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Reproducibility of high-throughput platereader experiments in synthetic biology Michael Chavez, Jonathan Ho, and Cheemeng Tan ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.6b00198 • Publication Date (Web): 31 Oct 2016 Downloaded from http://pubs.acs.org on November 1, 2016

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ACS Synthetic Biology

Reproducibility of high-throughput plate-reader experiments in synthetic biology Michael Chavez1, Jonathan Ho2, and Cheemeng Tan1* 1Department

2College

of Biomedical Engineering, University of California Davis, Davis USA.

of Biological Sciences, University of California Davis, Davis USA.

*Correspondence: Cheemeng Tan, Department of Biomedical Engineering, University of California Davis, 3009 Ghausi Hall, One Shields Avenue, Davis, CA 95616, USA email: [email protected]

Abstract Plate-reader assays are commonly conducted to quantify the performance of synthetic biological systems. However, based on a survey of 100 publications, we find that most publications do not report critical experimental settings of plate-reader assays, suggesting wide spread issues in their reproducibility. Specifically, critical platereader settings, including shaking time and covering method, either vary between labs or are not reported by the publications. Here, we demonstrate that the settings of platereader assays have significant impact on bacterial growth, recombinant gene expression, and biofilm formation. Furthermore, we show that the plate-reader settings affect the apparent activity, sensitivity, and chemical kinetics of synthetic constructs, as well as alter the apparent effectiveness of antibiotics. Our results suggest the critical need for consistent reporting of plate-reader protocols, in order to ensure the

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reproducibility of the protocols. In addition, our work provides data for the setup of plate-reader protocols in synthetic biology experiments. Keywords Reproducibility, Plate reader, Synthetic Biology standards, Gene circuits


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Environmental factors have long been known to affect bacterial physiology and growth1, 2. Factors such as temperature, oxygen availability, nutrient supply, and physical perturbation all substantially impact the growth dynamics of a bacterial culture3. These factors are well-controlled in experiments using flasks and culture tubes through standardized experimental design4. In recent years, however, microwell plates have emerged as a common approach to culture bacteria. Microwell plate-readers can incubate many low volume cultures and allow real-time tracking of bacterial absorbance, luminescence, and fluorescence. Consequently, microwell plate-readers allow for more quantitative and high-throughput experiments than traditional culture methods and have become essential for studies of synthetic biological systems5, antibiotics6, biofilm7, and the microbiome8. However, microwell plate-readers control the growth environment of bacteria through two fundamental parameters that have not been standardized: covering methods that inhibit evaporation from the wells and shaking durations that distribute nutrients, remove wastes, and exchange oxygen. Each of the parameters change the microenvironment of bacteria and affect bacterial growth dynamics. In a review of one hundred papers in the fields of synthetic biology and antibiotic studies, we find that 68% of papers did not report the time spent shaking the culture per measurement interval (Figure 1A). Similarly, 76% of papers did not report the covering method used (Figure 1B). Furthermore, those who did report the shaking time or covering method often used different parameters than others in their field (Table S1). This lack of both consensus and accurate reporting can potentially reduce the reproducibility of experiments, which is becoming an important issue in scientific



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research9. Here, we reveal the impact of the experimental setup on the quantitative outcomes of experiments in synthetic biology and antibiotic treatment. Through the literature review, we identified two common shaking times (shaking in intermittent bursts and almost continuous shaking), as well as three common covering methods (lids that rest on top of the entire plate, stickers that seal the wells, and oils that sit on top of the culture). To start, we investigated the efficiency of each cover method in reducing evaporation of media by calculating evaporation rates per well created in a Tecan m1000-pro plate reader (Figure 1C). We used a plastic plate topper to model lid covers, a qPCR sticker to model sticker covers, and 50 µL of mineral oil added directly to each well to model oil covers. Most notably, lid cover gives rise to high evaporation rates in the periphery-wells of the plate, with the highest in the four cornerwells (1.55 ± .08 µL hr-1) and less but still significant in the side-wells (1.07 ± .03 µL hr1).

