How does diet affect our gut microbe health? - Microbiome composition of Boozy Flies by measured by next-gen deep sequencing.

With further awareness of the intricacies of our bodies as the product of not only human but also microbial cell populations, the interplay of microbial contributions to health has been pushed to the forefront of popular science. While our microbiome is tailored to each individual and may help maintain health, many factors can alter microbial composition which may lead to disbiosis: the unbalance of microbial populations that may lead to downstream disease or pathology. With many University student’s diet being supplemented with regular drinking, we are interested in the effect of an ethanol supplemented diet on gut microbiome composition. To elucidate this question, will use a fruit fly (Drosophila melanogaster) model fed with either conventional or alcohol supplemented (0.5% Ethanol) diets for 1 week. Microbial populations will be determined using deep sequencing of bacterial 16S DNA and compared between the two diets.

Previous studies in mice have found significant shifts in abundance of different microbial populations such as Bacteriodetes and Firmicutes in addition to Proteobacteria, Actinobacteria and Corynebacterium in response to alcohol supplemented diets, although these animals were fed a high alcohol diet (5% ethanol) to simulate chronic alcohol pathology. This equates roughly to a bender of standard 5% ABV beer for 7 weeks, and while sounds fun, hopefully is not representative of the standard drinking experience. We expect to see shifts in similar microbial populations in our boozy flies, though likely to a much lesser extent than seen in previous pathology based models.

Methods

Flies and modified diets
Vials were seeded with 10 Drosophila melanogaster flies. Only virgin female flies were chosen to prevent gender biases as well as population expansion. Vials were raised on either a chemically defined agar based holidic diet, or a holidic diet containing 0.5% ethanol at 25˚C for 14 days. Flies were then sedated and euthanized at -20˚C.

Isolation amplification and sequencing of bacterial 16S DNA
Homogenization and DNA purification using an Ultraclean Microbial DNA Isolation Kit following slightly modified manufacturer’s protocol. Bacterial 16S DNA was then amplified via conventional PCR using primers specific for a broad range of bacterial, but not fly 16S DNA. PCR products were quantified using Quant-iT high-sensitivity DNA assay which measures dsDNA concentration based on fluorescence of intercalating dye. Amplified DNA was tagmented (fragmented and tagged) with 2 sets of indexes specific for each group’s library. Fragments were sequenced using an Illumina sequence by synthesis multiplexed cluster technique. Unfortunately, a mechanical failure occurred during the sequencing run, so data analyzed was provided by Dr. Edan Foley from a previous experimental trial run on male flies. Sequence data was given to Dr. Bart Hazes, and analyzed for microbial diversity using Unix based bioinformatics programs and databases. Illumina sequence by synthesis is explained in detail in the video below from Illumina.
                                          

Results and Discussion
It is commonly thought that the composition of the gut microbiome is incredibly important for host health. With the huge amount of high sugar or fat content foods readily available alongside the myriad diets coming in and out of fashion, it is important to determine the effect that nutrition has on our gut flora. Specifically, the effect of alcohol on microbial populations interested us greatly, being among one of the most common nutritional additives to diet relative to the student lifestyle.

Upon sequencing gut microbes from flies fed an alcohol supplemented diet, we found that overall, the diversity of gut microbiome decreased (Figure 1). This drop in diversity was calculated quantitatively using Shannon diversity scores which take into account not only number of species, but relative abundance of each (Table 1). Control flies fed a ‘holidic’ diet with a known composition yielded a score of 5.92, while flies fed a holidic diet with 0.5% ethanol decreased the Shannon score from 4.613.
 

Figure 1. Gut floral composition of ethanol diet Drosophila by 16S DNA sequence. Male Drosophila melanogaster flies fed either holidic control or alcohol supplemented diets for 7 days and then euthanized. Bacterial 16S ribosomal DNA was isolated from whole fly homogenates are amplified using conventional PCR. Bacterial DNA was then tagmented with known adapters prior to Ilumina sequence by synthesis. Identity and proportions of bacteria were calculated based on abundance and sequence identity. a) Proportion of bacterial population on family levels. b) Proportion of population on genus level. Teal indicates population consisting of 39 various Acetobacterea genus'. Other colors correspond to numerous other small microbial populations.

