Name: genetic variant detection in the calr gene using high resolution melting analysis
Uploaded: 2020-08-26T13:00:00
Description: Watch this Scientific Journal Video about Genetic Variant Detection in the CALR gene using High Resolution Melting Analysis at JoVE.com

The authors would like to thank all the academic experts and employees at the Specialized Hematology Laboratory, Department of Hematology, Division of Internal Medicine, University Medical Centre Ljubljana.

The study was approved by the Committee of Medical Ethics of the Republic of Slovenia. All procedures were in accordance with the Helsinki declaration.
1. Fluorescence-based quantitative real-time PCR (qPCR) and post-qPCR analysis by HRM
<ol>
	<li>Resuspend primers listed in the Table of Materials to 100 &#181;M with sterile, RNase and DNase free H2O (see Table of Materials). Make a 10 &#181;M working concentration primer. Primers used in the prot...

<ol>
	<li>Nangalia, 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=Somatic+CALR+mutations+in+myeloproliferative+neoplasms+with+nonmutated+JAK2.">Somatic CALR mutations in myeloproliferative neoplasms with nonmutated JAK2.</a> New England Journal of Medicine. 369, 2391-2405 (2013).</li><li>Klampfl, T., 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=Somatic+mutations+of+calreticulin+in+myeloproliferative+neoplasms.">Somatic mutations of calreticulin in myeloproliferative neoplasms.</a> New England Journal of Medicine. 369, 2379-2390 (2013).</li><li>Vainchenker, W., Kralovics, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+basis+and+molecular+pathophysiology+of+classical+myeloproliferative+neoplasms.">Genetic basis and molecular pathophysiology of classical myeloproliferative neoplasms.</a> Blood. 129, 667-679 (2017).</li><li>Pietra, 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=Differential+clinical+effects+of+different+mutation+subtypes+in+CALR-mutant+myeloproliferative+neoplasms.">Differential clinical effects of different mutation subtypes in CALR-mutant myeloproliferative neoplasms.</a> Leukemia. 30, 431-438 (2016).</li><li>Lim, K. H., 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=Rapid+and+sensitive+detection+of+CALR+exon+9+mutations+using+high-resolution+melting+analysis.">Rapid and sensitive detection of CALR exon 9 mutations using high-resolution melting analysis.</a> Clinica Chimica Acta. 440, 133-139 (2015).</li><li>Lay, M. J., Wittwer, C. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+fluorescence+genotyping+of+factor+V+leiden+during+rapid-cycle+PCR.">Real-time fluorescence genotyping of factor V leiden during rapid-cycle PCR.</a> Clinical Chemistry. 43, 2262-2267 (1997).</li><li>Vossen, R. H. A. M., Aten, E., Roos, A., Den Dunnen, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+melting+analysis+(HRMA)+-+More+than+just+sequence+variant+screening.">High-resolution melting analysis (HRMA) - More than just sequence variant screening.</a> Human Mutation. 30, 860-866 (2009).</li><li>Barbui, T., Thiele, J., Vannucchi, A. M., Tefferi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rationale+for+revision+and+proposed+changes+of+the+WHO+diagnostic+criteria+for+polycythemia+vera,+essential+thrombocythemia+and+primary+myelofibrosis.">Rationale for revision and proposed changes of the WHO diagnostic criteria for polycythemia vera, essential thrombocythemia and primary myelofibrosis.</a> Blood Cancer Journal. 5, 337-338 (2015).</li><li>Reed, G. H., Wittwer, C. 