Name: heterogeneity mapping of protein expression in tumors using quantitative immunofluorescence
Uploaded: 2011-10-25T13:00:00
Duration: 7 min 54 s
Description: Watch this Scientific Journal Video about Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence at JoVE.com

This work was supported in part by the Scottish Funding Council (Grant Number HR07005; <a href="http://www.sfc.ac.uk/">http://www.sfc.ac.uk/</a>), Medical Research Scotland (<a href="http://www.medicalresearchscotland.org.uk/">http://www.medicalresearchscotland.org.uk/</a>), the Cancer Research UK Experimental Cancer Medicine Centre (<a href="http://www.cancerresearchuk.org/">http://www.cancerresearchuk.org/</a>), and Breakthrough Breast Cancer (<a href="http://breakthrough.org.uk/">http://breakthrough.org.uk/</a>).

1. Tissue preparation
<ol class="lalpha">
 <li>Microtomy
 <ol class="lroman">
 <li>Section formalin fixed paraffin embedded tumor blocks at 4 &mu;m thickness using a rotary microtome.</li>
 <li>Place the sections onto positively charged microscopic slides.</li>
 <li>Incubate slides in a 37 &deg;C oven overnight.</li>
 </ol>
 </li>
 <li>Storage
 <ol>
 <li>Store sections in a -18 &deg;C to -25 &deg;C freezer, in order to prevent loss of antigenicity.</li>
 </ol>
 </li>
</ol>
2. Immunofluorescence of tissue sections
<ol class="lalpha">
 <li>Dewaxing and Rehydration
 <ol class="lroman">
 <li>Dewax slides in xylene twice for 5 min.</li>
 <li>Rehydrate slides in 99%, 99%, 80%, 50% of ethanol and running tap water for 2 min of each in order.</li>
 </ol>
 </li>
 <li>Antigen Retrieval
 <ol class="lroman">
 <li>Perform the heat induced antigen retrieval, using 0.15 mM Sodium Citrate, pH 6.0 buffer in a pressure cooker for 5 min in the microwave.</li>
 <li>Cool down slides for 20 min.</li>
 <li>Rinse slides in 0.05% PBST for 5 min on the rocker.</li>
 </ol>
 </li>
 <li>Blocking procedure
 <ol class="lroman">
 <li>Treat sections in 3% hydrogen peroxide for 10 min.</li>
 <li>Rinse slides in 0.05% PBST for 5 min on the rocker.</li>
 <li>Add the sections to Sequenza Immunostaining rack.</li>
 <li>Treat sections in Serum free protein block for 10 min.</li>
 </ol>
 </li>
 <li>Primary antibody incubation
 <ol class="lroman">
 <li>Incubate Primary antibody diluted to optimal dilution in Dako antibody diluent for 1 hour at room temperature.</li>
 <li>Rinse in 0.05% PBST three times for 5 min each.</li>
 </ol>
 </li>
 <li>Epithelial mask (Cytokeratin) incubation
 <ol class="lroman">
 <li>Incubate mouse anti-pan cytokeratin, diluted 1:50 in Dako antibody diluent overnight at 4 &deg;C.</li>
 </ol>
 </li>
 <li>Epithelial mask visualization
 <ol class="lroman">
 <li>Prepare a 1:25 dilution of the goat anti-mouse Alexa555 secondary antibody in the pre-diluted Envision goat-rabbit HRP antibody solution. Incubate slides in the dark for 1.5 hrs at room temperature.</li>
 <li>Rinse in 0.05% PBST three times for 5min each.</li>
 </ol>
 </li>
 <li>Target visualization
 <ol class="lroman">
 <li>Transfer the slides from the Sequenza to the humidity chamber.</li>
 <li>Combine Target signal amplification diluent (HistoRx tub E) and the Cy5 Tyramide (HistoRx tube F) at 1:50 concentration. Vortex to mix thoroughly. Incubate slides in the dark for 10 min at room temperature.</li>
 <li>Rinse in 0.05% PBST three times for 5 min each.</li>
 <li>Dehydrate slides in 80% ethanol for 1 min.</li>
 <li>Air dry slides in the dark.</li>
 </ol>
 </li>
 <li>Counterstaining and coverslipping
 <ol class="lroman">
 <li>Apply DAPI mounting medium on the coverslips and place the coverslips over the tissue sections.</li>
 <li>Dry slides overnight in the dark.</li>
 <li>After the slides have dried, secure with nail polish to ensure long term preservation and keep in a 4 &deg;C refrigerator.</li>
 </ol>
 </li>
</ol>
3. Image capture using whole section scanning
<ol class="lalpha">
 <li>Load up to five slides in the slide cassette of the Aperio ScanScope FL slide scanner.</li>
 <li>For each slide, select the general region to image using the interface in the ScanScope Console. Select the region to encompass all the tissue on the slide, regardless of staining pattern or other observable aspects of the sample.</li>
