and the optimization
of its concentration. Various approaches to evaluate the PCIS performance
will be demonstrated and processes for which a PCIS cannot correct
will be discussed. Finally, we will show the potential of PCIS as
a replacement of SIL-ISTDs and as a tool enabling absolute quantification
based on a calibration curve of standards in neat solution instead
of matrix. 

Chemicals and Biologicals LC grade
acetonitrile (ACN),
ULC grade isopropyl alcohol (IPA), ULC grade methanol absolute (MeOH)
and ULC grade ethanol absolute (EtOH) were purchased from Biosolve
(Valkenswaard, The Netherlands). Acetic acid, ethylenediaminetetraacetic
acid (EDTA), butylated hydroxytoluene (BHT), 1-Butanol (BuOH) and
MTBE were obtained from Sigma-Aldrich (Zwijndrecht, The Netherlands).
Citric acid (monohydrate) and disodium hydrogen phosphate were purchased
from Merck (Darmstadt, Germany), as was the Ultrapure Milli-Q system
which was used for water. Both the unlabeled standards (linoleoyl
ethanolamide (LEA), docosahexaenoyl ethanolamide (DHEA), arachidonoyl
ethanolamide (AEA), N-arachidonoyl dopamine (NADA), palmitoyl ethanolamide
(PEA), 1-arachidonoyl glycerol (1-AG), 2-arachidonoyl glycerol (2-AG),
oleoyl ethanolamide (OEA) and stearoyl ethanolamide (SEA)), the deuterated
standards (linoleoyl ethanolamide-d4 (d4-LEA), docosahexaenoyl ethanolamide-d4
(d4-DHEA), arachidonoyl ethanolamide-d8 (d8-AEA), N-arachidonoyl dopamine-d8
(d8-NADA), palmitoyl ethanolamide-d4 (d4-PEA), 2-arachidonoyl glycerol-d8
(d8–2-AG), oleoyl ethanolamide-d4 (d4-OEA) and stearoyl ethanolamide-d3
(d3-SEA), and the PCIS standard arachidonoyl-2′-fluoroethylamide
(2F-AEA) were obtained from Cayman Chemical (Ann Harbor, MI, USA,
via SanBio, Uden, The Netherlands). The SI (Table S1) gives an overview of the structures and classification of the used compounds. K2EDTA plasma from 9
diverse healthy donors was purchased from BioIVT (Westbury, NY, USA).
A K 2 EDTA plasma pool of 10 donors, here referred to as
lab plasma pool, was obtained from Innovative Research, Inc. (Novi,
MI, USA). Sample Preparation Samples were prepared by liquid–liquid
extraction according to a validated method − with slight
optimization. Briefly, 150 μL plasma aliquots were (on ice)
mixed with 5 μL of antioxidant solution (0.4 mg/mL BHT in MeOH:
0.4 mg/mL EDTA in water, 1:1 v:v), 40 μL of 0.4 mg/mL BHT solution
in MeOH, 150 μL of a 0.2 M citric acid and 0.4 M disodium hydrogen
phosphate buffer in water, and 1 mL of BuOH:MTBE (1:1 v:v). After
resting for 20 min on ice, they were mixed in a bullet blender (2
min, highest speed) and centrifuged (10 min, 4 °C, 15700 g).
A volume of 850 μL of the upper organic phase was transferred
to a 1.5 mL Eppendorf tube and dried overnight in a SpeedVac (ThermoFisher
Savant) at 30 °C. The dried samples were reconstituted in 50
μL of ice-cold injection solution (90% MeOH, 10% Milli-Q, 100
nM CUDA) to prepare blank plasma or in 50 uL of a combination of the
injection solution, (deuterated) standard mix and 0.4 mg/mL BHT in
MeOH. Samples were vortex mixed and centrifuged (10 min, 4 °C,
15700 g) prior to being transferred to an autosampler vial with an
insert. Preparation of Standards and Mixes From the endocannabinoid
reference standards or solutions, individual stock solutions were
prepared of 1 mM in ACN (1-AG and 2-AG), 1 mM in EtOH (LEA, DHEA,
AEA, PEA and SEA) or 0.1 mM in EtOH (NADA). A mixed calibration stock
solution of unlabeled standards was prepared in ACN with concentrations
of 27 μM of PEA, 13.5 μM of LEA, DHEA, AEA, 1-AG, 1-AG,
OEA and SEA, and 1.35 μM of NADA. This solution was 18 times
diluted to obtain the CAL9 solution, which was serially diluted 1:1
v:v with 0.4 mg/mL BHT in MeOH, to obtain CAL8, CAL7 etc. until CAL2. Individual stock solutions of each deuterated endocannabinoid were
made in ACN at 0.10 mM, only for 2AG-d8 at 0.26 mM. A mixed stock
solution of deuterated standards was prepared by mixing the right
volumes of each individual stock and diluting with ACN to concentrations
of 6.25 μM for all deuterated analytes, except for d8-NADA which
had a concentration of 0.625 μM. This stock mix was further
diluted with 0.4 mg/mL BHT in MeOH to a mixed working solution with
concentrations of 0.188 μM (and 10 times lower for d8-NADA). LC-MS/MS Analysis and Postcolumn Infusion Chromatographic
separation and MS/MS analysis was performed according to the low pH
method described by Di Zazzo et al., using
the same ternary mobile phase system and column (Acquity BEH C18 column
(50 mm × 2.1 mm, 1.7 μM) (Waters, Milford, MA, USA)), but
transferred to a Shimadzu Nexara X2 UHPLC system (Shimadzu, Kyoto,
Japan) coupled to a Sciex QTRAP 6500+ MS (Sciex, Framingham, MA, USA)
with electrospray ionization (ESI). The injection volume was 10 μL
and mobile phase A was used as a stacking solution to avoid breakthrough.
While the method was developed and validated to analyze a variety
of metabolic classes, including oxylipins, oxidative stress markers
and bile acids, this study only investigated endocannabinoids. The
LC and MS method details can be found in the SI (Table S2). Because of uncontrolled isomerization between 1-AG and
2-AG, isomer peaks of 1-AG and 2-AG were integrated as a double peak
(and referred to as 1/2-AG). Also d8–2AG was integrated as
a double peak. Postcolumn infusion was done using either the
QTRAP built-in syringe pump with a 1.0 mL Hamilton syringe (Sigma-Aldrich,
Saint Louis, USA) (for matrix effect exploration and PCIS concentration
optimization) or an Agilent 1260 Infinity pump (Agilent Technologies,
Santa Clara, USA) with a degasser (for all other experiments). Unless
otherwise indicated, the flow rate was set at 50 μL/min. The
flows were combined after the divert valve and before entering the
MS using a T-piece (Upchurch Scientific, Idex Health and Science LLC,
Oak Harbor, WA, USA). A schematic drawing of the instrument setup
is included in the SI (Figure S1). Exploration
of Matrix Effect To explore the extent
of matrix effect in our example method, the deuterated endocannabinoid
working solution was diluted 1:1 v/v with 50% ACN in water and infused
postcolumn by syringe, while injecting a plasma sample applying the
LC-MS conditions as described above. The MRM transitions of the infused
deuterated standards were monitored throughout the whole chromatogram. PCIS Concentration Optimization To optimize the PCIS
concentration, solutions of 25, 50, 100, 200, and 300 ng/mL 2F-AEA
were postcolumn infused (at 0.05 mL/min) for 2 min using isocratic
mobile phase conditions reflecting the gradient composition at 1 min
(73% A, 26% B, 1% C, 0.7 mL/min). The signal of the 2F-AEA MRM transition
( m / z 350.3 to 269.2) between 0.2
and 1.8 min was exported to Excel, smoothed (3-point moving average)
and used to calculate the mean intensity and signal stability (relative
standard deviation, RSD). The same solutions were postcolumn
infused while injecting a plasma sample (fortified with the CAL4 mix
of nonlabeled endocannabinoids) on the column, running the described
gradient separation. PCIS induced matrix effects were calculated as
the mean ratio of peak areas in plasma while infusing each PCIS solution
versus infusing plain ACN, whereby the latter was performed in triplicate. A lower limit of quantification (LLOQ) for each PCIS signal was
set at either 1% of the mean PCIS signal intensity during infusion
(at isocratic conditions) or 10 times the mean noise measured over
the first 0.5 min of the plasma injections (when the PCIS solution
was still diverted to waste), whichever was lowest. The number of
data points below LLOQ of the smoothed signal between 10 and 14 min
were counted for each PCIS concentration. PCIS Data Preprocessing Raw vendor data files (.wiff)
were converted to mzML files using MSConvert (v. 3.0, Proteo Wizard ). A script was developed for peak integration
for LC-MS/MS based experiments with PCIS correction. As input, the
script requires the mzML files, a list of targets and a list of PCISs
with their parameters. Using the target list, the software detects
and extracts the MRM signals, followed by resampling and smoothing.
It calculates PCIS corrected signals as the scan-by-scan ratio of
the analyte signal and the PCIS signal, and peak areas as the sum
of data points within a specified height of the peak (here set at
99% below maximum). It outputs a data report, including peak areas
and PCIS corrected peak areas, as well as pdf-files with chromatograms
of before and after correction to enable checking the peak integration
and, if necessary, adapting input parameters. The script and a set
of demo files have been published in the Github repository github.com/leidenuniv-lacdr-abs/endoc_pciis.
Further data processing was done in Excel (Microsoft). Evaluating
the Ability of PCIS to Correct for Differences in
Matrix Effect Three methods were applied to test the performance
of 2F-AEA as a PCIS. For Method 1, 9 diverse plasma samples (numbered
1 to 9) and an aliquot of the lab plasma pool were prepared. They
were reconstituted in 50 μL, consisting of 30 μL of injection
solution, plus 5, 10, or 20 μL of mixed working solution of
deuterated standards and 15, 10, or 0 μL of 0.4 mg/mL BHT in
MeOH to prepare concentration levels at LOW (18.8 nM of d4-LEA, d4-DHEA,
d8-AEA, d8–2-AG, d4-OEA, d3-SEA and d4-PEA and 1.88 nM of d8-NADA
in plasma 1, 2 and 3), MID (37.6 nM of d4-LEA, d4-DHEA, d8-AEA, d8–2-AG,
d4-OEA, d3-SEA and d4-PEA and 3.76 nM of d8-NADA in plasma 4, 5, 6
and the lab plasma pool) and HIGH (75.2 nM of d4-LEA, d4-DHEA, d8-AEA,
d8–2-AG, d4-OEA, d3-SEA and d4-PEA and 7.52 nM of d8-NADA in
plasma 7, 8, 9) concentration, respectively. For each level, a neat
(matrix-free) solution was prepared identically. Samples were analyzed
in triplicate using the described LC-MS/MS method with 2F-AEA (150
ng/mL) as a PCIS. For Method 2 and 3, 8 150-μL-aliquots
of the lab plasma pool were spiked with 20 uL of the CAL4 solution
of endocannabinoids and prepared until dryness. They were reconstituted
in 24 μL of injection solution (concentration factor (CF) 6.25),
pooled and distributed over vials to create a dilution series containing
0, 2, 4, 8, 16, 24, 32, or 40 μL of CF 6.25 plasma, 10 μL
of mixed working solution of deuterated standards and 40, 38, 32,
24, 16, 8, or 0 μL of injection solution, respectively. This
resulted in plasma samples with a CF ranging from 5 to 0.25, which
were analyzed in triplicate using LC-MS/MS with 2F-AEA as a PCIS.
In this study, the same set of samples was used for Method 2 and 3.
However, as the deuterated standards are not being used in the calculations
of Method 3, samples for this method could also have been prepared
without addition of the SIL standards. Evaluation of the ability
of the matrix effect correction by the
PCIS was in Method 1 done for each SIL-ISTD by dividing its peak area
in each sample by its peak area in the neat solution at the MID concentration,
with and without PCIS correction. In method 2 it was done for each
SIL-ISTD by dividing its peak area in a plasma sample with a certain
CF by its peak area in the neat solution, both with and without PCIS
correction. In method 3, the evaluation was done for each nonlabeled
compound-of-interest by (i) linearly fitting the peak areas over the
plasma CF and determining the coefficient of determination and (ii)
by calculating the coefficient of variation of the ratios of the peak
area and the plasma CF, again both with and without PCIS correction. From Relative to Absolute Quantification To test whether
the PCIS 2F-AEA enables endocannabinoid quantification using a calibration
curve in neat solution, 12 aliquots of the lab plasma pool were prepared
until dryness, reconstituted in 24 μL of injection solution
and pooled. A plasma calibration curve was created mixing 20 μL
of plasma pool with 20 μL of a CAL-solution (CAL2 up to CAL8)
and 10 μL of mixed working solution of deuterated standards.
Also, a blank (nonspiked plasma) was included, wherein 0.4 mg/mL BHT
in MeOH replaced the CAL-solution. Neat calibration standards were
prepared similarly, only the plasma concentrate was replaced by injection
solution. Samples were analyzed in duplicate using LC-MS/MS with 2F-AEA
as a PCIS. 

