Here, we describe a phenotypic assay applicable to the High-throughput/High-content screens of small-interfering synthetic RNA (siRNA), chemical compound, and Mycobacterium tuberculosis mutant libraries. This method relies on the detection of fluorescently labeled Mycobacterium tuberculosis within fluorescently labeled host cell using automated confocal microscopy.
Despite the availability of therapy and vaccine, tuberculosis (TB) remains one of the most deadly and widespread bacterial infections in the world. Since several decades, the sudden burst of multi- and extensively-drug resistant strains is a serious threat for the control of tuberculosis. Therefore, it is essential to identify new targets and pathways critical for the causative agent of the tuberculosis, Mycobacterium tuberculosis (Mtb) and to search for novel chemicals that could become TB drugs. One approach is to set up methods suitable for the genetic and chemical screens of large scale libraries enabling the search of a needle in a haystack. To this end, we developed a phenotypic assay relying on the detection of fluorescently labeled Mtb within fluorescently labeled host cells using automated confocal microscopy. This in vitro assay allows an image based quantification of the colonization process of Mtb into the host and was optimized for the 384-well microplate format, which is proper for screens of siRNA-, chemical compound- or Mtb mutant-libraries. The images are then processed for multiparametric analysis, which provides read out inferring on the pathogenesis of Mtb within host cells.
Among the emerging and re-emerging infectious pathogens reported during the last years, Mycobacterium tuberculosis (Mtb) holds a prominent place being responsible for 1.4 million deaths and 8.7 million new infections in 2011 (Global tuberculosis report 2012, www.who.int/topics/tuberculosis/en/). Despite the availability of multidrug therapies, the number of infected people is still on the rise and multidrug resistant (MDR) as well as extensively drug resistant (XDR) Mtb are quickly spreading all over the world1. Moreover, when taking into consideration the presence of Mtb antigens, it is evident that one third of the global population is considered as being latently infected by Mtb. Statistically, in one case out of ten, there is evolution towards the active form of the disease with subsequent clinical symptoms2. Therefore, new means to fight Mtb are urgently needed. In this context, we developed an in vitro visual phenotypic assay relying on monitoring Mtb invasion and multiplication into host cells by automated confocal fluorescence microscopy3. The adaptation of the assay in 384-well microtiter plates in combination with automated image acquisition and analysis, allowed High-content/High-throughput Screening (HC/HTS) of medium scale libraries of compounds, siRNAs and bacterial mutants. The screening of a genome wide RNAi library on this phenotypic assay thus enabled the identification of the key host-factors involved in Mtb trafficking and intracellular replication but also the elucidation of host-pathways exploited by the tubercle bacillus. Another adaptation of this particular phenotypic assay was for the identification of bacterial factors essential to Mtb intra-phagosomal persistence. For instance, the arrest of phagosome maturation is considered as one of the major mechanisms that facilitates the survival and replication of Mtb in macrophage. The monitoring of the subcellular localization of Mtb knock-out mutants in fluorescently labeled-acidic compartments allowed for the identification of bacterial genes involved in the survival process4. Finally, the high-content imaging of Mtb also offers an excellent method to quantify drug efficiency for inhibiting various phenomena like intracellular bacterial growth3. Altogether, this type of high throughput phenotypic assay allows accelerating drug discovery against TB and the data collected by these different approaches contribute to a better understanding of the host manipulation exerted by Mtb.
1. High-throughput Genome-wide siRNA Screening
Screening performed in a human Type-II pneumocytes model A549 cell line upon infection with Mtb H37Rv expressing Green Fluorescent Protein (GFP). This procedure is outlined in Figure 1A.
Note: This protocol is optimized to study the effect of gene silencing on the intracellular Mtb growth. Mtb is a slow-growth bacterium which divides every 20 hr in optimal conditions. After 5 days post-infection the amount of extracellular Mtb is still low in absence of cell lysis and didn't affect the quality of the analysis. This protocol must be optimized in terms of length of antibiotic treatment and incubation time to be adapted for siRNA screens using fast-growth bacteria like Mycobacteria smegmatis and Escherichia coli that are extensively released and can infect new cells.
2. High-throughput Compound Screening
Screening performed on Mtb H37Rv infected host cells. This procedure is outlined in Figure 1B.