 <li>Using the ScanScope Console, for each channel (DAPI, Cy3 and Cy5) determine an optimal exposure as follows:
 <ol class="lroman">
 <li>Select a region of tissue to interrogate - this area should, ideally, be in the tumor region of tissue to provide the best representation.</li>
 <li>Using the ScanScope Console autoexposure feature, evaluate the exposure time suggested for each channel, along with image focus.</li>
 <li>Repeat over up to 3-5 regions of each sample to confirm stability of the value.</li>
 </ol>
 </li>
 <li>Select an additional region (as shown by a blue diamond on the ScanScope console image) away from the tissue, yet still within the coverslipped region of the slide, to define a background area where flat field correction images will be acquired for each filter.</li>
 <li>Review these images prior to image acquisition. If the images appear to have high background or other image artifacts, the image region should be moved and new images acquired.</li>
 <li>The acquired digital slide images are uploaded automatically into the Aperio Spectrum database.</li>
</ol>
4. AQUA automated image analysis
<ol class="lalpha">
 <li>View the digital whole slide images in the Spectrum database and annotate tumor areas of interest in the context of the entire tissue and staining pattern(s).</li>
 <li>Select the slide to analyze using AQUAnalysis from the digital slide list in the Spectrum database by double clicking the thumbnail image (alternatively, select the check box next to the image and then select 'View Images' from the menu at the top of the list). This automatically opens the ImageScope software and color merged image of the tissue sample.</li>
 <li>Use this software to navigate the image using the tools provided.</li>
 <li>Using the accompanying H&amp;E (either the physical slide or an annotated image), annotate the region of interest on the fluorescent image. Multiple regions of interest, individual circled areas, can be selected on a single sample. There is also a 'negative' tool which is used to exclude areas within a selected region (i.e. damaged tissue, areas of poor histology, non-tumor areas, debris, etc.).</li>
 <li>Save the annotation layer(s) on the image.</li>
 <li>Return to the Spectrum main menu and select the check box next to the image that was just annotated. Select 'Analyze' from the menu strip across the top of the list.</li>
 <li>In the Analysis window which will come up, select "HistoRx AQUA Analysis" from the pull-down menu.</li>
 <li>If there are multiple layers of annotations, use the check boxes to select the appropriate layer(s) for analysis.</li>
 <li>Press the 'Analyze' button when ready to begin.</li>
 <li>At this point, a minimized console window will appear in the Windows taskbar. This will display the progress of the transfer of images from the Spectrum database to the local PC (the 'pull' operation).</li>
 <li>Once the data is transferred, AQUAnalysis will launch. The user can then use all of the internal functions of the software to view, navigate and analyze images.</li>
 <li>The annotated region of the image from the Spectrum database is broken into 512x512 pixel image tiles - each of which will be scored individually.</li>
 <li>For this study, an unsupervised AQUA scoring algorithm, based on data clustering, was used8. In this algorithm, AQUA scores are generated as follows:
 <ol class="lroman">
 <li>The pan-cytokeratin image signal is thresholded above background and those pixels are used to define a tumor 'mask' which is then used for subsequent calculations. The high -expression pan-cytokeratin pixels also define the cytoplasmic/non-nuclear regions of the tumor cells.</li>
 <li>Pixels with high signal in the DAPI channel that are within the tumor mask are identified as nuclear in nature.</li>
 <li>The clustering algorithm also examines pixels which have low intensity for either DAPI staining or cytokeratin expression and attempts to partially attribute them to either the nuclear or cytoplasmic compartments, or background.</li>
