Name: bidirectional retroviral integration site pcr methodology and quantitative data analysis workflow
Uploaded: 2017-06-14T15:00:00
Description: Watch this Scientific Journal Video about Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow at JoVE.com

Funding was provided by the National Institutes of Health Grants R00-HL116234, U19 AI117941, and R56 HL126544; the National Science Foundation Grant DMS-1516675; the National Research Foundation of Korea (NRF-2011-0030049, NRF-2014M3C9A3064552); and the KRIBB initiative program.

1. Generating Upstream (left)- and Downstream (right)-junction Sequence Libraries
<ol>
	<li>DNA linker preparation:
	<ol>
		<li>Prepare a 10 &#181;L linker DNA solution by adding 2 &#181;L of 100 &#181;M LINKER_A oligos (final: 20 &#181;M), 2 &#181;L of 100 &#181;M LINKER_B oligos (final: 20 &#181;M), 2 &#181;L of 5 M NaCl (final: 1M), and 4 &#181;L of nuclease-free water in a PCR tube. See Table 1 for the linker sequences.</li>
		<li>Incubate the linker DNA solution at 95 &#176;C for ...

<ol>
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The bidirectional assay enables the simultaneous analysis of both the upstream (left) and downstream (right) vector-host DNA junction sequences and is useful in a number of gene therapy, stem cell, and cancer research applications. The use of GTAC-motif enzymes (RsaI and CviQI) and the bidirectional PCR approach significantly improves the chances of detecting an integrome (or a clonal population) when compared to previous TCGA-motif enzyme (Taq&#945;I)-based assays2,<sup class=...

Retroviruses insert their genomic DNA into the host genome at various sites. This unique property, which may contribute to the development of cancers and other forms of viral pathogenesis, has the ironic benefit of making these viruses highly amenable to cellular engineering for gene therapy and basic biology research. The viral Integration Site (IS) &#8211; the location on the host genome where a foreign DNA (virus) is integrated &#8211; has important implications for the fate of both the integrated viruses and the host cells. IS assays have been used in various biological and clinical research settings to study retroviral integration site selection and pathogenesis, cancer development, stem cell biology, and developmental biology1,2,3,4. Low detection sensitivity, poor data reproducibility, and frequent cross-contamination are among the key factors limiting the applications of IS assays to current and planned studies.
Many IS analysis technologies have been developed. Restriction enzyme-based integration site assays, including Linker-Mediated (LM) Polymerase Chain Reaction (PCR)5, inverse PCR6, and Linear-Amplification-Mediated (LAM) PCR7, are the most widely used. The use of site-specific restriction enzymes, however, generates a bias during the retrieval of the IS, allowing only a subset of integromes (a foreign DNA integrated into the host genome) in the vicinity of the restriction site to be recovered4. Assay technologies that more comprehensively assess vector IS have also been introduced in recent years. These assays employ various strategies, including Mu transposon-mediated PCR8, nonrestrictive (nr)-LAM PCR9, type-II restriction enzyme-mediated digestion10, mechanical shearing11, and random hexamer-based PCR (Re-free PCR)12, to fragment genomic DNAs and amplify IS. Current technologies have varying levels of detection sensitivity, genome coverage, target specificity, high-throughput capacity, complexity of assay procedures, and biases in detecting the relative frequencies of target sites. Given the varying qualities of the existing assays and the variety of purposes for which they can be used, the optimal assay approach should be carefully selected.
This work provides detailed experimental procedures and a computational data analysis workflow for a bidirectional assay that significantly improves detection rates and sequence quantification accuracy by simultaneously analyzing the IS upstream and downstream of the integrated target DNA (see Figure 1 for a schematic view of the assay procedures). This approach also provides the means to characterize the retroviral integration process (for example, the fidelity of target site duplication and variations in the genomic sequences of upstream and downstream insertions). Other bidirectional methods have been used primarily for cloning and sequencing both ends of the target DNA11,13,14. This assay is extensively optimized for the high-throughput and reproducible quantification of vector-marked clones, using the well-established LM-PCR method and computational analysis mapping, and for quantifying both upstream and downstream junctions. Bidirectional analysis with the Taq&#945;I enzyme has proven useful for high-throughput clonal quantification in stem cell gene therapy preclinical studies2,15. This paper describes a modified method using a more frequent cutter (RsaI/CviQI - motif: GTAC) that doubles the chances of detecting integromes compared to a Taq&#945;I-based assay. Detailed experimental and data analysis procedures that use GTAC motif enzymes for lentiviral (NL4.3 and its derivatives) and gamma-retroviral (pMX vectors) vector IS analysis are described. The oligonucleotides used in the assay are listed in Table 1. An in-house programming script for IS sequence analysis is provided in the supplemental document.

