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.

1.&#160;Media and culture preparation
<ol>
	<li>Prepare stock solution of 1,000x Carbenicillin (50 mg/mL) or relevant antibiotic.
	<ol>
		<li>.Weigh 0.5 g of carbenicillin. Add 10 mL of sterile H2O. Dissolve completely.</li>
		<li>Sterilize carbenicillin stock through a 0.22 &#181;m syringe filter. Aliquot the antibiotic solution and store at -20 &#176;C.</li>
	</ol>
	</li>
	<li>To prepare lysogeny&#160;broth (LB) plates, mix 5 g of tryptone, 5 g of NaCl, 2.5 g of yeast extract and 7.5 g of Bacto agar with 0.5 L of sterile H2O. Autoclave the solution at 121 &#176;C for 20 min.
	<ol>
		<li>Partially submerge the molten gel-mix in a 50 &#176;C water bath.Add 1,000 &#181;L of carbenicillin (50 mg/mL).</li>
		<li>Prepare Petri dishes in a sterile environment. Leave the plates to set before storing them in the fridge.</li>
	</ol>
	</li>
	<li>For M9 minimal media, prepare separate stock solutions of the following: 5x M9 salts (56.4 g/L), 2 M glucose and 2% biotin-free casamino acids. Autoclave the solutions at 121 &#176;C for 20 min.</li>
	<li>To prepare 5 Petri plates, mix 1125 mg of low melting agar and 400 mg of agar with 89.2 mL of minimal media (1x M9). Add 10 mL of 2% casamino acids (2% [vol/vol]) in a 250 mL Erlenmeyer flask. 
	NOTE: Make sure to pour the media on the inner lips of the flask.</li>
	<li>Microwave the solution in short bursts of 3 to 4 seconds. Repeat until the solution is clear. 
	NOTE: Make sure not to reach boiling point.</li>
	<li>Place the 250 mL Erlenmeyer flask in a hot water bath (60 &#176;C) to further mix by diffusion, and leave it to cool until its temperature falls to about 45-50&#176;C.</li>
	<li>Light the flame at the plate-pouring bench.</li>
	<li>Add quickly all the solutions in the following order: 
	800 &#181;L of 50% glycerol (0.4% [vol/vol]) 
	100 &#181;L of thiamine (B1) 
	1100 &#181;L of Glucose (2M) 
	100 &#181;L of Carbenicillin (50 mg/mL).</li>
	<li>Swirl the Erlenmeyer flask to ensure even distribution of all ingredients throughout the agar.</li>
	<li>Open one plate at a time next to the flame and begin pouring.
	<ol>
		<li>Leave the plates on the bench for a few minutes until initial solidification.</li>
		<li>Turn the plates upside down to prevent water condensation from dripping onto the gel.</li>
		<li>Leave the plates to solidify at room temperature for about 2 h.</li>
		<li>Once the plates have solidified and dried, they can be stored at 4 &#176;C for about 3 months.</li>
	</ol>
	</li>
</ol>
2.&#160;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 10&#946;, using a standard heat shock protocol24. The final construct was transformed into Escherichia coli MG1655 wild type strain for testing.
<ol>
	<li>Transform the desired plasmid into Escherichia coli MG1655 cells with the standard heat shock protocol24.</li>
	<li>Grow the transformed cells on an LB agar plate overnight at 37&#176;C. 
	NOTE: It is possible to keep the Petri dishes from step 2.2 up to 3 days for microscopy use.</li>
	<li>Inoculate a single colony into 5 mL of LB broth supplemented with the relevant antibiotics in a glass tube.</li>
	<li>Grow cells at 37 &#176;C with 250 rpm shaking speed in incubator for 2 h until liquid is cloudy.</li>
	<li>Prepare 1 mL of dilution solution as follows: 
	892 &#181;L of minimal media (1x M9) 
	8 &#181;L of 50% glycerol (0.4% [vol/vol]) 
	100 &#181;L of 2% Casamino acids (0.2% [wt/vol]) 
	1 &#181;L of thiamine (B1) 
	11 &#181;L of Glucose (2 M) 
	&#8203;1 &#181;L of relevant antibiotics
	<ol>
		<li>Mix and spin down.</li>
	</ol>