LC grade
acetonitrile (ACN),
ULC grade isopropyl alcohol (IPA), ULC grade methanol absolute (MeOH)
and ULC grade ethanol absolute (EtOH) were purchased from Biosolve
(Valkenswaard, The Netherlands). Acetic acid, ethylenediaminetetraacetic
acid (EDTA), butylated hydroxytoluene (BHT), 1-Butanol (BuOH) and
MTBE were obtained from Sigma-Aldrich (Zwijndrecht, The Netherlands).
Citric acid (monohydrate) and disodium hydrogen phosphate were purchased
from Merck (Darmstadt, Germany), as was the Ultrapure Milli-Q system
which was used for water. Both the unlabeled standards (linoleoyl
ethanolamide (LEA), docosahexaenoyl ethanolamide (DHEA), arachidonoyl
ethanolamide (AEA), N-arachidonoyl dopamine (NADA), palmitoyl ethanolamide
(PEA), 1-arachidonoyl glycerol (1-AG), 2-arachidonoyl glycerol (2-AG),
oleoyl ethanolamide (OEA) and stearoyl ethanolamide (SEA)), the deuterated
standards (linoleoyl ethanolamide-d4 (d4-LEA), docosahexaenoyl ethanolamide-d4
(d4-DHEA), arachidonoyl ethanolamide-d8 (d8-AEA), N-arachidonoyl dopamine-d8
(d8-NADA), palmitoyl ethanolamide-d4 (d4-PEA), 2-arachidonoyl glycerol-d8
(d8–2-AG), oleoyl ethanolamide-d4 (d4-OEA) and stearoyl ethanolamide-d3
(d3-SEA), and the PCIS standard arachidonoyl-2′-fluoroethylamide
(2F-AEA) were obtained from Cayman Chemical (Ann Harbor, MI, USA,
via SanBio, Uden, The Netherlands). The SI (Table S1) gives an overview of the structures and classification of the used compounds. K2EDTA plasma from 9
diverse healthy donors was purchased from BioIVT (Westbury, NY, USA).
A K 2 EDTA plasma pool of 10 donors, here referred to as
lab plasma pool, was obtained from Innovative Research, Inc. (Novi,
MI, USA). 

