The authors would like to thank Branden Moriarity, David Largaespada, and Vincent Keng at the University of Minnesota, and Adam Dupuy at the University of Iowa for their assistance in developing the protocol described above.

1. Breeding and Aging of Transgenic Animals
<ol>
 <li>Select the strains of transgenic mice appropriate for your experiment. A typical experiment will use three transgenic lines; a conditional transposase mouse, a mouse harboring a concatamer of transposons, and a mouse expressing Cre Recombinase in the cells believed to be the cells of origin for the desired cancer. If the cancer being modeled has a known mutation in a large percentage of the patients it may be desirable to include this mutation at the start. Examples of these "predisposing" mutations include activated Kras, dominant negative Tp53 or a floxed Pten allele. By adding in a predisposing mutation the model may better represent the human cancer (see 1.3). Several Sleeping Beauty transposase and transposon mice are available from the National Cancer Institute Mouse Repository (<a href="http://mouse.ncifcrf.gov/" target="_blank">http://mouse.ncifcrf.gov/</a>).</li>
 <li>Breed conditional transposase mice with mice harboring the transposons to eventually obtain mice homozygous for both alleles if possible. Breed homozygous offspring to mice expressing Cre Recombinase to generate your triple transgenic experimental mice (Figure 2). Note: Obtaining homozygous mice are not always possible or ideal. For example, homozygous p53 dominant negative mice are much more difficult to breed than heterozygous mice. In addition, it is helpful to make sure that litters follow Mendelian genetics to confirm that the breeding scheme is successful.</li>
 <li>If using a predisposed background, first breed mice with the transposase to mice with the predisposing allele. Simultaneously, breed mice expressing Cre Recombinase to mice carrying the transposons. Create homozygous mice for each allele if possible. Finally, breed the homozygous offspring from the previous two breeding schemes to generate quadruple transgenic experimental animals.</li>
 <li>In addition to the experimental animals, generate control cohorts of mice. The control group normally consists of littermates from the experimental breeding that only inherit two of the three alleles (Figure 2). In the case of a predisposed background, mice including three out of the four alleles will also be necessary.</li>
 <li>Monitor experimental and control mice daily for tumor development and/or moribundity. Follow your institutional animal care and use committee (IACUC) requirements for animal husbandry. Also, it is typical to determine an age in which you will sacrifice an animal if it has not yet become moribund. Eighteen months is a typical age at which mice are euthanized.</li>
</ol>
2. Necropsy of Transgenic Animals
<table border="1">
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 </tr>
 <tr>
 <td>Sure-Seal Induction Chamber</td>
 <td>Brain Tree Scientific</td>
 <td>EZ-177</td>
 </tr>
 <tr>
 <td>Cryovial</td>
 <td>Bioexpress</td>
 <td>C-3355-2</td>
 </tr>
 <tr>
 <td>10% Buffered Formalin</td>
 <td>Sigma Aldrich</td>
 <td>HT501128-4L</td>
 </tr>
 </tbody>
</table>
Table 1. Reagents required.
<ol>
 <li>When a mouse is moribund or has reached the end of the determined life span, euthanize animal using a CO2 chamber following your IACUC guidelines. Generally, place the animal in a clean gas chamber, open the gas tank and adjust the regulator to read no higher than 5 pounds per square inch. Fill slowly to minimize nasal and ocular irritation and aversion to CO2. Make sure that the animal's heart is no longer beating and does not respond when touched in the eye.</li>
 <li>Spray exterior of sacrificed mouse with 70% ethanol.</li>
 <li>Place mouse on a necropsy board and secure with dissecting pins. Use scissors and tweezers to open mouse and remove organs. Determining which organs should be collected is project dependent. However, it is recommended to always collect the spleen and liver as these organs often give important insight into why the mouse was moribund. Also collect the sternum for the potential analysis of bone marrow. In addition, it is always beneficial to collect any organ or tissue that appears abnormal.</li>
 <li>Tissues and tumors that are collected should be divided into four sections. One section is fixed in 10% buffered formalin for 18 hr followed by 70% ethanol. This section can be used for H&amp;E staining and/or IHC. The remaining three sections, which are generally 50 to 100 mg in size, are snap frozen in liquid nitrogen. These can be used for DNA, RNA and protein isolation.</li>
 <li>Dispose of carcass and remaining tissues according to IACUC guidelines.</li>
</ol>
3. Isolating Genomic DNA from Tumors
<table border="1">
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 </tr>
 <tr>
 <td>Protein Precipitation solution</td>
 <td>Qiagen</td>
 <td>158910</td>
 </tr>
 <tr>
 <td>Cell lysis buffer</td>
 <td>Qiagen</td>
 <td>158906</td>
 </tr>
 <tr>
 <td>Proteinase K</td>
 <td>Qiagen</td>
 <td>158920</td>
 </tr>
 <tr>
 <td>RNase A</td>
 <td>Qiagen</td>
 <td>158924</td>
 </tr>
 <tr>
 <td>TE Buffer</td>
 <td>Promega</td>
 <td>V6232</td>
 </tr>
 </tbody>
</table>
Table 2. Reagents required.
<ol>
 <li>Finely mince the frozen tumor sample (50 to 100 mg) using a clean razor blade.</li>
 <li>Place minced tissue in a 1.5 ml microcentrifuge tube and add 1 ml of cell lysis buffer containing 5 &mu;l Proteinase K solution (20 mg/ml).</li>
 <li>Vortex thoroughly.</li>
 <li>Incubate in a 55 &deg;C shaker set at 250 rpm for at least 4 hr, preferably overnight.</li>
 <li>Add 1 &mu;l RNase A solution (10 mg/ml) and invert tube 25 times.</li>
 <li>Incubate at 37 &deg;C for 30 min.</li>
 <li>Cool to room temperature by placing on ice for 3 min.</li>
 <li>Add 333 &mu;l Protein Precipitation solution and vortex vigorously.</li>
 <li>Centrifuge at 14,000 rpm 10 min. Proteins should pellet. If pellet is very small, vortex again, incubate on ice for 5 min, and then re-centrifuge.</li>
 <li>Pour off supernatant containing DNA into a clean 15 ml centrifuge tube.</li>
 <li>Add equal volume of 100% isopropanol and mix by gently inverting 50 times.</li>
 <li>Centrifuge at 3,500 rpm for 3 min.</li>
 <li>Discard supernatant.</li>
 <li>Add 1 ml of 70% ethanol and invert tube several times to wash the pellet.</li>
 <li>Transfer the washed pellet along with the ethanol to a 1.5 ml microcentrifuge tube.</li>
 <li>Centrifuge at 14,000 rpm for 5 min at 4 &deg;C.</li>
 <li>Pour off ethanol and invert tube to air dry (approximately 5 to 30 min).</li>
 <li>Resuspend DNA pellet in 50 to 500 &mu;l of TE depending on size of DNA pellet.</li>
 <li>Optional incubation at 37 &deg;C to resuspend DNA.</li>
 <li>Store at 4 &deg;C, or -20 &deg;C for long term storage.</li>
</ol>
4. Linker Mediated-PCR Using Genomic DNA from Tumors
<table border="1">
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 </tr>
 <tr>
 <td>BfaI</td>
 <td>New England Biolabs</td>
 <td>R0568S</td>
 </tr>
 <tr>
 <td>NlaIII</td>
 <td>New England Biolabs</td>
 <td>R0125S</td>
 </tr>
 <tr>
 <td>MinElute 96 well plates</td>
 <td>Qiagen</td>
 <td>28051</td>
 </tr>
 <tr>
 <td>QIAvac 96 Vacuum manifold</td>
 <td>Qiagen</td>
 <td>19504</td>
 </tr>
 <tr>
 <td>Multi-MicroPlate Genie, 120V</td>
 <td>Scientific Industries, Inc.</td>
 <td>SI-4000</td>
 </tr>
 <tr>
 <td>T4 DNA ligase with 5x ligation Buffer</td>
 <td>Invitrogen</td>
 <td>15224-041</td>
 </tr>
 <tr>
 <td>BamHI</td>
 <td>New England Biolabs</td>
 <td>R0136S</td>
 </tr>
 <tr>
 <td>25 mM dNTPs</td>
 <td>Bioexpress</td>
 <td>C-5014-200</td>
 </tr>
 <tr>
 <td>Platinum Taq</td>
 <td>Invitrogen</td>
