Clinical Chemistry 47: 730-738, 2001;
(Clinical Chemistry. 2001;47:730-738.)
© 2001 American Association for Clinical Chemistry, Inc.
Plasma Protein Contents Determined by Fourier-Transform Infrared Spectrometry
Cyril Petibois1,2,
Georges Cazorla2,
André Cassaigne3 and
Gérard Déléris1,a
1
INSERM U443, Equipe de Chimie Bio-Organique,
2
Faculté des Sciences du Sport et de l’Education Physique, and
3
Département de Biochimie Médicale et Biologie Moléculaire, Laboratoire de Biochimie, CHU Bordeaux, Université Victor Segalen Bordeaux 2, 146 rue Léo Saignat, 33076 Bordeaux, France.
a Author for correspondence. Fax 33-5-5757-1002; e-mail
gerard.deleris{at}bioorga.u-bordeaux2.fr.
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Abstract
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Background: Fourier-transform infrared (FT-IR) spectrometry has
been used to measure small molecules in plasma. We wished to extend
this use to measurement of plasma proteins.
Methods: We analyzed plasma proteins, glucose, lactate, and urea
in 49 blood samples from 35 healthy subjects and 14 patients. For
determining the concentration of each biomolecule, the method used the
following steps: (a) The biomolecule was sought for
which the correlation between spectral range areas of plasma FT-IR
spectra and concentrations determined by comparison method was
greatest. (b) The IR absorption of the biomolecule at
the most characteristic spectral range was calculated by analyzing pure
samples of known concentrations. (c) The plasma
concentration of the biomolecule was determined using the FT-IR
absorption of the pure compound and the integration value obtained for
the plasma FT-IR spectra. (d) The spectral contribution
of the biomolecule was subtracted from the plasma FT-IR spectra, and
the resulting spectra were saved for further analyses.
(e) The same method was then applied to determining the
concentrations of other biomolecules by sequentially comparing the
resulting FT-IR spectra.
Results: Results agreed with those obtained by clinical methods
for the following biomolecules when analyzed in the following order:
albumin, glucose, fibrinogen, IgG2, lactate,
IgG1, 
1-antitrypsin,
2-macroglobulin, transferrin, apolipoprotein
(Apo)-A1, urea, Apo-B, IgM, Apo-C3, IgA,
IgG4, IgG3, IgD, haptoglobin, and
1-acid glycoprotein.
Conclusion: FT-IR spectrometry is a useful tool for
determining concentrations of several plasma biomolecules.
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Introduction
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Fourier-transform infrared (FT-IR) spectrometry is a global,
sensitive, and highly reproducible physicochemical analytical technique
that identifies structural moieties of biomolecules on the basis of
their IR absorption (1)(2). Because a
biomolecule is determined by its unique structure, each biomolecule
will exhibit a unique FT-IR spectrum, representing the vibrations of
its structural bonds (3). Furthermore, every biomolecule
present in the sample will exhibit more or less specific FT-IR
absorption peaks (4). Thus, a plasma FT-IR spectrum will
exhibit absorption peaks related to its major components. FT-IR
analytical applications have allowed determination of blood contents
using various materials and sample preparations. Concentrations of
glucose (1)(2)(5)(6),
total proteins, creatinine, urea, triglycerides (1), and
cholesterol (2) in blood, plasma, or serum have been
determined with clinical accuracy. However, extensive sample
preparation, manipulation of spectra, and mathematical treatments have
been necessary to obtain such results. Clinical analyses require
methods that involve minimal sample manipulations because every
handling step may be a source of quantitative error, affecting the
predictive performance of the method used
(7)(8).
With FT-IR spectrometry, fluctuations in absorption related to water
content because of environmental conditions (e.g., variations in air
temperature, humidity, and atmospheric pressure) have necessitated
manipulation of samples and/or spectra (or both)
(1)(2)(5)(6)(8).