Samples were prepared by liquid–liquid
extraction according to a validated method − with slight
optimization. Briefly, 150 μL plasma aliquots were (on ice)
mixed with 5 μL of antioxidant solution (0.4 mg/mL BHT in MeOH:
0.4 mg/mL EDTA in water, 1:1 v:v), 40 μL of 0.4 mg/mL BHT solution
in MeOH, 150 μL of a 0.2 M citric acid and 0.4 M disodium hydrogen
phosphate buffer in water, and 1 mL of BuOH:MTBE (1:1 v:v). After
resting for 20 min on ice, they were mixed in a bullet blender (2
min, highest speed) and centrifuged (10 min, 4 °C, 15700 g).
A volume of 850 μL of the upper organic phase was transferred
to a 1.5 mL Eppendorf tube and dried overnight in a SpeedVac (ThermoFisher
Savant) at 30 °C. The dried samples were reconstituted in 50
μL of ice-cold injection solution (90% MeOH, 10% Milli-Q, 100
nM CUDA) to prepare blank plasma or in 50 uL of a combination of the
injection solution, (deuterated) standard mix and 0.4 mg/mL BHT in
MeOH. Samples were vortex mixed and centrifuged (10 min, 4 °C,
15700 g) prior to being transferred to an autosampler vial with an
insert. 

From the endocannabinoid
reference standards or solutions, individual stock solutions were
prepared of 1 mM in ACN (1-AG and 2-AG), 1 mM in EtOH (LEA, DHEA,
AEA, PEA and SEA) or 0.1 mM in EtOH (NADA). A mixed calibration stock
solution of unlabeled standards was prepared in ACN with concentrations
of 27 μM of PEA, 13.5 μM of LEA, DHEA, AEA, 1-AG, 1-AG,
OEA and SEA, and 1.35 μM of NADA. This solution was 18 times
diluted to obtain the CAL9 solution, which was serially diluted 1:1
v:v with 0.4 mg/mL BHT in MeOH, to obtain CAL8, CAL7 etc. until CAL2. Individual stock solutions of each deuterated endocannabinoid were
made in ACN at 0.10 mM, only for 2AG-d8 at 0.26 mM. A mixed stock
solution of deuterated standards was prepared by mixing the right
volumes of each individual stock and diluting with ACN to concentrations
of 6.25 μM for all deuterated analytes, except for d8-NADA which
had a concentration of 0.625 μM. This stock mix was further
diluted with 0.4 mg/mL BHT in MeOH to a mixed working solution with
concentrations of 0.188 μM (and 10 times lower for d8-NADA). 

Chromatographic
separation and MS/MS analysis was performed according to the low pH
method described by Di Zazzo et al., using
the same ternary mobile phase system and column (Acquity BEH C18 column
(50 mm × 2.1 mm, 1.7 μM) (Waters, Milford, MA, USA)), but
transferred to a Shimadzu Nexara X2 UHPLC system (Shimadzu, Kyoto,
Japan) coupled to a Sciex QTRAP 6500+ MS (Sciex, Framingham, MA, USA)
with electrospray ionization (ESI). The injection volume was 10 μL
and mobile phase A was used as a stacking solution to avoid breakthrough.
While the method was developed and validated to analyze a variety
of metabolic classes, including oxylipins, oxidative stress markers
and bile acids, this study only investigated endocannabinoids. The
LC and MS method details can be found in the SI (Table S2). Because of uncontrolled isomerization between 1-AG and
2-AG, isomer peaks of 1-AG and 2-AG were integrated as a double peak
(and referred to as 1/2-AG). Also d8–2AG was integrated as
a double peak. Postcolumn infusion was done using either the
QTRAP built-in syringe pump with a 1.0 mL Hamilton syringe (Sigma-Aldrich,
Saint Louis, USA) (for matrix effect exploration and PCIS concentration
optimization) or an Agilent 1260 Infinity pump (Agilent Technologies,
Santa Clara, USA) with a degasser (for all other experiments). Unless
otherwise indicated, the flow rate was set at 50 μL/min. The
flows were combined after the divert valve and before entering the
MS using a T-piece (Upchurch Scientific, Idex Health and Science LLC,
Oak Harbor, WA, USA). A schematic drawing of the instrument setup
is included in the SI (Figure S1). 

To explore the extent
of matrix effect in our example method, the deuterated endocannabinoid
working solution was diluted 1:1 v/v with 50% ACN in water and infused
postcolumn by syringe, while injecting a plasma sample applying the
LC-MS conditions as described above. The MRM transitions of the infused
deuterated standards were monitored throughout the whole chromatogram. 

To optimize the PCIS
concentration, solutions of 25, 50, 100, 200, and 300 ng/mL 2F-AEA
were postcolumn infused (at 0.05 mL/min) for 2 min using isocratic
mobile phase conditions reflecting the gradient composition at 1 min
(73% A, 26% B, 1% C, 0.7 mL/min). The signal of the 2F-AEA MRM transition
( m / z 350.3 to 269.2) between 0.2
and 1.8 min was exported to Excel, smoothed (3-point moving average)
and used to calculate the mean intensity and signal stability (relative
standard deviation, RSD). The same solutions were postcolumn
infused while injecting a plasma sample (fortified with the CAL4 mix
of nonlabeled endocannabinoids) on the column, running the described
gradient separation. PCIS induced matrix effects were calculated as
the mean ratio of peak areas in plasma while infusing each PCIS solution
versus infusing plain ACN, whereby the latter was performed in triplicate. A lower limit of quantification (LLOQ) for each PCIS signal was
set at either 1% of the mean PCIS signal intensity during infusion
(at isocratic conditions) or 10 times the mean noise measured over
the first 0.5 min of the plasma injections (when the PCIS solution
was still diverted to waste), whichever was lowest. The number of
data points below LLOQ of the smoothed signal between 10 and 14 min
were counted for each PCIS concentration. 

Raw vendor data files (.wiff)
were converted to mzML files using MSConvert (v. 3.0, Proteo Wizard ). A script was developed for peak integration
for LC-MS/MS based experiments with PCIS correction. As input, the
script requires the mzML files, a list of targets and a list of PCISs
with their parameters. Using the target list, the software detects
and extracts the MRM signals, followed by resampling and smoothing.
It calculates PCIS corrected signals as the scan-by-scan ratio of
the analyte signal and the PCIS signal, and peak areas as the sum
of data points within a specified height of the peak (here set at
99% below maximum). It outputs a data report, including peak areas
and PCIS corrected peak areas, as well as pdf-files with chromatograms
of before and after correction to enable checking the peak integration
and, if necessary, adapting input parameters. The script and a set
of demo files have been published in the Github repository github.com/leidenuniv-lacdr-abs/endoc_pciis.
Further data processing was done in Excel (Microsoft). 