 <td>10966-034</td>
 </tr>
 <tr>
 <td>FastStart Taq DNA Polymerase</td>
 <td>Roche</td>
 <td>12032929001</td>
 </tr>
 <tr>
 <td>Agarose</td>
 <td>Promega</td>
 <td>V3121</td>
 </tr>
 <tr>
 <td>TAE Buffer</td>
 <td>Promega</td>
 <td>V4271</td>
 </tr>
 <tr>
 <td>TE Buffer</td>
 <td>Promega</td>
 <td>V6232</td>
 </tr>
 <tr>
 <td>Ethidium bromide</td>
 <td>Promega</td>
 <td>H5041</td>
 </tr>
 </tbody>
</table>
Table 3. Reagents required.
Linker Sequences
BfaI linker + 5' GTAATACGACTCACTATAGGGCTCCGCTTAAGGGAC 3'
BfaI linker- 5' Phos-TAGTCCCTTAAGCGGAG 3'NH2
NlaIII linker+ 5'GTAATACGACTCACTATAGGGCTCCGCTTAAGGGACCATG 3'
NlaIII linker- 5' Phos-GTCCCTTAAGCGGAGCC 3'
Primer Sequences
Primary PCR forward right side (NlaIII): Spl IRDR R1 1 GCTTGTGGAAGGCTACTCGAAATGTTTGACCC
Primary PCR forward left side (BfaI): Spl IRDR L1 1 CTGGAATTTTCCAAGCTGTTTAAAGGCACAGTCAAC
Primary PCR reverse for both right and left side: Spl Link1 1 GTAATACGACTCACTATAGGGC
Secondary PCR forward right side: Example of an Illumina barcoded secondary primer 5' AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNNNNNNNaGgtgtatgtaaacttccgacttcaa (The N's represent the 12 bp barcode, sequence in upper case are required for the Illumina platform, and the sequence in lower case will bind to the transposon.)
Secondary PCR forward left side: Example of an Illumina barcoded secondary primer 5'AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNNNNNNNaAgtgtatgtaaacttccgacttcaa (The N's represent the 12 bp barcode, sequence in upper case are required for the Illumina platform, and the sequence in lower case will bind to the transposon.)
Secondary PCR reverse for both right and left side: linker nested reverse primer 5' CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTAGGGCTCCGCTTAAGGGAC
<ol>
 <li>Anneal the + and - linkers for both BfaI linkers and NlaII linkers by mixing 50 &mu;l of linker+ (100 &mu;M) with 50 &mu;l of linker- (100 &mu;M) and add 2 &mu;l NaCl (5 M) in a 1.5 ml microcentrifuge tube. Place in 95 &deg; C heat block for 5 min. Turn off heating block and let slowly cool to room temperature (this takes an hour or more). After cooling, annealed linkers may be stored in a -20 &deg;C freezer until needed.</li>
 <li>Prepare two aliquots of the tumor DNA, each aliquot containing 1 &mu;g of DNA. One aliquot will be digested with a restriction enzyme that will cut close to the "right" end of the transposon. The second aliquot will be digested with a restriction enzyme that cuts close to the "left" end of the transposon. This is done to maximize the chances of amplifying every transposon insertion site.</li>
 <li>Digest the tumor DNA in one aliquot using the "right" side enzyme NlaII, and digest the tumor DNA in the other aliquot using the "left" side enzyme BfaI: combine 1 &mu;l of enzyme, 5 &mu;l 10x NEB Buffer #4, DNA (1 &mu;g) and ddH2O to a total volume of 50 &mu;l. Digest DNA overnight at 37 &deg; C in water bath or incubator.</li>
 <li>Heat inactivate enzymes (BfaI = 80 &deg;C for 20 min, NlaIII = 65 &deg;C for 20 min).</li>
 <li>Clean the digested samples by placing samples in MinElute 96 well plate, place plate in vacuum manifold and vacuum for about 15 min until wells look dry.</li>
 <li>Remove plate from vacuum manifold and blot bottom of 96 well plate with paper towel to remove all liquid</li>
 <li>Add 30 &mu;l ddH2O to each well and shake on an orbital shaker set on high for 2 min, or pipette up and down 20 to 40 times.</li>
 <li>Transfer entire volume in wells (30 &mu;l) from MinElute 96 well plate into clean 96 well plates.</li>
 <li>Perform linker ligation reactions with digested DNA from step 4.8 and linkers from step 4.1. Mix 10 &mu;l digested DNA, 4 &mu;l 5x ligation buffer, 5.5 &mu;l annealed linkers (950 &mu;g/&mu;l), 0.5 &mu;l T4 ligase (5 U/&mu;l).</li>
 <li>Incubate for 4 hr to overnight at 16 &deg;C.</li>
 <li>Heat inactivate the T4 DNA ligase at 65 &deg;C for 10 min.</li>
 <li>Clean up ligation using MinElute 96 well plates and a vacuum manifold as described in steps 4.5 - 4.6.</li>
 <li>Resuspend in 30 &mu;l H2O and transfer to a clean 96 well plate.</li>
 <li>Perform a second DNA digestion in order to destroy remaining concatamer transposons. This is achieved by cutting the DNA a second time using an enzyme that cuts the plasmid vector DNA that was used to create the transgenic mice containing the transposon concatamer.</li>
 <li>Digest DNA using BamHI (both left and right side): Mix 25 &mu;l of linker-ligated DNA from step 4.13, 5 &mu;l 10x NEB buffer #3, 0.5 &mu;l BamHI (20 U/&mu;l), 0.5 &mu;l BSA (10 mg/ml), and 19 &mu;l ddH2O.</li>
 <li>Digest 3-6 hr or overnight at 37 &deg;C.</li>
 <li>Perform primary PCR using a primer specific for the transposon and a primer specific for the linker. These will vary depending upon what transposon and linker you are using. The primers listed in the primer table above work for the T2/Onc and T2/Onc2 transposons.</li>
 <li>Primary PCR: 5 &mu;l 10x buffer, 2.0 &mu;l MgCl2 (50 mM), 4 &mu;l dNTPs (2.5 nM), 1 &mu;l Primer 1 (20 &mu;M), 1 &mu;l Primer 2 (20 &mu;M), 0.25 &mu;l Platinum Taq (5 U/&mu;l), 3 &mu;l Digested DNA with linkers (amount can vary), up to 50 &mu;l ddH2O.</li>
 <li>PCR Program: 94 &deg;C for 5 min, 30 cycles of 94 &deg;C for 30 sec, 60 &deg;C for 30 sec, 72 &deg;C for 90 sec. Final extension at 72 &deg;C for 5 min.</li>
 <li>Clean up PCR product using MinElute 96 well plates and a vacuum manifold as described above.</li>
 <li>Resuspend in 30 &mu;l H2O and transfer to a clean 96 well plate.</li>
 <li>Make a 1:75 dilution of the 1 &deg; PCR by diluting 2 &mu;l of DNA from step 4.21 into 148 &mu;l ddH2O</li>
 <li>Perform secondary PCR using nested versions of the primers that also have the required sequence for the Illumina GAIIx machine as well as a 12 base pair barcode that is unique for each tumor sample processed (see primer table above for examples of these primers).</li>
 <li>Secondary PCR: 10 &mu;l 10x Buffer with MgCl2, 8 &mu;l dNTPs (2.5 mM), 1 &mu;l Illumina barcoded secondary primer (20 &mu;M), 1 &mu;l Illumina Nest 1 secondary primer (20 &mu;M), 0.25 &mu;l FastStart Taq (5 U/&mu;l), 2 &mu;l DNA from primary PCR diluted 1:75, up to 100 &mu;l ddH2O.</li>
 <li>PCR Program: 94 &deg;C for 2 min, 30 cycles of 94 &deg;C for 30 sec, 53 &deg;C for 45 sec, 72 &deg;C for 90 sec. Final extension at 72 &deg;C for 5 min.</li>
 <li>Run 45 &mu;l of this PCR reaction on a 1% agarose gel containing ethidium bromide (0.5 &mu;g/ml). There should be a "smear" of PCR products representing amplicons of the many different transposon insertions in the tumor (Figure 3).</li>
 <li>Clean up remaining 50 &mu;l of PCR product using MinElute 96 well plates and a vacuum manifold as described above. Wash one time by placing 50 &mu;l ddH2O in the wells and vacuuming again.</li>
 <li>Resuspend in 30 &mu;l TE and transfer to a clean 96 well plate.</li>
 <li>Determine the concentration of DNA in each sample. Combine equal amounts of DNA in a single 1.5 ml microfuge tube. This will be the library that is sequenced in a single lane of an Illumina HiSeq 2000 machine. It is possible to sequence several hundred tumors in a single lane of an Illumina run.</li>
 <li>Submit the single tube containing equal amounts of DNA from all the tumors for sequencing. This can be done by your institution or commercially.</li>
</ol>
5. Analysis of Transposon Insertions
<ol>
 <li>Software for analyzing transposon insertion data is available for free download via SourceForge (<a href="http://sourceforge.net/projects/tapdancebio/" target="_blank">http://sourceforge.net/projects/tapdancebio/</a>). The program, called TapDance, requires the sequence file from the Illumina platform and a barcode to library map text file.</li>
</ol>

