SIMPLIFIED TWO-DIMENSIONAL CAPILLARY ELECTROPHORESIS-MASS
SPECTROMETRY MAPPING: ANALYSIS OF PROTEOLYTIC DIGESTS.
Guillaume L. Erny and Alejandro Cifuentes*
Institute of Industrial Fermentations (CSIC), Juan de la Cierva 3, 28006 Madrid, Spain
*Corresponding author:
Dr. Alejandro Cifuentes, Fax#: 34-91-5644853, e-mail: acifuentes@ifi.csic.es
Abbreviations: EIE, extracted ions electropherogram; TIE, total ions electropherogram;
CYC-B, cytochrome c from bovine; CYC-R, cytochrome c from rabbit; CYC-H, cytochrome
c from horse.
Keywords: 2D, mapping, CE-MS, proteomics, peptides, proteins, chemometric.
1
Abstract
Capillary electrophoresis-mass spectrometry (CE-MS) has demonstrated to be a very useful
hyphenated technique for Proteomic studies. However, the huge amount of data stored in a
single CE-MS run makes necessary to account with procedures able to extract all the relevant
information made available by CE-MS. In this work, we present a new and easy approach
able to generate a simplified two-dimensional map from CE-MS raw data. This new approach
provides the automatic detection and characterization of the most abundant ions from the CEMS data including their m/z values, ion intensities and analysis times. It is demonstrated that
visualization of CE-MS data in this simplified 2D format allows (i) an easy and simultaneous
visual inspection of large datasets, (ii) an immediate perception of relevant differences in
closely related samples, (iii) a rapid monitoring of data quality levels in different samples and
(iv) a fast discrimination between comigrating polypeptides and ESI-MS fragmentation ions.
The strategy proposed in this work does not rely on an excellent mass accuracy for peak
detection and filtering since MS values obtained from an ion trap analyzer are used.
Moreover, the methodology developed works directly with the CE-MS raw data, without
interference by the user, giving simultaneously a simplified 2D map and a much easier and
more complete data evaluation. Besides, this procedure can easily be implemented in any CEMS laboratory. The usefulness of this approach is validated by studying the very similar
trypsin digests from bovine, rabbit and horse cytochrome c. It is demonstrated that this
simplified 2D approach allows obtaining in a fast and simple way specific markers for each
species.
2
1. Introduction
1.1 General aspects
Proteins are fundamental components of all living beings and include many substances such
as enzymes, hormones, antibodies, etc, necessary for the proper functioning of any organism.
Separation and identification of proteins became in the last years of great importance
impulsed by the development of the Proteomics field and the seeking for a better
understanding of some biologic functions. However, it is well known that to get a complete
knowledge on the proteins content of any organism in a given moment is an extremely
complex task since organisms usually contain thousands of proteins of very different
concentration, size, hydrophobicity and charge. Moreover, enzymatic digestion of these
proteins is usually required increasing enormously the amount of compounds to analyze,
which illustrates the difficulty of this task [1].
Some recent developments have focused on the separation of proteins without aiming to
identify all of them, but intending to provide protein profiles or fingerprintings under specific
conditions. This fingerprinting (usually displayed in a form of a 2D-map using color coding
for intensity) can be used to establish Proteomic patterns for diagnostic purpose or to easily
obtain a biomarker specific for a particular disease. Moreover, these profiling techniques can
be useful not only for clinical applications but also for food analysis including e.g., food
adulterations, detection of genetically modified organisms, etc [2]. Alternatively, proteolysis
patterns are sometime favored as the peptidic fragments are more soluble, stable, and usually
easier to separate [3]. However, as each protein gives rise to numerous fragments, the pattern
complexity is significantly increased in this case.
3
Protein or peptide maps are usually achieved using 2D separation techniques, 2D-PAGE
being the most common procedure. Other 2D techniques have also been used such as
HPLC/HPLC, HPLC/CZE, CZE/MEKC, [4-6] as well as hyphenated techniques with MS as
second dimension (e.g., CE-MS or HPLC-MS) [7,8]. The great advantage of MS is that
allows identification of a given compound based on its relative molecular mass (Mr). In this
latter case data visualization of MS data in the format of a map (fraction number or retention
time as y-axis and m/z as x-axis, with a color color-coding for signal intensity) seems to be
suitable [9]. However, one of the main problems when using MS as second dimension is the
increased complexity due to fragmentation of parent ions. Although the use of soft ionization
procedures such as electrospray for HPLC-MS [10] and CE-MS [11] reduces significantly the
internal energy during the ionization, and thus limits the fragmentation process, the ionization
will never be “soft” enough to ensure that a particular detected ion does not result from the
fragmentation of a parent ion.