Three methods were applied to test the performance
of 2F-AEA as a PCIS. For Method 1, 9 diverse plasma samples (numbered
1 to 9) and an aliquot of the lab plasma pool were prepared. They
were reconstituted in 50 μL, consisting of 30 μL of injection
solution, plus 5, 10, or 20 μL of mixed working solution of
deuterated standards and 15, 10, or 0 μL of 0.4 mg/mL BHT in
MeOH to prepare concentration levels at LOW (18.8 nM of d4-LEA, d4-DHEA,
d8-AEA, d8–2-AG, d4-OEA, d3-SEA and d4-PEA and 1.88 nM of d8-NADA
in plasma 1, 2 and 3), MID (37.6 nM of d4-LEA, d4-DHEA, d8-AEA, d8–2-AG,
d4-OEA, d3-SEA and d4-PEA and 3.76 nM of d8-NADA in plasma 4, 5, 6
and the lab plasma pool) and HIGH (75.2 nM of d4-LEA, d4-DHEA, d8-AEA,
d8–2-AG, d4-OEA, d3-SEA and d4-PEA and 7.52 nM of d8-NADA in
plasma 7, 8, 9) concentration, respectively. For each level, a neat
(matrix-free) solution was prepared identically. Samples were analyzed
in triplicate using the described LC-MS/MS method with 2F-AEA (150
ng/mL) as a PCIS. For Method 2 and 3, 8 150-μL-aliquots
of the lab plasma pool were spiked with 20 uL of the CAL4 solution
of endocannabinoids and prepared until dryness. They were reconstituted
in 24 μL of injection solution (concentration factor (CF) 6.25),
pooled and distributed over vials to create a dilution series containing
0, 2, 4, 8, 16, 24, 32, or 40 μL of CF 6.25 plasma, 10 μL
of mixed working solution of deuterated standards and 40, 38, 32,
24, 16, 8, or 0 μL of injection solution, respectively. This
resulted in plasma samples with a CF ranging from 5 to 0.25, which
were analyzed in triplicate using LC-MS/MS with 2F-AEA as a PCIS.
In this study, the same set of samples was used for Method 2 and 3.
However, as the deuterated standards are not being used in the calculations
of Method 3, samples for this method could also have been prepared
without addition of the SIL standards. Evaluation of the ability
of the matrix effect correction by the
PCIS was in Method 1 done for each SIL-ISTD by dividing its peak area
in each sample by its peak area in the neat solution at the MID concentration,
with and without PCIS correction. In method 2 it was done for each
SIL-ISTD by dividing its peak area in a plasma sample with a certain
CF by its peak area in the neat solution, both with and without PCIS
correction. In method 3, the evaluation was done for each nonlabeled
compound-of-interest by (i) linearly fitting the peak areas over the
plasma CF and determining the coefficient of determination and (ii)
by calculating the coefficient of variation of the ratios of the peak
area and the plasma CF, again both with and without PCIS correction. 

To test whether
the PCIS 2F-AEA enables endocannabinoid quantification using a calibration
curve in neat solution, 12 aliquots of the lab plasma pool were prepared
until dryness, reconstituted in 24 μL of injection solution
and pooled. A plasma calibration curve was created mixing 20 μL
of plasma pool with 20 μL of a CAL-solution (CAL2 up to CAL8)
and 10 μL of mixed working solution of deuterated standards.
Also, a blank (nonspiked plasma) was included, wherein 0.4 mg/mL BHT
in MeOH replaced the CAL-solution. Neat calibration standards were
prepared similarly, only the plasma concentrate was replaced by injection
solution. Samples were analyzed in duplicate using LC-MS/MS with 2F-AEA
as a PCIS. 