Recently, we described FT-IR analysis of glucose concentrations in
dried serum samples (9). By avoiding water absorption in
serum spectra, we obtained results that correlated well with those
obtained by the glucose oxidase method (10); sample
manipulations were limited to a precise dilution with water and
desiccation under moderately reduced pressure after deposition of 35
µL of this solution onto a multiple sample holder. No other
manipulations of the sample or the spectrum were necessary for
obtaining highly reproducible results. The characteristic IR absorption
peaks for glucose were determined before those of biomolecules
exhibiting more intense absorption contributions to serum FT-IR spectra
(namely, proteins); we choose this approach because the glucose
absorption appears in a specific spectral area, the 
C
O
absorption region (1300–900 cm-1), in which
major protein absorption is absent.
However, this analytical method is rather limited. Only 
80 spectral
ranges, i.e., deformations such as peaks, shoulders, and bands, are
found on a FT-IR spectrum, whereas plasma contains thousands of
biomolecules at various concentrations. We previously had shown that
subtraction of the spectral contribution of a previously determined
metabolite allowed subsequent analysis, e.g., subtraction of the
glucose spectrum allowed determination of lactatemia (11).
These results for lactate were in good agreement with an enzymatic
comparison method but could not have been obtained before this
subtraction because the glucose absorption partially obscured the
lactate absorption in the complete serum FT-IR spectra of subjects.
Glucose and lactate concentrations could be determined because the
broadest protein IR absorption peaks (for C
O and
NH: 1700–400 cm-1) were
outside the spectral region in which most characteristic absorption
peaks for glucose and lactate were found (CO:
1300–900 cm-1). We now apply this methodology
to the analysis of plasma proteins, the major component of plasma
(accounting for 75 g/L of the 100 g/L of dried matter present in
plasma).
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Materials and Methods
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sample collection
Venous blood samples from 35 anonymous healthy blood donors,
admitted to hospital for routine analyses, were used for this study.
Although these subjects did not present with pathological situations,
several presented with some protein concentrations outside the
physiological range for healthy adults (Table 1
). The mean (± SD) age
of these subjects was 35.4 ± 5.1 years (range, 27–43 years).
Blood was sampled between 0730 and 0830 after an overnight fast of
10 h. Blood was drawn into sterile and gel-barrier collection
tubes (6-mL red-brown top and 7-mL red top Vacutainer Tubes; Becton
Dickinson). One gel-barrier collection tube was used for FT-IR
measurements. Venous blood was sampled by an antecubital venipuncture
of the right arm through a Teflon catheter. Samples collected in
gel-barrier collection tubes were centrifuged immediately for 10 min at
4000g; plasma was collected in the red-brown top tubes and
stored at -20 °C before analysis.
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Table 1. Plasma concentrations of glucose, lactate, urea, and
various proteins as determined by comparison
methods.
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Venous blood samples from 14 age-matched patients (37.1 ± 4.4
years; range, 30–44 years) admitted to hospital for severe infection
(n = 4), renal disorder (n = 4), or anemia (n = 6) were
used for validation of the methodology implemented for the 35 blood
samples from the relatively healthy subjects.
comparison methods
Glucose and urea concentrations were measured with an accepted
enzymatic method on a Dax 48 analyzer and using calibrators and
controls from Bayer. Lactate was also measured by an enzymatic method
(Microzym-L; SGI). Protein concentrations were determined
immunonephelometrically with a BN II apparatus and calibrators and
controls from Dade Behring, except that apolipoproteins
A1, B, and C3
(Apo-A1, Apo-B, Apo-C3) and
IgD were evaluated by radial immunodiffusion with reagents from Daiichi
Pure Chemicals Co. (for the apolipoproteins) and Dade Behring (for
IgD).
