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>

<ol>
	<li>Gire, S. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+surveillance+elucidates+Ebola+virus+origin+and+transmission+during+the+2014+outbreak.">Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak.</a> Science. 345 (6202), 1369-1372 (2014).</li><li>The Ebola Outbreak Epidemiology Team. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Outbreak+of+Ebola+virus+disease+in+the+Democratic+Republic+of+the+Congo,+April-May+2018:+an+epidemiological+study.">Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April-May 2018: an epidemiological study.</a> Lancet. 392 (10143), 213-221 (2018).</li><li>Zumla, A., Hui, D. S., Perlman, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Middle+East+respiratory+syndrome.">Middle East respiratory syndrome.</a> Lancet. 386 (9997), 995-1007 (2015).</li><li>Plourde, A. R., Bloch, E. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+literature+review+of+Zika+virus.">A literature review of Zika virus.</a> Emerg Infect Dis. 22 (7), 1185-1192 (2016).</li><li>Yuan, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+and+perspective+diagnostic+techniques+for+COVID-19.">Current and perspective diagnostic techniques for COVID-19.</a> ACS Infect Dis. 6 (8), 1998-2016 (2020).</li><li>Minhaj, F. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monkeypox+outbreak+-+nine+states,+May+2022.">Monkeypox outbreak - nine states, May 2022.</a> MMWR Morb Mortal Wkly Rep. 71 (23), 764-769 (2022).</li><li>Bao, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Challenges+and+opportunities+for+clustered+regularly+interspaced+short+palindromic+repeats+based+molecular+biosensing.">Challenges and opportunities for clustered regularly interspaced short palindromic repeats based molecular biosensing.</a> ACS Sens. 6 (7), 2497-2522 (2021).</li><li>Broughton, J. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CRISPR-Cas12-based+detection+of+SARS-CoV-2.">CRISPR-Cas12-based detection of SARS-CoV-2.</a> Nat Biotechnol. 38 (7), 870-874 (2020).</li><li>Chen, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CRISPR-Cas12a+target+binding+unleashes+indiscriminate+single-stranded+DNase+activity.">CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity.</a> Science. 360 (6387), 436-439 (2018).</li><li>Gootenberg, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nucleic+acid+detection+with+CRISPR-Cas13a/C2c2.">Nucleic acid detection with CRISPR-Cas13a/C2c2.</a> Science. 356 (6336), 438-442 (2017).</li><li>Kellner, M. J., Koob, J. G., Gootenberg, J. S., Abudayyeh, O. O., Zhang, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SHERLOCK:+nucleic+acid+detection+with+CRISPR+nucleases.">SHERLOCK: nucleic acid detection with CRISPR nucleases.</a> Nat Protoc. 14 (10), 2986-3012 (2019).</li><li>Mukama, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+ultrasensitive+and+specific+point-of-care+CRISPR/Cas12+based+lateral+flow+biosensor+for+the+rapid+detection+of+nucleic+acids.">An ultrasensitive and specific point-of-care CRISPR/Cas12 based lateral flow biosensor for the rapid detection of nucleic acids.</a> Biosens Bioelectron. 159, 112143 (2020).</li><li>Schwank, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+repair+of+CFTR+by+CRISPR/Cas9+in+intestinal+stem+cell+organoids+of+cystic+fibrosis+patients.">Functional repair of CFTR by CRISPR/Cas9 in intestinal stem cell organoids of cystic fibrosis patients.</a> Cell Stem Cell. 13 (6), 653-658 (2013).</li><li>Yin, L., Man, S., Ye, S., Liu, G., Ma, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CRISPR-Cas+based+virus+detection:+Recent+advances+and+perspectives.">CRISPR-Cas based virus detection: Recent advances and perspectives.</a> Biosens Bioelectron. 193, 113541 (2021).</li><li>Dronina, J., Bubniene, U. S., Ramanavicius, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+application+of+DNA+polymerases+and+Cas9+as+representative+of+DNA-modifying+enzymes+group+in+DNA+sensor+design+(review).">The application of DNA polymerases and Cas9 as representative of DNA-modifying enzymes group in DNA sensor design (review).</a> Biosens Bioelectron. 175, 112867 (2021).