This study
aims to demonstrate
how postcolumn infusion of standards
(PCIS) can be used to move toward absolute quantification in metabolomics
LC-MS methods. Although the example that we use to illustrate this
is based on LC-MS/MS, the strategies can also be applied to LC-high
resolution mass spectrometry (LC-HRMS). To incorporate PCIS
into LC-MS methods we use 4 steps: exploration
of the extent of matrix effect during ionization, selection of candidate
analytes for PCIS, optimizing the concentration of the PCIS solution
and evaluating the PCIS correction. Each of these steps will be discussed
in the following sections. Exploration of Matrix Effect In
LC-MS analyses, postcolumn
infusion of analytes-of-interest while injecting a matrix sample is
a common procedure to get insight in how and when the analytes are
affected by matrix effect. To assess
the potential of PCIS correction for a given LC-MS method, it is useful
to start by infusing a mix of analytes-of-interest and exploring the
infusion profiles. Even though PCIS correction can in general be helpful
to correct for any unexpected sources of matrix effect, it will be
particularly useful for methods with analytes eluting in known high-suppression
zones. Next to providing information on the extent of matrix effect,
we propose using the infusion experiment data to estimate the number
of different PCISs necessary to correct for the complete set of analytes.
It is known that an analyte might be more or less prone to matrix
effect depending on its ionization energy and proton affinity. Also other (physicochemical) properties have
been reported in models predicting ionization efficiencies. It could be expected that analytes with
similar ionization efficiencies can be corrected with a single PCIS.
This is exemplified with the method for endocannabinoids, for which
the result of the matrix effect exploration is visualized in A. Notably, using
stable isotope labeled standards for this exploration is beneficial
as it avoids endogenous analyte peaks in the matrix effect profiles. A shows that the
PCI profiles are all relatively stable between 1 and 10 min. However,
as more clearly visible in B, the signals drop frequently during the last 4 min,
and this high-suppression zone coincides with the elution region of
our analytes. The variability in the matrix effect over the last minutes
of the run shows that quantification would be compromised if only
one or two ISTDs would be spiked to a sample to correct the whole
set of analytes for matrix effect and that this method could benefit
from PCIS correction. The similarity of the response to matrix effect
was assessed by
calculating the Pearson correlation coefficients of the signals of
the infused standards along the high-suppression zone of the LC-MS/MS
run (10.5–14 min). The table in C shows that d8-NADA and d8–2-AG had
correlation coefficients below 0.91 with all other analytes, while
for the remaining analytes the correlation was mostly >0.95. In
addition,
the signals of d8-NADA and d8–2-AG did not correlate well with
each other (0.75). From a structural perspective, a difference in
ionization between most endocannabinoids and d8–2-AG, which
lacks the amide group, might have been expected. For d8-NADA it was
suspected that the low correlation was partially influenced by other
compounds with the same transition and by its overall low intensity. Based on these results of this simple infusion experiment and assuming
a 0.95 correlation between signals is sufficient to share a PCIS,
it can be estimated that 3 PCIS compounds would be needed to correct
for the matrix effect on these 8 targets, one for 2-AG, one for NADA
and one for the others. Selection of a PCIS Candidate To
successfully correct
for matrix effects in LC-MS/MS analysis and to be applied in routine
analysis, we here propose a set of characteristics that a PCIS candidate
should possess. A high-potential PCIS candidate: 1. Is similar in its response to matrix
effect as the analyte-of-interest. Obviously, in this respect a stable
isotope (having the same physicochemical properties) would be ideal.
Still, their limited availability and cost leads to the next best
option, which is a structural analogue. The traditional disadvantage
of structural analogues (not necessarily coeluting with the analyte
and therefore experiencing different ionization conditions) is overcome
by the continuous PCIS signal over time. 2. Is commercially available and affordable.
It should be considered that the consumption of ISTD per sample will
be higher if it is infused as compared to if it is added to the sample. 3. Ideally, has a commercially
available
and affordable SIL, which can be used as a spiked ISTD to correct
for sample preparation recovery or injection volume. Since a spiked
ISTD can also suffer from differences in matrix effect between samples,
it should also be corrected by a PCIS. To prevent multiplication of
errors, it is even more important for a spiked ISTD that the similarity
in response to matrix effect between ISTD and PCIS is very close to
perfect (as a stable isotope would be). 4. Is stable in solution for at least
the time of an analysis batch at lab temperature (or, if necessary,
with appropriate cooling). Namely, degradation of a PCIS during a
batch would result in an increasing overcorrection. 5. Is measurable with a specific signal.
When measuring in multiple reaction monitoring (MRM) mode, it should
have a selective MRM transition, giving limited to no signal in matrix
without infusing the PCIS candidate and should not interfere with
the MRM transitions of the analytes. The presence of a nonendogenous
atom, like fluorine, can help in achieving that. 6. Is easily fragmentable to the selective
fragment. The most selective MRM transition may not be the most sensitive
MRM transition and might require a higher concentration to give a
stable PCIS signal. However, as discussed in the next section, a too
high PCIS concentration can cause (significant) matrix effect by itself,
resulting in a lower sensitivity for the analytes-of-interest 7. Preferably has [M + H] + or
[M-H] − as the main ion with limited adduct formation
or in-source fragmentation. This is because using an adduct may cause
a higher variability in signal and if
adduct formation is significant, the signal of the (de)protonated
ion may be less stable. However, when quantifying an analyte based
on its adduct, a PCIS forming the same adduct would be preferable. Considering these characteristics, fluorinated
anandamide
(2F-AEA) was selected as a PCIS candidate for DHEA, SEA, LEA, OEA,
PEA and AEA. It was anticipated that NADA and 2-AG would probably
require an alternative PCIS, but for this example we will focus only
on a single PCIS, of which the following sections report the results. PCIS Concentration Optimization After selecting the
PCIS candidate, its signal intensity requires optimization. This intensity
depends on both the flow rate of the postcolumn infusion and its concentration.
Increasing the flow rate results in not only a higher PCIS intensity
but also a dilution of the LC-flow and a loss of sensitivity for targets.
Therefore, once a precise flow is achieved, it is recommended to optimize
the concentration. Generally, it is advisible to set the intensity
level of the PCIS as low as possible (to minimize costs and matrix
effect induced by the PCIS), but without compromising the stability
of the signal and without the signal going below the LLOQ during a
chromatographic run. The intensity level can be optimized by varying
the PCIS concentration and measuring the RSD and the intensity of
the PCIS signal when combined with an isocratic LC-flow, as shown
in A. The use
of an isocratic flow prevents peaks or dips in the signal associated
with the injected sample affecting the calculated RSD. As expected,
the PCIS signal intensity linearly increased with the concentration.
The RSD stabilized around 5% with concentrations at or above 50 ng/mL,
but it is important to realize that the signal RSD is more related
to its intensity than the concentration. It also means that if the
PCIS intensity when injecting a biological sample is lower than during
the isocratic infusion experiment (which could occur due to suppression),
the PCIS concentration must be increased to reachieve the intensity
level offering the desired stability. The matrix effect caused by the PCIS and the number
of data points
below the LLOQ in the elution area of the analytes-of-interest also
need evaluation to optimize the concentration. This requires the injection
of biological samples while infusing the different PCIS concentrations.
For the example method, as B shows, the matrix effect caused by the PCIS showed
a slight decreasing trend with increasing concentration, with a mean
of 82% at 25 ng/mL to 73% at 300 ng/mL. It should be noted, that because
no correction could be applied, there was high variation between the
triplicate measurements. Still, we considered the matrix effect caused
by the PCIS to be acceptable across all concentration levels. However,
the number of data points below the LLOQ in the endocannabinoid elution
area was >5 up to 100 ng/mL ( C). As each of these data points potentially disturbs
the correction, a concentration of 150 ng/mL was selected as a compromise
between costs, induced matrix effect and reliable signal measurement. When doing method development with more than one PCIS, an equal-intensity
(as opposed to equal-concentration) mix can be used at various levels
to evaluate the matrix effect caused by the PCIS mix and to evaluate
the number of data points below the LLOQ of the most suppressed signal. Evaluating the Ability of PCIS to Correct for Differences in
Matrix Effect With the PCIS concentration optimized, the
next step is to test its ability to correct. The best way for this
is to induce a matrix effect by varying the matrix or its concentration
and assess the ability of the PCIS to correct for this. Various methods
exist to do this, and we will here present three alternatives using
the endocannabinoid analysis as an example. The method of choice for
future method development with PCIS will depend on the availability
of SIL standards and diverse sources of matrix, as illustrated in
the flowchart of . If diverse matrices and SILs are available, then
the PCIS performance
can be tested by calculating the matrix effect and precision across
matrices, both with and without correction by the PCIS. The Method
1 pane in shows
the results of such an analysis for the endocannabinoids. Here, 9
diverse plasma samples and the lab plasma pool were spiked (after
sample preparation) with a “LOW” (50%), “MID”
(100%) or “HIGH” (200%) concentration of deuterated
endocannabinoids. The left plot of the pane shows that without correction,
d4-LEA suffers from relative matrix effect (d4-LEA is more suppressed
in plasma 3 and the plasma pool than in other samples) and nonlinearity
(the relative peak area increase between the levels is not constant).
Both issues are solved by PCIS correction as all corrected peak areas
fall within the 20% region of the nominal value. The plot on the right
gives the mean matrix effect and coefficient of variation (CV, also
referred to as precision and visualized by the error bars) for each
labeled endocannabinoid across all samples and levels. While this
plot clearly shows that 2F-AEA is an unsuitable PCIS for d8-NADA,
it also shows that for the other analytes, the matrix effect was improved
from 30% - 121% to 85% - 123%, and the precision from 21% - 90% to
5% - 20%. If the availability of different sources of matrix
is limited,
the concentration factor of a single matrix could be varied to test
the ability of the PCIS to correct for the matrix effect induced by
the increased matrix (Method 2 in ). When applied to the endocannabinoid example,
plasma with a concentration factor (CF) between 5 and 0.25 (= four
times diluted plasma) was spiked with SIL standards. As the left plot
in the Method 2 pane shows for d4-LEA, the plasma CF clearly affected
the uncorrected peak area. The matrix effect logarithmically decreased
with increasing the CF, in line with previous studies. Looking at the summary plot at the right, 2F-AEA
had the most difficulty to correct for NADA and 2-AG, agreeing with
the initial exploration. For the other compounds the PCIS correction
improved the matrix effect from 9% - 98% to 86% - 118%. In absence
of various sources of matrix and SIL standards, PCIS
performance can be assessed by its ability to improve the linearity
and the precision of the endogenous analytes across a dilution series
(Method 3 of ). In this case, plasma was spiked with the compounds-of-interest,
but interestingly, if the sensitivity to the endogenous analyte concentration
allows, this is even possible without standards. When plotting the
noncorrected peak area of LEA against the plasma CF, (left in the
Method 3 pane) it gives a flattening curve with a coefficient of determination
(r2) of only 0.86. The matrix effect causing this was effectively
corrected using 2F-AEA as PCIS, as demonstrated by the increased r2
to 0.99. In the absence of matrix effect, dividing the peak area by
the CF would give the same value across all CFs. This ratio is plotted
for LEA, and it can again be observed that the matrix effect has effectively
been corrected with 2F-AEA. The CV of these values indicates the precision
of the dilution series and these, as well as the r2 values are plotted
for each endocannabinoid at the right Method 3 pane. Excluding d8-NADA,
which was not well corrected by the PCIS used, the range of r2 values
improved from 0.10–0.97 to 0.9–0.99 and the precision
from 54% - 110% to 5% - 27%. Since these values might still
seem suboptimal, a few points need
to be taken into consideration. First of all, it should be noted that
the acceptance criteria for endogenous compounds in a concentration
series might be chosen less stringent than for usual calibration curves,
as also proposed in the draft ICH guideline M10 on bioanalytical method
validation advising a limit of 30% for
the precision in a dilution series. The guideline describes dilution
in blank matrix, but when measuring endogenous compounds, achieving
these standards is even more difficult because of the different matrix
for each sample in the calibration curve. Method 3 has some
more disadvantages. The concentration series
is limited in the minimum concentration factor (because of sensitivity
to the endogenous analyte) and its maximum (because of solubility
issues during reconstitution into a smaller volume). In the presented
example, the minimum concentration factor (0.25) still gave a considerable
peak for PEA, but was around the detection limit for DHEA, causing
high variability (22%) and a suboptimal r2 (0.90). Finally, Method
3 does not allow to test the performance of the PCIS in correcting
for absolute matrix effect (which is a requirement for absolute quantification)
due to lack of a neat solution with the same concentration. Due to
these limitations, this method is probably more useful to compare
the performance of different PCISs rather than assessing the performance
of a single one. From Relative to Absolute Quantification The value
of metabolomics studies could be greatly enhanced if absolute concentrations
would be reported. In the absence of an analyte-free matrix, to quantify
analytes that are also endogenous compounds, calibration curves can
be made using the standard addition method (SAM), background subtraction,
surrogate analyte or surrogate matrix approach. The SAM requires multiple analyses of the same sample spiked
at different levels making it time-consuming and impractical. The
background subtraction method uses a pooled sample to build the calibration
curve, meaning that part of the samples will have a concentration
below the lowest calibration point requiring extrapolation of the
curve. The surrogate analyte approach needs a surrogate analyte per
endogenous analyte (preferably a SIL analogue) to build the calibration
curve in matrix, resulting in extreme costs when measuring many analytes.
Finally, the surrogate matrix approach, using an alternative matrix
(e.g., a neat solution) for the calibration curves, also needs an
ISTD per analyte, unless there is (virtually) no matrix effect. If
PCIS could correct for this matrix effect, it becomes possible to
quantify endogenous analytes based on a calibration curve in neat
solution instead of matrix. Before testing this, it is important
to consider the limitations of PCIS. If working well, it can correct
for matrix effect during ionization, but LC-MS/MS analyses have additional
sources of variation. Correcting variability in sample preparation
recovery or matrix effects other than during ionization (such as differences
in target solubility or adsorption to vial surfaces between matrix
and neat solution) still requires an ISTD spiked at the start of the
sample preparation. To correct for sample injection volume, an external
standard (ESTD) could be spiked in the last step of the sample preparation.
When using PCIS correction, these spiked ISTDs or ESTDs should also
be corrected with a PCIS. Instead of a spiked ISTD per analyte, a
single ISTD is expected to correct for the sample preparation recovery
of a whole range of analytes. With the aim of the present study being
to show the potential of PCIS, sources of variability other than during
ionization are not within the scope of this study and all samples
were spiked after the sample preparation. To justify the use
of neat standards as a surrogate calibration
line for matrix samples, parallelism should be evaluated, which is done by slope comparison in . A shows calibration curves
for LEA in plasma and neat solution without any correction, and B after correction
with PCIS. For comparison, a traditional peak area correction with
spiked SIL-ISTD LEA-d4 was performed ( C). The same was done for the other endocannabinoids
and the resulting slope differences (% difference of the slope in
plasma as compared to that in neat solution) are plotted in D. This last figure
reconfirms that the plasma matrix severely suppresses the signal of
the endocannabinoids, as reflected by the slopes being 33% (PEA) up
to 87% (SEA) lower in plasma as compared to neat solution. Quantifying
these endocannabinoids without any correction based on the calibration
curve in neat solution would result in a serious underestimation of
the concentration. Surprisingly, when comparing the slope differences
between the
PCIS and the SIL-ISTD in D, the PCIS correction out- performed the correction
using the spiked SIL-ISTD, which is considered the golden standard.
Considering the high variability in suppression in this chromatographic
area, the suboptimal performance of the SIL-ISTD correction may be
due to the minor retention time difference between the analyte and
its slightly more polar deuterated analogue. Indeed, when overlaying peaks of LEA and d4-LEA (see SI Figure S2) it becomes clear that LEA-d4 elutes
slightly earlier and thereby suffers more from ion suppression, resulting
in an overcorrection of the LEA peak (and a steeper slope of the calibration
curve). These results indicate that in areas of serious and variable
matrix effect, a perfectly coeluting structural analogue (like the
PCIS) might correct even better for matrix effect than a spiked SIL-ISTD
with a slight difference in retention time. Taking into account
the difficulty to correct the slopes for matrix
effect even with a spiked SIL-ISTD, we consider a difference in slope
up to ±15% preferable, but up to ±20% acceptable; the latter
being a cutoff applied before by Godoy et al. comparing slopes of
calibration curves of metabolites in serum and urine versus a synthetic
surrogate matrix. The difference in slope
of SEA (−24%) was outside of the acceptable limit. In the earlier
tests, SEA, being the last eluting endocannabinoid and almost completely
suppressed by the matrix, was also at the limits of the acceptance
criteria. The slope differences of LEA, AEA, DHEA, PEA, OEA and even
2-AG easily fall within the preferable range (with a maximum of +9%).
Therefore, these compounds could all be quantified using 2F-AEA as
a PCIS to correct for matrix effect and calibration standards in neat
solution. 

