Spatial distribution of heme species in erythrocytes infected with Plasmodium falciparum by use of resonance Raman imaging and multivariate analysis
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- Bonifacio, A., Finaurini, S., Krafft, C. et al. Anal Bioanal Chem (2008) 392: 1277. doi:10.1007/s00216-008-2414-0
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The multivariate algorithm hierarchical cluster analysis is applied to sets of resonance Raman spectra collected from human erythrocytes infected with the malaria parasite Plasmodium falciparum. The images obtained yield information about the distribution of hemoglobin and hemozoin (or malaria pigment) within the parasitized cells and about their molecular structure. This method has the advantage of conveying more information than other imaging approaches based on resonance Raman spectroscopy, and it is a promising tool to study the hemozoin formation process and its interaction with antimalarial drugs within unstained, well-preserved parasites.
KeywordsMalariaPlasmodium falciparumHemozoinImagingResonance RamanMultivariate analysis
Malaria is a parasitic disease transmitted by mosquitoes occurring in tropical and subtropical regions of the world. Among the four species causing human malaria, Plasmodium falciparum is the most aggressive and lethal, causing up to two million deaths every year .
In the human body, the parasites multiply in the liver and then inside host erythrocytes, where they digest a major proportion of red cell hemoglobin in an acidic digestive vacuole . Free heme [ferriprotoporphyrin IX, or Fe(III)PPIX], a product of hemoglobin digestion, is dangerous for the parasite because it can cause harmful oxidant species, eventually leading to irreversible damage of biological membranes. The parasites detoxify Fe(III)PPIX by converting it into an insoluble crystal named “hemozoin,” or “malaria pigment.” The mechanism of hemozoin formation is still not completely clear and many hypotheses have been proposed .
The detoxification of Fe(III)PPIX into hemozoin is an important drug target: 4-aminoquinoline antimalarials are believed to form a π−π stacking with the planar aromatic structures of Fe(III)PPIX, resulting in inhibition of hemozoin formation and consequent buildup of toxic heme [4, 5]. A better understanding of the mechanisms of hemozoin formation and of drug action would be extremely valuable for the design of new drugs to increase efficacy and overcome resistance, which is presently the major drawback of antimalarial chemotherapy [6, 7].
Resonance Raman (RR) spectroscopy proved to be a useful tool in malaria research, in particular to study the structure and properties of hemozoin both in situ and ex situ, its structural analogy with the synthetic compound β-hematin, and its interaction with antimalarial drugs . This nondestructive technique is based on the inelastic scattering of laser radiation whose wavelength is in resonance with an electronic transition of the molecule studied (i.e., a chromophore); hence the name “resonance Raman effect” [8, 9]. In RR spectra the intensities of bands corresponding to vibrational modes of a chromophore (e.g., heme or hemozoin) are enhanced over vibrations due to other molecules which may be present in the sample. Since chromophore vibrations are related to the molecular structure, information about heme species such as hemozoin (or its synthetic analogue β-hematin) may be readily inferred from RR data, even when these species are found in complex mixtures such as those present in a cellular environment [8, 10–14].
The use of microscope optics to focus a laser on the sample and collect the scattered light (RR microspectroscopy) permits one to couple the structural and chemical information contained in Raman spectra with spatial information, with a lateral resolution which is in principle only limited by light diffraction. Spectral characteristics can be “mapped” on the sample, in the approach named “Raman mapping,” by superimposing a two-dimensional grid of points over the sample and collecting a whole spectrum from each point. Unlike traditional histopathological methods (e.g., Giemsa staining), this approach allowed imaging of the distribution of hemozoin within unlabeled, undyed parasitized erythrocytes, by mapping the spectral intensity at frequencies characteristic of the malaria pigment .
The chemical and structural information in an image based on a Raman map can be increased upon processing data according to multivariate data analysis. Cluster analysis (CA) has been successfully employed to process nonresonant Raman maps in order to visualize simultaneously different subcellular structures and their chemical components in single cells [15–17]. CA classifies spectra into groups (clusters) according to their spectral similarity, and the resulting images are “chemical maps” in which the sample is divided into areas (i.e., group of pixels) having different colors, where each color corresponds to a different cluster.
In this paper, the distributions of hemoglobin and hemozoin are shown simultaneously in RR maps of parasitized erythrocytes processed with the hierarchical cluster analysis (HCA), a particular CA method . The advantages of HCA applied to RR imaging are discussed in view of future applications.
Materials and methods
Parasite cultures and sample preparation
P. falciparum culture of D10 strain was maintained at 5% hematocrit (human type A-positive red blood cells) in RPMI 1640 medium (EuroClone, Celbio, Milan, Italy) with the addition of 1% AlbuMAXII (Gibco, Invitrogen, Italy), 0.01% hypoxanthine (Sigma-Italia, Milan, Italy), 20 mM N-(2-hydroxyethyl)piperazine-N′-ethanesulfonic acid, and 2 mM glutamine (EuroClone, Celbio), as described in . All the cultures were maintained at 37 °C in a standard gas mixture consisting of 1% O2, 5% CO2, and 94% N2. Smears of normal or parasitized red blood cells at the trophozoite stage were prepared on glass microscope slides, fixed in methanol (Sigma-Italia, Milan, Italy), and dried under ambient conditions. The parasitemia in the samples was between 2 and 5% (reproducing that of severe malaria in adults).
Human hemoglobin (lyophilized powder) was purchased from Sigma, whereas β-hematin was synthesized from hemin (Sigma) dissolved in methanol (2 mg/ml) by the addition of 1 molar equiv of acetic acid under stirring. After overnight incubation at 70 °C, the precipitate was washed with distilled water and unreacted hemin was removed by extracting the precipitate twice for 2 h in 0.1 M sodium bicarbonate buffer at pH 9.1. The final product was washed twice with distilled water and desiccated.
