A multiway approach for classification and characterization of rabbit liver apothioneins by...

Research Article

A multiway approach for classification andcharacterization of rabbit liver apothioneinsby CE-ESI-MS

We applied a multiway approach to extract information from the analysis of protein

isoforms by CE-ESI-MS. Metallothioneins (MT) are low-molecular-weight proteins

(6–7 kDa) with a strong affinity for heavy-metal ions. Rabbit liver MT-I and MT-II

fractions are purified from MT samples. At low pH, the bound metal ions were released

from the amino acid structures, giving rise to apothioneins. MT-I, MT-II and MT

apothioneins, which are complex mixtures of protein isoforms, were analyzed by CE-ESI-

MS. After data pre-processing, parallel factor analysis (PARAFAC) and multivariate curve

resolution-alternating least squares (MCR-ALS) were applied to the data sets. In both

cases, the models enabled classification of the protein samples and identification of their

characteristic sub-isoforms using a set of three components. MCR-ALS required an

initial estimate of the pure mass spectra of the three components. Thus, PARAFAC

loadings were used to initialize the MCR-ALS optimization. The classifications obtained

with MCR-ALS were slightly better than those obtained with PARAFAC, probably

because MCR-ALS was less affected by the small migration time shifts of the pre-

processed electropherograms. However, no differences were found between the pure

mass spectra of the three components in either model. Finally, MCR-ALS allowed us to

obtain an individual electrophoretic profile of each of the three components for each of

the samples analyzed, which proved valuable for characterization and quantification

purposes.

Keywords:

CE-MS / Isoforms / MCR-ALS / Metallothioneins / PARAFACDOI 10.1002/elps.200800212

1 Introduction

CE is one of the techniques of choice for separation of the

protein isoforms that are a result of microheterogeneity

arising in the biosynthesis of a large number of proteins

[1–4]. In CE, protein isoforms (e.g. apothioneins of metallo-

forms, metalloforms or glycoforms) are primarily separated

according to their charge-to-mass ratios, and despite its

limited selectivity, UV absorbance detection is used

extensively for detection [5–7]. In recent years, CE coupled

online with ICP-MS has proved to be useful for quantitative

speciation of several metals in metalloproteins [8]. However,

CE-ICP-MS is unsuitable for obtaining molecular mass

(Mm) information from the different apothionein metallo-

forms. For this purpose, CE-ESI-MS is preferred [1–4, 6–7,

9]. Several CE-ESI-MS have been described for the selective

separation and characterization of protein isoforms [1–4,

6–7, 9]. However, the performance of CE-ESI-MS is limited

and resolution problems could arise when handling

complex mixtures of protein isoforms, such as human

erythropoietin, which is a mixture of around 100 glycoforms

[9]. In such cases, the methods traditionally used for the

analysis of MS data may be excessively time consuming.

Chemometrics-assisted multiway data analysis is an

excellent alternative for handling these complex data sets

[10–14].

Multiway data analysis methods in combination with

CE-UV have been used for sample classification and char-

acterization, peak purity analysis, peak resolution and

quantification [10]. In general, UV absorbance at a single

wavelength as a function of migration time (first-order data

for a single sample or two-way data for a set of samples) or

UV spectra, acquired with a DAD, as a function of migration

time (second-order data for a single sample or three-way

data for a set of samples) have been employed for the

Fernando Benavente1

Balbina Andon1

Estela Gimenez1

Alejandro C. Olivieri2

Jose Barbosa1

Victoria Sanz-Nebot1

1Departamento de QuımicaAnalıtica, Universidad deBarcelona, Barcelona, Espana

2Departamento de QuımicaAnalıtica, Facultad de CienciasBioquımicas y Farmaceuticas,Universidad Nacional deRosario, Rosario, Argentina

Received March 31, 2008Revised June 19, 2008Accepted June 20, 2008

Abbreviations: ALS, alternating least squares; MCR,

multivariate curve resolution; Mm, molecular mass; MT,

metallothioneins; PARAFAC, parallel factor analysis; TIE,

total iron electropherogram

Correspondence: Dr. Fernando Benavente, Departamento deQuımica Analıtica, Universidad de Barcelona, Diagonal 647,E-08028 Barcelona, EspanaE-mail: [email protected]: 134-934021233

& 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.electrophoresis-journal.com

Electrophoresis 2008, 29, 4355–4367 4355

analysis of low-molecular-weight pharmaceuticals, biologi-

cal samples and foodstuffs [10]. Using three-way data,

determinations in the presence of unknown interferences

are possible, when the instrumental signals are separated

mathematically by achieving the second-order advantage [11].

We have recently demonstrated that a first-order multivariate

calibration method with partial least squares (PLS) can be

used to characterize mixtures of different erythropoietin

samples based on the analysis of the electrophoretic separa-

tions of their glycoforms at a single wavelength [12].

However, the use of second-order CE-DAD data for multiway

analysis of individual protein isoforms is precluded because

the UV spectra of the comigrating isoforms are indis-

tinguishable from each other. In order to circumvent this

problem, a detector with enhanced selectivity, such as a mass

spectrometer [1–4], is necessary. To date, a few multiway data

analysis methodologies have been described using CE-ESI-

MS data [13, 14], but to the best of our knowledge, none is

related to the analysis of protein isoforms.

Metallothioneins (MT) form a large family of low-

molecular-weight proteins (6–7 kDa) primarily present in

almost all life forms. MT bind heavy metal ions like

cadmium, zinc or copper, because of their significant

cysteine content [15, 16]. As a result, these proteins contri-

bute to many biological processes involving metal ions [17].

Dysfunctions in MT metabolism have been related to

Alzheimer’s and Wilson’s diseases, several cancers and

immunological disorders [18, 19]. For these reasons, MT

have been postulated as biomarkers of metal pollution or

possible diagnostic tools [18–20].

In addition to polymorphism due to its variable metallic

content, an MT of a particular species exists as a mixture of

several isoforms with slight differences in their amino acid

sequences. Historically, MT have been classified into two

main groups of isoforms on the basis of their elution order

by anionic-exchange chromatography: MT-I and MT-II

[17, 21]. In mammals, this conventional classification based

on charge differences currently coexists with another based

on the absence (MT-1) or presence (MT-2) of an acidic

amino acid residue (Asp (D)) at position 11 or 12 of the

sequence [21]. MT-1 and MT-2 are the isoforms most widely

expressed in tissues and they have received the most

attention. Isoforms with minor differences, such as one

amino acid residue, are classified as subgroups of these two

major isoforms and termed sub-isoforms. The sub-isoforms

are designated by a lower-case letter, e.g. MT-1a. At low pH,

the bound metal ions of MT sub-isoforms are released from

the amino acid structures, giving rise to apothionein sub-

isoforms. High-performance separations coupled to high-

resolution characterization techniques are key tools for

clarifying the specific biological role of metallated and apo

sub-isoforms, which is still unknown [1–4, 8]. Furthermore,

exploring multiway data analysis models for the study

of the relatively simple CE-ESI-MS data sets obtained

for the analysis of different MT samples constitutes an

excellent benchmark for the study of proteins with a greater

microheterogeneity.

