Clinical Chemistry 49: 1125-1132, 2003;
10.1373/49.7.1125
(Clinical Chemistry. 2003;49:1125-1132.)
© 2003 American Association for Clinical Chemistry, Inc.
Infrared Spectroscopic Identification of ß-Thalassemia
Kan-Zhi Liu1,a,
Kam Sze Tsang2,
Chi Kong Li3,
R. Anthony Shaw1 and
Henry H. Mantsch1
1 Institute for Biodiagnostics, National Research Council of Canada, 435 Ellice Ave., Winnipeg, Manitoba, R3B 1Y6 Canada.
Departments of
2
Anatomical & Cellular Pathology and
3 Pediatrics, The Chinese University of Hong Kong, Cancer Center, Prince of Wales Hospital, Hong Kong, Peoples Republic of China.
aAuthor for correspondence. Fax 204-984-4572; e-mail Kan-Zhi.Liu{at}nrc.ca.
 |
Abstract
|
|---|
Background: The aim of this study was to investigate the potential of infrared (IR) spectroscopy as a fast and reagent-free adjunct tool in the diagnosis and screening of ß-thalassemia.
Methods: Blood was obtained from 56 patients with ß-thalassemia major, 1 patient with hemoglobin H disease, and 35 age-matched controls. Hemolysates of blood samples were centrifuged to remove stroma. IR absorption spectra were recorded for duplicate films dried from 5 µL of hemolysate. Differentiation between the two groups of hemoglobin spectra was by two statistical methods: an unsupervised cluster analysis and a supervised linear discriminant analysis (LDA).
Results: The IR spectra revealed changes in the secondary structure of hemoglobin from ß-thalassemia patients compared with that from controls, in particular, a decreased 
-helix content, an increased content of parallel and antiparallel ß-sheets, and changes in the tyrosine ring absorption band. The hemoglobin from ß-thalassemia patients also showed an increase in the intensity of the IR bands from the cysteine -SH groups. The unsupervised cluster analysis, statistically separating spectra into different groups according to subtle IR spectral differences, allowed separation of control hemoglobin from ß-thalassemia hemoglobin spectra, based mainly on differences in protein secondary structure. The supervised LDA method provided 100% classification accuracy for the training set and 98% accuracy for the validation set in partitioning control and ß-thalassemia samples.
Conclusion: IR spectroscopy holds promise in the clinical diagnosis and screening of ß-thalassemia.
|
Introduction
|
|---|
The thalassemias comprise a group of genetic disorders of hemoglobin (Hb)1
synthesis caused by partial or total mutations that reduce or abolish the synthesis of the - or ß-globin chains in Hb. The vast majority of ß-thalassemias are caused by point mutations or small deletions or insertions of the ß-globin gene, whereas major deletions of the ß-globin cluster are rarely encountered (1). ß-Thalassemia can be classified as ß0-thalassemia (ß-thalassemia major) or ß+-thalassemia (ß-thalassemia trait), depending on whether the genetic mutations lead to an absence or a variable reduction in the ß-globin chains, respectively (2)(3). An excess of -chains attributable to imbalanced globin chain synthesis is the hallmark of ß-thalassemia. Unbound -chains denature, precipitate, and eventually lead to defective and ineffective erythropoiesis and a shortening of red cell survival (4)(5).
More than 300 000 infants are born every year with severe inherited Hb disorders, and the vast majority of them remain undiagnosed, untreated, or undertreated (6). In the case of ß-thalassemia major, the affected babies appear normal up to 3–6 months; thereafter, they gradually develop severe anemia, and regular blood transfusions are required to maintain growth and development (7). Although chelating agents can alleviate the adverse effects of transfusion-mediated iron deposition, they are expensive and may require long-term administration. Prenatal diagnosis and screening of carriers are the most effective means to reduce disease incidence and lessen the burden on both the parents and the healthcare system (8). Screening and diagnosis of abnormal Hb and thalassemia may need to be undertaken antenatally, neonatally, and in certain hematologic situations. Preliminary tests include a complete blood count, electrophoresis at pH 9.2, and quantification of Hb A2 and Hb F. Additional techniques include globin chain separation, isoelectric focusing, and a heat/isopropanol stability test to identify unstable and abnormal Hb (9). The introduction of cation-exchange HPLC systems has streamlined the recommended analytic tests and provides an important advance in technology for the detection and quantification of Hb A2, Hb F, and Hb variants (10)(11). However, meticulous and stringent calibration and standardization with internal and external control samples are prerequisites for acquiring accurate data for interpretation (12).