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Table 1. Diversity indexes of gut flora in flies fed an alcoholic diet. Shannon diversity scores were calculated using operational taxonomic units generated by analysis of deep sequencing data. Higher scores are achieved by a combination of variety and abundance of individual species. A score of 0 reflects a purely homogeneous population. Experimental data derived from given data, paper data derived from an upcoming publication from the Foley lab.

When looking at the effects of alcohol on specific populations of gut bacteria, we found a ~20% increase in population of members within the Acetobacteraceae family. This population consisted largely of Acetobacter species (52% and 63% of control and alcohol groups respectively) with the remaining populations consisting of 39 various other genus’. These results are not surprising, as acetic acid bacteria are able to utilize oxidation of ethanol into acetic acid. In addition, the vast majority of other bacterial populations decreased in size. This result was again unsurprising, as ethanol is toxic to most cells that are unable to metabolize it. While low concentrations of 0.5% may seem insignificant, long term exposure may have had the combined effect of increasing the growth of ethanol oxidizing bacteria (ie. Acetobacter) while slowing growth of other species. 

Although it’s hard to point out the biological significance of these results with looking solely at microbial composition, there are several important implications that could be made. The decrease in microbial diversity may have long term effects on nutrient access/ absorption, as many vitamins and other nutrients required for optimal growth are microbial metabolites. Reduction or depletion of these populations may also deplete the host of their nutritional benefits.

Significantly, Shin et all found that Acetobacter species modify Drosophila insulin and insulin growth factor signalling which regulate a huge range of physiological parameters such as body size, metabolism and rate of development (2). Thus, the effect of alcohol on microbial populations likely plays a role in the development of embryos and young children that require precisely coordinated signals while growing. It seems possible that the effect of fetal alcohol syndrome may be, at least in part, due to altered host-microbiome cross-talk. Additionally, depletion of microbial diversity may be affecting myriad possible interactions between resident gut microbes and the host that have yet to be described which could have varying impacts on an equally vast array of physiological parameters.

This experiment highlights the drastic changes in gut microbe composition that can be caused by even small changes in diet. Although this experiment provides important information of the impact of alcohol on the microbial populations, it does not provide any insight into what impact these population shifts have on the host. It is also important to realize that individuals within heterogeneous populations can have a very different gut microflora, and that each microbiome may act differently to identical changes in diet, especially given the complexity of interactions between the host and it’s commensals.


Technical discussion

Although the calculated Shannon scores for our experiment are 5-20 fold higher than those seen in the Foley paper, the trends seen are the same, strengthening the robustness of our results. The difference in magnitude is likely due to the way the data was analyzed. Each bacterial 16S DNA sequence is included into an Operational taxonomic unit (OUT), since sequence doesn’t necessarily describe a species. Stringency of OTUs can be adjusted to be more precise, giving more clusters of association, or less stringent, giving clusters of more broadly related microbes. We obtained 954 individual OTUs across all samples using a stringency of 97% sequence resemblance, which is ~30 fold higher than the 30 species known to be Drosophila gut commensals. This huge number is likely what increased the magnitude of our diversity scores and was likely caused by using whole fly homogenates where all external bacteria, commensal or otherwise, would be included. Homogenized isolated fly guts used in the Foley paper would include only gut microbes and likely a much smaller number of OTUs, though total OTUs obtained in their results was not included in the paper. Another reason is possible contamination from processing samples outside of a biosafety cabinet.  In addition, since only bacterial 16S ribosomal DNA was amplified, the effect of alcohol on eukaryotic commensals such as yeasts remain unknown.



Now with the final post of my MMI 590 blog, its time to embrace the student lifestyle and transition this experiment to ‘human trials’ with a pint or two.

Cheers!

