The authors have nothing to disclose.

1. Preparing the Growth Medium
NOTE: The chemical composition of minimal medium M63 is as follows: 62 mM K2HPO4, 39 mM KH2PO4, 15 mM (NH4)2SO4, 1.8 &#181;M FeSO4, 15 &#181;M thiamine-HCl, 0.2 mM MgSO4, and 22 mM glucose. M63 is made by mixing three stock solutions: Five X solution, 20% glucose and MgSO4 thiamine solution. Store all solutions at 4 &#176;C.
<ol>
	<li>Preparing the Five X solution
	<ol>
		<li>To prepare FeSO4 solution, use an electrical pipette and a disposable serological pipette to add ddH2O to a 50-mL centrifuge tube. Use a P-200 to add 0.06 mL HCl to obtain 0.01 M HCl. Add 36 mg FeSO4-7H2O and mix well.</li>
		<li>Measure 160 mL ddH2O in a measuring cylinder and add it to a 500-mL beaker. Add the following in this order: 10.72 g K2HPO4, 5.24 g KH2PO4, 2.0 g (NH4)2SO4, and 0.5 mL FeSO4 solution (prepared in step 1.1.1).</li>
		<li>Using a precision pH meter, adjust the pH to 7.0 by adding 2 M KOH. Pour the solution in a measuring cylinder and add ddH2O to a total volume of 200 mL. Sterilize the solution using a 250-mL filtration unit (PVDF, 0.22 &#181;M).</li>
	</ol>
	</li>
	<li>Preparing the 20% glucose solution
	<ol>
		<li>Measure 200 mL ddH2O in a measuring cylinder and pour it to a 500-mL beaker. While mixing using a magnetic stirrer, add 50 g of glucose. Dilute the solution in a measuring cylinder to a total volume of 250 mL. Sterilize the solution as described in step 1.1.3.</li>
	</ol>
	</li>
	<li>Preparing the MgSO4 thiamine solution
	<ol>
		<li>Add 150 mL ddH2O to a 500-mL beaker. While mixing with a magnetic stirrer, add 1.0 g of thiamine-HCl and 10 g of MgSO4-7H2O. Dilute the solution in a measuring cylinder to a total volume of 200 mL. Sterilize the solution as described in step 1.1.3.</li>
	</ol>
	</li>
	<li>Preparing minimal medium M63
	<ol>
		<li>Place the Five X solution, the 20% glucose solution, the MgSO4 thiamine solution, an electronic pipette, a P-200 pipette, and 200-&#181;L pipette tips on a clean bench.</li>
		<li>Measure 155.8 mL ddH2O in a measuring cylinder and pour it into a 500-mL beaker.</li>
		<li>Using the electronic pipette and disposable serological pipette, add 40 mL of Five X solution and 4 mL 20% glucose solution to the measured ddH2O. Then, add 0.2 mL MgSO4 thiamine solution with the P-200. Sterilize the solution as described in step 1.1.3.</li>
	</ol>
	</li>
</ol>
2. Preparing the Glycerol Stock
<ol>
	<li>Cell culture 
	NOTE: The bacterial strains (e.g., W3110 and its reduced genomes) are available from strain bank organizations. The strains are usually obtained in the form of colonies on agar plates.
	<ol>
		<li>Place five sterilized glass tubes with silicone rubber stoppers, an electronic pipette, P-1,000, 1,000-&#181;L pipette tips, P-200, 200-&#181;L pipette tips, and the target strains (colonies on plates) on a clean bench.</li>
		<li>Expose the mouth of the glass tube to a Bunsen burner before opening the silicone rubber stopper. Expose the silicone rubber stopper to the flame after the tube is opened, and then lightly place the cap back on the glass tube.</li>
		<li>Use the electronic pipette and disposable serological pipette to add 5 mL M63 to one of the glass tubes and 4.5 mL M63 to the other four tubes.</li>
		<li>Use the P-200 tip to pick a colony and inoculate it in the glass tube containing 5 mL M63.</li>
		<li>Vortex the tube to make a suspension. Then, dilute the solution 10-fold by transferring 0.5 mL of this solution to one of the four tubes containing 4.5 mL M63.</li>
		<li>Repeat the process described in step 2.1.5 for the remaining tubes. A dilution series with five different concentrations (dilutions of 1, 10, 100, 1000, and 10,000) is now ready.</li>
		<li>Sterilize the mouths of the glass tubes and the silicone rubber stoppers as described in step 2.1.2. Cap the tubes with the stoppers. Avoid contamination by not wrinkling the silicone rubber stoppers.</li>
		<li>Place the five tubes in a pre-warmed shaking incubator at 37 &#176;C and shake at 200 rpm. Incubate the culture overnight or for 10 to 30 h.</li>
	</ol>
	</li>
	<li>Selection of the culture for the glycerol stock
