Application of DNA chip scanning technology for automatic detection of Chlamydia trachomatis and Chlamydia pneumoniae inclusions

Abstract

Chlamydiae are obligate intracellular bacteria that propagate in the inclusion, a specific niche inside the host cell. The standard method for counting chlamydiae is immunofluorescent staining and manual counting of chlamydial inclusions. High- or medium-throughput estimation of the reduction in chlamydial inclusions should be the basis of testing antichlamydial compounds and other drugs that positively or negatively influence chlamydial growth, yet low-throughput manual counting is the common approach. To overcome the time-consuming and subjective manual counting, we developed an automatic inclusion-counting system based on a commercially available DNA chip scanner. Fluorescently labeled inclusions are detected by the scanner, and the image is processed by ChlamyCount, a custom plug-in of the ImageJ software environment. ChlamyCount was able to measure the inclusion counts over a 1-log-unit dynamic range with a high correlation to the theoretical counts. ChlamyCount was capable of accurately determining the MICs of the novel antimicrobial compound PCC00213 and the already known antichlamydial antibiotics moxifloxacin and tetracycline. ChlamyCount was also able to measure the chlamydial growth-altering effect of drugs that influence host-bacterium interaction, such as gamma interferon, DEAE-dextran, and cycloheximide. ChlamyCount is an easily adaptable system for testing antichlamydial antimicrobials and other compounds that influence Chlamydia-host interactions.

FIG 1

Comparison of the images produced by a DNA chip scanner and confocal microscopy. (A) C. trachomatis serovar D-infected McCoy cells grown in a 16-well plastic chamber attached to a glass slide. After fixation, chlamydial inclusions are fluorescently labeled and scanned by a DNA chip scanner at a 5-μm resolution. A scanned image of a well of the 16-well chamber slide is shown. (B) Magnified portion (boxed area of panel A) of the scanned image. (C) The same boxed area from panel A visualized by fluorescent confocal laser scanning microscopy. Bar = 100 μm. (D) Further magnification of the fluorescent structure at the lower right of panels B and C reveals a single infected cell. Bar = 10 μm.

FIG 2

ChlamyCount image adjustments and report. (A) Infected host cells are grown in a 16-well chamber slide. Chlamydial inclusions are stained by direct immunofluorescence and scanned by a DNA chip scanner. Before image analysis, the user can adjust the inclusion intensity and area thresholds for detection. (B) The effect of the applied threshold changes on the number and location of the detected inclusions can be readily checked by magnification of the quadrants at the left uppermost and lowest wells of the chamber. On the basis of the applied thresholds, ChlamyCount performs the image analysis. (C) ChlamyCount reports the numerical data as .txt, .xls, and .pdf files. The .pdf file contains the numerical data as well as the images of the 16 scanned areas.

FIG 3

Detection of chlamydial inclusions. McCoy cells were infected with serially diluted C. trachomatis serovar D (A), C. trachomatis serovar L2 (B), and C. pneumoniae (C). C. pneumoniae infections were performed by centrifugation (400 × g, 60 min, RT). Each infection at a particular MOI was performed in parallel wells. MOIs are shown as simple fractions instead of decimal numbers in order to follow the serial dilutions more easily. The last two wells were uninfected. The images of scanned and ChlamyCount-processed wells and the numerical data are shown for each species and serovar. For easier comparison of the theoretical and measured inclusion counts, the first theoretical inclusion count was made the same as the first measured inclusion count. Data are means ± standard deviations for the parallel wells.

FIG 4

Estimation of MICs of known and novel antimicrobial compounds. McCoy cells were infected with C. trachomatis serovar D (MOI, 1) in the presence of various concentrations of moxifloxacin (A), tetracycline (B), and PCC00213 (C). Each infection with a particular antibiotic concentration was performed using parallel wells. The inclusion counts were enumerated by ChlamyCount and manual confocal microscopy on the same slide. The scanned and ChlamyCount-processed well images and the numerical data are shown. Data are means ± standard deviations for the parallel wells. (D) Correlation between the inclusion numbers enumerated by the ChlamyCount method and manual counting. Each data point represents the inclusion number detected in a single well of the 16-well chamber slide.

FIG 5

Measuring the effects of IFN-γ, DEAE-dextran, and cycloheximide on the direct inclusion counts of C. trachomatis serovar D and C. pneumoniae. McCoy cells were infected with C. trachomatis serovar D (MOI, 1) in the presence of various concentrations of human (A) and murine (A, B) IFN-γ. HeLa cells were infected with C. trachomatis serovar D (MOI, 0.5) (C) and C. pneumoniae (MOI, 1) (D) after pretreatment with 1% DEAE-dextran (15 min, RT), and/or 1 μg/ml cycloheximide was applied during the infection. Each infection with a particular compound concentration was performed in two parallel wells. The scanned and ChlamyCount-processed well images and the numerical data are shown. Data are means ± standard deviations for the parallel wells. Student's t test was applied for statistical comparison of the treated versus the nontreated samples. *, P < 0.05; **, P < 0.01. NT, nontreated; Dex, DEAE-dextran treated; CH, cycloheximide treated. The results of a representative experiment are shown.