</li><li>Fozouni, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amplification-free+detection+of+SARS-CoV-2+with+CRISPR-Cas13a+and+mobile+phone+microscopy.">Amplification-free detection of SARS-CoV-2 with CRISPR-Cas13a and mobile phone microscopy.</a> Cell. 184 (2), 323-333.e9 (2021).</li><li>Kumar, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FnCas9-based+CRISPR+diagnostic+for+rapid+and+accurate+detection+of+major+SARS-CoV-2+variants+on+a+paper+strip.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrasensitive+CRISPR-based+diagnostic+for+field-applicable+detection+of+Plasmodium+species+in+symptomatic+and+asymptomatic+malaria.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hands-free+smartphone-based+diagnostics+for+simultaneous+detection+of+Zika,+Chikungunya,+and+Dengue+at+point-of-care.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smartphone-based+fluorescent+diagnostic+system+for+highly+pathogenic+H5N1+viruses.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Democratising+deep+learning+for+microscopy+with+ZeroCostDL4Mic.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virus+detection+and+identification+in+minutes+using+single-particle+imaging+and+deep+learning.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mixed-UNet:+Refined+class+activation+mapping+for+weakly-supervised+semantic+segmentation+with+multi-scale+inference.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AI-assisted+forward+modeling+of+biological+structures.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two-stage+selective+ensemble+of+CNN+via+deep+tree+training+for+medical+image+classification.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RCMNet:+A+deep+learning+model+assists+CAR-T+therapy+for+leukemia.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stroke+prediction+from+electrocardiograms+by+deep+neural+network.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+of+deep+learning+on+medical+image+analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+learning+of+spontaneous+arousal+fluctuations+detects+early+cholinergic+defects+across+neurodevelopmental+mouse+models+and+patients.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepLearnMOR:+a+deep-learning+framework+for+fluorescence+image-based+classification+of+organelle+morphology.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+transferable+are+features+in+deep+neural+networks.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+and+all-solution+phase+African+Swine+Fever+Virus+(ASFV)+detection+using+CRISPR-Cas12a+and+fluorescence+based+point-of-care+system.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ImageNet+classification+with+deep+convolutional+neural+networks.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+COVID-19+from+CT+lung+scans+using+transfer+learning.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+frog+virus+3+by+integrating+RPA-CRISPR/Cas12a-SPM+with+deep+learning.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+shrimp+hemocyte+iridescent+virus+by+recombinase+polymerase+amplification+assay.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+rapid+and+ultrasensitive+dual+detection+platform+based+on+Cas12a+for+simultaneous+detection+of+virulence+and+resistance+genes+of+drug-resistant+Salmonella.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanistic+insights+of+CRISPR/Cas+nucleases+for+programmable+targeting+and+early-stage+diagnosis:+A+review.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+detection+and+tracking+of+Omicron+variant+of+SARS-CoV-2+using+CRISPR-Cas12a-based+assay.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+RNA+viruses+in+living+mammalian+cells+by+fluorescence+microscopy.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging,+tracking+and+computational+analyses+of+virus+entry+and+egress+with+the+cytoskeleton.">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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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/&#160;</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&#160;</td>
			<td>Electro-Technic&#160; 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