	</li>
	<li>Dilute the cell culture (1:30) from step 2.4 into a 2 mL tube by adding 30 &#181;L of Escherichia coli growth to 1000 &#181;L of dilution solution (step 2.5).</li>
	<li>Incubate the tube for 1 h with shaking (250 rpm) at 37 &#176;C.</li>
	<li>Grow 40 &#181;L - 60 &#181;L on M9 plates prepared in step 1.10. Incubate the plate at 37 &#176;C overnight. 
	NOTE: The Optical Density (OD600nm) for plating should be around 0.1.</li>
	<li>Place up to three 35 mm glass bottom plates on the bench. 
	NOTE: Make sure the plate&#39;s glass has the correct thickness for the microscope lenses used.</li>
	<li>Prepare the culture for seeding on gel plates, repeat steps 2.3 to 2.6 for colonies from plate prepared at step 2.9 microscope measurements.</li>
	<li>Prepare the following solution to make three microscope plates.
	<ol>
		<li>Preheat the water bath to 60 &#176;C.</li>
		<li>Mix 112.5 mg of low melting agar and 40 mg of agar with 8.92 mL of minimal media (1x M9) and add 1 mL of 2% casamino acids (0.2% [vol/vol]) in a 25 mL Erlenmeyer flask. 
		NOTE: Make sure to pour the media on the inner lips of the flask.</li>
	</ol>
	</li>
	<li>Microwave the solution in short bursts of 2 to 3 seconds. Repeat until the solution is clear. 
	NOTE:Make sure not to reach boiling point.</li>
	<li>Place the 25 mL Erlenmeyer flask in a hot water bath (60 &#176;C) to further mixing by diffusion, and leave it to cool on the bench until its temperature falls to about 45-50 &#176;C.</li>
</ol>
3.&#160;Preparation of agarose pads
<ol>
	<li>Clean bench with 70% ethanol. Stretch tape on the cleaned bench. Make sure the tape is smooth and leveled.</li>
	<li>Prepare two coverslips: one on the tape and a second one nearby. Prepare a coverlid.</li>
	<li>Remove flask (prepared at step 2.14) from water bath, wipe the outside of the flask clean and leave it to cool until its temperature falls to about 45-50 &#176;C.</li>
	<li>To make the gel solution, mix quickly all the solutions in the following order: 
	80 &#181;L of 50% glycerol (0.4% [vol/vol]) 
	1 mL of 2% casamino acids (0.2% [wt/vol]) 
	10 &#181;L of thiamine (B1) 
	110 &#181;L of glucose (2 M) 
	10 &#181;L of relevant antibiotics.</li>
	<li>Pour 1.5 mL of the gel on the coverslip and cover it with the second piece making a &#34;sandwich&#34;.</li>
	<li>Return the Erlenmeyer to the hot water bath. Cover the sandwich with the lid and set timer for 20 minutes.</li>
	<li>At the same time, incubate the tube from step 2.11 for 1 h at 37 &#176;C with shaking (250 rpm)</li>
	<li>After 20 minutes flip the sandwich (step 3.5), cover it and leave to rest for 1 h. 
	NOTE: For better results leave the &#34;sandwich&#34; to rest at 4 &#176;C for the duration of step 3.8.</li>
	<li>Seed Escherichia coli culture from step 3.7 by pipetting the sample onto the 35 mm dish. 
	NOTE: Pipetting 6 &#181;L of cells as separate small drops gives the best results.</li>
	<li>Allow the drops to dry, for at least 15 minutes and up to 30 minutes.</li>
	<li>Expose the solidified gel of the sandwich from step 3.8 by sliding the coverslip away.</li>
	<li>Cut sandwich into small individual pads with biopsy punch or tip.</li>
	<li>Lean it gently on the sample (step 3.9). Leave the dish for 20 minutes on the bench.</li>
</ol>
4.&#160;Preparing the sample for microscopy imaging
<ol>
	<li>Remove the flask from water bath from step 3.6. Wipe the outside of the flask clean and let cool to room temperature (25 &#176;C). 
	NOTE: Remove the flask at step 4.1 about 3 minutes before the timer ends.</li>