	<ol>
		<li>Place the pre-warmed room temperature M63 medium, P-1,000, 1,000-&#181;L pipette tips, and a disposable cuvette on a clean bench.</li>
		<li>Add 1000 &#181;L M63 to a disposable cuvette with a P-1,000. Place the disposable cuvette in a spectrophotometer, start the program at a fixed wavelength of 600 nm, and measure the blank.</li>
		<li>Move the five glass tubes from the shaking incubator to the clean bench.</li>
		<li>Discard M63 from the disposable cuvette and add 1,000 &#181;L culture to the same disposable cuvette with a P-1,000. Measure the optical turbidity of the cell culture (OD600) as described in step 2.2.2. 
		NOTE: To avoid any contamination and to achieve a precise measurement, expose the glass tubes and the stoppers to the flame as described and vortex the culture before sampling. To ensure reliable results, repeated measurements are recommended, particularly when the cell density is low.</li>
		<li>Of the five cell cultures, choose one that is in the early exponential growth phase (OD600= 0.01 - 0.05) for the glycerol stock. 
		NOTE: If multiple cultures have densities within the optimal range, the one closest to 0.05 is commonly selected.</li>
	</ol>
	</li>
	<li>Make the glycerol stocks for repeated tests. 
	NOTE: This is described for preparing ten stocks. Larger or smaller amounts can be made according to the experimental requirements.
	<ol>
		<li>Place the sterilized 60% glycerol solution, ten sterilized 1.5-mL microtubes, P-1,000 and P-200, 1,000- and 200-&#181;L pipette tips, and a microtube stand on a clean bench.</li>
		<li>Add 250 &#181;L sterilized 60% glycerol solution and 750 &#181;L of the selected cell culture to the 1.5-mL microtube and mix by pipetting. 
		NOTE: The stock volume is variable, but always maintain a 1:3 ratio of 60% glycerol to cell culture; this results in a final concentration of 15% glycerol.</li>
		<li>Place the remaining nine microtubes in the microtube stand and dispense 100 &#181;L of the mixture prepared in step 2.3.2 to each tube. There are now ten identical glycerol stocks for future use.</li>
		<li>Store the stocks in a deep freezer at -80 &#176;C.</li>
	</ol>
	</li>
</ol>
3. Acquiring the Growth Curves
<ol>
	<li>Setting up the microplate reader. 
	NOTE: The terms shown in quotation marks show the specific phrasing used on the plate reader used here (see the Table of Materials).
	<ol>
		<li>Open the software. Open &#34;Protocols&#34; in &#34;Task Manager&#34; and choose &#34;Create New&#34;. Choose &#34;Standard Protocol&#34;.</li>
		<li>Open &#34;Procedure&#34;, and adjust the settings. Open &#34;Set Temperature&#34;, and select &#34;Incubator On&#34;. Set &#34;Temperature&#34; to 37 &#176;C and &#34;Gradient&#34; to 0 &#176;C. Check &#34;Preheat&#34; before continuing with the next step. Open &#34;Start Kinetic&#34;, set &#34;Run time&#34; for 24:00:00 or 48:00:00, and &#34;Interval&#34; for 00:30:00 or 01:00:00. 
		NOTE: It takes approximately 1 min to read an entire 96 well plate.</li>
		<li>Open &#34;Shake&#34; and set &#34;Shake Mode&#34; as &#34;Linear&#34;. Check &#34;Constitution Shake&#34; and set &#34;Frequency&#34; at 567 cpm. Open &#34;Read&#34;, check &#34;Absorbance&#34;, &#34;Endpoint/Kinetic&#34;, and &#34;Monochromators.&#34; Set &#34;Wavelength&#34; to 600.</li>
		<li>Click &#34;Validate&#34; to confirm that the procedure is correct. Click &#34;Save&#34; to save it as a new program for future use.</li>
	</ol>
	</li>
	<li>Real-time recording of growth
	<ol>
		<li>Draw a 96-well plate pictogram (8 &#215; 12 table) to indicate the positions of the inoculated culture samples on the 96-well plate. Print out the table and use it as a reference for the experiment. 
		NOTE: The wells located on the edges of the microplate should only contain blank medium because of evaporation.</li>
		<li>Place a sterilized 96-well flat bottom microplate with lid, P-1000, 1000-&#181;L pipette tips, P-200, 200-&#181;L pipette tips, several 1.5-mL microtubes, an 8-multichannel pipette, a sterilized reagent reservoir, room temperature M63, and the glycerol stocks (prepared in step 2.3) on a clean bench.</li>
