Name: quantitative mass spectrometric profiling of cancer-cell proteomes derived from liquid and solid tumors
Uploaded: 2015-02-27T13:00:00
Duration: 8 min 8 s
Description: Watch this Scientific Journal Video about Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors at JoVE.com

The authors thank Uwe Plessmann, Monika Raabe und Silvia M&#252;nch for technical support.

All experiments on human tissue or blood samples must be approved by an ethics committee and conducted according to any guidelines given in the ethics vote.
1. Cellular Enrichment
<ol>
	<li>Liquid tumors/Fluorescence-activated cell sorting.
	<ol>
		<li>Isolate mononuclear cells from bone marrow or blood sample by performing erythrocyte lysis using ammonium chloride or by Ficoll density-gradient centrifugation.
		<ol>
			<li>For erythrocyte lysis, combine 1 ml of sample with 4 ml of ammonium chloride solution. Incubate the solution on ice for 5 - 10 min. Add 40 ml of phosphate buffered saline (PBS) + 2% fetal calf serum (FCS) and centrifuge for 5 min at 400 x g and 4&#160;&#176;C.</li>
			<li>For Ficoll gradient centrifugation, layer 10 ml of sample on 15 ml of Ficoll-Hypaque to form 2 distinct layers (be careful not to disturb the layers). Centrifuge at 400 x g for 30 min at RT (15 - 25 &#176;C) carefully and harvest the mononuclear cells that collect at the interphase.</li>
			<li>Spin down the cells and take them up in PBS + 2% FCS. Centrifuge at 400 x g for 5 min and take up in 1 ml PBS + 2% FCS. Take one aliquot as negative control and one aliquot for each fluorescent dye used in the final stain.</li>
		</ol>
		</li>
		<li>Incubate with fluorescence-labeled antibodies useful to isolate the leukemic cell population in dilution according to manufacturer&#8217;s instructions on ice for 30 min. Also stain the aliquots used for compensation with each antibody individually. Wash twice with PBS + 2% FCS. 
		NOTE: The antibodies used were mouse anti-human against CD117-PE (Clone 95C3), CD34-FITC (Clone 8G12) and CD33-PE (Clone P67.6).</li>
		<li>Filter cells through 35 &#181;m cell strainer, ideally using tubes with cell strainer integrated in cap.</li>
		<li>Add cell viability stain (e.g., 7-AAD). Sort cells using an appropriate cell sorter11 (Figure 2). Collect cells in Iscove&#39;s Modified Dulbecco&#39;s Medium (IMDM) containing 10% FCS.</li>
	</ol>
	</li>
	<li>Solid tumors/Laser-capture microdissection.
	<ol>
		<li>For the described experiments, FFPE samples from lung cancer specimen were used. For this purpose lung cancer tissue was immediately fixed in 4% buffered formalin after surgical resection and pieces of about 3 x 2 x 1 cm where embedded in paraffin for further analysis.</li>
		<li>Cut sections of 5 - 10 &#181;m thickness from the formalin-fixed paraffin-embedded (FFPE) sample with a microtome. Mount sections on film-covered membrane slides and dry at 37 &#176;C for 1 hr. Deparaffinize and rehydrate the mounted sections by successive incubation in xylene, absolute ethanol, 70% and water, each for 1 min.</li>
		<li>Stain the sections with hematoxylin for 20 sec and then rinse with tap water. Collect the cell population of interest by using a laser-capture microdissection system (also see 12).</li>
	</ol>
	</li>
</ol>
2. Protein Extraction
<ol>
	<li>Liquid tumors.
	<ol>
		<li>Centrifuge the cell suspension from step 1.1.5 at 400 x g, 4&#160;&#176;C for 4 min and discard the supernatant. Wash twice with 500 &#181;l of cold PBS and centrifugation for 5 min at 400 x g and 4&#160;&#176;C</li>
		<li>Add 40 &#181;l lysis buffer per 106 cells and incubate for 15 min on ice (105 cells (approx. 10 &#181;g total protein) should be the minimum cell number). Centrifuge the lysate at 14,000 x g, 4&#176;&#160;C for 10 min and transfer the supernatant (cleared cellular lysate) to a new reaction tube. Discard the pellet.</li>
	</ol>
	</li>
	<li>Solid tumors.
	<ol>
		<li>Add 60 &#181;l of tissue lysis buffer to the microdissected tissue and incubate for 15 min on ice, collect the fluid by short centrifugation and transfer the suspension to a new reaction tube. Sonicate the lysate on ice for 3 min.</li>
		<li>Add 15 &#181;l of 20% sodium dodecyl sulfate (SDS) to a reach a final SDS concentration of 4%. Incubate the microdissected tissue at 99 &#176;C in a heating block for 1 hr and agitated at 600&#160;rpm. Centrifuge the lysate at 16,000 x g, 18 &#176;C for 10 min and transfer the supernatant to a new tube.</li>
	</ol>
	</li>
</ol>
3. Establishment of a SILAC Spike-in Quantification Standard (Super-SILAC Standard)