Evaporation rates at the center wells under lid cover (0.43 ± .01 µL hr-1) are

comparable to the evaporation rates seen using a sticker cover (.40 ± .01 µL hr-1) and an oil cover (no statistically significant evaporation). Furthermore, we measured the cover’s impact on the optical measurements of the plate reader to normalize all subsequent measurements (Figure S1, Table S2). Next, we elucidated how the experimental setup affects the growth of a model Escherichia coli strain (Bl21Pro pCS-pLux) over time (Figure S2). Using absorbance at 600nm (OD600) as a measure of cell density, we first quantified the maximum density and growth rates (∆OD600 · ∆min-1) achieved by bacteria under all combinations of covers, shake times, and evaporation rates (Figure 1D & 1E). Under intermittent shaking (5 sec of shaking every 10 min read interval), cover method does not affect the


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maximum density and growth rates. In contrast, continuous shaking (9 min of shaking every 10 min interval) generally increases the maximum density and growth rates, and leads to substantially higher bacterial growth with the lid cover. Evaporation rates show no significant effect on bacterial growth (Figure S3&S4). Finally, the oil cover and high evaporation rate give rise to a larger coefficient of variance between experiments than other covers or evaporation rates (Figure S5). In addition, we investigated how the experimental setup modulates recombinant gene expression. Using a strain carrying the Lux operon from Aliivibrio fischeri (E. coli BL21Pro pCS-pLux), we tracked the luminescence of bacterial culture with respect to relative OD600 (OD600 normalized by maximum OD600, Figure S6). Increasing the amount of shaking dramatically increases the luminescent intensity of the bacteria (Figure 1F). Furthermore, oil covering substantially inhibits expression of the operon. We also find that different evaporation rates do not substantially change the maximum level of luminescence (Figure S7). Lastly, we measure the correlation between luminescence and OD600 because luminescence is commonly used as a proxy of live bacterial densities (Figure 1G, S8&S9). Lid and sticker allow for a high level of linearity between luminescent intensities and bacterial densities, however oil under intermittent shaking dramatically decreases this linear relationship. Plate-reader setup also affects biofilm formation, which in turn affects gene expression and antibiotic tolerance. To this end, we measured the amount of biofilm created under each parameter by counting live cells (colony forming units, CFU) in biofilm after overnight incubation (Figure S10, See Online Methods). Comparing



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between cover methods, lid cover gives rise to the highest number of live cells in biofilm, likely because it causes the highest total bacterial densities. To understand how these changes in growth dynamic affect the quantitative outcome of experiments, we investigate how shaking duration and covering method affect experiments in synthetic biology and antibiotics. One of the original goals of synthetic biology is to create a registry of standardized parts with quantitative data to design and simulate synthetic circuits10-12. However, standardization remains a central challenge as synthetic constructs are inherently context dependent and therefore perform differently between labs13. Here, we quantify the dose response of three different synthetic constructs under various experimental setups using the MichaelisMenten equation (Figure S11-S13). If the part is standardized, the synthetic constructs would exhibit the same kinetics regardless of experimental setup. However, our results show that covering method and shaking protocol dramatically change the activity (Vmax), sensitivity (Km), and chemical kinetics (n) of synthetic constructs (Figure 2A). Our results suggest that the lack of reporting on plate-reader assays can reduce reproducibility of synthetic constructs. Furthermore, as in silico design of synthetic constructs become increasingly prevalent, this information on plate-reader protocol is vital as user-defined constraints in order to accurately model the system14. In addition, synthetic biology approaches have been applied in studies of antibiotics. To this end, we investigated how shake times and cover methods affect antibiotic killing (Figure S14). Here, we quantify the final cell density (OD600) of a model E. coli strain (BL21Pro pCS-pLux) grown under various doses of carbenicillin and experimental setups. We then fit a dose response curve to find the concentration at