<ol>
	<li>Fearon, E. R., Vogelstein, B., 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=A+genetic+model+for+colorectal+tumorigenesis.">A genetic model for colorectal tumorigenesis.</a> Cell. 61, 759-767 (1990).</li><li>Wood, L. 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=The+Genomic+Landscapes+of+Human+Breast+and+Colorectal+Cancers.">The Genomic Landscapes of Human Breast and Colorectal Cancers.</a> Science. 318, 1108-1113 (2007).</li><li>Ivics, Z., Hackett, P. B., Plasterk, R. H., Izsv&#225;k, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+Reconstruction+of+Sleeping+Beauty,+a+Tc1-like+Transposon+from+Fish,+and+Its+Transposition+in+Human+Cells.">Molecular Reconstruction of Sleeping Beauty, a Tc1-like Transposon from Fish, and Its Transposition in Human Cells.</a> Cell. 91, 501-510 (1997).</li><li>Starr, T. K., Largaespada, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+gene+discovery+using+the+Sleeping+Beauty+transposon.">Cancer gene discovery using the Sleeping Beauty transposon.</a> Cell Cycle. 4, 1744-1748 (2005).</li><li>Mueller, P. R., Wold, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+Vivo+Footprinting+of+a+Muscle+Specific+Enhancer+by+Ligation+Mediated+PCR.">In Vivo Footprinting of a Muscle Specific Enhancer by Ligation Mediated PCR.</a> Science. 246, 780-786 (1989).</li><li>Bergemann, T. 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=New+methods+for+finding+common+insertion+sites+and+co-occurring+common+insertion+sites+in+transposon-+and+virus-based+genetic+screens.">New methods for finding common insertion sites and co-occurring common insertion sites in transposon- and virus-based genetic screens.</a> Nucleic acids research. 40, 3822-3833 (2012).</li><li>Copeland, N. G., Jenkins, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Harnessing+transposons+for+cancer+gene+discovery.">Harnessing transposons for cancer gene discovery.</a> Nature Reviews Cancer. 10, 696-706 (2010).</li><li>Collier, L. S., Carlson, C. M., Ravimohan, S., Dupuy, A. J., Largaespada, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+gene+discovery+in+solid+tumours+using+transposon-based+somatic+mutagenesis+in+the+mouse.">Cancer gene discovery in solid tumours using transposon-based somatic mutagenesis in the mouse.</a> Nature. 436, 272-276 (2005).</li><li>Starr, T. 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=A+transposon-based+genetic+screen+in+mice+identifies+genes+altered+in+colorectal+cancer.">A transposon-based genetic screen in mice identifies genes altered in colorectal cancer.</a> Science. 323, 1747-1750 (2009).</li><li>Keng, V. W., 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=A+conditional+transposon-based+insertional+mutagenesis+screen+for+genes+associated+with+mouse+hepatocellular+carcinoma.">A conditional transposon-based insertional mutagenesis screen for genes associated with mouse hepatocellular carcinoma.</a> Nature. 27, 264-274 (2009).</li><li>Dupuy, A. J., Akagi, K., Largaespada, D. A., Copeland, N. G., Jenkins, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mammalian+mutagenesis+using+a+highly+mobile+somatic+Sleeping+Beauty+transposon+system.">Mammalian mutagenesis using a highly mobile somatic Sleeping Beauty transposon system.</a> Nature. 436, 221-226 (2005).</li></ol>