In this paper, parallel factor analysis (PARAFAC)

[22–24] and multivariate curve resolution- alternating least-

squares (MCR-ALS) [25, 26] are applied to the development

of second-order multiway data analysis methods for inves-

tigation of different samples of rabbit liver apothioneins.

Several data pre-processing steps are proposed before model

optimization. The performance of the two models is then

compared and major advantages and disadvantages are

discussed in detail. In general, the results confirm that both

PARAFAC and MCR-ALS give a rapid and simple classifi-

cation of the protein samples and identification of their

characteristic sub-isoforms.

2 Materials and methods

2.1 Chemicals and reagents

All chemicals were of analytical reagent grade and used as

received. Acetic acid (glacial) and formic acid (98–100%) for

the separation electrolytes were purchased from Merck

(Darmstadt, Germany). Trifluoroacetic acid employed for

sample pre-treatment and 2-propanol for sheath liquid

preparation were also supplied by Merck. Water with a

specific conductivity lower than 0.05 ms/cm was obtained

by using a Milli-Q water purification system (Millipore,

Molsheim, France).

Rabbit liver MT (batch no. 20K7000, 4.7% Cd and 0.5%

Zn), MT-I (batch no. 80K7012, 8.0% Cd and 1.2% Zn) and

MT-II (batch no. 20K70130, 7.9% Cd and 1.4% Zn) were

obtained from Sigma-Aldrich (St. Louis, MO, USA). The

manufacturer reported that MT-I and MT-II were obtained

from MT samples by means of a double-step procedure.

After purification of a rabbit liver extract by size exclusion

chromatography, the isolated MT-containing fraction was

subjected to anion-exchange chromatography at neutral pH,

obtaining MT-I and MT-II fractions that contained sub-

isoforms that differed by a single global charge. Stock

solutions of MT were prepared by dissolving 1 mg of protein

in 1 mL of water. They were stored at�181C in the dark

when not in use.

2.2 CE-ESI-MS

All CE-ESI-MS experiments were performed using an

Agilent Technologies HP3DCE system (Waldbronn,

Germany) coupled to an MSD Ion Trap mass spectrometer

with a G1603A sheath-flow CE-ESI-MS interface (Agilent

Technologies) [6]. The sheath liquid was delivered by an

infusion pump KD Scientific 100 Series (Holliston, MA,

USA) at a flow rate of 3.3 mL/min. A 100 cm LT� 75 mm id

bare fused-silica capillary supplied by Polymicro Technolo-

gies (Phoenix, AZ, USA) was used for the electrophoretic

separations. The parameters of the mass spectrometer were

automatically tuned by direct infusion of a 1 mg/L solution

of the MT-II sample. The sample solution was infused at

Electrophoresis 2008, 29, 4355–43674356 F. Benavente et al.


50 mbar through the separation capillary, while the signal

for one of the multiply charged molecular ions of the

predominant MT-2a sub-isoform ([MT-2a+5H]5+ 5 1226.1

m/z, see Table 1) was maximized. Full scan mass spectra

were acquired in the m/z range from 500 to 2200 m/z at

intervals of 0.1 m/z (as an average of every seven scans). All

experiments were done in positive mode and the ESI voltage

and the end plate offset were set at 4100 and �500 V,

respectively. Voltages on capillary exit and skimmer were

240 and 42 V, respectively. Octopole voltages were set at 24

and 1 V and octopole radiofrequency at 128 Vpp. Lens were

�7 and �67 V and trap drive 133 (arbitrary units). Nebulizer

gas (N2) pressure was 7 p.s.i., drying gas (N2) flow rate was

2 L/min and drying temperature was set at 3001C. Instru-

ment control, data acquisition and data processing were

performed using the CE/MSD Trap Software 6.1 (Agilent

Technologies).

pH was measured with a Crison 2002 potentiometer

(Crison Instruments, Barcelona, Spain), equipped with a

ROSS electrode 8102 (Orion Research, Boston, MA, USA).

2.3 Experimental procedures

2.3.1 Sample pre-treatment

Cadmium and zinc from rabbit liver MT were eliminated

before CE-ESI-MS analysis; 100 mL of 1 mg/mL solutions of

MT, MT-I and MT-II samples were acidified with TFA (final

concentration was 0.1% v/v). The acidic samples were

desalted by size exclusion filtration through MicroSpin

G-25 microcolumns containing a SephadexTM sorbent

(Amersham Biosciences, Uppsala, Sweden), following the

manufacturer’s instructions. Apothionein samples resulting

from this treatment were injected immediately after their

preparation to avoid oxidation. For ease of understanding,

apothionein isoforms and sub-isoforms are abbreviated as

MT throughout the text.

All samples were passed through a 0.45 mm nylon filter

(MSI, Westboro, MA, USA) before analysis and were stored

at 41C when not in use.

2.3.2 CE-ESI-MS

The CE-ESI-MS method was developed in an earlier study

using a TOF-MS detector [6]. The separation electrolyte

contained 50 mM of acetic acid and 50 mM of formic acid

(pH 2.3) and was passed through a nylon filter of 0.45 mm

(MSI) before analysis. A sheath liquid of 50:50 v/v

2-propanol:water with 1% v/v of acetic acid resulted in

optimum detection sensitivity. It was degassed for 10 min by

sonication before use. All capillary rinses were performed at

930 mbar. New capillaries were flushed for 20 min with

aqueous 1 M NaOH, followed by 15 min with water and

30 min with separation electrolyte solution. The system was

finally equilibrated by applying the separation voltage for

15 min. The activation procedure was performed off-line

and ESI voltage was switched off in order to avoid the

unnecessary entrance of NaOH into the MS system.

Samples were hydrodynamically injected at 30 mbar for

5 s. Analyses were carried out at 251C under normal polarity.

A separation voltage of 25 kV was employed for the

electrophoretic separations, while the ESI voltage was

applied at the MS entrance. MT-I , MT-II and MT samples

were analyzed on four different days using a new separation

capillary each day, resulting in 10, 10 and 3+3 electro-

phoretic runs each day, respectively. Between runs, the

capillary was rinsed for 3 min with separation buffer.

Capillaries were discarded after each working day. The

separation electrolyte and the sheath liquid were stored at

41C when not in use.