Infrared (IR) spectroscopy, in which the individual vibrations of chemical groups are recorded, has been used to study the structures of various Hbs. For example, IR spectra provided insight into the binding of ligands to the heme iron and revealed protein structural changes attributable to various factors (13)(14). The IR absorption bands of the thiol groups in the cysteine residues of Hb have also been explored (15), and it was found that the conformational sensitivity of the thiol IR bands can be used to study changes in the tertiary and quaternary structure of Hb induced by perturbations of pH, temperature, and effector molecules (16). In addition, IR bands produced by amide I, from the carbonyl groups in the peptide bonds that constitute the linkages between the amino acid residues of the Hb molecule, can be used for quantitative analysis of the -helix, random coil, ß-sheet, and turn structures (17).
More recently, the scope of IR spectroscopy has been extended to the study of clinically relevant problems, such as differentiation of leukemia cells from healthy lymphocytes, detection of drug-resistant cell populations in leukemia patients for chemotherapy guidance, or monitoring of glucose fluctuations in diabetic patients (18)(19). Success in these areas has been realized largely by combining the ever-improving sensitivity of modern spectrometers with powerful multivariate quantification and classification methods that permit essentially all of the relevant information latent in the IR spectrum to be usefully extracted. In the current study, we combined both traditional and advanced IR techniques to examine the spectral differences between the Hb from controls and patients with ß-thalassemia major and evaluated the feasibility of diagnosing ß-thalassemia major based on IR spectra in combination with multivariate classification methods.
|
Materials and Methods
|
|---|
patients and materials
The study included 92 patients admitted to the Prince of Wales Hospital in Hong Kong: 56 with ß-thalassemia major, 1 with Hb H disease, and 35 who were age-matched non-thalassemia inpatients undergoing blood tests for other diagnostic screening purposes. Informed consent was obtained from all adult patients or, in the case of children, their parents or guardians.
ß-Thalassemia major patients presented with severe anemia with the presence of hypochromia, microcytosis, anisocytosis, poikilocytosis, nucleated red cells, and target cells. Hemoglobinopathy screening revealed increased Hb F and Hb A2, and the patients received regular blood transfusions at 3- to 4-week intervals. Myoglobin and sickle cell Hb (Hb S) were obtained from Sigma-Aldrich.
preparation of HB lysates
The blood samples from patients with ß-thalassemia major were drawn during visits to the clinic or before regular blood transfusions. Complete blood counts were performed. To prepare hemolysates, we centrifuged a total of 3 mL of peripheral blood anticoagulated with acid-citrate-dextrose at 400g for 10 min at 4 °C to pack red blood cells (RBCs). Plasmas were discarded, and the RBCs were washed three times in Hanks’ balanced salt solution (Invitrogen). RBCs were lysed by the addition of two volumes of distilled water to the washed packed RBCs and centrifugation at 20 000g for 30 min to pellet cell stroma.
ir spectra
For each specimen, duplicate 5-µL aliquots of the hemolysates were evenly spread on IR-transparent calcium fluoride windows (13 mm diameter) and allowed to dry under reduced pressure (25 kPa) to produce glassy films. The IR spectra of such films were recorded with a Bio-Rad FTS-40A IR spectrometer (Digilab, LLC.) at a nominal resolution of 2 cm-1 (256 co-added interferograms, apodized with a triangular smoothing function before Fourier transformation). To resolve and illustrate otherwise overlapping spectral features, we used Fourier self-deconvolution with a 
of 2.5 and Bessel smoothing factor of 70% to enhance resolution. Clustering and classification trials made use of the second-derivative spectra (15-point Savitzky–Golay), vector normalized over the region 1000–1800 cm-1.
multivariate statistical analysis
Both clustering and classification approaches were used to seek diagnostic rules distinguishing the spectra of Hb from ß-thalassemia patients from the spectra of control Hb. The first trial was an unsupervised cluster analysis using Ward’s minimum variance algorithm and Euclidean distances as distance measure (Statistica 5.1; Statsoft) (20). In this approach, the Euclidean distance between spectra is calculated and the pair of spectra with the least distance is grouped to create a cluster. The separation between this cluster and all other spectra is then calculated, and two other closest spectra/clusters are joined to form a new cluster. This procedure continues until all spectra/clusters are combined. In this unsupervised classification, no information about the disease state of the samples is provided to the algorithm; only the similarity or dissimilarity of their IR spectra is used for this classification.