acquisition of ft-ir spectra
The method for acquiring spectra has been described elsewhere
(9). Briefly, after samples returned to room temperature
(15 min at 20–25 °C), 20-µL aliquots were diluted with 80 µL
of water. The diluted samples were homogenized with an agitator (Vortex
Reax 2000; Heidolf) at 1000g for 10 s. Thirty-five
microliters of each solution was deposited exactly within the cell
limits of a zinc selenide wheel with 15 sample cells (Bruker). The
loaded wheel was subsequently placed in a low-pressure [266 Pa (2
mmHg)] drier to evaporate the water in the sample (45 min), after
which a KBr cover was attached to protect the wheel. The sample-bearing
wheel was then inserted into the analysis compartment of a Bruker IFS
28/B spectrometer equipped with a Globar (MIR) source (7 V), a KBr beam
splitter, and a DTGS/B detector (18–28 °C) from Bruker. Beam
diameter at the sample location was 6 mm. In all experiments, we used a
resolution of 2.0 cm-1 and performed 32 scans
for data acquisition. All analyses were performed in triplicate on
three successive wheels.
comparison ft-ir spectra of biomolecules
FT-IR spectra obtained for pure components (98–99% purity;
Sigma-Aldrich) were used as comparison FT-IR spectra. These were the
spectra used for all subtractions of biomolecule absorption peaks. The
concentration of the dried matter in plasma is 100 g/L. We
previously demonstrated (9)(11) that FT-IR
analyses of plasma diluted fourfold with water provided a higher
signal-to-noise ratio (determined in the 2100–2200
cm-1 region, where no absorption attributable to
plasma biomolecules is found) and greater signal reproducibility for
successive analyses. Because these fourfold-diluted solutions contained
20 g/L dried matter, we used pure component solutions ranging in
concentration from 12 to 28 g/L to obtain FT-IR spectra that would be
comparable in absorption values to those of the plasma FT-IR spectra.
Application of pure component solutions onto the analytical wheel,
desiccation, and FT-IR spectrum acquisition were performed exactly as
described for plasma FT-IR spectrum acquisitions. For every
biomolecule, linearity between the absorption signals detected and
these concentrations was assessed by total area integration over
4000–500 cm-1, representing the whole FT-IR
absorption range for the biomolecules (with P <0.01).
analyses and calculations
The method for iterative determination of biomolecule
concentrations (Fig. 1
) was assessed as follows:
- (1) Spectral ranges (peaks, bands, shoulders) were chosen as
approximate absorption bands within the plasma FT-IR spectra. The
precise abscissa limits for the spectral ranges were assigned using a
subroutine of OPUS 3.0 software (Bruker). In brief, once two
wavenumbers had been selected outside the absorption band, the software
found the lowest absorption values that allowed the drawing of a line,
no other point on which was in common with the spectrum curve.
- (2) For each defined spectral range common to the 35 plasma FT-IR
spectra of our experiments, the area between the line drawn and the
spectrum was integrated by the software and expressed in arbitrary
units (U).
- (3) A correlation matrix was used to compare all integration
results with the concentrations of all biomolecule measured with the
comparison clinical analytical methods. The spectral range that led to
the greatest correlation between integration values and the results
obtained by the comparison method for each of the biomolecules
considered was selected for subsequent use.
- (4) A series of accurately determined calibration solutions of the
selected biomolecule allowed us to acquire and integrate the spectral
area of the pure compound over the previously determined spectral
range. These results were expressed as the biomolecule concentration
per arbitrary unit of spectral area:
g · L-1 · U-1.
- (5) For each FT-IR plasma spectrum, the concentration of the
selected biomolecule was then calculated using its absorption value,
based on the spectrum of the pure component, and the spectral area
integration result obtained in step 2.
- (6) The contributing spectrum of the biomolecule, determined from
its pure component spectrum and its plasma concentration (as measured
with a comparison method), was subsequently subtracted from the plasma
FT-IR spectrum.
- (7) The resulting spectrum was then saved and subjected to the
same iterative method (steps 1 to 6) to determine the concentration of
the next biomolecule to be analyzed. This process was repeated for each
of the biomolecules being evaluated.
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Figure 1. Flow diagram of the steps in the iterative procedure for
predicting concentrations of plasma compounds from plasma FT-IR
spectra.