NOTE: The quantification standard consists of a SILAC-labeled protein mixture derived from 4 - 6 cells lines that match the tumor type of interest. To achieve the maximum overlap between the SILAC-labeled reference proteome and the tumor-derived proteome, a principal component analysis needs to be performed before cell lines are selected for the quantification standard14.
<ol>
	<li>Principal component analysis (PCA).
	<ol>
		<li>To determine the protein expression patterns of cell lines, cultivate about ten different cell lines in appropriate cell culture media. Lyse the cells as described in step 2.1. Prepare cellular lysates for mass spectrometry as described in step 6.1.</li>
		<li>Analyze the protein expression pattern of each cell line on a high-resolution, liquid chromatography-coupled mass spectrometer (nanoLC-MS/MS).</li>
		<li>Analyze the resulting raw data using a free ware MaxQuant and perform a principal-component analysis using the associated software such as Perseus16,17.</li>
		<li>Select 4 - 6 cell lines that show the greatest diversity between their protein-expression profiles and use them to generate the Super-SILAC spike-in standard.</li>
	</ol>
	</li>
	<li>SILAC-labeling and label check.
	<ol>
		<li>Cultivate the selected cell lines for at least five cell cycles in appropriate SILAC cell culture medium, in which arginine and lysine are labeled with stable isotopes of carbon and nitrogen (SILAC medium)4.</li>
		<li>Lyse the cells as described in step 3.1 and prepare cellular lysates for mass spectrometry as described in step 6.1. Measure the incorporation efficiency of SILAC labeling by nanoLC-MS/MS. Analyze the resulting raw MS data with MaxQuant and determine the efficiency of SILAC-labeling. This is achieved by counting the numbers of peptides identified in their labeled (heavy) and their endogenous forms (light), and calculating the ratio (heavy)/(heavy+light). Labeling efficiency should exceed 98%.</li>
	</ol>
	</li>
	<li>Combination of SILAC proteomes and validation.
	<ol>
		<li>Cultivate and expand selected cell lines according to manufactures instructions in appropriate SILAC medium. Lyse the cells as described in step 2.1 for liquid tumors or as described in step 3.2 for solid tumors and determine protein concentration for each cellular lysate.</li>
		<li>Mix equimolar protein amounts of each cell line and divide the mixture into aliquot. Snap-freeze the aliquots and store at -80&#176;C until measurement. For one experiment you need 20 - 50 &#181;g of Super-SILAC standard. Take care to prepare the standard in excess as changing the standard within a series of experiments should be avoided.</li>
	</ol>
	</li>
</ol>
4. Measurement of Protein Concentration and Spike-in
<ol>
	<li>Liquid tumors.
	<ol>
		<li>Using protein quantitation assay determine the protein concentrations of the respective cellular lysates and the Super-SILAC standard.</li>
		<li>Mix equal amounts of cleared cellular lysate and the Super-SILAC standard and subsequently add lithium dodecyl sulfate (LDS) buffer (25% of the sample volume) and reducing agent (10% of the sample volume).</li>
		<li>Heat the resulting solution in a heating block at 72 &#176;C for 10 min. Optionally, store the resulting denatured proteins at -80&#176;C.</li>
	</ol>
	</li>
	<li>Solid tumors.
	<ol>
		<li>For protein concentration measurement on a plate reader, mix a standard bovine serum albumin (BSA) dilution solution series, the lysate and the appropriate Super-SILAC standard with an commercially available protein assay in a 96-well plate, and shake for 1 min, incubate for indicated time and measure absorbance as indicated by the manufacturer. 
		NOTE: The high concentration of SDS and dithiothreitol (DTT) in the lysate is a problem for most available assays for protein concentration determination.</li>
		<li>Mix equal amounts of clarified lysate and the Super-SILAC standard with 200 &#956;l of urea in the filter unit and centrifuge at 14,000 x g for 30 min at 20 &#176;C. Do not use more than 50 &#181;l of clarified lysate and the Super-SILAC standard. Avoid temperatures below 15 &#176;C, so that urea does not crystallize out.</li>
	</ol>
	</li>
</ol>
5. Sample Separation and Protein Digest
<ol>
	<li>Liquid tumors.
	<ol>
		<li>Separate 30 - 100 &#181;g total protein per lane on a 4 - 12% gradient SDS-PAGE Gel. Stain proteins with Coomassie Blue O/N. Remove excess Coomassie stain by two subsequent washes with water.</li>
		<li>Cut each lane from the gel and divide it into 23 equal-sized slices irrespective of the pattern of gel staining. Process the gel slices separately, each in a in 0.6 ml polypropylene vial.</li>