The second analysis method, which makes use of linear discriminant analysis (LDA), is known as a supervised method because the class identity of each sample is supplied to the classification algorithm and used in establishing boundaries to distinguish among groups of spectra in different classes (21)(22). Taking the spectra and corresponding diagnoses (class designations) as input, it explicitly chooses the boundaries that best separate the classes; class assignment of any given spectrum involves computing its distance from all class centroids (the representative class-average spectra) and allotting it to the class whose centroid is nearest.
Although the raw spectra may in principle be used as the basis for LDA classification, the accuracy of the method is inevitably improved by preprocessing the spectra to enhance diagnostic patterns and minimize (or omit) superfluous spectral features. To that end, these spectra were preprocessed by applying an algorithm, the optimal region selection genetic algorithm, which identifies a set of discrete spectral subregions that maximally enhance the differentiation among the various spectral subtypes. These preprocessed spectra, each reduced to a set of n intensity values (integrated intensity values for each of the n spectral regions) were then subjected to LDA as described above.
To properly assess the predictive value of this classification procedure, we split the spectra into a training set, which was used as the basis to identify distinguishing patterns (spectral regions), and an independent test set to assess the accuracy of the trained algorithm in classifying samples of unknown origin. To that end, we designated approximately two-thirds of the samples as the training set and the remaining one-third as the test set. There were 56 ß-thalassemia Hb samples and 35 control Hb samples. The training set therefore comprised 37 ß-thalassemia and 24 control samples, and the test set comprised 19 ß-thalassemia and 11 control samples. Each sample was represented by both of the duplicate spectra, so that the training set comprised 74 ß-thalassemia and 48 control spectra and the test set comprised 38 ß-thalassemia and 22 control spectra.
|
Results
|
|---|
patient characteristics
Blood samples from 56 patients with ß-thalassemia, 1 patient with Hb H disease, and 35 nonthalassemic individuals were studied, and their laboratory findings are shown in Table 1
. Among these laboratory findings are routine blood counts of RBC, white blood cells, platelets, and specific RBC indices. Because ß-thalassemia is associated with impaired Hb synthesis, which leads to microcytic and hypochromic anemia, the Hb concentrations, RBC counts, and hematocrits in these patients were substantially lower than those in the control group.
ir spectra of HB
Class-average spectra of Hb from control individuals and patients with ß-thalassemia major are shown in Fig. 1
. As expected, the IR spectrum of isolated Hb revealed basic information about the protein. For example, in Fig. 1
, there are two prominent absorption bands, one at 1652 cm-1 arising from the amide C=O stretching (the amide I band) and another at 1542 cm-1 originating from the amide N-H bending (the amide II band) vibrations of the peptide groups in proteins. The bands at 1452 and 1387 cm-1 originate from the bending vibrations of -CH2 and -CH3 groups of amino acids in the protein side chains. The -SH group vibrations from the cysteine residues appear around 2550 cm-1. The bands between 2875 and 3000 cm-1 are the symmetric and asymmetric -CH2 and -CH3 stretching vibrations from protein side chains. Other protein bands are the amide B band at 3050 cm-1 and the amide A at 3290 cm-1. The amide band most widely used in studies of protein secondary structure is the amide I mode, which has been used extensively to quantify -helices, ß-sheets, turns, and nonordered structures in proteins (14).
View larger version (20K):
[in this window]
[in a new window]
|
Figure 1. Mean IR spectra of Hb from control (solid line) and ß-thalassemia major (dashed line) samples.
|
|
Although the two mean spectra from control Hb and Hb from patients with ß-thalassemia major appeared superficially similar (Fig. 1
), there are ways to enhance the information available from the broad protein bands. One way is to use computational procedures to decrease the widths of individual band subcomponents, allowing better visualization of the overlapping absorption bands making up the composite band contour (23). Another approach is to generate a difference spectrum to clearly highlight those absorptions whose intensities differ for the two spectra of interest. Features in the difference spectrum may then be assigned and interpreted to reveal structural and/or conformational features distinguishing the two molecular species.