500 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

Intranuclear Microinjection of DNA into Dissociated Adult Mammalian Neurons

Primary neuronal cell cultures are valuable tools to study protein function since they represent a more biologically relevant system compared to immortalized cell lines. However, the post-mitotic nature of primary neurons prevents effective heterologous protein expression using common procedures such as electroporation or chemically-mediated transfection. Thus, other techniques must be employed in order to effectively express proteins in these non-dividing cells.
In this article, we describe the steps required to perform intranuclear injections of cDNA constructs into dissociated adult sympathetic neurons. This technique, which has been applied to different types of neurons, can successfully induce heterologous protein expression. The equipment essential for the microinjection procedure includes an inverted microscope to visualize cells, a glass injection pipet filled with cDNA solution that is connected to a N2(g) pressure delivery system, and a micromanipulator. The micromanipulator coordinates the injection motion of microinjection pipet with a brief pulse of pressurized N2 to eject cDNA solution from the pipet tip. 
This technique does not have the toxicity associated with many other transfection methods and enables multiple DNA constructs to be expressed at a consistent ratio. The low number of injected cells makes the microinjection procedure well suited for single cell studies such as electrophysiological recordings and optical imaging, but may not be ideal for biochemical assays that require a larger number of cells and higher transfection efficiencies. Although intranuclear microinjections require an investment of equipment and time, the ability to achieve high levels of heterologous protein expression in a physiologically relevant environment makes this technique a very useful tool to investigate protein function.

Direct intranuclear injection of cDNA is an effective transfection technique for post-mitotic cells. This method provides high levels of heterologous protein expression from single or multiple cDNA constructs and enables protein function to be studied in a physiologically relevant environment with a variety of single cell assays.

Primary neuronal cell cultures are valuable tools to study protein function since they represent a more biologically relevant system compared to ...

Genetic Studies of Human DNA Repair Proteins Using Yeast as a Model System 

Understanding the roles of human DNA repair proteins in genetic pathways is a formidable challenge to many researchers. Genetic studies in mammalian systems have been limited due to the lack of readily available tools including defined mutant genetic cell lines, regulatory expression systems, and appropriate selectable markers. To circumvent these difficulties, model genetic systems in lower eukaryotes have become an attractive choice for the study of functionally conserved DNA repair proteins and pathways. We have developed a model yeast system to study the poorly defined genetic functions of the Werner syndrome helicase-nuclease (WRN) in nucleic acid metabolism. Cellular phenotypes associated with defined genetic mutant backgrounds can be investigated to clarify the cellular and molecular functions of WRN through its catalytic activities and protein interactions. The human WRN gene and associated variants, cloned into DNA plasmids for expression in yeast, can be placed under the control of a regulatory plasmid element. The expression construct can then be transformed into the appropriate yeast mutant background, and genetic function assayed by a variety of methodologies. Using this approach, we determined that WRN, like its related RecQ family members BLM and Sgs1, operates in a Top3-dependent pathway that is likely to be important for genomic stability. This is described in our recent publication [1] at <a href="http://www.impactaging.com">www.impactaging.com</a>. Detailed methods of specific assays for genetic complementation studies in yeast are provided in this paper.

Genetic Studies of Human DNA Repair Proteins Using Yeast as a Model System

Genetic studies in yeast can be employed to investigate the molecular and cellular functions of human genes in cellular DNA metabolism. Methods are described for the genetic characterization of the human WRN gene product defective in the premature aging disorder Werner syndrome in functionally conserved pathways using yeast as a tractable model system.

Understanding the roles of human DNA repair proteins in genetic pathways is a formidable challenge to many researchers.  Genetic studies in mammalian ...

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Genetic information encoded by DNA is organized in a complex and highly regulated chromatin structure. Each chromosome occupies a specific territory, that may change according to stage of development or cell cycle. Gene expression can occur in specialized transcriptional factories where chromatin segments may loop out from various chromosome territories, leading to co-localization of DNA segments which may exist on different chromosomes or far apart on the same chromosome. The Associated Chromosome Trap (ACT) assay provides an effective methodology to identify these long-range DNA associations in an unbiased fashion by extending and modifying the chromosome conformation capture technique. The ACT assay makes it possible for us to investigate mechanisms of transcriptional regulation in trans, and can help explain the relationship of nuclear architecture to gene expression in normal physiology and during disease states.