2.4 Data analysis

2.4.1 Software

Matlabs for Windows (version 7.0) was used for data pre-

processing, programming, calculations and graphical repre-

sentation, unless otherwise indicated. MassLynx (version

3.5) was the software supplied with the mass spectrometers

of Micromass (Manchester, UK) for control, data acquisition

and data processing. DataBridge was a file converter

provided with Masslynx. ESI mass spectra were deconvo-

luted using MaxEnt1 (Micromass), which uses an algorithm

based on the method of maximum entropy to find the

simplest zero charge Mm spectrum that could account for

the observed m/z data. EDit was a free C++ program for

conversion of MassLynx continuum spectra into a matrix

format suitable for direct introduction into scientific

graphing packages [27]. A laboratory-written Matlab routine

was employed for the rest of the data-pre-processing. This

routine needed Moving_average2 for smoothing, which

Table 1. Molecular mass and m/z of the most abundant

molecular ions of rabbit liver apothioneins (MT) in

CE-ESI-MS

MT Molecular

massa) (Da)

[M+5H]5+ [M+6H]6+ [M+7H]7+

Isoform Sub-isoform

MT-I MT-1a 6145.4 1230.1 1025.2 878.9

MT-II MT-2a 6125.3 1226.1 1021.9 876.0

MT-II MT-2b 6146.4 1230.3 1025.4 879.1

MT-II MT-2c 6155.4 1232.1 1026.9 880.3

MT-I MT-2d 6214.5 1243.9 1036.7 888.8

MT-I MT-2e 6240.6 1249.1 1041.1 892.5

a) The molecular mass was calculated from the amino acidicsequence. The molecular mass of the detected non-N-terminal acetylated subisoforms differ �42.0 Da withrespect to the values shown for the acetylated species(6103.4 MT-1a non-ac, 6083.3 MT-2a non-ac, 6172.5 MT-2dnon-ac and 6198.5 MT 2e non-ac).

Electrophoresis 2008, 29, 4355–4367 CE and CEC 4357


was available through the file exchange service of the

MATLAB website [28]. The routine for PARAFAC was freely

available on the internet as part of an N-way Toolboxfor MATLAB [29]. A graphical user-friendly interface for

MCR-ALS was also freely available online on the authors’

website [30].

2.4.2 Data pre-processing

Recovery of data from the CE-ESI-MS electropherograms of

each sample in an appropriate text format was a trouble-

some procedure. When the CE-ESI-MS raw files were

directly converted into ASCII format by use of the CE/MSD

Trap Software 6.1 provided with the instrument, the

resulting files were useless for further data analysis. As an

alternative, we found it necessary to first convert these raw

files into continuum NetCDF format by use of the CE/MSD

Trap Software 6.1. Then the NetCDF files were converted

into the MassLynx environment employing the DataBridge

program, prior to finally turning the continuum Masslynx

files into ASCII format, again using DataBridge. Following

this procedure, each of the generated text files consisted of a

sequence over time of the acquired spectra listed as m/zversus intensity [27]. EDit was employed to convert the ASCII

files obtained with Masslynx into a matrix in comma

separated value format, which was readable by numerical

computing programs such as Matlab. A matrix obtained

with EDit consisted of a single data matrix, the columns of

which contained the mass spectra at the different migration

times, while the rows contained the electrophoretic profiles

at the different m/z values. The first column and the first

row did not contain intensity values, but rather the m/zvalues and the time values, respectively. The dimensions of

the EDit matrix could be specified before the conversion in

order to resize the original raw data. In our case, a

preliminary inspection of the raw CE-ESI-MS electropher-

ograms with the CE/MSD Trap Software 6.1 was helpful for

reducing both the m/z and the time dimensions of the data

matrix. An m/z range between 600.1 and 1499.9 (at intervals

of 0.1 m/z) was selected for all the electropherograms

according to the m/z values of the [M+5H]5+, [M+6H]6+and

[M+7H]7+ molecular ions of the MT-I, MT-II and MT sub-

isoforms, which were the most abundant molecular ions in

their mass spectra [6] (see Table 1). In contrast, a different

time window was selected for each electropherogram, but

always centered on the region where the electrophoretic

peaks were appearing, and with the precaution of selecting

the same number of migration times, i.e. columns. In this

way, 8999� 240 data matrices were obtained after

being processed with EDit. The first column was immedi-

ately eliminated because it contained the same m/z values in

all cases.

Each 8999� 239 matrix obtained as indicated above was

processed by the following procedure. The mass spectra of

the first 20 columns, where no electrophoretic peaks were

detected, were averaged in order to obtain a background

mass spectrum. The background mass spectrum was

subtracted from the mass spectra of each column. Then, the

first 20 columns of the matrix were discarded. The intensity

values of the background-subtracted 8999� 219 matrix were

smoothed by a moving average function (X, M, N), which

smoothed the matrix X by averaging each element with the

surrounding elements that fit in a box of (2M+1)� (2N+1)

centered on that element (M and N were both 1). In order to

correct for the variability in the migration times, once the

intensity values of the matrix were smoothed, the migration

timescale of the first row was converted into a migration

time ratio (t/tr) scale [31]. As MT-1a and MT-2b sub-isoforms

had the same electrophoretic mobility at the separation pH

value [6], in all cases it was employed as a time reference (tr),

the time corresponding to the maximum intensity at

an m/z value of 1230.1 (70.4), which corresponded to the

[MT-1a+5H]5+ molecular ion in MT-I and MT samples and

to the [MT-2b+5H]5+ molecular ion in MT-II and MT

samples (Table 1). All the intensity values were normalized

to the maximum intensity found in the matrix. The final

data set for multivariate data analysis consisted of 25 pre-

processed matrices (8999� 219), 10 for the MT-I, 10 for the

MT-II and 6 for the MT sample. The pre-processed matrices

could be represented as a total ion electropherogram (TIE)

where the intensities in each row were summed over all the

m/z values.

2.4.3 Multiway data analysis

Both PARAFAC [22–24, 29, 32, 33] and MCR-ALS [25, 26,

30, 34, 35] models have been discussed in detail elsewhere

and only a brief description is presented here. Before data

analysis, the first row from the pre-processed matrices,

which contained the t/tr scale, was eliminated, the

minimum value of intensity was summed to all the matrix

elements in order to avoid negative values and the resulting

matrix transposed. Each J�K DI matrix (I 5 25) consisted of

a two-way array with J times and K m/z ratios (J 5 219 and

K 5 8998). When all DI matrices were stacked one on top of

another, a three-way data array Dpar with dimensions

I� J�K, was obtained for processing with PARAFAC.

Alternatively, for MCR-ALS analysis, a column-wise

augmented data matrix (Daug) was created, with dimensions

(IJ)�K.

Neither PARAFAC nor MCR-ALS routines could be run

on our PC (Intel Core 2 Duo E6600 at 2.4 GHz with 4 GB

RAM) using 219� 8998 DI matrices after building Dpar or

Daug. Thus, two sets of reduced DI matrices were

generated. A set of 219� 3000 DI matrices was obtained

after taking into account one of every three m/z values of

the original 219� 8998 DI matrix for each row. In

addition, a set of 219� 1286 DI matrices was generated

after considering one of every seven m/z values. PARAFAC

could be run employing both reduced DI sets. For MCR-ALS

it was necessary to build Daug from the 219� 1286 DI

matrices.