Note: This protocol can be adapted for Mtb mutant library screening by replacing compounds by mutants expressing a fluorescent protein (One well/One mutant) (Figure 1C, see also Brodin et al.4). Fluorescent mutants are first seeded in wells (20 μl of bacterial suspension per well). Bacteria are then recovered by 30 μl of cell suspension. After centrifugation at 350 x g for 1 min, the plate is incubated at 37 °C in an atmosphere containing 5% CO2. Incubation time and MOI depend on the assay. As an example, for visualization of early cellular events such as phagosome acidification, the cells can be infected for 2 hr with MOI ranging from 1-20. Lysosomes are stained using Lysotracker dye at 2 μM for 1.5 hr at 37 °C in an atmosphere containing 5% CO2 and then fixed with either 10% formalin or 4% paraformaldehyde (PFA). Confocal images are acquired and finally analyzed using image analysis scripts featuring appropriate algorithms for lysosomes detection and subcellular localization4.
3. Green Fluorescent Protein Expressing Mycobacterium tuberculosis H37Rv (GFP-H37Rv) Culture Conditions
For long term storage, GFP-H37Rv were frozen in D-PBS (around 1 x 108 mycobacteria per vial).
4. Human Peripheral Blood Monocyte Cells Purification from Whole-blood or Buffy-coat Preparation
High-throughput genome-wide siRNA screening
Mtb is able to colonize immune cells in vitro as well as several other lung epithelial cells. For instance, Mtbis able to infect and damage A549 epithelial cells that are commonly used as a model for human type II pneumocytes5-7. Dectin-1 was reported as a host cell receptor involved in Mtb uptake, proinflammatory response and antibacterial effect on intracellular mycobacterial growth in A549 cells8. siRNA condition described in Protocol 1 led to 85% of silencing efficiency (data not shown). Silencing Dectin-1 expression with siRNA led to a decrease of intracellular mycobacteria amount in A549 cells. Indeed, after 3 days of silencing and 5 days of infection, the percentage of infected cells is reduced twice in Dectin-1 silenced A549 cells compared to cells transfected with nontargeting scramble siRNA (Figures 2A and 2B). We applied sample-based normalization of siRNA targeted Dectin-1 compared to scramble to define Z-score. As shown in Figure 2C, we obtained a Z-score average around -15 for siRNA targeted Dectin-1. Dectin-1 can be used as positive control for the siRNA screen to discover other novel host factor involved in Mtb colonization in pneumocytes that could have the same phenotype as that with the Dectin siRNA. The use of a siRNA impacting on the phenotype as a control on each microplate during the screen allows normalization of each plate, which is useful when one wants to perform whole genome screen analysis. The statistical parameter Z' was 0.1 using control based normalization on scramble and siRNA targeted Dectin1, which is an acceptable value for the validation of the siRNA screening data.
High-content compound screening
Compound efficiency on intracellular bacterial growth is evaluated by establishing a dose response curve (DRC) and normalized to the reference positive compound and negative compound solvent controls. Representative DRC of two reference compounds, isoniazid (INH) and rifampicin (RIF), active against Mtb growth are shown in Figure 3. These curves are obtained in human primary macrophages infected by a GFP-expressing Mtb H37Rv strain, with readout after 5 days post-infection. DMSO and INH at a final concentration of respectively 1% and 0.1 μg/ml are commonly used as negative and positive controls, providing basal levels of efficiency (0 and 100%) (Figure 3A). Following the image analysis process detailed in Figure 4B, multiparametric data are extracted from confocal fluorescence images of infected-human macrophages taken by automated confocal microscope. Active compounds impacting on the intracellular replication of Mtb in host cells led to a decrease of mycobacterial load, which corresponds to the area of the GFP signal in cells on pictures (Figure 3B). The ratio between intracellular bacterial area and total cell area, calculated using image-based analysis software, is plotted in function of compound concentration, which generates the DRC (Figure 3B). These curves allow the determination of both the concentration required to decrease the bacterial load by 50% (IC50) and the minimal concentration required to inhibit 99% of the bacterial replication (MIC99) (Figure 3C). Z' based on the control DMSO 1% and the control INH 0.1 μg/ml was 0.49. This Z', really close to 0.5, is acceptable to validate this assay.