<table border="0" cellpadding="0" cellspacing="0" width="1004">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Thermostable DNA polymerase</td>
			<td style="width:208px;">Agilent</td>
			<td style="width:119px;">600424</td>
			<td style="width:347px;">PicoMaxx Polymerase</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Thermostable DNA polymerase buffer</td>
			<td style="width:208px;">Agilent</td>
			<td>600424</td>
			<td style="width:347px;">PicoMaxx Polymerase buffer</td>
		</tr>
		<tr height="26">
			<td height="26" style="height:27px;">Deoxynucleotide (dNTP) solution mix</td>
			<td style="width:208px;">New England Biolabs</td>
			<td>N0447L</td>
			<td>dNTP solution mix (10mM each)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">PCR tubes</td>
			<td style="width:208px;">VWR International</td>
			<td style="width:119px;">53509-304</td>
			<td style="width:347px;">PCR Strip Tubes With Individual Attached Caps</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">2ml microcentrifuge tube</td>
			<td style="width:208px;">Molecular Bioproducts</td>
			<td style="width:119px;">3453</td>
			<td>microcentrifuge tubes</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">PCR purification kit</td>
			<td style="width:208px;">Qiagen</td>
			<td>28106</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">RsaI&#160;</td>
			<td style="width:208px;">New England Biolabs</td>
			<td>R0167L</td>
			<td>restriction enzyme</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">CviQI</td>
			<td style="width:208px;">New England Biolabs</td>
			<td>R0639L</td>
			<td>restriction enzyme</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Buffer A</td>
			<td style="width:208px;">New England Biolabs</td>
			<td>B7204S</td>
			<td>NEB CutSmart buffer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">DNA Polymerase I large (klenow) fragment&#160;</td>
			<td style="width:208px;">New England Biolabs</td>
			<td>M0210L</td>
			<td>Blunting</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">streptavidin beads solution</td>
			<td>Invitrogen&#160;</td>
			<td>60101</td>
			<td>Dynabeads kilobaseBINDER kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Binding Solution</td>
			<td>Invitrogen&#160;</td>
			<td>60101</td>
			<td>Dynabeads kilobaseBINDER kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Washing Solution</td>
			<td>Invitrogen&#160;</td>
			<td>60101</td>
			<td>Dynabeads kilobaseBINDER kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">magnetic stand</td>
			<td style="width:208px;">ThermoFisher</td>
			<td>12321D</td>
			<td>DynaMag&#8482;-2 Magnet</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">T4 DNA ligase</td>
			<td style="width:208px;">New England Biolabs</td>
			<td>M0202L</td>
			<td>T4 DNA ligase</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">10X T4 DNA ligase buffer</td>
			<td style="width:208px;">New England Biolabs</td>
			<td>B0202S</td>
			<td>T4 DNA ligase reaction buffer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">5X T4 DNA ligase buffer</td>
			<td style="width:208px;">Invitrogen&#160;</td>
			<td>46300-018</td>
			<td>T4 DNA ligase buffer with polyethylene glycol-8000</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">UV-Vis spectrophotometer</td>
			<td style="width:208px;">Fisher Scientific</td>
			<td>S06497</td>
			<td style="width:347px;">Nanodrop 2000</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">pvuII</td>
			<td style="width:208px;">new England Biolabs</td>
			<td>R0151L</td>
			<td>restriction enzyme</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">sfoI</td>
			<td style="width:208px;">new England Biolabs</td>
			<td>R0606L</td>
			<td>restriction enzyme</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Buffer B</td>
			<td style="width:208px;">new England Biolabs</td>
			<td>B7203S</td>
			<td>NEB buffer 3.1</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Nuclease free water</td>
			<td style="width:208px;">Integrated DNA Technologies</td>
			<td>11-05-01-14</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Capillary electrophoresis</td>
			<td style="width:208px;">Qiagen</td>
			<td>9001941</td>
			<td style="width:347px;">QIAxcel capillary electrophoresis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Veriti 96-well Fast Thermal Cycler</td>
			<td style="width:208px;">Thermo Fisher Scientific</td>
			<td>4375305</td>
			<td>PCR Instrument</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">Rotating wheel (or Roller)</td>
			<td style="width:208px;">Eppendorf</td>
			<td>M10534004</td>
			<td>Cell Culture Roller Drums</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">DNA size marker</td>
			<td style="width:208px;">Qiagen</td>
			<td>929559</td>
			<td>QX size marker (100-2,500 bp)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">DNA size marker</td>
			<td style="width:208px;">Qiagen</td>
			<td>929554</td>
			<td>QX size marker (50-1,500 bp)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">DNA alignment markers</td>
			<td style="width:208px;">Qiagen</td>
			<td>929524</td>
			<td>QX DNA Alignment Marker</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:331px;">genomc DNA</td>
			<td style="width:208px;">Not Available</td>
			<td style="width:119px;">Not Available</td>
			<td>Sample genomc DNA from in vivo or in vitro experiments</td>
		</tr>
	</tbody>
</table>

bidirectional retroviral integration site pcr methodology and quantitative data analysis workflow

