This work is supported by the National Natural Science Foundation of China 31970752, Science, Technology, Innovation Commission of Shenzhen Municipality JCYJ20190809180003689, JSGG20200225150707332, JSGG20191129110812708, WDZC20200820173710001; Shenzhen Bay Laboratory Open Funding, SZBL2020090501004; China Postdoctoral Science Foundation 2020M680023; and General Administration of Customs of the People&#39;s Republic of China 2021HK007.

1. Processing of samples
<ol>
	<li>Take Frog Virus 3 (FV3, genera Ranavirus, family Iridoviridae), a double-stranded DNA virus. Select the major capsid (mcp) gene as the target for the detection of FV3 since it is highly conserved and usually regarded as the target for ranavirus detection. The target sequence selected is shown in Table 1. 
	NOTE: Frog Virus 3 is taken as an example in this protocol.</li>
	<li>For target DNA fragments preparation, use DNA fragments of the mcp gene from FV3 and Infectious Spleen and Kidney Necrosis Virus (ISKNV, another Ranavirus). 
	NOTE: In this study, the target DNA fragments were commercially obtained from a company listed in the Table of Materials. They are regarded as the target and the control of subsequent detection.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Name</td>
			<td colspan="3">Sequence</td>
		</tr>
		<tr>
			<td rowspan="2">FV3 MCP</td>
			<td colspan="3">NTS: 5&#8217; &#8230;gtaacccggctttcGGGCAGCAGTTTCGGTCGGCGTtcccaggtcg&#8230; 3&#8217; (240 bp)</td>
		</tr>
		<tr>
			<td colspan="3">TS: 5&#8217; &#8230;ccgacctgggaACGCCGACCGAAACTGCTGCCCtgctgcccgaaagc&#8230; 3&#8217; (240 bp)</td>
		</tr>
		<tr>
			<td rowspan="2">ISKNV MCP</td>
			<td colspan="3">NTS: 5&#8217; &#8230;ggccatgccaatttTGGGCAGGAGTTTAGTGTGACGgtggcgaggg&#8230; 3&#8217; (231 bp)</td>
		</tr>
		<tr>
			<td colspan="3">TS: 5&#8217; &#8230;ccctcgccaccgtcACACTAAACTCCTGCCCAAAATtggcatggcc&#8230; 3&#8217; (231 bp)</td>
		</tr>
	</tbody>
</table>
Table 1: Target sequence selected in this method.
2. RPA reaction
<ol>
	<li>Design and synthesize the RPA primer pairs for the target sequence. The sequences of RPA primer pairs are described in Table 2.</li>
	<li>Prepare the 5x RPA reaction buffer (Table 3) and MgCl2 (100 mM).</li>
	<li>Mix the four key RPA enzymes (UvsX, UvsY, GP32, Bsu protein) in the 1x RPA reaction buffer, along with the primers designed in advance, as detailed in Table 4.</li>
	<li>Vortex the mixture thoroughly.</li>
	<li>Add 1 &#181;L of the target obtained from step 1 for each RPA reaction, and mix it thoroughly by vortex again.</li>
	<li>Add 7 &#181;L of MgCl2 (100 mM) to initiate the reaction. The final volume of each RPA reaction is 50 &#181;L.</li>
	<li>Perform the assay at 37 &#176;C for 30 min.</li>
	<li>The RPA product can be stored at 4 &#176;C for a few days. As RPA products degrade, use for further detection as soon as possible to obtain better diagnosis results.</li>
	<li>[Optional] Conduct DNA gel electrophoresis.
	<ol>
		<li>Take out 5 &#181;L of RPA products, add an appropriate volume of 6x DNA loading buffer, and conduct DNA gel electrophoresis in the Tris-acetate EDTA (TAE) buffer.</li>
		<li>Run the electrophoresis under 120 V for about 20 min until the bands of loading buffer reach the bottom of the gel.</li>
		<li>Determine whether the target sequence is successfully amplified from the sample by comparing the size of the bands of the sample with the bands of the marker.</li>
	</ol>
	</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Name</td>
			<td>Sequence</td>
		</tr>
		<tr>
			<td>RPA primer F</td>
			<td>ATGTCTTCTGTAACTGGTTCAGGTATCACA</td>
		</tr>
		<tr>
			<td>RPA primer R</td>
			<td>GGCGTTGAGGATGTAATCCCCCGACCTGGG</td>
		</tr>
	</tbody>
</table>
Table 2: RPA primers used in this method.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Component</td>
			<td>Original concentration</td>
			<td>Addition</td>
		</tr>
		<tr>
			<td>PEG 20,000</td>
			<td>-</td>
			<td>114 mg</td>
		</tr>
		<tr>
			<td>ATP</td>
			<td>100 mM</td>
			<td>125 &#956;L</td>
		</tr>
		<tr>
			<td>dNTPs</td>
			<td>25 mM</td>
			<td>48 &#956;L</td>
		</tr>
		<tr>
			<td>Tris-HCl</td>
			<td>1 M</td>
			<td>125 &#956;L</td>
		</tr>
		<tr>
			<td>DTT</td>
			<td>1 M</td>
			<td>125 &#956;L</td>
		</tr>
		<tr>
			<td>Phosphocreatine</td>
			<td>1 M</td>
			<td>250 &#956;L</td>
		</tr>
		<tr>
			<td>Creatine kinase</td>
			<td>10 &#956;g/&#956;L</td>
			<td>50 &#956;L</td>
		</tr>
		<tr>
			<td>ddH2O</td>
			<td>-</td>
			<td>277 &#956;L</td>
		</tr>
		<tr>
			<td>Total Volume</td>
			<td>-</td>
			<td>1 mL</td>
		</tr>
	</tbody>
</table>
Table 3: The composition of the 5x RPA reaction buffer (pH 7.5).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Component</td>
			<td>Original concentration</td>
			<td>Addition</td>
		</tr>
		<tr>
			<td>5x RPA reaction buffer</td>
			<td>-</td>
			<td>10 &#956;L</td>
		</tr>
		<tr>
			<td>UvsX protein</td>
			<td>5 mg/mL</td>
			<td>2.6 &#956;L</td>
		</tr>
		<tr>
			<td>UvsY protein</td>
			<td>5 mg/mL</td>
			<td>0.9 &#956;L</td>
		</tr>
		<tr>
			<td>GP32 protein</td>
			<td>5 mg/mL</td>
			<td>2.54 &#956;L</td>
		</tr>
		<tr>
			<td>Bsu protein</td>
			<td>5 mg/mL</td>
			<td>0.88 &#956;L</td>
		</tr>
		<tr>
			<td>forward primer</td>
			<td>100 &#956;M</td>
			<td>0.25 &#956;L</td>
		</tr>
		<tr>
			<td>reversed primer</td>
			<td>100 &#956;M</td>
			<td>0.25 &#956;L</td>
		</tr>
		<tr>
			<td>ddH2O</td>
			<td>-</td>