Figure 1. Visual High Content Screening Approaches. Schematic representation of Mtb infection model system used for the siRNA (A), chemical compounds (B) and Mtb mutants (C) screens. (A) siRNA library screen: the cells were transfected with siRNA for 3 days in 384-well plates using reverse transfection method. siRNA transfected cells were infected with GFP-Mtb and incubated for 5 days at 37 °C in an atmosphere containing 5% CO2. The cells were then stained and images were collected using an automated confocal microscope. (B) Compounds library screen: the compounds were distributed in 384-well plates. Cell suspension and GFP-Mtb were incubated together for 2 hr at 37 °C. Infected cells were seeded in the plates and incubated for 5 days at 37 °C in an atmosphere containing 5% CO2. The cells were then stained and images were collected using an automated confocal microscope. (C) Fluorescent-Mtb mutant library: the fluorescent Mtb mutants were seeded in 384-well plates and the host cell suspension was distributed. The incubation time varied depending on the assay. The cells or cellular vesicles were stained before automated image acquisition. Click here to view larger image.
Figure 2. Dectin-1 silencing impacts on Mtb colonization in A549 cells. (A) Representative confocal images (10X air lens) of A549 cells transfected with nontargeting siRNA (scramble) or with siRNA specific for Dectin-1 and infected with GFP-Mtb H37Rv (MOI5) for 5 days. The scale bar represents 200 μm (A) GFP-Mtb H37Rv were visualized in green and the cells in red. The number of cells (A. Cell detection) and the intracellular GFP-Mtb H37Rv load (A. Bacterial area) were determined using image-based analysis software. (B) Graphic representation of the percentage of infected A549 cells in 5 replicates (w1 to w5) of Scramble siRNA (blue circles) and Dectin-1 siRNA (red circles). (C) Graphic representation of Z-score average of Scramble siRNA and Dectin-1 siRNA. (*** p-value < 0.0001). Click here to view larger image.
Figure 3. Dose response curve of active reference compounds against Mtb in human macrophages. (A and B) Representative confocal images (20X water lens) of human macrophages (red, cell labeled with red fluorescent dye) infected with GFP-Mtb H37Rv (green) with a MOI1 for 5 days. The scale bar represents 50 μm. (A) Images of infected-cells incubated with DMSO 1% used as negative control in the assay. (B) Images of infected cells incubated with increasing concentration of two reference compounds isonazid (INH) and rifampicin (RIF). (C) Dose-response curves (DRC) of INH and RIF. Image-based analysis allowed determination of the DRC for each compound tested. DRC represents the ratio between intracellular GFP-bacterial area and total cell area (Y axis), in function of the compound concentration (log scale, x-axis). In each graph, the DRC of compound was normalized to that of the negative control DMSO 1% (0% inhibition) and the positive control INH at a concentration of 0.1 μg/ml (100% inhibition). For each compound, the concentration required to inhibit 50% of the bacterial colonization (IC50) and the minimal inhibitory concentration (MIC99) were calculated from the DRC. Click here to view larger image.
Figure 4. Standard image-based analysis to determine fluorescent intracellular mycobacteria. Images from 384-well plates were acquired using an automated confocal microscope. In this case, 4 different images of the same well (fields) were recorded. Each field was then analyzed using image-based analysis software Acapella 2.6 (Perkin Elmer). (A) Each field contained two channels (two colors), one for the bacteria (green) and one for the cell nuclei (blue channel), that were segmented using the following algorithm: i) nuclei detection using a built-in Acapella procedure, ii) cytoplasm detection, based on the nuclei population, using a built-in Acapella procedure, iii) bacteria detection by keeping only pixels which intensity are higher than a manually-defined threshold, iv) merging cells position with bacteria position to identify infected cells. Final results, expressed as an average of the four fields, are the total bacterial area, the total number of cell, the percentage of infected cells and the bacterial area per cell (average of all infected cells). (B) Each field contained two channels, one for the bacteria (green channel) and one for the cell nuclei and cytoplasm (far-red channel), that were segmented using the following algorithm: i) filtering the original channel using an anti-median filter, ii) keeping only pixels which intensity are higher than a manually-defined threshold (each channel has its own threshold), iii) counting the remaining number of pixel for each channel, iv) merging channels and counting the number of pixel shared by both bacteria and nuclei to quantify intracellular bacteria. Final results, expressed as an average of the four fields, are the total bacterial area, the total cellular area, the total area of intracellular bacteria and the ratio between intracellular bacterial area and total area of cells. Click here to view larger image.