Apart of the above mentioned limitations, the huge amount of data in different formats
produced by Proteomic techniques has determined the urgent need for procedures able to
extract the relevant information from the MS spectra [12]. Specific tools have already been
developed to display m/z ratios in conjunction with data from a separation step [13-15]. These
procedures cannot only be used for MS mapping, but also for visual analysis and comparison
between various datasets through adequate normalization. The increasing activity in this field
underlines the need for flexible data visualization tools that can easily be applied to a wide
variety of experimental setups. As evident, all these technologies are still burdened with
certain limitations. The most severe limitation, however, might not be the technical aspect of
MS and/or separation (data accumulation) but rather the subsequent data evaluation.
4
The aim of this paper is to demonstrate the possibilities of a new and easy approach
developed at our lab able to provide a simplified 2D mapping of CE-MS data. The approach
is based on the automatic detection and characterization of the main peaks and m/z values
from the raw data. The simplified 2D mapping will be obtained by performing a classical
peak analysis for every m/z containing important information. To our knowledge, this is the
first time that such approach has been proposed. The strategy proposed in this work does not
rely on an excellent mass accuracy (like the one provided by more expensive MS analyzers as
e.g., TOF-MS or FT-ICR-MS) as an attribute for peak detection and filtering since data
obtained from an ion trap analyzer are used. Moreover, the methodology developed works
directly with the CE-MS raw data, without interference by the user, providing simultaneously
a simplified 2D map and a much easier and complete data evaluation. The usefulness of this
approach is validated by analyzing the trypsin digests of cytochrome c from three different
species, namely, bovine (CYC-B), rabbit (CYC-R) and horse (CYC-H). It is shown that this
simplified 2-D procedure permits the detection of specific markers for each species even from
very similar proteolytic digests.
1.2 Theoretical section.
The chemometric tool developed in this work allows carrying out the following three steps in
an automatic way (see Figure 1). First, raw data from a given CE-MS run are automatically
converted in a 2 dimensional matrix, namely, time  m/z (step 1 in Figure 1). In the second
step of Figure 1, the m/z values containing useful information are detected (vide infra), and a
series of extracted ions electropherograms (EIE) are reconstructed based on those principal
m/z ions. In the last step, each individual EIE is automatically analyzed to obtain the main m/z
values together with their mass incertitude, peak area and analysis time (see table in Figure 1,
step 3). These three steps provide an automatic and drastic reduction in the data size, making
5
easier the following simplified 2-D representation and allowing a better study and
visualization of the CE-MS results.
In order to automatically detect the m/z values of interest, the standard deviation (SD) of the
ionic intensity values obtained for each m/z is calculated along the time scale using our
approach. Logically, the most interesting m/z values will have the highest SD as a result of the
large ionic intensity variation observed along the time. Therefore, m/z values of interest are
selected as those with a SD higher than a certain threshold. EIEs are then reconstructed by
summing for each time the ionic intensities obtained inside an m/z interval centered at the
detected main m/z value plus twice the mass incertitude (0.5 m/z in our case). However, in the
case where two detected m/z ions are close to each other (less than 0.4 m/z), they will be
processed in a single EIE (see Figure 1, Step 2). The resulting data will be a series of array,
each of them representing an EIE and indexed by the average m/z and mass incertitude.
Peaks are then detected in each EIE as a succession of data points whose signal is 10 times
higher than the average noise calculated using the 20 first points of every EIE. For each
detected peaks two electrophoretic parameters were measured, the peak area, A, and the peak
migration time, tm, by
A  t  I i
(1)
i
and
tm 
1
 ti  I i
A i
(2)
where the summation i is over every point that defines a particular peak for a given m/z, being
Ii and ti the intensity and time respectively. As can be seen, the migration time has been
6
calculated using the first statistical moment [16] and will slightly differ from the peak
maximal for asymmetrical peaks. However, the use of this parameter allows a higher
precision than the peak maximal whose precision can be limited by the sampling rate [17].
