We thank James Shan for his efforts in field video recording. We thank Jon Byron and Matthew Semerau for their work in video editing. We thank Ansen Luo, Harry Du, and Frank Deng for their support in locating the test field. We thank Maria Rubinsky for her valuable comments on the whole manuscript. All the acknowledged persons are employees of Herbalife International of America, Inc.

1. Sample collection
<ol>
	<li>Set up a testing area in the field with a flat and horizontal surface.</li>
	<li>Identify a representative plant that reflects the characteristics of majority of the plants in the chamomile flower field (Figure 4).</li>
	<li>Pick a flower head from the representative plant using sterile gloves.</li>
	<li>Place the sample into a 2.0 mL collection tube.</li>
	<li>Repeat steps 1.3 to 1.4 and collect a leaflet (approximately 0.5&#8211;0.7 cm long) from the same plant. 
	NOTE: M. chamomilla flower and leaf are small enough to sit at the bottom of a 2.0 mL collection tube. For other botanicals with larger surface area, a paper punch or scissors (rinsed in 70% ethanol prior to use) can be used to isolate tissue samples for testing. When multiple sampling is required, rinse the paper punch or scissors between handling of different samples.</li>
</ol>
2. DNA extraction
<ol>
	<li>Preheat the dry bath incubator to 95 &#176;C.</li>
	<li>To each collection tube, add 100 &#181;L of the extraction solution from the plant DNA extraction kit (listed in Table of Materials). For better DNA extraction efficiency, grind the tissue sample in the extraction solution using a tissue pestle.</li>
	<li>Close the tube. Ensure that the botanical tissue is covered with the extraction solution throughout the extraction process.</li>
	<li>Place the collection tubes in a preheated dry bath incubator and incubate the collection tubes at 95 &#176;C for 10 min.</li>
	<li>After 10 min, take the tubes out of the dry bath incubator.</li>
	<li>Add 100 &#181;L of the dilution solution from the same DNA extraction kit and mix the solution by pipetting up and down several times.</li>
	<li>Repeat the same step for leaflet extraction.</li>
	<li>Shake to mix the solution further. The plant tissue usually does not appear to be degraded after this treatment. The liquid color may change and become cloudy. 
	NOTE: The diluted solution can be stored at room temperature overnight if not proceeding immediately. It is not necessary to remove the cellular debris from plant tissue before storage. The liquid in the tubes holds the DNA templates for downstream PCR amplification.</li>
</ol>
3. PCR reaction setup
<ol>
	<li>Configure the qPCR instrument thermocycling conditions according to the manufacturer&#39;s specifications. Apply the PCR thermocycling profile listed in (Table 1), which starts with a constant temperature step for initial denaturation, followed by 25 cycles of amplification, and ends with temperature ramping to obtain a high-resolution melting curve.</li>
	<li>Thaw the qPCR Master Mix and primers (Table 2) at room temperature prior to use.</li>
	<li>Plan the reaction that will be loaded in each well: wells containing positive control with target species, positive control with non-target species, samples, and negative control (Figure 5).
	<ol>
		<li>In this example, ten wells are used &#8211; five for the German chamomile identification test and the remaining five for the Roman chamomile identification test. For each type of species identification test, one well contains positive control with DNA extracted from targeted species&#8217; reference material, one well contains positive control with DNA extracted from non-targeted species&#8217; reference material, two wells are filled with flower and leaf DNA samples extracted from the field, and one well is allocated for a negative control. Table 3 describes each well type.</li>
	</ol>
	</li>
	<li>Prepare a reaction master-mix according to the manual for each botanical species identification test. A typical reaction master-mix contains universal qPCR Master Mix (2x), forward and reverse species-specific primers, and nuclease-free water. Table 4 lists the reaction system composition. 
	NOTE: If not using immediately, store the qPCR reaction master-mix at +2 &#176;C to +8 &#176;C in a cooler or mini-fridge.</li>
	<li>Thoroughly mix the reaction master-mix by pipetting before use.</li>
	<li>Place the qPCR cartridge face-up on a flat and stable surface.</li>
	<li>Load 18 &#181;L of the reaction master-mix configured in step 3.4 into the cartridge wells according to the wells defined in step 3.3. For this demonstration, add the German chamomile identification test reaction master mix into wells labeled for GC test (GCT in wells 1, 3, 5, 7, 9) and the Roman chamomile identification test reaction master-mix into wells labeled for RC test (RCT in wells 2, 4, 6, 8, 10) (see Figure 5).</li>
	<li>Transfer 2 &#181;L of sample DNA from the supernatant of DNA extraction tubes and pre-extracted DNA positive controls into cartridge wells preloaded with qPCR master mix. After adding each DNA template to the qPCR master mix, gently mix the solution by pipetting. 
	NOTE: Avoid floating cellular debris when transferring DNA from the DNA extraction tube. Use minicentrifuge to separate the supernatant and cellular debris, if necessary.</li>
	<li>Carefully seal the cartridge with adhesive film. Load the cartridge onto the thermocycling chamber and close it.</li>
	<li>Set the qPCR instrument to run.</li>
</ol>

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We certify that Zhengxiu Yang, Zheng Quan, Tiffany Chua, Leo Li, Yanjun Zhang, Silva Babajanian, Tricia Chua, Peter Chang, Gary Swanson, Zhengfei Lu are employees of Herbalife International of America, Inc. We certify that Francesco Buongiorno, Isabella Della Noce, and Lorenzo Colombo are employees of Hyris Ltd. that produces the portable qPCR instrument used in this article.

