A high-content imaging assay for the quantification of the Burkholderia pseudomallei induced multinucleated giant cell (MNGC) phenotype in murine macrophages
© Pegoraro et al.; licensee BioMed Central Ltd. 2014
Received: 20 December 2013
Accepted: 11 April 2014
Published: 22 April 2014
Burkholderia pseudomallei (Bp), a Gram-negative, motile, facultative intracellular bacterium is the causative agent of melioidosis in humans and animals. The Bp genome encodes a repertoire of virulence factors, including the cluster 3 type III secretion system (T3SS-3), the cluster 1 type VI secretion system (T6SS-1), and the intracellular motility protein BimA, that enable the pathogen to invade both phagocytic and non-phagocytic cells. A unique hallmark of Bp infection both in vitro and in vivo is its ability to induce cell-to-cell fusion of macrophages to form multinucleated giant cells (MNGCs), which to date are semi-quantitatively reported following visual inspection.
In this study we report the development of an automated high-content image acquisition and analysis assay to quantitate the Bp induced MNGC phenotype. Validation of the assay was performed using T6SS-1 (∆hcp1) and T3SS-3 (∆bsaZ) mutants of Bp that have been previously reported to exhibit defects in their ability to induce MNGCs. Finally, screening of a focused small molecule library identified several Histone Deacetylase (HDAC) inhibitors that inhibited Bp-induced MNGC formation of macrophages.
We have successfully developed an automated HCI assay to quantitate MNGCs induced by Bp in macrophages. This assay was then used to characterize the phenotype of the Bp mutants for their ability to induce MNGC formation and identify small molecules that interfere with this process. Successful application of chemical genetics and functional reverse genetics siRNA approaches in the MNGC assay will help gain a better understanding of the molecular targets and cellular mechanisms responsible for the MNGC phenotype induced by Bp, by other bacteria such as Mycobacterium tuberculosis, or by exogenously added cytokines.
KeywordsBurkholderia pseudomallei High Content imaging phagocytosis Multinucleated giant cells Macrophages
Burkholderia pseudomallei (Bp) is a Gram-negative bacterial pathogen and the causative agent of melioidosis, a potentially fatal disease if misdiagnosed or left untreated[1, 2]. Bp is endemic to Southeast Asia, Northern Australia, South America, Africa, Middle East, China and India and the pathogen can be commonly isolated from soil and surface waters[1, 3, 4]. Both acute and chronic infections with Bp can be acquired by inhalation, percutaneous inoculation and in rare circumstances by ingestion. The clinical symptoms of melioidosis are broad and may present as acute or chronic pneumonia, internal organ abscesses (lung, liver and spleen), fulminating septicemia and uncommonly individuals can be asymptomatic. In fact, and due to the facultative intracellular lifestyle of Bp, dormant cases have been reported with the most notable being 62 years after initial exposure. With the relative ease of genetic manipulation, environmental availability and intrinsic antibiotic resistance, Bp is listed as a category B select agent by the U.S. Centers for Disease Control and Prevention.
Macrophages and monocytes play critical roles in both the innate and adaptive arms of the immune system and are the first line of host defense mediating immunological responses to foreign antigens[7, 8]. These cells have diverse functions within the host including phagocytosis of bacterial, fungal, parasitic and viral pathogens, cytokine and chemokine biosynthesis for inflammatory mediated responses to invading pathogens as well as regulation of cellular metabolic processes including fatty acid metabolism, iron reprocessing and mineral reabsorption[9–11]. In response to certain biological triggers, monocytes or macrophages form multinucleated giant cells (MNGCs), which involves the fusion of adjacent cells and results in a multinucleated cell with a single cytoplasmic compartment. MNGCs are a well characterized phenotype in tissue granuloma formation in response to bacterial infection, with the most notable being associated with Mycobacterium tuberculosis (Mtb). Using various animal, human, in vitro cell culture and explant tissue models of Mtb infection it has been demonstrated that monocytes develop into various MNGC types, which is essential in the confinement of Mtb within infectious granulomas[13–20]. Likewise, monocyte and macrophage MNGC formation can be induced in vitro using various conditioned mediums containing exogenous cytokines, lectin, phorbol myristate acetate and even select antibodies[21–32]. The most notable cytokines associated with monocyte and macrophage differentiation into MNGCs are Interleukin-4 (IL-4) and Interferon gamma (IFN-γ). However, recent reports have also demonstrated that MNGC formation is dependent on diverse range of cellular proteins including CD36, TREM-2, E-cadherin, CCL2 and Rac1, MMP9, DC-STAMP, E-cadherin and Syk; all of which are involved in intracellular signaling, cell surface communication, proteolysis, chemotaxis and cellular transcription[28, 33–43].