			<td>24.58 &#956;L</td>
		</tr>
		<tr>
			<td>Target</td>
			<td>-</td>
			<td>1 &#956;L</td>
		</tr>
		<tr>
			<td>*MgCl2</td>
			<td>100 mM</td>
			<td>7 &#956;L</td>
		</tr>
		<tr>
			<td>Total Volume</td>
			<td></td>
			<td>50 &#956;L</td>
		</tr>
		<tr>
			<td colspan="3">*MgCl2 needs to be lastly added to initiate the RPA reaction.</td>
		</tr>
	</tbody>
</table>
Table 4: The composition of the RPA reaction.
3. CRISPR/Cas12a detection without SPM
<ol>
	<li>Use the crRNAs (Table of Materials) of the target sequence and the ssDNA reporter probe linking a fluorophore and a quencher for CRISPR/Cas12a. Here, carboxy tetramethylrhodamine (TAMRA) is linked as the fluorophore at the 5&#39; ends of ssDNA reporter probes and Black Hole Quencher-2 (BHQ2) as the quencher at the 3&#39; ends. The detailed sequence of crRNA and ssDNA reporter are described in Table 5.</li>
	<li>Prepare the Lachnospiraceae bacterium Cas12a (LbCas12a) protein with 10x CRISPR/Cas12a reaction buffer.</li>
	<li>Dissolve 1 &#181;L of RPA reaction product from section 2 in 1x CRISPR/Cas12a reaction buffer with LbCas12a-crRNA complexes and 500 nM ssDNA reporter probe in a 100 &#181;L reaction volume.</li>
	<li>After mixing LbCas12a and crRNA, let the mixture sit for at least 5 min to form a functional complex. After incubation, add other components to the reaction mixture and carry out the whole reaction at 37 &#176;C. The final volume of each CRISPR/Cas12a reaction is 100 &#181;L. The detailed concentrations of each component in each CRISPR/Cas12a reaction are described in Table 6.</li>
	<li>Perform the 100 &#181;L CRISPR/Cas12a detection reaction at 37 &#176;C for 30 min.</li>
	<li>Examine the fluorescence signals constantly by a microplate reader at an excitation wavelength of 535 nm and an emission wavelength of 595 nm with a gain of 60. 
	NOTE: The detection of different wavelengths of excitation and emission light depends on the choice of the fluorophore and quencher in the previously designed ssDNA reporter probes.</li>
	<li>For these collected data, divide the control value by the positive samples&#39; measurement to normalize all the data, then integrate them for a two-sample t-test analysis.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Name</td>
			<td colspan="2">Sequence</td>
		</tr>
		<tr>
			<td>LbCas12a crRNA for FV3</td>
			<td colspan="2">uaauuucuacuaaguguagauGGGCAGCAGTTTTCGGTCGGCGT</td>
		</tr>
		<tr>
			<td>ssDNA reporter</td>
			<td colspan="2">/5TAMRA/TTATT/3BHQ2</td>
		</tr>
	</tbody>
</table>
Table 5: Sequences of CRISPR/Cas12a crRNA and ssDNA reporter used in this method.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Component</td>
			<td>Original concentration</td>
			<td>Addition</td>
		</tr>
		<tr>
			<td>NEBuffer r2.1&#160;</td>
			<td>-</td>
			<td>10 &#956;L</td>
		</tr>
		<tr>
			<td>Lba Cas12a (Cpf1)</td>
			<td>10 &#956;M</td>
			<td>0.5 &#956;L</td>
		</tr>
		<tr>
			<td>crRNA</td>
			<td>10 &#956;M</td>
			<td>0.625 &#956;L</td>
		</tr>
		<tr>
			<td colspan="3">Wait for at least 5 min to let the LbCas12a/crRNA complex combine.</td>
		</tr>
		<tr>
			<td>DNA reporter</td>
			<td>100 &#956;M</td>
			<td>0.5 &#956;L</td>
		</tr>
		<tr>
			<td>ddH2O</td>
			<td>-</td>
			<td>87.375 &#956;L</td>
		</tr>
		<tr>
			<td>Target</td>
			<td>-</td>
			<td>1 &#956;L</td>
		</tr>
		<tr>
			<td>Total Volume</td>
			<td>-</td>
			<td>100 &#956;L</td>
		</tr>
	</tbody>
</table>
Table 6: The composition of the CRISPR/Cas12a Reaction.
4. SPM setup
<ol>
	<li>For the excitation path, set a laser beam to pass through the neutral density (ND) filters to attenuate the laser intensity.</li>
	<li>Generate a collimated beam from an aspherical lens (hereinafter referred to as L1) and reflect it by the dichroic mirror (DM).</li>
	<li>Direct the light onto the glass slide where the sample is placed through the objective (20x) to illuminate and excite the fluorescence of the sample. The sample stage enables precise focal plane adjustment, directing the beam to the objective&#39;s back focal plane. The steps mentioned above form the excitation path of the SPM instrument.</li>
	<li>For the emission path, position an external lens (hereinafter referred to as L2) to form an intermediate image on the other side of the objective. The objective simultaneously illuminates the sample and collects the emission signal.</li>
	<li>Record the fluorescence signal from the sample using the smartphone placed at the end of the emission path. Use a stable bracket to avoid shaking.</li>
	<li>Set a bandpass filter between L1 and the smartphone&#39;s camera to filter out the excited light while allowing only the emitted light from the sample to reach the camera, which can optimize the detection.</li>
	<li>Immobilize the SPM setup on a breadboard for portable deployment. 
	NOTE: The schematic and the physical appearance of the SPM device for fluorescence detection based on the RPA-CRISPR/Cas12a reaction are shown in Figure 2. The CRISPR/Cas12a detection takes place on the pre-treated glass slide described in the next step.</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64833/64833fig02.jpg" /> 