		<li>Add approximately 25 mL M63 to the reagent reservoir. Use this reservoir stock for all the following steps.</li>
		<li>Add 900 &#181;L M63 to the microtubes in preparation for making serial dilutions.</li>
		<li>Thaw the glycerol stock at room temperature. Add 900 &#181;L M63 to the thawed glycerol stock and vortex. This results in a 10-fold dilution of the original glycerol stock.</li>
		<li>Transfer 100 &#181;L of the 10-fold dilution to another microtube containing 900 &#181;L of M63 and vortex. This results in a 100-fold dilution.</li>
		<li>Repeat step 3.2.6 until the desired number of dilutions are achieved.</li>
		<li>Fill the wells at the edge of the microplate with 200 &#181;L M63 using an 8-channel pipette (P-200). 
		NOTE: These wells can be used as the blank.</li>
		<li>Load 200 &#181;L of each diluted sample prepared in steps 3.2.4 - 3.2.7 to the microplate wells according to the reference table (step 3.2.1). Vortex the diluted samples prior to loading and load the same sample in multiple wells at varied locations on the plate.</li>
		<li>Place the 96-well microplate on the plate reader.</li>
		<li>Open &#34;Read Now&#34; in &#34;Task Manager&#34; and choose the program (section 3.1). Click &#34;OK&#34; to start measuring. Save the recording as a new experimental file for data analysis (section 4).</li>
	</ol>
	</li>
</ol>
4. Data Analysis
<ol>
	<li>Export the results of the real-time growth-rate data (section 3.2) to a USB memory stick as a text file. 
	NOTE: A 96-well formatted results (curves) will display on the plate reader in real-time. The hourly records (OD values) can be exported as a table; the rows and columns represent the well numbers (e.g., A1, B1) and the measuring time (e.g., 00:00:59), respectively.</li>
	<li>Open the text file with a spreadsheet software.</li>
	<li>Copy the hourly OD600 reads of the 96-well microplate to a new worksheet for further analysis.</li>
	<li>Subtract the background reads at time zero from the hourly reads of each sample well. 
	NOTE: For convenience, the mean value of the blank wells containing M63 (step 3.2.8) can be used as the background value.</li>
	<li>Calculate the mean of five consecutive OD600 reads to estimate the maximal population density. 
	NOTE: The largest mean value of the five consecutive OD600 reads is defined as the maximal OD600 of the corresponding growth curve.</li>
	<li>Calculate the growth rate, &#181; (h-1), by applying the following equation for all pairs of consecutive values of OD600: 
	<img alt="Equation" src="/files/ftp_upload/56197/56197eq1.jpg" /> 
	NOTE: In this equation, Ci and Ci+1 represent the OD600 values of any two consecutive time points (ti and ti+1, respectively). LN indicates natural logarithm.</li>
	<li>Calculate the changes in the growth rate over time based on the hourly OD600 reads according to step 4.6. Calculate the mean and the standard deviation of five consecutive growth rates to estimate the maximal growth rate. 
	NOTE: The largest mean with the smallest standard deviation is defined as the maximal growth rate of the corresponding growth curve.</li>
</ol>
5. Confirming the Global Bias of the 96-well Reads (Optional)
NOTE: Both the plate reader and the consumable 96-well plate can cause biased measurements. To achieve highly precise and reproducible quantitative results, confirming the global bias of the 96-well plate is highly recommended.
<ol>
	<li>Prepare the 96-well plate and the plate reader for the bias test as described in section 3.2.2.</li>
	<li>Add 20 mL M63 to a sterilized 50-mL centrifuge tube. Add the glycerol stock to the same centrifuge tube and vortex. Transfer the suspended solution to a sterilized reagent reservoir.</li>
	<li>Use an 8-channel pipette to transfer 200 &#181;L of the suspended solution to the 96-well plate.</li>
	<li>Place the 96-well plate on the plate reader and start the measurement.</li>
	<li>Record the hourly OD600 reads and analyze as described in section 4. Compare the calculated maximal growth rate and population density of each well to determine locational bias of the 96-well reads.</li>
</ol>