2.4.3.1 PARAFAC

PARAFAC can be considered as a generalization of bilinear

PCA for higher-order data. The PARAFAC trilinear model

for the three-way data array Dpar was described as

Dpar i;j;k ¼XN

n¼1

ai;nbj;nck;n þ ei;j;k ð1Þ

where i, j and k in Dpar i;j;k indicated the sample number, the

time and the m/z of each element of the array Dpar, N was

the total number of components, ai;n, bj;n and ck;n were the

elements of the loading matrices A (I�N), B (J�N) and C(K�N) and ei;j;k were the elements of the residual array E(I� J�K), which contained the variance not captured by the

model. For CE-ESI-MS data, A was related to the relative

concentration of each individual component in each of the Isamples, and B and C to the migration profile over time and

the pure mass spectra of each individual component,

respectively. The decomposition of Dpar was accomplished

through an ALS minimization [22–24, 29]. The selection of a

model with an appropriate number of components is crucial

in order to avoid overfitting [22–24, 29]. In our case, as the

sub-isoforms of MT-I and MT-II samples were assumed to

be present in MT samples, which at the same time

contained other sub-isoforms, a three-component model

was selected. In addition, depending on the system under

study, some constraint can be applied to the elements of

matrices A, B and C [22–24, 29, 32, 33]. However, for our

CE-ESI-MS data, no constraints were applied.

For the graphical representation of the migration profile

over time of any of the three components, a single

normalized timescale vector was obtained by averaging the

t/tr vectors of each of the 25 pre-processed matrices. In a

similar way, a vector containing the m/z values was

appended to the C matrix in order to represent the pure

mass spectra of each of the components using Masslynx,

after using DataBridge to convert the ASCII file into the

Masslynx environment.

2.4.3.2 MCR-ALS

In MCR the three-way data array Daug mentioned above was

described as a bilinear model:

Daug i;j;k ¼XN

n¼1

caug i;j;nsTn;k þ eaug i;j;k ð2Þ

where i, j and k in Daug i;j;k again indicated the sample

number, time and m/z of each element of the matrix Daug,

N was the total number of components/factors, caug i;j;n and

sTn;k were the elements of the loading matrices Caug (IJ)�N

and ST (N�K) and eaug i;j;k were the elements of the residual

array Eaug (IJ)�K. In contrast to PARAFAC, Caug was the

augmented matrix that contained the migration profiles over

time of each of the N components selected for the

decomposition in each of the 25 electrophoretic runs. In

other words, a separate and quantitative migration profile

over time for each of the N resolved components was now

obtained for each of the electrophoretic runs of the data set.

On the other hand, similar to PARAFAC, ST was the matrix

of the pure mass spectra of each of the individual Ncomponents. Finally, Eaug was the augmented matrix that

contained the variation not captured by the model. The

decomposition of Daug was again achieved following an

alternating least-squares approach [25, 26, 30]. A three-

component model was selected according to the criterion

explained before. In order to start the ALS optimization,

initial estimates of the three components either in Caug or in

ST matrices were required. Several alternatives have been

described for performing this task [25, 26, 30, 34, 35]. In our

case, the matrix loading C of the three-component

PARAFAC model was taken as the best initial estimate for

the pure mass spectra of the three components. In order to

obtain a unique solution with physical meaning the non-

negativity constraint was applied to mass spectra and

migration profiles, because both were only defined as

positive or zero [25, 26, 30, 34, 35].

For the graphical representation of the migration profile

over time of any of the three components in a certain

sample, the t/tr vector corresponding to the pre-processed

sample matrix was considered. The pure mass spectra of the

three components contained in the ST matrix were repre-

sented using Masslynx, as explained before for the

PARAFAC model. Finally, the pure mass spectra of the

three components were deconvoluted to zero charge mass

spectra using MaxEnt1.

3 Results and discussion

MT-I, MT-II and MT samples were analyzed by CE-ESI-MS

with an ion trap using a methodology that was previously

developed for MT-I and MT-II samples using an equivalent

setup but with a TOF analyzer [6]. At that time, MT samples

were not studied. Figure 1A and B shows the typical TIE for

MT-I and MT-II samples, which were similar to those

previously obtained [6]. In addition, we have observed that

separation reproducibility in terms of migration times and

peak areas was lower for MT than for MT-I and MT-II

samples, probably because of adsorption of protein sub-

isoforms or other impurities on the inner wall of the bare-

fused silica capillaries. MT-I and MT-II samples were

supposed to be less problematic to analyze using bare-fused

silica capillaries because, as explained in the experimental

section, both are purified from MT samples using anion-

exchange chromatography. Figure 1C and D shows the

typical TIE obtained for MT samples on two different days.

As expected, the electrophoretic profiles were more compli-

cated than those obtained for MT-I and MT-II samples

(Fig. 1A and B). On the other hand, there were marked

differences in S/Ns migration times and peak areas between

both days due to the limited reproducibility indicated above.

To date, six sub-isoforms of rabbit liver MT-1 and MT-2

isoforms have been reported, i.e. MT-1a, MT-2a, MT-2b,

MT-2c, MT-2d and MT-2e [36] (Swiss-Prot Database,



http://www.expasy.ch). Their presence in MT-I, MT-II and

MT samples was confirmed in our previous studies using

LC-ESI-MS and CE-ESI-MS [6, 37]. The data generated in

those studies were processed as usual in order to investigate

the detected compounds. First of all, in order to confirm the

presence of a sub-isoform of known Mm, the extracted ion

electropherogram or chromatogram of the most abundant

molecular ions expected in its mass spectra was obtained

(Table 1). Later, in order to tentatively identify some of the

unknown compounds, the acquired mass spectra were

studied in short time ranges around or at each electro-

phoretic or chromatographic peak. In this way, and as

indicated in Table 1, it was concluded that MT-1a, MT-2d

and MT-2e were the most abundant sub-isoforms in

MT-I samples while MT-2a, MT-2b and MT-2c were found

in MT-II samples. In addition, some of the non-N-terminal

acetylated variants of these sub-isoforms were identified in

MT-I and MT-II samples at low concentration [6, 37]. As

MT-I and MT-II fractions are purified from an MT sample,

the presence of all the most-abundant sub-isoforms was also

confirmed in MT samples. Finally, some of the peaks found

in the MT samples were tentatively attributed to degradation

products or oxidized MT sub-isoforms [6, 37]. However, the

unambiguous identification of an unknown solely based on

an Mm value calculated from a molecular mass spectrum is

not completely reliable. As shown in the TIE of Fig. 1A–D,

0.0

0.5

1.0

1.5

8 10 12 14 16 18 20 22

0.0

0.5

1.0

1.5

0.5

1.0

0.5

1.0

1.5

Inte

nsit

y*10

8

A MT-I

B MT-II

C MT-Day1

time (min.)