Figure 2: Schematic and physical appearance of the SPM device used for fluorescence detection. (A) The physical appearance of the SPM device for fluorescence image collection after the RPA-CRISPR/Cas12a reaction. (B) Schematic of the SPM device for fluorescence detection based on the RPA-CRISPR/Cas12a reaction. This figure is modified (adjusted image position and color) with permission from Lei et al.35. <a href="https://www.jove.com/files/ftp_upload/64833/64833fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
5. Treatment of glass slide for detection with SPM
<ol>
	<li>Prepare polydimethylsiloxane (PDMS) by mixing the base and curing agent with a ratio of 10:1, followed by baking on a hotplate at 80 &#176;C for 2 h.</li>
	<li>Treat both PDMS and the glass slide (Length: 75 mm; Height: 50 mm) with oxygen plasma treatment for 120 s, then press them together.</li>
	<li>Bake the glass/PDMS at 95 &#176;C for 2 h; it is sealed permanently by the Si-O-Si bond. PDMS has high transparency and no auto-fluorescence, which is conducive to SPM detection. The treatment of the glass slide and PMDS is performed as per He et al.32</li>
</ol>
6. CRISPR/Cas12a detection with SPM
<ol>
	<li>Use the crRNAs (Table of Materials) of the target sequence and the ssDNA reporter probe linking a fluorophore and a quencher for CRISPR/Cas12a. In this method, carboxy tetramethylrhodamine (TAMRA) is linked as the fluorophore at the 5&#39; ends of ssDNA reporter probes and Black Hole Quencher-2 (BHQ2) as the quencher at the 3&#39; ends. The detailed sequence of crRNA and ssDNA reporter are described in Table 5.</li>
	<li>Prepare the Lachnospiraceae bacterium Cas12a (LbCas12a) protein with 10x CRISPR/Cas12a Reaction Buffer.</li>
	<li>Dissolve 1 &#181;L of RPA reaction product from section 2 in 1x CRISPR/Cas12a reaction buffer with LbCas12a-crRNA complexes and 500 nM ssDNA reporter probe in a 100 &#181;L reaction volume.</li>
	<li>After mixing LbCas12a and crRNA, let the mixture sit for at least 5 min to form a functional complex. After incubation, add other components to the reaction mixture and complete the reaction at 37 &#176;C. The final volume of each CRISPR/Cas12a reaction is 100 &#181;L. The detailed concentrations of each component in each CRISPR/Cas12a reaction are described in Table 6.</li>
	<li>Perform the 100 &#181;L CRISPR/Cas12a detection reaction on the pre-treated glass slide and cover it with a coverslip. Incubate the glass slide with reaction at RT for 10 min.</li>
	<li>Measure the fluorescence signals by the SPM. Put the glass slide with the detection reaction on the stage of the SPM, maintain an appropriate distance, adjust the focal length and clarity, then look for the field of view of the reaction and focus it to capture an image. 
	&#8203;NOTE: A standard curve should be obtained first so that the data from the samples can be scaled to the approximate concentration range. Various concentrations of purified targets are used, including 10 nM, 1 nM, 100 pM, and 10 pM, another virus as negative control (before RPA).</li>
</ol>
7. Dataset and data augmentation
<ol>
	<li>Collect the fluorescence images from the detection assay in section 6 as the datasets. Repeat at least three parallel detections for each sample to ensure data parallelism.
	<ol>
		<li>Some appropriate ways to achieve higher parallelism can be approved. For example, when collecting the fluorescence images from each sample, manually focus and look for a relatively brighter field to take images. At the same time, photograph each sample to attain the fluorescence signal in more than five different locations.</li>
	</ol>
	</li>
	<li>Measure the mean grey value of each image and the standard deviation of the mean gray value in a concentration group by ImageJ.</li>
	<li>Set an intensities range [median - standard deviation, median + standard deviation] for data cleaning. 
	&#8203;NOTE: In the images obtained in the early steps, there may be images with large differences, so it is necessary to screen the images. If the images&#39; intensities are out of the set threshold, they should be considered outliers and excluded.</li>
	<li>Label the images for the purified target at increasing concentrations with 0-6 in ascending order, respectively.</li>
	<li>To enhance the system&#39;s robustness and prevent over-fitting, implement image augmentation techniques such as horizontal flipping, vertical flipping, and random noise through transform functions in Python. This helps to introduce variations in the dataset.</li>
</ol>
8. Transfer learning
<ol>
	<li>As the backbone network, adopt the deep learning model AlexNet33 for classification.</li>
	<li>To satisfy the constraint of the pre-trained model in section 7, reshape the input images to 224 pixels x 224 pixels x 3 channels&#160;(height and width of 224 pixels and a depth of 3 channels&#160;for the red, green, and blue color channels) through transform functions in Python. 
	NOTE: This step is a common preprocessing for heterogeneous data in transfer learning, including transform.</li>
	<li>Use a pre-trained backbone network with the ImageNet dataset to extract features while leveraging the weights of intermediate hidden layers that were learned.</li>
	<li>In the fluorescence classification job context, substitute the final fully connected layer of the neural network, which originally included 1000 neurons for the ImageNet task, with a fully connected layer with either 2 or 7 neurons.</li>
	<li>Evaluate the performance of the setup training model by using a series of metrics, including confusion matrix, accuracy, precision, recall, and F1-score based on Lawton and Viriri34.</li>
</ol>