Raman imaging/spectroscopy data collection
Raman spectra and images were collected using an inVia Raman system (Renishaw, Wotton-under-Edge, UK). The laser (514.5 nm argon-ion laser, LaserPhysics, West Jordan, UT, USA) was focused on the sample by a ×100 objective (0.85 numerical aperture).
RR maps were acquired by moving the sample in steps of 1 μm with a ProScanTMII motorized stage (Prior, Cambridge, UK) coupled to the microscope to sequentially illuminate with the laser a grid of points, while collecting a Raman spectrum (850–1,700 cm-1) for each point under control of the Renishaw software program Wire 2.0. The exposure time to the CCD detector for each point was 30 s, and the laser power at the sample was 1 mW throughout the measurements. The CytoSpecTM software package (http://www.cytospec.com) was used for data preprocessing and analysis.
Spectra of β-hematin and hemoglobin were acquired from small quantities of the compounds deposited on glass microscope slides, using a laser power at the sample of 1 mW and exposure times of 300 and 100 s, respectively.
Data preprocessing and hierarchical cluster analysis
All sets of spectra were preprocessed before HCA . Preprocessing involved the elimination of spikes generated by cosmic rays and the vector normalization of spectra (see the electronic supplementary material).
HCA was performed on the spectral region 1,300–1,650 cm-1 (where more intense and characteristic bands are present) by applying Euclidean distances to calculate the dissimilarity coefficients and Ward’s algorithm as a clustering algorithm to separate Raman spectra into clusters [18, 20]. As results of HCA, average spectra, dendrograms, and maps colored according to cluster membership were automatically produced with the CytoSpec software package. The average spectra were calculated using the original unnormalized data on the basis of the HCA.
Results and discussion
The application of CA tells us how many different kinds of spectra are observed in the RR map and how these clusters are spatially distributed over the two-dimensional map. Since Raman spectra convey information about chemical structure and composition, CA shows how different chemical species are distributed in a sample, at the same time giving information about their molecular structure through the average spectrum (or “centroid”) evaluated for each cluster. In HCA, spectra having a certain degree of similarity are merged into groups (clusters) whose average spectra constitute a new data set, and this agglomerative process is reiterated in several steps until all spectra are grouped into one cluster.
The assignment of red and blue clusters to hemozoin and hemoglobin, respectively, is consistent with the spatial distribution of these clusters in the maps. In fact, hemoglobin is the most abundant species in erythrocytes and is assumed to be homogeneously distributed throughout the cell. On the other hand, hemozoin is localized in the food vacuole of the parasite, and therefore it is concentrated in one specific region inside the cell. According to this interpretation, the erythrocyte in Fig. 1 might be infected by two parasites, a feature characteristic of P. falciparum, since hemozoin is clearly observed in two separated areas. Since in Raman spectroscopy the intensity is roughly proportional to the concentration, the green cluster might correspond to areas of the sample containing low quantities of hemoglobin. This assignment is consistent with the fact that in Fig. 1 most of the green pixels are adjacent to blue pixels. They can indicate a transition from the erythrocyte to the background, as they are bordering the upper edge of the cell.
Besides Raman mapping, images can be obtained by using the unfocused laser to illuminate at once a large sample area, acquiring at the same time on a CCD camera the image formed by the Raman photons scattered from the sample (global Raman imaging ). Different from mapping, in global Raman imaging no spectra are collected: the image is obtained directly from the sample with the use of filters that let only the scattered Raman light of a specific frequency reach the CCD (similarly to fluorescence microscopy). However, global Raman imaging requires the species to be imaged to have an intense, spectrally isolated band, which should be characteristic of that species only. In fact, images obtained with this approach usually show the distribution of only one species at a time (i.e., in one image). The same limitation occurs in images showing the distribution of Raman intensity at a certain wavenumber which are built using Raman maps (intensity mapping ). Moreover, both global Raman imaging and intensity mapping may have problems in separating the contributions from other species which have bands overlapping with that of the species to be imaged (for an example of this limitation in intensity mapping, see the electronic supplementary material). Since HCA relies on differences in a whole spectral region, and not only at one wavenumber, it discriminates between different species better than global imaging and intensity mapping, and allows the spatial distribution of more than one species to be simultaneously shown in one single image. Moreover, spectral averaging in HCA-processed maps allows one to obtain spectra with reasonable quality even if spectra from single points have poor signal-to-noise ratio. It should be stressed that this averaging operation in HCA-processed maps, in which spatial resolution is unaffected, is different from the averaging between adjacent data points or pixels (i.e., binning) in which spatial resolution is lost.
In summary, RR mapping and HCA has been proven to be a useful tool in malaria research, and since polarization-resolved RR spectra of hematin are altered upon interaction with chloroquine , in the near future HCA maps based on polarized RR measurements could produce images showing the distribution of drugs in drug-treated infected erythrocytes as further experimental evidence for the direct drug–heme interaction.
V.S. and A.B. acknowledge partial financial support from BINASP/INFRAEUR and IRCCS Burlo Garofolo, respectively. The work by D.T. and S.P. was generated in the context of the AntiMal project, funded under the 6th Framework Programme of the European Community (contract no. IP-018834). S.F. is a PhD student of the AntiMal International PhD Programme run in collaboration with the European Molecular Biology Laboratory (Heidelberg). The authors thank Nicoletta Basilico and Diego Monti for useful suggestions and critical review of the data.