TIE

D MT-Day2

600 700 800 900 1000 1100 1200 1300 1400

0

100Imax 1.46e5 10.7-14.8 min.

1248.8

1040.9

1036.5

1024.8892.2

867.4759.2 851.2

1229.6

1046.2

1051.4 1225.81056.2 1220.9

1255.4

1261.51273.2

1278.7

E MT-I

ESI Mass spectra

1001225.7

1021.6

992.61112.9

1038.8 1198.4

1238.1

1250.2

Imax 3.39e5 11.5-16.0 min.

0

F MT-II

0

1001112.8

1038.8

894.0819.6

756.7

702.6

974.0

910.0

983.4

1120.11090.2

1198.4

1225.61404.3

1298.21249.0

1383.0

1408.91413.21415.9

Imax 6.60e4 10.1-17.9 min.

m/z

G MT-Day1

Intensity

H MT-Day2

0

100

%

756.7

702.8

629.4

1208.0

819.7

974.0894.1 1038.7983.3 1112.9

1185.61119.9

1225.6

1284.91229.5

Imax 8.45e4 11.6-22.9 min.

MT-2d + MT-2e + MT-1a non-ac

MT-1aMT-2d non-ac+

MT-2e non ac

MT-2a + MT-2c

MT-2b+

MT-2a non-ac

MT-2d + MT-2e

MT-1a + MT-2b

MT-2a + MT-2c

MT-2d + MT-2e

MT-1a + MT-2b

MT-2a + MT-2c

Figure 1. CE-ESI-MS analysis of apothionein samples. TIE for (A) MT-I, (B) MT-II, (C) MT (first day) and (D) MT (second day). ESI massspectra obtained in the time range indicated with a box for (E) MT-I, (F) MT-II, (G) MT (first day) and (H) MT (second day).



the same findings in MT-I, MT-II and MT samples were

observed when the CE-ESI-MS data obtained in this work

were investigated as usual. As in our previous studies [6, 37],

some of the sub-isoforms comigrated and some of the

electrophoretic peaks could not be identified. As an example

of the typical ESI mass spectra for the mixtures of sub-

isoforms found in MT-I, MT-II and MT samples, Fig. 1E–H

shows the mass spectra obtained in the time range indicated

with a box in the TIE of Fig. 1A–D. As shown in Fig. 1E and

F for MT-I and MT-II samples, the m/z values of the

[M+5H]5+, [M+6H]6+ and, in some cases, [M+7H]7+ mole-

cular ions of the most-abundant sub-isoforms can be clearly

observed (compare with Table 1). In contrast, the mass

spectra of MT samples were more complex and noisy

(Fig. 1G and H), being consistent with the above comments

about complexity, sensitivity and reproducibility. It was even

difficult to ensure from visual inspection whether the MT

sample contained, among other compounds, the sub-

isoforms found in MT-I and MT-II samples. Thus, a

multivariate approach could be useful to investigate the

highly complex and overlapping electrophoretic profiles of

MT-I, MT-II and MT samples, which were mainly mixtures

of protein sub-isoforms with multiply-charged mass spectra,

and where peaks occurred that could not be assigned to any

particular compound, but that could also be useful as a

fingerprint for classification.

Before multivariate data analysis, pre-processing of the

raw CE-ESI-MS data was necessary [10, 13, 14, 32–35]. At the

moment, the use of bare-fused silica capillaries and sheath-

flow CE-ESI-MS interfaces are the best alternatives for the

analysis of protein isoforms at acidic pH, because of the

excellent column stability and the acceptable robustness of

the coupling [1–4, 6, 7, 9]. However, in general, the

heterogeneity of CE-ESI-MS data is higher than in LC-ESI-

MS due to the lower reproducibility of migration times and

peak areas [33, 34]. Several more or less sophisticated

methods have been described for peak alignment, time or

signal normalization, noise filtering, and baseline correction

of LC-UV or LC-ESI-MS raw data that could be applied to

CE-UV or CE-ESI-MS [10, 13, 14, 33, 34, 38]. Based on this

idea, the raw CE-ESI-MS electropherograms obtained for the

MT-I, MT-II and MT samples were pre-processed following

a simple strategy for background subtraction, smoothing

and normalization of time and intensity scales. The excel-

lent performance is seen by comparing Fig. 2A with Fig. 2B,

which show the TIE corresponding to the analysis

of the MT-I sample before and after completing data

pre-processing.

PARAFAC and MCR-ALS were selected for the multi-

way data analysis of the pre-processed CE-ESI-MS data

because they are well-known second-order decomposition

methods that have been extensively applied for the analysis

of spectroscopic data [22–26, 29, 30, 32–35]. For modelling,

both required an estimation of the number of compounds

(i.e. components or factors) present. Selection of an appro-

priate number of components can be performed in different

ways and can be a challenging task if no prior information is

available about the studied samples [22–26, 29, 30, 32–35].

In our case, three components were easily selected because,

as discussed before, MT-I, MT-II and MT owned a char-

acteristic CE-ESI-MS fingerprint. Before beginning the

three-component model optimization, the size of the 25 pre-

processed matrices was slightly reduced in order to be able

to run PARAFAC and MCR-ALS routines on our standard

personal computer. MCR-ALS required a slightly higher

amount of RAM memory for model optimization. Thus,

selection of one of every three m/z values from each matrix

row was enough for PARAFAC, while one of every seven

m/z values was necessary for MCR-ALS. However, as no

significant differences were found between results obtained

with PARAFAC using either set of size-reduced matrices,

only the results with PARAFAC and MCR-ALS for the

second size-reduced data set will be discussed.

Figure 3 shows the 3-D scores plot (Fig. 3A), the

resolved electrophoretic profiles of the three components

(Fig. 3B) and their pure mass spectra (Fig. 3C) when the

data were modelled with a three-component PARAFAC

model. The percentage of explained variance was 47.4%.

Inte

nsit

y*10

3

t/tr

8 10 12 14 16 18 200

2

4

6

8

10

12

14

16

Inte

nsit

y*10

7

time (min.)

MT-I raw TIE

MT-I pre-processed TIE

0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.50

1

2

3

4

5

6

7

8

A

B

Figure 2. TIE obtained for MT-I sample (A) before and (B) afterdata pre-processing for background subtraction, smoothing andnormalization of time and intensity scales.