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<li>Kumar, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((FnCas9-based+CRISPR+diagnostic+for+rapid+and+accurate+detection+of+major+SARS-CoV-2+variants+on+a+paper+strip%5BTitle%5D)+AND+%22eLife%22%5BJournal%5D)">FnCas9-based CRISPR diagnostic for rapid and accurate detection of major SARS-CoV-2 variants on a paper strip.</a> eLife. 10, e67130(2021).</li>
<li>Lee, R. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ultrasensitive+CRISPR-based+diagnostic+for+field-applicable+detection+of+Plasmodium+species+in+symptomatic+and+asymptomatic+malaria%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+U+S+A%22%5BJournal%5D)">Ultrasensitive CRISPR-based diagnostic for field-applicable detection of Plasmodium species in symptomatic and asymptomatic malaria.</a> Proc Natl Acad Sci U S A. 117 (41), 25722-25731 (2020).</li>
<li>Ganguli, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hands-free+smartphone-based+diagnostics+for+simultaneous+detection+of+Zika%2C+Chikungunya%2C+and+Dengue+at+point-of-care%5BTitle%5D)+AND+%22Biomed+Microdevices%22%5BJournal%5D)">Hands-free smartphone-based diagnostics for simultaneous detection of Zika, Chikungunya, and Dengue at point-of-care.</a> Biomed Microdevices. 19 (4), 73(2017).</li>
<li>Yeo, S. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Smartphone-based+fluorescent+diagnostic+system+for+highly+pathogenic+H5N1+viruses%5BTitle%5D)+AND+%22Theranostics%22%5BJournal%5D)">Smartphone-based fluorescent diagnostic system for highly pathogenic H5N1 viruses.</a> Theranostics. 6 (2), 231-242 (2016).</li>
<li>von Chamier, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Democratising+deep+learning+for+microscopy+with+ZeroCostDL4Mic%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Democratising deep learning for microscopy with ZeroCostDL4Mic.</a> Nat Commun. 12 (1), 2276(2021).</li>
<li>Shiaelis, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Virus+detection+and+identification+in+minutes+using+single-particle+imaging+and+deep+learning%5BTitle%5D)+AND+%22ACS+Nano%22%5BJournal%5D)">Virus detection and identification in minutes using single-particle imaging and deep learning.</a> ACS Nano. 17 (1), 697-710 (2020).</li>
<li>Liu, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mixed-UNet%3A+Refined+class+activation+mapping+for+weakly-supervised+semantic+segmentation+with+multi-scale+inference%5BTitle%5D)+AND+%22Front.+Comput.+Sci%22%5BJournal%5D)">Mixed-UNet: Refined class activation mapping for weakly-supervised semantic segmentation with multi-scale inference.</a> Front. Comput. Sci. 4, 1036934(2022).</li>
<li>Lawrimore, J., Doshi, A., Walker, B., Bloom, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((AI-assisted+forward+modeling+of+biological+structures%5BTitle%5D)+AND+%22Front+Cell+Dev+Biol%22%5BJournal%5D)">AI-assisted forward modeling of biological structures.</a> Front Cell Dev Biol. 7, 279(2019).</li>
<li>Yang, Y., Hu, Y., Zhang, X., Wang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Two-stage+selective+ensemble+of+CNN+via+deep+tree+training+for+medical+image+classification%5BTitle%5D)+AND+%22IEEE+Trans+Cybern%22%5BJournal%5D)">Two-stage selective ensemble of CNN via deep tree training for medical image classification.</a> IEEE Trans Cybern. 52 (9), 9194-9207 (2022).</li>
<li>Zhang, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RCMNet%3A+A+deep+learning+model+assists+CAR-T+therapy+for+leukemia%5BTitle%5D)+AND+%22Comput+Biol+Med%22%5BJournal%5D)">RCMNet: A deep learning model assists CAR-T therapy for leukemia.</a> Comput Biol Med. 150, 106084(2022).</li>
<li>Xie, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Stroke+prediction+from+electrocardiograms+by+deep+neural+network%5BTitle%5D)+AND+%22Multimed+Tools+Appl%22%5BJournal%5D)">Stroke prediction from electrocardiograms by deep neural network.</a> Multimed Tools Appl. 80, 17291-17297 (2021).</li>
<li>Wang, J., Zhu, H., Wang, S., Zhang, Y. -D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+review+of+deep+learning+on+medical+image+analysis%5BTitle%5D)+AND+%22Mobile+Netw+Appl%22%5BJournal%5D)">A review of deep learning on medical image analysis.</a> Mobile Netw Appl. 26, 351-380 (2021).</li>
<li>Artoni, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Deep+learning+of+spontaneous+arousal+fluctuations+detects+early+cholinergic+defects+across+neurodevelopmental+mouse+models+and+patients%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+U+S+A%22%5BJournal%5D)">Deep learning of spontaneous arousal fluctuations detects early cholinergic defects across neurodevelopmental mouse models and patients.</a> Proc Natl Acad Sci U S A. 117 (38), 23298-23303 (2020).</li>
<li>Li, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DeepLearnMOR%3A+a+deep-learning+framework+for+fluorescence+image-based+classification+of+organelle+morphology%5BTitle%5D)+AND+%22Plant+Physiol%22%5BJournal%5D)">DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology.</a> Plant Physiol. 186 (4), 1786-1799 (2021).</li>
<li>Yosinski, J., Clune, J., Bengio, Y., Lipson, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((How+transferable+are+features+in+deep+neural+networks%5BTitle%5D)+AND+%22Proceedings+of+the+27th+International+Conference+on+Neural+Information+Processing+Systems%22%5BJournal%5D)">How transferable are features in deep neural networks.</a> Proceedings of the 27th International Conference on Neural Information Processing Systems. 2, 3320-3328 (2014).</li>
<li>He, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-throughput+and+all-solution+phase+African+Swine+Fever+Virus+%28ASFV%29+detection+using+CRISPR-Cas12a+and+fluorescence+based+point-of-care+system%5BTitle%5D)+AND+%22Biosens+Bioelectron%22%5BJournal%5D)">High-throughput and all-solution phase African Swine Fever Virus (ASFV) detection using CRISPR-Cas12a and fluorescence based point-of-care system.</a> Biosens Bioelectron. 154, 112068(2020).</li>
<li>Krizhevsky, A., Sutskever, I., Hinton, G. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ImageNet+classification+with+deep+convolutional+neural+networks%5BTitle%5D)+AND+%22Commun.+ACM%22%5BJournal%5D)">ImageNet classification with deep convolutional neural networks.</a> Commun. ACM. 60 (6), 84-90 (2017).</li>
<li>Lawton, S., Viriri, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detection+of+COVID-19+from+CT+lung+scans+using+transfer+learning%5BTitle%5D)+AND+%22Comput+Intell+Neurosci%22%5BJournal%5D)">Detection of COVID-19 from CT lung scans using transfer learning.</a> Comput Intell Neurosci. 2021, 5527923(2021).</li>
<li>Lei, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detection+of+frog+virus+3+by+integrating+RPA-CRISPR%2FCas12a-SPM+with+deep+learning%5BTitle%5D)+AND+%22ACS+Omega%22%5BJournal%5D)">Detection of frog virus 3 by integrating RPA-CRISPR/Cas12a-SPM with deep learning.</a> ACS Omega. 8 (36), 32555-32564 (2023).</li>
<li>Chen, Z., Huang, J., Zhang, F., Zhou, Y., Huang, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detection+of+shrimp+hemocyte+iridescent+virus+by+recombinase+polymerase+amplification+assay%5BTitle%5D)+AND+%22Mol+Cell+Probes%22%5BJournal%5D)">Detection of shrimp hemocyte iridescent virus by recombinase polymerase amplification assay.</a> Mol Cell Probes. 49, 101475(2020).</li>
<li>Fu, X., Sun, J., Ye, Y., Zhang, Y., Sun, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+rapid+and+ultrasensitive+dual+detection+platform+based+on+Cas12a+for+simultaneous+detection+of+virulence+and+resistance+genes+of+drug-resistant+Salmonella%5BTitle%5D)+AND+%22Biosens+Bioelectron%22%5BJournal%5D)">A rapid and ultrasensitive dual detection platform based on Cas12a for simultaneous detection of virulence and resistance genes of drug-resistant Salmonella.</a> Biosens Bioelectron. 195, 113682(2022).</li>
<li>Habimana, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mechanistic+insights+of+CRISPR%2FCas+nucleases+for+programmable+targeting+and+early-stage+diagnosis%3A+A+review%5BTitle%5D)+AND+%22Biosens+Bioelectron%22%5BJournal%5D)">Mechanistic insights of CRISPR/Cas nucleases for programmable targeting and early-stage diagnosis: A review.</a> Biosens Bioelectron. 203, 114033(2022).</li>
<li>Liang, Y., Lin, H., Zou, L., Deng, X., Tang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Rapid+detection+and+tracking+of+Omicron+variant+of+SARS-CoV-2+using+CRISPR-Cas12a-based+assay%5BTitle%5D)+AND+%22Biosens+Bioelectron%22%5BJournal%5D)">Rapid detection and tracking of Omicron variant of SARS-CoV-2 using CRISPR-Cas12a-based assay.</a> Biosens Bioelectron. 205, 114098(2022).</li>
<li>Sivaraman, D., Biswas, P., Cella, L. N., Yates, M. V., Chen, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detecting+RNA+viruses+in+living+mammalian+cells+by+fluorescence+microscopy%5BTitle%5D)+AND+%22Trends+Biotechnol%22%5BJournal%5D)">Detecting RNA viruses in living mammalian cells by fluorescence microscopy.</a> Trends Biotechnol. 29 (7), 307-313 (2011).</li>
<li>Wang, I. H., Burckhardt, C. J., Yakimovich, A., Greber, U. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Imaging%2C+tracking+and+computational+analyses+of+virus+entry+and+egress+with+the+cytoskeleton%5BTitle%5D)+AND+%22Viruses%22%5BJournal%5D)">Imaging, tracking and computational analyses of virus entry and egress with the cytoskeleton.</a> Viruses. 10 (4), 166(2018).</li>
</ol>