	<li>Pour 3 mL of the gel constantly to the plate perimeter in a circular motion. Leave to solidify for a few minutes.</li>
	<li>Seal plates with tape and pierce several holes with 25 G needle. Flip all the dishes to prevent water condensation dripping onto the gel.</li>
	<li>Incubate all the dishes at 4 &#176;C for 30 min to allow full solidification while preventing cell mitosis. 
	NOTE:&#160;Leave samples at 4 &#176;C for successive measurements, no more than one day.</li>
	<li>Start the microscope per manufacturer instructions.</li>
	<li>Find the initial focus using lowest amplification lens and engage automatic focus system (AFS).</li>
	<li>Use oil if needed, drown the lens with oil and spread it carefully by moving the plate with a platform controller (not manually) and AFS can be engaged again.</li>
	<li>Use relative cross-section in Z direction, following default suggestion for Z step cross-sections. 
	NOTE: We took bright field images every 5 minutes and fluorescent images every 20 minutes. Sometimes the focus needs to be adjusted during the first 30 minutes. Preheating the microscope incubator box and oil, while refrigerating the sample tends to help reduce the initial loss of focus.</li>
</ol>
5.&#160;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.
<ol>
	<li>First, define the following parameters in main_code.m.
	<ol>
		<li>Define the folder of the acquisition images.</li>
		<li>Define the image time period - bright field channel time step in minutes.</li>
		<li>Define the GFP frequency - rate of acquisition of GFP images.</li>
		<li>Define the microscope resolution (i.e., how many pixels equals 1 &#181;m).</li>
		<li>Define histogram bin range - cell area range. 
		NOTE: The process of classifying the data according to cell area is similar to the principles of gating in flow cytometry data analysis. Gates are placed around populations of cells with common characteristics, usually forward scattered and side scattered, to isolate and quantify these populations of interest. Microscopy allows to gate, investigate and quantify several cell groups.</li>
		<li>Define convolution kernel (3,3) - this parameter detects cell boundaries by global thresholding (count_cells.m). 
		NOTE: This setup is needed only when changing the lab or microscope. Software requires the input bright field channel to be labeled with index c1, fluorescence channel labeled as c2.</li>
	</ol>
	</li>
	<li>Run main_code.m, which will run all other scripts automatically (count_cells.m, cell_growth_rate.m).
	<ol>
		<li>Program automatically segments bright field images (see Supplementary Figure 2-4).</li>
		<li>Combine the segmentation image with fluorescent image (GFP) to extract intensity level per cell.</li>
		<li>Calculate the graph of amount of cells by time and fit according to exponential growth.</li>
		<li>Calculate the mean and standard deviation (STD) for each cell area range.</li>
		<li>Calculate the signal to noise ratio (SNR) for each cell area range.</li>
		<li>Plot and fit the distribution of amount of cells by intensity.</li>
		<li>Calculate the coefficient of Variance (CV) and compare to flow analyzer data. 
		NOTE: Software will give as output the final segmentation images for adjusting conv_kernel in a new folder &#34;yourfolder/Segmented&#34;. Software will give as output the graphs in a new folder &#34;yourfolder/Graphs.</li>
	</ol>
	</li>
	<li>In order to compare between experiments, use compare_experiments.m.
	<ol>
		<li>Define directories for the saved graphs and the address for the \CompareResult directory.</li>
		<li>Run the file.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