Shown in Fig. 2
are the mean spectra of Hb from control and ß-thalassemia samples after band narrowing by Fourier self-deconvolution (bottom spectrum) and the difference spectrum generated by subtracting the mean spectrum of Hb from patients with ß-thalassemia from that of the control (top spectrum). After Fourier self-deconvolution, previously overlapping components in the amide I band, such as -helix (1657 cm-1) or parallel and antiparallel ß-sheets (1640 and 1680 cm-1, respectively), are apparent. In addition, the "breathing" vibration of the tyrosine ring (1517 cm-1) originating from the side chains of the protein also becomes prominent. From the difference spectrum, one can observe that the -helix content in the Hb from patients with ß-thalassemia is decreased and that of ß-sheets is increased compared with that in the control group. This observation suggests that the differences in secondary protein structure in the two groups of mean spectra reflects an altered protein profile in Hb from patients with ß-thalassemia attributable to gene mutations.
View larger version (24K):
[in this window]
[in a new window]
|
Figure 2. IR spectra of Hb from controls and ß-thalassemia patients in the range of 1400–1800 cm-1.
(Bottom), mean IR spectra of Hb from controls and ß-thalassemia patients after band narrowing by Fourier self-deconvolution. (Top), difference spectrum between control and ß-thalassemia major Hb.
|
|
We also compared the IR spectra of Hb from controls and from patients with ß-thalassemia with those of Hb H and Hb S (Fig. 3
). Hb H is an -thalassemia in which there is an accumulation of tetramers of ß-chains attributable to the defect in -chain synthesis. Again there are major differences in the protein structure that are reflected in the spectra. These spectra demonstrate the sensitivity of IR spectroscopy for detecting subtle differences in the structure of control and other mutated Hbs and provide a basis for anticipating success in applying pattern recognition methods for the classification of individual samples/spectra.
View larger version (29K):
[in this window]
[in a new window]
|
Figure 3. IR spectra of control (normal) and mutated Hbs in the range of 1400–1800 cm-1 after band narrowing by Fourier self-deconvolution.
Spectra for control and ß-thalassemia major samples are the mean spectra, whereas those for the other two are individual spectra.
|
|
Another useful IR band in the spectra of Hb is the -SH stretching vibration around 2550 cm-1, which originates from the thiols of cysteine residues. Although sulfhydryl groups give rise to very weak absorption bands, they appear in a spectral region devoid of other bands and have been exploited to probe changes in amino acid sequence, oxidation state, ligand binding, pH, and other factors (13)(14). Human Hb has six cysteine residues per tetramer; in each symmetrical half-molecule, two are in the ß-chain (ß-93 and ß-112) and one is in the -chain (-104) (24). Fig. 4
displays IR spectra in the -SH stretching region of control Hb and various mutated Hbs. For the control, we used horse myoglobin because there is no cysteine residue group and thus no -SH bands in this particular protein (25). As shown in Fig. 4
, there are two IR absorption bands originating from -SH stretch vibrations, one at 
2553 cm-1 from the -104 cysteine and the other at 2589 cm-1 from the ß-93 cysteine. As can be seen from Fig. 4
, the intensities of these bands and the band positions differ among these Hb variants.
View larger version (19K):
[in this window]
[in a new window]
|
Figure 4. IR spectra of control (normal) and mutated Hbs in the range of 2500–2600 cm-1 after band narrowing by Fourier self-deconvolution.
|
|
statistical analysis
Various spectral features differed between the mean spectra of Hb from controls and Hb from ß-thalassemia patients, as well as among the individual spectra acquired for Hb H and Hb S. In our next step, therefore, we applied an unsupervised cluster analysis to determine whether the individual IR spectra can be classified according to their clinical diagnoses. This method uses a statistical approach to classify spectra into groups according to subtle spectral differences. The outcome of clustering can be visualized with a dendrogram in which the cluster formation is plotted as a horizontal, interconnecting line located at the distance at which the cluster is being formed. Spectra are presented to the algorithm without diagnostic labels, and the algorithm partitions them into subgroups based on spectral similarities. In the ideal scenario, Hb spectra from ß-thalassemia patients form a cluster separate from that for Hb specimens form controls; however, it is only after this analysis that each spectrum is marked retrospectively as Hb from controls or from patients with ß-thalassemia.