Mike Wong
University of Alberta


1. Bull-Otterson et al. Metagenomic Analyses of Alcohol Induced Pathogenic Alterations in the Intestinal Microbiome and the Effect of Lactobacillus rhamnosus GG Treatment (2013). PLoS ONE. DOI: 10.1371/journal.pone.0053028

2. Shin et al. Drosophila microbiome modulates host developmental and metabolic homeostasis via insulin signaling. Science. Vol.334 no. 6056 pp. 670-674 DOI: 10.1126/science.1212782

Fluorescence microscopy and live cell imaging

Fluorescence microscopy has long been employed to observe biological processes within fixed cells or tissue sections. We are now more than ever able to expand our field of knowledge using the plethora of dyes and fluorophores to stain these samples and gain higher resolution of multiple processes in multiparametric fashion. Although using these dyes on fixed samples from different intervals (ie. Fixed at different times post treatment) can reveal kinetic trends, it does not allow the resolution to observe biological processes in real time. With the advent of live cell imaging, we are now capable of viewing cellular kinetics in real time within individual cells. Here in we demonstrate the use of live cell imaging to track the movement of mitochondria within live cells, and the effect of photo-bleaching when doing live cell experiments. We also demonstrate the use of live cell imaging to follow kinetics of nuclear NFκB translocation to the nucleus in response to recombinant TNF-α.

Methods

Mitochondria movement
Protocols are outlined in the MMI 490 Fall Semester 2015 lab manual with minor changes. Briefly, HeLa cells were seeded onto glass coverslips at 20% confluence in cDMEM (10% FCS) and incubated at 37°C with 5% CO₂. Slips were transferred to 6 well plates and stained with 1:333 Pico green, 0.5µM Mitotracker red CMX ros and 0.8µM Hoescht 33258 for 1 hour. Cover slips were individually transferred to live cell imaging chambers containing 0.5mL media before sampling on a Leica SP5 scanning confocal fluorescence microscope. Images were acquired once a minute for 40 minutes and analyzed using FIJI image analysis software to obtain mean fluorescence for whole cells in each channel.

Transfection of GFP-NFκB and NFκB translocation
HeLa cells were seeded onto glass cover slips at 20% confluence overnight. 100µL 1:33 Fugene 6 HD in Opti-MEM was mixed with 1 volume 200µM GFP-NFκB encoding plasmid in Opti-MEM. This mixture was incubated at 37°C for 20 minutes before drop-wise addition to cells in 1mL Opti-MEM (3% FCS). Cells were incubated at 37C 5% CO₂ before transfer of coverslips to a live cell imaging chamber. GFP distribution was then analyzed using the Olympus Wave FX 1 spinning disk fluorescence microscope. Data obtained from Dr. Steve Ogg contains images that were captured every 12 seconds for 1 hour with TNF addition at 3 minutes into the run. Fluorescence was measured using FIJI analysis software by comparing mean pixel fluorescence of the nucleus to that of the entire cell.

Results and Discussion

Mitochondrial movement in live HeLa cells
Mitochondria are often thought of as stationary blobs within the cell, but in fact their movement can be quite pronounced. Although this is the case, the health and state of cells can impact the mitochondrial behaviour, where our time lapse videos failed to show much significant movement at all. This could be due to many factors including density, activation states, temperature or cell ‘happiness’ outside of 5% CO₂ since the SP5 is not equipped with a live cell incubation unit. Another issue we had was the predominant staining with Pico green, an intercalating dye that binds to the small groove of dsDNA (2). We believe that the addition of Pico green at high concentration before the addition of Hoescht bound all the nuclear DNA and excluded the Hoechst dye from being displayed (Figure 1) since green signal can be seen dispersed throughout the cell, but also very brightly over the nucleus. The mitrotracker red displayed great affinity for mitochondria, which was expected since it localizes to mitochondrial membrane potential.

Figure 1. Photo-bleaching of vital dyes during live cell imaging. Hela cells were stained with pico green and mitotracker red previous to image capture on an SP5 spinning disk fluorescence microscope. Images were captured once a minute for 40 minutes. Images displayed from the beginning of the run and at the end, in both green and red channels.

Figure 2. Quantification of mean fluorescence of vital dyes during live cell imaging. Mean whole cell fluorescence was measured for each channel respectively in each acquired image and compared to the starting frame as calculated by (frame (x) mean fluorescence/ start frame mean fluorescence). 