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T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+DNA+melting+analysis+for+simple+and+efficient+molecular+diagnostics.">High-resolution DNA melting analysis for simple and efficient molecular diagnostics.</a> Pharmacogenomics. 8, 597-608 (2007).</li><li>Gonzalez-Bosquet, 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=Detection+of+somatic+mutations+by+high-resolution+DNA+melting+(HRM)+analysis+in+multiple+cancers.">Detection of somatic mutations by high-resolution DNA melting (HRM) analysis in multiple cancers.</a> PLoS One. 6, 14522 (2011).</li><li>Van Der Stoep, 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=Diagnostic+guidelines+for+high-resolution+melting+curve+(HRM)+analysis:+An+interlaboratory+validation+of+BRCA1+mutation+scanning+using+the+96-well+LightScanner.">Diagnostic guidelines for high-resolution melting curve (HRM) analysis: An interlaboratory validation of BRCA1 mutation scanning using the 96-well LightScanner.</a> Human Mutation. 30, 899-909 (2009).</li><li>Singdong, 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=Characterization+and+Prognosis+Significance+of+JAK2+(V617F),+MPL,+and+CALR+Mutations+in+Philadelphia-Negative+Myeloproliferative+Neoplasms.">Characterization and Prognosis Significance of JAK2 (V617F), MPL, and CALR Mutations in Philadelphia-Negative Myeloproliferative Neoplasms.</a> Asian Pacific Journal of Cancer Prevention. 17, 4647-4653 (2016).</li><li>Erali, M., Wittwer, C. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+resolution+melting+analysis+for+gene+scanning.">High resolution melting analysis for gene scanning.</a> Methods. 50, 250-261 (2010).</li><li>Bilbao-Sieyro, C., 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+Resolution+Melting+Analysis:+A+Rapid+and+Accurate+Method+to+Detect+CALR+Mutations.">High Resolution Melting Analysis: A Rapid and Accurate Method to Detect CALR Mutations.</a> PLoS One. 9, 103511 (2014).</li><li>S&#322;omka, M., Sobalska-Kwapis, M., Wachulec, M., Bartosz, G., Strapagiel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+resolution+melting+(HRM)+for+high-throughput+genotyping-limitations+and+caveats+in+practical+case+studies.">High resolution melting (HRM) for high-throughput genotyping-limitations and caveats in practical case studies.</a> International Journal of Molecular Sciences. 18, 2316 (2017).</li><li>Li, 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=Comparison+of+three+common+DNA+concentration+measurement+methods.">Comparison of three common DNA concentration measurement methods.</a> Analytical Biochemistry. 451, 18-24 (2014).</li><li>Voytas, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Agarose+Gel+Electrophoresis.">Agarose Gel Electrophoresis.</a> Current Protocols in Immunology. 2, (1992).</li><li>Lee, P. Y., Costumbrado, J., Hsu, C. Y., Kim, Y. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Agarose+Gel+Electrophoresis+for+the+Separation+of+DNA+Fragments.">Agarose Gel Electrophoresis for the Separation of DNA Fragments.</a> Journal of Visualized Experiments. , e3923 (2012).</li><li>Helling, R. B., Goodman, H. M., Boyer, H. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Endonuclease+R&#183;EcoRI+Fragments+of+DNA+from+Lambdoid+Bacteriophages+and+Other+Viruses+by+Agarose-Gel+Electrophoresis.">Analysis of Endonuclease R&#183;EcoRI Fragments of DNA from Lambdoid Bacteriophages and Other Viruses by Agarose-Gel Electrophoresis.</a> Journal of Virology. , 1235-1244 (1974).</li></ol>