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral gene therapy studies, IS assays, in combination with next-generation sequencing, have been used as a cell-tracking tool to characterize clonal stem cell populations sharing the same IS. For the accurate comparison of repopulating stem cell clones within and across different samples, the detection sensitivity, data reproducibility, and high-throughput capacity of the assay are among the most important assay qualities. This work provides a detailed protocol and data analysis workflow for bidirectional IS analysis. The bidirectional assay can simultaneously sequence both upstream and downstream vector-host junctions. Compared to conventional unidirectional IS sequencing approaches, the bidirectional approach significantly improves IS detection rates and the characterization of integration events at both ends of the target DNA. The data analysis pipeline described here accurately identifies and enumerates identical IS sequences through multiple steps of comparison that map IS sequences onto the reference genome and determine sequencing errors. Using an optimized assay procedure, we have recently published the detailed repopulation patterns of thousands of Hematopoietic Stem Cell (HSC) clones following transplant in rhesus macaques, demonstrating for the first time the precise time point of HSC repopulation and the functional heterogeneity of HSCs in the primate system. The following protocol describes the step-by-step experimental procedure and data analysis workflow that accurately identifies and quantifies identical IS sequences.

The bidirectional IS assay generated different sizes of PCR amplicons for both the upstream (left) and downstream (right) vector host junctions (Figure 2). The size of a PCR amplicon is dependent on the location of the nearest GTAC motif upstream and downstream from an integrome. The assay also produced internal DNA PCR amplicons: retroviral sequences near the polypurine tract and the primer binding site were concomitantly amplified during left- and right-jun...

Watch this Scientific Journal Video about Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow at JoVE.com

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

This manuscript describes the experimental procedure and software analysis for a bidirectional integration site assay that can simultaneously analyze upstream and downstream vector-host junction DNA. Bidirectional PCR products can be used for any downstream sequencing platform. The resulting data are useful for a high-throughput, quantitative comparison of integrated DNA targets.

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral ...

The overall goal of this procedure is to identify retroviral vector integration sites in the host genome and quantify the relative frequencies of clonal cell populations each sharing the same integration site. This method can help answer key questions in retroviral gene therapy, such as which part of the host genome vector has integrated, how a genetically engineered cell will repopulate after the transplant, and how they are functionally different. The main advantage of this technique is that retro-integration site sequences can be analyzed with higher accuracy and sensitivity by sequencing both upstream and downstream vector host DNA junctions simultaneously.Though this method can provide insight into the safety of vectors and gene engineered cell in gene therapy studies, it can also be applied to other basic biology studies to investigate individual cell behavior in vivo. The first step in this procedure is to determine the DNA concentration and the ratio of absorbance at 260 and 280 nanometers of the genomic DNA to be analyzed. After this, dilute one to two micrograms of the sample genomic DNA to a final volume of 170 microliters of genomic DNA solution using nuclease free water.Prepare a 200 microliter PCR reaction by mixing the following in a microcentrifuge tube. 2.5 microliters each of HIV-1 specific biotin primers, or a total of 10 microliters for four lentivirus-specific primers, 20 microliters of 10X thermostable DNA polymerase buffer, four microliters of 10 millimolar dNTPs, three microliters of thermostable DNA polymerase, and 163 microliters of genomic DNA. Divide the solution equally into four PCR tubes and carry out a single extension cycle under the following condition.94 degrees Celsius for 5 minutes, 56 degrees Celsius for 3 minutes, 72 degrees Celsius for 5 minutes, and 4 degrees C for storage. Pool all four PCR reactions into a two milliliter microcentrifuge tube. Follow the manufacturer's procedure of a PCR purification kit, and elute with 50 microliters of elution buffer.Prepare for a 100 microliter digestion by adding the following to a microcentrifuge tube. 50 microliters of DNA, 10 microliters of 10X buffer A, two microliters of RSAI enzyme, and 38 microliters of nuclease-free water. Incubate at 37 degrees Celsius in a thermal cycler for one hour.Next, add one microliter of CviQI enzyme to the reaction and incubate at 25 degrees Celsius for 30 minutes. When the RSAI and CviQI digestion is complete, proceed immediately with blunt ending. Prepare a 4.5 microliter mixture containing 2.5 microliters of DNA polymerase I, Large Fragment, and 2 microliters of 10 millimolar dNTPs.Transfer 1 microliter of the mixture to the DNA sample. The total volume will be 102 microliters. Mix well by vortexing and incubate at 25 degrees Celsius for one hour.Immediately proceed with streptavidin bead binding when the incubation is complete. Prepare the streptavidin beads by briefly vortexing the bead solution and transferring 50 microliters to a new two milliliter microcentrifuge tube. Remove the supernatant using a magnetic stand and wash the beans with 200 microliters of binding solution.Resuspend the beads in 100 microliters of binding solution and place the tube away from the magnetic stand. Transfer 100 microliters of the sample DNA to the 100 microliters of resuspended bead solution and mix carefully by pipetting to avoid any foaming of the solution. Incubate the tube at room temperature for 3 hours on a rotating wheel.Use the magnetic stand to capture the beads and discard the supernatant. Wash the beads twice in 400 microliters of washing solution and twice in 400 microliters of 1X T4 DNA ligase buffer. Resuspend the beads in 200 microliters of 1X T4 DNA ligase buffer and place it away from the magnetic stand.Linker ligation should be performed immediately after the streptavidin bead binding. Prepare a 400 microliter ligation reaction solution by mixing the following in a two milliliter microcentrifuge tube. 0.5 microliters of the DNA linker, 10 microliters of 10X T4 DNA ligase buffer, 20 microliters of 5X T4 DNA ligase buffer, 5 microliters of T4 DNA ligase, 164.5 microliters of nuclease free water, and 200 microliters of the resuspended beads.Place the reaction tube on a rotating wheel at room temperature for 3 hours. Wash the beads twice with the washing solution, and twice with 1X thermostable DNA polymerase buffer. Resuspend the beads in 50 microliters of 1X thermostable DNA polymerase PCR buffer, and place it away from the magnetic stand.To begin this procedure prepare a 200 microliter PCR reaction by adding the following to the resuspended beads. 10 microliters of one 1L-primer, 10 microliters of 1R-primer, 20 microliters of primer Link1, 10 microliters of 10X thermostable DNA polymerase buffer, four microliters of 10 milimolar dNTPs, eight microliters of thermostable DNA polymerase, and 88 microliters of nuclease-free water. Aliquot the reaction mixture into four PCR tubes and carry out PCR with the following condition.94 degrees Celsius for 2 minutes, 25 cycles of 94 degrees Celsius for 20 seconds, 56 degrees Celsius for 25 seconds, and 72 degrees Celsius for two minutes, and a final extension at 72 degrees Celsius for 5 minutes. Pool all four PCR reactions into a 2 ml microcentrifuge tube. Follow the procedure of the PCR purification kit and elute with 50 microliters of elution buffer.Determine the DNA concentration and the ratio of absorbance at 260 and 280 nanometers using a UV visible spectrophotometer. The DNA can now be stored at minus 20 degrees Celsius until ready for the next step. Since the procedures for the left junction and right junction specific amplifications are identical except for the primers used in the PCR, only the left junction amplification will be demonstrated.Prepare a 50 microliter PCR reaction by mixing the following in a PCR tube. Five microliters of DNA, five microliters of 10 micromolar 2L-primer, five microliters of 10 micromolar primer Link2, 5 microliters of 10X thermostable DNA polymerase buffer, one microliter of 10 milimolar dNTPs, two microliters of thermostable DNA polymerase, and 27 microliters of nuclease-free water. Carry out PCR with the following condition.94 degrees Celsius for 3 minutes, 8 to 15 cycles of 94 degrees Celsius for 20 seconds, 56 degrees Celsius for 25 seconds, and 72 degrees Celsius for two minutes, and a final extension at 72 degrees Celsius for 5 minutes. Use the PCR purification kit to purify the DNA and elute with 50 microliters of elution buffer. Determine the DNA concentration and ratio of absorbance at 260 and 280 nanometers.The last step in generating left and right junction sequence libraries is to analyze the PCR Amplicon length variations by performing capillary electrophoresis. This representative result shows varying lengths of PCR Amplicons in lentiviral vector integrations sites for both the upstream and downstream vector host junctions. The DNA size marker is in the M lane.After sequencing the PCR Amplicons, computational integration site sequence analysis is performed using an in-house programming script. The bi-directional integration site assay generates different sizes of PCR Amplicons for both the upstream and downstream vector host junctions visualized by capillary electrophoresis. A comparison of percent integration in genes Alu and L1 repeats of lentiviral vectors in humanized mouse repopulating cells within silico generated random integration events revealed that integration sites are significantly enriched in genes.The relative detection frequencies of vector integromes were calculated using only the sequence counts of integration site junctions generating smaller than 500bp PCR Amplicons. Approximately 77%of the vectors could be quantitatively analyzed by the bi-directional approach. In the strategy for clonal quantification we presume that each individual clone shares the same vector integrome.The relative frequencies of the left and right junctions are combined to represent the relative quantities of the clonal populations, or QVI. This color scheme shows the relative frequencies of 44 QVI clones in humanized mouse repopulating cells. Based on in silico 10, 000 random integration analysis, a 1.25 fold overestimation of clonal frequencies is expected when using GTAC motif enzymes, and a 2.56 overestimation is expected when using a TCGA motif enzyme.Once mastered, this technique can be divided in parts and completed in two days, if performed properly. While attempting this procedure it is important to remember that PCR steps are extremely sensitive, and precautions should be take to avoid any contamination. This technique has paved the way to examining the safety of therapeutic retroviral vectors, and to explore long-term repopulation patterns of hemotopoietic stem cell clones following transplant in rhesus macaques.

bidirectional-retroviral-integration-site-pcr-methodology

Research

JoVE Journal

Genetics

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Human signal transducer and activator of transcription 3 (STAT3) is one of many genes containing a tandem splicing site. Alternative donor splice sites 3 nucleotides apart result in either the inclusion (S) or exclusion (&#916;S) of a single residue, Serine-701. Further downstream, splicing at a pair of alternative acceptor splice sites result in transcripts encoding either the 55 terminal residues of the transactivation domain (&#945;) or a truncated transactivation domain with 7 unique residues (&#946;). As outlined in this manuscript, measuring the proportions of STAT3's four spliced transcripts (S&#945;, S&#946;, &#916;S&#945; and &#916;S&#946;) was possible using absolute qPCR (quantitative polymerase chain reaction). The protocol therefore distinguishes and measures highly similar splice variants. Absolute qPCR makes use of calibrator plasmids and thus specificity of detection is not compromised for the sake of efficiency. The protocol necessitates primer validation and optimization of cycling parameters. A combination of absolute qPCR and efficiency-dependent relative qPCR of total STAT3 transcripts allowed a description of the fluctuations of STAT3 splice variants' levels in eosinophils treated with cytokines. The protocol also provided evidence of a co-splicing interdependence between the two STAT3 splicing events. The strategy based on a combination of the two qPCR techniques should be readily adaptable to investigation of co-splicing at other tandem splicing sites.

Tandem splicing events occur at sites less than 12 nucleotides apart. Quantifying ratios of such splice variants is feasible using an absolute quantitative PCR approach. This manuscript describes how splice variants of the gene STAT3, in which two splicing events results in Serine-701 inclusion/exclusion and &#945;/&#946; C-termini, can be quantified.

Human signal transducer and activator of transcription 3 (STAT3) is one of many genes containing a tandem splicing site. Alternative donor splice sites 3 ...

Retroviral Scanning: Mapping MLV Integration Sites to Define Cell-specific Regulatory Regions

Moloney murine leukemia (MLV) virus-based retroviral vectors integrate predominantly in acetylated enhancers and promoters. For this reason, mLV integration sites can be used as functional markers of active regulatory elements. Here, we present a retroviral scanning tool, which allows the genome-wide identification of cell-specific enhancers and promoters. Briefly, the target cell population is transduced with an mLV-derived vector and genomic DNA is digested with a frequently cutting restriction enzyme. After ligation of genomic fragments with a compatible DNA linker, linker-mediated polymerase chain reaction (LM-PCR) allows the amplification of the virus-host genome junctions. Massive sequencing of the amplicons is used to define the mLV integration profile genome-wide. Finally, clusters of recurrent integrations are defined to identify cell-specific regulatory regions, responsible for the activation of cell-type specific transcriptional programs.
The retroviral scanning tool allows the genome-wide identification of cell-specific promoters and enhancers in prospectively isolated target cell populations. Notably, retroviral scanning represents an instrumental technique for the retrospective identification of rare populations (e.g. somatic stem cells) that lack robust markers for prospective isolation.

Here, we describe a protocol for genome-wide mapping of the integration sites of Moloney murine leukemia virus-based retroviral vectors in human cells.

Moloney murine leukemia (MLV) virus-based retroviral vectors integrate predominantly in acetylated enhancers and promoters. For this reason, mLV ...

A Standard Methodology to Examine On-site Mutagenicity As a Function of Point Mutation Repair Catalyzed by CRISPR/Cas9 and SsODN in Human Cells

Combinatorial gene editing using CRISPR/Cas9 and single-stranded oligonucleotides is an effective strategy for the correction of single-base point mutations, which often are responsible for a variety of human inherited disorders. Using a well-established cell-based model system, the point mutation of a single-copy mutant eGFP gene integrated into HCT116 cells has been repaired using this combinatorial approach. The analysis of corrected and uncorrected cells reveals both the precision of gene editing and the development of genetic lesions, when indels are created in uncorrected cells in the DNA sequence surrounding the target site. Here, the specific methodology used to analyze this combinatorial approach to the gene editing of a point mutation, coupled with a detailed experimental strategy to measuring indel formation at the target site, is outlined. This protocol outlines a foundational approach and workflow for investigations aimed at developing CRISPR/Cas9-based gene editing for human therapy. The conclusion of this work is that on-site mutagenesis takes place as a result of CRISPR/Cas9 activity during the process of point mutation repair. This work puts in place a standardized methodology to identify the degree of mutagenesis, which should be an important and critical aspect of any approach destined for clinical implementation.

This protocol outlines the workflow of a CRISPR/Cas9-based gene editing system for the repair of point mutations in mammalian cells. Here, we use a combinatorial approach to gene editing with a detailed follow-on experimental strategy for measuring indel formation at the target site&#8212;in essence, analyzing onsite mutagenesis.

Combinatorial gene editing using CRISPR/Cas9 and single-stranded oligonucleotides is an effective strategy for the correction of single-base point ...

High-Throughput Quantitative RT-PCR in Single and Bulk C. elegans Samples Using Nanofluidic Technology

This paper presents a high-throughput reverse transcription quantitative PCR (RT-qPCR) assay for Caenorhabditis elegans that is fast, robust, and highly sensitive. This protocol obtains precise measurements of gene expression from single worms or from bulk samples. The protocol presented here provides a novel adaptation of existing methods for complementary DNA (cDNA) preparation coupled to a nanofluidic RT-qPCR platform. The first part of this protocol, named &#8216;Worm-to-CT&#8217;, allows cDNA production directly from nematodes without the need for prior mRNA isolation. It increases experimental throughput by allowing the preparation of cDNA from 96 worms in 3.5 h. The second part of the protocol uses existing nanofluidic technology to run high-throughput RT-qPCR on the cDNA. This paper evaluates two different nanofluidic chips: the first runs 96 samples and 96 targets, resulting in 9,216 reactions in approximately 1.5 days of benchwork. The second chip type consists of six 12 x 12 arrays, resulting in 864 reactions. Here, the Worm-to-CT method is demonstrated by quantifying mRNA levels of genes encoding heat shock proteins from single worms and from bulk samples. Provided is an extensive list of primers designed to amplify processed RNA for the majority of coding genes within the C. elegans genome.

High-Throughput Quantitative RT-PCR in Single and Bulk C. elegans Samples Using Nanofluidic Technology

In this article a high-throughput protocol for fast and reliable determination of gene expression levels in single or bulk C. elegans samples is described. This protocol does not require RNA isolation and produces cDNA directly from samples. It can be used together with high-throughput multiplexed nanofluidic real-time qPCR platforms.

This paper presents a high-throughput reverse transcription quantitative PCR (RT-qPCR) assay for Caenorhabditis elegans that is fast, robust, and highly ...

Site-Directed &#966;C31-Mediated Integration and Cassette Exchange in Anopheles Vectors of Malaria

Functional genomic analysis and related strategies for genetic control of malaria rely on validated and reproducible methods to accurately modify the genome of Anopheles mosquitoes. Amongst these methods, the &#966;C31 system allows precise and stable site-directed integration of transgenes, or the substitution of integrated transgenic cassettes via recombinase-mediated cassette exchange (RMCE). This method relies on the action of the Streptomyces &#966;C31 bacteriophage integrase to catalyze recombination between two specific attachment sites designated attP (derived from the phage) and attB (derived from the host bacterium). The system uses one or two attP sites that have been integrated previously into the mosquito genome and attB site(s) in the donor template DNA. Here we illustrate how to stably modify the genome of attP-bearing Anopheles docking lines using two plasmids: an attB-tagged donor carrying the integration or exchange template and a helper plasmid encoding the &#966;C31 integrase. We report two representative results of &#966;C31-mediated site-directed modification: the single integration of a transgenic cassette in An. stephensi and RMCE in An. gambiae mosquitoes. &#966;C31-mediated genome manipulation offers the advantage of reproducible transgene expression from validated, fitness neutral genomic sites, allowing comparative qualitative and quantitative analyses of phenotypes. The site-directed nature of the integration also substantially simplifies the validation of the single insertion site and the mating scheme to obtain a stable transgenic line. These and other characteristics make the &#966;C31 system an essential component of the genetic toolkit for the transgenic manipulation of malaria mosquitoes and other insect vectors.

Site-Directed &lt;em&gt;&amp;#966;C31&lt;/em&gt;-Mediated Integration and Cassette Exchange in &lt;em&gt;Anopheles&lt;/em&gt; Vectors of Malaria

The protocol describes how to achieve site-directed modifications in the genome of Anopheles malaria mosquitoes using the &#966;C31 system. Modifications described include both the integration and the exchange of transgenic cassettes in the genome of attP-bearing docking lines.

Functional genomic analysis and related strategies for genetic control of malaria rely on validated and reproducible methods to accurately modify the ...

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A variety of biological sequence classification tasks, such as species classification, gene function classification and viral host classification, are expected processes in many metagenomic data analyses. Since metagenomic data contain a large number of novel species and genes, high-performing classification algorithms are needed in many studies. Biologists often encounter challenges in finding suitable sequence classification and annotation tools for a specific task and are often not able to construct a corresponding algorithm on their own because of a lack of the necessary mathematical and computational knowledge. Deep learning techniques have recently become a popular topic and show strong advantages in many classification tasks. To date, many highly packaged deep learning packages, which make it possible for biologists to construct deep learning frameworks according to their own needs without in-depth knowledge of the algorithm details, have been developed. In this tutorial, we provide a guideline for constructing an easy-to-use deep learning framework for sequence classification without the need for sufficient mathematical knowledge or programming skills. All the code is optimized in a virtual machine so that users can directly run the code using their own data.

This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic data.

A variety of biological sequence classification tasks, such as species classification, gene function classification and viral host classification, are ...

Detection of Homologous Recombination Intermediates via Proximity Ligation and Quantitative PCR in Saccharomyces cerevisiae

DNA damage, including DNA double-stranded breaks and inter-strand cross-links, incurred during the S and G2 phases of the cell cycle can be repaired by homologous recombination (HR). In addition, HR represents an important mechanism of replication fork rescue following stalling or collapse. The regulation of the many reversible and irreversible steps of this complex pathway promotes its fidelity. The physical analysis of the recombination intermediates formed during HR enables the characterization of these controls by various nucleoprotein factors and their interactors. Though there are well-established methods to assay specific events and intermediates in the recombination pathway, the detection of D-loop formation and extension, two critical steps in this pathway, has proved challenging until recently. Here, efficient methods for detecting key events in the HR pathway, namely DNA double-stranded break formation, D-loop formation, D-loop extension, and the formation of products via break-induced replication (BIR) in Saccharomyces cerevisiae are described. These assays detect their relevant recombination intermediates and products with high sensitivity and are independent of cellular viability. The detection of D-loops, D-loop extension, and the BIR product is based on proximity ligation. Together, these assays allow for the study of the kinetics of HR at the population level to finely address the functions of HR proteins and regulators at significant steps in the pathway.

Detection of Homologous Recombination Intermediates via Proximity Ligation and Quantitative PCR in Saccharomyces cerevisiae

The D-loop capture (DLC) and D-loop extension (DLE) assays utilize the principle of proximity ligation together with quantitative PCR to quantify D-loop formation, D-loop extension, and product formation at the site of an inducible double-stranded break in Saccharomyces cerevisiae.

DNA damage, including DNA double-stranded breaks and inter-strand cross-links, incurred during the S and G2 phases of the cell cycle can be repaired by ...

Accurate Telomere Length Assessment in Human Population Studies

Monochrome Multiplex Quantitative PCR Telomere Length Measurement 

Telomeres are ribonucleoprotein structures at the end of all eukaryotic chromosomes that protect DNA from damage and preserve chromosome stability. Telomere length (TL) has been associated with various exposures, biological processes, and health outcomes. This article describes the monochrome multiplex quantitative polymerase chain reaction (MMqPCR) assay protocol routinely conducted in our laboratory for measuring relative mean TL from human DNA. There are several different PCR-based TL measurement methods, but the specific protocol for the MMqPCR method presented in this publication is repeatable, efficient, cost-effective, and suitable&#160;for population-based studies. This detailed protocol outlines all information necessary for investigators to establish this assay in their laboratory. In addition, this protocol provides specific steps to increase the reproducibility of TL measurement by this assay, defined by the intraclass correlation coefficient (ICC) across repeated measurements of the same sample. The ICC is a critical factor in evaluating expected power for a specific study population; as such, reporting cohort-specific ICCs for any TL assay is a necessary step to enhance the overall rigor of population-based studies of TL. Example results utilizing DNA samples extracted from peripheral blood mononuclear cells demonstrate the feasibility of generating highly repeatable TL data using this MMqPCR protocol.