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...

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&#160;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&#39;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&#160;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.&#160;

<table border="0" cellpadding="0" cellspacing="0" style="width:748px;" width="747">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">35mm glass dish</td>
			<td style="width:216px;">mattek</td>
			<td style="width:137px;">P35G-0.170-14-C</td>
			<td style="width:172px;">thickness corresponding with microscope lense.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Agarose&#160;</td>
			<td style="width:216px;">Lonza</td>
			<td style="width:137px;">5004</td>
			<td>LB preperation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">AHL&#160;</td>
			<td style="width:216px;">Sigma-Aldrich</td>
			<td style="width:137px;">K3007</td>
			<td>inducer</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:223px;">Bacto tryptone&#160;</td>
			<td style="width:216px;">BD - Becton, Dickinson and Company&#160;</td>
			<td style="width:137px;">211705</td>
			<td>LB preperation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Carb</td>
			<td style="width:216px;">Invitrogen</td>
			<td style="width:137px;">10177-012</td>
			<td>antibiotic</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Carb</td>
			<td style="width:216px;">Formedium</td>
			<td style="width:137px;">CAR0025</td>
			<td>antibiotic</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:223px;">Casamino acids&#160;</td>
			<td style="width:216px;">BD - Becton, Dickinson and Company&#160;</td>
			<td style="width:137px;">223050</td>
			<td>minimal media solution</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">eclipse Ti</td>
			<td style="width:216px;">nikon</td>
			<td style="width:137px;"></td>
			<td>inverted microscope</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Glucose</td>
			<td style="width:216px;">Sigma-Aldrich</td>
			<td style="width:137px;">G5767</td>
			<td>minimal media solution</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Glyserol</td>
			<td style="width:216px;">Bio-Lab</td>
			<td style="width:137px;">000712050100</td>
			<td>minimal media substrate</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Immersol 518F</td>
			<td style="width:216px;">zeiss</td>
			<td style="width:137px;">4449600000000</td>
			<td>immersion oil</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">M9 salt solution&#160;</td>
			<td style="width:216px;">Sigma-Aldrich</td>
			<td style="width:137px;">M6030</td>
			<td>minimal media solution</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">NaCl</td>
			<td style="width:216px;">Bio-Lab</td>
			<td style="width:137px;">214010</td>
			<td>LB preperation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Noble agar</td>
			<td style="width:216px;">Sigma-Aldrich</td>
			<td style="width:137px;">A5431</td>
			<td>minimal media substrate</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">parafilm tape</td>
			<td style="width:216px;">Bemis</td>
			<td style="width:137px;">PM-996</td>
			<td>refered to as tape in text</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Seaplaque GTG Agarose</td>
			<td style="width:216px;">Lonza</td>
			<td style="width:137px;">50111</td>
			<td>minimal media substrate</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">thaymine B1</td>
			<td style="width:216px;">Sigma-Aldrich</td>
			<td style="width:137px;">T0376&#160;</td>
			<td>minimal media solution</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:223px;">Yeast Extract</td>
			<td style="width:216px;">BD - Becton, Dickinson and Company&#160;</td>
			<td style="width:137px;">212750</td>
			<td>LB preperation</td>
		</tr>
	</tbody>
</table>

continuous measurement of biological noise in escherichia coli using time-lapse microscopy

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).

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...

Watch this Scientific Journal Video about Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy at JoVE.com

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

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.

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

The protocol developed here offers a tool to enable computer tracking of Escherichia coli division and fluorescent levels over several hours. The process ...

continuous-measurement-biological-noise-escherichia-coli-using-time

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JoVE Journal

Bioengineering

3.5K Views.  Technion - Israel Institute of Technology. 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. 

Video: Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Three-dimensional Optical-resolution Photoacoustic Microscopy