We describe here the methods required for a phenotypic assay using a GFP-expressing Mtb H37Rv strain to infect fluorescently labeled host cells, which makes it appropriate for High-content/High-throughput screens. This protocol could be applied to a broad range of compounds, fluorescent probes and Mtb mutants. For each protocol described above, fixation and immunolabeling steps could be performed prior to image acquisition. We use an automated fluorescent confocal microscope equipped with a 20X (NA 0.70) or 60X (NA 1.2) water lens to acquire images. The confocal microscope is equipped with 405, 488, 561, and 640 nm excitation lasers. The emitted fluorescence is captured using 3 cameras associated with a set of filters covering a detection wavelength ranging from 450-690 nm. It is important to note that the adjustment of the microscope excitation and emission settings depends on the type of dyes or fluorochromes used in each experiment. For phenotypic assays, DAPI or Hoechst are commonly used at 5 μg/ml for 10 min to stain nuclei. However, cells can also be stained with different fluorescent dyes specific for the detection of cytoplasm, membrane or cytoskeleton. After image acquisition, pictures should be analyzed using an image-based analysis software. Cells segmentation algorithms based on intensity of each pixel should be used to respectively ascertain the number of cells or the intracellular bacterial area (see Figure 4). The generated data should be weighed against a statistically based acceptance criteria to validate the robustness and the accuracy of the assay.
Large-scale high-throughput screens are time- and resource-consuming experiments. Therefore, it is of prime importance to assess beforehand the suitability of the assay. The data collected from phenotypic screens were visualized using spreadsheet software like Excel and generally normalized per plate. The most common quality metrics used for both siRNA and small-molecules screens is the Z. The Z is defined with mean and standard deviation of both positive and negative controls9. The Z quantifies whether the assay response is large enough to validate its application for a full-scale screen of samples. The range of the Z is negative infinity to 1, with >0.5 as a very good assay, >0 an acceptable assay and <0 an unacceptable assay. Compared to the small-molecule screens for which the strength of the controls allows the validation of the assay with Z > 0.5, the variability of siRNA screens impacts the Z which tend to be lower (frequently between 0.0-0.5)10. Indeed, the success of siRNA screens depends on the optimization of i) the efficiency of siRNA delivery into the cells, ii) the cytotoxicity induced by the transfection and iii) the assay condition for the efficiency of gene silencing. The siRNA can easily be transfected in various cell lines, such as HeLa, following the manufacturer's transfection protocol, but efficient siRNA transfection in some cell lines including macrophages still remains a challenge. Nevertheless, viral vector-mediated expression of short hairpin RNAs (shRNA) could represent a good alternative11. To escape the difficulty of interpretation, the data collected from siRNA screens are frequently normalized relative to the normal standard distribution to define the Z-score (also called Z-value) for each point10,12. Then, hit-samples were ranked according to the Z-score which typically belongs to less than -2 or more than +2. Finally, selected hits in the primary screen were retested in a secondary screen including more replicates in order to confirm the phenotype observed in the primary screen.
Our protocol could easily be applied for small scale screens with equipment. To achieve this, assay miniaturization in the micro-titer plates and manipulation of library of molecules are needed to optimize the process of conservation, dispensing and mixing13. Furthermore, it is recommended to use an automated robotic platform, including dispensers, in order to accurately and reproducibly control assay conditions in the micro-titer plates. The huge amount of data produced by high throughput screening (HTS) should also be managed by database and an adapted pipeline for image analysis of the confocal picture, data storage and transfer. The assays presented in this report were performed in Biosafety Level 3 facility (BSL3). The strict adherence to safety in BSL3 increases the difficulty of the screens. Indeed, many equipment required for the screens must be available in BSL3 and isolated to protect the worker and the environment from contamination. Therefore, the installation of the adapted pipeline in BSL3 required a lot of space. For this reason, our protocol was developed to have a maximum of steps performed out the BSL3 like siRNA transfection and compound transfer to the plates. The cell infection thru image acquisition steps were performed in BSL3 conditions. The images were then transferred in a dedicated server and analyzed out of the BSL3.