		<li>Wash the gel slices with water and methanol/water (50:50, v/v), reduce with 10 mM DTT by incubating for 30 min at 56 &#176;C. Alkylate the gel slices with 55 mM iodoacetamide (IAA) by incubating at 60 min at RT in dark.</li>
		<li>Between sample-handling steps, wash slices with acetonitrile for 15 min and dry in a SpeedVac to remove excess solvent and improve uptake of reagent solution.</li>
		<li>Perform protease cleavage by rehydrating dried gel slices with the minimum amount of porcine trypsin solution (12.5 ng/&#181;l in 0.025 M aqueous ammonium bicarbonate) for 16 hr at 37&#176;C.</li>
		<li>Add 10 &#181;l water to gel slice and incubate for 15 min at 37 &#176;C. Add 80 &#181;l acetonitrile and incubate for 15 min at 37 &#176;C. Centrifuge at 15,800 x g, for 1 min. Collect supernatant and store in a separate 0.6 ml tube.</li>
		<li>Add 65 &#181;l of 5% formic acid solution, vortex and incubate for 15 min at 37 &#176;C. Add 65 &#181;l acetonitrile and incubate for 15 min at 37 &#176;C. Centrifuge at 15,800 x g for 1 min. Collect the supernatant and add it to the supernatant from the previous step. Evaporate the combined supernatant to dryness in a vacuum concentrator.</li>
	</ol>
	</li>
	<li>Solid tumors.
	<ol>
		<li>To remove SDS from tissue lysate use the following FASP protocol, also described as in reference15.</li>
		<li>After the first centrifugation described in step 5.2.3 add a further 200 &#956;l of 8 M urea on the filter, and further centrifuge at 14,000 x g for 20 min at 20 &#176;C. Discard the flow-through filtrate.</li>
		<li>Add 100 &#956;l IAA and mix in a thermomixer at 600 rpm for 1 min. Incubate the filter for 20 min at 20 &#176;C in the dark. Centrifuge the filter at 14,000 x g for 10 min at 20 &#176;C.</li>
		<li>Add 100 &#956;l of urea to the filter and centrifuge at 14,000 x g, for 15 min at 20 &#176;C. Repeat this step one more time.</li>
		<li>Add 100 &#956;l of NH4HCO3 to the filter and centrifuge at 14,000 x g for 10 min at 20 &#176;C. Repeat this step twice.</li>
		<li>Add 40 &#956;l NH4HCO3 + 1 &#181;l (= 0.4 &#181;g) trypsin and mix in thermomixer at 20 &#176;C for 600 rpm, 1 min. Incubate the filter O/N in a wet chamber at 37 &#176;C. Transfer the filter to new collection tubes.</li>
		<li>Centrifuge the filter at 14,000 x g for 10 min at 20 &#176;C. Add 50 &#956;l of NH4HCO3 and centrifuge the filter at 14,000 g for 10 min at 20&#176;C. Store the resulting peptides at -20&#176;C until mass spectrometric measurement.</li>
		<li>For peptide concentration measurement, dispense 50 &#181;l of the resulting flow-through and a series of appropriate dilutions of neat tryptophan into a 96-well plate. Measure tryptophan fluorescence. Convert the resulting concentration of tryptophan to peptide concentration, as 0.1 &#956;g of tryptophan corresponds to 9 &#956;g protein.</li>
	</ol>
	</li>
</ol>
6. Liquid Chromatography and Mass Spectrometric Analysis
<ol>
	<li>Redissolve peptides in 30 &#181;l loading buffer for 5 min in a sonication bath. Spin down in a centrifuge at 15,800 x g for 1 min and pipette the clear solution into an MS autosampler vial.</li>
	<li>Inject 5 &#181;l of sample per analysis using the autosampler of the nanoLC-MS/MS system. Concentrate and desalt peptides online on a reversed phase C18 pre-column (0.15 mm ID x 20 mm with 5 &#181;m pore size C18 material) mounted in either a vented column setup or a precolumn switching setup.</li>
	<li>Separate peptides on a reversed phase C18 microcolumn (0.075 mm ID x 200 mm self-packed with 3 &#956;m or smaller pore size C18 material in a self-pack nanospray column such as PicoFrit, using a 90 min gradient of 5&#62;35% acetonitrile vs. 0.1% aqueous formic acid at 300 nl/min. Transfer eluent into a hybrid quadrupole/orbitrap mass spectrometer via a nanospray ion source.</li>
	<li>Analyze peptides using a Top15 data dependent acquisition method (MS m/z range 350 - 1600, resolution target 70.000 FWHM, AGC target 1 x 106, max fill time 60 msec. MS/MS start mass 100, resolution target 17.500 FWHM, AGC target 2 x 105, max fill time 60 msec. MS/MS threshold 3 x 104, include charge states 2 - 5, Normalized Collision Energy NCE 25%, dynamic exclusion 15 sec).</li>
</ol>
7. Data Analysis
<ol>
	<li>For data analysis use the freely available MaxQuant16 software. A detailed protocol for bioinformatic data analysis is described in16,17. For analysis, combine raw data files from all slices from an SDS-PAGE lane into a single experiment17.</li>
</ol>