In general, the amount of variance explained when analyz-

ing second-order data from complex biological samples or

processes is expected to be lower than the values usually

obtained in the analysis of data from less-troublesome

samples [35]. The low percentage of variance explained by

PARAFAC could be due to the lack of reproducibility in

migration times, which led to lack of trilinearity in the

processed data, one of the conditions required for successful

PARAFAC decomposition. Introducing more than three

components led to a similar fit, because the higher-order

components were likely to fit mostly noise, as they were not

related to another chemical species [22–26, 29, 30, 32–35]. In

the 3-D scores plot of Fig. 3A, each point represents a

particular MT-I, MT-II and MT analysis. As can be observed,

the PARAFAC model allowed very good separation between

the three different protein groups. According to the contri-

bution of each of the three components to the different

groups, the variance of MT-II and MT-I was mainly

explained by the first and the second components, respec-

tively, while all three were necessary for MT. At this point,

600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250 1300 1350 1400 14500

100

0

100

0

100

Imax6.27e31020.8

992.1850.7833.9 973.2

1225.2

1031.3

1190.21041.8

1237.1

1248.3

1040.4

1036.2

1024.3892.0866.8744.3

759.0

1242.7

1228.7

1046.0 1254.6

1267.2

1038.3

893.4819.2

756.2

702.3 824.1

973.2

909.5915.8

983.0

985.1

1112.5

1092.21042.5

1197.9

1116.7

1403.7

1297.31199.3

1228.71241.3

1412.1

Imax5.34e3

I max 3.02e3

i) Score 1

ii) Score 2

iii) Score 3

m/z

Inte

nsit

y

00.1

0.20.3

0.4

0

0.2

0.4

0.6

0.80

0.1

0.2

0.3

0.4

0.5

A B

C

Score 1Score 2

Scor

e 3

MT-I

MT-II

MT_Day 1

MT_Day 2

0.7 0.8 0.9 1 1.1 1.2 1.30

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

t/tr (average)

Inte

nsit

y

Score 1Score 2

Score 3

Figure 3. Three-component PARAFAC model. (A) 3-D scores plot, (B) resolved electrophoretic profiles of the three components and (C)pure mass spectra of (i) the first, (ii) the second and (iii) the third components.



PARAFAC did not provide further information to explain

the slight separation between the MT samples analyzed on

two different days, which may be related to the limited inter-

day reproducibility, and which was not completely solved

with the simple data-pre-processing method proposed.

However, in both cases, the position of the MT samples in

the 3-D plot was consistent with the fact that the MT sample

contained the sub-isoforms found in MT-I and MT-II toge-

ther with other compounds that were modelled by the third

component. A rapid comparison between the typical mass

00.1

0.20.3

0.4

00.2

0.40.6

0.80

0.1

0.2

0.3

0.4

0.5

A

B C D

Score 1Score 2

Scor

e 3 MT-I

MT-II

MT_Day 1

MT_Day 2

MT-I MT-II MT

0

40

80

120

160

200

0

40

80

120

160

0.7 0.8 0.9 1 1.1 1.2 1.3

0

40

80

120

160

t/tr t/tr

Analysis 2

Analysis 6

Analysis 9

Inte

nsit

y

Score 2

Score 3

Score 1

0

50

100

150

0

40

80

120

0.7 0.8 0.9 1 1.1 1.2 1.3

0

50

100

150

Analysis 2

Analysis 6

Analysis 9

Score 1

Score 2

Score 3

0

40

80

120

160

0.7 0.8 0.9 1 1.1 1.2 1.30

20

40

60

t/ tr

Day 1Analysis 2

Day 2Analysis 2

Score 1Score 2

Score 3

Score 2

Score 3

Score 1

Score 2

Score3

Score1

Score 1

Score 2

Score 3

Score 1

Score2

Score3

Score 1

Score 2

Score 3



spectra of MT-I, MT-II and MT samples (Fig. 1E–H) and the

pure mass spectra of each of the components retrieved by

PARAFAC (Fig. 3C) confirmed that the first, the second and

the third components were, respectively, related to MT-II,

MT-I and the unknown compounds of the MT samples. As

shown in Fig. 3B, PARAFAC resolved a unique electro-

phoretic profile for each component taking into account

the information from all the analyses. The unknown

compounds of the MT sample migrated first, the sub-

isoforms of the MT-I sample next and the sub-isoforms of

the MT-II last.

In contrast to PARAFAC, MCR-ALS required an initial

estimate of the mass spectra of the three components in

order to start model optimization [25, 26, 30, 34, 35]. In our

case, as the loadings from the three-component PARAFAC

model were available, they were selected as the best initial

estimates for the pure spectra of each of the three compo-

nents. Figure 4 shows the 3-D scores plot (Fig. 4A), the

resolved electrophoretic profiles of the three components in

different analyses (Fig. 4B, C and D) and their pure mass

spectra (Fig. 4E) when the data were modelled with a three-

component MCR-ALS model. The percentage of explained

variance was 71.6%, indicating that the model fit was better

than with PARAFAC, probably because MCR-ALS had an

improved performance with data that show migration time

shifts. As shown by the 3-D scores plot of Fig. 4A, which has

the same axis ranges of Fig. 3A, the different protein

samples were separated into the same three groups (i.e. MT-I,

MT-II and MT). However, according to the improved fit, the

dispersion within the same group was now lower, and the

separation among groups was better. From the position of

the different samples in the 3-D scores plot (Fig. 4A),

similar conclusions could be derived about the contribution

of each of the three components to MT-I, MT-II and MT

samples. The variance in MT-II and MT-I samples was

mainly explained by the first and the second components,

respectively, while all three were necessary for MT samples.

In this latter case, as with PARAFAC before, the MT

samples analyzed on two different days were separated

along the third-component axis. This can be better explained

by Fig. 4B, C and D, which shows the electrophoretic

profiles of each of the components for several analyses. A

great advantage of MCR-ALS was that it resolved an indi-

vidual electrophoretic profile of each component for each

analysis of the data set, something that could be a benefit for

characterization and quantification purposes. As seen in

Fig. 4B, C and D, once again the migration order of the

three components coincided with PARAFAC (see Fig. 3B),

600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250 1300 1350 1400 1450

0

100

0

100

0

100 I max 5.84e3

Imax5.38e3

I max3.71e3

i) Score 1

ii) Score 2

iii) Score 3

m/z

Inte

nsit

y

1020.8

992.1850.7833.9

973.2

1225.2

1031.3

1190.21041.8

1237.1

1248.3

1040.4

1036.2

892.0866.8744.3

1024.3

1242.7

1228.71050.9

1220.31260.9

1267.2

1038.3819.2

756.2

702.3628.8

893.4

824.1

983.0973.2

899.0 1024.3

1112.5

1092.21042.5

1403.71197.9

1116.7

1228.7

1297.31254.6

1412.1

E

Figure 4. Three-component MCR-ALS model. (A) 3-D scores plot, (B)–(D) resolved electrophoretic profiles of the three components for(B) MT-I samples (i) second analysis, (ii) sixth analysis and (iii) ninth analysis; (C) MT-II samples, (i) second analysis, (ii) sixth analysis and(iii) ninth analysis; (D) MT samples, (i) second analysis (first day), (ii) second analysis (second day); and (E) pure mass spectra of(i) the first, (ii) the second and (iii) the third components.