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which half the cells are killed by the antibiotic (K, Eq. S2, See Online Methods, Figure 2B). Here, K is more sensitive to shake time with the oil cover method than other covers. This result demonstrates that standardization of plate-reader protocols is needed because some experimental setups are more robust to variation than others. A few overarching patterns emerge from this work. Lid covers and sticker covers give rise to the highest growth rates of bacteria and expression rates of recombinant proteins. While the lid-covering method results in high evaporation rates in the periphery wells, the evaporation of media shows little to no effect on bacterial growth dynamics. Despite its common applications in synthetic biology15, the oil-covering method gives rise to the lowest growth rates of bacteria and recombinant protein expression, as well as increases the noise between experiments. The direct generalization of our results to other plate-readers will require careful investigation due to different internal hardware of plate-readers. Despite the limitation, our results reinforce the premise of the work that without consistent, accurate reporting and standardization of plate-reader protocols, reproducibility will likely remain low in high-throughput experiments using plate-readers. Our work also provides data for the optimization and setup of plate-reader protocols for synthetic biology and antibiotic experiments. In contrast to the current focus to standardize synthetic biological parts and the units to measure them16, 17, our results suggest that transparent reporting of plate-reader protocols is paramount to their reproducibility.



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Supporting Information: Raw curves, evaporation data, additional metrics, Michaelis-Menten fits, and antibiotic fits in Figures S1-S14. Literature search results in Table S1. Normalization values in Table S2.

Acknowledgements The work is supported by the Branco-Weiss Fellowship (CT) and the Human Frontier Science Program (CT).


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Methods Literature Search: One hundred papers (51 in antibiotics and 49 in synthetic biology) were reviewed to enumerate the reporting of experimental setup. All papers used a highthroughput plate-reader to incubate bacterial cultures. Each paper was read to see if shake time and cover method were reported. Shake time was calculated by dividing the reported shaking time per read interval by the time of the read interval. General terms such as “grown with shaking” were treated as not reported; only quantified times were taken into account. Cover methods were binned into five categories; unreported, oil (if oil was added to the well), sticker (if wells were sealed with some membrane), lid (if a cover was rested on top of the entire plate) or no lid (if they stated they did not add a lid). Shake time was binned into three groups; unreported (or unquantified), less than half (if shaking time was less than half the incubation time), and greater than half (if shaking time was greater than half the incubation time).

Evaporation Profiles: All 96 wells of a microplate (Black, Corning 96 well plate with clear, flat bottoms) were filled with 200 µL of LB media (Affymetrix Luria Broth, Ultrapure) and then covered with either a plastic lid topper, a qPCR sticker (Biotix, Plate Sealing Films), or 50 µL of mineral oil (Sigma). The plate was then incubated in a plate reader (Tecan m1000pro) for 16 hours at 37 oC with 20 seconds of shaking every 10 minutes. The Tecan m100pro plate reader utilizes a block heater and fan to control temperature, which may affect the evaporation profile in a different way than other plate readers. For plates incubated with sticker and lid covers, the contents of each well were then weighed and evaporation rates were measured by weight difference compared to



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the weight of 200 uL of LB. For oil, the contents of select wells were weighed and checked if evaporation was statistically significant by performing a student’s t-test with this data point against a population of unevaporated wells filled with oil.

Plate-reader Parameters: For all experiments, a set of parameters were used to model the wide variety of high-throughput culture methods used in the field. For covers, we used a plastic lid topper, a qPCR sticker, or 50 µL of mineral oil to model lid covers, sticker covers, and oil covers respectively. For shake time, we used 5 seconds of shaking at the beginning of every ten-minute measurement interval as a model for intermittent shaking and 9 minutes of shaking at the beginning of every ten-minute measurement interval as a model for continuous shaking. For evaporation, we used three categories; high evaporation rates by growing bacteria in the four corners of the plate under a lid cover, medium evaporation rates using the top edge (wells A2-A11) of the plate with a lid cover, and low evaporation rates using the center of the plate with lid cover, or using either the sticker or oil covers.

