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Method Article
* Wspomniani autorzy wnieśli do projektu równy wkład.
This work presents a microscopy method that allows live imaging of a single cell of Escherichia coli for analysis and quantification of the stochastic behavior of synthetic gene circuits.
The protocol developed here offers a tool to enable computer tracking of Escherichia coli division and fluorescent levels over several hours. The process starts by screening for colonies that survive on minimal media, assuming that only Escherichia coli harboring the correct plasmid will be able to thrive in the specific conditions. Since the process of building large genetic circuits, requiring the assembly of many DNA parts, is challenging, circuit components are often distributed between multiple plasmids at different copy numbers requiring the use of several antibiotics. Mutations in the plasmid can destroy transcription of the antibiotic resistance genes and interject with resources management in the cell leading to necrosis. The selected colony is set on a glass-bottom Petri dish and a few focus planes are selected for microscopy tracking in both bright field and fluorescent domains. The protocol maintains the image focus for more than 12 hours under initial conditions that cannot be regulated, creating a few difficulties. For example, dead cells start to accumulate in the lenses' field of focus after a few hours of imaging, which causes toxins to buildup and the signal to blur and decay. Depletion of nutrients introduces new metabolic processes and hinder the desired response of the circuit. The experiment's temperature lowers the effectivity of inducers and antibiotics, which can further damage the reliability of the signal. The minimal media gel shrinks and dries, and as a result the optical focus changes over time. We developed this method to overcome these challenges in Escherichia coli, similar to previous works developing analogous methods for other micro-organisms. In addition, this method offers an algorithm to quantify the total stochastic noise in unaltered and altered cells, finding that the results are consistent with flow analyzer predictions as shown by a similar coefficient of variation (CV).
Synthetic biology is a multidisciplinary field that has emerged in the past decade and aims to translate engineering design principles into rational biological design1,2,3, in an effort to achieve multi-signal integration and processing in living cells for understanding the basic science4,5, diagnostic, therapeutic and biotechnological applications6,7,8,9,10. Our ability to quantify the input-output response of synthetic gene circuits has been revolutionized by recent advances in single-cell technology, including the flow analyzer and live cell imaging using automated time-lapse microscopy11. A flow analyzer is often used to measure the response of these circuits at the steady state1,12, and inverted microscopy is used to measure the dynamic response of synthetic gene circuits at the level of a single cell3. For example, one of the early works in synthetic biology involved the construction of genetic oscillator networks in living cells using negative feedback loops with a delay3. Later on, the genetic oscillator circuits were applied to understand metabolic control in the dynamic environment of living cells4. Automated time-lapse microscopy is one method to characterize such circuits. We hypothesize that the host cells, Escherichia coli, synchronize when forming micro colonies, allowing measurement of signal and calculation of noise without tracking exact mother-daughter relations.
Noise is a fundamental, inherent aspect of biological systems often arising from multiple sources. Consider for example, biochemical reactions involving signals that originate from the transport of discrete random carriers such as diffusion of proteins13. These signals propagate with random fluctuations14. Other noise sources are resource availability, cell division and variations in environmental conditions such as temperature, humidity and pressure. Biological signals that propagate in synthetic gene circuits often have a very low signal to noise ratio (SNR), which disturbs the performance of such circuits. Therefore, genetic circuit design remains one of the most challenging aspects of genetic engineering15. For example, in contrast to most approaches which calculate only the mean gene expression (measured over the entire cell population), the variance of the measured signal is considered in order to engineer predictable behavior through synthetic gene networks12. As such, the levels of variability or noise in the protein expression play a dominant role in design and performance of analog and digital gene circuits1,16,17.
Many approaches has been developed to quantify cell-to-cell variability, including in Escherichia coli3,7,18. These methods are often used to study gene activation and metabolic pathways, however with less focus on the study of stochastic noise dynamics, like measuring and disentangling specific noise sources, especially for genetic circuits in living cells where this is a fundamental challange19,20,21. Several factors, both inherited to the circuit itself (intrinsic) and derived from the host cells (extrinsic), can disturb the continuous performance of genetic circuits. In this paper, we developed a protocol that aims to quantify the total noise in Escherichia coli cells, including the intrinsic and extrinsic noise sources6,22. By quantifying the total noise and then evaluating the SNR23, the design of gene circuits can be improved. This method can be modified to measure independent noise sources separately, by monitoring several fluorescent proteins6,20. For the protocol described here, we keep the environmental conditions well controlled and continuously measure the activity of cells without the influence of external factors. We measure the signal from fluorescent proteins in single cells over time and simultaneously image them under an agarose substrate. The resulting images are analyzed using the laboratory's custom MATLAB.
Ideally, continuous measurement of the real time activity of fluorescent proteins inside a cell will produce accurate data through the growth and division of the cells. However, it is challenging to acquire such data. This is due to degradation of fluorescent proteins, known as photo-bleaching7, when they are exposed to radiation in the excitation process. Furthermore, Escherichia coli cells are also sensitive for the excitation, which might lead to phototoxicity7. Both issues limit the amount of photo frames that can be acquired and the time between acquisitions. The substrate and medium types (e.g., lysogeny broth) that is used to grow the cells during imaging also have a critical role. We strongly recommend using minimal medium, which minimizes non-fluorescent background and extends cell division time.