The associated chromosome trap (ACT) assay is a novel unbiased method for identifying long-range DNA interactions. The characterization of long range DNA interactions will allow us to determine the relationship of nuclear architecture to gene expression in both normal physiology and in diseased states.

Genetic information encoded by DNA is organized in a complex and highly regulated chromatin structure.  Each chromosome occupies a specific territory, ...

Visualization of DNA Replication in the Vertebrate Model System DT40 using the DNA Fiber Technique

Maintenance of replication fork stability is of utmost importance for dividing cells to preserve viability and prevent disease. The processes involved not only ensure faithful genome duplication in the face of endogenous and exogenous DNA damage but also prevent genomic instability, a recognized causative factor in tumor development.
Here, we describe a simple and cost-effective fluorescence microscopy-based method to visualize DNA replication in the avian B-cell line DT40. This cell line provides a powerful tool to investigate protein function in vivo by reverse genetics in vertebrate cells1. DNA fiber fluorography in DT40 cells lacking a specific gene allows one to elucidate the function of this gene product in DNA replication and genome stability. Traditional methods to analyze replication fork dynamics in vertebrate cells rely on measuring the overall rate of DNA synthesis in a population of pulse-labeled cells. This is a quantitative approach and does not allow for qualitative analysis of parameters that influence DNA synthesis. In contrast, the rate of movement of active forks can be followed directly when using the DNA fiber technique2-4. In this approach, nascent DNA is labeled in vivo by incorporation of halogenated nucleotides (Fig 1A). Subsequently, individual fibers are stretched onto a microscope slide, and the labeled DNA replication tracts are stained with specific antibodies and visualized by fluorescence microscopy (Fig 1B). Initiation of replication as well as fork directionality is determined by the consecutive use of two differently modified analogues. Furthermore, the dual-labeling approach allows for quantitative analysis of parameters that influence DNA synthesis during the S-phase, i.e. replication structures such as ongoing and stalled forks, replication origin density as well as fork terminations. Finally, the experimental procedure can be accomplished within a day, and requires only general laboratory equipment and a fluorescence microscope.

DT40, a model vertebrate genetic system, provides a powerful tool to analyze protein function. Here we describe a simple method that allows qualitative analysis of parameters that influence DNA synthesis during the S-phase in DT40 cells at the single molecule level.

Maintenance of replication fork stability is of utmost importance for dividing cells to preserve viability and prevent disease. The processes involved not ...

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Retroviruses exhibit signature integration preferences on both the local and global scales. Here, we present a detailed protocol for (1) generation of diverse libraries of retroviral integration sites using ligation-mediated PCR (LM-PCR) amplification and next-generation sequencing (NGS), (2) mapping the genomic location of each virus-host junction using BEDTools, and (3) analyzing the data for statistical relevance. Genomic DNA extracted from infected cells is fragmented by digestion with restriction enzymes or by sonication. After suitable DNA end-repair, double-stranded linkers are ligated onto the DNA ends, and semi-nested PCR is conducted using primers complementary to both the long terminal repeat (LTR) end of the virus and the ligated linker DNA. The PCR primers carry sequences required for DNA clustering during NGS, negating the requirement for separate adapter ligation. Quality control (QC) is conducted to assess DNA fragment size distribution and adapter DNA incorporation prior to NGS. Sequence output files are filtered for LTR-containing reads, and the sequences defining the LTR and the linker are cropped away. Trimmed host cell sequences are mapped to a reference genome using BLAT and are filtered for minimally 97% identity to a unique point in the reference genome. Unique integration sites are scrutinized for adjacent nucleotide (nt) sequence and distribution relative to various genomic features. Using this protocol, integration site libraries of high complexity can be constructed from genomic DNA in three days. The entire protocol that encompasses exogenous viral infection of susceptible tissue culture cells to integration site analysis can therefore be conducted in approximately one to two weeks. Recent applications of this technology pertain to longitudinal analysis of integration sites from HIV-infected patients.