The design of primers and template selection are the crucial steps in obtaining an efficient and specific qPCR amplification. After identifying a suitable template, primer design software is typically used to aid selection of primers based on design variables such as primer length, melting temperature, and GC content33,34. Optimization and validation can be performed under the expected experimental conditions of the assay to ensure specificity, sensitivity, and robustness of a PCR reaction35. Sub-optimal primer design may result in primer-dimer formation, wherein primer interactions produce non-specific products36.
The no template controls (NTC) used in this study check for both DNA contamination and the presence of primer-dimers that could affect the assay. Results showed no amplification, a good indication that both DNA contamination and primer-dimers are not a concern. DNA contamination and primer-dimers are manifested in melt curves through no template controls, and as extra peaks in melt curves of positive controls. Typically, the melt curve of a positive control is expected to contain a single peak, unless AT-rich subdomains in the template cause uneven melting. Double peaks could be predicted by simulating melting assays using the uMELT software37. In this study, the gold standard of running the PCR product on agarose gel was used to confirm the presence of target PCR product and absence of contamination and primer-dimers.
A considerable challenge in botanical material molecular analysis is obtaining good-quality DNA following the botanical DNA extraction process. Botanical materials are traded and consumed for the active chemical compounds that are associated with health benefits. In the process of DNA extraction, these chemical compounds will also be released into the DNA extraction solution, potentially causing PCR inhibition, thereby resulting in failures in PCR amplification. Various plant DNA purification kits using organic solvents and columns have been developed to remove chemical compounds derived from botanicals38. However, fume hood and high-speed centrifuge required to assist these kits are not available in the field.
In the current protocol, the simplified DNA extraction method uses a commercial plant DNA extraction kit (see Table of Materials for details). It had the ability to neutralize common inhibitory substances for reproducible results and produced consistent results for M. chamomilla and C. nobile. Both M. chamomilla and C. nobile flower heads and leaves yielded PCR amplicons with specific melt peaks, indicating that the presence of PCR inhibitors was not a concern. For other plants with higher levels of PCR inhibitors, amplifying DNA in their original extraction may be less efficient. To reduce inhibition and improve amplification efficiency, with access to the whole plant, other plant parts with lower polysaccharide and polyphenol content can also be used for identification purposes. If there is limited access to different plant parts, younger leaves or petals dissected from flower heads, which typically have lower phenolic content39, may offer a better chance of success. Since DNA sequences are consistent across the whole plant, any plant part may be used to confirm species identity. If PCR amplification is still suboptimal, the original DNA extract can be further diluted before PCR, or more sophisticated laboratory purification protocols can be used.
Another challenge for PCR analysis is false positive results caused by DNA contamination, which can negatively impact data interpretation. It is usually controlled by active housekeeping, using dedicated equipment, and restricting work to designated areas. Using qPCR, all PCR analysis can be accomplished in a closed system, which greatly reduces the chance of PCR amplicon contamination in an environment that is not well controlled. Besides, environmental DNA should also not show a false positive due to the specificity of the assay, according to a previous validation study40.
There is room for improvement. In the protocol presented here, intercalating dye was used to show target fragment amplification in real-time. The specificity of the method is further confirmed by the characteristic melting temperature, which is distinct between M. chamomilla and C. nobile amplicons. Therefore, intercalating dye-based PCR can efficiently answer the question &#8220;What is this plant species?&#8221; in the field. However, in addition to the need of performing botanical identification on a single plant isolated from the field, in many circumstances, botanical powders or extracts in the warehouse will also benefit from an on-site rapid identity assessment. For these types of materials, additional questions may need to be addressed, such as &#8220;What is in this material?&#8221;, &#8220;Does it contain the botanical species I am looking for?&#8221;, &#8220;Does it contain adulterants I want to avoid?&#8221;, and &#8220;Is it substituted wholly or partially by other botanical species that are harmful?&#8221;. Instead of using intercalating dye, different qPCR probes can be designed to target amplicons from different botanicals in one reaction system, while maintain high specificity and efficiency of the assay. Development of probe-based qPCR and utilization of a portable qPCR system that offers multiple channel detection can further extend the application of field testing as a fit-for-purpose assay to a broader environment setting, such as botanical material warehouses and distribution centers to answer more complicated questions. In addition, using multiple probes also allows the user to include internal amplification in each reaction system, so that more information will be available when PCR inhibition is suspected.
The protocol presented here has the following advantages compared to existing technologies used for the same purpose. First, for traditional morphological and chemical identification methods, the procedure and its results need to be conducted and interpreted by experts. qPCR-based identification tests can be conducted by people with basic molecular biology training and interpreted in a more standardized manner. Second, compared to qPCR-based species identification and differentiation normally performed in the laboratory, the field identification protocol using a portable instrument does not require instruments with a large footprint, such as a high-speed centrifuge, DNA quality evaluation equipment, thermal cycler with fluorescence detector, and a computer running a special software. Thus, DNA-based species identification can be performed in any setting without delay. Third, searching for botanical materials is a task that requires a global operation. With advancements in cloud services and artificial intelligence, a portable device can potentially receive methods developed and validated by experts in the laboratory, be operated by non-experts in remote areas, and produce objective certifications from third parties. Therefore, this option is more compelling than ever with remote work becoming the trend.
In summary, the protocol here demonstrated field identification of M. chamomilla using a portable qPCR system. The successful application of this technique will generate highly accurate results on botanical identification and help botanical manufacturers and suppliers qualify botanical materials in a timely and cost-efficient manner.