For each detected peak, area and migration time, as well as m/z and its incertitude are
recorded in a table as indicated in step 3 of Figure 1. These data are next used to get the
simplified 2D CE-MS representation.
2. Experimental section
2.1 Chemicals
Ammonia (30%) was from Panreac (Barcelona, Spain), methanol (HPLC grade) from
Scharlau (Barcelona, Spain) and formic acid from Merck (Darmstadt, Germany). Trypsin and
cytochrome c from bovine heart (CYC-B), horse heart (CYC-H) and rabbit heart (CYC-R)
were from Sigma (St. Louis, MO, USA). Water was deionized with a Milli-Q system
(Millipore, Bedford, MA, USA).
2.2 Protein hydrolysis
Cytochrome c from the different species were dissolved in a buffer solution containing 200
mM sodium acetate, 20 mM Tris and 0.2 mM calcium chloride at a concentration of 2 mg/ml.
Trypsin was dissolved in water at a concentration of 2 mg/ml. CYC and trypsin were mixed at
a ratio of 10 to 1, and the digestion was allowed to proceed for 16 h at 37°C. The enzymatic
digestion was stopped by increasing the temperature to 80°C for 10 min. Proteins digest were
stored at -4°C.
7
2.3 Capillary Electrophoresis-Electrospray-Mass Spectrometry (CE-ESIMS)
CE-ESI-MS analyses were carried out in a PACE/5500 CE instrument (Beckman, Fullerton,
CA, USA) coupled to a Bruker Daltonic Esquire 2000 ion-trap mass spectrometer (Bruker
Daltonik, Bremen, Germany) using commercial coaxial sheath-flow interface. The separation
method was adapted from Simo et al. [18,19]. Briefly, the MS was operated in the positive
ion mode, and scanned from 200 to 1100 m/z at 13000 u/s. ESI parameters were: nebulizer
pressure, 27579 Pa; dry gas flow, 8L/h; dry gas temperature, 120°C; and a sheath liquid made
of methanol-water (50/50) at a flow rate of 4 μL min-1. Separation was performed in a 90 cm
long capillary (50 μm i.d., from Composite Metal services, Worcester, England) using a
buffer made of 0.9 M formic acid adjusted to pH 2 with ammonium hydroxide. Between runs
the capillary was rinsed for 3 min with water and 1 min with buffer. CYC hydrolysates were
injected without any dilution or purification step for 20 sec at 3447 Pa.
2.4 Data analysis and programming
For this work, different computer tools were used. The software integrated with the
instrument (DataAnalysis version 3.0, Bruker Daltonic Bremen, Germany) was used to obtain
the extracted ion electropherograms (EIEs) as well as to convert the raw data in ASCII
format. Visual basic (Visual Basic 6.0, Microsoft) was used to program the different filtering
routines, and the computation of the electrophoretic figures of merits. Results were recorded
in an Excel spreadsheet (Excel 2000, Microsoft) for further analysis.
3. Results and discussion
8
As mentioned above, the usefulness of this new approach was validated by comparing the 2D
mapping obtained after digestion with trypsin of cytochrome c from three species. Namely,
bovine (CYC-B), horse (CYC-H) and rabbit (CYC-R) cytochrome c digested with trypsin
were compared. An additional aim was to find a CE-MS marker for each species, which could
be used as quality control to detect e.g., adulterations of minced meat [20-22]. Logically, this
approach for 2D-CE-MS mapping can be useful in many other applications including the
finding of biomarkers, the identification of therapeutic polypeptide targets, the establishment
of patterns for diagnostic purposes [9], etc.
The total-ion electropherograms (TIEs) obtained by CE-MS of the three cytochromes digested
with trypsin are shown in Figure 2. As can be seen, few differences can directly be detected
from these CE-MS electropherograms. Indeed, although peak 1 could be used as a marker for
CYC-R, no unique feature can be observed for CYC-B and CYC-H. For example, if peak 2 is
no present in CYC-R, it is present in CYC-B and CYC-H, similarly peak 3 is not present in
CYC-H but present in CYC-B and CYC-R and peak 4 is not present in CYC-R, but present in
CYC-H and CYC-B. The same applies to the group of peaks labeled as 5 in Figure 2.