A unique phenotypic characteristic of Bp infection, in addition to Burkholderia mallei (Bm) and Burkholderia thailandensis (Bt), is the ability to induce host cell MNGC formation following cellular uptake, in both tissue culture cells (i.e. murine macrophages) and in primary human cells (patients with active melioidosis)[44–47]. MNGC formation has been demonstrated in both phagocytic and non-phagocytic cells in addition to patient tissue(s) with active melioidosis[46–54]. The importance of Bp-mediated MNGC formation during infection is currently unknown, but it is possible that cell to cell spread via MNGC allows the pathogen to avoid immune surveillance in vivo. The Bp genome encodes a diverse range of specialized protein secretion systems including three type III secretion systems (T3SS) and six type VI secretion systems (T6SS)[1, 55, 56]. Mutation of the Bp T3SS-3, which is homologous to the Shigella Mxi-Spa and Salmonella SPI-1 T3SSs, results in loss of Bp induced MNGC formation, inability of endosomal escape and loss of virulence in animal models of Bp infection[50, 53, 57]. Likewise, disruption of components making up the T6SS-1 reduced animal virulence and hindered MNGC formation in RAW264 macrophages. In addition, it has been shown that the Bp alternative sigma factor RpoS, which is involved in genome-wide regulation of bacterial adaptation to environmental stress (i.e. nutrient limitation), plays a role in Bp induced MNGC formation. Recently, the molecular mechanism of Bp MNGC formation was revealed by Toesca et al.. The T6SS-1 valine-glycine repeat tail spike protein (VgrG1) possesses a novel fusogenic domain at its C-terminus that mediates cell fusion and allows Bp cell to cell spread.
Automated high content imaging (HCI) microscopy is a powerful technique to quantitatively characterize cellular phenotypes at the single cell level in response to bacterial and viral infection, exposure to drug agonists and antagonists and for drug mechanism of action determination[61–69]. This work describes the development of a cell-based HCI immunofluorescence assay to quantitatively characterize the MNGC phenotype induced in murine macrophages upon infection with Bp K96243. As a proof of principle for its applicability in a relevant biological setting, this assay was validated using mutants of Bp that were previously described to be defective for MNGC formation in mouse macrophages[58, 70]. Furthermore, we used the MNGC HCI assay to screen a focused small molecule library to identify compounds that interfere with MNGC formation induced by Bp. Together, the results of these experiments indicated that the HCI MNGC assay can be used in a medium-throughput format to identify and characterize Bp mutants that are defective in their ability to induce MNGCs and to identify small molecules that inhibit this phenotype.
Results & discussion
Optimization of the MNGC assay
As observed in the fluorescence microscopy images, Bp infection induced cell-to-cell fusion, clustering of the nuclei and cell body enlargement in a substantial fraction of infected macrophages when compared to mock infected samples (Figure 1A). These cellular objects fit the definition of MNGC. A large number of Bp bacterial spots were found to be either internalized or in close proximity with the boundaries of infected cell bodies. In these experimental conditions not all the infected cells appear to be part of an MNGC object (Figure 1A). Hence, it was important to develop an HCI analysis that would recognize and distinguish MNGC objects from non-MNGC objects in a heterogeneous population of infected cells. To address this issue, we took advantage of the close proximity of the nuclei in MNGC’s to recognize and classify MNGC clusters. Briefly, and as shown in Figure 1B, cell nuclei were first identified by using the Hoechst 33342 channel image, thus obtaining a population of objects that was named “Nuclei”. The cell body edges were identified by expanding the body of the nucleus detected in the previous step. The cell body borders were then detected by using the CellMask DeepRed channel image.
Bp spots were identified using the Bp antibody channel image. Several cellular attributes were calculated for the Nuclei population, the most relevant being: number of objects, cell body area and number of bacterial spots per object. The next step in the image analysis consisted in recursively clustering distinct Nuclei objects together into a single “Cluster” object, provided that their nuclei were either touching or adjacent. All the cellular attributes of the Nuclei population calculated on a single-object basis were then summed into the corresponding Cluster object. In addition, the number of Nuclei per Cluster (Polynucleation) was calculated. Finally, based on visual inspection of images analyzed with this strategy, the Cluster population was further classified into either MNGC (>3 Nuclei per Cluster) or non-MNGC (≤3 Nuclei per Cluster) sub-populations (Figure 1B). This approach was then used to quantitatively measure MNGC formation in RAW264.7 macrophages infected with wild-type Bp K96243. As seen in Figure 1C, the results of these experiments indicate that the HCI MNGC analysis can be used at the well level to detect MNGC formation in Bp K96243-infected populations when compared to mock infected samples. In particular, and as expected, infected cells had a 4.3-fold increase in Cluster Area, a 2.4-fold increase in Number of Nuclei per Cluster, and a 21-fold increase in the Percentage of MNGC when compared to non-infected samples.