In
LC-MS analyses, postcolumn
infusion of analytes-of-interest while injecting a matrix sample is
a common procedure to get insight in how and when the analytes are
affected by matrix effect. To assess
the potential of PCIS correction for a given LC-MS method, it is useful
to start by infusing a mix of analytes-of-interest and exploring the
infusion profiles. Even though PCIS correction can in general be helpful
to correct for any unexpected sources of matrix effect, it will be
particularly useful for methods with analytes eluting in known high-suppression
zones. Next to providing information on the extent of matrix effect,
we propose using the infusion experiment data to estimate the number
of different PCISs necessary to correct for the complete set of analytes.
It is known that an analyte might be more or less prone to matrix
effect depending on its ionization energy and proton affinity. Also other (physicochemical) properties have
been reported in models predicting ionization efficiencies. It could be expected that analytes with
similar ionization efficiencies can be corrected with a single PCIS.
This is exemplified with the method for endocannabinoids, for which
the result of the matrix effect exploration is visualized in A. Notably, using
stable isotope labeled standards for this exploration is beneficial
as it avoids endogenous analyte peaks in the matrix effect profiles. A shows that the
PCI profiles are all relatively stable between 1 and 10 min. However,
as more clearly visible in B, the signals drop frequently during the last 4 min,
and this high-suppression zone coincides with the elution region of
our analytes. The variability in the matrix effect over the last minutes
of the run shows that quantification would be compromised if only
one or two ISTDs would be spiked to a sample to correct the whole
set of analytes for matrix effect and that this method could benefit
from PCIS correction. The similarity of the response to matrix effect
was assessed by
calculating the Pearson correlation coefficients of the signals of
the infused standards along the high-suppression zone of the LC-MS/MS
run (10.5–14 min). The table in C shows that d8-NADA and d8–2-AG had
correlation coefficients below 0.91 with all other analytes, while
for the remaining analytes the correlation was mostly >0.95. In
addition,
the signals of d8-NADA and d8–2-AG did not correlate well with
each other (0.75). From a structural perspective, a difference in
ionization between most endocannabinoids and d8–2-AG, which
lacks the amide group, might have been expected. For d8-NADA it was
suspected that the low correlation was partially influenced by other
compounds with the same transition and by its overall low intensity. Based on these results of this simple infusion experiment and assuming
a 0.95 correlation between signals is sufficient to share a PCIS,
it can be estimated that 3 PCIS compounds would be needed to correct
for the matrix effect on these 8 targets, one for 2-AG, one for NADA
and one for the others. 