The authors have nothing to disclose.

A forward genetic screen using the Sleeping Beauty transposon system provides a method for identifying mutations that cause cancer. By selecting the appropriate promoter to control Cre Recombinase in addition to any predisposing mutations, the SB screen will identify known and novel candidate cancer genes. 
The success of an SB screen is largely dependent on the mice selected for creating the screen. For the transposon, we recommend using two different mouse strains carrying the concatamer of oncogenic transposons on different chromosomes7. Careful thought should be used when choosing the tissue-specific Cre Recombinase that will activate the SB transposase. There should be evidence that the Cre enzyme is active in the cell type that is suspected of being the cell of origin of the cancer being modeled. Successful examples include Villin-Cre (GI tract cancer), Albumin-Cre (hepatocellular carcinoma), and Aid-Cre (lymphoma).8&#x2013;10
The number of mice required for a successful screen will depend upon the tumor penetrance. In general, more tumors and more mice will result in a more robust screen. To detect a 2-fold difference in cancer latency there should be at least 60 animals in both the control and experimental groups.11
The versatile nature of these screens is powerful in respect to other techniques in that one can specify the nature of disease, follow the progression, and analyze the genetic composition. This technique has been successful in a number of screens that have yielded CIS lists that identify new potential cancer driving genes. With this information, directed studies may be performed to understand the function of these novel mutations. Overall, the use of this technique may lead to the eventual design of new drug therapies aimed to eliminate the results of these specific genetic mutations.

<table border="1">
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 <td>Comments (optional)</td>
 </tr>
 <tr>
 <td>Sure-Seal Induction Chamber</td>
 <td>Brain Tree Scientific</td>
 <td>EZ-177</td>
 <td></td>
 </tr>
 <tr>
 <td>Cryovial</td>
 <td>Bioexpress</td>
 <td>C-3355-2</td>
 <td></td>
 </tr>
 <tr>
 <td>10% Buffered Formalin</td>
 <td>Sigma Aldrich</td>
 <td>HT501128-4L</td>
 <td></td>
 </tr>
 <tr>
 <td>Protein Precipitation solution</td>
 <td>Qiagen</td>
 <td>158910</td>
 <td></td>
 </tr>
 <tr>
 <td>Cell lysis buffer</td>
 <td>Qiagen</td>
 <td>158906</td>
 <td></td>
 </tr>
 <tr>
 <td>Proteinase K</td>
 <td>Qiagen</td>
 <td>158920</td>
 <td></td>
 </tr>
 <tr>
 <td>RNase A</td>
 <td>Qiagen</td>
 <td>158924</td>
 <td></td>
 </tr>
 <tr>
 <td>TE Buffer</td>
 <td>Promega</td>
 <td>V6232</td>
 <td></td>
 </tr>
 <tr>
 <td>BfaI</td>
 <td>New England Biolabs</td>
 <td>R0568S</td>
 <td></td>
 </tr>
 <tr>
 <td>NlaIII</td>
 <td>New England Biolabs</td>
 <td>R0125S</td>
 <td></td>
 </tr>
 <tr>
 <td>MinElute 96 well plates</td>
 <td>Qiagen</td>
 <td>28051</td>
 <td></td>
 </tr>
 <tr>
 <td>QIAvac 96 Vacuum manifold</td>
 <td>Qiagen</td>
 <td>19504</td>
 <td></td>
 </tr>
 <tr>
 <td>Multi-MicroPlate Genie, 120V</td>
 <td>Scientific Industries, Inc.</td>
 <td>SI-4000</td>
 <td></td>
 </tr>
 <tr>
 <td>T4 DNA ligase with 5x ligation Buffer</td>
 <td>Invitrogen</td>
 <td>15224-041</td>
 <td></td>
 </tr>
 <tr>
 <td>BamHI</td>
 <td>New England Biolabs</td>
 <td>R0136S</td>
 <td></td>
 </tr>
 <tr>
 <td>25mM dNTPs</td>
 <td>Bioexpress</td>
 <td>C-5014-200</td>
 <td></td>
 </tr>
 <tr>
 <td>Platinum Taq</td>
 <td>Invitrogen</td>
 <td>10966-034</td>
 <td></td>
 </tr>
 <tr>
 <td>FastStart Taq DNA Polymerase</td>
 <td>Roche</td>
 <td>12032929001</td>
 <td></td>
 </tr>
 <tr>
 <td>Agarose</td>
 <td>Promega</td>
 <td>V3121</td>
 <td></td>
 </tr>
 <tr>
 <td>TAE Buffer</td>
 <td>Promega</td>
 <td>V4271</td>
 <td></td>
 </tr>
 <tr>
 <td>TE Buffer</td>
 <td>Promega</td>
 <td>V6232</td>
 <td></td>
 </tr>
 <tr>
 <td>Ethidium bromide</td>
 <td>Promega</td>
 <td>H5041</td>
 <td></td>
 </tr>
 </tbody>
</table>