Optical microscopy, providing valuable insights at the cellular and organelle levels, has been widely recognized as an enabling biomedical technology. As the mainstays of in vivo three-dimensional (3-D) optical microscopy, single-/multi-photon fluorescence microscopy and optical coherence tomography (OCT) have demonstrated their extraordinary sensitivities to fluorescence and optical scattering contrasts, respectively. However, the optical absorption contrast of biological tissues, which encodes essential physiological/pathological information, has not yet been assessable. 
The emergence of biomedical photoacoustics has led to a new branch of optical microscopy optical-resolution photoacoustic microscopy (OR-PAM)1, where the optical irradiation is focused to the diffraction limit to achieve cellular1 or even subcellular2 level lateral resolution. As a valuable complement to existing optical microscopy technologies, OR-PAM brings in at least two novelties. First and most importantly, OR-PAM detects optical absorption contrasts with extraordinary sensitivity (i.e., 100%). Combining OR-PAM with fluorescence microscopy3 or with optical-scattering-based OCT4 (or with both) provides comprehensive optical properties of biological tissues. Second, OR-PAM encodes optical absorption into acoustic waves, in contrast to the pure optical processes in fluorescence microscopy and OCT, and provides background-free detection. The acoustic detection in OR-PAM mitigates the impacts of optical scattering on signal degradation and naturally eliminates possible interferences (i.e., crosstalks) between excitation and detection, which is a common problem in fluorescence microscopy due to the overlap between the excitation and fluorescence spectra. 
Unique for optical absorption imaging, OR-PAM has demonstrated broad biomedical applications since its invention, including, but not limited to, neurology5, 6, ophthalmology7, 8, vascular biology9, and dermatology10. In this video, we teach the system configuration and alignment of OR-PAM as well as the experimental procedures for in vivo functional microvascular imaging.

Optical-resolution photoacoustic microscopy (OR-PAM) is an emerging technology capable of imaging optical absorption contrasts in vivo with cellular resolution and sensitivity. Here, we provide a visualized instruction on the experimental protocols of OR-PAM, including system configuration, system alignment, typical in vivo experimental procedures, and functional imaging schemes.

Optical microscopy, providing valuable insights at the cellular and organelle levels, has been widely recognized as an enabling biomedical technology. As ...

Fluorescent Nanoparticles for the Measurement of Ion Concentration in Biological Systems

Tightly regulated ion homeostasis throughout the body is necessary for the prevention of such debilitating states as dehydration.1 In contrast, rapid ion fluxes at the cellular level are required for initiating action potentials in excitable cells.2 Sodium regulation plays an important role in both of these cases; however, no method currently exists for continuously monitoring sodium levels in vivo 3 and intracellular sodium probes 4 do not provide similar detailed results as calcium probes. In an effort to fill both of these voids, fluorescent nanosensors have been developed that can monitor sodium concentrations in vitro and in vivo.5,6 These sensors are based on ion-selective optode technology and consist of plasticized polymeric particles in which sodium specific recognition elements, pH-sensitive fluorophores, and additives are embedded.7-9 Mechanistically, the sodium recognition element extracts sodium into the sensor. 10 This extraction causes the pH-sensitive fluorophore to release a hydrogen ion to maintain charge neutrality within the sensor which causes a change in fluorescence. The sodium sensors are reversible and selective for sodium over potassium even at high intracellular concentrations.6 They are approximately 120 nm in diameter and are coated with polyethylene glycol to impart biocompatibility. Using microinjection techniques, the sensors can be delivered into the cytoplasm of cells where they have been shown to monitor the temporal and spatial sodium dynamics of beating cardiac myocytes.11 Additionally, they have also tracked real-time changes in sodium concentrations in vivo when injected subcutaneously into mice.3 Herein, we explain in detail and demonstrate the methodology for fabricating fluorescent sodium nanosensors and briefly demonstrate the biological applications our lab uses the nanosensors for: the microinjection of the sensors into cells; and the subcutaneous injection of the sensors into mice.

Fluorescent nanoparticles produced in our lab are used for imaging ion concentrations and ion fluxes in biological systems such as cells during signaling and interstitial fluid during physiological homeostasis.

Tightly regulated ion homeostasis throughout the body is necessary for the prevention of such debilitating states as dehydration.1 In contrast, rapid ion ...