To
successfully correct
for matrix effects in LC-MS/MS analysis and to be applied in routine
analysis, we here propose a set of characteristics that a PCIS candidate
should possess. A high-potential PCIS candidate: 1. Is similar in its response to matrix
effect as the analyte-of-interest. Obviously, in this respect a stable
isotope (having the same physicochemical properties) would be ideal.
Still, their limited availability and cost leads to the next best
option, which is a structural analogue. The traditional disadvantage
of structural analogues (not necessarily coeluting with the analyte
and therefore experiencing different ionization conditions) is overcome
by the continuous PCIS signal over time. 2. Is commercially available and affordable.
It should be considered that the consumption of ISTD per sample will
be higher if it is infused as compared to if it is added to the sample. 3. Ideally, has a commercially
available
and affordable SIL, which can be used as a spiked ISTD to correct
for sample preparation recovery or injection volume. Since a spiked
ISTD can also suffer from differences in matrix effect between samples,
it should also be corrected by a PCIS. To prevent multiplication of
errors, it is even more important for a spiked ISTD that the similarity
in response to matrix effect between ISTD and PCIS is very close to
perfect (as a stable isotope would be). 4. Is stable in solution for at least
the time of an analysis batch at lab temperature (or, if necessary,
with appropriate cooling). Namely, degradation of a PCIS during a
batch would result in an increasing overcorrection. 5. Is measurable with a specific signal.
When measuring in multiple reaction monitoring (MRM) mode, it should
have a selective MRM transition, giving limited to no signal in matrix
without infusing the PCIS candidate and should not interfere with
the MRM transitions of the analytes. The presence of a nonendogenous
atom, like fluorine, can help in achieving that. 6. Is easily fragmentable to the selective
fragment. The most selective MRM transition may not be the most sensitive
MRM transition and might require a higher concentration to give a
stable PCIS signal. However, as discussed in the next section, a too
high PCIS concentration can cause (significant) matrix effect by itself,
resulting in a lower sensitivity for the analytes-of-interest 7. Preferably has [M + H] + or
[M-H] − as the main ion with limited adduct formation
or in-source fragmentation. This is because using an adduct may cause
a higher variability in signal and if
adduct formation is significant, the signal of the (de)protonated
ion may be less stable. However, when quantifying an analyte based
on its adduct, a PCIS forming the same adduct would be preferable. Considering these characteristics, fluorinated
anandamide
(2F-AEA) was selected as a PCIS candidate for DHEA, SEA, LEA, OEA,
PEA and AEA. It was anticipated that NADA and 2-AG would probably
require an alternative PCIS, but for this example we will focus only
on a single PCIS, of which the following sections report the results. 

After selecting the
PCIS candidate, its signal intensity requires optimization. This intensity
depends on both the flow rate of the postcolumn infusion and its concentration.
Increasing the flow rate results in not only a higher PCIS intensity
but also a dilution of the LC-flow and a loss of sensitivity for targets.
Therefore, once a precise flow is achieved, it is recommended to optimize
the concentration. Generally, it is advisible to set the intensity
level of the PCIS as low as possible (to minimize costs and matrix
effect induced by the PCIS), but without compromising the stability
of the signal and without the signal going below the LLOQ during a
chromatographic run. The intensity level can be optimized by varying
the PCIS concentration and measuring the RSD and the intensity of
the PCIS signal when combined with an isocratic LC-flow, as shown
in A. The use
of an isocratic flow prevents peaks or dips in the signal associated
with the injected sample affecting the calculated RSD. As expected,
the PCIS signal intensity linearly increased with the concentration.
The RSD stabilized around 5% with concentrations at or above 50 ng/mL,
but it is important to realize that the signal RSD is more related
to its intensity than the concentration. It also means that if the
PCIS intensity when injecting a biological sample is lower than during
the isocratic infusion experiment (which could occur due to suppression),
the PCIS concentration must be increased to reachieve the intensity
level offering the desired stability. The matrix effect caused by the PCIS and the number
of data points
below the LLOQ in the elution area of the analytes-of-interest also
need evaluation to optimize the concentration. This requires the injection
of biological samples while infusing the different PCIS concentrations.
For the example method, as B shows, the matrix effect caused by the PCIS showed
a slight decreasing trend with increasing concentration, with a mean
of 82% at 25 ng/mL to 73% at 300 ng/mL. It should be noted, that because
no correction could be applied, there was high variation between the
triplicate measurements. Still, we considered the matrix effect caused
by the PCIS to be acceptable across all concentration levels. However,
the number of data points below the LLOQ in the endocannabinoid elution
area was >5 up to 100 ng/mL ( C). As each of these data points potentially disturbs
the correction, a concentration of 150 ng/mL was selected as a compromise
between costs, induced matrix effect and reliable signal measurement. When doing method development with more than one PCIS, an equal-intensity
(as opposed to equal-concentration) mix can be used at various levels
to evaluate the matrix effect caused by the PCIS mix and to evaluate
the number of data points below the LLOQ of the most suppressed signal. 

With the PCIS concentration optimized, the
next step is to test its ability to correct. The best way for this
is to induce a matrix effect by varying the matrix or its concentration
and assess the ability of the PCIS to correct for this. Various methods
exist to do this, and we will here present three alternatives using
the endocannabinoid analysis as an example. The method of choice for
future method development with PCIS will depend on the availability
of SIL standards and diverse sources of matrix, as illustrated in
the flowchart of . If diverse matrices and SILs are available, then
the PCIS performance
can be tested by calculating the matrix effect and precision across
matrices, both with and without correction by the PCIS. The Method
1 pane in shows
the results of such an analysis for the endocannabinoids. Here, 9
diverse plasma samples and the lab plasma pool were spiked (after
sample preparation) with a “LOW” (50%), “MID”
(100%) or “HIGH” (200%) concentration of deuterated
endocannabinoids. The left plot of the pane shows that without correction,
d4-LEA suffers from relative matrix effect (d4-LEA is more suppressed
in plasma 3 and the plasma pool than in other samples) and nonlinearity
(the relative peak area increase between the levels is not constant).
Both issues are solved by PCIS correction as all corrected peak areas
fall within the 20% region of the nominal value. The plot on the right
gives the mean matrix effect and coefficient of variation (CV, also
referred to as precision and visualized by the error bars) for each
labeled endocannabinoid across all samples and levels. While this
plot clearly shows that 2F-AEA is an unsuitable PCIS for d8-NADA,
it also shows that for the other analytes, the matrix effect was improved
from 30% - 121% to 85% - 123%, and the precision from 21% - 90% to
5% - 20%. If the availability of different sources of matrix
is limited,
the concentration factor of a single matrix could be varied to test
the ability of the PCIS to correct for the matrix effect induced by
the increased matrix (Method 2 in ). When applied to the endocannabinoid example,
plasma with a concentration factor (CF) between 5 and 0.25 (= four
times diluted plasma) was spiked with SIL standards. As the left plot
in the Method 2 pane shows for d4-LEA, the plasma CF clearly affected
the uncorrected peak area. The matrix effect logarithmically decreased
with increasing the CF, in line with previous studies. Looking at the summary plot at the right, 2F-AEA
had the most difficulty to correct for NADA and 2-AG, agreeing with
the initial exploration. For the other compounds the PCIS correction
improved the matrix effect from 9% - 98% to 86% - 118%. In absence
of various sources of matrix and SIL standards, PCIS
performance can be assessed by its ability to improve the linearity
and the precision of the endogenous analytes across a dilution series
(Method 3 of ). In this case, plasma was spiked with the compounds-of-interest,
but interestingly, if the sensitivity to the endogenous analyte concentration
allows, this is even possible without standards. When plotting the
noncorrected peak area of LEA against the plasma CF, (left in the
Method 3 pane) it gives a flattening curve with a coefficient of determination
(r2) of only 0.86. The matrix effect causing this was effectively
corrected using 2F-AEA as PCIS, as demonstrated by the increased r2
to 0.99. In the absence of matrix effect, dividing the peak area by
the CF would give the same value across all CFs. This ratio is plotted
for LEA, and it can again be observed that the matrix effect has effectively
been corrected with 2F-AEA. The CV of these values indicates the precision
of the dilution series and these, as well as the r2 values are plotted
for each endocannabinoid at the right Method 3 pane. Excluding d8-NADA,
which was not well corrected by the PCIS used, the range of r2 values
improved from 0.10–0.97 to 0.9–0.99 and the precision
from 54% - 110% to 5% - 27%. Since these values might still
seem suboptimal, a few points need
to be taken into consideration. First of all, it should be noted that
the acceptance criteria for endogenous compounds in a concentration
series might be chosen less stringent than for usual calibration curves,
as also proposed in the draft ICH guideline M10 on bioanalytical method
validation advising a limit of 30% for
the precision in a dilution series. The guideline describes dilution
in blank matrix, but when measuring endogenous compounds, achieving
these standards is even more difficult because of the different matrix
for each sample in the calibration curve. Method 3 has some
more disadvantages. The concentration series
is limited in the minimum concentration factor (because of sensitivity
to the endogenous analyte) and its maximum (because of solubility
issues during reconstitution into a smaller volume). In the presented
example, the minimum concentration factor (0.25) still gave a considerable
peak for PEA, but was around the detection limit for DHEA, causing
high variability (22%) and a suboptimal r2 (0.90). Finally, Method
3 does not allow to test the performance of the PCIS in correcting
for absolute matrix effect (which is a requirement for absolute quantification)
due to lack of a neat solution with the same concentration. Due to
these limitations, this method is probably more useful to compare
the performance of different PCISs rather than assessing the performance
of a single one. 