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statistics
Data are expressed as the mean ± SD. In each clinical
reference data series, the dispersion [mean dispersion (CV), expressed
as a percentage] around the mean value was calculated. When the CV
exceeded 5%, the concentration values were considered significantly
heterogeneous (P <0.05). Linear regressions were performed
to compare the data series. The results obtained by the clinical
analytical method and the spectral data were considered comparable when
P was <0.05. When the data were comparable, dispersion data
around the regression lines were estimated by the mean standard error
of prediction:
where xi corresponds to the predicted
analyte value, µ is the value determined by the comparison method,
and n is the total number of samples in the prediction data set. For
results to be acceptable, the regression line had to show a slope close
to 1 and an intercept close to 0. Differences between methods were
tested using a standard t-test for paired data.
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Results
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clinical data
The plasma concentrations of glucose, lactate, urea, and proteins
for the first set of 35 venous blood samples are presented in Table 1
. Immunoglobulins, haptoglobin, 1-acid
glycoprotein, and Apo-C3 exhibited the broadest
distribution of concentrations (CV = 8–10%). This heterogeneity
in plasma protein content allowed us to assess the analytical potential
of FT-IR spectrometry for determining concentrations of successive
biomolecules.
ft-ir spectrum acquisition
For the set of 35 plasma FT-IR spectra, the mean signal-to-noise
ratio in the 2100–2200 cm-1 region was 687
± 19. The baselines between repeated samples were also homogeneous,
varying by 0.011 ± 0.006 at 4000 cm-1. The
between-run CV was 1.3% for triplicate FT-IR measurements. The mean
plasma FT-IR spectrum area was 472 ± 45 U (integration range,
4000–500 cm-1). Results were comparable for the
set of FT-IR spectra from the plasma samples from 14 patients, except
that the mean plasma FT-IR spectrum area for the latter was 461 ±
76 U (integration range, 4000–500 cm-1).
ft-ir spectra of biomolecules
For each series of pure component solutions, we studied the
correlations between the corresponding 4000–500
cm-1 spectral area and the biomolecule
concentration. The correlations were 0.95–0.99 for all analytes
evaluated in the concentration range 12–28 g/L (Table 2
). The FT-IR spectra for the biomolecules presented important IR
absorption dissimilarities, even for biomolecules belonging to the same
families, e.g., apolipoproteins (Fig. 2
) and IgG (Fig. 3
).
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Table 2. Relationships between concentrations and corresponding
4000–500 cm-1 total spectral areas obtained from series
of pure component
solutions.
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Figure 2. FT-IR spectra (1850–500 cm-1) of
IgG1–IgG4 in 20 g/L solutions.
Several absorption peaks differ among these biomolecules: 802
cm-1 for IgG1, 1087 and 982 cm-1
for IgG2, 1185 cm-1 for IgG3, and
893 cm-1 for IgG4.
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Figure 3. FT-IR spectra (1850–500 cm-1) of
Apo-A1, -B, and -C3 in 20 g/L solutions.
Several absorption peaks are highly specific for these biomolecules:
1466 and 1164 cm-1 for Apo-A1, 1218 and 984
cm-1 for Apo-B, and 997 cm-1 for
Apo-C3.
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analyses
The iterative procedure for determining the concentrations of
biomolecules was tested for the set of 35 venous blood samples. Table 3
presents, in the order of analysis of the biomolecules, details
on the spectral ranges used for integration (left and right abscissa
limits), the FT-IR absorption data for the pure components in the same
spectral ranges used to measure the concentrations of the biomolecules
in plasma, the statistical significance of any correlation with results
by the comparison methods, and a summary of the statistical indices for
linearity between methods. After multiple analyses of the highest
statistical values found in the correlation matrices, only one sequence
of biomolecule analysis was found to give results that provided
sufficient agreement with the 35 results obtained with the clinically
used comparison methods.