High-resolution melting of DNA is a simple solution for genotyping and genetic variant scanning14. It depends on sequence differences that result in heteroduplexes that change the shape of the melting curve. Different types of genetic variants with diverse frequencies can be observed in the gene specific for a certain group of patients with cancer1,2,15,16,<su...

Somatic genetic variants in the calreticulin gene (CALR) were recognized in 2013 in patients with myeloproliferative neoplasms (MPN) such as essential thrombocythemia and primary myelofibrosis1,2. Since then, more than 50 genetic variants in the CALR gene have been discovered, inducing a +1 (&#8722;1+2) frameshift3. The two most frequent CALR genetic variants are a 52 bp deletion (NM_004343.3 (CALR):c.1099_1150del52, p.(Leu367Thrfs*46)), also called type 1 mutation, and a 5 bp insertion (NM_004343.3 (CALR):c.1154_1155insTTGTC, p.(Lys385Asnfs*47)), also called type 2 mutation. These two genetic variants represent 80% of all CALR genetic variants. The other ones have been classified as type 1&#8211;like or type 2&#8211;like using algorithms based on the preservation of an &#945; helix close to wild type CALR4. Here, we present one of the highly sensitive and rapid methods for CALR genetic variant detection, the high resolution melting analysis method (HRM). This method enables the rapid detection of type 1 and type 2 genetic variants, which represent the majority of CALR mutations5. HRM was introduced in combination with real time &#187;polymerase chain reaction&#171; (qPCR) in 1997 as a tool to detect the mutation in factor V Leiden6. In comparison to Sanger sequencing that represents the golden standard technique, HRM is a more sensitive and less specific method5. The HRM method is a good screening method that enables a rapid analysis of a large number of samples with a great cost-benefit5. It is a simple PCR method performed in the presence of a fluorescent dye and does not require specific skills. Another benefit is that the procedure itself does not damage or destroy the analyzed sample that allows us to reuse the sample for electrophoresis or Sanger sequencing after the HRM procedure7. The only disadvantage is that it is sometimes difficult to interpret the results. Additionally, HRM does not detect the exact mutation in patients with non-type 1 or type 2 mutations8. In these patients, Sanger sequencing should be performed (Figure 1).
HRM is based on the amplification of the specific DNA region in the presence of saturating DNA fluorescent dye, which is incorporated in double-stranded DNA (dsDNA). The fluorescent dye emits light when incorporated in the dsDNA. After a progressive increase in temperature, dsDNA breaks down into single stranded DNA, which can be detected on the melting curve as a sudden decrease in fluorescence intensity. The shape of the melting curve depends on the DNA sequence that is used to detect the mutation. Melting curves of samples are compared to melting curves of known mutations or wild type CALR. Distinct melting curves represent a different mutation that is non type 1 or type 29.
The algorithm for the somatic genetic variant detection in the CALR gene by HRM, agarose gel electrophoresis and sequencing method (Figure 1) was used and validated in the retrospective study published before10.

<table>
	<tbody>
		<tr>
			<td>E-Gel EX 4% Agarose</td>
			<td>Invitrogen, Thermo Fischer Scientific</td>
			<td>G401004</td>
			<td></td>
		</tr>
		<tr>
			<td>Fuorometer 3.0 QUBIT</td>
			<td>Invitrogen, Thermo Fischer Scientific</td>
			<td>Q33216</td>
			<td></td>
		</tr>
		<tr>
			<td>Invitrogen E-Gel iBase and E-Gel Safe Imager Combo Kit</td>
			<td>Invitrogen, Thermo Fischer Scientific</td>
			<td>G6465EU</td>
			<td></td>
		</tr>
		<tr>
			<td>MeltDoctor HRM MasterMix 2X</td>
			<td>Applied Biosystem, Thermo Fischer Scientific</td>
			<td>4415440</td>
			<td>Components: AmpliTaqGold 360 DNA Polymerase, MeltDoctor trade HRM dye, dNTP blend including dUTP, Magnesium salts and other buffer components, precisely formulated to obtain optimal HRM results</td>
		</tr>
		<tr>
			<td>MicroAmp Fast 96-well Reaction Plate (0.1 mL)</td>
			<td>Applied Biosystems, Thermo Fischer Scientific</td>
			<td>4346907</td>
			<td></td>
		</tr>
		<tr>
			<td>MicroAmp Optical adhesive film</td>
			<td>Applied Biosystems, Thermo Fischer Scientific</td>
			<td>4311971</td>
			<td></td>
		</tr>
		<tr>
			<td>NuGenius</td>
			<td>Syngene</td>
			<td>NG-1045</td>
			<td>Gel documentation systems</td>
		</tr>
		<tr>
			<td>Primer CALRex9 Forward</td>
			<td>Eurofins Genomics</td>
			<td></td>
			<td>Sequence: 5&#39;GGCAAGGCCCTGAGGTGT&#39;3 (High-Purity, Salt-Free)</td>
		</tr>
		<tr>
			<td>Primer CALRex9 Reverse</td>
			<td>Eurofins Genomics</td>
			<td></td>
			<td>Sequence: 5&#39;GGCCTCAGTCCAGCCCTG&#39;3 (High-Purity, Salt-Free)</td>
		</tr>
		<tr>
			<td>QIAamp DNA Mini Kit</td>
			<td>QIAGEN</td>
			<td>51306</td>
			<td>DNA isolation kit with the buffer for DNA dilution.</td>
		</tr>
		<tr>
			<td>Qubit Assay Tubes</td>
			<td>Invitrogen, Thermo Fischer Scientific</td>
			<td>Q32856</td>
			<td></td>
		</tr>
		<tr>
			<td>QUBIT dsDNA HS assay</td>
			<td>Invitrogen, Thermo Fischer Scientific</td>
			<td>Q32854</td>
			<td></td>
		</tr>
		<tr>
			<td>Trackit 100bp DNA Ladder</td>
			<td>Invitrogen, Thermo Fischer Scientific</td>
			<td>10488058</td>
			<td>Ladder consists of 13 individual fragments with the reference bands at 2000, 1500, and 600 bp.</td>
		</tr>
		<tr>
			<td>ViiA7 Real-Time PCR System</td>
			<td>Applied Biosystems, Thermo Fischer Scientific</td>
			<td>4453534</td>
			<td></td>
		</tr>
		<tr>
			<td>Water nuclease free</td>
			<td>VWR, Life Science</td>
			<td>436912C</td>
			<td>RNase, DNase and Protease free water</td>
		</tr>
	</tbody>
</table>