The authors have nothing to disclose.

In this method, we develop a fast, easy-to-implement, highly sensitive, sequence-specific, and POC DNA virus detection system with AI assistance. After obtaining samples, RPA is applied to amplify the target sequence, and then CRISPR/Cas12a can recognize the target DNA and release fluorescence, which enlarges the detection signal. Portable smartphone microscopy is built to take fluorescence images, and deep learning models with transfer learning are used for binary classification of the positive and negative samples' ima...

In recent years, epidemics of infectious diseases caused by different viruses have occurred frequently, including the Ebola virus disease (EVD) epidemic in 20141 and 20182, the Middle East Respiratory Syndrome (MERS) in 20153, the Zika virus disease epidemic in 20154, the Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)5 and the continuing Monkeypox caused by Monkeypox virus (MKPV) in 20226. These sudden outbreaks of epidemic infectious diseases cause a large number of deaths and bring huge economic losses and social unrest. A rapid and accurate detection system is urgently required to quickly diagnose the infection and prevent the virus&#39;s further spread.
Recently, clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins have gained worldwide attention and have shown promising results in nucleic acid detection7,8,9,10,11,12,13,14,15. The CRISPR/Cas12a protein, guided by CRISPR RNA (crRNA), binds to and cleaves the target DNA. This activity leads to the release of nonspecific single-stranded DNA (ssDNA), known as trans-cleavage, and can be utilized to enhance the detection signal for nucleic acid detection. Some traditional detection methods like polymerase chain reaction (PCR), quantitative real-time PCR (qPCR), and enzyme-linked immunosorbent assay (ELISA) are complicated, time-consuming, and costly for point-of-care (POC) detection. Our previous work successfully developed an automated, integrated, and cost-effective detection system for the African swine fever virus (ASFV) based on CRISPR/Cas12a technology. In this system, we achieved a detection limit of 1 pM within a 2-h time frame without the need for amplification. The CRISPR/Cas12a system and recombinase polymerase amplification (RPA) are combined to improve the sensitivity and specificity for trace DNA detection. Compared with other isothermal amplification techniques, RPA is simple in design and convenient in operation since it has a shorter reaction time without sophisticated temperature control equipment.
For POC detection of pathogens, instruments such as smartphone microscopy (SPM), handheld fluorimeter, or lateral flow strips are developed for the results readouts16,17,18. SPM captures images through a camera and uploads them to some mobile applications for fast data analysis. Such microscopy makes a portable, cheap, and miniatured signal acquisition system with high sensitivity and has shown advantages in detecting pathogens such as H5N1, the Zika virus, and SARS-CoV-219,20.&#160;Therefore, we build a portable SPM to catch the fluorescence signals triggered by RPA-CRISPR/Cas12a detection of the target DNA virus. The ssDNA reporter probe linking a fluorophore and a quencher will be cleaved when CRISPR/Cas12a recognizes the target DNA virus, and the fluorescence emitted by the fluorophore can be captured by SPM.
Compared to the professional software usually used to obtain the results information from the fluorescence images from SPM21,&#160;some experts use machine learning and deep learning to quantify the concentrations of virus DNA after attaining fluorescence images22, which is more time-consuming. When it comes to classifying medical images, conventional neural networks (CNNs) are often used to learn features from the raw pixelated images in an end-to-end manner23,24,25,26. Popular CNN-based deep learning models like AlexNet, DenseNet-121, and EfficientNet-B7 have been successfully applied in this field27,28. However, obtaining large data sets in specific domains can be challenging, necessitating transfer learning29,30. This approach pre-trains a deep learning model with a large dataset, and the pre-trained model is used as the starting point for a new task with a small dataset. This technique can reduce the need for large data sets, combat overfitting, and reduce training time31. Herein, we use deep learning models with transfer learning for the binary classification of the positive and negative samples&#39; fluorescence images.
In this method, we combine RPA and the CRISPR/Cas12a system for the trace detection of DNA viruses. Target DNA is amplified and recognized by RPA and CRISPR/Cas12a separately, which triggers the collateral cleavage activity of Cas12a that cleaves a fluorophore-quencher labeled DNA reporter and generalizes fluorescence. We build a portable SPM to take the fluorescent images for POC detection and develop deep learning models for binary classification. The schematic of the built POC detection system is shown in Figure 1.&#160;Without skilled operators and bulky instruments, the RPA-CRISPR/Cas12a-SPM with artificial intelligence (AI) assisted classification shows great potential for POC DNA virus detection.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64833/64833fig01.jpg" /> 
Figure 1: The schematic of the RPA-CRISPR/Cas12-SPM detection system along with AI classification for collected images. The nucleic acids of animal-derived samples are released by PINDBK. The Target DNA of the virus is amplified and recognized specifically by the RPA-CRISPR/Cas12a system. CRISPR/Cas12a bonds with crRNA and the Cas12a-crRNA complex bonds with target DNA, which triggers the collateral cleavage of CRISPR/Cas12a on the ssDNA reporter probes. The fluorophore on the reporter is released, and the fluorescence is detected by a commercialized plate reader or the SPM we build. Three different deep learning models, including AlexNet, DenseNet-121, and EfficientNet-B7 with transfer learning, are used to classify the fluorescence images. This figure is reused with permission from Lei et al.35. <a href="https://www.jove.com/files/ftp_upload/64833/64833fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>20x amplification</td><td>OLYMPUS</td><td>OPLN20X</td><td></td></tr><tr><td>532 nm green laser</td><td>Thorlabs</td><td>PL201</td><td>with 0.9 mW output power</td></tr><tr><td>535 nm cutoff wavelength</td><td>chrome</td><td>AT535</td><td></td></tr><tr><td>6x DNA loading buffer</td><td>Thermo scientific</td><td>R0611</td><td></td></tr><tr><td>96-well black microplate</td><td>Corning Incorporated</td><td>3603</td><td>Black with flat clear bottom</td></tr><tr><td>Aspherical lens</td><td>Lubang</td><td>N/A</td><td></td></tr><tr><td>Bandpass filter</td><td>SEMROCK</td><td>FF01-542/27-25</td><td></td></tr><tr><td>Bsu DNA Polymerase</td><td>ATG Biotechnology</td><td>M103</td><td>Large Fragment</td></tr><tr><td>crRNA</td><td>Sangon Biotech</td><td>N/A</td><td></td></tr><tr><td>DNA fragments</td><td>Sangon Biotech</td><td>N/A</td><td></td></tr><tr><td>Dichroic holders</td><td>Ruicage</td><td>N/A</td><td></td></tr><tr><td>Dichroic mirror</td><td>SEMROCK</td><td>FF555-Di03-25x36</td><td>with a cutoff wavelength of 535 nm</td></tr><tr><td>E.Z.N.A Gel Extraction Kit</td><td>Omega Biotek</td><td>D2500-02</td><td></td></tr><tr><td>EnGen Lba Cas12a (Cpf1)</td><td>New England Biolabs (Beijing) LTD</td><td>M0653T</td><td></td></tr><tr><td>Filter holders</td><td>Ruicage</td><td>N/A</td><td></td></tr><tr><td>Fluorophore-ssDNA-Quencher reporter probes</td><td>Sangon Biotech</td><td>N/A</td><td>TAMRA (carboxy tetramethylrhodamine) as the fluorophore at the 5 ends; BHQ2 (Black Hole Quencher-2) as the quencher at the 3 ends</td></tr><tr><td>GP32</td><td>ATG Biotechnology</td><td>M104</td><td></td></tr><tr><td>ImageJ</td><td>Open-source</td><td>Version 1.53t 24</td><td>Downloaded from https://imagej.nih.gov/ij/&amp;nbsp;</td></tr><tr><td>Microplate reader</td><td>SPARK, TECAN</td><td>N/A</td><td></td></tr><tr><td>Multi-Block thermal Cycler PCR instrument</td><td>LongGene</td><td>N/A</td><td></td></tr><tr><td>NanoDrop 2000/2000c Spectrophotometers</td><td>Thermo Scientific</td><td>ND-2000</td><td></td></tr><tr><td>NEBuffer r2.1</td><td>New England Biolabs (Beijing) LTD</td><td>B6002S</td><td>10x CRISPR/Cas12a Reaction buffer</td></tr><tr><td>Oxygen plasma treatment&amp;nbsp;</td><td>Electro-Technic&amp;nbsp; Products</td><td>N/A</td><td></td></tr><tr><td>Pathogen Inactivate, Nucleic acid extraction-free, Direct-to-PCR Buffer with Proteinase K (PINDBK)</td><td>Ebio</td><td>PINDBK -25mL</td><td></td></tr><tr><td>PCR primer pairs</td><td>Sangon Biotech</td><td>N/A</td><td></td></tr><tr><td>PDMS</td><td>Dow Corning</td><td>Sylgard 184</td><td></td></tr><tr><td>RPA primer pairs</td><td>Sangon Biotech</td><td>N/A</td><td></td></tr><tr><td>Smartphone</td><td>Huawei</td><td>Mate10</td><td></td></tr><tr><td>Translation stages</td><td>Ruicage</td><td>N/A</td><td></td></tr><tr><td>Transmitted neutral density filters</td><td>Thorlabs</td><td>ND40A</td><td></td></tr><tr><td>Triplet achromatic lenses</td><td>Thorlabs</td><td>TRH127-020-A</td><td></td></tr><tr><td>UvsX</td><td>ATG Biotechnology</td><td>M105</td><td></td></tr><tr><td>UvsY</td><td>ATG Biotechnology</td><td>M106</td><td></td></tr></tbody></table>