0

1006236.5

6211.0

5198.5

4969.05174.5

5038.0

6139.0

5248.0

7484.5

7277.56298.0

7244.5

6358.0 7123.0

7453.07367.5

7558.0

Imax2.26e3

6269.54990.0

F Score 2In

tens

ity

0

1006122.0

5100.0

4896.0

4956.0

5948.0

5832.05152.0

7346.0

6182.07212.0

6938.07420.0

7494.0

I max3.54e3

6150

E Score 1

Molecular Mass (Da)

4800 5200 5600 6000 6400 6800 7200 76000

100 6668.05556.0

4910.0

4862.0

5186.0

4940.0

5116.0

5356.0

5208.05454.0

5610.0

6248.0

5984.0

5890.05834.05646.0

6140.0

6042.0

6546.0

6286.0

7012.0

6872.06806.0

7180.0

7058.0

7366.0

7262.0

7440.0

7636.07552.0

Imax958G Score 3

0

1006239.5

6213.5

5178.0

5119.0

4971.55200.0

5253.0 6100.0

6271.5

6302.56364.0

Imax5.21e5

6143.0

6174.5

Molecular Mass (Da)

A MT-I

0

1006123.0

5101.06081.5

6154.5

6671.56242.0

Imax1.06e6

6186.5

6144.0

Inte

nsit

y

B MT-II

4800 5200 5600 6000 6400 6800 7200 7600

0

1006671.5

4789.55189.0

4912.0

4971.5

5559.5

5358.5

5613.0

5731.06227.0

6034.55995.5

5837.0

6251.56549.5

6486.06480.06619.5

7265.0

7144.56810.0

6876.5

7039.56906.0

7368.5

7470.0

7716.0

IZmax1.45e5C MT-Day1

0

1006124.0

6036.0

5838.0

4912.0

5190.0

5604.0

5630.0

5996.0

5906.0

6550.0

6214.0

6252.0

6440.0

6266.06420.06362.0

7558.0

7144.06876.0

6802.06672.0

7020.0

6952.0

7462.07264.0

Imax1.16e5D MT-Day2

MT-2e

MT-2d

MT-1a

MT-1a non ac

MT-2d non ac

6200.0MT-2e non ac

MT-2a

MT-2b

MT-2c

MT-2a non ac

MT-2e

MT-2d

MT-1a

6142.1

MT-2a

MT-2b

MT-2c

Figure 5. Mass spectra deconvoluted from the raw ESI mass spectra of Fig. 1A–D, (A) MT-I, (B) MT-II, (C) MT (first day) and (D) MT(second day) samples. Mass spectra deconvoluted from the pure mass spectra of the three components of the MCR-ALS model (E) firstcomponent, (F) second component and (G) third component.



and it was clear that the variance of MT-I and MT-II samples

was mainly explained by the second and the first compo-

nents respectively, while all three were necessary for MT

samples. Furthermore, the electrophoretic profiles of the

three components within the same group of samples were

similar, even for the MT samples analyzed on two different

days (Fig. 4D). The separation of MT samples into two

groups in the 3-D scores plot of Fig. 4A could be tentatively

explained by the differences in the ranges of the y-axis of

Fig. 4D plots, which were due to the lower S/N ratio of the

CE-ESI-MS electropherograms acquired for the MT samples

during the second day (see the TIE of Fig. 1C and D). In this

sense, the quantitative information that can be extracted

from the electrophoretic profiles resolved by MCR-ALS

allowed improved sample characterization. Figure 4E shows

the pure mass spectra of each of the components retrieved

with MCR-ALS, which were identical to those obtained with

PARAFAC (Fig. 3C). This confirmed the goodness of the

pure mass spectra obtained by PARAFAC, which were an

excellent alternative for initializing MCR-ALS optimization.

Figure 5A–D shows the zero charge mass spectra

deconvoluted from the ESI mass spectra of Fig. 1E–H. In a

similar way, Fig. 5E–G shows those obtained from the pure

mass spectra of each of the three MCR-ALS components.

Consistent with the TIE of Fig. 1A and B and Table 1, the

Mm values of MT-1a, MT-2d, MT-2e, MT-1a non-ac, MT-2d

non-ac and MT-2e non-ac were observed in the deconvoluted

mass spectra of the MT-I sample (Fig. 5A) and those of

MT-2a, MT-2b, MT-2c and MT-2a non-ac were found in the

deconvoluted mass spectra of the MT-II sample (Fig. 5B).

The other Mm values, which as explained before were

difficult to unambiguously identify, could be tentatively

attributed to some degradation products or oxidized MT sub-

isoforms [6, 37]. However, from the point of view of a

multivariate data analysis, the Mm information from the

unidentified compounds is also useful as part of the indi-

vidual fingerprint of each MT-I, MT-II and MT sample. As

the first and the second components were related with

MT-II and MT-I samples, respectively, their deconvoluted

mass spectra (Fig. 5E and F) contained most of the infor-

mation observed in the deconvoluted mass spectra of MT-II

and MT-I samples (Fig. 5B and A), respectively. In addition,

there were some other minor signals, because both

components were also explaining to some extent the

variance of the rest of the data set. With reference to

the information related with MT-II and MT-I samples in the

first and the second components (Fig. 5E and F, respec-

tively), it was possible to clearly identify the Mm values of

the most-abundant sub-isoforms, which differed by only a

few mass units (1–4 Da) from the Mm values obtained for

MT-I and MT-II samples (Fig. 5A and B). The accuracy of

the Mm values obtained with MCR-ALS or PARAFAC will

have important implications when an reliable identification

is necessary, and it could be improved by using an MS

detector with improved mass accuracy and resolution [9].

However, at this point, it is important to emphasize the

excellent accuracy of the Mm data obtained from both

multivariate data analysis models. In accordance with the

ESI mass spectra of Fig. 1G and H, the deconvoluted mass

spectra of the MT samples were more complex and noisy

(Fig. 5C and D). Again, it was difficult to observe from visual

inspection whether the MT sample contained, among other

compounds, the sub-isoforms found in MT-I and MT-II

samples. In a similar way, it was complicated to assert

whether the deconvoluted mass spectra of the MT samples

(Fig. 5C and D) contained the information retrieved by the

deconvoluted mass spectra of the three components

(Fig. 5E–G). However, all the results discussed above ensure

that the pure spectra retrieved by MCR-ALS were repre-

sentative of the compounds in the MT-I, MT-II and MT

samples and that the Mm values obtained from the decon-

voluted mass spectra could be used for identification and

confirmation purposes.