identification of sleeping beauty transposon insertions in solid tumors using linker-mediated pcr

Genomic, proteomic, transcriptomic, and epigenomic analyses of human tumors indicate that there are thousands of anomalies within each cancer genome compared to matched normal tissue. Based on these analyses it is evident that there are many undiscovered genetic drivers of cancer1. Unfortunately these drivers are hidden within a much larger number of passenger anomalies in the genome that do not directly contribute to tumor formation. Another aspect of the cancer genome is that there is considerable genetic heterogeneity within similar tumor types. Each tumor can harbor different mutations that provide a selective advantage for tumor formation2. Performing an unbiased forward genetic screen in mice provides the tools to generate tumors and analyze their genetic composition, while reducing the background of passenger mutations. The Sleeping Beauty (SB) transposon system is one such method3. The SB system utilizes mobile vectors (transposons) that can be inserted throughout the genome by the transposase enzyme. Mutations are limited to a specific cell type through the use of a conditional transposase allele that is activated by Cre Recombinase. Many mouse lines exist that express Cre Recombinase in specific tissues. By crossing one of these lines to the conditional transposase allele (e.g. Lox-stop-Lox-SB11), the SB system is activated only in cells that express Cre Recombinase. The Cre Recombinase will excise a stop cassette that blocks expression of the transposase allele, thereby activating transposon mutagenesis within the designated cell type. An SB screen is initiated by breeding three strains of transgenic mice so that the experimental mice carry a conditional transposase allele, a concatamer of transposons, and a tissue-specific Cre Recombinase allele. These mice are allowed to age until tumors form and they become moribund. The mice are then necropsied and genomic DNA is isolated from the tumors. Next, the genomic DNA is subjected to linker-mediated-PCR (LM-PCR) that results in amplification of genomic loci containing an SB transposon. LM-PCR performed on a single tumor will result in hundreds of distinct amplicons representing the hundreds of genomic loci containing transposon insertions in a single tumor4. The transposon insertions in all tumors are analyzed and common insertion sites (CISs) are identified using an appropriate statistical method5. Genes within the CIS are highly likely to be oncogenes or tumor suppressor genes, and are considered candidate cancer genes. The advantages of using the SB system to identify candidate cancer genes are: 1) the transposon can easily be located in the genome because its sequence is known, 2) transposition can be directed to almost any cell type and 3) the transposon is capable of introducing both gain- and loss-of-function mutations6. The following protocol describes how to devise and execute a forward genetic screen using the SB transposon system to identify candidate cancer genes (Figure 1).

After the breeding scheme has been established, breeders should produce a litter every 19-21 days. Litter size will vary between 5 and 12 pups, depending on the age of the breeders and the genetic background. In addition, make sure that the genotype of the litter follows Mendelian genetics as a way to confirm that the breeding scheme is both correct and successful. 
For the experiment to be successful, tumor incidence in the experimental animals should be significantly greater than tumor incidence in the control animals. If no "predisposing" mutation was included in the experiment there should be few to no tumors in the control animals. If a "predisposing" mutation was included in the experiment, tumor incidence in these animals should be much lower than the incidence in animals with the mutation and SB activity. 
LM-PCR should amplify hundreds of transposon insertions in a single tumor. After running half of the secondary PCR product on a 1% gel, there should be a "smear" of DNA representing this amplification (Figure 3). If there are only one or two bands, either SB was not active in the tumor or an error was made in the protocol (Figure 4).
Sequencing one to two hundred tumors in a single lane of the Illumina GAIIx platform should produce over 30 million sequences of 100 bp each (Figure 5).
Table 1. Reagents required for section 2.
Table 2. Reagents required for section 3.
Table 3. Reagents required for section 4.
<img src="/files/ftp_upload/50156/50156fig1.jpg" alt="Figure 1" fo:content-width="6in" fo:src="/files/ftp_upload/50156/50156fig1highres.jpg"/> 
Figure 1. Flow chart showing the steps to carry out a forward genetic screen using the Sleeping Beauty system. First, a mouse model is generated. Mice are then necropsied when moribund and tumors are collected and isolated. Tumor DNA is purified and linker mediated-PCR performed. Finally, amplified tumor DNA with transposon insertions is sequenced and common insertion sites identified. 
<img src="/files/ftp_upload/50156/50156fig2.jpg" alt="Figure 2"/> 
Figure 2. Breeding scheme to generate experimental cohort. Mice containing the transposase (Rosa26-LsL-SB11) are crossed to mice harboring the concatamer of transposons (T2-Onc). The offspring are then mated to mice carrying the designated tissue specific promoter driven Cre Recombinase (Cre). 
<img src="/files/ftp_upload/50156/50156fig3.jpg" alt="Figure 3"/> 
Figure 3. Representative results of Secondary PCR. The expected product has a smear like appearance ranging from 100 to 600 base pairs. 
<img src="/files/ftp_upload/50156/50156fig4.jpg" alt="Figure 4"/> 
Figure 4. If LM-PCR is unsuccessful, there will be an absence of the "smear" of DNA. Instead, you will see definitive bands of DNA.
<img src="/files/ftp_upload/50156/50156fig5.jpg" alt="Figure 5" fo:src="/files/ftp_upload/50156/50156fig5highres.jpg" fo:content-width="4.6in"/> 
Figure 5. Example data from a sequencing run that demonstrates a single lane of the Illumina GAIIx platform should produce over 30 million sequences of 100 base pairs each.