The practice of using botanicals to maintain and improve health dates back to thousands of years. Due to stresses on the supply chain brought by increased consumer demand1, unsustainable harvesting practices and climate change2, botanical adulteration is becoming a growing concern in the food and dietary supplement industry3. The presence of undeclared or misidentified botanical species may lead to reduced efficacy, or even safety issues. For example, black cohosh (Actaea racemosa), used for treating premenstrual discomfort, may be substituted with a low-priced Asian species with limited clinical data support for its efficacy4. In a more serious case, substitution of Aristolochia fangchi for Stephania terandra in a clinical study for weight loss using Chinese herbs led to severe nephrotoxicity and renal failure in some participants5,6. The two different species shared a Chinese common name &#8220;Fang Ji&#8221;. These cases highlight the need for more stringent quality control, starting with the identification of raw materials7, preferably as close to the point of origin of the supply chain as possible, so resources can be efficiently allocated to the material of correct identity.
A number of orthogonal approaches can be used for botanical identification. Traditionally, botanical identification is performed through morphological assessment8,9 and chemical analytical methods10,11,12,13. Morphological identification is based on differences in macroscopic and microscopic features of plant materials if differences exist (Figure 1). However, the lack of training programs on classical botany in the recent years has resulted in a shortage of experts14, making this approach impractical for routine quality control. Its application in powdered botanical materials is also limited. Chemical analytical methods are widely used in pharmacopoeias and laboratories, but are not ideal for field testing due to the size of instruments such as High Performance Thin Layer Chromatography (HPTLC), High Performance Liquid Chromatography (HPLC), and Nuclear Magnetic Resonance Spectroscopy (NMR) (Figure 2), and environment requirements. Recently, genomic methods have emerged as an alternative technique for botanical species&#39; authentication and substitution detection and has proven to be efficient and precise. Genomic methods exploit the high fidelity and specificity of genetic information in plant materials15,16,17,18,19. Molecular diagnostic tools are available in the form of portable devices, and often include automated data interpretation tools that lower the barrier to technology use, making this approach ideal for field identification20,21,22,23,24. Once the molecular analysis method has been designed and validated25,26,27, it can be performed by any personnel with basic molecular biology training. Among the different portable tools available, real-time PCR on DNA sequences is one of the cost-effective choices28. The combination of a portable device, together with customized and validated molecular analysis, allows verification of botanical species and ingredients outside the laboratory, such as in farms and botanical material warehouses, reducing the time and costs associated with traditional methods.
The goal of this protocol is to introduce a method for botanical identification in situations where access to laboratory equipment and expertise is limited or unavailable, using a portable qPCR system. The method is demonstrated on a field of Matricaria chamomilla (Figure 3A), commonly known as German chamomile, widely used for its anti-inflammatory and antioxidant properties29. It can be confused with related species of similar appearance or odor, especially from the genera Chamaemelum, Tanacetum, and Chrysanthemum30,31,32. Among the related species, Chamaemelum nobile, also known as Roman chamomile, is a noticeable one with comparable production levels in commerce (Figure 3B). The method demonstrated was designed to not only identify the target botanical species M. chamomilla, but also detect its close relative, C. nobile, based on specific amplification of DNA sequences.
This article explains, in detail, how to perform field botanical identification of M. chamomilla using intercalating dye-based qPCR and melt curve analysis on a portable device. The protocol includes the collection of botanical samples from the field, on-site DNA extraction, and set up of real-time PCR reaction. To ensure a valid conclusion, target botanical M. chamomilla and non-target botanical C. nobile genomic DNA, pre-extracted from certified botanical reference materials, are used as positive control. The specificity of this method is demonstrated by performing both M. chamomilla and C. nobile identification tests individually on samples and controls. Non-template negative control is used to exclude false positive results caused by PCR contamination.

<table><tbody><tr><td>Battery</td><td>TNE</td><td>78000mAh</td><td>Provide field power supply</td></tr><tr><td>bCUBE</td><td>HYRIS</td><td>bCUBE 2.0</td><td>Portable qPCR instrument</td></tr><tr><td>Cartridges(16 Well)</td><td>HYRIS</td><td>NA</td><td>Consumables for bCUBE</td></tr><tr><td>Electric pipette</td><td>Eppendorf</td><td>NA</td><td>Handling liquid</td></tr><tr><td>Extract-N-Amp&lt;sup&gt;TM&lt;/sup&gt; plant PCR kit</td><td>SIGMA</td><td>XNAP2-1KT</td><td>Plant DNA extraction kit</td></tr><tr><td>German chamomile (&lt;em&gt;Matricaria chamomilla L&lt;/em&gt;) flower botanical reference materials</td><td>AHP</td><td>2264</td><td>Used as positive control</td></tr><tr><td>Mini dry bath</td><td>Yooning</td><td>MiniH-100L</td><td>For DNA extraction</td></tr><tr><td>Nuclease-free water</td><td>AMBION</td><td>AM9937</td><td>qPCR reaction</td></tr><tr><td>Primer</td><td>Thermo Fisher Scientific</td><td>NA</td><td>qPCR reaction</td></tr><tr><td>Roman chamomile (&lt;em&gt;Chamaemelum nobile&lt;/em&gt;) flower botancial reference materials</td><td>ChromaDex</td><td>ASB-00030806-005</td><td>Used as positive control</td></tr><tr><td>Luna universal qPCR master mix</td><td>NEB</td><td>M3003L</td><td>qPCR reaction</td></tr></tbody></table>

field identification of matricaria chamomilla using a portable qpcr system

Quality control in botanical products begins with the raw material supply. Traditionally, botanical identification is performed through morphological assessment and chemical analytical methods. However, the lack of availability of botanists, especially in recent years, coupled with the need to enhance quality control to combat the stresses on the supply chain brought by increasing consumer demand and climate change, necessitates alternative approaches. The goal of this protocol is to facilitate botanical species identification using a portable qPCR system on the field or in any setting, where access to laboratory equipment and expertise is limited. Target DNA is amplified using dye-based qPCR, with DNA extracted from botanical reference materials serving as a positive control. The target DNA is identified by its specific amplification and matching its melting peak against the positive control. A detailed description of the steps and parameters, from hands-on field sample collection, to DNA extraction, PCR amplification, followed by data interpretation, has been included to ensure that readers can replicate this protocol. The results produced align with traditional laboratory botanical identification methods. The protocol is easy to perform and cost-effective, enabling quality testing on raw materials as close to the point of origin of the supply chain as possible.

Following the protocol described in section 1, botanical DNA from flower head and leaf were extracted into the supernatant after heat incubation of the collection tube at 95 &#176;C for 10 min. In the current study, the supernatant showed a yellow and greenish color for both flower and leaf, indicating that a variety of natural compounds were released into the supernatant with botanical DNA (Figure 6). Although reliable PCR amplification was achieved later in triplicate for all field extracted DNA template, DNA quality assessment was performed in the laboratory as reference. The concentration of flower head DNA extract, determined by fluorometry, ranged from 3.69&#8211;5.36 ng/&#181;L, while the concentration of leaf DNA extract ranged from 6.42&#8211;9.29 ng/&#181;L. The A260/A280 and A260/A230 absorbance ratios of flower and leaf DNA extracts were measured by spectrophotometry. However, due to the overlap between DNA and phytochemical UV absorption spectrum, these ratios could not be reliability measured (data not shown).
Intercalating fluorescent dye was used to monitor the amplification of target fragments in real-time. Since both the specific primers M. chamomilla and C. nobile target the internal transcribed spacer 2 (ITS2) region, which has tens to hundreds of copies in the plant genome, 25 PCR amplification cycles are sufficient to generate enough amplicons for the identification of chamomile species. In Figure 7, the Ct value for M. chamomilla positive control in M. chamomilla identification test was less than 25 (GCP_GCT), while after 25 amplification cycles, the fluorescence of the same control in C. nobile identification test was below the detection threshold (GCP_RCT). On the other hand, after 25 cycles, the fluorescence for C. nobile positive control in M. chamomilla identification test was below the detection threshold (RCP_GCT), while the Ct value for the same control in C. nobile identification test was less than 25 (RCP_RCT). The amplification of target and non-target positive controls in their respective assays demonstrate the specificity of the M. chamomilla identification assay. For sample DNA, field flower head and leaf DNA extract yielded Ct values of 15.18 and 19.41 in M. chamomilla identification test, respectively (Sample1(FLOWER)_GCT and Sample2(LEAF)_GCT). Both of these samples were not amplified in C. nobile identification test (Sample1(FLOWER)_RCT and Sample2(LEAF)_RCT). The amplification patterns of both the samples matched the amplification pattern of M. chamomilla positive control. Negative controls were not amplified in both M. chamomilla and C. nobile identification tests (NC_GCT and NC_RCT), excluding the possibility of false positives caused by PCR contamination. To further confirm specific amplification in positive controls and samples, fractions of PCR end product from each well were run on 2% agarose gel in the laboratory (Figure 8). For M. chamomilla identification test, both field samples yielded amplicons running at the same position as the M. chamomilla positive control with an estimated size slightly above 100 bp (theoretical size 102 bp). For C. nobile identification test, non-target species C. nobile positive control yielded a band between 50 and 100 bp, fitting the theoretical size of 65 bp. The rest of the lanes showed no specific amplification product, which was in agreement with the absence of fluorescent signal for these wells, as observed in field testing.
Following PCR amplification, a melting curve analysis was performed to assess the dissociation characteristics of double-stranded DNA (dsDNA) during heating. As the temperature ramped up during the final cycle for melt curve analysis, increases in temperature caused the double-strand amplicons to dissociate. The intercalating fluorescent dye was gradually released into the solution, decreasing fluorescence intensity (Figure 9A). The inflection point of the first derivative curve was used to determine the melting temperature (Tm) (Figure 9B), which depends mainly on DNA fragment length and GC content. Combining Ct value with melting temperature can increase the specificity of qPCR analysis. In the current study, the melting temperature peak of M. chamomilla positive control PCR amplicon occurred at 85.6 &#176;C (GCP_GCT) and it was distinct from the melting temperature peak of C. nobile positive control PCR amplicon at 79.1 &#176;C (RCP_RCT). The PCR amplicon from field flower head and leaf produced melting temperature peaks at 85.2 &#176;C and 84.8 &#176;C, respectively (Sample1(FLOWER)_GCT and Sample2(LEAF)_GCT). To assess melting temperature variations measured by the portable qPCR system, additional datapoints were collected to confirm that sample melting temperatures were always close to the melting temperature obtained from M. chamomilla positive control (within 2 &#176;C) and were far away from the melting temperature of C. nobile positive control amplicon (Figure 10). Melting temperature peaks were sometimes reported in other wells. However, their Ct values were not less than 25 and melting temperature peaks were not close to M. chamomilla or C. nobile positive control (more than 2 &#176;C apart).
In summary, field M. chamomilla identification test can be interpreted based on decision rules summarized in Table 5. With all the positive controls testing positive for the putative botanical species, negative for the other species, and negative controls testing negative, both field samples were determined to contain M. chamomilla but not C. nobile. In addition, to align field testing results with other analytical techniques, field conclusions were further confirmed by a previously validated DNA barcoding method25 (data not shown).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60940/60940fig01.jpg" /> 
Figure 1: Morphological identification of botanical materials.&#160;(A) Hibiscus rosa-sinensis flowers, Curcuma longa roots, Malva Sylvestris leaves, Rosmarinus officinalis leaves, Coriandrum sativum seeds, Zingiber officinale roots. (B) Petroselinum crispum and Apium graveolens flakes are difficult to differentiate. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60940/60940fig02.jpg" /> 
Figure 2: Chemical identification of botanical materials.&#160;(A) HPTLC instrument and a representative HPTLC chromatogram. (B) HPLC instrument and a representative HPLC chromatogram. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60940/60940fig03.jpg" /> 
Figure 3: Matricaria chamomilla and Chamaemelum nobile in the field.&#160;(A) Matricaria chamomilla, adapted from Wikipedia under CC BY-SA 3.0, https://en.wikipedia.org/wiki/Matricaria_chamomilla#/media/File:Matricaria_February_2008-1.jpg. (B) Chamaemelum nobile, adapted from Wikipedia under CC BY-SA 3.0, https://en.wikipedia.org/wiki/Chamomile#/media/File:Chamaemelum_nobile_001.JPG. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60940/60940fig04.jpg" /> 
Figure 4: Collecting M. chamomilla plant parts from the field. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60940/60940fig05.jpg" /> 
Figure 5: Layout of testing wells in the demonstration. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60940/60940fig06.jpg" /> 
Figure 6: Field DNA extract in collection tubes.&#160;Botanical tissue remains in the original tube and is covered by yellowish DNA extract. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/60940/60940fig07.jpg" /> 
Figure 7: Fluorescence plot showing the accumulation of PCR products over 25 cycles of thermocycling.&#160;M. chamomilla positive control and C. nobile positive control show Ct values less than 25 in M. chamomilla and C. nobile identification tests, respectively. The field flower and leaf samples were amplified by M. chamomilla identification test with Ct values of 15.18 and 19.41. The rest of the wells were not amplified. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/60940/60940fig08.jpg" /> 
Figure 8: Gel electrophoresis of field PCR amplification products. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/60940/60940fig09.jpg" /> 
Figure 9: Melting temperature analysis.&#160;(A) The fluorescence signal in each well decreases with the increasing temperature. (B) The identity of the PCR products was confirmed by the melting temperature peak in melting curve analysis. The field flower and leaf samples show peaks at 85.2 &#176;C and 84.8 &#176;C. These are close to the peak produced by M. chamomilla positive control. The C. nobile positive control produced a peak at 79.1 &#176;C, which is different from the other three samples. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/60940/60940fig10.jpg" /> 
Figure 10: Melting temperature peak variation between positive control and field samples. <a href="https://www.jove.com/files/ftp_upload/60940/60940fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Stage</td>
			<td>Cycle</td>
			<td>Temperature</td>
			<td>Time</td>
		</tr>
		<tr>
			<td>Constant Temperature</td>
			<td>1</td>
			<td>95 &#176;C</td>
			<td>60s</td>
		</tr>
		<tr>
			<td rowspan="2">Amplification</td>
			<td rowspan="2">25</td>
			<td>95 &#176;C</td>
			<td>30s</td>
		</tr>
		<tr>
			<td>60 &#176;C</td>
			<td>30s</td>
		</tr>
		<tr>
			<td rowspan="2">Melting Curve</td>
			<td rowspan="2">1</td>
			<td>60 &#176;C</td>
			<td rowspan="2">Ramp 0.05 &#176;C/s</td>
		</tr>
		<tr>
			<td>95 &#176;C</td>
		</tr>
	</tbody>
</table>
Table 1: qPCR thermocycling conditions for M. chamomilla and C. nobile identification tests.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Assay</td>
			<td>Primer name</td>
			<td colspan="2">Sequence 5&#39;-3&#39;</td>
			<td>Position</td>
			<td>Region</td>
			<td>Amplicon Size</td>
		</tr>
		<tr>
			<td rowspan="2">Matricaria recutita</td>
			<td>ZL3</td>
			<td colspan="2">TCGTCGGTCGCAAGGATAAG</td>
			<td>Forward</td>
			<td rowspan="2">ITS2</td>
			<td rowspan="2">102 bp</td>
		</tr>
		<tr>
			<td>ZL4</td>
			<td colspan="2">TAAACTCAGCGGGTAGTCCC</td>
			<td>Reverse</td>
		</tr>
		<tr>
			<td rowspan="2">Chamaemelum nobile</td>
			<td>ZL11</td>
			<td colspan="2">TGTCGCACGTTGCTAGGAAGCA</td>
			<td>Forward</td>
			<td rowspan="2">ITS2</td>
			<td rowspan="2">65 bp</td>
		</tr>
		<tr>
			<td>ZL12</td>
			<td colspan="2">TCGAAGCGTCATCCTAAGACAAC</td>
			<td>Reverse</td>
		</tr>
	</tbody>
</table>
Table 2: Primer pairs for M. chamomilla and C. nobile identification tests.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Well position</td>
			<td>Well name</td>
			<td colspan="2">Description</td>
		</tr>
		<tr>
			<td>1</td>
			<td>GC_PosCtrl_GC_Test</td>
			<td colspan="2">German chamomile positive control under GC Test</td>
		</tr>
		<tr>
			<td>2</td>
			<td>GC_PosCtrl_RC_Test</td>
			<td colspan="2">German chamomile positive control under RC Test</td>
		</tr>
		<tr>
			<td>3</td>
			<td>RC_PosCtrl_GC_Test</td>
			<td colspan="2">Roman chamomile positive control under GC Test</td>
		</tr>
		<tr>
			<td>4</td>
			<td>RC_PosCtrl_RC_Test</td>
			<td colspan="2">Roman chamomile positive control under RC Test</td>
		</tr>
		<tr>
			<td>5</td>
			<td>Field_Sample_GC_Test</td>
			<td colspan="2">Sample of leaf tissue under GC Test</td>
		</tr>
		<tr>
			<td>6</td>
			<td>Field_Sample_RC_Test</td>
			<td colspan="2">Sample of leaf tissue under RC Test</td>
		</tr>
		<tr>
			<td>7</td>
			<td>Field_Sample_GC_Test</td>
			<td colspan="2">Sample of flower tissue under GC Test</td>
		</tr>
		<tr>
			<td>8</td>
			<td>Field_Sample_RC_Test</td>
			<td colspan="2">Sample of flower tissue under RC Test</td>
		</tr>
		<tr>
			<td>9</td>
			<td>NegCtrl_GC_Test</td>
			<td colspan="2">Negative control sample under GC Test</td>
		</tr>
		<tr>
			<td>10</td>
			<td>NegCtrl_RC_Test</td>
			<td colspan="2">Negative control sample under RC Test</td>
		</tr>
	</tbody>
</table>
Table 3: Well types and descriptions for M. chamomilla and C. nobile identification tests.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Reagent</td>
			<td>GC_Test</td>
			<td>RC_Test</td>
		</tr>
		<tr>
			<td>Universal qPCR Mix*</td>
			<td>10 &#181;L</td>
			<td>10 &#181;L</td>
		</tr>
		<tr>
			<td>ZL3 primer (10 &#181;M)</td>
			<td>0.4 &#181;L</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>ZL4 primer (10 &#181;M)</td>
			<td>0.4 &#181;L</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>ZL11 primer (10 &#181;M)</td>
			<td>NA</td>
			<td>0.4 &#181;L</td>
		</tr>
		<tr>
			<td>ZL12 primer (10 &#181;M)</td>
			<td>NA</td>
			<td>0.4 &#181;L</td>
		</tr>
		<tr>
			<td>H2O (Nuclease-free)</td>
			<td>7.2 &#181;L</td>
			<td>7.2 &#181;L</td>
		</tr>
		<tr>
			<td colspan="3">* contains Hot Start Taq DNA Polymerase</td>
		</tr>
	</tbody>
</table>
Table 4: Master-mix composition for M. chamomilla and C. nobile identification tests.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Well Name</td>
			<td rowspan="2">Expected Result</td>
			<td>Positive Result Criteria</td>
			<td>Negative Result Criteria</td>
		</tr>
		<tr>
			<td>Detected / Present</td>
			<td>Not Detected / Absent</td>
		</tr>
		<tr>
			<td>GC_PosCtrl_GC_Test</td>
			<td>Detected</td>
			<td>Ct &#60; 25 and 84 &#60;= Tm &#60;= 86</td>
			<td>-</td>
		</tr>
		<tr>
			<td>GC_PosCtrl_RC_Test</td>
			<td>Not Detected</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>RC_PosCtrl_GC_Test</td>
			<td>Not Detected</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>RC_PosCtrl_RC_Test</td>
			<td>Detected</td>
			<td>Ct &#60; 25 and 79 &#60;= Tm &#60;= 81</td>
			<td>-</td>
		</tr>
		<tr>
			<td>Field_Sample_Leaf_GC_Test</td>
			<td>Present</td>
			<td>Ct &#60; 25 and 84 &#60;= Tm &#60;= 86</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>Field_Sample_Leaf_RC_Test</td>
			<td>Absent</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>Field_Sample_Flower_GC_Test</td>
			<td>Present</td>
			<td>Ct &#60; 25 and 84 &#60;= Tm &#60;= 86</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>Field_Sample_Flower_RC_Test</td>
			<td>Absent</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>NegCtrl_GC_Test</td>
			<td>Not Detected</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
		<tr>
			<td>NegCtrl_RC_Test</td>
			<td>Not Detected</td>
			<td>-</td>
			<td>No Ct value within 25 cycles</td>
		</tr>
	</tbody>
</table>
Table 5: Rules for qPCR result interpretation.

Watch this Scientific Journal Video about Field Identification of Matricaria chamomilla using a Portable qPCR System at JoVE.com

Field Identification of Matricaria chamomilla using a Portable qPCR System

Presented here is a protocol for field identification of Matricaria chamomilla using a portable qPCR system. This easy-to-perform protocol is ideal as a method to confirm the identity of a botanical species at locations where access to laboratory equipment and expertise is limited, such as farms and warehouses.

Field Identification of Matricaria chamomilla using a Portable qPCR System

Quality control in botanical products begins with the raw material supply. Traditionally, botanical identification is performed through morphological ...

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Research

JoVE Journal

Biology

6.8K Views.  Herbalife NatSource (Hunan) Natural Products Co., Ltd.. De praktijk van het gebruik van plantaardige producten om de gezondheid te verbeteren dateert van duizenden jaren. De laatste tijd heeft de toegenomen vraag, in combinatie met niet-duurzame oogstpraktijken en klimaatverandering, de supply chain onder druk gezet. Hierdoor wordt botanische vervalsing een groeiende zorg, waardoor botanische identificatie een steeds belangrijker onderdeel wordt van kwaliteitscontrole.Idealiter wordt identificatie zo dicht mogelijk bij de bron uitgevoerd, z...

Video: Field Identification of Matricaria chamomilla using a Portable qPCR System

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those populations. Single-cell gene expression measurements can help assess the role of noise in gene expression, identify correlations in the expression of pairs of genes, and reveal subpopulations of cells that respond differently to a stimulus. Here, we describe a procedure to measure the expression of up to 96 genes in single mammalian cells isolated from a population growing in tissue culture. Cells are sorted into lysis buffer by fluorescence-activated cell sorting (FACS), and the mRNA species of interest are reverse-transcribed and amplified. Gene expression is then measured using a microfluidic real-time PCR machine, which performs up to 96 qPCR assays on up to 96 samples at a time. We also describe the generation and use of PCR amplicon standards to enable the estimation of the absolute number of each transcript. Compared with other methods of measuring gene expression in single cells, this approach allows for the quantification of more distinct transcripts than RNA FISH at a lower cost than RNA-Seq.

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those ...

Genetics

On-Site Molecular Detection of Soil-Borne Phytopathogens Using a Portable Real-Time PCR System

On-site diagnosis of plant diseases can be a useful tool for growers for timely decisions enabling the earlier implementation of disease management strategies that reduce the impact of the disease. Presently in many diagnostic laboratories, the polymerase chain reaction (PCR), particularly real-time PCR, is considered the most sensitive and accurate method for plant pathogen detection. However, laboratory-based PCRs typically require expensive laboratory equipment and skilled personnel. In this study, soil-borne pathogens of potato are used to demonstrate the potential for on-site molecular detection. This was achieved using a rapid and simple protocol comprising of magnetic bead-based nucleic acid extraction, portable real-time PCR (fluorogenic probe-based assay). The portable real-time PCR approach compared favorably with a laboratory-based system, detecting as few as 100 copies of DNA from Spongospora subterranea. The portable real-time PCR method developed here can serve as an alternative to laboratory-based approaches and a useful on-site tool for pathogen diagnosis.

Rapid and accurate detection of plant pathogens on-site, especially soil-borne pathogens, is crucial to prevent further inoculum production and proliferation of plant diseases in the field. The method developed here using a portable real-time PCR detection system enables on-site diagnosis under field conditions.

On-site diagnosis of plant diseases can be a useful tool for growers for timely decisions enabling the earlier implementation of disease management ...

Environment

A Loop-mediated Isothermal Amplification (LAMP) Assay for Rapid Identification of Bemisia tabaci

The whitefly Bemisia tabaci (Gennadius) is an invasive pest of considerable importance, affecting the production of vegetable and ornamental crops in many countries around the world. Severe yield losses are caused by direct feeding, and even more importantly, also by the transmission of more than 100 harmful plant pathogenic viruses. As for other invasive pests, increased international trade facilitates the dispersal of B. tabaci to areas beyond its native range. Inspections of plant import products at points of entry such as seaports and airports are, therefore, seen as an important prevention measure. However, this last line of defense against pest invasions is only effective if rapid identification methods for suspicious insect specimens are readily available. Because the morphological differentiation between the regulated B. tabaci and close relatives without quarantine status is difficult for non-taxonomists, a rapid molecular identification assay based on the loop-mediated isothermal amplification (LAMP) technology has been developed. This publication reports the detailed protocol of the novel assay describing rapid DNA extraction, set-up of the LAMP reaction, as well as interpretation of its read-out, which allows identifying B. tabaci specimens within one hour. Compared to existing protocols for the detection of specific B. tabaci biotypes, the developed method targets the whole B. tabaci species complex in one assay. Moreover the assay is designed to be applied on-site by plant health inspectors with minimal laboratory training directly at points of entry. Thorough validation performed under laboratory and on-site conditions demonstrates that the reported LAMP assay is a rapid and reliable identification tool, improving the management of B. tabaci.

A Loop-mediated Isothermal Amplification LAMP Assay for Rapid Identification of Bemisia tabaci

This paper reports the protocol for a rapid identification assay for Bemisia tabaci based on loop-mediated isothermal amplification (LAMP) technology. The protocol requires minimal laboratory training and can, therefore, be implemented on-site at points of entry for plant imports such as seaports and airports.

The whitefly Bemisia tabaci (Gennadius) is an invasive pest of considerable importance, affecting the production of vegetable and ornamental crops in many ...

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in immunocompromised patients. In most cases only few data, mostly published as case reports, are available which investigate the role of such pathogens as an infectious agent. Therefore, in order to clarify the pathogenic character of such microorganisms, it is necessary to conduct epidemiologic studies which include large numbers of these bacteria. The methods used in such a surveillance study have to meet the following criteria: the identification of the strains has to be accurate according to the valid nomenclature, they should be easy to handle (robustness), economical in routine diagnostics and they have to generate comparable results among different laboratories. Generally, there are three strategies for identifying bacterial strains in a routine setting: 1) phenotypic identification characterizing the biochemical and metabolic properties of the bacteria, 2) molecular techniques such as 16S rRNA gene sequencing and 3) mass spectrometry as a novel proteome based approach. Since mass spectrometry and molecular approaches are the most promising tools for identifying a large variety of bacterial species, these two methods are described. Advances, limitations and potential problems when using these techniques are discussed.

Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and molecular techniques (16S rRNA gene sequencing) permit the identification of rare bacterial pathogens in routine diagnostics. The goal of this protocol lies in the combination of both techniques which leads to more accurate and reliable data.

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in ...

Immunology and Infection

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Quantitative real-time PCR (QPCR) has emerged as an accurate and valuable tool in profiling gene expression levels.  One of its many advantages is a lower detection limit compared to other methods of gene expression profiling while using smaller amounts of input for each assay.  Automated qPCR setup has improved this field by allowing for greater reproducibility.  Its convenient and rapid setup allows for high-throughput experiments, enabling the profiling of many different genes simultaneously in each experiment.  This method along with internal plate controls also reduces experimental variables common to other techniques.  
We recently developed a qPCR assay for profiling of pre-microRNAs  (pre-miRNAs) using a set of 186 primer pairs.  MicroRNAs have emerged as a novel class of small, non-coding RNAs with the ability to regulate many mRNA targets at the post-transcriptional level.  These small RNAs are first transcribed by RNA polymerase II as a primary miRNA (pri-miRNA) transcript, which is then cleaved into the precursor miRNA (pre-miRNA).  Pre-miRNAs are exported to the cytoplasm where Dicer cleaves the hairpin loop to yield mature miRNAs.  Increases in miRNA levels can be observed at both the precursor and mature miRNA levels and profiling of both of these forms can be useful.  There are several commercially available assays for mature miRNAs; however, their high cost may deter researchers from this profiling technique.  Here, we discuss a cost-effective, reliable, SYBR-based qPCR method of profiling pre-miRNAs.  Changes in pre-miRNA levels often reflect mature miRNA changes and can be a useful indicator of mature miRNA expression.  However, simultaneous profiling of both pre-miRNAs and mature miRNAs may be optimal as they can contribute nonredundant information and provide insight into microRNA processing.  Furthermore, the technique described here can be expanded to encompass the profiling of other library sets for specific pathways or pathogens.

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR qPCR Arrays

We will demonstrate the setup and analysis of pre-microRNA 96-well arrays for QPCR using a robot as well as by hand with a Thermo Scientific Matrix multichannel pipette.

Quantitative real-time PCR (QPCR) has emerged as an accurate and valuable tool in profiling gene expression levels.  One of its many advantages is a lower ...

Visual Detection of Multiple Nucleic Acids in a Capillary Array

Multi-target, short time, and resource-affordable methodologies for the detection of multiple nucleic acids in a single, easy to operate test are urgently needed in disease diagnosis, microbial monitoring, genetically modified organism (GMO) detection, and forensic analysis. We have previously described the platform called CALM (Capillary Array-based Loop-mediated isothermal amplification for Multiplex visual detection of nucleic acids). Herein, we describe improved fabrication and performance processes for this platform. Here, we apply a small, ready-to-use cassette assembled by capillary array for multiplex visual detection of nucleic acids. The capillary array is pre-treated into a hydrophobic and hydrophilic pattern before fixing loop-mediated isothermal amplification (LAMP) primer sets in capillaries. After assembly of the loading adaptor, LAMP reaction mixture is loaded and isolated into each capillary, due to capillary force by a single pipetting step. The LAMP reactions are performed in parallel in the capillaries. The results are visually read out by illumination with a hand-held UV flashlight. Using this platform, we demonstrate monitoring of 8 frequently appearing elements and genes in GMO samples with high specificity and sensitivity. In summary, the platform described herein is intended to facilitate the detection of multiple nucleic acids. We believe it will be widely applicable in fields where high-throughput nucleic acid analysis is required.

This protocol describes the fabrication of a small, ready-to-use cassette that can be applied for visual detection of multiple nucleic acids in a single, test that is easy to operate. In this approach, a capillary array was used for multiplex and highly efficient detection of GMO targets.

Multi-target, short time, and resource-affordable methodologies for the detection of multiple nucleic acids in a single, easy to operate test are urgently ...

Bioengineering

A PCR-based Genotyping Method to Distinguish Between Wild-type and Ornamental Varieties of Imperata cylindrica

Wild-type I. cylindrica (cogongrass) is one of the top ten worst invasive plants in the world, negatively impacting agricultural and natural resources in 73 different countries throughout Africa, Asia, Europe, New Zealand, Oceania and the Americas1-2. Cogongrass forms rapidly-spreading, monodominant stands that displace a large variety of native plant species and in turn threaten the native animals that depend on the displaced native plant species for forage and shelter. To add to the problem, an ornamental variety [I. cylindrica var. koenigii (Retzius)] is widely marketed under the names of Imperata cylindrica 'Rubra', Red Baron, and Japanese blood grass (JBG). This variety is putatively sterile and noninvasive and is considered a desirable ornamental for its red-colored leaves. However, under the correct conditions, JBG can produce viable seed (Carol Holko, 2009 personal communication) and can revert to a green invasive form that is often indistinguishable from cogongrass as it takes on the distinguishing characteristics of the wild-type invasive variety4 (Figure 1). This makes identification using morphology a difficult task even for well-trained plant taxonomists. Reversion of JBG to an aggressive green phenotype is also not a rare occurrence. Using sequence comparisons of coding and variable regions in both nuclear and chloroplast DNA, we have confirmed that JBG has reverted to the green invasive within the states of Maryland, South Carolina, and Missouri. JBG has been sold and planted in just about every state in the continental U.S. where there is not an active cogongrass infestation. The extent of the revert problem in not well understood because reverted plants are undocumented and often destroyed.
Application of this molecular protocol provides a method to identify JBG reverts and can help keep these varieties from co-occurring and possibly hybridizing. Cogongrass is an obligate outcrosser and, when crossed with a different genotype, can produce viable wind-dispersed seeds that spread cogongrass over wide distances5-7. JBG has a slightly different genotype than cogongrass and may be able to form viable hybrids with cogongrass. To add to the problem, JBG is more cold and shade tolerant than cogongrass8-10, and gene flow between these two varieties is likely to generate hybrids that are more aggressive, shade tolerant, and cold hardy than wild-type cogongrass. While wild-type cogongrass currently infests over 490 million hectares worldwide, in the Southeast U.S. it infests over 500,000 hectares and is capable of occupying most of the U.S. as it rapidly spreads northward due to its broad niche and geographic potential3,7,11. The potential of a genetic crossing is a serious concern for the USDA-APHIS Federal Noxious Week Program. Currently, the USDA-APHIS prohibits JBG in states where there are major cogongrass infestations (e.g., Florida, Alabama, Mississippi). However, preventing the two varieties from combining can prove more difficult as cogongrass and JBG expand their distributions. Furthermore, the distribution of the JBG revert is currently unknown and without the ability to identify these varieties through morphology, some cogongrass infestations may be the result of JBG reverts. Unfortunately, current molecular methods of identification typically rely on AFLP (Amplified Fragment Length Polymorphisms) and DNA sequencing, both of which are time consuming and costly. Here, we present the first cost-effective and reliable PCR-based molecular genotyping method to accurately distinguish between cogongrass and JBG revert.

A PCR-based Genotyping Method to Distinguish Between Wild-type and Ornamental Varieties of Imperata cylindrica

We provide a cost-effective and rapid molecular genotyping protocol that employs variety-specific PCR primers that target DNA sequence differences within the chloroplast trnL-F spacer region to differentiate between varieties of Imperata cylindrica (cogongrass) that cannot be distinguished by morphology alone. These varieties include the federally listed noxious weed, cogongrass and closely-related, wide-spread ornamental variety, I. cylindrica var. koenigii (Japanese blood grass).

Wild-type I. cylindrica (cogongrass) is one of the top ten worst invasive plants in the world, negatively impacting agricultural and natural resources in ...

Detection of Live Escherichia coli O157:H7 Cells by PMA-qPCR

A unique open reading frame (ORF) Z3276 was identified as a specific genetic marker for E. coli O157:H7. A qPCR assay was developed for detection of E. coli O157:H7 by targeting ORF Z3276. With this assay, we can detect as low as a few copies of the genome of DNA of E. coli O157:H7. The sensitivity and specificity of the assay were confirmed by intensive validation tests with a large number of E. coli O157:H7 strains (n = 369) and non-O157 strains (n = 112). Furthermore, we have combined propidium monoazide (PMA) procedure with the newly developed qPCR protocol for selective detection of live cells from dead cells. Amplification of DNA from PMA-treated dead cells was almost completely inhibited in contrast to virtually unaffected amplification of DNA from PMA-treated live cells. Additionally, the protocol has been modified and adapted to a 96-well plate format for an easy and consistent handling of a large number of samples. This method is expected to have an impact on accurate microbiological and epidemiological monitoring of food safety and environmental source.

Detection of Live Escherichia coli O157:H7 Cells by PMA-qPCR

A qPCR assay was developed for detection of Escherichia coli O157:H7 targeting a unique genetic marker, Z3276. The qPCR was combined with propidium monoazide (PMA) treatment for live cell detection. This protocol has been modified and adapted to a 96-well plate format for easy and consistent handling of numerous samples

A unique open reading frame (ORF) Z3276 was identified as a specific genetic marker for E. coli O157:H7. A qPCR assay was developed for detection of E. ...

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic chromosome, synIII1, and one synthetic chromosome arm, synIXR2, have been constructed and their in vivo function validated in the absence of the corresponding wild type chromosomes. An important design feature of Sc2.0 chromosomes is the introduction of PCRTags, which are short, re-coded sequences within open reading frames (ORFs) that enable differentiation of synthetic chromosomes from their wild type counterparts. PCRTag primers anneal selectively to either synthetic or wild type chromosomes and the presence/absence of each type of DNA can be tested using a simple PCR assay. The standard readout of the PCRTag assay is to assess presence/absence of amplicons by agarose gel electrophoresis. However, with an average PCRTag amplicon density of one per 1.5 kb and a genome size of ~12 Mb, the completed Sc2.0 genome will encode roughly 8,000 PCRTags. To improve throughput, we have developed a real time PCR-based detection assay for PCRTag genotyping that we call qPCRTag analysis. The workflow specifies 500 nl reactions in a 1,536 multiwell plate, allowing us to test up to 768 PCRTags with both synthetic and wild type primer pairs in a single experiment.

Designer chromosomes of the Synthetic Yeast Genome project, Sc2.0, can be distinguished from their native counterparts using a PCR-based genotyping assay called PCRTagging, which has a presence/absence endpoint. Here we describe a high-throughput real time PCR detection method for PCRTag genotyping.

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic ...

Field-Deployable Candidatus Liberibacter asiaticus Detection Using Recombinase Polymerase Amplification Combined with CRISPR-Cas12a

The early detection of Candidatus Liberibacter asiaticus (CLas) by citrus growers facilitates early intervention and prevents the spread of disease. A simple method for rapid and portable Huanglongbing (HLB) diagnosis is presented here that combines recombinase polymerase amplification and a fluorescent reporter utilizing the nuclease activity of the clustered regularly interspaced short palindromic repeats/CRISPR-associated 12a (CRISPR-Cas12a) system. The sensitivity of this technique is much higher than PCR. Furthermore, this method showed similar results to qPCR when leaf samples were used. Compared with conventional CLas detection methods, the detection method presented here can be completed in 90 min and works in an isothermal condition that does not require the use of PCR machines. In addition, the results can be visualized through a handheld fluorescent detection device in the field.

Field-Deployable Candidatus Liberibacter asiaticus Detection Using Recombinase Polymerase Amplification Combined with CRISPR-Cas12a

In this work, a rapid, sensitive, and portable detection method for Candidatus Liberibacter asiaticus based on recombinase polymerase amplification combined with CRISPR-Cas12a was developed.

The early detection of Candidatus Liberibacter asiaticus (CLas) by citrus growers facilitates early intervention and prevents the spread of disease. A ...

Field Identification of Matricaria chamomilla using a Portable qPCR System

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