Photo-bleaching is the degradation of fluorophores by light sources and is concern when doing any type of fluorescent assay. It is especially important for time-lapse imaging, since bleaching can prevent capture of further time points. Hence it is important to consider longer image capture times on longer assays to minimize exposure to high energy light and thus prevent extensive photo-bleaching. In this assay, one image was taken per minute in 3 channels for 40 minutes. Even at this setting, both detectable fluorophores (Mitotracker Red and Pico green) displayed significant levels of photo-bleaching with visually detectable differences from beginning to end (Figure 1). When quantified using FIJI (is just ImageJ), end mean fluorescence for individual cells was seen to drop by ~30% and 50% from start mean fluorescence for Pico green and Mitotracker red respectively (Figure 2). If more images had been taken during the same time frame, the images may have been dimmer or non-existent due to higher light exposures. This is especially problematic since bleaching is known to be exponential, and a lack of signal prevents any further data from being extracted from the assay.

NFkB translocation in TNFα stimulated HeLa cells
NFκB is a critical transcription factor involved in stimulating inflammatory reactions. Many studies observe the percent of cells that have translocated after specific treatments, etc., but that percentage is just a snap shot of what is happening in a cell. Using live cell imaging, we are able to show the kinetics of how fast HeLa cells translocate NFκB to the nucleus in response to TNFα stimulation. In data obtained from Dr. Steve Ogg, we observed translocation in all 3 cells found in one frame (Figure 3a), but not those found in another. When total cell fluorescence was compared to that of the nucleus, we found 3 cells experienced a mean nuclear intensity that increased steadily from 70-80% to 80-100% of that of the total cell (Figure 4). While each cell had slightly different kinetics, indicated by varying slopes of each line, each cell experiences filling in of the nucleus with the GFP tagged NFkB that is evident in the image. One the other hand, another set of cells did not display any translocation, with nuclear intensity actually dropping after treatment with TNF (Figure 3b). This could be due to the state of growth of the cells, the density on the cover slip, relative health, or crowding as outlined by the Pelkmans group in their 2009 Nature publication¹. They found that heterogeneity of cells within a single dish was not due to random variation, but could be predicted based on morphology and relative niche of each grouping of cells due to differential growth kinetics, activity, etc. Further, they could predict lower infectivity of certain cell subsets based on their positioning based on growth phase dependent expression of certain phospholipids that simian virus 40 (SV40) uses for entry.

                                                                                     

Figure 3. Translocation of NFkB-GFP in TNF stimulated HeLa cells. Transfected HeLa cells were imaged for GFP translocation to the nucleus for 30 minutes after stimulation with recombinant TNFa. a) Cells that exhibited translocation, cells 1-3. b) Cells that showed no translocation (cell 4). Yellow lines show representative regions of interest used to quantify fluorescence in Figure 4. c) Response to cell 4. Time lapse video of d) NFkB translocating cells e) non-translocating cells. (Apologies for the sloppy figure, Blogger doesn't seem to like embedding videos very much!)



d-e

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Figure 4. Quantification of NFkB translocation via nuclear fluorescence intensity. Individual cells indicated in Figure 3 were quantified for mean fluorescence in the nucleus compared to that of the whole cell for one frame every 2 minutes. A reading of 1 indicates a mean fluorescence of the nucleus identical to the whole cell. the red arrow indicates TNF addition at time = 3min.



Likely, this may contribute to why our personal experiments didn’t work, as we may have chosen to image cells that either fell into a growth phase that doesn’t express much TNF receptor, or that is somehow inhibited in the NFkB pathway to prevent proliferation of activated cells. Antibody staining for TNFR could be used to rule out the first possibility. Alternatively, it could have been due to the TNF becoming non-functional from too many freeze thaws, as we performed 3 separate technical replicates to try to obtain our own data. Problems with a speckle reducer, a module in the scope which smooths out light coming through the 50 fiber optic threads within the cable, prevented quality image acquisition on our first run. Additionally, a faulty temperature probe allowed the cell incubator to reach over 44 °C which likely had a detrimental effect on our cells. Though, as with all science one cannot expect results from the first run, and patience must be observed while trying to troubleshoot possible sources of error to fully optimize an assay.

1. Snijder, B. et al. Population context determines cell-to-cell variability in endocytosis and virus infection. Nature 461, 520–523 (2009).

2. Dragan, A. et al. Characterization of PicoGreen interaction with dsDNA and the origin of its fluorescence enhancement upon binding. Journal of Biophysics (2010).
doi: 10.1016/j.bpj.2010.09.012.