Modulation of Optical Measurements by Cover Method: E. coli DH5αpro p15αTetCFP, and E. coli BL21-DE3 pLysS pET15b-mCherry were grown in tube cultures for an hour and a half and then induced with 80 ng/µL of aTc and 1 µM of IPTG respectively. These two constructs and E. coli BL21Pro pCS-pLux were then grown overnight separately in tubes at 37 oC and continuous shaking (200 rpm). The overnight cultures were then separately added to a 96 well plate and serial diluted (1 in 2 for 12 dilutions) with fresh LB media, leaving 200 µL in each well. The plate was then measured for OD600, 10 ACS Paragon Plus Environment

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luminescence, CFP fluorescence (ex. 454, em. 477), and mCherry fluorescence (ex. 587, em. 610) under all three cover methods and no cover method. A line was then fitted to the measurements with non-covered wells vs measurements with covered wells and the linear fit was used to normalize all subsequent data.

Growth over Time Experiments: Overnight cultures of E. coli BL21Pro pCS-pLux were grown in tubes with LB media supplemented with 30 µg/mL of kanamycin at 37 oC and continuous shaking. Overnight cultures were then diluted 1 in 250 in fresh kanamycin supplemented LB media and 200 µL of this culture was added to the wells of a 96 well plate. One of the three cover methods and one of the two shake times were chosen. If lid cover was chosen, the corners, edges, and center of the plate were treated as different experimental groups. The plate was incubated at 37 oC and read for OD600 every 10 min for six hours in a plate reader. Shaking of the plate happened at the beginning of each interval (for example, for intermittent shaking, there was 5 seconds of shaking, then 9 min 55 seconds of wait, then read).

Maximum OD600 Calculation: To calculate maximum OD600, we separated each growth curve into their respective experimental setups (oil cover and intermittent shake, oil cover and continuous shake, lid cover and intermittent shake, etc.). For each curve, we measured the maximum OD600 using MATLAB’s max() function. The mean and standard error of the mean (SEM) for maximum OD600 was calculated between all six replicates of the same experimental group. Indicated differences were statistically



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analyzed using an unpaired Student’s t-test (MATLAB’s ttest2() function) at a 5% significance level.

Maximum Growth Rate Calculation: To calculate maximum growth rates, we separated each growth curve into their respective experimental setups. For each curve, we transformed the data by using MATLAB’s diff() function to compute ∆OD600 · ∆min-1. The maximum of the transformed data was then calculated for this curve, and the mean and SEM were calculated using all six replicates of the same experimental group. Indicated differences were statistically analyzed using an unpaired Student’s t-test (MATLAB’s ttest2() function) at a 5% significance level.

Coefficient of Variance Calculation: To calculate the differences in noise between experimental groups, we first separated each growth curve into their respective experimental groups. For each curve, we took the Final OD600 and calculated the coefficient of variance, or the standard deviation normalized by the mean, between replicates (6 in total) of an experimental group.

Luminescence vs OD600 Experiments: Overnight cultures of E. coli BL21Pro pCS-pLux were grown in tubes with LB media supplement with 30 µg/mL of kanamycin at 37 oC and continuous shaking. Overnight cultures were then diluted 1 in 250 with fresh kanamycin supplemented LB media and 200 µL of this was added to the wells of a 96 well plate. One of the three cover methods and one of the two shake times were chosen. 12 ACS Paragon Plus Environment

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If lid cover was chosen, the corners, edges, and center of the plate were treated as different experimental groups. The plate was incubated at 37 oC and read for OD600 and luminescence every 10 min for six hours in a plate reader. Shaking of the plate happened at the beginning of each interval (for example, for intermittent shaking there was 5 seconds of shaking, then 9 min 55 seconds of wait, then read).

