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. 2010 Dec;59(4):565-70.
doi: 10.1016/j.parint.2010.07.007. Epub 2010 Aug 3.

High-content imaging for automated determination of host-cell infection rate by the intracellular parasite Trypanosoma cruzi

L L Nohara  1 , C Lema, J O Bader, R J Aguilera, I C Almeida
Affiliations

High-content imaging for automated determination of host-cell infection rate by the intracellular parasite Trypanosoma cruzi

L L Nohara et al. Parasitol Int. 2010 Dec.

Abstract

Chagas disease affects 8-11 million people, mostly in Latin America. Sequelae include cardiac, peripheral nervous and/or gastrointestinal disorders, thus placing a large economic and social burden on endemic countries. The pathogenesis and the evolutive pattern of the disease are not fully clarified. Moreover, available drugs are partially effective and toxic, and there is no vaccine. Therefore, there is an urgent need to speed up basic and translational research in the field. Here, we applied automated high-content imaging to generate multiparametric data on a cell-by-cell basis to precisely and quickly determine several parameters associated with in vitro infection of host cell by Trypanosoma cruzi, the causative agent of Chagas disease. Automated and manual quantifications were used to determine the percentage of T. cruzi-infected cells in a 96-well microplate format and the data generated was statistically evaluated. Most importantly, this automated approach can be widely applied for discovery of potential drugs as well as molecular pathway elucidation not only in T. cruzi but also in other human intracellular pathogens.

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Figures

Figure 1
Figure 1
General workflow of high-content imaging applied for automated analysis of infection rate by T. cruzi. A high-resolution fluorescence bioimager system was applied for image acquisition, data processing, and analysis of T. cruzi-infected, GFP-expressing NIH3T3 cells, in a 96-well plate format. Images of GFP fluorescence (host cell) and DAPI (host-cell and parasite DNA) from nine contiguous image fields (3 × 3 montage) were acquired per well with a 20× 0.75 NA objective. For optimal segmentation and accurate quantification several image processing and segmentation tools were combined in order to optimize the image analysis protocol. GFP was used to define the host-cell body (boundary object), whereas DAPI was used to define the intracellular parasites (sub-object) and the host-cell nuclei. Numerical data was used for the analysis of multiparametric data associated with infection of host cells by T. cruzi.
Figure 2
Figure 2
Representative cropped, segmented images (20×, 0.75 NA) of GFP-expressing NIH 3T3 cells infected with T. cruzi. A. Host-cell body segmentation based on GFP expression. B. Host-cell nucleus segmentation based on DAPI staining. C. T. cruzi segmentation based on DAPI staining of parasite nuclear and kinetoplast DNA, and size threshold. Yellow arrows denote parasite DNA. D. Segmentation mask of the same image generated after sub-object analysis depicting host-cell bodies (red), host-cell nuclei (blue), and parasites (yellow). Small numbers in A and B denote host-cell bodies and nuclei, respectively. Insets in A–D indicate higher magnification of areas delimited by white squares within the main images.
Figure 3
Figure 3
Scatter plots of manual versus automated intracellular T. cruzi counting methods. A. Scatter plot comparing manual versus automated method for counting the number of parasites per 100 cells with the fitted regression line and the 95% confidence bounds for individual values. B. Scatter plot comparing manual versus automated method for determining the percentage of infected cells with the fitted regression line and the 95% confidence bounds for individual values. In both plots, the scatter about the fitted regression line is quite small and the slope of the line is positive (small manual values correspond to small automated values; large manual values correspond to large automated values), thus indicating a positive correlation between manual and automated methods.
Figure 4
Figure 4
Comparison of data point means between manual and automated T. cruzi counting methods. A. Mean number of parasites per 100 cells, by treatment and method. B. Mean percentage of infected cells, by treatment and method.
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