Watch this Scientific Journal Video about Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR at JoVE.com

Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR

A method of identifying unknown drivers of carcinogenesis using an unbiased approach is described. The method uses the Sleeping Beauty transposon as a random mutagen directed to specific tissues. Genomic mapping of transposon insertions that drive tumor formation identifies novel oncogenes and tumor suppressor genes

Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR

Genomic, proteomic, transcriptomic, and epigenomic analyses of human tumors indicate that there are thousands of anomalies within each cancer genome ...

identification-sleeping-beauty-transposon-insertions-solid-tumors

Universidad San Sebastian

Research

JoVE Journal

Medicine

14.0K Views.  University of Minnesota, Minneapolis. A method of identifying unknown drivers of carcinogenesis using an unbiased approach is described. The method uses the Sleeping Beauty transposon as a random mutagen directed to specific tissues. Genomic mapping of transposon insertions that drive tumor formation identifies novel oncogenes and tumor suppressor genes

Video: Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of areas of distinct differentiation into recognized histological subtypes, or different pathological grade, often there are more subtle differences in phenotype which defy accurate classification (Figure 1). Ultimately, since morphology is dictated by the underlying molecular phenotype, areas with visible differences are likely to be accompanied by differences in the expression of proteins which orchestrate cellular function and behavior, and therefore, appearance. The significance of visible and invisible (molecular) heterogeneity for prognosis is unknown, but recent evidence suggests that, at least at the genetic level, heterogeneity exists in the primary tumor1,2, and some of these sub-clones give rise to metastatic (and therefore lethal) disease. 
Moreover, some proteins are measured as biomarkers because they are the targets of therapy (for instance ER and HER2 for tamoxifen and trastuzumab (Herceptin), respectively). If these proteins show variable expression within a tumor then therapeutic responses may also be variable. The widely used histopathologic scoring schemes for immunohistochemistry either ignore, or numerically homogenize the quantification of protein expression. Similarly, in destructive techniques, where the tumor samples are homogenized (such as gene expression profiling), quantitative information can be elucidated, but spatial information is lost. Genetic heterogeneity mapping approaches in pancreatic cancer have relied either on generation of a single cell suspension3, or on macrodissection4. A recent study has used quantum dots in order to map morphologic and molecular heterogeneity in prostate cancer tissue5, providing proof of principle that morphology and molecular mapping is feasible, but falling short of quantifying the heterogeneity. Since immunohistochemistry is, at best, only semi-quantitative and subject to intra- and inter-observer bias, more sensitive and quantitative methodologies are required in order to accurately map and quantify tissue heterogeneity in situ.
We have developed and applied an experimental and statistical methodology in order to systematically quantify the heterogeneity of protein expression in whole tissue sections of tumors, based on the Automated QUantitative Analysis (AQUA) system6. Tissue sections are labeled with specific antibodies directed against cytokeratins and targets of interest, coupled to fluorophore-labeled secondary antibodies. Slides are imaged using a whole-slide fluorescence scanner. Images are subdivided into hundreds to thousands of tiles, and each tile is then assigned an AQUA score which is a measure of protein concentration within the epithelial (tumor) component of the tissue. Heatmaps are generated to represent tissue expression of the proteins and a heterogeneity score assigned, using a statistical measure of heterogeneity originally used in ecology, based on the Simpson's biodiversity index7.
To date there have been no attempts to systematically map and quantify this variability in tandem with protein expression, in histological preparations. Here, we illustrate the first use of the method applied to ER and HER2 biomarker expression in ovarian cancer. Using this method paves the way for analyzing heterogeneity as an independent variable in studies of biomarker expression in translational studies, in order to establish the significance of heterogeneity in prognosis and prediction of responses to therapy.

Here we describe a method to quantify molecular heterogeneity in histological sections of tumor material using quantitative immunofluorescence, image analysis, and a statistical measure of heterogeneity. The method is intended for use in clinical biomarker development and analysis.

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of ...

MicroRNA Detection in Prostate Tumors by Quantitative Real-time PCR (qPCR)