In order to obtain more information (including specific markers for each species), the classical
procedure would be to analyze the full MS spectra for every peak and to compare these results
among the different species. However, this procedure is labor intensive and time consuming.
Alternately, a straight 2D mapping of the samples could be compared. An example of such
representation for the hydrolysis of the CYC-B is shown in Figure 3. In our case, this 2D map
was obtained from the original 2 dimensional matrix (step 1 in Figure 1), that was pasted in an
excel spreadsheet. For size and speed consideration, the m/z values have been compressed by
a factor of 40. As can be seen, much more information is obtained in this case. However, as
evident from the wealth of data, it was impossible to evaluate the raw data using
9
commercially available software. For example, with our MS set-up (mass scan from 200 to
1100) a 2D matrix as the one shown in Figure 3 will easily represent more than 10 Mbyte.
More importantly, such representation can provide an overloaded of information that can hide
important differences [9].
Therefore, the usefulness of the new approach described under Theory for achieving a
simplified 2D-CE-MS mapping was tested. The original TIE of a given trypsin digest
analyzed by CE-MS is shown in Figure 4A, and its corresponding graph of the measured
standard deviations (SD) for each m/z is shown in Figure 4B. As can be observed, the m/z
values with high SD values agree with the most intense spots shown in Figure 3. For example,
it can be seen in Figure 4b that the EIEs corresponding to m/z = 584.9 and 589.2 will have
important information (highest SD in Figure 4B). Those two m/z values correspond to two of
the most intense spots in Figure 3. Moreover, some ions that contribute in a large extent to the
noise (e.g., m/z = 282.2 in Figure 3) do not give a high SD. The highest standard deviation
can be found between m/z of 500 and m/z of 700 in good agreement with the results of Figure
3. The insert shown in Figure 4B corresponds to a zoom of the m/z values (x axis) between
500 and 510, and standard deviations (y axes) between 0 and 5000. As can be seen, each peak
is extremely sharp with a peak width at half height well below 0.5 m/z. This will allow to
automatically obtaining relevant extracted ion electropherograms with a suitable mass
accuracy. Indeed, taking in Figure 4b a threshold of 2000, corresponding to 1% of the
maximum SD, 735 different EIEs have been automatically generated out of the 9000 m/z
possible. Figure 4C shows the TIC obtained by summation of the 735 selected EIE, as can be
seen, no difference can be visually observed between electropherograms of Figure 4A and 4C,
what is further corroborated by the electropherogram of Figure 4d that shows the differences
between Figures 4A and 4C. Namely, Figure 4D shows that the residual between the two
figures will never represent more than 10% of the full peak, and is usually below 5%. Figure
10
4E shows the total ion electropherogram after the step 3 of our approach (see Figure 1). To
obtain this Figure, all points that were not detected as part of a peak were set to zero in the
original matrix, points being part of a peak were baseline corrected. As can be seen, the
elimination of the data that do not add information provides a significant increase in
sensitivity, as also demonstrated by the insert of Figure 4E. In Figure 4F the differences
between Figure 4E and Figure 4A are plotted. As can be seen, although a certain amount of
information can be lost, this is basically due to a multitude of small peaks resulting from the
fragmentation of the main ions that are not included.
As an example, the software has detected and measured 1628 peaks for the hydrolysis of
bovine cytochrome c spanning four orders of magnitude (area between 3000 and 15000000).
After applying the procedure proposed in this work, the resulting file contains less than 150
Kbytes, a reduction by more than 50 times from the original data set recorded by the MS
instrument (7.5 Mbytes). The 2D mapping using this new set of data is shown in Figure 5.