Author Spotlight: Exploring the Impact of Trauma on Cellular Aging

Here we present a protocol for the measurement of relative telomere length (TL) using the monochrome multiplex quantitative polymerase chain reaction (MMqPCR) assay. The MMqPCR assay is a repeatable, efficient, and cost-effective method for measuring TL from human DNA in population-based studies.

Telomeres are ribonucleoprotein structures at the end of all eukaryotic chromosomes that protect DNA from damage and preserve chromosome stability. ...

Unsupervised Analysis of Multi-Omics Data in Coronary Syndromes Using MOFA

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a valuable resource to generate more insights into those processes, but the analysis of such datasets can be challenging due to the high dimensionality resulting, for example, from different disease conditions, timepoints, and omics capturing the process at different resolutions.
Here, we showcase an approach to analyze and explore such a complex multiomics dataset in an unsupervised way by applying multi-omics factor analysis (MOFA) to a dataset generated from blood samples that capture the immune response in acute and chronic coronary syndromes. The dataset consists of several assays at differing resolutions, including sample-level cytokine data, plasma-proteomics and neutrophil prime-seq, and single-cell RNA-seq (scRNA-seq)&#160;data. Further complexity is added by having several different time points measured per patient and several patient subgroups.
The analysis workflow outlines how to integrate and analyze the data in several steps: (1) Data pre-processing and harmonization, (2) Estimation of the MOFA model, (3) Downstream analysis. Step 1 outlines how to process the features of the different data types, filter out low-quality features, and normalize them to harmonize their distributions for further analysis. Step 2 shows how to apply the MOFA model and explore the major sources of variance within the dataset across all omics and features. Step 3 presents several strategies for the downstream analysis of the captured patterns, linking them to the disease conditions and potential molecular processes governing those conditions.
Overall, we present a workflow for unsupervised data exploration of complex multi-omics datasets to enable the identification of major axes of variation composed of differing molecular features that can also be applied to other contexts and multi-omics datasets (including other assays as presented in the exemplary use case).

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

We present a flexible, extendible Jupyter-lab-based workflow for the unsupervised analysis of complex multi-omics datasets that combines different pre-processing steps, estimation of the multi-omics factor analysis model, and several downstream analyses.

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a ...

Preparation, Pre-Processing, and Harmonization of Data for MOFA Analysis

Begin by adding all the multiomics input data sets to the input data folder. Here they contain data from patients with stable, chronic, and acute coronary syndromes. To pre-process the data, click on the folder symbol, then double-click on the mofa_workflow scripts and configurations to access the configuration folder.Double-click on the data configuration CSV file to open it. In the value column, enter the paths to the input data and results folders. In the configuration name-value column, specify a name to be added as a file extension for all saved files.To save the changes, select File and Save CSV File from the menu at the top. Then, using the navigation menu on the left side, click on scripts to go to the scripts folder. Double click on 00_Configuration_Update.ipynb to open the initialization notebook. To run the script, click on the Restart Kernel and Run All Cells button at the top, and then click Restart in the popup. To navigate to the configurations folder, double-click on configurations.Then double-click on 01_Pre_Processing_SC_Data. csv to open the file. Verify the automatically filled in values, select File and Save CSV file from the menu at the top to save the changes.Then use the navigation menu on the left side and click on scripts to navigate to the scripts folder. Double-click on 01_Prepare_Pseudobulk. ipynb to open the notebook.To run the script, click on the Restart Kernel and Run All Cells button at the top, and then click Restart in the popup. To navigate to the figures folder, double-click first on figures, and then on 01_figures. Open the newly generated plot, FIG01_Amount_of_Cells overview.Then examine the plot to identify cell type clusters with a very low number of cells per sample. Note down the names of those cluster IDs to exclude them in the subsequent steps. To navigate back to the configuration folder, click on dots and double click on configurations.Then open the file 02_Pre_Processing_Configuration_SC.csv. Add all cluster IDs identified for exclusion in the previous step, separated by commas in the cell_type_exclusion column. To save the changes, select File and Save CSV File from the menu at the top.Now open the file 02_Pre_Processing_Configuration. CSV, and adjust the pre-processing configuration for each dataset included and stored in the data input folder. Adjust the parameters in the columns as needed depending on which pre-processing steps should be applied.Save the changes by selecting File and Save CSV File. To navigate to the scripts folder, click on scripts. Open the notebook 02_Integrate_and_Normalize_Data_Sources.ipynb.Click on the Restart Kernel and Run All Cells button at the top, and then click Restart in the popup. Next, navigate to the generated 02_results folder. Click on the folder symbol, then double click on results and 02_results.Verify that it includes the file 02_Combined_Data, configuration name, INTEGRATED CSV containing the combined pre-process data input file.