The closest relationship between integration results and the values
obtained by the clinical comparison methods was for albumin, the
biomolecule present in the highest concentration in plasma. This
relationship involved the spectral area located between 1600 and 1488
cm-1 (NH region; 23.3 ±
0.3 U vs 40.9 ± 3.8 g/L; r = 0.99; P
<0.001). Albumin represents 65% of plasma proteins and 45% of the
total plasma mass. The FT-IR absorption of albumin between 1600 and
1488 cm-1 was 1.76
g · L-1 · U-1.
The albumin concentration measured by FT-IR spectrometry was 41.0
± 3.9 g/L (correlation with the comparison method, r =
0.99; P <0.001). Regression analysis of the two methods
showed a slope of 1.00 with an intercept of 0.17 g/L (Fig. 4
). The spectral contribution of albumin (4000–500
cm-1) represented 39.9% of the total plasma
FT-IR spectral area. Subtracting the albumin absorption left a spectral
area of 230 ± 21 U (Fig. 5
).
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Figure 4. Relationship between albumin concentration measured by the
comparison method and by FT-IR spectrometry in plasma samples from 35
healthy subjects.
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Figure 5. Subtraction of IgG1 absorption spectrum (for
7.66 g/L IgG1 sample) from plasma FT-IR spectrum.
The mean spectra (1500–500 cm-1) for samples from 35
healthy subjects before (plasma) and after
(resulting) subtraction are shown.
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Evaluation of the remaining area showed that glucose was the second
biomolecule whose concentration closely correlated with absorption area
integration values (5.2 ± 0.4 mmol/L;
COC absorption at 1033–997
cm-1 = 7.27
mmol · L-1 · U-1;
plasma spectral area at 1033–997 cm-1 =
0.71 ± 0.05 U). The glucose concentration measured by FT-IR
spectrometry was 5.22 ± 0.17 mmol/L (correlation with the
comparison method, r = 0.97; P <0.001;
regression slope = 0.99; intercept = 0.04 mmol/L). Regardless
of whether the albumin absorption was subtracted before or after
glucose concentration analysis in the resulting spectra, the results
for glucose were in accord with those from our previous study
(9).
For subsequent spectra obtained after the spectral contributions of
specific biomolecules were subtracted, the greatest correlations were
found between the concentrations measured by the comparison method and
the results obtained by spectral integration in the following
descending order: fibrinogen, IgG2, lactate,
IgG1, 1-antitrypsin,
2-macroglobulin, transferrin,
Apo-A1, urea, Apo-B, IgM,
Apo-C3, IgA, IgG4,
IgG3, IgD, haptoglobin, and
1-acid glycoprotein (Tables 2
and 3
). Urea was
included in this analysis because it exhibits strong absorption bands
between 1800 and 1100 cm-1, a spectral range in
which IgM, IgG3, and IgG4
are also examined. In the 1500–1300 cm-1
region, several of the spectral ranges used for determining biomolecule
concentrations were very similar, i.e., those for fibrinogen,
haptoglobin, IgG1, IgA, and IgM. Utilization of
these ranges was possible, however, because the absorption bands for
the pure compounds were not found in exactly the same locations
(
max and 
) on the spectrum for each of the
other pure components (Fig. 6
).
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Figure 6. FT-IR spectra of 20 g/L fibrinogen, haptoglobin,
IgG1, IgA, and IgM in the 1500–1300 cm-1
spectral region.
In each spectrum, vertical bars give abscissa limits and
top of spectral ranges used for determination of biomolecule
concentrations. The upper limits of the spectral ranges were 1398
cm-1 for fibrinogen, 1401 cm-1 for
haptoglobin, 1405 cm-1 for IgG1, 1411
cm-1 for IgM, and 1408 cm-1 for IgA (see
Table 3
for abscissa limits).
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Haptoglobin was one the last biomolecules analyzed in our sequence and
presented important concentration variations among subjects.