Comparing this mapping with the original one displayed in Figure 3, it can be seen that all
important information have indeed been conserved. Moreover, a much higher resolution is
observed in the time scale in Figure 5 than in Figure 3. This is striking when comparing in
Figure 5 the alignments of the spots from peaks 7, 9, 11 and 12 with the one from peaks 1, 2
and 8. This result came from the uses of electrophoretic parameters. Indeed, peaks in the m/z
dimension resulting from the ESI-MS fragmentation of the same parent compound will have
the same peak shape. However, peaks in the m/z dimension resulting from different parent
compounds will have different peak shapes. The accurate measurement of the electrophoretic
parameters (migration time, but also peak variance, peak asymmetry…) allows highlighting
small differences in the peak shapes. This is illustrated in Figure 6, where the full MS spectra
of peak 10 (Figure 6A), and the EIEs obtained using the five more abundant ions from Figure
6A (Figure 6B) are compared with the full MS spectra for peak 8 (Figure 6C), and the EIEs
11
obtained from the five more abundant ions in Figure 6C (Figure 6D) including m/z values
higher than 300 (typically, m/z values lower than 300 have a high contribution to the noise
signal). Using the ten most intense spots for peak 10, an average migration time of 26.648
min was obtained with a standard deviation of 0.008 min (i.e., a relative standard deviation of
less than 0.05%), showing the very high precision of the procedure proposed to determine the
peak center. Moreover, this result shows that our simplified 2D mapping can be of great help
to differentiate CE comigrating polypeptides from those produced by ESI-MS fragmentation.
Furthermore, the easy manipulation of the obtained data makes also possible to achieve other
interesting 2D representations. For example, in capillary electrophoresis, migration time can
be related to the mobility, a parameter that depends on the analyte and the separation buffer.
Although different equations have been proposed to calculate the mobilities from
experimental data [23], an adequate procedure for CE-MS can be the use of two reference
peaks for standardization. The equation originally proposed by Ikuta and colleagues use one
analyte and the electroosmotic flow (eof) marker as reference peaks [24]. However, as in CEMS with a very low pH buffer no eof marker is usually observed, two analytes will be used in
this work as reference. The original equation has to be accordingly modified to

t ref 2 t ref1  t 
 ref2   ref 1    ref1
t t ref 1  t ref2 
(3)
where t and  are the migration time and mobility of the peak of interest, tref1 and tref2 are the
migration times of the two reference peaks, and ref1 and ref2 are their corresponding
theoretical mobilities. In order to choose the adequate two reference peaks, data were sorted
by ionic intensity showing that two out of the three most intense spots are common to the
three species (corresponding to the peaks marked with an asterisk in Figure 2) ((tref1)CYC-B =
12
27.552, (m/z) = 589.3, (tref2)CYC-B = 18.002, (m/z) = 585.2; ((tref1)CYC-H = 27.111, (m/z) = 589.3,
(tref2)CYC-H = 17.778, (m/z) = 585.3; ((tref1)CYC-R = 27.134, (m/z) = 589.3, (tref2)CYC-R = 17.748,
(m/z) = 585.2). Therefore, these two peaks were used as references for the three samples and
their analysis time and ref1 and ref2 were used in equation 3.
Using this approach, electrophoretic mobility values were then automatically calculated for
the main ions. This was automatically done for all the extracted data recorded in an excel
spreadsheet (Figure 1, step 3). The electrophoretic mobility values provided by equation 3 are
logically independent on the applied voltage and capillary dimensions [24]. This point needs
to be highlighted since in CE-MS a large part of the capillary is usually not thermostated.
Therefore, by using equation 3 the negative thermal effect on reproducibility is practically
eliminated [25]. Although in this study reference points could easily be determined, in more
complex samples this point cannot be so easy. However, the use of internal standards will
provide the same results in these more complex matrices.
Once the electrophoretic mobility values were obtained, the simplified 2D map shown in
Figure 7 was obtained representing mobility  m/z for the three proteolytic digests (bovine,
horse and rabbit). Only the most intense spots have been plotted, corresponding the three
different symbols to the three studied species (  bovine; x  rabbit; +  horse) and the
different colors to the ion intensity (black  high intensity; red  intermediate intensity; blue
 low intensity).
The strikingly reduced amount of information shown in Figure 7, their high matching, as well
as the high precision in the mobility and m/z parameters allow now an easy and accurate
comparison of the CE-MS results obtained for the proteolytic digests from these three species.