Maximum Luminescence per OD600 Calculation: To calculate maximum luminescence intensities, we separated each luminescence curve into their respective experimental setups. The data was then transformed by dividing each luminescence data point by its respective OD600. MATLAB’s max() function was then used to calculate the maximum luminescence per OD600. The mean and SEM of the maximum luminescence per OD600 was calculated using all six replicates of the same experimental setup. Indicated differences were statistically analyzed using an unpaired Student’s t-test (MATLAB’s ttest2() function) at a 5% significance level.

Drop-off OD600 Calculation: The drop-off OD600 here is defined as the point at which luminescence and OD600 no longer correlate. To begin, raw OD600 curves were transformed into relative OD600 by normalizing all OD600 values by the maximum OD600 achieved. Luminescence vs relative OD600 was then graphed and the drop-off OD600 was the local maximum of the resultant curve. For each individual curve, the drop-off OD600 was calculated using MATLAB’s findpeaks() function. The mean and SEM of the drop-off OD600 was calculated using all six replicates of the same



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parameter set. Indicated differences were statistically analyzed using an unpaired Student’s t-test (MATLAB’s ttest2() function) at a 5% significance level.

Linearity before Drop-off Calculation: Here, we use R2 as a measurement of linearity. For each luminescence vs relative OD600 curve, we take all values in the domain of 0 to its calculated drop-off OD600 and group the curves by experimental setup. We then calculate the R2 of all six replicates of an experimental setup group together.

Preparation of Biofilm: E. coli BL21DE3 pTacCFP was grown overnight in 3 mL of LB broth in a 17x100 mm culture tube. The tube was incubated at 37℃ and shaken at 200 rpm. The cap of the tube was left loose. Overnight culture was diluted at a ratio 1 in 250 and 200 µL of the diluted culture was aliquoted into the wells of a 96-well plate. For the microplates designated for covering by lid, the representative wells from the corner, edge, and center wells were aliquoted with diluted culture. All other wells were aliquoted with 200 µL of LB broth. The plate was then covered with either a lid, qPCR sticker, or 50 µL mineral oil. Finally, the microplate was incubated in the plate reader at 37℃ with one of the shaking protocols for 16 hours.

Biofilm Quantification Assay: 24-well plates were aliquoted with 1 mL of autoclaved LB agar (AMRESCO) in each well and allowed to cool. The microplate that contains biofilm was removed from the incubator. The media from each well with biofilm was carefully removed. These wells were subsequently washed with 1X PBS two times. The plate was 14 ACS Paragon Plus Environment

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air dried for 15 minutes. The biofilm was resuspended with 200 µL of LB broth. In a clear round bottom 96 well plate, each biofilm resuspension was diluted 10 fold six times. Then 10 µL of each of the last four dilutions was inoculated to its own well in the prepared 24-well agar plates. The 24-well agar plates were manually rocked to spread the bacteria. The agar plates were then covered with a lid and shaken at 80 rpm at room temperature for 5 minutes. Finally, they were incubated without shaking at 37℃; colonies were counted after 14 hours. The mean and SEM of the CFU counts were quantified between replicates. Lid cover and intermittent shaking was used to compare different evaporation rate. Continuous shaking and low evaporation rates was used to compare different cover methods. Lid cover and low evaporation rate was used to compare different shake times. Indicated differences were statistically analyzed using an unpaired Student’s t-test (MATLAB’s ttest2() function) at a 5% significance level.