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We thank Kohei Tsuchiya for providing the CFU assay example. This work was partially financially supported by a Grant-in-Aid for Scientific Research (C) no. 26506003 (to BWY) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Critical steps in the protocol include the preparation of a common stock of exponentially growing cells and the replication of the same samples in multiple wells at various positions on the microplate. Previously, microbiologists started the culture from an overnight pre-culture. While this method may reduce the lag time of bacterial growth, it is difficult to achieve reproducible growth curves. As shown in Figure 2, the independent measurements using the common glycerol stocks resulted in n...

Microbiological studies often start with the culture of bacterial cells and the assessment of the bacterial growth curves, which represent a fundamental phenomenon of bacterial physiology1,2,3. Basic culture principles are widely available in the published research literature and textbooks because bacterial culture is a fundamental methodology. At the bench level, substantial attention has traditionally been focused on optimizing growth media and culturing conditions, but controlling the growth rate, which would likely provide even greater understanding of microbial physiology, has not been extensively studied4. For exponentially growing bacteria, a key parameter of the cellular state is the growth rate, which has been reported to be coordinated with the genome, transcriptome, and proteome5,6,7,8. Thus, quantitative evaluation of bacterial growth is crucial for understanding microbial physiology.
To evaluate bacterial growth, the experimental methods used to estimate biomass are well established9,10 and are based on the detection of biochemical, physical, or biological parameters, such as optical turbidity. In addition, the analytical methods used to capture the dynamic properties of growth changes are commonly based on established nonlinear models11,12,13, for example, logistic equations. Growth dynamics are generally acquired by timed sampling of cell growth in culture by either measuring optical turbidity or performing colony-forming unit (CFU) assays. The limitation of these culturing and detection methods is that the data points are not a true reflection of population dynamics because the measurement intervals are often in hours and because the culture condition (e.g., changes in temperature and aeration) is disturbed at the time of sampling. Culture and analysis techniques must be updated using recent developments in technology and understanding. Recent advances in microplate readers allow the real-time observation of bacterial growth and significantly decrease labor costs. Using these advanced devices, the latest studies on bacterial growth have reported analytical methods for high-throughput measurements14,15.
The purpose of this protocol is to evaluate the precise growth dynamics in a high-throughput manner, which will be valuable for quantitative studies that ultimately address the questions of how the growth rate is determined and what factors affect the growth rate. The protocol addresses all factors that should be taken into account for the repeatable and precise quantitation of bacterial growth. The experimental method and analysis are described in detail in the main text. This method permits the precise and reproducible analysis of bacterial growth in a high-throughput manner. Microbiologists can use this protocol to derive additional quantitative results from their experimental evidence. This protocol can also be used for studies in systems biology that attempt to draw conceptual conclusions or to achieve a theoretical overview of growth.