<ol>
	<li>Walther, T. C., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+spectrometry-based+proteomics+in+cell+biology.">Mass spectrometry-based proteomics in cell biology.</a> J Cell Biol. 190 (4), 491-500 (2010).</li><li>Lenz, C., Urlaub, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Separation+methodology+to+improve+proteome+coverage+depth.">Separation methodology to improve proteome coverage depth.</a> Expert Rev Proteomics. 11 (4), 409-414 (2014).</li><li>Olsen, J. V., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Status+of+large-scale+analysis+of+post-translational+modifications+by+mass+spectrometry.">Status of large-scale analysis of post-translational modifications by mass spectrometry.</a> Mol Cell Proteomics. 12 (12), 3444-3452 (2013).</li><li>Ong, S. E., 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=Stable+isotope+labeling+by+amino+acids+in+cell+culture,+SILAC,+as+a+simple+and+accurate+approach+to+expression+proteomics.">Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.</a> Mol Cell Proteomics. 1 (5), 376-386 (2002).</li><li>Jimenez, C. R., Verheul, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+spectrometry-based+proteomics:+from+cancer+biology+to+protein+biomarkers,+drug+targets,+and+clinical+applications.">Mass spectrometry-based proteomics: from cancer biology to protein biomarkers, drug targets, and clinical applications.</a> Am Soc Clin Oncol Educ Book. , e504-e510 (2014).</li><li>Tang, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+cancer+stem+cell+heterogeneity+and+plasticity.">Understanding cancer stem cell heterogeneity and plasticity.</a> Cell Res. 22 (3), 457-472 (2012).</li><li>Evans, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+insight+into+iTRAQ:+where+do+we+stand+now.">An insight into iTRAQ: where do we stand now.</a> Anal Bioanal Chem. 404 (4), 1011-1027 (2012).</li><li>Ostasiewicz, P., Zielinska, D. F., Mann, M., Wisniewski, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteome,+phosphoproteome,+and+N-glycoproteome+are+quantitatively+preserved+in+formalin-fixed+paraffin-embedded+tissue+and+analyzable+by+high-resolution+mass+spectrometry.">Proteome, phosphoproteome, and N-glycoproteome are quantitatively preserved in formalin-fixed paraffin-embedded tissue and analyzable by high-resolution mass spectrometry.</a> J Proteome Res. 9 (7), 3688-3700 (2010).</li><li>Malmstr&#246;m, J., Picotti, P., Aebersold, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Perspectives+of+targeted+mass+spectrometry+for+protein+biomarker+verification.">Perspectives of targeted mass spectrometry for protein biomarker verification.</a> Curr Opin Chem Biol. 13 (5-6), 518-525 (2009).</li><li>Gillet, L. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeted+data+extraction+of+the+MS/MS+spectra+generated+by+data-independent+acquisition:+a+new+concept+for+consistent+and+accurate+proteome+analysis.">Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.</a> Mol Cell Proteomics. 11 (6), (2012).</li><li>Basu, S., Campbell, H. M., Dittel, B. N., Ray, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Purification+of+specific+cell+population+by+fluorescence+activated+cell+sorting+(FACS).">Purification of specific cell population by fluorescence activated cell sorting (FACS).</a> J Vis Exp. 41, (2010).</li><li>Edwards, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laser+capture+microdissection+of+mammalian+tissue.">Laser capture microdissection of mammalian tissue.</a> J Vis Exp. 8, 309 (2007).</li><li>Liu, N. Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteomics+pipeline+for+biomarker+discovery+of+laser+capture+microdissected+breast+cancer+tissue.">Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue.</a> J Mammary Gland Biol Neoplasia. 17 (2), 155-164 (2012).</li><li>Geiger, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+stable+isotope+labeling+by+amino+acids+in+cell+culture+as+a+spike-in+standard+in+quantitative+proteomics.">Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics.</a> Nat Protoc. 6 (2), 147-157 (2011).</li><li>Wisniewski, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteomic+sample+preparation+from+formalin+fixed+and+paraffin+embedded+tissue.">Proteomic sample preparation from formalin fixed and paraffin embedded tissue.</a> J Vis Exp. (79), (2013).</li><li>Cox, J., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MaxQuant+enables+high+peptide+identification+rates,+individualized+p.p.b.-range+mass+accuracies+and+proteome-wide+protein+quantification.">MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.</a> Nat Biotechnol. 26 (12), 1367-1372 (2008).</li><li>Cox, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+practical+guide+to+the+MaxQuant+computational+platform+for+SILAC-based+quantitative+proteomics.">A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics.</a> Nat Protoc. 4 (5), 698-705 (2009).</li><li>Rappsilber, J., Mann, M., Ishihama, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protocol+for+micro-purification,+enrichment,+pre-fractionation+and+storage+of+peptides+for+proteomics+using+StageTips.">Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips.</a> Nat Protoc. 2 (8), 1896-1906 (2007).</li><li>Sarvi, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CD133++cancer+stem-like+cells+in+small+cell+lung+cancer+are+highly+tumorigenic+and+chemoresistant+but+sensitive+to+a+novel+neuropeptide+antagonist.">CD133+ cancer stem-like cells in small cell lung cancer are highly tumorigenic and chemoresistant but sensitive to a novel neuropeptide antagonist.</a> Cancer Res. 74 (5), 1554-1565 (2014).</li><li>Shlush, L. I., 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=Identification+of+pre-leukaemic+haematopoietic+stem+cells+in+acute+leukaemia.">Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia.</a> Nature. 506 (7488), 328-333 (2014).</li><li>Schaab, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+phosphoproteome+analysis+of+human+bone+marrow+reveals+predictive+phosphorylation+markers+for+the+treatment+of+acute+myeloid+leukemia+with+quizartinib.">Global phosphoproteome analysis of human bone marrow reveals predictive phosphorylation markers for the treatment of acute myeloid leukemia with quizartinib.</a> Leukemia. 28 (3), 716-719 (2014).</li><li>H&#252;ttenhain, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducible+quantification+of+cancer-associated+proteins+in+body+fluids+using+targeted+proteomics.">Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics.</a> Sci Transl Med. 4 (142), 142ra94 (2012).</li><li>Burgess, M. 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=Simplified+and+efficient+quantification+of+low-abundance+proteins+at+very+high+multiplex+via+targeted+mass+spectrometry.">Simplified and efficient quantification of low-abundance proteins at very high multiplex via targeted mass spectrometry.</a> Mol Cell Proteomics. 13 (4), 1137-1149 (2014).</li><li>Boja, E. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analytical+Validation+Considerations+of+Multiplex+Mass+Spectrometry-based+Proteomic+Platforms+for+Measuring+Protein+Biomarkers.">Analytical Validation Considerations of Multiplex Mass Spectrometry-based Proteomic Platforms for Measuring Protein Biomarkers.</a> J Proteome Res. , (2014).</li></ol>