Moreover, the sample needs to be prepared considering the following requirements (1) Low cell division rate allows for less frequent exposures for closely imaging the division cycle and reducing the probability of phototoxicity and photobleaching. We set the acquisition time to about half of the predicted mitosis time (2) Low cell density at the beginning of the experiment allows for better uniformity and trackability of division. Cell density is affected by the dilution ratio of the Escherichia coli cells, which is a significant parameter for the success of this protocol and needs to be determined for every lab. In order to establish the ratio, each new Escherichia coli strain or media used should be fitted with growth rate graphs (Supplementary Figure 1). An appropriate ratio has been achieved if cells can grow without additional shaking after a short incubation from an initial density of about OD600nm = 0.1. Cells at this phase will divide according to the environment temperature only (3) Restriction of cell movement: cell movement strongly depends on substrate (agarose pad) firmness. The substrate firmness depends on the amount of total agarose and the gel solidification time. Gels cannot be left to solidify overnight at room temperature, as the Escherichia coli will undergo mitosis. Other factors that affect substrate stability include the amount of water in the sample and humidity. Additional issues are discussed in detail in the Representative Results. This protocol provides many details and gradually moves from one step to another. The protocol offers long stability for imaging experiments and provides a basic image processing tool.
1. Media and culture preparation
2. Bacterial strains and plasmids construction
NOTE:The genetic circuit contains one part; a Green Fluorescent Protein (GFP) driven by a PtetO promoter resulting in constitutive expression. All the plasmids in this work were constructed using basic molecular cloning techniques and were transformed into Escherichia coli 10β, using a standard heat shock protocol24. The final construct was transformed into Escherichia coli MG1655 wild type strain for testing.
3. Preparation of agarose pads
4. Preparing the sample for microscopy imaging
5. Data analysis
NOTE: In order to process microscopy data, we designed a computer-based software in MATLAB. This software facilitates identification of cell boundaries from bright field tiff images and segments and sorts cells by area. The output of this image analysis can be used as a mask on fluorescent tiff images to derive cell intensity levels and cancel artifacts in the fluorescent domain such as cell halo due to microscope resolution limits. The software developed was inspired from similar works7,25,26,27,28,29,30 and provides an elegant solution tailored for the lab.
The software analyzes bright field domain images that are off-white and black. The Escherichia coli will look like black oblong shapes on an off-white background and dynamic range of luminance should show a spike at its center (Figure 1). In fluorescent images cells may have a small halo but individual cells with oblong shapes can still be resolved. A mitosis event should be first detected after 30 minutes. Microscope focus should remain stable over time and although cells might mov...
In this work, we developed a protocol that enables computer tracing of Escherichia coli live cells, following division and fluorescent levels over a period of hours. This protocol allows us to quantify the stochastic dynamics of genetic circuits in Escherichia coli by measuring the CV and SNR in real time. In this protocol, we compared the stochastic behaviors of two different circuits as shown in Figure 10. It has been shown that plasmids with low copy numbers are more prone to stochas...
The authors have nothing to disclose.
We thank Mr. Gil Gelbert (Faculty of electric Engineering, Technion) for assisting with the MATLAB code. We thank Dr. Ximing Li (Faculty of bio-medical Engineering, Technion) for assisting with proofing this article. This research was partially supported by the Neubauer Family Foundation and Israel Ministry of Science, grant 2027345.
Name | Company | Catalog Number | Comments |
35mm glass dish | mattek | P35G-0.170-14-C | thickness corresponding with microscope lense. |
Agarose | Lonza | 5004 | LB preperation |
AHL | Sigma-Aldrich | K3007 | inducer |
Bacto tryptone | BD - Becton, Dickinson and Company | 211705 | LB preperation |
Carb | Invitrogen | 10177-012 | antibiotic |
Carb | Formedium | CAR0025 | antibiotic |
Casamino acids | BD - Becton, Dickinson and Company | 223050 | minimal media solution |
eclipse Ti | nikon | inverted microscope | |
Glucose | Sigma-Aldrich | G5767 | minimal media solution |
Glyserol | Bio-Lab | 000712050100 | minimal media substrate |
Immersol 518F | zeiss | 4449600000000 | immersion oil |
M9 salt solution | Sigma-Aldrich | M6030 | minimal media solution |
NaCl | Bio-Lab | 214010 | LB preperation |
Noble agar | Sigma-Aldrich | A5431 | minimal media substrate |
parafilm tape | Bemis | PM-996 | refered to as tape in text |
Seaplaque GTG Agarose | Lonza | 50111 | minimal media substrate |
thaymine B1 | Sigma-Aldrich | T0376 | minimal media solution |
Yeast Extract | BD - Becton, Dickinson and Company | 212750 | LB preperation |
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