We describe a protocol for amplifying retroviral integration sites from the genomic DNA of infected cells, sequencing the amplified virus-host junctions, and then mapping these sequences to a reference genome. We also describe techniques to quantify the distribution of integration sites relative to various genomic annotations using BEDTools.

Retroviruses exhibit signature integration preferences on both the local and global scales. Here, we present a detailed protocol for (1) generation of ...

SA-&#946;-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell culture, termed senolytics, can reduce the senescent cell burden in vivo and extend healthspan. Multiple classes of senolytics have been identified to date including HSP90 inhibitors, Bcl-2 family inhibitors, piperlongumine, a FOXO4 inhibitory peptide and the combination of Dasatinib/Quercetin. Detection of SA-&#946;-Gal at an increased lysosomal pH is one of the best characterized markers for the detection of senescent cells. Live cell measurements of senescence-associated &#946;-galactosidase (SA-&#946;-Gal) activity using the fluorescent substrate C12FDG in combination with the determination of the total cell number using a DNA intercalating Hoechst dye opens the possibility to screen for senotherapeutic drugs that either reduce overall SA-&#946;-Gal activity by killing of senescent cells (senolytics) or by suppressing SA-&#946;-Gal and other phenotypes of senescent cells (senomorphics). Use of a high content fluorescent image acquisition and analysis platform allows for the rapid, high throughput screening of drug libraries for effects on SA-&#946;-Gal, cell morphology and cell number.

SA-&amp;#946;-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

Cellular senescence is the key factor in the development of chronic age-related pathologies. Identification of therapeutics that target senescent cells show promise for extending healthy aging. Here, we present a novel assay to screen for the identification of senotherapeutics based on measurement of senescence associated &#946;-Galactosidase activity in single cells.

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell ...

Visualizing and Quantifying Endonuclease-Based Site-Specific DNA Damage

Cells are continuously exposed to various DNA damaging agents, inducing different cellular responses. Applying biochemical and genetic approaches is essential in revealing cellular events associated with the recruitment and assembly of DNA repair complexes at the site of DNA damage. In the last few years, several powerful tools have been developed to induce site-specific DNA damage. Moreover, novel seminal techniques allow us to study these processes at the single-cell resolution level using both fixed and living cells. Although these techniques have been used to study various biological processes, herein we present the most widely used protocols in the field of DNA repair, Fluorescence Immunostaining (IF) and Chromatin Immunoprecipitation (ChIP), which in combination with endonuclease-based site-specific DNA damage make it possible to visualize and quantify the genomic occupancy of DNA repair factors in a directed and regulated fashion, respectively. These techniques provide powerful tools for the researchers to identify novel proteins bound to the damaged genomic locus as well as their post-translational modifications necessary for their fine-tune regulation during DNA repair.

This article introduces essential steps of immunostaining and chromatin immunoprecipitation. These protocols are commonly used to study DNA damage-related cellular processes and to visualize and quantify the recruitment of proteins implicated in DNA repair.

Cells are continuously exposed to various DNA damaging agents, inducing different cellular responses. Applying biochemical and genetic approaches is ...

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The technique has several limitations, however, that make analyzing the data it generates time-intensive and difficult. Hand segmenting a single tomogram can take from hours to days, but a microscope can easily generate 50 or more tomograms a day. Current deep learning segmentation programs for cryo-ET do exist, but are limited to segmenting one structure at a time. Here, multi-slice U-Net convolutional neural networks are trained and applied to automatically segment multiple structures simultaneously within cryo-tomograms. With proper preprocessing, these networks can be robustly inferred to many tomograms without the need for training individual networks for each tomogram. This workflow dramatically improves the speed with which cryo-electron tomograms can be analyzed by cutting segmentation time down to under 30 min in most cases. Further, segmentations can be used to improve the accuracy of filament tracing within a cellular context and to rapidly extract coordinates for subtomogram averaging.