<table><tbody><tr><td>K&lt;sub&gt;2&lt;/sub&gt;HPO&lt;sub&gt;4&lt;/sub&gt;</td><td>Wako</td><td>164-04295</td><td></td></tr><tr><td>KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;</td><td>Wako</td><td>166-04255</td><td></td></tr><tr><td>(NH&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt;</td><td>Wako</td><td>019-03435</td><td></td></tr><tr><td>MgSO&lt;sub&gt;4&lt;/sub&gt;-7H&lt;sub&gt;2&lt;/sub&gt;O</td><td>Wako</td><td>138-00415</td><td></td></tr><tr><td>Thiamine-HCl</td><td>Wako</td><td>201-00852</td><td></td></tr><tr><td>glucose</td><td>Wako</td><td>049-31165</td><td></td></tr><tr><td>HCl</td><td>Wako</td><td>080-01066</td><td></td></tr><tr><td>Iron (II) sulfate heptahydrate (FeSO&lt;sub&gt;4&lt;/sub&gt;-7H&lt;sub&gt;2&lt;/sub&gt;O)</td><td>Wako</td><td>094-01082</td><td></td></tr><tr><td>KOH</td><td>Wako</td><td>168-21815</td><td></td></tr><tr><td>Glycerol</td><td>Wako</td><td>075-00611</td><td></td></tr><tr><td>Centrifuge tube (50 mL, sterilized)</td><td>WATSON</td><td>1342-050S</td><td></td></tr><tr><td>Pipette Tips, 200 &amp;micro;L</td><td>WATSON</td><td>110-705Y</td><td></td></tr><tr><td>Pipette Tips, 1,000 &amp;micro;L</td><td>WATSON</td><td>110-8040</td><td></td></tr><tr><td>Microtube (1.5 mL)</td><td>WATSON</td><td>131-715C</td><td></td></tr><tr><td>8 multichannel-pipette</td><td>WATSON</td><td>NT-8200</td><td></td></tr><tr><td>PASORINA STIRRER</td><td>AS ONE</td><td>2-4990-02</td><td></td></tr><tr><td>Glass cylinder (200 mL)</td><td>AS ONE</td><td>1-8562-07</td><td></td></tr><tr><td>Precision pH mater</td><td>AS ONE</td><td>AS800 / 1-054-01</td><td></td></tr><tr><td>Pipetman P-200</td><td>GILSON</td><td>1-6855-05</td><td></td></tr><tr><td>Pipetman P-1000</td><td>GILSON</td><td>1-6855-06</td><td></td></tr><tr><td>Disposable Serolocical Pipettes (10 mL)</td><td>SANPLATEC</td><td>SAN27014</td><td></td></tr><tr><td>Disposable Serolocical Pipettes (25 mL)</td><td>SANPLATEC</td><td>SAN27015</td><td></td></tr><tr><td>Microtube stand</td><td>BM Bio</td><td>801-02Y</td><td></td></tr><tr><td>Vortex</td><td>BM Bio</td><td>BM-V1</td><td></td></tr><tr><td>Corning Costar 96-well microplate with lid (Flat bottom, Clear)</td><td>Sigma-Aldrich</td><td>Corning, 3370</td><td></td></tr><tr><td>Corning Costar reagent reservoir (50 mL)</td><td>Sigma-Aldrich</td><td>Corning, 4870</td><td></td></tr><tr><td>Stericup GV PVDF (250 mL, 0.22 &amp;micro;M)</td><td>Merck Millipore</td><td>SCGVU02RE</td><td></td></tr><tr><td>Pipet-Aid XP</td><td>DRUMMOND</td><td>4-000-101</td><td></td></tr><tr><td>Bioshaker (BR-23UM MR)</td><td>TAITEC</td><td>0053778-000</td><td></td></tr><tr><td>Disposal cell (1.5 mL)</td><td>Kartell</td><td>1938 / 2-478-02</td><td></td></tr><tr><td>DU 730 Life Science UV/Vis Spectrophotometer</td><td>Beckman Coulter</td><td>A23616</td><td></td></tr><tr><td>EPOCH2</td><td>BioTek</td><td>2014-EP2-002 / EPOCH2T</td><td></td></tr><tr><td>Beaker (500 mL)</td><td>IWAKI</td><td>82-0008</td><td></td></tr><tr><td>BIO clean bench</td><td>Panasonic</td><td>MCV-B131F</td><td></td></tr><tr><td>Glass tubes</td><td>NICHIDEN RIKA GLASS</td><td>P-10M~P-30 /101019</td><td></td></tr><tr><td>Silicone rubber stoppers</td><td>ShinEtsu Polymer</td><td>T-19</td><td></td></tr><tr><td>Bacterial strains</td><td>Strain bank organization; National Bio Resource Project (NBRP) in Japan</td><td></td><td></td></tr></tbody></table>

precise, high-throughput analysis of bacterial growth

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the systems level. Recent studies have reported correlations between bacterial growth and genome-wide events, such as genome reduction and transcriptome reorganization. Correctly analyzing bacterial growth is crucial for understanding the growth-dependent coordination of gene functions and cellular components. Accordingly, the precise quantitative evaluation of bacterial growth in a high-throughput manner is required. Emerging technological developments offer new experimental tools that allow updates of the methods used for studying bacterial growth. The protocol introduced here employs a microplate reader with a highly optimized experimental procedure for the reproducible and precise evaluation of bacterial growth. This protocol was used to evaluate the growth of several previously described Escherichia coli strains. The main steps of the protocol are as follows: the preparation of a large number of cell stocks in small vials for repeated tests with reproducible results, the use of 96-well plates for high-throughput growth evaluation, and the manual calculation of two major parameters (i.e., maximal growth rate and population density) representing the growth dynamics. In comparison to the traditional colony-forming unit (CFU) assay, which counts the cells that are cultured in glass tubes over time on agar plates, the present method is more efficient and provides more detailed temporal records of growth changes, but has a stricter detection limit at low population densities. In summary, the described method is advantageous for the precise and reproducible high-throughput analysis of bacterial growth, which can be used to draw conceptual conclusions or to make theoretical observations.