Results and Analysis
               The Operetta High content imaging system is equipped with a spinning disk confocal microscope and is capable of up to 4 fluorescent colors and 3 different objective lenses. This allows for high resolution light or fluorescence microscopy in each individual well in a plate. The Operetta is also equipped with a robotic arm (Figure 1) which manipulates plates in/out of the imaging system and allows for full automation of the system. This factors greatly into the system’s utility, as a single run can take a huge amount of time to process. For example, the Harmony software that runs the machine estimated that our small run of 10x 384 well plates with 4 images per well would take ~16 hours to finish. You could imagine that larger screens would become uncomfortably time consuming with constantly switching out plates by hand.
 

       

      Figure 1.  The Operetta imaging system. a) A robotic arm feeds plates in and out of the imaging system while scanning plate information, allowing continual autonomous screening once initiated. b) The trade off for convenience is the danger of the robotic arm becoming sentient and giving extreme hugs.

      As described in the previous post, our assay was designed to find molecules that would inhibit TNFα induced NFκB nuclear translocation as a means of discovering a novel anti-inflammatory therapeutic, particularly for chronic inflammation. With this in mind, we had to set up the screen to examine parameters that were relevant to this assay, as the Operetta platform is capable of analyzing a large array of characteristics from apoptosis, cell cycle, morphology change, internalization, protein expression to even counting of viral factories.  Using the Harmony software, a few wells from a random plate were imaged and strategies for counting and distinguishing individual cells were established using nuclear and cytoplasmic (NFκB) stains (Figure 2).  Using the intensity of green NFκB- FITC fluorescence in the cytoplasm vs area overlapping with the nucleus (stained by Hoescht), a threshold is then set for the program to distinguish cells that have high nuclear NFκB vs those that do not, while omitting partial cells or background fluorescence. Once these parameters were set, the run was started with plates A-J taking 4 image fields per well. The program then compiled all data collected in each field of view, analyzed total cell numbers and the percentage of cells with high nuclear NFκB is reported.

Figure 2. Fluorescent imaging of nuclear translocation in cultured cells. HeLa human cervical carcinoma cells treated with various small molecule inhibitors were treated with TNF-a then fixed and stained with anti NFkB (FITC) and Hoescht nuclear stain. Images were aquired using the Operetta high throughput imaging system. Orange arrow indicates a low nuclear NFkB cell, white arrow indicates a high nuclear NFkB cell. 

Figure 3. Z scores are used to determine 'hits'. Histograms were generated for individual wells on technical replicate plates (A and B) using raw data (%high NFkB) or z-score. Orange and blue arrows represent cut off points for 2 standard deviations below the median for plates A and B respectively. Red arrow indicates the same cut off point in z-score.








               Due to the ‘plate effect’, or variability between plates due to handling (by different lab groups in this case), relative humidity, or myriad other factors, a threshold of percent of translocated cells can’t be used to determine if a certain well or drug is a ‘hit’ and blocked NFκB translocation.The difference between distributions is illustrated in Figure 3, where plate A has a much tighter distribution than plate B. The figure also illustrates that choosing an arbitrary % NFkB translocated cells as a 'hit' would not be equal for each plate. Since each plate should in fall into a normal distribution, and in theory each well on individual plates are treated the same, standard deviation is instead used to determine hits. We can see that plates A and B have a similar number of 'hits' when z-scores are used to determine drug efficacy. The median number of percent translocated cells was used to determine a Z score for percent translocated cells for each well, which equates to the number of standard deviations away from the median. We then chose to look for Z scores below -2, which would equate to wells that had a number of high nuclear NFκB cells two standard deviations or more lower than the plate mean. This correlates to roughly 2.5% of a normal distribution, and will allow us to quickly find and sort through low % NFkB translocation outliers.

               Once we found all of our ‘hits’, we disregarded any controls found in this cohort and proceeded to cross-reference our wells to the LOPAC drug library to find out which drugs were present in our target wells.

 To look for the possibility of experimental error, a heat map was generated using z-scores obtained from our assay (Figure 4). Problems could arise from uneven evaporation, heating, other factors that could cause false positives. If there are no adverse effects on plates, hits should be distributed randomly unless the drugs are organized by category and a certain category of inhibitor can block TNF-a induced NFkB translocation. 