To capitalize on the distinctive patterns already identified as distinguishing the mean spectra of Hb from controls and Hb from patients with ß-thalassemia, we used the spectral ranges encompassing both the protein amide I and amide II bands (1400–1800 cm-1) and the -SH groups (2500–2600 cm-1) as the basis for cluster analysis (Figs. 5
and 6
). The cluster analysis based on the -SH bands from 150 spectra (70 control samples, 80 ß-thalassemia samples) yielded three clusters. Cluster 1 consisted of 24 spectra from control Hb and 6 from ß-thalassemia Hb samples. The spectra in cluster 2 were mainly from Hb from patients with ß-thalassemia (34 spectra) with 4 spectra from controls. Cluster 3 was a mixed group, with 48 spectra from control Hb and 32 from ß-thalassemia Hb. The mean spectra of the three clusters were generated and are plotted in the right panel. The major difference between the three clusters was readily apparent and comprised the different intensities of the -104 -SH stretching vibration (see Fig. 5
), with cluster 3 being intermediate as a mixture.
View larger version (19K):
[in this window]
[in a new window]
|
Figure 5. Dendrogram generated from 150 spectra of Hb samples from controls and patients with ß-thalassemia major.
The mean spectra in the range of 2500–2600 cm-1 generated from each cluster are shown on the right after band narrowing by Fourier self-deconvolution.
|
|
View larger version (22K):
[in this window]
[in a new window]
|
Figure 6. Dendrogram generated from 150 spectra of Hb samples from controls and patients with ß-thalassemia major.
The mean spectra in the range of 900-1800 cm-1 generated from each subcluster are shown on the right after band narrowing by Fourier self-deconvolution.
|
|
On the basis of the spectral segment 1400–1800 cm-1 (encompassing absorption bands characterizing the protein secondary structure), we obtained four clusters (Fig. 6
). All of the spectra in clusters 1 and 2 originated from ß-thalassemia Hb samples from patients with ß-thalassemia, whereas clusters 3 and 4 were all from controls. The mean spectra generated from the clustering process (right panel) clearly delineated the differences in the amide I and II regions that are responsible for this clustering. Although the differentiation between nonthalassemia and ß-thalassemia Hbs for this clustering was better than that from the thiol groups (Fig. 5
), there was an undesirable mixing between clusters 2 and 3. Clearly, the unsupervised classification method was not good enough to be used for clinical screening of ß-thalassemia.
The optimal region selection/LDA approach was very effective in distinguishing ß-thalassemia from control Hb spectra. A systematic set of trials quickly led to a classifier based on six spectral regions (indicated by the shaded areas in Fig. 7
) that provided the basis for LDA classification summarized in Table 2
. The overall accuracy for this spectral classification trial was 100% for the training set and 98% in the test set, with only a single misclassification. It is worth noting that the single spectral misclassification was "fuzzy", i.e., it was classified as control and ß-thalassemia Hb with almost equal weights, whereas the duplicate counterpart spectrum for the sample was classified with no ambiguity as ß-thalassemia. The consensus classification for the sample in question (the sum of the classification weights for the two duplicate spectra) was therefore correct.
View larger version (20K):
[in this window]
[in a new window]
|
Figure 7. Mean IR spectrum for control Hb samples, indicating the six spectral regions (shaded areas) selected by the optimal region selection algorithm for maximum LDA differentiation between control and ß-thalassemia Hb (see Table 2
).
|
|
View this table:
[in this window]
[in a new window]
|
Table 2. Diagnostic accuracy of IR spectroscopy: Comparison of spectroscopy-based classification with true Hb types for six-region optimal region selection/LDA trial.
|
|
|
Discussion
|
|---|
Thalassemias and hemoglobinopathies are the most common single-gene disorders in humans on a worldwide basis (26). ß-Thalassemia is characterized by quantitative defects in the ß-globin chain, one of the backbone proteins of the Hb molecule. It is estimated by the WHO, the World Bank, and other international agencies that there is a massive increase in the number of patients with different forms of thalassemia in many emerging countries and that it is imperative to establish more surveys of the frequency of the disease and set up centers for its control and management.