The described method provides a means to capture dynamic bacterial growth in a continuous, high-throughput manner by utilizing a 96-well format reader that takes multiple optical density measurements at various time intervals (from minutes to hours to days). The growth curves of an assortment of E. coli strains expressing various genomes can be precisely acquired in a single experiment (Figure 1A). In comparison to the described method, the tradition...

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Precise, High-throughput Analysis of Bacterial Growth

Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the ...

precise-high-throughput-analysis-of-bacterial-growth

Research

JoVE Journal

Biology

23.9K Views.  University of Tsukuba. Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.

Video: Precise, High-throughput Analysis of Bacterial Growth

An Assay for Quantifying Protein-RNA Binding in Bacteria

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding of an RNA binding protein (RBP) to the initiation region of the mRNA, which interferes with ribosome binding. In the presented method, we utilize this blocking phenomenon to quantify the binding affinity of RBPs to their cognate and non-cognate binding sites. To do this, we insert a test binding site in the initiation region of a reporter mRNA and induce the expression of the test RBP. In the case of RBP-RNA binding, we observed a sigmoidal repression of the reporter expression as a function of RBP concentration. In the case of no-affinity or very low affinity between binding site and RBP, no significant repression was observed. The method is carried out in live bacterial cells, and does not require expensive or sophisticated machinery. It is useful for quantifying and comparing between the binding affinities of different RBPs that are functional in bacteria to a set of designed binding sites. This method may be inappropriate for binding sites with high structural complexity. This is due to the possibility of repression of ribosomal initiation by complex mRNA structure in the absence of RBP, which would result in lower basal reporter gene expression, and thus less-observable reporter repression upon RBP binding.

In this method, we quantify the binding affinity of RNA binding proteins (RBPs) to cognate and non-cognate binding sites using a simple, live, reporter assay in bacterial cells. The assay is based on repression of a reporter gene.

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding ...

Genetics

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

A competitive index is a common method used to assess bacterial fitness and/or virulence. The utility of this approach is exemplified by its ease to perform and its ability to standardize the fitness of many strains to a wild-type organism. The technique is limited, however, by available phenotypic markers and the number of strains that can be assessed simultaneously, creating the need for a great number of replicate experiments. Concurrent with large numbers of experiments, the labor and material costs for quantifying bacteria based on phenotypic markers are not insignificant. To overcome these negative aspects while retaining the positive aspects, we have developed a molecular-based approach to directly quantify microorganisms after engineering genetic markers onto bacterial chromosomes. Unique, 25 base pair DNA barcodes were inserted at an innocuous locus on the chromosome of wild-type and mutant strains of Salmonella. In vitro competition experiments were performed using inocula consisting of pooled strains. Following the competition, the absolute numbers of each strain were quantified using digital PCR and the competitive indices for each strain were calculated from those values. Our data indicate that this approach to quantifying Salmonella is extremely sensitive, accurate, and precise for detecting both highly abundant (high fitness) and rare (low fitness) microorganisms. Additionally, this technique is easily adaptable to nearly any organism with chromosomes capable of modification, as well as to various experimental designs that require absolute quantification of microorganisms.

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

This molecular-based approach for determining bacterial fitness facilitates precise and accurate detection of microorganisms using unique genomic DNA barcodes that are quantified via digital PCR. The protocol describes calculating the competitive index for Salmonella strains; however, the technology is readily adaptable to protocols requiring absolute quantification of any genetically-malleable organism.

A competitive index is a common method used to assess bacterial fitness and/or virulence. The utility of this approach is exemplified by its ease to ...

Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader

This study aimed to develop a repeatable, reliable, high-throughput protocol to monitor bacterial growth in 96-well plates and analyze the maximum growth rate. The growth curves and maximum growth rates of two bacterial species were determined. Issues including (i) lid condensation, (ii) pathlength correction, (iii) inoculation size, (iv) sampling time interval, and (v) spatial bias were investigated. The repeatability of the protocol was assessed with three independent technical replications, with a standard deviation of 0.03 between the runs. The maximum growth rates of Bacillus mycoides and Paenibacillus tundrae were determined to be&#160;(mean&#160;&#177; SD) 0.99 h&#8722;1 &#177;&#160; 0.03 h&#8722;1 and 0.85 h&#8722;1 &#177; 0.025 h&#8722;1, respectively. These bacteria are more challenging to monitor optically due to their affinity to clump together. This study demonstrates the critical importance of inoculation size, path length correction, lid warming, sampling time intervals, and well-plate spatial bias to obtain reliable, accurate, and reproducible data on microplate readers. The developed protocol and its verification steps can be expanded to other methods using microplate readers and high-throughput protocols, reducing the researchers&#39; innate errors and material costs.

Here, a high-throughput protocol is presented to measure growth data, including growth curves, growth rate, and maximum growth rate. The protocol was verified and validated using two biofilm-producing bacteria. The results and approach applied in this study can be expanded to other high-throughput protocols using microplate readers.

This study aimed to develop a repeatable, reliable, high-throughput protocol to monitor bacterial growth in 96-well plates and analyze the maximum growth ...

Environment

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are affected both by the doubling time of the microorganism and by any delay in growth upon transfer from one condition to another, the lag. The ScanLag method enables the characterization of these two independent properties at the level of colonies originating each from a single cell, generating a two-dimensional distribution of the lag time and of the growth time. In ScanLag, measurement of the time it takes for colonies on conventional nutrient agar plates to be detected is automated on an array of commercial scanners controlled by an in house application. Petri dishes are placed on the scanners, and the application acquires images periodically. Automated analysis of colony growth is then done by an application that returns the appearance time and growth rate of each colony. Other parameters, such as the shape, texture and color of the colony, can be extracted for multidimensional mapping of sub-populations of cells. Finally, the method enables the retrieval of rare variants with specific growth phenotypes for further characterization. The technique could be applied in bacteriology for the identification of long lag that can cause persistence to antibiotics, as well as a general low cost technique for phenotypic screens.

ScanLag is a high-throughput method for measuring the delay in growth, namely lag time, as well as the growth rate of colonies for thousands of cells in parallel. Screening using ScanLag enables the discrimination between long lag-time and slow growth at the level of a single variant.

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are ...

Immunology and Infection

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysis by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.

Here, we present phenomic approaches for the functional characterization of putative phage genes. Techniques include a developed assay capable of monitoring host anabolic metabolism, the Multi-phenotype Assay Plates (MAPs), in addition to the established method of metabolomics, capable of measuring effects to catabolic metabolism.

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage ...

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).

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.

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

Bioengineering

Multi-scale Analysis of Bacterial Growth Under Stress Treatments

Analysis of the bacterial ability to grow and survive under stress conditions is essential for a wide range of microbiology studies. It is relevant to characterize the response of bacterial cells to stress-inducing treatments such as exposure to antibiotics or other antimicrobial compounds, irradiation, non-physiological pH, temperature, or salt concentration. Different stress treatments might disturb different cellular processes, including cell division, DNA replication, protein synthesis, membrane integrity, or cell cycle regulation. These effects are usually associated with specific phenotypes at the cellular scale. Therefore, understanding the extent and causality of stress-induced growth or viability deficiencies requires a careful analysis of several parameters, both at the single-cell and at the population levels. The experimental strategy presented here combines traditional optical density monitoring and plating assays with single-cell analysis techniques such as flow cytometry and real time microscopy imaging in live cells. This multiscale framework allows a time-resolved description of the impact of stress conditions on the fate of a bacterial population.

This protocol allows a time-resolved description of bacterial growth under stress conditions at the single-cell and the cell population levels.

Analysis of the bacterial ability to grow and survive under stress conditions is essential for a wide range of microbiology studies. It is relevant to ...