Spots labeled as “ref” are the ones used for mobility calibration. Thus, as can be seen in
13
Figure 7, numerous spots are common to the three species, with a very high level of
confidence. For example the measured mobility and m/z values for spots labeled as 1 in
Figure 7 are CYC-B = 2.03610-8 m2 V-1 s-1, m/z CYC-B = 617.3 ± 0.7; CYC-R = 2.03210-8 m2
V-1 s-1, m/z CYC-R = 617.4 ± 0.8; CYC-H = 2.03210-8 m2 V-1 s-1, m/z CYC-H = 617.4 ± 0.5. More
interestingly, specific differences between the three species can now be easily recognized. For
example spots labeled from 2 to 5 could be selected to distinguish the three species. Thus,
spot 2 or 5 can be selected as marker for CYC-H, spot 3 as marker for CYC-B and spot 4 as
marker for CYC-R. Not only the marker can easily be identified using this simplified
mapping but also its intensity, giving rise in that way to the most favorable choice among the
different markers that could be selected. For instance, spots labeled as A or B in Figure 7
could also be selected as markers, however, since their intensity is lower than the markers
chosen above, their use to differentiate species would be less favorable.
Interestingly, an additional proof of the usefulness of this 2-D representation is that in some
cases only the combination of the information from both dimensions makes possible to
understand the proteolytic profile. A representative example of this idea can be seen
considering the two fragments marked as 6 in Figure 7 (  bovine and x  rabbit). From a
first sight of the 2-D map it is observed that the match between these two fragments is not as
good as the match observed for other fragments (see for instance in Figure 7 the exact match
for spots with a mobility between 15 and 1810-9 m2 V-1 s-1) indicating that they are different
compounds. Thus, as can be seen in the 2-D map these fragments have the same m/z value
(m/z CYC-B = 600.4 ± 0.7, m/z CYC-R = 600.4 ± 0.7) but different mobility (CYC-B = 0.88810-8
m2 V-1 s-1, CYC-R = 0.87710-8 m2 V-1 s-1). Interestingly, the corresponding full MS spectra of
these spots were also very similar. This made even more difficult to detect some significant
difference directly from the CE-MS electropherogram (i.e., the only difference was the
presence of a secondary ion at m/z equal 1005.9 for CYC-B, and at m/z equal to 999.0 for
14
CYC-R). Based on the visual information provided by the 2-D map these two spectra were
studied in more detail confirming that they correspond to a fragment from bovine cytochrome
c identified [19] as GITWGEETLMEYLENPK (Mr = 2010.2) and, considering similar
cleavage into the rabbit sequence, to the fragment GITWGEDTLMEYLENPK (Mr = 1996.2),
corresponding the aforementioned ion of m/z = 600.4 to the common fragment LENPK.
Interestingly, this very small difference (glutamic acid for aspartic acid) between the two
sequences can also explain their different mobility. Indeed, using a theoretical model to
predict the CE migration of peptides [25] the mobility of GITWGEETLMEYLENPK has
been estimated to be 0.82710-8 m2 V-1 s-1 and the mobility of GITWGEDTLMEYLENPK as
0.82410-8 m2 V-1 s-1. The fact that such small differences can easily be detected through the
simplified 2D map confirms the accuracy and usefulness of the proposed approach.
Two different samples containing (i) 95% of CYC-B + 5% CYC-H and (ii) 95% of CYC-B +
5% CYC-R were prepared with the aim to demonstrate the usefulness of the selected markers
to selectively identify the species. Samples were digested with trypsin and then analyzed by
CE-MS. The m/z scan range was decreased to 540 to 760 (target mass 650 m/z) as the selected
markers are in this range. This allowed an increase in the signal/noise ratio by a factor of ca.
1.5 (data not show). The EIEs obtained for the three m/z values used as markers (i.e. m/z
728.9 ± 0.2 for CYC-B, m/z 736.0 ± 0.2 for CYC-H and m/z 665.4 ± 0.2 for CYC-R) are
shown in Figure 8. Namely, the sample (i) composed of 95% CYC-B + 5% CYC-H is shown
in Figure 8-IA to IC, while the sample (ii) composed of 95 % CYC-B + 5% CYC-R is shown
in Figure 8-IIA to IIC. As expected, the bovine marker is present in all samples, while the
horse marker is only present in the 5% horse sample (Figure 8-IB) and the rabbit marker is
only present in the 5% rabbit sample (Figure 8-IIC). This result corroborates the usefulness of
our procedure to provide selective species-markers. Moreover, this procedure also allows
overcoming the migration time shifts observed in CE. As an example, the electrophoretic
15
mobility of the bovine marker (m/z = 728.9) determined according to equation 3 was
1.04010-8 m2 V-1 s-1 in Figure 7 and 1.04610-8 m2 V-1 s-1 in Figure 8 showing again the
advantages offered by this 2D methodology.