The authors have no conflict of interest or other issues to disclose.

Mass spectrometric profiling of patient-derived cancer-cell proteomes is needed for the discovery of new diagnostic/predictive biomarkers as well as to give a better understanding of cancer-cell biology, which might in turn lead to the identification of new potential drug targets. However, such mass spectrometric analyses are highly challenging, in particular because various pre-analytical issues have to be solved, if one is to obtain robust and biologically relevant results.
The experimental workflow described here allows for quantitative proteomic characterization of proteomes derived from cellular subpopulations of both liquid and solid tumors. The initial enrichment of tumor cells by either FACS-based cell-sortingor microdissection is needed to avoid contamination by cells of the tumor microenvironment. Furthermore, these techniques allow one to isolate cellular subpopulations of interest. Recent cell-biological studies have demonstrated that certain cellular subpopulations have tumor-initiating properties and are thus highly relevant for cancer pathogenesis19,20. As mass spectrometry has become more sensitive in recent years, quantitative proteomic analyses are feasible for the small amounts of protein that can be derived from a few thousand cells, making it possible to focus on functionally relevant cell populations.
The set-up presented here can be used to identify and validate novel diagnostic biomarkers in FFPE samples. Hence, it promises to bea useful tool for the improvement of clinical diagnostics, as to date there is still a lack of molecular biomarkers in adequate number and quality for many cancer types. Important examples of difficult differential diagnoses, for which biomarkers are lacking, are the discrimination between primary lung cancer from metastases in the lung, intrapancreatic cholangiocellular carcinoma and pancreatic adenocarcinoma, as well as differentiation of benign neurofibroma from highly malignant peripheral nerve-sheath tumors. Moreover, we and others have shown that quantitative elucidation of proteomic signatures can be useful in studying cancer cell biology in general, and for revealing predictive biomarkers of therapeutic response in cancer patients21.
Two current drawbacks of the method presented here are the requirement for extensive manual sample processing and the demand on nanoLC-MS/MS acquisition time. While the former can be addressed by moving sample preparation to e.g., 96-well formats and using robotic processing, the latter will require a change in mass spectrometric acquisition strategy. Once subsets of target proteins have been identified that can be associated with e.g., tumor classification, we envisage the design of targeted mass spectrometry methods that provide quantitative readouts for these subsets with a greatly reduced separation effort, and therefore with a correspondingly reduced acquisition time. If the required acquisition time required could be reduced from 24 - 36 hr (liquid tumors) or 3 hr (solid tumors) to e.g., 1 hr using targeted mass spectrometry and a simple one-dimensional separation of peptides, then the resulting gain in throughput could be used to increase significantly the numbers of biological and technical replicates examined, with corresponding improvements in the significance of quantification results. Targeted mass spectrometric approaches have already been demonstrated to be a suitable tool for the verification of cancer-associated protein biomarker-candidates22, and have been developed to a point where they show promise for validation or even as a potential tool for routine clinical use23,24.

Mass spectrometry-based proteomics has emerged and is now a widely used discipline in cell biology and translational biomedical research. Technical advances in the field have made it possible to study complex cellular processes in cell lines and animal models, as thousands of proteins can be identified in a single mass spectrometric experiment1,2. Similar progress has been made for the analysis of many posttranslational modifications such as phosphorylation or ubiquitination, although this usually requires bespoke workflows for enrichment and data analysis for each type of modification2,3. Moreover, the involvement of chemical and metabolic labeling strategies, including SILAC, enables accurate relative quantification of proteins and PTMs, making this method particularly attractive for the discovery of basic cellular processes, diagnostic biomarkers and potential drug targets in human cancer4.
However, several challenges have to be overcome with regard to proteomic analysis of primary human cancers5. First of all, human cancer samples often show a high degree of cellular heterogeneity that is due to the presence of various cell types belonging to the tumor microenvironment, including immune cells and fibroblasts. Secondly, clonal evolution leads to genetic diversity within the tumors themselves, resulting in the existence of several cellular subpopulations with distinct functional properties. According to the current concept of a few cancer stem cells being the driving forces behind cancer development and progression, proteomic analysis of such (functionally highly relevant) cellular subpopulations is expected to be of major importance for a better understanding of oncogenic mechanisms relevant for clinical application6. Thirdly, quantitative protein expression profiles of large sets of samples are often required for the identification of robust clinical biomarkers; this cannot be accomplished by chemical labeling such as iTRAQ7, while metabolic labeling strategies &#8211; relying, as they do, on cellular proliferation4&#8211; are likewise inapplicable. Fourthly, most available solid tumor samples are formalin-fixed, which complicates mass spectrometric proteome analysis due to the formation of protein crosslinks8. Finally, most existing proteomics workflows require significant amounts of sample processing and data acquisition, rendering the analysis of sample numbers relevant for clinical research difficult, and calling for new workflow paradigms9,10.
To address these obstacles we developed an experimental setup that combines either flow-cytometric cell sorting11 or laser-capture microdissection12,13 for cellular enrichment with a Super-SILAC14 strategy to introduce a global internal standard for comprehensive mass spectrometric analysis. By using the method described here, it is possible to quantify up to 8,000 proteins in single human tumor samples derived from either liquid or solid cancers.