Single cell analysis of the Bp K96243 infected macrophages
Validation of the MNGC assay to detect mutants defective in their ability to induce MNGC
Screening of a small molecule library in the MNGC assay
In summary, we have successfully developed an automated HCI assay to quantitate MNGCs induced by Bp in macrophages. This assay was then used to characterize the phenotype of the Bp mutants for their ability to induce MNGC formation and identify small molecules that interfere with this process. This assay can also be applied to identify the molecular targets and mechanisms responsible for the Bp induced phenotype, which to date are poorly understood. In addition, this assay has potential application for characterizing bacterial isolates as well as the identification of immune modulators such as cytokines that induce or inhibit this phenotype. Currently, we are not aware of a robust and direct HCI method to unambiguously distinguish cell clumps from MNGC. Nevertheless, in the experimental conditions described in the manuscript, and in the absence of tested compounds, the detection of MNGC via our HCI method is clearly dependent on infection by Bp (Figures 1,4 and5). Compounds that induce cell clumping rather than MNGC-formation might be counter-screened by measuring MNGC formation in mock infected/compound treated cells. In addition, it will be of future interest to develop and implement calculated cellular attributes (such as Cell Area) or the IF staining of additional cellular structures (such as Actin or Tubulin) to further refine and improve the HCI analysis of MNGC.
Burkholderia pseudomallei K96243 was maintained in either Luria-Bertani (LB) broth, on LB plates or on 1.5% agar plates containing 5% sheep blood (SBA). Broth cultures were grown at 37°C with shaking at 250 rpm and agar plates were incubated at 37°C. For macrophage infections, Bp was grown on LB plates for ~18 h at 37°C and a loopful of the culture was suspended in 10 ml of Dulbecco's Modified Eagle Medium (DMEM) (Gibco, Carlsbad, CA). Bacterial concentrations were determined by measuring the OD600 and cell suspensions were adjusted to a multiplicity of infection (MOI) of 30 using a conversion factor of 5 × 108 CFU/ml per unit of optical density at 600 nm. All Bp manipulations were performed in biosafety level 3 laboratories.
Construction of a B. pseudomallei ΔbsaZ type three secretion mutant
Genomic DNA from Bp Δ sctUBp3 was purified and used as template DNA for PCR amplification of the ΔbsaZ gene. Gene amplification was performed using the forward primer bsaZ-FXb 5’-CATGTCTAGA CTTCACGTCACGTCATGCCGAGCGACACG-3’ and reverse primer bsaZ-RH 5’-CATGAAGCTT TGTTGGCTAGTGGTCGTTCCC-3’ with the Epicentre FailSafe Kit with buffer “D” (Epicentre Technologies, Madison, WI) using the following conditions: one cycle at 94°C for 5 min; 30 cycles at 94°C for 30 sec, 56°C for 30 sec, and 72°C for 1 min; followed by a final 7 min extension at 72°C. Characters in boldface in the above primer pair represents the XbaI and HindIII sites incorporated into the oligonucleotides for directional cloning. PCR products were resolved on a 2% agarose gel and excised using the GeneClean III kit (Qbiogene, Carlsbad, California). Purified PCR fragments were digested with XbaI and HindIII (New England Biolabs, Ipswich, MA) using buffer “2”, cleaned as described above and cloned into the similarly digested sacB-based suicide plasmid pMo130 to generate pMoΔbsaZ. Ligations were transformed into chemically competent Escherichia coli TOP10 (Invitrogen, Carlsbad, CA) and recombinant plasmids were purified using the Wizard Plus SV miniprep kit (Promega, Madison, WI). pMoΔbsaZ was electroporated into E. coli S17-1 and mobilized into Bp K96243 as previously described[75, 76]. pMoΔbsaZ was resolved from transconjugants by culturing the isolates in LB without NaCl containing 10% (wt/vol) sucrose for 3–4 days at 25°C. Deletion of the Bp bsaZ gene was confirmed using PCR and apparent by a reduction in the amplicon size of ~1060 bp.
Tissue culture and macrophage infections
The RAW264.7 cell line was maintained in DMEM (Gibco) containing 10% FBS (Hyclone, Logan, UT), 1% non-essential amino acids (Sigma, St. Louis, MO), 1% HEPES buffer (Gibco) and 1% L-Glutamine at 37°C under an atmosphere of 5% CO2. For macrophage infections, BD Falcon 96-well plates (Franklin Lakes, NJ) were seeded with ~2 × 104 cells/per well and incubated overnight as described above to obtain ~4 × 104 cells/well. Macrophages were infected with Bp at a MOI of 30 (or otherwise noted) for 2 h, monolayers washed three times with PBS to remove extracellular bacteria and either macrophages were fixed (2 h infection) or pre-warmed DMEM containing 10% fetal bovine serum and 250 μg/ml of kanamycin (Sigma) was added to reduce extracellular bacterial growth. Infections were continued for an additional 8 h (or otherwise noted) and monolayers were fixed for ~18-24 h with 10% formalin prior to antibody staining.