MicroRNAs (miRNAs) are single-stranded, 18&#x2013;24 nucleotide long, non-coding RNA molecules. They are involved in virtually every cellular process including development1, apoptosis2, and cell cycle regulation3. MiRNAs are estimated to regulate the expression of 30% to 90% of human genes4 by binding to their target messenger RNAs (mRNAs)5. Widespread dysregulation of miRNAs has been reported in various diseases and cancer subtypes6. Due to their prevalence and unique structure, these small molecules are likely to be the next generation of biomarkers, therapeutic agents and/or targets.
Methods used to investigate miRNA expression include SYBR green I dye- based as well as Taqman-probe based qPCR. If miRNAs are to be effectively used in the clinical setting, it is imperative that their detection in fresh and/or archived clinical samples be accurate, reproducible, and specific. qPCR has been widely used for validating expression of miRNAs in whole genome analyses such as microarray studies7. The samples used in this protocol were from patients who underwent radical prostatectomy for clinically localized prostate cancer; however other tissues and cell lines can be substituted in. Prostate specimens were snap-frozen in liquid nitrogen after resection. Clinical variables and follow-up information for each patient were collected for subsequent analysis8.
Quantification of miRNA levels in prostate tumor samples. The main steps in qPCR analysis of tumors are: Total RNA extraction, cDNA synthesis, and detection of qPCR products using miRNA-specific primers. Total RNA, which includes mRNA, miRNA, and other small RNAs were extracted from specimens using TRIzol reagent. Qiagen's miScript System was used to synthesize cDNA and perform qPCR (Figure 1). Endogenous miRNAs are not polyadenylated, therefore during the reverse transcription process, a poly(A) polymerase polyadenylates the miRNA. The miRNA is used as a template to synthesize cDNA using oligo-dT and Reverse Transcriptase. A universal tag sequence on the 5' end of oligo-dT primers facilitates the amplification of cDNA in the PCR step. PCR product amplification is detected by the level of fluorescence emitted by SYBR Green, a dye which intercalates into double stranded DNA. Specific miRNA primers, along with a Universal Primer that binds to the universal tag sequence will amplify specific miRNA sequences. 
The miScript Primer Assays are available for over a thousand human-specific miRNAs, and hundreds of murine-specific miRNAs. Relative quantification method was used here to quantify the expression of miRNAs. To correct for variability amongst different samples, expression levels of a target miRNA is normalized to the expression levels of a reference gene. The choice of a gene on which to normalize the expression of targets is critical in relative quantification method of analysis. Examples of reference genes typically used in this capacity are the small RNAs RNU6B, RNU44, and RNU48 as they are considered to be stably expressed across most samples. In this protocol, RNU6B is used as the reference gene.

MicroRNA Detection in Prostate Tumors by Quantitative Real-time PCR qPCR

Quantitative Real Time polymerase chain reaction (qPCR) is a rapid and sensitive method to investigate the expression levels of various microRNA (miRNA) molecules in tumor samples. Using this method expression of hundreds of different miRNA molecules can be amplified, quantified, and analyzed from the same cDNA template.

MicroRNAs (miRNAs) are single-stranded, 18&#x2013;24 nucleotide long, non-coding RNA molecules. They are involved in virtually every cellular process ...

In vitro Organoid Culture of Primary Mouse Colon Tumors

Several human and murine colon cancer cell lines have been established, physiologic integrity of colon tumors such as multiple cell layers, basal-apical polarity, ability to differentiate, and anoikis are not maintained in colon cancer derived cell lines. The present study demonstrates a method for culturing primary mouse colon tumor organoids adapted from Sato T et al. 1, which retains important physiologic features of colon tumors. This method consists of mouse colon tumor tissue collection, adjacent normal colon epithelium dissociation, colon tumor cells digestion into single cells, embedding colon tumor cells into matrigel, and selective culture based on the principle that tumor cells maintain growth on limiting nutrient conditions compared to normal epithelial cells.
The primary tumor organoids if isolated from genetically modified mice provide a very useful system to assess tumor autonomous function of specific genes. Moreover, the tumor organoids are amenable to genetic manipulation by virus meditated gene delivery; therefore signaling pathways involved in the colon tumorigenesis could also be extensively investigated by overexpression or knockdown. Primary tumor organoids culture provides a physiologic relevant and feasible means to study the mechanisms and therapeutic modalities for colon tumorigenesis.

In vitro Organoid Culture of Primary Mouse Colon Tumors

A simple method to establish primary murine colon tumor organoid is described. This method utilizes the feature that colon tumor cells survive and grow into organoids in media containing limited growth factors, whereas normal colon epithelial do not.

Several  human and murine colon cancer cell lines have been established, physiologic  integrity of colon tumors such as multiple cell layers, basal-apical ...

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

Flexible Colonoscopy in Mice to Evaluate the Severity of Colitis and Colorectal Tumors Using a Validated Endoscopic Scoring System

The use of modern endoscopy for research purposes has greatly facilitated our understanding of gastrointestinal pathologies. In particular, experimental endoscopy has been highly useful for studies that require repeated assessments in a single laboratory animal, such as those evaluating mechanisms of chronic inflammatory bowel disease and the progression of colorectal cancer. However, the methods used across studies are highly variable. At least three endoscopic scoring systems have been published for murine colitis and published protocols for the assessment of colorectal tumors fail to address the presence of concomitant colonic inflammation. This study develops and validates a reproducible endoscopic scoring system that integrates evaluation of both inflammation and tumors simultaneously. This novel scoring system has three major components: 1) assessment of the extent and severity of colorectal inflammation (based on perianal findings, transparency of the wall, mucosal bleeding, and focal lesions), 2) quantitative recording of tumor lesions (grid map and bar graph), and 3) numerical sorting of clinical cases by their pathological and research relevance based on decimal units with assigned categories of observed lesions and endoscopic complications (decimal identifiers). The video and manuscript presented herein were prepared, following IACUC-approved protocols, to allow investigators to score their own experimental mice using a well-validated and highly reproducible endoscopic methodology, with the system option to differentiate distal from proximal endoscopic colitis (D-PECS).

Over the past few years, new generation endoscopes have emerged as important diagnostic research aids for evaluating murine colitis and colorectal tumors. We present herein a detailed protocol for endoscopic assessment of inflammation and colorectal tumors in mice, as well as a novel scoring system that uses decimal identifiers to document the endoscopic severity of colitis and colorectal tumors.

The use of modern endoscopy for research purposes has greatly facilitated our understanding of gastrointestinal pathologies. In particular, experimental ...