Nevertheless, although fibrinogen, IgG1, IgA, and
IgM concentrations were determined before those of haptoglobin, we
obtained very good relationships between spectral and clinical
reference values for these five biomolecules. Finally, after
subtraction for 20 biomolecules, which corresponded to 69 ± 4 g/L
of the dried matter in plasma, the mean total spectral area of the
remaining spectrum was 114.5 ± 11.2 U, i.e., 24.2% of the
initial plasma FT-IR spectral area (Fig. 7
).
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Figure 7. Plasma FT-IR spectrum (4000–500 cm-1) after
subtraction of absorption spectra of 20 biomolecules.
The FT-IR spectrum is the mean for samples from 35 healthy subjects.
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validation with plasma samples from 14 patients
The results obtained for plasma samples from the 14 patients are
presented in Table 4
. Patients presented with broad concentration heterogeneities
for albumin, IgA, IgD, IgG1,
IgG2, IgG3,
IgG4, IgM, urea, Apo-B,
Apo-C3, and haptoglobin. By comparing these
differences in plasma contents with the results obtained for the
first set of plasma samples, we could test the validity of the
sequence used for biomolecule analysis. For this set of 14 plasma
samples, we used exactly the same spectral ranges and sequence order
that were used for the previous analyses of 35 plasma samples. As
shown in Table 4
, all concentration values obtained correlated well
with the concentrations determined by the comparison methods.
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Discussion
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We previously demonstrated that FT-IR spectrometry could determine
glucose and lactate concentrations with clinical accuracy in the
1300–900 cm-1 absorption region
(9)(11). These measurements were facilitated by
the absence of marked absorption by proteins, mainly albumin, within
this spectral region. This opportunity has to be related to the absence
of glycosylation of albumin, this protein being by far the most
abundant compound in plasma. However, until now, no more than a few
characteristic absorption peaks have been used successfully for
measuring the individual concentrations of several biomolecules in a
sample. Only statistical methods, such as multivariate regression,
allowed determination of concentrations of other biomolecules in
plasma, using FT-IR spectra. These methods looked for a strong
relationship between a wide spectral region (500
cm-1 or higher) that contains several absorption
bands for a biomolecule and the concentration of the molecule in the
analyzed sample (1)(2). The aim of multivariate
regression methods is to correlate variations in blood FT-IR absorption
spectra with the known changes in the absorption spectrum of a pure
component when the concentration of the pure component varies within
samples. Such methods have been used to determine the plasma
concentrations of total proteins (1) and cholesterol
(2). However, a strong limitation of these methods is that a
direct analysis, i.e., one showing any characteristic absorption of the
biomolecules in the spectrum, has been unavailable. Indeed, defined
concentrations of biomolecules could not be determined as had been done
for glucose or lactate. In the FT-IR spectra of complex biological
samples, such as plasma, the absorption spectra of some biomolecules
may first be subtracted to uncover other absorption patterns. This has
been clearly shown for the absorption pattern of lactate in the
1300–900 cm-1 spectral region, which is
obscured by glucose absorption (11). Lactate absorption in
this spectral region could be exploited only after the absorption
attributable to glucose had been subtracted.
Our aim in this study was to determine the concentrations of various
proteins in plasma on the basis of their most characteristic IR
absorption peaks. For albumin, the best correlation with results
obtained by a comparison method was found using the NH
absorption region (1600–1480 cm-1) common to
all plasma proteins. However, the physiological concentration of
albumin is 37–45 g/L, and the biomolecule that contributes the most
intense absorption to plasma FT-IR spectra after albumin is
IgG1, the concentration of which usually is 6–8
g/L. We found a high correlation between IgG1
concentrations measured by the comparison method and spectral data for
the 1419–1361 cm-1 absorption region, which is
outside of the spectral region used for determining the albumin
concentration.