Experiments using Synthetic Promoters: To quantify the dynamics of synthetic constructs, we grew three different synthetic constructs under all combinations of shake times and cover methods. The first construct DH5αPro pTetCFP, expresses cyan fluorescent protein (CFP) in the presence of anhydrous tetracycline (aTc). The second construct, BL21DE3 pT7/LacO1- mCherry, expresses the mCherry fluorescent protein in the presence of isopropyl β-D-1-thiogalactopyranoside (IPTG). The third construct BL21AI pBAD-mCherry expresses mCherry fluorescent protein in the presence of arabinose. The three constructs were grown overnight in separate tubes using 3 mL of LB media supplemented with either 30 µg/mL of kanamycin or 50 µg/mL carbenicillin at 37 oC and continuous shaking. The cultures were then diluted 1 in 250 with fresh LB



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media and 200 µL was added to the wells of a 96 well plate. The plate was then incubated for an hour and a half at 37 oC and continuous shaking (200 rpm) under a lid cover in a shaker so that the cells reached the exponential phase. A range of inducer concentrations for each strain was then added to separate wells. The three constructs were then grown at 37 oC until stationary phase (six hours) in a plate reader under one of the three cover methods and one of the two shaking times. The final fluorescence intensities were normalized by the final OD600 as a proxy for the final fluorescent intensity per cell. The Michaelis – Menten equation in the form of: ி௎

= ை஽

௏೘ೌೣ ሾூሿ೙ ೙ ାሾூሿ೙ ௄೘

… [Eq. s1]

was then fitted to the dose response curves using the measured FU · OD600-1 as the dependent variable and [I], or the inducer concentration, as the independent variable. The fit yielded the maximum expression level Vmax, half-maximal inducer concentration Km, and Hill coefficient n. The mean and SEM for each of these fitted parameters of a single experimental setup was taken using the four replicates. Experiments using Antibiotics: Overnight cultures of E. coli BL21Pro pCS-pLux were grown in tubes with LB media supplemented with 30 µg/mL of kanamycin at 37 oC and continuous shaking. Overnight cultures were then diluted 1 in 250 in fresh kanamycin supplemented LB and 200 µL was added to the wells of a 96 well plate. Specific concentrations of carbenicillin were added in each well and one of the two shake times and either sticker or oil cover was chosen (sticker and lid gave similar dynamics in all other experiments, and only one of them was tested here). The plate was then incubated in a plate reader at 37 oC for six hours, and OD600 was measured every 10 min. The final five OD600 measurements were taken for each dose of carbenicillin and all six 16 ACS Paragon Plus Environment

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replicates of an experimental setup were grouped together. The mean of these points were evaluated and the dose response was fitted to the equation: ܱ‫ܦ‬600 =

௔ ௄ାሾ஼ሿ

+ܾ

… [Eq. s2]

where OD600 is the measured dependent variable and [C], or the carbenicillin concentration, is the independent variable. Here, since b is similar for all experimental setups (the lower limit of detection for the plate reader), we use K to indicate the concentration at which half the cells are killed by antibiotics.



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Figure Legends: Figure 1: Reproducibility of bacterial phenotypes across different plate-reader settings. (A) Based on 100 publications in the fields of synthetic biology and antibiotic treatment, 68% of papers did not report or did not quantify shaking time (gray), while 23% used intermittent shaking time (shaking for less than half of incubation time, orange) and 9% used continuous shaking (shaking for greater than half of incubation time, blue). See Table S1 for the list of papers and the reported plate-reader settings. (B) 76% of publications did not report a covering method (gray), while 8% used an oil cover (blue), 5% used a sticker cover (orange), 10% used a lid cover (yellow), and 1% did not use a cover method (green). See Table S1 for the list of papers and the reported plate-reader settings. (C) Evaporation rate profile of 96 well plate with lid cover (top), oil cover (middle), and sticker cover (bottom). Lid cover gives rise to high evaporation rates in periphery wells, while sticker and oil give rise to low evaporation rates comparable to the middle wells under a lid cover. (D) The maximum OD600 achieved using each cover method under intermittent shaking (gray) and continuous shaking (white). Maximum cell density increases substantially from intermittent shaking to continuous shaking, while lid cover gives rise to the highest density under continuous shaking. Error bars represent the SEM of 6 replicates. Differences in groups a and b are not statistically significant (p > .05). All other results are statistically significant.