Time-lapse Fluorescence Imaging of Arabidopsis Root Growth with Rapid Manipulation of The Root Environment Using The RootChip

The root functions as the physical anchor of the plant and is the organ responsible for uptake of water and mineral nutrients such as nitrogen, phosphorus, sulfate and trace elements that plants acquire from the soil. If we want to develop sustainable approaches to producing high crop yield, we need to better understand how the root develops, takes up a wide spectrum of nutrients, and interacts with symbiotic and pathogenic organisms. To accomplish these goals, we need to be able to explore roots in microscopic detail over time periods ranging from minutes to days.
We developed the RootChip, a polydimethylsiloxane (PDMS)- based microfluidic device, which allows us to grow and image roots from Arabidopsis seedlings while avoiding any physical stress to roots during preparation for imaging1 (Figure 1). The device contains a bifurcated channel structure featuring micromechanical valves to guide the fluid flow from solution inlets to each of the eight observation chambers2. This perfusion system allows the root microenvironment to be controlled and modified with precision and speed. The volume of the chambers is approximately 400 nl, thus requiring only minimal amounts of test solution.
Here we provide a detailed protocol for studying root biology on the RootChip using imaging-based approaches with real time resolution. Roots can be analyzed over several days using time lapse microscopy. Roots can be perfused with nutrient solutions or inhibitors, and up to eight seedlings can be analyzed in parallel. This system has the potential for a wide range of applications, including analysis of root growth in the presence or absence of chemicals, fluorescence-based analysis of gene expression, and the analysis of biosensors, e.g. FRET nanosensors3.

This article provides a protocol for cultivation of Arabidopsis seedlings in the RootChip, a microfluidic imaging platform that combines automated control of growth conditions with microscopic root monitoring and FRET-based measurement of intracellular metabolite levels.

The root functions as the physical anchor of the plant and is the organ responsible for uptake of water and mineral nutrients such as nitrogen, ...

In situ TEM of Biological Assemblies in Liquid

Researchers regularly use Transmission Electron Microscopes (TEMs) to examine biological entities and to assess new materials. Here, we describe an additional application for these instruments- viewing viral assemblies in a liquid environment. This exciting and novel method of visualizing biological structures utilizes a recently developed microfluidic-based specimen holder. Our video article demonstrates how to assemble and use a microfluidic holder to image liquid specimens within a TEM. In particular, we use simian rotavirus double-layered particles (DLPs) as our model system. We also describe steps to coat the surface of the liquid chamber with affinity biofilms that tether DLPs to the viewing window. This permits us to image assemblies in a manner that is suitable for 3D structure determination. Thus, we present a first glimpse of subviral particles in a native liquid environment.

In situ TEM of Biological Assemblies in Liquid

Here we describe a procedure to image viral complexes in liquid at nanometer resolution using a transmission electron microscope.

Researchers regularly use Transmission Electron Microscopes (TEMs) to examine biological entities and to assess new materials. Here, we describe an ...

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture cellular behaviors involved in developmental processes.&#160; The ability to track and follow cell movements is limited by sample movements that occur due to drift of the sample or, in some cases, growth during image acquisition. Tracking cells in datasets affected by drift and/or growth will incorporate these movements into any analysis of cell position. This may result in the apparent movement of static structures within the sample. Therefore prior to cell tracking, any sample drift should be corrected. Using the open source Fiji distribution 1 &#160;of ImageJ 2,3 and the incorporated LOCI tools 4, we developed the Correct 3D drift plug-in to remove erroneous sample movement in confocal datasets. This protocol effectively compensates for sample translation or alterations in focal position by utilizing phase correlation to register each time-point of a four-dimensional confocal datasets while maintaining the ability to visualize and measure cell movements over extended time-lapse experiments.

Time-lapse microscopy allows the visualization of developmental processes. Growth or drift of samples during image acquisition reduces the ability to accurately follow and measure cell movements during development. We describe the use of open source image processing software to correct for three dimensional sample drift over time.

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture ...