4. Concluding remarks
The procedure presented in this work to obtain simplified 2D maps allows an automatic
representation of raw CE-MS data. It is demonstrated that this new approach provides
simplified 2D maps and a reduction of the initial amount of data by a factor of 50 without any
major loss of information. This tool has been tested studying trypsin digests of cytochrome c
from three different species (bovine, horse and rabbit). For the three species more than 1500
spots were generated, each of them indexed by three parameters: migration time, ionic
intensity and m/z value.
It has been shown that the developed 2D procedure also helps to differentiate between CE
comigrating compounds and ESI-MS fragmentation-ions. Moreover, spots can be easily
distinguished based on very subtle differences in their mobilities or m/z values using the
generated 2D maps. As an example, two very similar fragments from CYC-B and CYC-R
were visually distinguished in the 2D map. These fragments could not be differentiated based
on their standard CE-MS electropherograms.
Since this approach makes full use of the advantages derived from a CE separation prior to
MS analysis, it can be foreseen that working under the same CE conditions and with the same
ESI-MS settings, reproducible and specific fingerprintings could ideally be obtained.
Moreover, the very good results obtained with this simple approach suggest that this could
routinely be used to simplify CE-MS data as well as LC-MS or GC-MS data.
16
Acknowledgements
G.L.E. thanks the Spanish MEC for a postdoctoral grant. Authors are grateful to the
AGL2005-05320-C02-01 Project (Ministerio de Educacion y Ciencia) and the S-505/AGR0153 Project (Comunidad Autonoma de Madrid, CAM) for financial support of this work.
17
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19
FIGURE LEGENDS
Figure 1. Schematic representation of the steps performed before obtaining the simplified 2D
CE-MS map.
Figure 2. CE-ESI-MS total ion electropherograms of the trypsin hydrolysates from bovine,
rabbit and horse cytochrome c. Running buffer, 0.9 M formic acid adjusted to pH 2 with
ammonium hydroxide; injection at 3447 Pa for 20s; running voltage 20 kV; capillary length
90 cm (total and detection length). MS conditions: sheath liquid methanol/water (50/50) at 4
μL/min; nebulizer gas, 27579 Pa and 8L/min at a temperature of 120 °C; MS scan range, m/z
200-1100 (target mass: 650 m/z). The two peaks marked with an asterisk are used for mobility
normalization (see text for more details), peaks marked with a number are used in the text to
compare the three electropherograms.
Figure 3. CE-ESI-MS full two-dimensional map of bovine cytochrome c digested with
trypsin. Color code: Black, intensity > 180 000; brown, intensity > 100 000; red, intensity >
56 000; orange, intensity > 32 000; yellow, intensity > 18 000; blue, intensity > 10 000. All
experimental conditions as in Figure 2.
Figure 4. (A). CE-ESI-MS total ion electropherogram of. (B) Measured standard deviation
(SD) for each m/z value of A. (C) Reconstructed total ion electropherogram using only the m/z
values whith SD > 2000. (D) Residual between (A) and (C). (E) Reconstructed total ion
electropherogram using only the part of every m/z where a peak has been detected (Step 3 in
Figure 1). (F) Residual between (E) and (A). All experimental conditions as in Figure 2.
20
Figure 5. Simplified 2D CE-MS map of bovine cytochrome c digested with trypsin. Color
code: black, area > 570 000; brown, area > 320 000; red, area > 180 000; orange, area > 100
000; yellow, area > 56 000; bleu, area > 5 000. All experimental conditions as in Figure 2.
Figure 6. (A) Full MS spectra and (B) EIEs of the most intense fragments of peak 10 in
Figure 5 and (C) full MS spectra and (D) EIEs of the most intense fragments of peak 8 in
Figure 5.
Figure 7. Comparison of the simplified 2D CE-MS map of hydrolysed cytochrome c from
bovine heart (), rabbit heart (x) and horse heart (+). Colour code: black, area superior or
equal to 50 % of the highest area; red, area superior or equal to 20% of the highest area; blue,
area superior or equal to 10% of the highest area.
Figure 8. Extracted ion electropherograms from digested samples containing (I) 95% bovine
+ 5 % horse cytochrome c and (II) 95% bovine + 5 % rabbit cytochrome c. EIEs
corresponding to (A) m/z 728.9 ± 0.2, (B) m/z 736.0 ± 0.2 and (C) m/z 665.4 ± 0.2. The MS
scan range was set at m/z 540-760 (target mass 650 m/z). Rest of conditions as in Figure 2.