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

MicroRNAs (miRNAs) constitute a potent layer of gene regulation by guiding RISC to target sites located on mRNAs and, consequently, by modulating their translational repression. Changes in miRNA expression have been shown to be involved in the development of all major complex diseases. Furthermore, recent findings showed that miRNAs can be secreted to the extracellular environment and enter the bloodstream and other body fluids where they can circulate with high stability. The function of such circulating miRNAs remains largely elusive, but systematic high throughput approaches, such as miRNA profiling arrays, have lead to the identification of miRNA signatures in several pathological conditions, including neurodegenerative disorders and several types of cancers. In this context, the identification of miRNA expression profile in the cerebrospinal fluid, as reported in our recent study, makes miRNAs attractive candidates for biomarker analysis.
There are several tools available for profiling microRNAs, such as microarrays, quantitative real-time PCR (qPCR), and deep sequencing. Here, we describe a sensitive method to profile microRNAs in cerebrospinal fluids by quantitative real-time PCR. We used the Exiqon microRNA ready-to-use PCR human panels I and II V2.R, which allows detection of 742 unique human microRNAs. We performed the arrays in triplicate runs and we processed and analyzed data using the GenEx Professional 5 software.
Using this protocol, we have successfully profiled microRNAs in various types of cell lines and primary cells, CSF, plasma, and formalin-fixed paraffin-embedded tissues.

We describe a protocol of real time PCR to profile microRNAs in the cerebrospinal fluid (CSF). With the exception of RNA extraction protocols, the procedure can be extended to RNA extracted from other body fluids, cultured cells, or tissue specimens.

MicroRNAs (miRNAs) constitute a potent layer of gene regulation by guiding RISC to target sites located on mRNAs and, consequently, by modulating their ...

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo monitoring of physiopathological processes labeled with a fluorescent marker. Mouse models (brain tumor and arthritis) were used to evaluate the usefulness of this method. Saposin C (SapC)-dioleoylphosphatidylserine (DOPS) nanovesicles tagged with CellVue Maroon (CVM) fluorophore were administered intravenously. Animals were then placed in the rotational holder (MARS) of the in vivo imaging system. Images were acquired in 10&#176; steps over 380&#176;. A rectangular&#160;region of interest (ROI) was placed across the full image width at the model disease site. Within the ROI, and for every image, mean fluorescence intensity was computed after background subtraction. In the mouse models studied, the labeled nanovesicles were taken up in both the orthotopic and transgenic brain tumors, and in the arthritic sites (toes and ankles). Curve analysis of the multi angle image ROIs determined the angle with the highest signal. Thus, the optimal angle for imaging each disease site was characterized. The MAROI method applied to imaging of fluorescent compounds is a noninvasive, economical, and precise tool for in vivo quantitative analysis of the disease states in the described mouse models.

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo quantitation of a fluorescent marker delivered by saposin C (SapC)-dioleoylphosphatidylserine (DOPS) nanovesicles. Employing mouse models of cancer and arthritis, we demonstrate how the MAROI signal curve analysis can be used for the precise mapping and biological characterization of disease processes.

We describe a multi-angle rotational optical imaging (MAROI) system for in vivo monitoring of physiopathological processes labeled with a fluorescent ...

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.

In-depth analyses of cancer cell proteomes facilitate identification of novel drug targets and diagnostic biomarkers. We describe an experimental workflow for quantitative analysis of (phospho-)proteomes in cancer cell subpopulations derived from liquid and solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry.

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic ...

Spatial Measurements of Perfusion, Interstitial Fluid Pressure and Liposomes Accumulation in Solid Tumors

The heterogeneous intra-tumoral accumulation of liposomes is a critical determinant of their efficacy. Both the chaotic tumor microcirculation and elevated IFP are linked to the heterogeneous intra-tumoral distribution of nanotechnology-based drug delivery systems such as liposomes. In the present study, the relationship between tumor microcirculation, elevated IFP, and accumulation of nanoparticles was investigated through in vivo experimentation. This was accomplished by evaluation of the tumor microcirculation using dynamic contrast enhanced computed tomography (DCE-CT) and measurement of tumor IFP using a novel image-guided robotic needle placement system connected to the micro-CT scanner. The intra-tumoral accumulation of liposomes was determined by CT image-based assessment of a nanoparticle liposomal formulation that stably encapsulate the contrast agent iohexol (CT-liposomes). CT imaging allowed for co-localization of the spatial distribution of tumor hemodynamics, IFP and CT-liposome accumulation in an individual subcutaneous xenograft mouse model of breast cancer. Measurements led to the discovery that perfusion and plasma volume fraction are strong mediators of the intra-tumoral distribution of liposomes. Furthermore, the results suggest that IFP plays an indirect role in mediating liposome distribution through modulating blood flow.

The heterogeneous intra-tumoral accumulation of liposomes has been linked to an abnormal tumor microenvironment. Herein methods are presented to measure tumor microcirculation by perfusion imaging and elevated interstitial fluid pressure (IFP) using an image-guided robotic system. Measurements are compared to the intra-tumoral accumulation of liposomes, determined using volumetric micro-CT imaging.

The heterogeneous intra-tumoral accumulation of liposomes is a critical determinant of their efficacy. Both the chaotic tumor microcirculation and ...

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Three-dimensional (3D) printing technologies offer the possibility of visualizing patient-specific pathologies in a physical model of correct dimensions. The model can be used for planning and simulating critical steps of a surgical approach. Therefore, it is important that anatomical structures such as blood vessels inside a tumor can be printed to be colored not only on their surface, but throughout their whole volume. During simulation this allows for the removal of certain parts (e.g., with a high-speed drill) and revealing internally located structures of a different color. Thus, diagnostic information from various imaging modalities (e.g., CT, MRI) can be combined in a single compact and tangible object.
However, preparation and printing of such a fully colored anatomical model remains a difficult task. Therefore, a step-by-step guide is provided, demonstrating the fusion of different cross-sectional imaging data sets, segmentation of anatomical structures, and creation of a virtual model. In a second step the virtual model is printed with volumetrically colored anatomical structures using a plaster-based color 3D binder jetting technique. This method allows highly accurate reproduction of patient-specific anatomy as shown in a series of 3D-printed petrous apex chondrosarcomas. Furthermore, the models created can be cut and drilled, revealing internal structures that allow for simulation of surgical procedures.

The protocol describes the fabrication of fully colored three-dimensional prints of patient-specific, anatomical skull models to be used for surgical simulation. The crucial steps of combining different imaging modalities, image segmentation, three-dimensional model extraction, and production of the prints are explained.

Three-dimensional (3D) printing technologies offer the possibility of visualizing patient-specific pathologies in a physical model of correct dimensions. ...