Surface Potential Measurement of Bacteria Using Kelvin Probe Force Microscopy

Surface potential is a commonly overlooked physical characteristic that plays a dominant role in the adhesion of microorganisms to substrate surfaces. Kelvin probe force microscopy (KPFM) is a module of atomic force microscopy (AFM) that measures the contact potential difference between surfaces at the nano-scale. The combination of KPFM with AFM allows for the simultaneous generation of surface potential and topographical maps of biological samples such as bacterial cells. Here, we employ KPFM to examine the effects of surface potential on microbial adhesion to medically relevant surfaces such as stainless steel and gold. Surface potential maps revealed differences in surface potential for microbial membranes on different material substrates. A step-height graph was generated to show the difference in surface potential at a boundary area between the substrate surface and microorganisms. Changes in cellular membrane surface potential have been linked with changes in cellular metabolism and motility. Therefore, KPFM represents a powerful tool that can be utilized to examine the changes of microbial membrane surface potential upon adhesion to various substrate surfaces. In this study, we demonstrate the procedure to characterize the surface potential of individual methicillin-resistant Staphylococcus aureus USA100 cells on stainless steel and gold using KPFM.

Here, we present a protocol explaining the use of Kelvin probe force microscopy as a tool for generating high resolution nano-scale surface potential maps. This tool was applied to assess the role of surface potential on the binding capacity of microorganisms to substrate surfaces.

Surface potential is a commonly overlooked physical characteristic that plays a dominant role in the adhesion of microorganisms to substrate surfaces. ...

Design and Construction of Artificial Extracellular Matrix (aECM) Proteins from Escherichia coli for Skin Tissue Engineering

Recombinant technology is a versatile platform to create novel artificial proteins with tunable properties. For the last decade, many artificial proteins that have incorporated functional domains derived from nature (or created de novo) have been reported. In particular, artificial extracellular matrix (aECM) proteins have been developed; these aECM proteins consist of biological domains taken from fibronectin, laminins and collagens and are combined with structural domains including elastin-like repeats, silk and collagen repeats. To date, aECM proteins have been widely investigated for applications in tissue engineering and wound repair. Recently, Tjin and coworkers developed integrin-specific aECM proteins designed for promoting human skin keratinocyte attachment and propagation. In their work, the aECM proteins incorporate cell binding domains taken from fibronectin, laminin-5 and collagen IV, as well as flanking elastin-like repeats. They demonstrated that the aECM proteins developed in their work were promising candidates for use as substrates in artificial skin. Here, we outline the design and construction of such aECM proteins as well as their purification process using the thermo-responsive characteristics of elastin.

Design and Construction of Artificial Extracellular Matrix aECM Proteins from Escherichia coli for Skin Tissue Engineering

Recombinant technologies have enabled material designers to create novel artificial proteins with customized functionalities for tissue engineering applications. For example, artificial extracellular matrix proteins can be designed to incorporate structural and biological domains derived from native ECMs. Here, we describe the construction and purification of aECM proteins containing elastin-like repeats.

Recombinant technology is a versatile platform to create novel artificial proteins with tunable properties. For the last decade, many artificial proteins ...

Synthesis of Infectious Bacteriophages in an E. coli-based Cell-free Expression System

A new generation of cell-free transcription-translation (TXTL) systems, engineered to have a greater versatility and modularity, provide novel capabilities to perform basic and applied sciences in test tube reactions. Over the past decade, cell-free TXTL has become a powerful technique for a broad range of novel multidisciplinary research areas related to quantitative and synthetic biology. The new TXTL platforms are particularly useful to construct and interrogate biochemical systems through the execution of synthetic or natural gene circuits. In vitro TXTL has proven convenient to rapidly prototype regulatory elements and biological networks as well as to recapitulate molecular self-assembly mechanisms found in living systems. In this article, we describe how infectious bacteriophages, such as MS2 (RNA), &#934;&#935;174 (ssDNA), and T7 (dsDNA), are entirely synthesized from their genome in one-pot reactions using an all Escherichia coli, cell-free TXTL system. Synthesis of the three coliphages is quantified using the plaque assay. We show how the yield of synthesized phage depends on the biochemical settings of the reactions. Molecular crowding, emulated through a controlled concentration of PEG 8000, affects the amount of synthesized phages by orders of magnitudes. We also describe how to amplify the phages and how to purify their genomes. The set of protocols and results presented in this work should be of interest to multidisciplinary researchers involved in cell-free synthetic biology and bioengineering.