<table border="0" cellpadding="0" cellspacing="0" width="943">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:444px;">Name of Material/ Equipment</td>
			<td style="width:181px;">Company</td>
			<td style="width:151px;">Catalog Number</td>
			<td style="width:167px;">Comments/Description</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">660 nm Kit</td>
			<td style="width:181px;">Thermo scientific</td>
			<td style="width:151px;">22662</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Cell culture medium depleted of arginine and lysine</td>
			<td style="width:181px;">Thermo Scientific</td>
			<td style="width:151px;">88421</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Coomassie Brilliant Blue R-250 staining solution</td>
			<td style="width:181px;">Bio Rad</td>
			<td style="width:151px;">161-0436</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Dialyzed fetal calf serum (FCS)</td>
			<td style="width:181px;">PAA</td>
			<td style="width:151px;">A15-107</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Diffuser caps for microdissection</td>
			<td style="width:181px;">MMI</td>
			<td style="width:151px;">50202</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">FACS-sorter</td>
			<td style="width:181px;">BD</td>
			<td style="width:151px;">FACSAria III</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Ionic Detergent Compatibility Reagent</td>
			<td style="width:181px;">Thermo scientific</td>
			<td>22663</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Laser-capture microdissector</td>
			<td style="width:181px;">MMI&#160;</td>
			<td style="width:151px;">cell cut plus</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">LDS buffer&#160;</td>
			<td style="width:181px;">Life Technologies</td>
			<td style="width:151px;">NP0009</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Membrane slides for microdissection</td>
			<td style="width:181px;">MMI</td>
			<td style="width:151px;">50103</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Microcon YM-30</td>
			<td style="width:181px;">Millipore</td>
			<td style="width:151px;">MRCF0R030</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">NuPAGE 4-12% Bis-Tris Mini Gels</td>
			<td style="width:181px;">Life Technologies</td>
			<td style="width:151px;">NP0335PK2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:444px;">Picofrit Self-Pack Columns</td>
			<td style="width:181px;">New Objective</td>
			<td style="width:151px;">PF360-75-15-N-5</td>
			<td>Mass Spectrometry Column/Emitter</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Reducing agent</td>
			<td style="width:181px;">Life Technologies</td>
			<td>NP0007</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:444px;">Reprosil-Pur LC/MS/MS Column stationary phase</td>
			<td style="width:181px;">Dr. Maisch</td>
			<td style="width:151px;">120 C18-AQ, 3 &#181;m</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:444px;">Reprosil-Pur LC/MS/MS Precolumn stationary phase</td>
			<td style="width:181px;">Dr. Maisch</td>
			<td style="width:151px;">120 C18-AQ, 5 &#181;m</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">SILAC-labeled arginine</td>
			<td style="width:181px;">Eurisotop</td>
			<td style="width:151px;">CLM-2265-H-0.1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">SILAC-labeled lysine</td>
			<td style="width:181px;">Eurisotop</td>
			<td style="width:151px;">DLM-2640-0.25</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Trypsin, NB Sequencing Grade</td>
			<td style="width:181px;">Serva</td>
			<td style="width:151px;">3728301</td>
			<td>for in-gel digests</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Trypsin, Sequencing Grade</td>
			<td style="width:181px;">Promega</td>
			<td style="width:151px;">V5111</td>
			<td>for in-solution digests</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Buffer and solutions</td>
			<td style="width:181px;"></td>
			<td style="width:151px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td colspan="4" height="21" style="height:21px;">Cell lysis buffer: 150 mM NaCl, 50 mM Tris/HCl pH 7.8, 5mM NaF,&#160; 0,5% NP40, 0.1% laurylmaltoside, Roche complete protease inhibitor, 1 mM Na3VO4</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:444px;">Tissue lysis buffer: 100 mM Tris/HCl pH 7.8, 0.1 M DTT&#160;</td>
			<td style="width:181px;"></td>
			<td style="width:151px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Urea: 8 M urea in 0.1 M Tris-HCl, pH 8.5</td>
			<td style="width:181px;"></td>
			<td style="width:151px;"></td>
			<td>for FASP-protocoll</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">IAA: 0.05 M iodoacetamide, 8 M urea, 0.1 M Tris-HCl, pH 8.5</td>
			<td style="width:181px;"></td>
			<td style="width:151px;"></td>
			<td>for FASP-protocoll</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">0.05 M NH4HCO3</td>
			<td style="width:181px;"></td>
			<td style="width:151px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">10 mM dithiothreitol (DTT) in 0.1 M ammonium bicarbonate</td>
			<td style="width:181px;"></td>
			<td style="width:151px;"></td>
			<td>for in-gel digest</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">55 mM iodoacetamide (IAA) in 0.1 mM ammonium bicarbonate</td>
			<td style="width:181px;"></td>
			<td style="width:151px;"></td>
			<td>for in-gel digest</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">&#160;5% aqueous formic acid.</td>
			<td style="width:181px;"></td>
			<td style="width:151px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