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(E) The maximum growth rates achieved under each cover method for intermittent shaking (gray) and continuous shaking (white). Growth rate increases as shaking time increases. Under continuous shaking, lid cover allows for a higher growth rate than both sticker and oil cover. Error bars represent the SEM of 6 replicates. Differences in groups c and d are not statistically significant (p > .05). All other results are statistically significant. (F) The maximum luminescence achieved under each cover method for intermittent shaking (gray) and continuous shaking (white). Oil cover significantly decreases the expression of the luminescence operon in comparison to lid and sticker covers. Error bars represent the SEM of 6 replicates. Differences in group e are not statistically significant (p > .05). All other results are statistically significant. (G) The linearity of luminescent intensities with respect to relative OD600 under each cover method for intermittent shaking (gray) and continuous shaking (white). Both luminescence intensities and OD6oo are commonly used to measure bacterial densities. Oil cover results in a low correlation between these two metrics, while lid cover at intermittent shaking is the only setup that gives an R2 greater than .95.

Figure 2: Reproducibility of quantitative experiments in synthetic biology and antibiotic experiments across different plate-reader settings. (A) Experimental setup affects quantitative performance of three synthetic constructs: the PBAD promoter (left) activated by arabinose, the PT7/LacO1 (middle) activated by Isopropyl β-D-1-thiogalactopyranoside (IPTG), and PTet (right) activated by anhydrous



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tetracycline (aTc). S, O, and L represent sticker, oil, and lid covers respectively. I and C represent intermittent and continuous shaking respectively. If the constructs are not affected by experimental setup, all points would converge to a single point. However, covering method and shaking protocol dramatically change the activity (Vmax), sensitivity (Km), and chemical kinetics (n, indicated by the color of the data point) of a synthetic construct. The PBAD promoter grown under sticker and continuous shaking did not exhibit Michaelis-Menten dynamics and therefore is not shown. (B) Experimental setup alters bacterial response to antibiotics. The bacterial response is quantified as the concentration of carbenicillin required to reduce the final cell density of E. coli by 50%. S and O represent sticker, and oil covers respectively. I and C represent intermittent and continuous shaking respectively. The different cover methods result in differences in the apparent antibacterial efficacy of the antibiotic. Furthermore, antibiotic killing exhibits a high sensitivity to shake duration with an oil cover.