More than 150 globin gene mutations cause ß-thalassemia, usually single-base substitutions or small insertions/deletions of the ß-globin gene (27). Although the complete blood count can give clues for the diagnosis and screening of ß-thalassemia, confirmation requires additional analytical tests, such as electrophoresis, isoelectric focusing, and HPLC, to assay Hb derivatives and to detect the presence of any -globin chain tetramers. Recently, molecular analysis of DNA has emerged as an alternative method for early detection of mutant globin genes and alleles. These additional confirmation tests are usually complex and labor-intensive, however, and may be a burden to countries with limited resources.
Previous IR spectroscopic studies of Hb have focused on various distinctive spectral features, e.g., the strong C=O stretching IR band of carbon monoxide bound to Hb (HbCO) (28), the weak but well-isolated -SH stretching bands of the cysteine residues (24), and the protein side chain and backbone vibrations (29)(30). The technique has been useful for differentiating between CO bound to different subunits in human Hb mutants, where marked changes in heme environment produce distinctly different spectral bands for CO bound to - and ß-subunits (28). The assignment of different -SH stretching bands in the IR spectrum of human Hb to individual cysteine residues was achieved in an elegant way by comparison with the spectrum of horse Hb, which is devoid of cysteine ß-112, and that of bovine Hb, which contains only ß-93 cysteine (24). The -SH stretch vibrations of cysteine residues in Hb A are sensitive to the structural changes that result from the binding of ligands, such as O2, CO, and NO, at the heme iron (13)(31).
More relevant to our current investigation are studies of individual Hb variants. For example, the IR difference spectrum of Hb A and Hb Kempsey (a mutant in which Asp ß-99 is replaced by Asn) revealed a negative band at 1697 cm-1 originating with the C=O stretch of carboxylic acid, ascribed to the side chain of Asp ß-99 based on its known mutation (29). Wallace et al. (30) demonstrated that the vCO of carbon monoxide bound to the - and ß-chains in Hb Zurich (Hb Z; ß-63 His-Arg) shifted to 1950 and 1958 cm-1, respectively, compared with that of normal HbACO (1951 cm-1). Replacement of the distal histidine (ß-63) of Hb A by arginine in Hb Z enhances its susceptibility to autooxidation in the presence of "oxidant drugs". This observation provided a correlation of structure with the pathologic manifestations of Hb Z and supported the contention that IR spectroscopy is able to provide understanding of the origins of Hb diseases attributable to abnormal Hb structure.
In this pilot study, the IR spectra of hemolysates derived from a cohort of patients with ß-thalassemia major revealed important alterations in the protein secondary structure compared with the spectra of age-matched nonthalassemic individuals. Generally speaking, the prominent spectral changes in Hb of patients with ß-thalassemia major are as follows: decreased -helix content; increased parallel and antiparallel ß-sheet content; and decreases in the intensities of tyrosine absorption bands. Similar changes were observed in the hemolysate derived from a patient with Hb H disease. We believe that the major IR features, in particular those indicative of reduced -helix content and increased ß-structure, likely arise from the denatured unbound -chains characteristic of ß-thalassemia major. Although this appears the most likely origin for the major spectral features characterizing Hb from ß-thalassemia patients, further studies of hemolysates from patients with hereditary fetal hemoglobinemia are necessary to explore the spectroscopic influence of increased fetal Hb.
Finally, the present study extends these previous findings by use of multivariate clustering and classification (LDA) methods as the basis to designate individual spectra as Hb from controls or ß-thalassemia patients. Thus, although unsupervised cluster analysis can qualitatively differentiate Hb from patients with ß-thalassemia from its counterpart from nonthalassemic individuals, the solution really is a supervised methodology that can provide a first line of screening ß-thalassemia with use of a LDA classifier that is trained to distinguish the Hb spectra from ß-thalassemia major patients from those of control Hb. The accuracy of this approach was 100% for the training set and 98% for the validation set, suggesting a potential role of IR spectroscopy in the screening of ß-thalassemia carriers.