dna virus detection system based on rpa-crispr/cas12a-spm and deep learning

We report a fast, easy-to-implement, highly sensitive, sequence-specific, and point-of-care (POC) DNA virus detection system, which combines recombinase polymerase amplification (RPA) and CRISPR/Cas12a system for trace detection of DNA viruses. Target DNA is amplified and recognized by RPA and CRISPR/Cas12a separately, which triggers the collateral cleavage activity of Cas12a that cleaves a fluorophore-quencher labeled DNA reporter and generalizes fluorescence. For POC detection, portable smartphone microscopy is built to take fluorescent images. Besides, deep learning models for binary classification of positive or negative samples, achieving high accuracy, are deployed within the system. Frog virus 3 (FV3, genera Ranavirus, family Iridoviridae) was tested as an example for this DNA virus POC detection system, and the limits of detection (LoD) can achieve 10 aM within 40 min. Without skilled operators and bulky instruments, the portable and miniature RPA-CRISPR/Cas12a-SPM with artificial intelligence (AI) assisted classification shows great potential for POC DNA virus detection and can help prevent the spread of such viruses.

This method focuses on a fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for DNA viruses. Primer pairs design for the RPA reaction and crRNA design for CRISPR/Cas12a reaction are two of the essential parts since they will affect the efficiency of the RPA-CRISPR/Cas12a reaction and influence the subsequent detection and classification.
In this method, FV3 is regarded as an example of DNA virus detection. Some RPA primer pairs for FV3 are...

Watch this Scientific Journal Video about DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning at JoVE.com

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

We present a protocol that combines recombinase polymerase amplification with a CRISPR/Cas12a system for trace detection of DNA viruses and builds portable smartphone microscopy with an artificial intelligence-assisted classification for point-of-care DNA virus detection.

We report a fast, easy-to-implement, highly sensitive, sequence-specific, and point-of-care (POC) DNA virus detection system, which combines recombinase ...

dna-virus-detection-system-based-on-rpa-crisprcas12a-spm-deep

Research

JoVE Journal

Biology

667 Views.  Tsinghua-Berkeley Shenzhen Institute. We present a protocol that combines recombinase polymerase amplification with a CRISPR/Cas12a system for trace detection of DNA viruses and builds portable smartphone microscopy with an artificial intelligence-assisted classification for point-of-care DNA virus detection.

Video: DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Lentiviral Vector Platform for the Efficient Delivery of Epigenome-editing Tools into Human Induced Pluripotent Stem Cell-derived Disease Models

The use of hiPSC-derived cells represents a valuable approach to study human neurodegenerative diseases. Here, we describe an optimized protocol for the differentiation of hiPSCs derived from a patient with the triplication of the alpha-synuclein gene (SNCA) locus into Parkinson&#8217;s disease (PD)-relevant dopaminergic neuronal populations. Accumulating evidence has shown that high levels of SNCA are causative for the development of PD. Recognizing the unmet need to establish novel therapeutic approaches for PD, especially those targeting the regulation of SNCA expression, we recently developed a CRISPR/dCas9-DNA-methylation-based system to epigenetically modulate SNCA transcription by enriching methylation levels at the SNCA intron 1 regulatory region. To deliver the system, consisting of a dead (deactivated) version of Cas9 (dCas9) fused with the catalytic domain of the DNA methyltransferase enzyme 3A (DNMT3A), a lentiviral vector is used. This system is applied to cells with the triplication of the SNCA locus and reduces the SNCA-mRNA and protein levels by about 30% through the targeted DNA methylation of SNCA intron 1. The fine-tuned downregulation of the SNCA levels rescues disease-related cellular phenotypes. In the current protocol, we aim to describe a step-by-step procedure for differentiating hiPSCs into neural progenitor cells (NPCs) and the establishment and validation of pyrosequencing assays for the evaluation of the methylation profile in the SNCA intron 1. To outline in more detail the lentivirus-CRISPR/dCas9 system used in these experiments, this protocol describes how to produce, purify, and concentrate lentiviral vectors and to highlight their suitability for epigenome- and genome-editing applications using hiPSCs and NPCs. The protocol is easily adaptable and can be used to produce high titer lentiviruses for in vitro and in vivo applications.

Targeted DNA epigenome editing represents a powerful therapeutic approach. This protocol describes the production, purification, and concentration of all-in-one lentiviral vectors harboring the CRISPR-dCas9-DNMT3A transgene for epigenome-editing applications in human induced pluripotent stem cell (hiPSC)-derived neurons.

The use of hiPSC-derived cells represents a valuable approach to study human neurodegenerative diseases. Here, we describe an optimized protocol for the ...

Genetics

Digital-Droplet PCR to Detect Indels Mutations in Genetically Modified Anopheline Mosquito Populations

Recent advances in mosquito genomics and genetic engineering technologies have fostered a need for quick and efficient methods for detecting targeted DNA sequence variation on a large scale. Specifically, detecting insertions and deletions (indels) at gene-edited sites generated by CRISPR guide RNA (gRNA)/Cas9-mediated non-homologous end-joining (NHEJ) is important for assessing the fidelity of the mutagenesis and the frequency of unintended changes. We describe here a protocol for digital-droplet PCR (ddPCR) that is well-suited for high-throughput NHEJ analysis. While this method does not produce data that identifies individual sequence variation, it provides a quantitative estimate of the sequence variation within a population. Additionally, with appropriate resources, this protocol can be implemented in a field-site laboratory setting more easily than next-generation or Sanger sequencing. ddPCR also has a faster turn-around time for results than either of those methods, which allows a more quick and complete analysis of genetic variation in wild populations during field trials of genetically-engineered organisms.

This protocol provides the steps from DNA extraction to experimental set-up for digital droplet PCR (ddPCR), including analysis for the identification and quantification of non-homologous end-joining (NHEJ) events at target sites following gRNA-induced Cas9 cleavage and DNA repair. Other uses of this method include applications such as polymorphism detection and gene-editing variant verification.