genetic variant detection in the calr gene using high resolution melting analysis

High resolution melting analysis (HRM) is a powerful method for genotyping and genetic variation scanning. Most HRM applications depend on saturating DNA dyes that detect sequence differences, and heteroduplexes that change the shape of the melting curve. Excellent instrument resolution and special data analysis software are needed to identify the small melting curve differences that identify a variant or genotype. Different types of genetic variants with diverse frequencies can be observed in the gene specific for patients with a specific disease, especially cancer and in the CALR gene in patients with Philadelphia chromosome&#8211;negative myeloproliferative neoplasms. Single nucleotide changes, insertions and/or deletions (indels) in the gene of interest can be detected by the HRM analysis. The identification of different types of genetic variants is mostly based on the controls used in the qPCR HRM assay. However, as the product length increases, the difference between wild-type and heterozygote curves becomes smaller, and the type of genetic variant is more difficult to determine. Therefore, where indels are the prevalent genetic variant expected in the gene of interest, an additional method such as agarose gel electrophoresis can be used for the clarification of the HRM result. In some instances, an inconclusive result must be re-checked/re-diagnosed by standard Sanger sequencing. In this retrospective study, we applied the method to JAK2 V617F-negative patients with MPN.

The successfully amplified DNA region of interest with an exponential increase in fluorescence that exceeds the threshold between cycles 15 and 35 and very narrow values of the cycle of quantification (Cq) in all replicated samples and controls (Figure 2) is a prerequisite for the reliable identification of genetic variants by HRM analysis. This is achieved by using a precise determination of DNA with fluorescence staining and an equal amount of DNA in the qPCR HRM experiment (see step 1.2)....

Watch this Scientific Journal Video about Genetic Variant Detection in the CALR gene using High Resolution Melting Analysis at JoVE.com

Genetic Variant Detection in the CALR gene using High Resolution Melting Analysis

High resolution melting analysis (HRM) is a sensitive and rapid solution for genetic variant detection. It depends on sequence differences that result in heteroduplexes changing the shape of the melting curve. By combing HRM and agarose gel electrophoresis, different types of genetic variants such as indels can be identified.

Genetic Variant Detection in the CALR gene using High Resolution Melting Analysis

High resolution melting analysis (HRM) is a powerful method for genotyping and genetic variation scanning. Most HRM applications depend on saturating DNA ...

This method accurately defines calreticulin mutations in patients with suspected hematologic condition, especially where other diagnostic options are limited or there is an overlap with non-hematologic conditions. The main advantage of the high resolution mirror technique is detecting the common somatic genetic variants in the hotspot region of the calreticulin gene at the acceptable limit of detection and affordable cost. This technique enables rapid and noninvasive diagnosis of myeloproliferative disorders, especially essential thrombocythemia and primary myelofibrosis.Currently it has no effect on the choice of therapy in these patients. However, this may change in the future. Begin by preparing the qPCR HRM master mix as described in the text manuscript, making enough for three replicates of each DNA sample and control.Mix the reaction contents by gently tapping and inverting the tube and vortexing for two to three seconds, then briefly spin the tube to collect the scattered droplets. Transfer 19 microliters of the qPCR HRM master mix to each well of the 96 Well Optical Reaction Plate. Pipette one microliter of the negative controls, positive controls and samples into the appropriate wells of the plate for the no template control or NTC at one microliter of sterile RNAs and DNA's free water used for preparing the qPCR HRM master mix instead of DNA.Seal the reaction plate firmly with the optical adhesive film to prevent evaporation during the run. Then spin it at 780 times G at room temperature for one minute. Check that the liquid is at the bottom of the wells in the reaction plate and run the assay.Assign the controls and samples to the appropriate wells in the instrument system software and change the default instrument amplification protocol. Program the instrument as described in the text manuscript to perform the dissociation analysis immediately after qPCR. when the run end review and verify the amplification plot.In the experiment menu of the instrument system software select the amplification plot option. If no data are displayed, click the green analyze button. In the Amplification Plot tab from the plot type dropdown menu, select the plot that displays the amplification data as the fluorescence readings, normalized to the fluorescence from the passive reference as a function of cycle number, In the plot color dropdown menu, select sample.From the graph type dropdown menu, select the linear amplification graph type, verify that the baseline start and end cycle is set correctly. The end cycle should be set a few cycles before the cycle number where a significant fluorescent signal is detected. From the graph type dropdown menu, select the log 10 amplification graph type show the threshold line on the graph by selecting the threshold option and adjust the threshold.The threshold line should cross the exponential phase at the qPCR curves. Verify that all sample and positive control Wells have normal amplification curves and that there is no amplification in the NTC Wells. A normal amplification plot shows an exponential increase in florescence that exceeds the threshold between cycles 15 and 35.In the derivative melt curves review the pre and post melt regions or temperature lines. The pre and post melt regions should be within a flat area where there are no large peaks or slopes in the fluorescent levels. If needed set the start and stop of the pre and post melt temperature lines, approximately 0.5 degrees Celsius apart from each other.Restart the analysis if the parameters are adjusted by clicking on the analyze button. In the plot settings tab of the difference pot tab, choose one of the wild type control replicates as the reference DNA and restart the analysis by clicking on the analyze button. In the aligned Melt curves tab, confirm that all positive controls have the correct genotype and that the NTC failed to amplify.From the well table, select a control well to highlight the corresponding melt curve in the analysis plots and confirm that the color of the line corresponds to the correct genotype. In the aligned melt curves tab carefully review the plot displays for the unknown samples and compare them to the plot displays for controls. Select the Wells containing the unknown sample replicates and align the melt curves with the controls in an ordered sequence by selecting the wells containing controls one by one.Analyze the result for the unknown sample in the Difference Plot tab to verify that the results obtained are consistent. Select the wells containing the unknown sample replicates and align the melt curves with the controls in an ordered sequence, selecting the Wells containing controls one by one. Run qPCR HRM products on a 4%agarose precast gel containing a fluorescent nucleic acid strain.Run only one positive negative NTC and sample qPCR HRM replicates. Remove the precast gel and the cassette from the package then remove the comb and insert the gel into the apparatus according to the manufacturer's instructions. Dilute a 10 microliter sample to 20 microliters with sterile RNA and DNA free water and load each well with 20 microliters of the diluted sample.Dilute three microliters of DNA size standard solution to 20 microliters and load it into the marker well. Fill any empty wells with 20 microliters of sterile RNA and DNA's free water. Immediately select the program according to the percentage of the gel being run and set the runtime on the apparatus to 20 minutes.Start the electrophoresis within one minute of loading the samples. When the electrophoresis is completed, visualize the DNA and the gel by using blue light or UV transillumination. And successfully amplified DNA region of interest with an exponential increase in fluorescents that exceeds the threshold between cycles 15 and 35 and very narrow values of the cycle of quantification in all replicates and controls is a prerequisite for the reliable identification of genetic variance by high resolution melting analysis.HRM analysis is performed immediately after qPCR, the active melt regions of the samples, the controls and the NTC are used to create their aligned melt curve plots. Correctly set pre and post melt regions are important for properly visualizing and identifying genetic variance in the samples. Aligned melt curves and different pots are shown here.The unknown samples are tightly aligned with one of the positive controls. Incorrectly set pre and post melt regions or temperature lines result in aligned melt curves and different plots where correct identification of the genetic variance is more difficult. The genetic variant in the sample can be identified by comparing the band pattern of the sample to the controls.And by combining the HRM and agarose gel electrophoresis. Correct genetic variant identification can only be done for the samples that contain the same genetic variant as one of the controls used in the HRM assay. Samples containing rare calreticulin genetic variants differ in the HRM result and electrophoresis band pattern.When attempting this protocol, it is important to carefully compare the aligned melt curve plot displays of the known and control samples.

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Research

JoVE Journal

Genetics

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

High-resolution Melting PCR for Complement Receptor 1 Length Polymorphism Genotyping: An Innovative Tool for Alzheimer's Disease Gene Susceptibility Assessment

Complement receptor 1 (CR1), a transmembrane glycoprotein that plays a key role in the innate immune system, is expressed on many cell types, but especially on red blood cells (RBCs). As a receptor for the complement components C3b and C4b, CR1 regulates the activation of the complement cascade and promotes the phagocytosis of immune complexes and cellular debris, as well as the amyloid-beta (A&#946;) peptide in Alzheimer's disease (AD). Several studies have confirmed AD-associated single nucleotide polymorphisms (SNPs), as well as a copy-number variation (CNV) in the CR1 gene. Here, we describe an innovative method for determining the length polymorphism of the CR1 receptor. The receptor includes three domains, called long homologous repeats (LHR)-LHR-A, LHR-C, and LHR-D-and an n domain, LHR-B, where n is an integer between 0 and 3. Using a single pair of specific primers, the genetic material is used to amplify a first fragment of the LHR-B domain (the variant amplicon B) and a second fragment of the LHR-C domain (the invariant amplicon). The variant amplicon B and the invariant amplicon display differences at five nucleotides outside of the hybridization areas of said primers. The numbers of variant amplicons B and of invariant amplicons is deduced using a quantitative tool (high-resolution melting (HRM) curves), and the ratio of the variant amplicon B to the invariant amplicon differs according to the CR1 length polymorphism. This method provides several advantages over the canonical phenotype method, as it does not require fresh material and is cheaper, faster, and therefore applicable to larger populations. Thus, the use of this method should be helpful to better understand the role of CR1 isoforms in the pathogenesis of diseases such as AD.

Here, we describe an innovative method to determine complement receptor 1 (CR1) length polymorphisms for use in several applications, particularly the assessment of susceptibility to diseases such as Alzheimer&#39;s disease (AD). This method could be useful to better understand the role of CR1 isoforms in the pathogenesis of AD.

Complement receptor 1 (CR1), a transmembrane glycoprotein that plays a key role in the innate immune system, is expressed on many cell types, but ...

High Resolution Fluorescent In Situ Hybridization in Drosophila Embryos and Tissues Using Tyramide Signal Amplification

In our efforts to determine the patterns of expression and subcellular localization of Drosophila RNAs on a genome-wide basis, and in a variety of tissues, we have developed numerous modifications and improvements to our original fluorescent in situ hybridization (FISH) protocol. To facilitate throughput and cost effectiveness, all steps, from probe generation to signal detection, are performed using exon 96-well microtiter plates. Digoxygenin (DIG)-labelled antisense RNA probes are produced using either cDNA clones or genomic DNA as templates. After tissue fixation and permeabilization, probes are hybridized to transcripts of interest and then detected using a succession of anti-DIG antibody conjugated to biotin, streptavidin conjugated to horseradish peroxidase (HRP) and fluorescently conjugated tyramide, which in the presence of HRP, produces a highly reactive intermediate that binds to electron dense regions of immediately adjacent proteins. These amplification and localization steps produce a robust and highly localized signal that facilitates both cellular and subcellular transcript localization. The protocols provided have been optimized to produce highly specific signals in a variety of tissues and developmental stages. References are also provided for additional variations that allow the simultaneous detection of multiple transcripts, or transcripts and proteins, at the same time.

High Resolution Fluorescent In Situ Hybridization in Drosophila Embryos and Tissues Using Tyramide Signal Amplification

The described RNA in situ hybridization protocol allows the detection of RNA in whole Drosophila embryos or dissected tissues. Using 96-well microtiter plates and tyramide signal amplification, transcripts can be detected at high resolution, sensitivity, and throughput, and at a relatively low cost.

In our efforts to determine the patterns of expression and subcellular localization of Drosophila RNAs on a genome-wide basis, and in a variety of ...

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the spatio-temporal expression of their target genes through physical contact, often bridging considerable (in some cases hundreds of kilobases) genomic distances and bypassing nearby genes. The human genome harbors an estimated one million enhancers, the vast majority of which have unknown gene targets. Assigning distal regulatory regions to their target genes is thus crucial to understand gene expression control. We developed Promoter Capture Hi-C (PCHi-C) to enable the genome-wide detection of distal promoter-interacting regions (PIRs), for all promoters in a single experiment. In PCHi-C, highly complex Hi-C libraries are specifically enriched for promoter sequences through in-solution hybrid selection with thousands of biotinylated RNA baits complementary to the ends of all promoter-containing restriction fragments. The aim is to then pull-down promoter sequences and their frequent interaction partners such as enhancers and other potential regulatory elements. After high-throughput paired-end sequencing, a statistical test is applied to each promoter-ligated restriction fragment to identify significant PIRs at the restriction fragment level. We have used PCHi-C to generate an atlas of long-range promoter interactions in dozens of human and mouse cell types. These promoter interactome maps have contributed to a greater understanding of mammalian gene expression control by assigning putative regulatory regions to their target genes and revealing preferential spatial promoter-promoter interaction networks. This information also has high relevance to understanding human genetic disease and the identification of potential disease genes, by linking non-coding disease-associated sequence variants in or near control sequences to their target genes.

DNA regulatory elements, such as enhancers, control gene expression by physically contacting target gene promoters, often through long-range chromosomal interactions spanning large genomic distances. Promoter Capture Hi-C (PCHi-C) identifies significant interactions between promoters and distal regions, enabling the assignment of potential regulatory sequences to their target genes.

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the ...

High-Resolution Comparison of Bacterial Conjugation Frequencies

Bacterial conjugation is an important step in the horizontal transfer of antibiotic resistance genes via a conjugative DNA element. In-depth comparisons of conjugation frequency under different conditions are required to understand how the conjugative element spreads in nature. However, conventional methods for comparing conjugation frequency are not appropriate for in-depth comparisons because of the high background caused by the occurrence of additional conjugation events on the selective plate. We successfully reduced the background by introducing a most probable number (MPN) method and a higher concentration of antibiotics to prevent further conjugation in selective liquid medium. In addition, we developed a protocol for estimating the probability of how often donor cells initiate conjugation by sorting single donor cells into recipient pools by fluorescence-activated cell sorting (FACS). Using two plasmids, pBP136 and pCAR1, the differences in conjugation frequency in Pseudomonas putida cells could be detected in liquid medium at different stirring rates. The frequencies of conjugation initiation were higher for pBP136 than for pCAR1. Using these results, we can better understand the conjugation features in these two plasmids.

With an aim to understand the behaviors of various bacterial conjugative DNA elements under different conditions, we describe a protocol for detecting differences in conjugation frequency, with high resolution, to estimate how efficiently the donor bacterium initiates conjugation.

Bacterial conjugation is an important step in the horizontal transfer of antibiotic resistance genes via a conjugative DNA element. In-depth comparisons ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. While this has led to increased identification of likely pathogenic mutations associated with genetic syndromes, it has also dramatically increased the number of incidentally found genetic variants of unknown significance (VUS). Determining the clinical significance of these variants is a major challenge for both scientists and clinicians. An approach to assist in determining the likelihood of pathogenicity is signal-to-noise analysis at the protein sequence level. This protocol describes a method for amino acid-level signal-to-noise analysis that leverages variant frequency at each amino acid position of the protein with known protein topology to identify areas of the primary sequence with elevated likelihood of pathologic variation (relative to population &#34;background&#34; variation). This method can identify amino acid residue location &#34;hotspots&#34; of high pathologic signal, which can be used to refine the diagnostic weight of VUSs such as those identified by next generation genetic testing.

Amino acid-level signal-to-noise analysis determines the prevalence of genetic variation at a given amino acid position normalized to background genetic variation of a given population. This allows for identification of variant &#34;hotspots&#34; within a protein sequence (signal) that rises above the frequency of rare variants found in a population (noise).

Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. ...

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in regulating gene expression at the post-transcriptional level. Sequencing sRNA libraries by Next Generation Sequencing (NGS) methods has been widely employed to identify and analyze miRNA transcriptomes in the last decade, resulting in a rapid increase of miRNA discovery. However, two major challenges arise in plant miRNA annotation due to increasing depth of sequenced sRNA libraries as well as the size and complexity of plant genomes. First, many other types of sRNAs, in particular, short interfering RNAs (siRNAs) from sRNA libraries, are erroneously annotated as miRNAs by many computational tools. Second, it becomes an extremely time-consuming process for analyzing miRNA transcriptomes in plant species with large and complex genomes. To overcome these challenges, we recently upgraded miRDeep-P (a popular tool for miRNA transcriptome analyses) to miRDeep-P2 (miRDP2 for short) by employing a new filtering strategy, overhauling the scoring algorithm and incorporating newly updated plant miRNA annotation criteria. We tested miRDP2 against sequenced sRNA populations in five representative plants with increasing genomic complexity, including Arabidopsis, rice, tomato, maize and wheat. The results indicate that miRDP2 processed these tasks with very high efficiency. In addition, miRDP2 outperformed other prediction tools regarding sensitivity and accuracy. Taken together, our results demonstrate miRDP2 as a fast and accurate tool for analyzing plant miRNA transcriptomes, therefore a useful tool in helping the community better annotate miRNAs in plants.

A bioinformatics pipeline, namely miRDeep-P2 (miRDP2 for short), with updated plant miRNA criteria and an overhauled algorithm, could accurately and efficiently analyze microRNA transcriptomes in plants, especially for species with complex and large genomes.

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in ...

Identifying Mutations by High Resolution Melting in a TILLING Population of Rice

Target Induced Local Lesions In Genomes (TILLING) is a strategy of reverse genetics for the high-throughput screening of induced mutations. However, the TILLING system has less applicability for insertion/deletion (Indel) detection and traditional TILLING needs more complex steps, like CEL I nuclease digestion and gel electrophoresis. To improve the throughput and selection efficiency, and to make the screening of both Indels and single base substitions (SBSs) possible, a new high-resolution melting (HRM)-based TILLING system is developed. Here, we present a detailed HRM-TILLING protocol and show its application in mutation screening. This method can analyze the mutations of PCR amplicons by measuring the denaturation of double-stranded DNA at high temperatures. HRM analysis is directly performed post-PCR without additional processing. Moreover, a simple, safe and fast (SSF) DNA extraction method is integrated with HRM-TILLING to identify both Indels and SBSs. Its simplicity, robustness and high throughput make it potentially useful for mutation scanning in rice and other crops.

In this article, we present the protocol that is described as high-resolution melting analysis (HRM)-based Target Induced Local Lesions In Genomes (TILLING). This method utilizes fluorescence changes during the melting of the DNA duplex and is suitable for high-throughput screening of both insertion/deletion (Indel) and single base substition (SBS).

Target Induced Local Lesions In Genomes (TILLING) is a strategy of reverse genetics for the high-throughput screening of induced mutations. However, the ...

Tomato Root Transformation Followed by Inoculation with Ralstonia Solanacearum for Straightforward Genetic Analysis of Bacterial Wilt Disease

Ralstonia solanacearum is a devastating soil borne vascular pathogen that can infect a large range of plant species, causing an important threat to agriculture. However, the Ralstonia model is considerably underexplored in comparison to other models involving bacterial plant pathogens, such as Pseudomonas syringae in Arabidopsis. Research targeted to understanding the interaction between Ralstonia and crop plants is essential to develop sustainable solutions to fight against bacterial wilt disease but is currently hindered by the lack of straightforward experimental assays to characterize the different components of the interaction in native host plants. In this scenario, we have developed a method to perform genetic analysis of Ralstonia infection of tomato, a natural host of Ralstonia. This method is based on Agrobacterium rhizogenes-mediated transformation of tomato roots, followed by Ralstonia soil-drenching inoculation of the resulting plants, containing transformed roots expressing the construct of interest. The versatility of the root transformation assay allows performing either gene overexpression or gene silencing mediated by RNAi. As a proof of concept, we used this method to show that RNAi-mediated silencing of SlCESA6 in tomato roots conferred resistance to Ralstonia. Here, we describe this method in detail, enabling genetic approaches to understand bacterial wilt disease in a relatively short time and with small requirements of equipment and plant growth space.

Tomato Root Transformation Followed by Inoculation with Ralstonia Solanacearum for Straightforward Genetic Analysis of Bacterial Wilt Disease

Here, we present a versatile method for tomato root transformation followed by inoculation with Ralstonia solanacearum to perform straightforward genetic analysis for the study of bacterial wilt disease.

Ralstonia solanacearum is a devastating soil borne vascular pathogen that can infect a large range of plant species, causing an important threat to ...