There are several potential advantages and benefits in using the IR-based method to screen and diagnose ß-thalassemia carriers: (a) it is reagent-free, the IR "color patterns" of the species of interest provide the basis for detection and quantification; (b) it uses a small sample volume (5 µL), leaving ample material for other clinical tests, which is of particular importance in the fetus, for which blood sampling is difficult and limited; (c) it can be automated; and (d) it is simple (the expertise can be acquired after minimal training). For the purpose of this pilot study, IR spectra were obtained from hemolysates to avoid other possible influences from cell membranes and other cellular components. To simplify the method, whole blood would be preferable, which would avoid the need to prepare hemolysates, would reduce the cost, and would shorten the turnaround time for testing. Further studies are planned to explore this possibility.
|
Footnotes
|
|---|
1 Nonstandard abbreviations: Hb, hemoglobin; IR, infrared; RBC, red blood cell; and LDA, linear discriminant analysis. 
|
References
|
|---|
- Ho PJ, Thein SL. Gene regulation and deregulation: a ß globin perspective. Blood Rev 2000;14:78-93.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Weatherall DJ. Phenotype-genotype relationships in monogenic disease: lessons from the thalassemias. Nat Rev Genet 2001;2:245-255.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Mastropietro F, Modiano G, Cappabianca M, Foglietta E, D’Asero C, Mezzabotta M, et al. Factors regulating Hb F synthesis in thalassemic diseases. BMC Blood Disord 2002;6:2-9.[CrossRef]
- Wickramasinghe SN, Lee MJ, Furukawa T, Eguchi M, Reid CD. Composition of the intra-erythroblastic precipitates in thalassemia and congenital dyserythropoietic anemia (CDA): identification of a new type of CDA with intra-erythroblastic precipitates not reacting with monoclonal antibodies to - and ß-globin chains. Br J Hematol 1996;93:576-585.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Schrier SL. Pathophysiology of thalassemia. Curr Opin Hematol 2002;9:123-126.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Angastiniotis M, Modell B. Global epidemiology of hemoglobin disorders. Ann N Y Acad Sci 1998;850:251-269.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Bunn HG, Forget BG. Hemoglobin: molecular, genetic, and clinical aspects 1986:46-98 WB Saunders Philadelphia. .
- Waye JS, Eng B, Cai SP, Patterson M, Smith J, Tang W, et al. Carrier detection and prenatal diagnosis of hemoglobinopathies in Ontario. Clin Invest Med 1993;16:358-371.[Web of Science][Medline]
[Order article via Infotrieve]
- . International Committee for Standardization in Hematology. Recommendations of a system for identifying abnormal hemoglobins. Blood 1978;52:1065-1067.[Free Full Text]
- Fucharoen S, Winichagoon P, Wisedpanichkij R, Sae-Ngow B, Sriphanich R, Oncoung W, et al. Prenatal and postnatal diagnoses of thalassemias and hemoglobinopathies by HPLC. Clin Chem 1998;44:740-748.[Abstract/Free Full Text]
- Winichagoon P, Sriphanich R, Saengow B, Chowthaworn J, Tantisirin P, Kanokpongsakdi S, et al. Automated HPLC in prenatal diagnosis of thalassemia. Lab Hematol 2002;8:29-35.
- Fucharoen S, Winichagoon P, Piankijagum A. Standardization on laboratory diagnosis of thalassemia and abnormal hemoglobin. Southeast Asian J Trop Med Public Health 1999;30(Suppl 3):90-98.
- Dong A, Caughey WS. Infrared methods for study of hemoglobin reactions and structures. Methods Enzymol 1994;232:139-175.[Web of Science][Medline]
[Order article via Infotrieve]
- Surewicz WK, Mantsch HH. Infrared absorption methods for examining protein structure. Havel HA eds. Spectroscopic methods for determining protein structure in solution. 1996:135-158 VCH Publishers Weinheim. .