4 Concluding remarks

Multivariate data analysis based on PARAFAC and MCR-

ALS models have demonstrated to be excellent complemen-

tary tools with which to investigate, in a direct way and with

minimum prior knowledge, the highly complex and over-

lapping electrophoretic profiles that are usually obtained by

CE-ESI-MS for protein isoforms. Using both methods it was

possible to discriminate a characteristic fingerprint for MT-I,

MT-II and MT samples, based on the electrophoretic

profiles and the pure mass spectra of the three model

components, which contributed in a different way to

explaining the variance of each protein type. For MT-I

and MT-II samples it was also possible to identify the

Mm values of their main characteristic sub-isoforms after

deconvolution of the pure mass spectra of their specific

model component. The results with MCR-ALS in terms of

classification were slightly better than those obtained with

PARAFAC, because it performed better with the pre-

processed electropherograms, which showed small migra-

tion time shifts. However, no differences were found

between the pure mass spectra of the three components

for either model. MCR-ALS allowed us to resolve an

individual electrophoretic profile of each of the three

components for each of the analyzed samples, something

that was advantageous for characterization and quantifica-

tion purposes. The application of multivariate data analysis

methods to protein isoform separation and characterization

should be regarded as an efficient, novel alternative for

achieving a deeper understanding of the vast amount of data

obtained by hyphenated techniques for the analysis of

complex proteins.

B. A. is grateful to the University of Barcelona for awardinga doctoral fellowship. This study was supported in part by a grantfrom the Spanish Ministry of Science and Technology(CTQ2005-04357/BQU).

The authors have declared no conflict of interest.



5 References

[1] Haselberg, R., de Jong, G. J., Somsen, G. W. J.Chromatogr. A 2007, 1159, 81–109.

[2] Hernandez-Borges, J., Neussus, C., Cifuentes, A.,Pelzing, M., Electrophoresis 2004, 25, 2257–2281.

[3] Klampfl, C. W., Electrophoresis 2006, 27, 3–34.

[4] Stutz, H., Electrophoresis 2005, 26, 1254–1290.

[5] Sanz-Nebot, V., Benavente, F., Vallverdu, A., Guzman,N. A., Barbosa, J., Anal. Chem. 2003, 75, 5220–5229.

[6] Andon, B., Barbosa, J., Sanz-Nebot, V., Electrophoresis2006, 27, 3661–3670.

[7] Sanz-Nebot, V., Balaguer, E., Benavente, F., Neussus, C.,Barbosa, J., Electrophoresis 2007, 28, 1949–1957.

[8] Prange, A., Profrock, D., Anal. Bioanal. Chem. 2005, 383,372–389.

[9] Balaguer, E., Demelbauer, U. M., Pelzing, M., Sanz-Nebot,V. et al., Electrophoresis 2006, 27, 2638–2650.

[10] Sentellas, S., Saurina, J., J. Sep. Sci. 2003, 26,1395–1402.

[11] Booksh, K. S., Kowalski, B. R., Anal. Chem. 1994, 66,782A–791A.

[12] Benavente, F., Gimenez, E., Olivieri, A. C., Barbosa, J.,Sanz-Nebot, V., Electrophoresis 2006, 27, 4008–4015.

[13] Kaiser, T., Wittke, S., Just, I., Krebs, R. et al., Electro-phoresis 2004, 25, 2044–2055.

[14] Ullsten, S., Danielsson, R., Backstrom, D., Sjoberg, P.,Bergquist, J., J. Chromatogr. A 2006, 1117, 87–93.

[15] Gonzalez-Duarte, P., in: McCleverty (Ed.), Metallothioneins,Comprehensive Coordination Chemistry II, vol. 8, Elsevier,Oxford 2004, pp. 213–228.

[16] Stillman, M. J., Coord. Chem. Rev. 1995, 144,461–511.

[17] Nordberg, M., Talanta 1998, 46, 243–254.

[18] Klaasen, C. D., Liu, J., Choudhuri, S., Annu. Rev.Pharmacol. Toxicol. 1999, 39, 267–294.

[19] Nordberg, G., Jin, T., Leffler, P., Svensson, M.,Nordberg, M., Analysis 2000, 28, 396–400.

[20] Cosson, R. P. Amiard, J. C., in: Lagadic, L. et al. (Eds.),Use of Biomarkers for Environmental Quality Assess-ment, Science Publishers, Enfield 2000, pp. 79–111.

[21] Kojima, Y., Meth. Enzymol. 1991, 205, 8–10.

[22] Andersen, C. M., Bro, R., J. Chemom. 2003, 17, 200–215.

[23] Bro, R., Chemom. Intell. Lab. Syst. 1997, 38, 149–171.

[24] Paatero, P., Chemom. Intell. Lab. Syst. 1997, 38,223–242.

[25] Tauler, R., Chemom. Intell. Lab. Syst. 1995, 30, 133–146.

[26] Tauler, R., Smilde, A., Kowalski, B., J. Chemom. 1995, 9,31–58.

[27] Husheer, S. L. G., Forest, O., Henderson, M., McIndoe,J. S., Rapid Commun. Mass Spectrom. 2005, 19,1352–1354.

[28] Vargas, C. A., moving_average2, http://www.mathworks.com/matlabcentral/fileexchange.

[29] Andersson, C. A., Bro, R., Chemom. Intell. Lab. Syst.2000, 52, 1–4.

[30] Jaumot, J., Gargallo, R., de Juan, A., Tauler, R.,Chemom. Intell. Lab. Syst. 2005, 76, 101–110.

[31] Wang, J., Bose, S., Hage, D. S., J. Chromatogr. A 1996,735, 209–220.

[32] Munoz de la Pena, A., Espinosa-Mansilla, A., Gonzalez-Gomez, D., Olivieri, A. C., Goicoechea, H. C., Anal.Chem. 2003, 75, 2640–2646.

[33] Braga, J. W. B., Bottoli, C. B. G., Jardim, I. C. S. F.,Goicoechea, H. C. et al., J. Chromatogr. A 2007, 1148,200–210.

[34] Pere-Trepat, E., Lacorte, S., Tauler, R., Anal. Chim. Acta2007, 595, 228–237.

[35] Jaumot, J., Tauler, R., Gargallo, R., Anal. Biochem.2006, 358, 76–89.

[36] Hunziker, P. E., Kaur, P., Wan, M., Kanzig, A., Biochem.J. 1995, 306, 265–270.

[37] Sanz-Nebot, V., Andon, B., Barbosa, J., J. Chromatogr.B 2003, 796, 379–393.

[38] Tomasi, G., van den Berg, F., Andersson, C.,J. Chemom. 2004, 18, 231–241.



A multiway approach for classification and characterization of rabbit liver apothioneins by...

Documents

Transcript of A multiway approach for classification and characterization of rabbit liver apothioneins by...