Proteomic profiling of liquid and solid tumors from patients is a promising approach for the discovery of new diagnostic and predictive biomarkers. However, the sample preparation procedure and the mass spectrometric analysis are challenging, owing to the complexity of the samples and the need for accurate protein quantification in large sets of samples. The experimental procedure described above starts with the isolation of cells of interest, either by fluorescence-activated cell sorting or by laser-capture microdissection.
For this purpose, cells from patient-derived bone marrow aspirates or blood samples are stained with fluorescence-labeled antibodies against defined surface markers of interest before FACS-based sorting and cell lysis.
To enrich cellular subsets from tissue sections, these first have to be mounted on appropriate slides to allow for laser-capture microdissection. For this purpose mount the tissue sections on appropriate membrane slides and use a laser-capture microdissector according to manufacturer&#8217;s instructions. Selecting the areas and cells of interest requires appropriate knowledge about the histomorphology of healthy and neoplastic tissue. The extracted cells are then transferred into an appropriate collection tube. Initial experiments should be performed in order to define the amount of tissue necessary for extraction of sufficient amounts of protein (minimum 10 &#181;g total protein) for every new tissue of interest. Each microdissected sample is treated with 60 &#181;l of tissue lysis buffer. For efficient lysis, cells from tissue samples have to be sonicated for 3 min and after the addition of 15 &#181;l of SDS samples are incubated in a thermomixer at 99 &#176;C for 1 hr to remove formalin-induced protein crosslinks. Centrifugation will finally facilitate the collection of the cleared cellular lysate.
For every disease of interest (liquid and solid tumors) a suitable Super-SILAC quantification standard has to be established14. An appropriate standard should represent more than 90% of the proteins expressed in the samples of interest. For preparation of a quantification standard, cell lines related to the cancer type of interest should first be analyzed by mass spectrometry to determine their protein expression profiles and subsequently principal component analysis should be performed. 4 - 6 different cell lines with differential protein expression patterns should be chosen and SILAC-labeled with &#8220;heavy&#8221; arginine and lysine. Labeling efficiency should be checked by nanoLC-MS/MS and should be greater than 98%. Afterwards, cell lines should be lysed in the same way as the respective patient-derived sample and, subsequently, equimolar protein amounts of each sample and the SILAC spike-in standard should be mixed. A critical step for accurate protein quantification of clinical samples of interest is the accurate mixture of equimolar protein amounts of the patient-derived samples and the SILAC spike-in quantification standard. To determine the protein concentration of lysates derived from sorted cells, various protein quantification assays can be used. For cellular lysates from tissue-derived cells an assay is needed that can deal with high concentrations of SDS and DTT in the lysate. Protein concentrations of the patient-derived samples and the SILAC spike-in standard should be measured each time prior to combination with the respective clinical samples in order to ensure correct mixture, which is crucial in making even hundreds of samples comparable.
After the mixture of equal amounts of spike-in standard and lysate, samples obtained from liquid tumors are mixed with LDS, heated in a thermomixer at 72 &#176;C for 10 min and subsequently subjected to 1D-PAGE and in-gel digestion of the separated proteins with trypsin. Samples obtained from tissue are freed from SDS and digested with trypsin by using the FASP approach as established by Wisniewski et al.15
The tryptic peptides obtained can be quantified by measuring tryptophan fluorescence, which is of special interest for tissue samples as the amount of peptides released from the filter differs between 15% and 75% compared to the protein amounts initially loaded onto the filter. For this purpose we measure the fluorescence of tryptophan. Comparison with an appropriate tryptophan dilution series allows for the quantification of peptides, as 1.1 &#181;g of tryptophan corresponds to approximately 100 &#181;g of peptide. If necessary, samples derived especially from FASP processing can be pre-purified on either commercial or home-made stage tips packed with reversed phase-C18 (RP-C18) material before loading onto the nanoLC/MS/MS system18.
Mass-spectrometric analysis is performed on a high-resolution, high-sensitivity mass-spectrometry system. In short, peptide samples are desalted and preconcentrated on an RP-C18 precolumn, and separated on an RP-C18 analytical column coupled directly to the mass spectrometer. To achieve sufficient depth of analysis, we employ either a combination of SDS-PAGE protein prefractionation with 40 min RP-C18 gradients (for liquid tumors) or single injections with long 2 - 3 hr RP-C18 gradients (for solid tumors processed by FASP). MS spectra are acquired at a resolution of 70,000 FWHM or better, to allow accurate quantification of SILAC pairs by integration of chromatographic peak profiles. For protein identification, a Top15 data-dependent acquisition method is used to generate a large number of peptide MS/MS spectra for peptide and protein identification.
From the resulting raw data, protein identification and quantification are achieved by database searching with MaxQuant software against a UniProt Knowledgebase Human Complete Proteome sequence database17. In MaxQuant software (current version 1.5.0.25) peptides are identified from the acquired MS/MS spectra by peptide fragment-matching against spectra derived in silico from the protein sequence database. At the same time, precursor ion isotopic profiles are extracted around their chromatographic retention times, and their integrated peak areas are used for relative quantification of the light:heavy peptide pairs generated by SILAC labeling. Peptide identities and relative intensities are then assigned to the corresponding protein properties. Perseus software (current version 1.5.0.15) is then used to perform further downstream statistical evaluation of the MaxQuant processing results, including sample-to-sample comparisons, PCA and hierarchical clustering.
Using the experimental setup described we have identified and quantified up to 8,000 proteins from as little as 30 &#181;g of total protein derived from liquid tumors.
Up to 2,500 proteins from solid-tumor samples can be identified and quantified in a shotgun proteomic approach with an LC gradient of only 2 hr, allowing the analysis of hundreds of clinical samples in a relatively short time.
<img alt="Figure 1" src="/files/ftp_upload/52435/52435fig1highres.jpg" /> 
Figure 1. Experimental workflow. The main steps of cellular-subset enrichment, protein isolation, spike-in of the quantification standard and mass-spectrometric analysis are shown for liquid and solid tumors. <a href="https://www.jove.com/files/ftp_upload/52435/52435fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52435/52435fig2highres.jpg" /> 
Figure 2. Cellular enrichment strategies. FACS sorting and LCM. (A) Exemplary gating strategy depicting gating on a CD117-stained leukemic cell population acquired by a cell sorter. (B) Laser capture microdissection of solid tumor tissue. Tissue sections of 5 - 10 &#181;m thickness were mounted on film-covered membrane slides before staining. Region of interests was selected manually for laser capture microdissection. Shown are sections before microdissection with the region of interest marked yellow and after microdissection. A scale bar of 50 &#181;m is included in the figure. <a href="https://www.jove.com/files/ftp_upload/52435/52435fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52435/52435fig3highres.jpg" /> 
Figure 3. Cluster analysis of human cancer-cell proteomes. Unsupervised cluster analysis of protein expression profiles of liquid and solid tumors was performed using the computational platform Perseus. AC &#8211; Adenocarcinoma, SCC &#8211; Squamous cell carcinoma, SCC-Met &#8211; Squamous cell carcinoma metastases from head and neck cancer, R &#8211; technical replicate.

Watch this Scientific Journal Video about Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors at JoVE.com

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

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 ...

quantitative-mass-spectrometric-profiling-cancer-cell-proteomes

Research

JoVE Journal

Medicine

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 ...