Recent advances in mosquito genomics and genetic engineering technologies have fostered a need for quick and efficient methods for detecting targeted DNA ...

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Here we outline a next-generation RNA sequencing protocol that enables de novo assemblies and intra-host variant calls of viral genomes collected from clinical and biological sources. The method is unbiased and universal; it uses random primers for cDNA synthesis and requires no prior knowledge of the viral sequence content. Before library construction, selective RNase H-based digestion is used to deplete unwanted RNA &#8212; including poly(rA) carrier and ribosomal RNA &#8212; from the viral RNA sample. Selective depletion improves both the data quality and the number of unique reads in viral RNA sequencing libraries. Moreover, a transposase-based &#39;tagmentation&#39; step is used in the protocol as it reduces overall library construction time. The protocol has enabled rapid deep sequencing of over 600 Lassa and Ebola virus samples-including collections from both blood and tissue isolates-and is broadly applicable to other microbial genomics studies.

This protocol describes a rapid and broadly applicable method for unbiased RNA-sequencing of viral samples from human clinical isolates.

Here we outline a next-generation RNA sequencing protocol that enables de novo assemblies and intra-host variant calls of viral genomes collected from ...

Medicine

Combining Analysis of DNA in a Crude Virion Extraction with the Analysis of RNA from Infected Leaves to Discover New Virus Genomes

This metagenome approach is used to identify plant viruses with circular DNA genomes and their transcripts. Often plant DNA viruses that occur in low titers in their host or cannot be mechanically inoculated to another host are difficult to propagate to achieve a greater titer of infectious material. Infected leaves are ground in a mild buffer with optimal pH and ionic composition recommended for purifying most bacilliform Para retroviruses. Urea is used to break up inclusion bodies that trap virions and to dissolve cellular components. Differential centrifugation provides further separation of virions from plant contaminants. Then proteinase K treatment removes the capsids. Then the viral DNA is concentrated and used for next-generation sequencing (NGS). The NGS data are used to assemble contigs which are submitted to NCBI-BLASTn to identify a subset of virus sequences in the generated dataset. In a parallel pipeline, RNA is isolated from infected leaves using a standard column-based RNA extraction method. Then ribosome depletion is carried out to enrich for a subset of mRNA and virus transcripts. Assembled sequences derived from RNA sequencing (RNA-seq) were submitted to NCBI-BLASTn to identify a subset of virus sequences in this dataset. In our study, we identified two related full-length badnavirus genomes in the two datasets. This method is preferred to another common approach which extracts the aggregate population of small RNA sequences to reconstitute plant virus genomic sequences. This latter metagenomic pipeline recovers virus related sequences that are retro-transcribing elements inserted into the plant genome. This is coupled to biochemical or molecular assays to further discern the actively infectious agents. The approach documented in this study, recovers sequences representative of replicating viruses that likely indicate active virus infection.

Here we present a new approach to identify plant viruses with double-strand DNA genomes. We use standard methods to extract DNA and RNA from infected leaves and carry out next-generation sequencing. Bioinformatic tools assemble sequences into contigs, identify contigs representing virus genomes and assign genomes to taxonomic groups.

This metagenome approach is used to identify plant viruses with circular DNA genomes and their transcripts. Often plant DNA viruses that occur in low ...

Immunology and Infection

Confocal Imaging of Double-Stranded RNA and Pattern Recognition Receptors in Negative-Sense RNA Virus Infection

Double-stranded (ds) RNA is produced as a replicative intermediate during RNA virus infection. Recognition of dsRNA by host pattern recognition receptors (PRRs) such as the retinoic acid (RIG-I) like receptors (RLRs) RIG-I and melanoma differentiation-associated protein 5 (MDA-5) leads to the induction of the innate immune response. The formation and intracellular distribution of dsRNA in positive-sense RNA virus infection has been well characterized by microscopy. Many negative-sense RNA viruses, including some arenaviruses, trigger the innate immune response during infection. However, negative-sense RNA viruses were thought to produce low levels of dsRNA, which hinders the imaging study of PRR recognition of viral dsRNA. Additionally, infection experiments with highly pathogenic arenaviruses must be performed in high containment biosafety level facilities (BSL-4). The interaction between viral RNA and PRRs for highly pathogenic RNA virus is largely unknown due to the additional technical challenges that researchers need to face in the BSL-4 facilities. Recently, a monoclonal antibody (Mab) (clone 9D5) originally used for pan-enterovirus detection has been found to specifically detect dsRNA with a higher sensitivity than the traditional J2 or K1 anti-dsRNA antibodies. Herein, by utilizing the 9D5 antibody, we describe a confocal microscopy protocol that has been used successfully to visualize dsRNA, viral protein and PRR simultaneously in individual cells infected by arenavirus. The protocol is also suitable for imaging studies of dsRNA and PRR distribution in pathogenic arenavirus infected cells in BSL4 facilities.

Double-stranded RNA produced during RNA virus replication can be recognized by pattern recognition receptors to induce an innate immune response. For negative-sense RNA viruses, the interaction between the low-level dsRNA and PRRs remains unclear. We have developed a confocal microscopy method to visualize arenavirus dsRNA and PRR in individual cells.

Double-stranded (ds) RNA is produced as a replicative intermediate during RNA virus infection. Recognition of dsRNA by host pattern recognition receptors ...

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Synthetic gene Boolean gates and digital circuits have a broad range of applications, from medical diagnostics to environmental care. The discovery of the CRISPR-Cas systems and their natural inhibitors-the anti-CRISPR proteins (Acrs)-provides a new tool to design and implement in vivo gene digital circuits. Here, we describe a protocol that follows the idea of the "Design-Build-Test-Learn" biological engineering cycle and makes use of dCas9/dCas12a together with their corresponding Acrs to establish small transcriptional networks, some of which behave like Boolean gates, in Saccharomyces cerevisiae. These results point out the properties of dCas9/dCas12a as transcription factors. In particular, to achieve maximal activation of gene expression, dSpCas9 needs to interact with an engineered scaffold RNA that collects multiple copies of the VP64 activation domain (AD). In contrast, dCas12a shall be fused, at the C terminus, with the strong VP64-p65-Rta (VPR) AD. Furthermore, the activity of both Cas proteins is not enhanced by increasing the amount of sgRNA/crRNA in the cell. This article also explains how to build Boolean gates based on the CRISPR-dCas-Acr interaction. The AcrIIA4 fused hormone-binding domain of the human estrogen receptor is the core of a NOT gate responsive to &#946;-estradiol, whereas AcrVAs synthesized by the inducible GAL1 promoter permits to mimic both YES and NOT gates with galactose as an input. In the latter circuits, AcrVA5, together with dLbCas12a, showed the best logic behavior.

CRISPR-Cas systems and anti-CRISPR proteins were integrated into the scheme of Boolean gates in Saccharomyces cerevisiae. The new small logic circuits showed good performance and deepened the understanding of both dCas9/dCas12a-based transcription factors and the properties of anti-CRISPR proteins.

Synthetic gene Boolean gates and digital circuits have a broad range of applications, from medical diagnostics to environmental care. The discovery of the ...