Single-cell Microfluidic Analysis of Bacillus subtilis

Microfluidic technology overcomes many of the limitations to traditional analytical methods in microbiology. Unlike bulk-culture methods, it offers single-cell resolution and long observation times spanning hundreds of generations; unlike agarose pad-based microscopy, it has uniform growth conditions that can be tightly controlled. Because the continuous flow of growth medium isolates the cells in a microfluidic device from unpredictable variations in the local chemical environment caused by cell growth and metabolism, authentic changes in gene expression and cell growth in response to specific stimuli can be more confidently observed. Bacillus subtilis is used here as a model bacterial species to demonstrate a &#34;mother machine&#34;-type method for cellular analysis. We show how to construct and plumb a microfluidic device, load it with cells, initiate microscopic imaging, and expose cells to a stimulus by switching from one growth medium to another. A stress-responsive reporter is used as an example to reveal the type of data that may be obtained by this method. We also briefly discuss further applications of this method for other types of experiments, such as analysis of bacterial sporulation.

Single-cell Microfluidic Analysis of Bacillus subtilis

We present a method for the microfluidic analysis of individual bacterial cell lineages using Bacillus subtilis as an example. The method overcomes shortcomings of traditional analytical methods in microbiology by allowing observation of hundreds of cell generations under tightly controllable and uniform growth conditions.

Microfluidic technology overcomes many of the limitations to traditional analytical methods in microbiology. Unlike bulk-culture methods, it offers ...

Using Microtiter Dish Radiolabeling for Multiple In Vivo Measurements Of Escherichia coli (p)ppGpp Followed by Thin Layer Chromatography

The (p)ppGpp nucleotide functions as a global regulator in bacteria in response to a variety of physical and nutritional stress. It has a rapid onset, in seconds, which leads to accumulation of levels that approach or exceed those of GTP pools. Stress reversal occasions a rapid disappearance of (p)ppGpp, often with a half-life of less than a minute. The presence of (p)ppGpp results in alterations of cellular gene expression and metabolism that counter the damaging effects of stress. Gram-negative and Gram-positive bacteria have different response mechanisms, but both depend on (p)ppGpp concentration. In any event, there is a need to simultaneously monitor many radiolabeled bacterial cultures at time intervals that may vary from 10 seconds to hours during critical stress transition periods. This protocol addresses this technical challenge. The method takes advantage of temperature- and shaker-controlled microtiter dish incubators that allow parallel monitoring of growth (absorbance) and rapid sampling of uniformly phosphate-radiolabeled cultures to resolve and quantitate nucleotide pools by thin-layer chromatography on PEI-cellulose. Small amounts of sample are needed for multiple technical and biological replicates of analyses. Complex growth transitions, such as diauxic growth and rapid (p)ppGpp turnover rates can be quantitatively assessed by this method.

Using Microtiter Dish Radiolabeling for Multiple In Vivo Measurements Of Escherichia coli pppGpp Followed by Thin Layer Chromatography

The growth of radiolabeled bacterial cultures in microtiter dishes facilitates high throughput sampling that allows multiple technical and biological replicate assays of nucleotide pool abundance, including that of (p)ppGpp. The effects of growth transitions provoked by sources of physiological stress as well as recovery from stress can be monitored.

The (p)ppGpp nucleotide functions as a global regulator in bacteria in response to a variety of physical and nutritional stress. It has a rapid onset, in ...

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental inputs into stress tolerance, pathogenicity, and other key components of fitness. This manuscript describes a microscope-based assay that tracks approximately 105&#160;Saccharomyces cerevisiae microcolonies per experiment. After automated time-lapse imaging of yeast immobilized in a multiwell plate, microcolony growth rates are easily analyzed with custom image-analysis software. For each microcolony, expression and localization of fluorescent proteins and survival of acute stress can also be monitored. This assay allows precise estimation of strains&#39; average growth rates, as well as comprehensive measurement of heterogeneity in growth, gene expression, and stress tolerance within clonal populations.

Yeast growth phenotypes are precisely measured through highly parallel time-lapse imaging of immobilized cells growing into microcolonies. Simultaneously, stress tolerance, protein expression, and protein localization can be monitored, generating integrated datasets to study how environmental and genetic differences, as well as gene-expression heterogeneity among isogenic cells, modulate growth.

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental ...

Precise, High-throughput Analysis of Bacterial Growth

Summary

Explore More Videos

An Assay for Quantifying Protein-RNA Binding in Bacteria

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

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

Multi-scale Analysis of Bacterial Growth Under Stress Treatments

Single-cell Microfluidic Analysis of Bacillus subtilis

Using Microtiter Dish Radiolabeling for Multiple In Vivo Measurements Of Escherichia coli (p)ppGpp Followed by Thin Layer Chromatography

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression