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-AmpTM plant PCR kit</td>
			<td>SIGMA</td>
			<td>XNAP2-1KT</td>
			<td>Plant DNA extraction kit</td>
		</tr>
		<tr>
			<td>German chamomile (Matricaria chamomilla L) 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 (Chamaemelum nobile) 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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Biology

6.7K Views.  Herbalife NatSource (Hunan) Natural Products Co., Ltd.. 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.

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

Quantitative Real-Time PCR using the Thermo Scientific Solaris qPCR Assay

The Solaris qPCR Gene Expression Assay is a novel type of primer/probe set, designed to simplify the qPCR process while maintaining the sensitivity and accuracy of the assay. These primer/probe sets are pre-designed to &gt;98% of the human and mouse genomes and feature significant improvements from previously available technologies. These improvements were made possible by virtue of a novel design algorithm, developed by Thermo Scientific bioinformatics experts.
Several convenient features have been incorporated into the Solaris qPCR Assay to streamline the process of performing quantitative real-time PCR. First, the protocol is similar to commonly employed alternatives, so the methods used during qPCR are likely to be familiar. Second, the master mix is blue, which makes setting the qPCR reactions easier to track. Third, the thermal cycling conditions are the same for all assays (genes), making it possible to run many samples at a time and reducing the potential for error. Finally, the probe and primer sequence information are provided, simplifying the publication process.
Here, we demonstrate how to obtain the appropriate Solaris reagents using the GENEius product search feature found on the ordering web site (www.thermo.com/solaris) and how to use the Solaris reagents for performing qPCR using the standard curve method.

The Solaris qPCR Gene Expression Assays are novel pre-designed qPCR primer/probe combinations designed to simplify the qPCR process without sacrificing the specificity and robustness of the assay.

The Solaris qPCR Gene Expression Assay is a novel type of primer/probe set, designed to simplify the qPCR process while maintaining the sensitivity and ...

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 ...

Visualization of Caenorhabditis elegans Cuticular Structures Using the Lipophilic Vital Dye DiI

The cuticle of C. elegans is a highly resistant structure that surrounds the exterior of the animal1-4. The cuticle not only protects the animal from the environment, but also determines body shape and plays a role in motility4-6. Several layers secreted by epidermal cells comprise the cuticle, including an outermost lipid layer7.
Circumferential ridges in the cuticle called annuli pattern the length of the animal and are present during all stages of development8. Alae are longitudinal ridges that are present during specific stages of development, including L1, dauer, and adult stages2,9. Mutations in genes that affect cuticular collagen organization can alter cuticular structure and animal body morphology5,6,10,11. While cuticular imaging using compound microscopy with DIC optics is possible, current methods that highlight cuticular structures include fluorescent transgene expression12, antibody staining13, and electron microscopy1. Labeled wheat germ agglutinin (WGA) has also been used to visualize cuticular glycoproteins, but is limited in resolving finer cuticular structures14. Staining of cuticular surface using fluorescent dye has been observed, but never characterized in detail15. We present a method to visualize cuticle in live C. elegans using the red fluorescent lipophilic dye DiI (1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate), which is commonly used in C. elegans to visualize environmentally exposed neurons. This optimized protocol for DiI staining is a simple, robust method for high resolution fluorescent visualization of annuli, alae, vulva, male tail, and hermaphrodite tail spike in C. elegans.

Visualization of Caenorhabditis elegans Cuticular Structures Using the Lipophilic Vital Dye DiI

We present a method to visualize cuticle in live C. elegans using the red fluorescent lipophilic dye DiI (1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate), which is commonly used in C. elegans to visualize environmentally exposed neurons. With this optimized protocol, alae and annular cuticular structures are stained by DiI and observed using compound microscopy.

The cuticle of C. elegans is a highly resistant structure that surrounds the exterior of the animal1-4. The cuticle not only protects the animal from the ...

Identification of Protein Interacting Partners Using Tandem Affinity Purification

A critical and often limiting step in understanding the function of host and viral proteins is the identification of interacting cellular or viral protein partners. There are many approaches that allow the identification of interacting partners, including the yeast two hybrid system, as well as pull down assays using recombinant proteins and immunoprecipitation of endogenous proteins followed by mass spectrometry identification1. Recent studies have highlighted the utility of double-affinity tag mediated purification, coupled with two specific elution steps in the identification of interacting proteins. This approach, termed Tandem Affinity Purification (TAP), was initially used in yeast2,3 but more recently has been adapted to use in mammalian cells4-8.
As proof-of-concept we have established a tandem affinity purification (TAP) method using the well-characterized eukaryotic translation initiation factor eIF4E9,10.The cellular translation factor eIF4E is a critical component of the cellular eIF4F complex involved in cap-dependent translation initiation10. The TAP tag used in the current study is composed of two Protein G units and a streptavidin binding peptide separated by a Tobacco Etch Virus (TEV) protease cleavage sequence. The TAP tag used in the current study is composed of two Protein G units and a streptavidin binding peptide separated by a Tobacco Etch Virus (TEV) protease cleavage sequence8. To forgo the need for the generation of clonal cell lines, we developed a rapid system that relies on the expression of the TAP-tagged bait protein from an episomally maintained plasmid based on pMEP4 (Invitrogen). Expression of tagged murine eIF4E from this plasmid was controlled using the cadmium chloride inducible metallothionein promoter.
Lysis of the expressing cells and subsequent affinity purification via binding to rabbit IgG agarose, TEV protease cleavage, binding to streptavidin linked agarose and subsequent biotin elution identified numerous proteins apparently specific to the eIF4E pull-down (when compared to control cell lines expressing the TAP tag alone). The identities of the proteins were obtained by excision of the bands from 1D SDS-PAGE and subsequent tandem mass spectrometry. The identified components included the known eIF4E binding proteins eIF4G and 4EBP-1. In addition, other components of the eIF4F complex, of which eIF4E is a component were identified, namely eIF4A and Poly-A binding protein. The ability to identify not only known direct binding partners as well as secondary interacting proteins, further highlights the utility of this approach in the characterization of proteins of unknown function.

Tandem affinity purification is a robust approach for the identification of protein binding partners. As proof of concept, this methodology was applied to the well-characterized translation initiation factor eIF4E to co-precipitate the host cell factors involved in translation initiation. This method is easily adapted to any cellular or viral protein.

A critical and often limiting step in understanding the function of host and viral proteins is the identification of interacting cellular or viral protein ...

Pyrosequencing for Microbial Identification and Characterization

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate bacterial strains and detect genetic mutations that confer resistance to anti-microbial agents. The advantages of pyrosequencing for microbiology applications include rapid and reliable high-throughput screening and accurate identification of microbes and microbial genome mutations. Pyrosequencing involves sequencing of DNA by synthesizing the complementary strand a single base at a time, while determining the specific nucleotide being incorporated during the synthesis reaction. The reaction occurs on immobilized single stranded template DNA where the four deoxyribonucleotides (dNTP) are added sequentially and the unincorporated dNTPs are enzymatically degraded before addition of the next dNTP to the synthesis reaction. Detection of the specific base incorporated into the template is monitored by generation of chemiluminescent signals. The order of dNTPs that produce the chemiluminescent signals determines the DNA sequence of the template. The real-time sequencing capability of pyrosequencing technology enables rapid microbial identification in a single assay. In addition, the pyrosequencing instrument, can analyze the full genetic diversity of anti-microbial drug resistance, including typing of SNPs, point mutations, insertions, and deletions, as well as quantification of multiple gene copies that may occur in some anti-microbial resistance patterns.

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate bacterial strains, and detect genetic mutations that confer resistance to anti-microbial agents. In this video, the procedure for microbial amplicon generation, amplicon pyrosequencing, and DNA sequence analysis will be demonstrated.

Pyrosequencing is a versatile technique that facilitates microbial genome sequencing that can be used to identify bacterial species, discriminate ...

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. ...

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.&#160; Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. Utilizing normal-phased liquid chromatography coupled to high-resolution mass spectrometry with polarity switching and a rapid duty cycle, we describe a protocol to analyze the polar metabolic composition of biological material with high sensitivity, accuracy, and resolution.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting ...

qPCR Is a Sensitive and Rapid Method for Detection of Cytomegaloviral DNA in Formalin-fixed, Paraffin-embedded Biopsy Tissue

It is crucial to identify cytomegalovirus (CMV) infection in the gastrointestinal (GI) tract of immunosuppressed patients, given their greater risk for developing severe infection. Many laboratory methods for the detection of CMV infection have been developed, including serology, viral culture, and molecular methods. Often, these methods reflect systemic involvement with CMV and do not specifically identify local tissue involvement. Therefore, detection of CMV infection in the GI tract is frequently done by traditional histology of biopsy tissue. Hematoxylin and eosin (H&#38;E) staining in conjunction with immunohistochemistry (IHC) have remained the mainstays of examining these biopsies. H&#38;E and IHC sometimes result in atypical (equivocal) staining patterns, making interpretation difficult. It was shown that quantitative polymerase chain reaction (qPCR) for CMV can successfully be performed on formalin-fixed, paraffin-embedded (FFPE) biopsy tissue for very high sensitivity and specificity. The goal of this protocol is to demonstrate how to perform qPCR testing for the detection of CMV in FFPE biopsy tissue in a clinical laboratory setting. This method is likely to be of great benefit for patients in cases of equivocal staining for CMV in GI biopsies.

This protocol describes qPCR detection of cytomegalovirus in formalin-fixed, paraffin-embedded biopsy tissue, which is rapid, sensitive, specific, and useful for interpreting equivocal hematoxylin and eosin or immunohistochemical staining patterns.

It is crucial to identify cytomegalovirus (CMV) infection in the gastrointestinal (GI) tract of immunosuppressed patients, given their greater risk for ...

Identification of Kinase-substrate Pairs Using High Throughput Screening

We have developed a screening platform to identify dedicated human protein kinases for phosphorylated substrates which can be used to elucidate novel signal transduction pathways. Our approach features the use of a library of purified GST-tagged human protein kinases and a recombinant protein substrate of interest. We have used this technology to identify MAP/microtubule affinity-regulating kinase 2 (MARK2) as the kinase for a glucose-regulated site on CREB-Regulated Transcriptional Coactivator 2 (CRTC2), a protein required for beta cell proliferation, as well as the Axl family of tyrosine kinases as regulators of cell metastasis by phosphorylation of the adaptor protein ELMO. We describe this technology and discuss how it can help to establish a comprehensive map of how cells respond to environmental stimuli.

Protein phosphorylation is a central feature of how cells interpret and respond to information in their extracellular milieu. Here, we present a high throughput screening protocol using kinases purified from mammalian cells to rapidly identify kinases that phosphorylate a substrate(s) of interest.


We have developed a screening platform to identify dedicated human protein kinases for phosphorylated substrates which can be used to elucidate novel ...

Identification of Kinesin-1 Cargos Using Fluorescence Microscopy

Fluorescence microscopy is employed to identify Kinesin-1 cargos. Recently, the heavy chain of Kinesin-1 (KIF5B) was shown to transport the nuclear transcription factor c-MYC for proteosomal degradation in the cytoplasm. The method described here involves the study of a motorless KIF5B mutant for fluorescence microscopy. The wild-type and motorless KIF5B proteins are tagged with the fluorescent protein tdTomato. The wild-type tdTomato-KIF5B appears homogenously in the cytoplasm, while the motorless tdTomato-KIF5B mutant forms aggregates in the cytoplasm. Aggregation of the motorless KIF5B mutant induces aggregation of its cargo c-MYC in the cytoplasm. Hence, this method provides a visual means to identify the cargos of Kinesin-1. A similar strategy can be utilized to identify cargos of other motor proteins.

Here, a protocol is presented to identify Kinesin-1 cargos. A motorless mutant of the Kinesin-1 heavy chain (KIF5B) aggregates in the cytoplasm and induces aggregation of its cargos. Both aggregates are detected under fluorescence microscopy. A similar strategy can be employed to identify cargos of other motor proteins.

Fluorescence microscopy is employed to identify Kinesin-1 cargos. Recently, the heavy chain of Kinesin-1 (KIF5B) was shown to transport the nuclear ...