This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament tracing.

Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The ...

Generation of Brown Fat-Specific Knockout Mice Using a Combined Cre-LoxP, CRISPR-Cas9, and Adeno-Associated Virus Single-Guide RNA System

Brown adipose tissue (BAT) is an adipose depot specialized in energy dissipation that can also serve as an endocrine organ via the secretion of bioactive molecules. The creation of BAT-specific knockout mice is one of the most popular approaches for understanding the contribution of a gene of interest to BAT-mediated energy regulation. The conventional gene targeting strategy utilizing the Cre-LoxP system has been the principal approach to generate tissue-specific knockout mice. However, this approach is time-consuming and tedious. Here, we describe a protocol for the rapid and efficient knockout of a gene of interest in BAT using a combined Cre-LoxP, CRISPR-Cas9, and adeno-associated virus (AAV) single-guide RNA (sgRNA) system. The interscapular BAT is located in the deep layer between the muscles. Thus, the BAT must be exposed in order to inject the AAV precisely and directly into the BAT within the visual field. Appropriate surgical handling is crucial to prevent damage to the sympathetic nerves and vessels, such as the Sultzer's vein that connects to the BAT. To minimize tissue damage, there is a critical need to understand the three-dimensional anatomical location of the BAT and the surgical skills required in the technical steps. This protocol highlights the key technical procedures, including the design of sgRNAs targeting the gene of interest, the preparation of AAV-sgRNA particles, and the surgery for the direct microinjection of AAV into both BAT lobes for generating BAT-specific knockout mice, which can be broadly applied to study the biological functions of genes in BAT.

In this protocol, we describe the technical procedures to generate brown adipose tissue (BAT)-specific knockout mice leveraging a combined Cre-LoxP, CRISPR-Cas9, and adeno-associated virus (AAV) single-guide RNA (sgRNA) system. The described steps include the design of the sgRNAs, the preparation of the AAV-sgRNA particles, and the microinjection of AAV into the BAT lobes.

Brown adipose tissue (BAT) is an adipose depot specialized in energy dissipation that can also serve as an endocrine organ via the secretion of bioactive ...

Image Analysis of Immunohistochemically Stained Endometrial Tissue

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis

To evaluate the endometrial immune microenvironment of patients with recurrent miscarriage (RM), a digital immunohistochemistry image analysis platform was developed and validated to quantitatively analyze endometrial immune cells during the mid-luteal phase. All endometrium samples were collected during the mid-luteal phase of the menstrual cycle. Paraffin-embedded endometrial tissues were sectioned into 4 &#181;m thick slides, and immunohistochemistry (IHC)staining was carried out for detecting endometrial immune cells, including CD56+ uNK cells, Foxp3+ Tregs, CD163+ M2 macrophages, CD1a+ DCs, and CD8+ T cells. The panoramic slides were scanned using a digital slide scanner and a commercial image analysis system was used for quantitative analysis. The percentage of endometrial immune cells was calculated by dividing the number of immune cells in the total endometrial cells. Using the commercial image analysis system, quantitative evaluation of endometrial immune cells, which are difficult or impossible to analyze with conventional image analysis, could be easily, and accurately analyzed. This methodology can be applied to quantitatively characterize the endometrium microenvironment, including interaction between immune cells, and its heterogeneity for different reproductive failure patients. The platform for quantitative evaluation of endometrial immune cells may be of important clinical significance for the diagnosis and treatment of RM patients.

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells 

Here, a digital immunohistochemistry image analysis platform was developed and validated to quantitatively analyze the endometrial immune cells of patients with recurrent miscarriages in the window of implantation.

To evaluate the endometrial immune microenvironment of patients with recurrent miscarriage (RM), a digital immunohistochemistry image analysis platform ...