The value
of metabolomics studies could be greatly enhanced if absolute concentrations
would be reported. In the absence of an analyte-free matrix, to quantify
analytes that are also endogenous compounds, calibration curves can
be made using the standard addition method (SAM), background subtraction,
surrogate analyte or surrogate matrix approach. The SAM requires multiple analyses of the same sample spiked
at different levels making it time-consuming and impractical. The
background subtraction method uses a pooled sample to build the calibration
curve, meaning that part of the samples will have a concentration
below the lowest calibration point requiring extrapolation of the
curve. The surrogate analyte approach needs a surrogate analyte per
endogenous analyte (preferably a SIL analogue) to build the calibration
curve in matrix, resulting in extreme costs when measuring many analytes.
Finally, the surrogate matrix approach, using an alternative matrix
(e.g., a neat solution) for the calibration curves, also needs an
ISTD per analyte, unless there is (virtually) no matrix effect. If
PCIS could correct for this matrix effect, it becomes possible to
quantify endogenous analytes based on a calibration curve in neat
solution instead of matrix. Before testing this, it is important
to consider the limitations of PCIS. If working well, it can correct
for matrix effect during ionization, but LC-MS/MS analyses have additional
sources of variation. Correcting variability in sample preparation
recovery or matrix effects other than during ionization (such as differences
in target solubility or adsorption to vial surfaces between matrix
and neat solution) still requires an ISTD spiked at the start of the
sample preparation. To correct for sample injection volume, an external
standard (ESTD) could be spiked in the last step of the sample preparation.
When using PCIS correction, these spiked ISTDs or ESTDs should also
be corrected with a PCIS. Instead of a spiked ISTD per analyte, a
single ISTD is expected to correct for the sample preparation recovery
of a whole range of analytes. With the aim of the present study being
to show the potential of PCIS, sources of variability other than during
ionization are not within the scope of this study and all samples
were spiked after the sample preparation. To justify the use
of neat standards as a surrogate calibration
line for matrix samples, parallelism should be evaluated, which is done by slope comparison in . A shows calibration curves
for LEA in plasma and neat solution without any correction, and B after correction
with PCIS. For comparison, a traditional peak area correction with
spiked SIL-ISTD LEA-d4 was performed ( C). The same was done for the other endocannabinoids
and the resulting slope differences (% difference of the slope in
plasma as compared to that in neat solution) are plotted in D. This last figure
reconfirms that the plasma matrix severely suppresses the signal of
the endocannabinoids, as reflected by the slopes being 33% (PEA) up
to 87% (SEA) lower in plasma as compared to neat solution. Quantifying
these endocannabinoids without any correction based on the calibration
curve in neat solution would result in a serious underestimation of
the concentration. Surprisingly, when comparing the slope differences
between the
PCIS and the SIL-ISTD in D, the PCIS correction out- performed the correction
using the spiked SIL-ISTD, which is considered the golden standard.
Considering the high variability in suppression in this chromatographic
area, the suboptimal performance of the SIL-ISTD correction may be
due to the minor retention time difference between the analyte and
its slightly more polar deuterated analogue. Indeed, when overlaying peaks of LEA and d4-LEA (see SI Figure S2) it becomes clear that LEA-d4 elutes
slightly earlier and thereby suffers more from ion suppression, resulting
in an overcorrection of the LEA peak (and a steeper slope of the calibration
curve). These results indicate that in areas of serious and variable
matrix effect, a perfectly coeluting structural analogue (like the
PCIS) might correct even better for matrix effect than a spiked SIL-ISTD
with a slight difference in retention time. Taking into account
the difficulty to correct the slopes for matrix
effect even with a spiked SIL-ISTD, we consider a difference in slope
up to ±15% preferable, but up to ±20% acceptable; the latter
being a cutoff applied before by Godoy et al. comparing slopes of
calibration curves of metabolites in serum and urine versus a synthetic
surrogate matrix. The difference in slope
of SEA (−24%) was outside of the acceptable limit. In the earlier
tests, SEA, being the last eluting endocannabinoid and almost completely
suppressed by the matrix, was also at the limits of the acceptance
criteria. The slope differences of LEA, AEA, DHEA, PEA, OEA and even
2-AG easily fall within the preferable range (with a maximum of +9%).
Therefore, these compounds could all be quantified using 2F-AEA as
a PCIS to correct for matrix effect and calibration standards in neat
solution. 

This study aimed to
increase the appreciation
and use of postcolumn
infusion of standards (PCIS) as a tool to correct for matrix effect
in LC-MS analysis, with the eventual goal to generate absolute quantitative
data for metabolomics studies. To this end, we provided guidance in
PCIS method development and performance testing, and we demonstrated
its use with a LC-MS/MS method analyzing endocannabinoids in plasma. With a simple infusion experiment it was estimated that at least
six (LEA, AEA, DHEA, PEA, OEA and SEA) out of 8 (also including 2-AG
and NADA) targeted endocannabinoids could be corrected with the same
PCIS. Six characteristics of a promising PCIS candidate were defined
and used to select 2F-anandamide (2F-AEA) to test as a PCIS for the
endocannabinoids in this study. This choice was successful, as became
clear after optimizing its concentration and applying three approaches
to assess its performance, all leading to similar conclusions: the
PCIS 2F-AEA was not successful in the matrix effect correction of
NADA, but for the other analytes it could improve the matrix effect
values for various matrices, the precision over matrices and over
matrix concentrations, and the dilutional linearity and precision
to within acceptable limits. Keeping in mind that this study
only considered variability due
to matrix effect during ionization and a correction for variability
in sample preparation and injection volume would still be advisible,
we showed that using 2F-AEA as PCIS, LEA, AEA, DHEA, PEA, OEA and
2-AG could be quantified with good accuracy (slope difference with
plasma < ±9%) using a calibration series in neat solution.
In this case of severe and variable ion suppression PCIS correction
performed even better than the “gold standard” correction
with spiked SIL-ISTDs. With the endocannabinoid-related compounds
in this study exemplifying a worst-case scenario in terms of matrix
effect, the use of PCIS can be extended to other compound classes
and other fields analyzing large numbers of analytes, such as exposome
research. Based on the wide applicability of the technique and
the results
it showed here, we conclude that PCIS is indeed a powerful tool for
the necessary step toward absolute quantification in metabolomics. 