Synthesis of Infectious Bacteriophages in an E. coli-based Cell-free Expression System

A new generation of cell-free transcription-translation platforms has been engineered to construct biochemical systems in vitro through the execution of gene circuits. In this article, we describe how bacteriophages, such as MS2, &#934;&#935;174, and T7, are synthesized from their genome using an all E. coli cell-free TXTL system.

A new generation of cell-free transcription-translation (TXTL) systems, engineered to have a greater versatility and modularity, provide novel ...

Stiffness Measurement of Soft Silicone Substrates for Mechanobiology Studies Using a Widefield Fluorescence Microscope

Soft tissues in the human body typically have stiffness in the kilopascal (kPa) range. Accordingly, silicone and hydrogel flexible substrates have been proven to be useful substrates for culturing cells in a physical microenvironment that partially mimics in vivo conditions. Here, we present a simple protocol for characterizing the Young's moduli of isotropic linear elastic substrates typically used for mechanobiology studies. The protocol consists of preparing a soft silicone substrate on a Petri dish or stiff silicone, coating the top surface of the silicone substrate with fluorescent beads, using a millimeter-scale sphere to indent the top surface (by gravity), imaging the fluorescent beads on the indented silicone surface using a fluorescence microscope, and analyzing the resultant images to calculate the Young's modulus of the silicone substrate. Coupling the substrate's top surface with a moduli extracellular matrix protein (in addition to the fluorescent beads) allows the silicone substrate to be readily used for cell plating and subsequent studies using traction force microscopy experiments. The use of stiff silicone, instead of a Petri dish, as the base of the soft silicone, enables the use of mechanobiology studies involving external stretch. A specific advantage of this protocol is that a widefield fluorescence microscope, which is commonly available in many labs, is the major equipment necessary for this procedure. We demonstrate this protocol by measuring the Young's modulus of soft silicone substrates of different elastic moduli.

Substrates with stiffness in the kilopascal-range are useful to study the response of cells to physiologically relevant micro-environment stiffness. Using just a widefield fluorescence microscope, the Young's modulus of soft silicone gels can be determined using an indentation with a suitable sphere.

Soft tissues in the human body typically have stiffness in the kilopascal (kPa) range. Accordingly, silicone and hydrogel flexible substrates have been ...

Electric and Magnetic Field Devices for Stimulation of Biological Tissues

Electric fields (EFs) and magnetic fields (MFs) have been widely used by tissue engineering to improve cell dynamics such as proliferation, migration, differentiation, morphology, and molecular synthesis. However, variables such stimuli strength and stimulation times need to be considered when stimulating either cells, tissues or scaffolds. Given that EFs and MFs vary according to cellular response, it remains unclear how to build devices that generate adequate biophysical stimuli to stimulate biological samples. In fact, there is a lack of evidence regarding the calculation and distribution when biophysical stimuli are applied. This protocol is focused on the design and manufacture of devices to generate EFs and MFs and implementation of a computational methodology to predict biophysical stimuli distribution inside and outside of biological samples. The EF device was composed of two parallel stainless-steel electrodes located at the top and bottom of biological cultures. Electrodes were connected to an oscillator to generate voltages (50, 100, 150 and 200 Vp-p) at 60 kHz. The MF device was composed of a coil, which was energized with a transformer to generate a current (1 A) and voltage (6 V) at 60 Hz. A polymethyl methacrylate support was built to locate the biological cultures in the middle of the coil. The computational simulation elucidated the homogeneous distribution of EFs and MFs inside and outside of biological tissues. This computational model is a promising tool that can modify parameters such as voltages, frequencies, tissue morphologies, well plate types, electrodes and coil size to estimate the EFs and MFs to achieve a cellular response.

This protocol describes the step-by-step process to build both electrical and magnetic stimulators used to stimulate biological tissues. The protocol includes a guideline to simulate computationally electric and magnetic fields and manufacture of stimulator devices.

Electric fields (EFs) and magnetic fields (MFs) have been widely used by tissue engineering to improve cell dynamics such as proliferation, migration, ...