We determined an absorption-related sequence for the biomolecules to be
subtracted from plasma FT-IR spectra; that is, the concentrations of
several biomolecules could be determined only after the absorption
spectra of other biomolecules had been subtracted, even when the
absorption contributed to the plasma FT-IR spectrum by the molecules
subtracted earlier was several-fold less intense than the absorption
contributed by the remaining biomolecules. This was clearly illustrated
by the plasma concentrations of the biomolecules determined in the
following order: glucose (1 g/L), fibrinogen (3 g/L), lactate
(0.3 g/L), and IgG1 (8 g/L). Despite its
high concentration, IgG1 could not be determined
before the glucose, fibrinogen, and lactate IR absorption peaks were
subtracted. Glucose absorbs strongly in the 1419–1361
cm-1 spectral region, which is also the most
specific region for IgG1 absorption in plasma.
Similarly, fibrinogen exhibits a strong absorption centered at 1395
cm-1, and lactate exhibits a weak absorption
centered at 1399 cm-1. Furthermore, we were
unable to find another specific IgG1 absorption
peak in any other spectral region. Indeed, before we could ascertain
which absorption region could be used for determining the
IgG1 concentration in plasma, the glucose,
fibrinogen, and lactate absorption spectra had to be subtracted. The
sequence we found for determining biomolecule concentrations was the
one yielding the best correlation with the spectral range areas
calculated with respect to the resulting spectra for the other
biomolecules. Multivariate calibration methods, such as partial least
squares and principal component regression, effectively remove the
spectral features of each determined analyte sequentially, similar to
the method we have proposed. However, by subtracting the spectral
contribution of each biomolecule determined from the complete FT-IR
spectrum for plasma, we were able to determine the concentrations of
many more biomolecules than could be measured by other FT-IR
spectrometry methods.
Importantly, the FT-IR spectra of biomolecules belonging to the same
families contained important differences, as was observed for the IgGs.
This reflects structural specificities for these biomolecules; e.g.,
between 1300 and 900 cm-1 (CO
region), spectral differences reflect differences in the sugar content.
Thus, we were able to determine the concentration of each IgG form in
plasma according to the FT-IR spectrum. This was also true for
apolipoproteins, for which three different spectral regions were used,
representing the different FT-IR absorption spectra of these
biomolecules.
Another important finding of this study is that the concentrations of
several biomolecules, notably those for fibrinogen, haptoglobin,
IgG1, IgA, and IgM, could be determined using
spectral ranges that were very close to one another, i.e., 1500–1300
cm-1, but that differed as to the exact
locations and intensities of the absorption peaks. Even when
haptoglobin concentrations exhibited several-fold variations between
subjects, we were able to determine the concentrations of the other
biomolecules with clinical accuracy in the previous steps. Indeed, the
spectral ranges we used exhibited slight but sufficiently
characteristic differences in the 1500–1300
cm-1 absorption region, leading to determination
of the concentrations of these biomolecules. For the last biomolecule
determined in this series, 1-acid
glycoprotein, the results correlated well (r = 0.96)
with the values obtained by the comparison method.
We tested the sequence order we found for determining biomolecule
concentrations by assaying plasma samples from 14 patients in which the
concentrations of several proteins varied widely. We also assayed these
samples with the comparison methods for all biomolecules determined by
the FT-IR method. The results demonstrated that the spectral ranges we
used to determine biomolecule concentrations reflected structural
absorption peaks sufficiently characteristic of these biomolecules.
Moreover, the FT-IR absorption spectra of the biomolecules analyzed on
the basis of sequential plasma spectra were not altered by successive
subtractions of absorption. After the individual absorption peaks for
20 biomolecules were subtracted from the complete FT-IR spectrum of
plasma, the resulting spectra were not noise. We therefore consider it
possible to continue to use this method to determine the concentrations
of additional biomolecules, namely, triglycerides, cholesterol esters,
amino acids, and fatty acids.
In conclusion, the present study has demonstrated that FT-IR
spectrometry is a useful tool for determining concentrations of
multiple biomolecules in microsamples of plasma.
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Acknowledgments
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We are indebted to the Conseil Régional d’Aquitaine and the
Fédération Française de Rubgy for financial support
and technical assistance.
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Abstract
Introduction
