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. 2010 Aug 4;99(3):953-60.
doi: 10.1016/j.bpj.2010.04.065.

Quantitative imaging of human red blood cells infected with Plasmodium falciparum

Affiliations

Quantitative imaging of human red blood cells infected with Plasmodium falciparum

Alessandro Esposito et al. Biophys J. .

Abstract

During its 48 h asexual reproduction cycle, the malaria parasite Plasmodium falciparum ingests and digests hemoglobin in excess of its metabolic requirements and causes major changes in the homeostasis of the host red blood cell (RBC). A numerical model suggested that this puzzling excess consumption of hemoglobin is necessary for the parasite to reduce the colloidosmotic pressure within the host RBC, thus preventing lysis before completion of its reproduction cycle. However, the validity of the colloidosmotic hypothesis appeared to be compromised by initial conflicts between model volume predictions and experimental observations. Here, we investigated volume and membrane area changes in infected RBCs (IRBCs) using fluorescence confocal microscopy on calcein-loaded RBCs. Substantial effort was devoted to developing and testing a new threshold-independent algorithm for the precise estimation of cell volumes and surface areas to overcome the shortfalls of traditional methods. We confirm that the volume of IRBCs remains almost constant during parasite maturation, suggesting that the reported increase in IRBCs' osmotic fragility results from a reduction in surface area and increased lytic propensity on volume expansion. These results support the general validity of the colloidosmotic hypothesis, settle the IRBC volume debate, and help to constrain the range of parameter values in the numerical model.

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Figures

Figure 1
Figure 1
Confocal imaging of calcein-loaded infected and uninfected (cohorts) RBCs from the same culture: (A) cohort RBC, (B) IRBC with ring-stage parasite, (C) IRBC with trophozoite-stage parasite, and (D) IRBC with schizont-stage parasite. Images are representative of 119 cells analyzed. Scales are shown in the right panel.
Figure 2
Figure 2
Background-to-object transition and length estimates obtained with the FA algorithm. The procedure is illustrated with a 1D example using simulated fluorescent intensity versus distance diagrams; intensity and distance are expressed in arbitrary normalized units (au). (A) Background-to-object transition. The PSF of the microscope smoothes the sharp simulated background-to-object transition to an extent determined by the SD of the error function, illustrated here for values between 0 (steepest slope) and 1 (shallowest slope), in arbitrary normalized units. In panels B–D, boundary transitions to an object of length d are generated from computer simulations and are represented by gray circles; in panels B and C, the gray circles overlap and result in a gray outline. The continuous curves represent the fit obtained with the fitting algorithm. (B) Sharp transition. (C) With blurring. (D) With blurring and noise.
Figure 3
Figure 3
Comparison between the FA algorithm and GB methods for estimating the object-to-background transition and object volume. Panels A–C compare efficiencies in boundary estimates as a function of the SNR of the data set; efficiency is reported as the ratio of the CV to the SNR. Note that lower values equal better performance. In panel D, estimated volumes (in fL) are reported as a function of the number of synthetic objects simulated (sample number). Color codes: red (lower curve), FA algorithm; blue (upper curve), gradient filtering; green (middle curve), diffusional filtering. Dashed segments stress the superior precision of the FA algorithm for subpixel resolution. (A) Statistical error in length estimation normalized by Poissonian noise. (B) Bias in the estimate of the absolute position of the boundary. (C) Bias in the estimate of the length of the simulated segment. (D) Comparison of volume estimates on 75 different simulated 3D samples by the FA algorithm (red, lower curve) and GB isosurface rendering (blue, upper curve) at different isovalues from 15% to 30% of the maximal intensity. The dashed green line indicates the actual volume of the simulated object. Note that the FA algorithm approximates the simulated volume better than the GB methods at all initial threshold values.
Figure 4
Figure 4
Transmission, confocal, and 3D images of a cohort cell (A), ring-stage IRBC (B), trophozoite-stage IRBC (C), and schizont-stage IRBC (D and E). Fluorescence images are maximum intensity projections of the deconvolved confocal 3D image stacks. The selected images are representative of a total of 119 cells analyzed. Panels D and E show typical phenotypes exhibited by schizont-infected RBCs.
Figure 5
Figure 5
Volume and area estimates of IRBCs with parasites at different stages of development. Volume (A) and area (B) values are normalized to the corresponding means in the uninfected cohort RBCs (C). The columns report the mean ± SD. Column width reflects the probable time interval for each parasite developmental stage as selected by the criteria described in Materials and Methods. R: rings; T: trophozoites; S: schizonts. Horizontal lines above the columns report the statistical significance of differences between indicated groups (p < 0.01, ∗∗p < 0.001). The shadowed area in the top graph reports a subset of data from the malaria model (9), the significance of which is analyzed in the Discussion section.

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