Figure 4. Z scores of Plate A wells.

Z-scores generated from %high nuclear NFkB were plotted relative to their spacial arrangement on the plate. Hits used were z = (-2) or lower. D and B denote wells containing 4% DMSO loading or Bortezomib control treatments respectively.

Another important factor of high throughput screening is consistency. Due to the large number of samples being run, combined with very small cell numbers (5200 cells /well). To this effect, each plate was run in duplicate and then each well was compared for z- scores (Figure 5). In theory, when these points are plotted against each other, if each plate gives identical values, a trend-line with R=1 will be achieved. Since there is always variability, especially in biology, we use this value to assess whether our variation is within an acceptable range where we can trust the assay and thus the generated data. An R² =0.276 shows that our data is most likely fairly reliable. While there is deviation, the trend for each well tends to be fairly similar, and thus we can accept that our data is within an acceptable range of error. It can be noted, however, that Plate B wells tended to give less extreme Z-scores for the same wells in A, which could be due to the handling of individual plates or other factors previously discussed.

Figure 5. R² analysis of technical replicate 

results. Z scores for plates A and B were generated and graphed against each other. An R² goodness of fit test was performed. Trendline indicated by hashed line.

After checking for plate effects, and for reliability of our data, all hits were collected from the dataset. Control wells were omitted, yielding the remaining experimental hits (Table 1). Wells were cross-referenced against the LOPAC drug library and cell images were assessed for morphology. Some cells experienced adverse morphologies such as syncitium formation, blebbing, reduced processes, or complete death (Figure 6) which was factored into selecting 3 potential drugs, since it can give clues to how the drug works. For example, some hits were discounted because they killed the vast majority of cells, preventing therefore preventing proper NFkB translocation. In addition, it is likely these compounds would likely be too toxic to an animal if they kill tissue culture cells so quickly. Using this information, and specific drug activity found in the LOPAC library (Table 1) and in the literature, 3 hits were selected as molecules of interest identified as myrecetin, stattic and Bay11-7082.

Figure 6. Morphologies of HeLa cells treated with various small molecules. Images from each hit were visually assessed for morphologies like a) healthy, b) shrunken, c) blebbing/ apoptotic, d) syncitia formation.

Table 1. Experimental hits garnished from the TNF-a inhibition small molecule screen. Hits listed after bortezomib control hits were removed from the pool. Hits from whole class data shown with z-scores for all technical replicates. Selected compounds of interest highlighted in green.

In addendum - more details of hit selection and molecule activity to come!

Many of the candadates that prevented NFkB translocation, while not adversely affecting cell health, were generally not selected due to a lack of specificity, or because of potential off target effects. For example, chelerythine chloride was not chosen because it is a protein kinase C inhibitor. Since these kinases are involved in a huge array of cellular processes, and broad spectrum blocking of these enzymes would likely cause huge complications in animal models. It is also unlikely that the structure could be easily modified to be specific for a certain NFkB related PKC (like PKC-theta). Another example of a drug with off target activity is BTO-1, which inhibits Polo-like kinases that are involved in NFkB translocation, but are also involved in mitotic spindle formation.

Myricetin, Stattic and Bay11-7082 were selected for their activity directly in the NFkB pathway. Myricetin blocks casein kinase II which phosphorylates IkB and allows it to be targeted for ubiquitin dependent degradation. Stattic inhibits STAT3 activation, preventing it from enhancing NFkB promoter binding. Finally, Bay 11-7082 inhibits IkB-alpha phosphorylation. IkB binds to NFkB binding sites in the nucleus, blocking transcription. Phosphorylation leads to degradation and frees these sites. Therefore we propose these candidate drugs for potential therapeutics.

High throughput screening: A beginner’s guide to new age science wizardry

Science can often be tedious and time consuming, eating up hours upon hours of pipetting and processing. With the advancement of technology, we are now able to automate these processes which allows for the potential of high throughput screening. This technique takes advantage of robotics to rapidly process thousands of samples with incredible consistency and precision. Not only do these factors save time, but they eliminate the possibility of human error between samples, increasing the robustness of results. One of the main problems with high throughput screening is that many scientists think of it as a technique used only by Big Pharma, or very large research centers.