21
Step 1
Step 2
m1 + m1
Mass
Original CE-MS
Data
m2 + m2
Time
Time
Step 3
Peak
#1
#2
#3
…
m/z /Da
m1
m2
m2
…
Incertitude
m1
m2
m2
…
Peak area
A1
A2
A3
…
Migration time
tm1
tm2
tm3
…
Simplified
2D CE-MS map
(time  m/z)
Figure 1.
22
Intens. /107
1.5
* CYC Bovine
CYC Bovine
5
1
3
4
0.5
1
*
2
0
10
Intens. /107
1.5
14
18
Time /min
22
26
30
CYC Rabbit
CYC Rabbit
1
*
5
4
0.5
1
3
2
*
0
10
Intens. /107
1.5
14
18
*
CYC Horse
Time /min
22
26
30
CYC Horse
5
1
4
3
0.5
*
1
2
0
10
14
18
Time /min
22
26
30
Figure 2.
23
m/z
282.2
Figure 3.
24
1.5
2
B.
5
1
SD /10
Intens. /10
7
A.
1
0.5
0
0
10
14
18 Time /min 22
26
200
30
1.5
400
600
m/z
800
1000
0.15
D.
7
1
Intens. /10
Intens. /10
7
C.
0.5
0
0.05
0
10
14
18 Time /min 22
26
30
10
14
18 Time /min 22
26
30
14
18 Time /min 22
26
30
0.3
1.5
E.
F.
0.25
7
7
1
11
14
0.5
Intens. /10
Intens. /10
0.1
0.2
0.15
0.1
0
10
14
18 Time /min 22
26
30
10
Figure 4.
25
2
1
3
5
4
6
8
9
7
10
11
12
1000
m/z
800
600
400
200
10
12
14
16
18
20
22
24
26
28
30
Time /min
Figure 5.
26
Intens.
x10 4
Intens.
x10 4
All, 26.3-26.9min (#650-#666)
A.
All, 21.7-22.5min (#537-#558)
C.
494.2
600.2
656.9
1.25
1.5
487.2
763.3
282.2
1.00
607.3
282.2
1.0
0.75
512.1
244.1
0.5
358.2
713.7
0.50
1005.9
892.4
446.6
423.2
841.4
372.3
644.2
568.7
985.0
0.25
683.5
763.3
891.5
920.4
200
Intens.
x10 5
1070.0
0.00
0.0
300
400
500
600
700
800
900
1000
Intens.
x10 5
600.2±0.5
B.
200
m/z
763.3±0.5
300
400
500
600
700
D.
800
900
1000
m/z
656.9±0.5
3
487.2±0.5
494.2±0.5
1.5
512.1±0.5
2
607.3±0.5
1.0
1005.9±0.5
713.7±0.5
1
0.5
841.4±0.5
0
0.0
21.5
25.8
26.0
26.2
26.4
26.6
26.8
27.0
27.2
21.6
21.7
21.8
21.9
22.0
22.1
22.2
22.3
22.4
Time [min]
Time [min]
Figure 6.
27
900
CYC Bovine
CYC Rabit
CYC Horse
CYC Bovine
CYC Rabit
CYC Horse
B
5
2
3
600
4
A
Ref 6
1
m /z
Ref
300
0
6
8
10
12
14
-9
16
2
-1
18
20
22
-1
mobility /10 m V s
Figure 7.
28
1.5
1.5
I-A
Bovine marker
m/z = 728.9
Intens. /106
Intens. /106
Bovine marker
m/z = 728.9
II-A
0
0
30
1.5
35
Time /min
40
30
45
1.5
I-B
0
Time /min
40
45
II-B
Horse marker
m/z = 736.0
Intens. /106
Intens. /106
Horse marker
m/z = 736.0
35
0
30
1.5
35
Time /min
40
30
45
1.5
I-C
0
Time /min
40
45
II-C
Rabbit marker
m/z = 665.4
Intens. /106
Intens. /106
Rabbit marker
m/z = 665.4
35
0
30
35
Time /min
40
45
30
35
Time /min
40
45
Figure 8.
29