- Alben JO, Bare GH. Ligand-dependent heme-protein interactions in human hemoglobin studied by Fourier transform infrared spectroscopy. Effects of quaternary structure on chain tertiary structure measured at the -104(G11) cysteine-SH. J Biol Chem 1980;255:3892-3897.[Free Full Text]
- El Antri S, Zentz C, Alpert B. Implication of the 1ß1 interface in the hemoglobin affinity changes. A comparative study between normal and San Diego fully ligated hemoglobins. Eur J Biochem 1989;179:165-168.[Web of Science][Medline]
[Order article via Infotrieve]
- Mantsch HH, Jackson M. Molecular spectroscopy in biodiagnostics (from Hippocrates to Herschel and beyond). J Mol Struct 1995;347:187-206.[CrossRef]
- Liu KZ, Schultz CP, Mohammad RM, Al-Katib RM, Johnston JB, Mantsch HH. Similarities between the sensitivity to 2-chlordeoxyadenosin of lymphocytes for CLL patients and bryostatin 1 treated WSU-CLL cells: an infrared spectroscopic study. Cancer Lett 1998;127:185-193.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Petrich W, Dolenko B, Fruh J, Ganz M, Greger H, Jacob S, et al. Disease pattern recognition in infrared spectra of human sera with diabetes mellitus as an example. Appl Opt 2000;39:3372-3379.[Medline]
[Order article via Infotrieve]
- Schultz CP, Liu KZ, Johnston JB, Mantsch HH. Differentiation of leukemic from normal human lymphocytes by FT-IR spectroscopy and cluster analysis. Leukemia Res 1996;20:649-655.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Somorjai RL, Nikulin AE, Pizzi N, Jackson D, Scarth G, Dolenko B, et al. Computerized consensus diagnosis: a classification strategy for the robust analysis of MR spectra. I. Application to 1H spectra of thyroid neoplasms. Magn Reson Med 1995;33:257-263.[Web of Science][Medline]
[Order article via Infotrieve]
- Liu KZ, Schultz CP, Johnston JB, Mantsch HH. Comparison of infrared spectra of CLL cells with their ex vivo sensitivity (MTT assay) to chlorambucil and cladribine. Leukemia Res 1997;21:1125-1133.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Kauppinen JK, Moffatt DJ, Mantsch HH, Cameron DG. Fourier self-deconvolution: a method for resolving intrinsically overlapped bands. Appl Spectrosc 1981;35:271-282.[CrossRef]
- Moh PP, Fiamingo FG, Alben JO. Conformational sensitivity of ß-93 cysteine SH to ligation of hemoglobin observed by FT-IR spectroscopy. Biochemistry 1987;26:6243-6249.[CrossRef][Medline]
[Order article via Infotrieve]
- Bare GH, Alben JO, Bromberg PA. Sulfhydryl groups in hemoglobin. A new molecular probe at the 1 ß1 interface studied by Fourier transform infrared spectroscopy. Biochemistry 1975;14:1578-1583.[CrossRef][Medline]
[Order article via Infotrieve]
- Clarke GM, Higgins TN. Laboratory investigation of hemoglobinopathies and thalassemias: review and update. Clin Chem 2000;46:1284-1290.[Abstract/Free Full Text]
- Huisman THJ. The ß- and
-thalassemia repository. Hemoglobin 1992;16:237-258.[Web of Science][Medline]
[Order article via Infotrieve]
- Potter WT, Hazzard JH, Kawanishi S, Caughey WS. Direct measurement of carbon monoxide bound to different subunits of hemoglobin A in solution and in red cells by infrared spectroscopy. Biochem Biophys Res Commun 1983;116:719-725.[Web of Science][Medline]
[Order article via Infotrieve]
- Gregoriou VG, Jayaraman V, Hu X, Spiro TG. FT-IR difference spectroscopy of hemoglobins A and Kempsey: evidence that a key quaternary interaction induces protonation of Asp ß99. Biochemistry 1995;34:6876-6882.[CrossRef][Medline]
[Order article via Infotrieve]
- Wallace WJ, Volpe JA, Maxwell JC, Caughey WS. Properties of hemoglobin A and hemoglobin Zurich (ß63 histidine replaced by arginine): quantitative evaluation of functional abnormalities in hemoglobins. Biochem Biophys Res Commun 1976;68:1379-1386.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
- Sampath V, Zhao XJ, Caughey WS. Characterization of interactions of nitric oxide with human hemoglobin A by infrared spectroscopy. Biochem Biophys Res Commun 1994;198:281-287.[CrossRef][Web of Science][Medline]
[Order article via Infotrieve]
Top
Abstract
Introduction