This blog post aims to provide a point of view walk through of a screen (through the eyes of someone who has also never run one!) searching for a small molecule TNF-α inhibitor: a much sought after therapeutic for chronic inflammatory diseases. Here in we will walk through and exhibit the relative simplicity of the actual protocols of running this screen while hopefully demonstrating its usability to a wider scientific audeience.

The Technique
The most important step in a screen is to properly plan your experiment, as High throughput screens are very expensive. Our experiment aims to administer the Sigma LOPAC drug library to HeLa cells to access if any of these molecules can inhibit TNF-α induced NFκB translocation to the nucleus.

-HeLa human cervical carcinoma cells were seeded at 5200 cells/well in 10x 384 well plates (5 sets in duplicate) in 18µL DMEM. Cells were incubated with either:

LOPAC¹²⁸⁰ small molecule inhibitor library (Sigma) which are all characterized to be biologically active
Bortezomib (Santa Cruz #sc-217785) – 26S proteasome inhibitor which acts as a negative control by                                                                                      blocking Ikk degradation and therefore NFκB translocation.
DMSO – drug loading control to verify DMSO suspending drugs isn’t toxic to our cells
Water – to make sure TNF is working
*All liquid manipulations performed by a robotic Janus Workstation (Perkin Elmer)


* Figure generated by Dr. Edan Foley. University of Alberta. Department of Medical Microbiology and Immunology. MMI 590 lab manual. Page 29. Figure 3.

-Cells were then incubated at 37°C for 30 minutes.
-Recombinant TNFα (Sigma T0157) was then added to each well at 10ng/mL and further incubated for 1 hour
-Medium was aspirated and cells were washed with PBS.
-Cells fixed in 3.7% Formaldehyde for 20min (room temperature)
-Formaldehyde aspirated
-Cells then incubated with 50uL (dispensed with plate washer) PTX buffer (1xPBS, 0.01% Triton X-100) and incubated for 5 minutes (x3 with new PTX).
-15µL of blocking buffer (1x PBS, 5% Normal goat serum, 0.1% Tween-20) was added (using the workstation)
-Cells incubated for 30 minutes then blocking buffer was dumped from plates
-Added 15µL of 1:1000 Rabbit anti-NFκB (Santa Cruz D5796)
-Incubated overnight at 4°C
-Dumped primary antibody solution and washed x4 with PBT (1xPBS, Tween-20) using the plate washer
-added 15µLof 1:1000 Hoechst 33258 (Molecular probes H-3569) 1:1000 Goat anti-rabbit AF488 (Life Tech A11008) and incubated 1h at room temperature in the dark
-Antibody solution was dumped and plates were washed with PBT twice and once with PBS before being stored in PBS for subsequent analysis on a Perkin Elmer Operetta high content imaging system

More info on the sample analysis and subsequent discussion to come!

Analysis of phagocytosis induced TNF-α production of murine macrophage using traditional flow cytometry in a mixed population

Introduction
Phagocytosis is one of the most ancient immune defenses and is vastly conserved across evolution. It is critical not only in pathogen defence, but also in the clearance of dead or dying cells. Traditionally, phagocytosis was studied using light or fluorescence microscopy to quantify particle internalization, but this technique is meticulous, slow, and usually generates statistically weak data. By utilizing flow cytometry, fluorescent target particles associated (both surface bound and internalized) with tagged effector cells can be analyzed at high speed to generate large statistically robust datasets. In addition, other downstream effects of phagocytosis can be analyzed in a multi-parametric manner using other fluorescent markers and channels. Here in we demonstrate the use of traditional flow cytometry to analyze the production of TNF-α by murine macrophages in relation to bacterial internalization.