Quantitative Analysis of Chromatin Proteomes in Disease

In the nucleus reside the proteomes whose functions are most intimately linked with gene regulation. Adult mammalian cardiomyocyte nuclei are unique due to the high percentage of binucleated cells,1 the predominantly heterochromatic state of the DNA, and the non-dividing nature of the cardiomyocyte which renders adult nuclei in a permanent state of interphase.2 Transcriptional regulation during development and disease have been well studied in this organ,3-5 but what remains relatively unexplored is the role played by the nuclear proteins responsible for DNA packaging and expression, and how these proteins control changes in transcriptional programs that occur during disease.6 In the developed world, heart disease is the number one cause of mortality for both men and women.7 Insight on how nuclear proteins cooperate to regulate the progression of this disease is critical for advancing the current treatment options.
Mass spectrometry is the ideal tool for addressing these questions as it allows for an unbiased annotation of the nuclear proteome and relative quantification for how the abundance of these proteins changes with disease. While there have been several proteomic studies for mammalian nuclear protein complexes,8-13 until recently14 there has been only one study examining the cardiac nuclear proteome, and it considered the entire nucleus, rather than exploring the proteome at the level of nuclear sub compartments.15 In large part, this shortage of work is due to the difficulty of isolating cardiac nuclei. Cardiac nuclei occur within a rigid and dense actin-myosin apparatus to which they are connected via multiple extensions from the endoplasmic reticulum, to the extent that myocyte contraction alters their overall shape.16 Additionally, cardiomyocytes are 40% mitochondria by volume17 which necessitates enrichment of the nucleus apart from the other organelles. Here we describe a protocol for cardiac nuclear enrichment and further fractionation into biologically-relevant compartments. Furthermore, we detail methods for label-free quantitative mass spectrometric dissection of these fractions-techniques amenable to in vivo experimentation in various animal models and organ systems where metabolic labeling is not feasible.

Advances in mass spectrometry have allowed the high throughput analysis of protein expression and modification in a host of tissues. Combined with subcellular fractionation and disease models, quantitative mass spectrometry and bioinformatics can reveal new properties in biological systems. The method described herein analyzes chromatin-associated proteins in the setting of heart disease and is readily applicable to other in vivo models of human disease.

In the nucleus reside the proteomes whose functions are most intimately linked with gene regulation. Adult mammalian cardiomyocyte nuclei are unique due ...

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).

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

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

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 ...

Method for Obtaining Primary Ovarian Cancer Cells From Solid Specimens

Reliable tools for investigating ovarian cancer initiation and progression are urgently needed. While the use of ovarian cancer cell lines remains a valuable tool for understanding ovarian cancer, their use has many limitations. These include the lack of heterogeneity and the plethora of genetic alterations associated with extended in vitro passaging.&#160;Here we describe a method that allows for rapid establishment of primary ovarian cancer cells form solid clinical specimens collected at the time of surgery. The method consists of subjecting clinical specimens to enzymatic digestion for 30 min. The isolated cell suspension is allowed to grow and can be used for downstream application including drug screening. The advantage of primary ovarian cancer cell lines over established ovarian cancer cell lines is that they are representative of the original specific clinical specimens they are derived from and can be derived from different sites whether primary or metastatic ovarian cancer.

This study describes a detailed method for isolation and characterization of primary ovarian cancer cells from solid clinical specimens. Ovarian cancer clinical specimens are subjected to enzymatic digestion to obtain viable, fibroblast-free epithelial ovarian cancer (EOC) cells highly suitable for downstream applications.

Reliable tools for investigating ovarian cancer initiation and progression are urgently needed. While the use of ovarian cancer cell lines remains a ...

Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing

The use of patient-derived xenografts for modeling cancers has provided important insight into cancer biology and drug responsiveness. However, they are time consuming, expensive, and labor intensive. To overcome these obstacles, many research groups have turned to spheroid cultures of cancer cells. While useful, tumor spheroids or aggregates do not replicate cell-matrix interactions as found in vivo. As such, three-dimensional (3D) culture approaches utilizing an extracellular matrix scaffold provide a more realistic model system for investigation. Starting from subcutaneous or intracranial xenografts, tumor tissue is dissociated into a single cell suspension akin to cancer stem cell neurospheres. These cells are then embedded into a human-derived extracellular matrix, 3D human biogel, to generate a large number of microtumors. Interestingly, microtumors can be cultured for about a month with high viability and can be used for drug response testing using standard cytotoxicity assays such as 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and live cell imaging using Calcein-AM. Moreover, they can be analyzed via immunohistochemistry or harvested for molecular profiling, such as array-based high-throughput kinomic profiling, which is detailed here as well. 3D microtumors, thus, represent a versatile high-throughput model system that can more closely replicate in vivo tumor biology than traditional approaches.

Patient-derived xenografts of glioblastoma multiforme can be miniaturized into living microtumors using 3D human biogel culture system. This in vivo-like 3D tumor assay is suitable for drug response testing and molecular profiling, including kinomic analysis.

The use of patient-derived xenografts for modeling cancers has provided important insight into cancer biology and drug responsiveness. However, they are ...

Isolation and Profiling of MicroRNA-containing Exosomes from Human Bile

Exosome research in the last three years has greatly extended the scope towards identification and characterization of biomarkers and their therapeutic uses.
Exosomes have recently been shown to contain microRNAs (miRs). MiRs themselves have arisen as valuable biomarkers for diagnostic purposes. As specimen collection in clinics and hospitals is quite variable, miRNA isolation from whole bile varies substantially. To achieve robust, accurate and reproducible miRNA profiles from collected bile samples in a simple manner required the development of a high-quality protocol to isolate and characterize exosomes from bile. The method requires several centrifugations and a filtration step with a final ultracentrifugation step to pellet the isolated exosomes. Electron microscopy, Western blots, flow cytometry and multi-parameter nanoparticle optical analysis, where available, are crucial characterization steps to validate the quality of the exosomes. For the isolation of miRNA from these exosomes, spiking the lysate with a non-specific, synthetic miRNA from a species like Caenorhabditis elegans, i.e., Cel-miR-39, is important for normalization of RNA extraction efficiency. The isolation of exosome from bile fluid following this method allows the successful miRNA profiling from bile samples stored for several years at -80 &#176;C.

Bile fluid is a valuable source of extracellular vesicles/exosomes that contain potentially important biomarkers. This protocol represents a robust method to isolate exosomes from human bile for further analyses including miRNA profiling.

Exosome research in the last three years has greatly extended the scope towards identification and characterization of biomarkers and their therapeutic ...

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.