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References

1. Reisner, A., Krogfelt, K. A., Klein, B. M., Zechner, E. L., and Molin, S. (2006) In Vitro Biofilm Formation of Commensal and Pathogenic Escherichia coli Strains: Impact of Environmental and Genetic Factors, J. Bacteriol. 188, 3572-3581. 2. Sniegowski, P. D., Gerrish, P. J., and Lenski, R. E. (1997) Evolution of high mutation rates in experimental populations of E. coli, Nature 387, 703-705. 3. McDaniel, L. E., and Bailey, E. G. (1969) Effect of Shaking Speed and Type of Closure on Shake Flask Cultures, Appl. Microbiol. 17, 286-290. 4. Shiloach, J., and Fass, R. (2005) Growing E. coli to high cell density—A historical perspective on method development, Biotechnol. Adv. 23, 345-357. 5. Elowitz, M. B., and Leibler, S. (2000) A synthetic oscillatory network of transcriptional regulators, Nature 403, 335-338. 6. Chait, R., Craney, A., and Kishony, R. (2007) Antibiotic interactions that select against resistance, Nature 446, 668-671. 7. Jackson, D. W., Suzuki, K., Oakford, L., Simecka, J. W., Hart, M. E., and Romeo, T. (2002) Biofilm Formation and Dispersal under the Influence of the Global Regulator CsrA of Escherichia coli, J. Bacteriol. 184, 290-301. 8. Cullen, T. W., Schofield, W. B., Barry, N. A., Putnam, E. E., Rundell, E. A., Trent, M. S., Degnan, P. H., Booth, C. J., Yu, H., and Goodman, A. L. (2015) Antimicrobial peptide resistance mediates resilience of prominent gut commensals during inflammation, Science 347, 170-175. 9. Collins, F. S., and Tabak, L. A. (2014) NIH plans to enhance reproducibility, Nature 505, 612-613. 10. Arkin, A. (2008) Setting the standard in synthetic biology, Nat. Biotechnol. 11. Serrano, L. (2007) Synthetic biology: promises and challenges, Mol. Syst. Biol. 12. Canton, B., Labno, A., and Endy, D. (2008) Refinement and standardization of synthetic biological parts and devices, Nat. Biotechnol. 13. Purnick, P. E. M., and Weiss, R. (2009) The second wave of synthetic biology: from modules to systems, Nat. Rev. Mol. Cell Biol. 14. Nielsen, A. A. K., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., Ross, D., Densmore, D., and Voigt, C. A. (2016) Genetic circuit design automation, Science (New York, N.Y.) 352. 15. Ronen, M., Rosenberg, R., Shraiman, B. I., and Alon, U. (2002) Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics, Proc Natl Acad Sci U S A 99, 10555-10560. 16. Cheng, A. A., and Lu, T. K. (2012) Synthetic biology: an emerging engineering discipline, Annu. Rev. Biomed. Eng. 14, 155-178. 17. Kelly, J. R., Rubin, A. J., Davis, J. H., Ajo-Franklin, C. M., Cumbers, J., Czar, M. J., de Mora, K., Glieberman, A. L., Monie, D. D., and Endy, D. (2009) Measuring the activity of BioBrick promoters using an in vivo reference standard, J. Biol. Eng. 3, 1-13.


Covering 9%

23%

(C) 2 6

0.6

76%

8% 5% 1% 10%

8000

Continuous

0.4

Intermittent

a,b a

b

0.2

Oil

Sticker

0

e

e e

2000

Oil

Sticker

Lid

Oil

Sticker

Lid

(G)

(E)

1

0.2 0.15 c,d

0.1

c d

Linearity (R2)

Maximum Growth Rate (ΔOD600 Δ min-1)

3

4000

Lid

0.25

1

6000

0

0 4

Page 22 of 24

(F) Luminescent Intensity ∙ OD600-1

Shaking


(D)

68%

Evaporation Rate (μL hr-1)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

(B)

Maximum Cell Density (OD600)

(A)

0.9

0.8

0.7

0.05 ACS Paragon Plus Environment

0.6

0 Oil

Sticker

Lid

Page 23 of 24

(A)

mCherry

(B)

L,C

2000

L,I

5000

1.5

4000

L,C

1.8 1.6

3000

L,I O,C

2

0 0.1

0.2

0.15

1000

1

L,C

5

S,C 1

0

1

0

0.5

1

KM (IPTG, μM)

1.5

2

ACS Paragon Plus Environment

Half Maximum Concentration (µg/mL)

1.5

0.8

7

S,C S,I

L,I

1.2

S,C S,I O,C O,I 0.5

8

3

O,I

KM (Arabinose, % w/v)

0

PTet

4

S,I

1.2 1.1

6 x 10

1.4 2000

500

0.05

4

1.3

O,I S,I

0

PT7/LacO1

1.4

O,C

CFP

1.6

1500 1000

aTc

mCherry

PBAD

2500

VMax (FU OD-1)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42


IPTG

Arabinose

6 5 4 3

O,C 2

2

O,I

1

1 5

10

15

KM (aTc, ng/μL)

20


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

ACS Paragon Plus Environment

Page 24 of 24

Reproducibility of High-Throughput Plate-Reader ... - ACS Publications

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