Materials and Methods

Intracellular staining of RAW macrophages following phagocytosis of Eschericheri coli.
Cultured murine macrophage were incubated with GFP-E. coli according to MMI Lab 3 protocol. Briefly, 1x10⁶ RAW murine macrophage and Jurkat T-cells were seeded into complete DMEM with GFP expressing DH5α E. coli at a 21.1:1 target to effector ratio. Tubes were incubated with Golgiplug (BD) at 37°C and 5% CO₂. Samples were then spun down at 200 x g and washed twice with PBS before blocking with FACS buffer (2% serum) and incubated with rat anti-mouse αCD45-PE IgG (Biolegend) for 30 minutes, washed twice and resuspended in Cytofix buffer and held at room temperature for 20 minutes. Following PBS wash, cells were resuspended in permeablization buffer (0.01% Triton X-100) and held at room temp for 10 minutes before incubation with rat anti mouse αTNFα IgG-APC (Biolegend) for 30 minutes at 4°C. Samples were washed with permeablization buffer and spun at 326 x g twice before being stored overnight at 4°C in PBS then being run on a BD FACS Canto II. Small debris and potential clumps were gated out using gating techniques described in MMI 590 Lab 1.

Results and Discussion

Antibody staining allows for high resolution of gating on desired cell populations.
Although forward and side scatter can sometimes be used to separate out morphologically distinct cells, side scatter profiles often overlap and prevent analysis of a population of interest. In this experiment, RAW macrophage and Jurkat T-cells are seen to overlap quite heavily in SSC-FSC plots (Figure 1a). The use of fluorescently tagged αCD-45 antibodies allows a much higher level of resolution when separating these two populations. Although CD-45 is a pan- leukocyte marker, this particular antibody is against murine CD-45 and does not cross-react with human CD-45. Thus the antibody stains RAW cells, which are of murine origin, but not human Jurkat cells and allows for downstream analysis of each population respectively (Figure 1b).

Figure 1. Analysis of distinct cell types in a mixed population using surface markers. Co-incubated RAW murine macrophage and Jurkat human T-cells were acquired on a BD FACS Canto II. a) Forward and side scatter profile of the mixed population P1 gate (RAW cells orange, Jurkat cells Green based on P4/P5 gates) b) Total single cell events were gated based on fluorescence of anti-mouse CD45 antibody conjugated to a PE fluorochrome.

Raw macrophage exposed to E. coli produce TNFα regardless of bacterial internalization.
Macrophage are professional phagocytes, and tend to be some of the first responders in infection which recruit other cells to the site and activate them via chemokine/cytokine production. Thus, as expected, our RAW macrophages responded quickly when exposed to E. coli, being roughly 15% and 22% phagocytic by 30 and 60 minutes respectively (Figure 2, GFP+). This rate seems low in relation to total TNFα producing, APC+ RAW cells (85% and 95% at 30 and 60 minutes respectively), but may be due to paracrine action of cytokine production by phagocytic cells. Though this scenario seems unlikely since the addition of golgiplug should prevent the secretion of any produced cytokines. Most likely, these non phagocytic TNF producing cells are being activated through bacterial PAMPs present in the culture media binding through TLRs. A non-stimulated control could also be used to see if TNF production is constitutive in this cell line.

 Since Jurkat cells are lymphocytes, which are not known to be phagocytic except for in specific species, we expect them to not internalize any bacteria.  As expected, the vast majority of the Jurkat cells were negative for bacterial internalization. The very small proportions of GFP+ Jurkat cells (1.7% and 2.5% at 30 and 60 minutes respectively) could be due to a bacterial cell stuck to the outside of a Jurkat cell, which would likely not change the area enough to be removed as a clump using a height to surface area gate. This is where traditional flow cytometry falters, as it cannot distinguish associated fluorescence from true internalization. The use of imaging flow cytometry could eliminate these events and greatly increase confidence in the data.

 CD4+ T helper cells have been known to produce TNFα in macrophage co-cultures. Since Jurkat cell lines are CD4+ derived, we expected some level of TNFα production, but virtually no TNF staining was seen in these cell types. This could be explained by murine macrophages not being able to stimulate human T cells. Alternatively, there may be TNF production, but since we used an anti-mouse TNF antibody, there may simply be no cross-reactivity and thus the cells appear to be TNF(-). A repeated experiment using anti-human antibodies may be useful in deciphering these results.

Figure 2. Macrophage exposed to bacteria produce TNFα regardless of bacterial internalization. Mixed RAW macrophage and Jurkat T cells were incubated for 30 or 60 minutes with 22.1:1 GFP E. coli. Populations gated based on anti-mouse CD-45 staining. Samples were also stained with anti-mouse anti-TNFα antibody conjugated to APC. Population proportions are noted in the corner of each respective gate.