Bioengineering

Optimized CRISPR-Based Molecular Diagnostics Protocol for Point-of-Care Use

Point-of-care CRISPR-based Diagnostics with Premixed and Freeze-dried Reagents

Molecular diagnostics by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based detection have high diagnostic accuracy and attributes that are suitable for use at point-of-care settings such as fast turnaround times for results, convenient simple readouts, and no requirement of complicated instruments. However, the reactions can be cumbersome to perform at the point of care due to their many components and manual handling steps. Herein, we provide a step-by-step, optimized protocol for the robust detection of disease pathogens and genetic markers with recombinase-based isothermal amplification and CRISPR-based reagents, which are premixed and then freeze-dried in easily stored and ready-to-use formats. Premixed, freeze-dried reagents can be rehydrated for immediate use and retain high amplification and detection efficiencies. We also provide a troubleshooting guide for commonly found problems upon preparing and using premixed, freeze-dried reagents for CRISPR-based diagnostics, to make the detection platform more accessible to the wider diagnostic/genetic testing communities.

Author Spotlight: Development of Simplified CRISPR-Based Tests for Rapid Detection of Infectious Diseases

Here, we present the step-by-step preparation of premixed, lyophilized recombinase-based isothermal amplification and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based reactions, which can be used for the detection of nucleic acid biomarkers of infectious disease pathogens or other genetic markers of interest.

Molecular diagnostics by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based detection have high diagnostic accuracy and attributes ...

Analysis of SARS-CoV-2 Using Dual-Reporter and Multiplex Technology

Profiling of Surface Protein Epitopes on Viral Particles by Multiplex Dual-Reporter Strategy

Membrane proteins on enveloped viruses play an important role in many biological functions involving virus attachment to target cell receptors, fusion of viral particles to host cells, host-virus interactions, and disease pathogenesis. Furthermore, viral membrane proteins on virus particles and presented on host cell surfaces have proven to be excellent targets for antivirals and vaccines. Here, we describe a protocol to investigate surface proteins on intact severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) particles using the dual-reporter flow cytometric system. The assay exploits multiplex technology to obtain a triple detection of viral particles by three independent affinity reactions. Magnetic beads conjugated to recombinant human angiotensin-converting enzyme-2 (ACE2) were used to capture viral particles from the supernatant of cells infected with SARS-CoV-2. Then, two detection reagents labeled with R-phycoerythrin (PE) or Brilliant Violet 421 (BV421) were applied simultaneously. As a proof-of-concept, antibody fragments targeting different epitopes of the SARS-CoV-2 surface protein Spike (S1) were used. The detection of viral particles by three independent affinity reactions provides strong specificity and confirms the capture of intact virus particles. Dose-dependency curves of SARS-CoV-2 infected cell supernatant were generated with replicate coefficient variances (mean/SD) &#706;14%. Good assay performance in both channels confirmed that two virus surface target protein epitopes are detectable in parallel. The protocol described here could be applied for (i) high-multiplex, high-throughput profiling of surface proteins expressed on enveloped viruses; ii) detection of active intact viral particles; and (iii) assessment of specificity and affinity of antibodies and antiviral drugs for surface epitopes of viral antigens.The application can be potentially extended to any type of extracellular vesicles and bioparticles, exposing surface antigens in body fluids or other liquid matrices.

Author Spotlight: Advancing Antiviral Strategies Through Novel Immunocapture and Mass Spectrometry Techniques

Here, we describe a newly developed multiplex fluorescent immunoassay that uses a dual-reporter flow cytometric system to concurrently detect two unique spike protein epitopes on intact severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral particles that had been captured by angiotensin-converting enzyme-2-coupled magnetic microspheres.

Membrane proteins on enveloped viruses play an important role in many biological functions involving virus attachment to target cell receptors, fusion of ...

Rapid and Specific Detection of Acinetobacter baumannii Infections Using a Recombinase Polymerase Amplification/Cas12a-based System

Acinetobacter baumannii, a gram-negative bacterium, is notorious for causing severe infections with high mortality rates. Rapid and accurate detection of A. baumannii is crucial for prompt treatment, effective infection control, and curbing antibiotic resistance. However, there is no suitable method for rapid and easy on-site detection of A. baumannii. The DNA Endonuclease Targeted CRISPR Trans Reporter (DETECTR) system offers a rapid, precise, and sensitive approach to A. baumannii detection by integrating the target-specific recognition capabilities of Cas12a with the isothermal amplification efficiency of Recombinase Polymerase Amplification (RPA). This protocol details the detection of A. baumannii using RPA combined with LbaCas12a endonuclease. The following steps are described in this article: extraction of DNA, selection of a specific DNA sequence, design of primer and CRISPR RNA (crRNA), construction of positive recombinant plasmid, setup of Cas12a-RPA assay, optimization of the RPA amplification system, visualization of the RPA-CRISPR/Cas12a assay using a fluorescence detection tool such as a real-time PCR instrument, and evaluation of sensitivity and specificity evaluation.

To facilitate the rapid and precise detection of Acinetobacter baumannii, we present a protocol that employs Recombinase Polymerase Amplification (RPA) in conjunction with LbaCas12a endonuclease for identifying A. baumannii infections.

Acinetobacter baumannii, a gram-negative bacterium, is notorious for causing severe infections with high mortality rates. Rapid and accurate detection of ...

Single-Cell Multiplexed Fluorescence Imaging to Visualize Viral Nucleic Acids and Proteins and Monitor HIV, HTLV, HBV, HCV, Zika Virus, and Influenza Infection

Capturing the dynamic replication and assembly processes of viruses has been hindered by the lack of robust in situ hybridization (ISH) technologies that enable sensitive and simultaneous labeling of viral nucleic acid and protein. Conventional DNA fluorescence in situ hybridization (FISH) methods are often not compatible with immunostaining. We have therefore developed an imaging approach, MICDDRP (multiplex immunofluorescent cell-based detection of DNA, RNA and protein), which enables simultaneous single-cell visualization of DNA, RNA, and protein. Compared to conventional DNA FISH, MICDDRP utilizes branched DNA (bDNA) ISH technology, which dramatically improves oligonucleotide probe sensitivity and detection. Small modifications of MICDDRP enable imaging of viral proteins concomitantly with nucleic acids (RNA or DNA) of different strandedness. We have applied these protocols to study the life cycles of multiple viral pathogens, including human immunodeficiency virus (HIV)-1, human T-lymphotropic virus (HTLV)-1, hepatitis B virus (HBV), hepatitis C virus (HCV), Zika virus (ZKV), and influenza A virus (IAV). We demonstrated that we can efficiently label viral nucleic acids and proteins across a diverse range of viruses. These studies can provide us with improved mechanistic understanding of multiple viral systems, and in addition, serve as a template for application of multiplexed fluorescence imaging of DNA, RNA, and protein across a broad spectrum of cellular systems.

Presented here is a protocol for a fluorescence imaging approach, multiplex immunofluorescent cell-based detection of DNA, RNA, and protein (MICDDRP), a method capable of simultaneous fluorescence single-cell visualization of viral protein and nucleic acids of different type and strandedness. This approach can be applied to a diverse range of systems.

Capturing the dynamic replication and assembly processes of viruses has been hindered by the lack of robust in situ hybridization (ISH) technologies that ...