Macrophage and bacterial staining
Following macrophage fixation cells were washed and subsequently permeabilized for 15 minutes at room temperature with Cellomics 1× permeabilization buffer (Halethorpe, MD), washed twice with PBS and blocked (minimum of 1 h) with Cellomics 1x blocking buffer. Following incubation, blocking buffer was removed and replaced with 50 μL of a 1:1000 dilution of 2 mg/mL anti-Burkholderia pseudomallei monoclonal antibody (AB-BURK-P-MAB3, Critical Reagents Program, Frederick, MD) for 1 h. Unbound primary antibody was removed by two washes with PBS and a 1:500 dilution of Dylight 488 goat anti-mouse secondary antibody (Fisher Scientific, Waltham, MA) was added at room temperature for 30 min. Cells were washed two additional times with PBS and 1× CellMask DeepRed (Invitrogen) and 1:10,000 Hoechst nuclear stain (Invitrogen, Carlsbad, CA) were added.
Image acquisition and analysis
An Opera QEHS confocal system (PerkinElmer, Waltham, MA) was used for high-throughput image acquisition. 4 imaging fields per well were acquired with a 20X water objective in the Blue (Hoechst 33342), Green (Alexa488) and Far Red (CellMask DeepRed) channels on a single Z-plane in 2 sequential exposures. The first exposure utilized the 488 nm and 640 nm excitation lasers, the emitted light was first filtered by a 405/488/640 primary dichroic mirror, then collected on separate high resolution CCD cameras through 525/35 nm and 690/70 nm band pass filters, respectively. The second exposure used the 405 nm and the excitation light was filtered first through a 405/561/640 primary dichroic mirror, then through a 568 nm Detection dichroic mirror and finally through a 450/50 nm band pass filters. Images were imported into Columbus 2.3 database (PerkinElmer) and analyzed with Acapella 2.7 (PerkinElmer). For the MNGC assay, nuclei were first identified using the Hoechst33342 channel image as input, then the cell edges were determined using the CellMask DeepRed channel image, and bacterial spots were detected using the Alexa 488 channel image. The nuclei detection described above generated a first population of objects (Nuclei), for which cellular attributes were calculated (Cell Area, Number of Foci per Cell). Nuclei objects were then clustered together based on the distance of their nuclear bodies (Measured in pixels). Nuclei objects whose nuclear bodies were within a distance of 0 or 1 pixels, depending on the experiment, were considered as part of a single Cluster object. All the cellular attributes of the Nuclei population were then imported (As sums) into the corresponding Clusters and the number of Nuclei per Cluster attribute was also calculated. Clusters were then further classified into a MNGC subpopulation based on the number of nuclei present in the cluster (Nuclei per Cluster >3). The Percentage of MNGC was calculated as (Number of MNGC objects)/(Number of Cluster objects)*100. Values in the histograms represent the mean +/SD of 6 replicates on the same plate run on 3 separate days (n = 18). Statistical significance for differences in cellular and bacterial attributes between different samples was calculated using the t-test. For single cell analysis presented in Figure 2, images were directly analyzed after image acquisition with Acapella 2.6, (Using an image analysis strategy similar to the one just described above, Nuclear distance for clustering: 3 pixels) and the image analysis results were imported into FCSExpress4 (Denovo Software, Los Angeles, CA), which was used for single cell image cytometry measurements.
Small molecule screening in the MNGC assay
Where: Z-Scoreij = Z-Score for well in Row “i” and Column “j”, % Sampleij = Cellular attribute value for well in Row “i” and Column “j”, μN = Mean of the Cellular attribute for the negative controls on the plate, and σS = Standard Deviation of Cellular attribute for the negative controls on the plate. Compounds that had both Number of Nuclei Z-Scoreij > -3 (Cytotoxicity filter) and % MNGC Z-Scoreij > 3 (Activity filter) were considered as active compounds.
Multiplicity of infection
High content imaging
Multinucleated giant cells.
We would like to thank Paul Brett and Mary Burtnick for providing pMoΔbsaZ and Samuel Dickson for help with statistical analysis. This project was funded by the Department of Defense Chemical Biological Defense Program through the Defense Threat Reduction Agency (DTRA) JSTO-CBS.MEDBIO.02.10.RD.010 (to RGP). We would like to thank Oak Ridge Institute for Science and Engineering for participating in the Postgraduate Research Program at the U.S. Army Medical Research and Materiel Command. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the U.S. Army, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
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