The phenotypic assay described here was based on two methods of image analysis (Figure 4). The first method, used for siRNA screening, was designed to give the number of infected cells. This parameter was found to efficiently compare the effect of gene silencing on Mtb intracellular replication. When screening compounds however, a more basic read-out based on the total area of cells was sufficient to clearly identify active compounds. As this second method is faster and easier to implement, it was preferred for the analysis of images arising from compounds screening. To go further, more fluorescent dyes and/or labeling probes could be added and image analysis scripts could be optimized to generate multiparametric data such as colocalization, nuclei and cell morphology, cell death, bacterial aggregation, as well as intracellular trafficking of the bacteria. It is important to note that for intracellular trafficking or colocalization assays, it is essential to get Z-stacks in order to apply a cumulative assessment.
The authors have nothing to disclose.
Financial support for this work was provided by the European Community (ERC-STG INTRACELLTB Grant n° 260901, MM4TB Grant n° 260872), the Agence Nationale de Recherche, the Feder (12001407 (D-AL) Equipex Imaginex BioMed), and the Region Nord Pas de Calais. We gratefully acknowledge the technical assistance of Gaspard Deloison, Elizabeth Werkmeister, Antonino Bongiovanni and Frank Lafont from the platform BICeL.
µclear-plate black, 384 well | Greiner bio-one | 781091 | 127.8/86/15 MM with Lid, TC treated |
CellCarrier 384 well plate | PerkinElmer | 6007550 | Black, Clear Bottom, with Lid, TC treated |
V-bottom white , 384 well-plate | Greiner bio-one | 781280 | |
sealing tape, breathable, sterile | Corning | 3345 | |
Lipofectamine RNAiMax | Life Technologies | 13778150 | Transfection reagent |
Dimethyl sulfoxide | Sigma-Aldrich | 34943 | |
RPMI 1640 + GlutaMAX-I | Life Technologies | 61870-010 | Cell culture medium |
D-PBS 1X [-]MgCl2/[-]CaCl2 | Life Technologies | 14190-094 | Dulbecco's Phosphate Salin Buffer |
D-PBS 1X [+]MgCl2/[+]CaCl2 | Life Technologies | 14190-091 | Dulbecco's Phosphate Salin Buffer |
Fetal bovine serum | Life Technologies | 2610040-79 | |
FICOLL PAQUE PLUS | DUTSCHER | 17-1440-03 | Ficoll for Peripherical Blood Monocyte Cells purification |
CD14 MicroBeads, human | Miltenyi | 130-050-201 | Purification of CD14+ Monocytes |
Human M-CSF, premium gr. (1000 μg) | Miltenyi | 130-096-493 | Macrophage Colony Stimulating Factor |
LS Columns | Miltenyi | 130-042-401 | Columns for CD14+ Monocytes isolation |
Tween 80 | Euromedex | 2002-A | Mycobacteria culture |
Glycerol high purity | Euromedex | 50405-EX | Mycobacteria culture |
Middlebrook OADC enrichment | Becton-Dickinson | 211886 | Mycobacteria culture |
7H9 | Becton-Dickinson | W1701P | Mycobacteria culture |
Versene 1X | Life Technologies | 15040033 | Non enzymatic cell dissociation solution |
DAPI | Life Technologies | D1306 | Nuclei dye |
Hoechst 33342 | Life Technologies | H3570 | Nuclei dye |
Syto60 | Life Technologies | S11342 | Nuclei/cytoplasm dye |
Formalin | Sigma-Aldrich | HT5014 | Cell fixation solution |
siRNA targeting Dectin-1 | Santa-Cruz | sc-63276 | |
*siGenome* Non targeted siRNA pool | Dharmacon | D-001206-14 | |
Rifampicin | Sigma-Aldrich | R3501 | antibiotic |
Isoniazid (INH) | Sigma-Aldrich | I3377-50G | antibiotic |
Hygromycin B | Life Technologies | 10687-010 | antibiotic |
Amikacin | Sigma-Aldrich | A1774 | antibiotic |
Automated Confocal Microscope OPERA | PerkinElmer | Image acquisition | |
Columbus 2.3.1 Server Database | PerkinElmer | Data transfert, storage and analysis | |
Acapella 2.6 software | PerkinElmer | Image-based analysis | |
GraphPad Prism5 software | GraphPad | Statistical analysis | |
Excel 2010 | Microsoft | Statistical analysis |