 <li>Once all pixels are assigned to one of the tumor compartments or background, the intensity of signal from the target protein (ER or HER2) is then calculated within each of the compartments and normalized to the size of the compartments, thus producing AQUA scores.</li>
 <li>Images which did not have sufficient tumor representation were not scored (as defined by those with less than 5% of pixels representing tumor). These regions also produce a 'Final Result' value called 'Fail' and can thus be removed in subsequent statistical analyses.</li>
 <li>Additionally, if upon review, an operator identified fields which were not suitable for scoring due to sample or imaging artifacts (e.g. folded tissue, debris), those fields were redacted from scoring and produced a 'Final Result' called 'Fail'.</li>
 </ol>
 </li>
 <li>The results of AQUA scoring are tables of scores associated with each 512x512 pixel tile in the region of interest within a whole tissue section sample. These files, in .CSV format, can then be used for subsequent statistical analysis.</li>
</ol>
5. Statistical Analysis: All statistical analyses are performed in SPSS (IBM)
<ol>
 <li>Where possible, for large operations, generate SPSS syntax code files (.sps) to perform data manipulations and analysis steps. Example syntax code is provided as supplementary files.</li>
 <li>Read data files into SPSS. Remove unused variables in the raw data (e.g. instrument specific parameters, AQUA scores from non-utilized compartments).</li>
 <li>Where regions have a 'Final Result' of 'Fail' (see above), mark these values as SYSMIS, which removes them from the numerical analyses.</li>
 <li>Aggregate all data for all slides for each marker (ER or HER2) into a single master data set in SPSS format. Use this master file for all subsequent calculations.</li>
 <li>Using an SPSS syntax file (one for each marker: ER or HER2 (see supplementary syntax files) data was further validated and Simpson's Indices of diversity calculated as follows:
 <ol class="lroman">
 <li>Generate a master analytical file, in order to prevent modification of the master data set previously generated.</li>
 <li>Validate the master data set against the raw data and images generated.</li>
 <li>Produce summary statistics for AQUA scores for each sample (count of fields, mean score, median score, minimum score, maximum score, and standard deviation of scores for sample).</li>
 <li>Check a random sample of results against the original raw data output files and against the raw image data scored.</li>
 </ol>
 </li>
 <li>To generate the heat map images (shown in Figures 2 and 3):
 <ol class="lroman">
 <li>'Bin' the AQUA score data (using the 'Visual Binning' function in SPSS) into eight individual levels. For ER, nuclear scores were used, for HER2, cytoplasmic scores were used.</li>
 <li>Derive bins such that each bin represents an equal percentage of available cases from the entire data set.</li>
 <li>Use the SPSS function 'Split File' so that all subsequent output would be on a 'per slide/sample' level.</li>
 <li>Plot each region (corresponding to a 512x512 tile) by its x,y coordinates and color with a value corresponding to the bin into which it was allocated (see figures 2 and 3).</li>
 </ol>
 </li>
 <li>To generate heterogeneity scores, code was written to generate a Simpson's index of diversity using AQUA scores.
 <ol class="lroman">
 <li>The Simpson's index of diversity, as used here, is defined as: 
 <img alt="Equation 1" src="/files/ftp_upload/3334/3334eq1.jpg" /> 
 Where N is the total number of scores/fields used for a given sample and n is the number of scores/fields in a given group of standardized scores (produced as described below).</li>
 </ol>
 </li>
 <li>Use the master analytical file described above for data source. In the examples shown, all ER calculations used the nuclear AQUA score and HER2 calculations used the cytoplasmic AQUA score. Perform the calculations below for each sample/slide.</li>
 <li>Since fluorescence data is subject to variance instability, such that error magnitude propagates with intensity, apply logarithmic (base 2) normalization to all AQUA scores.</li>
 <li>Further transform the normalized (log transformed) data into z-(standard) scores.
 <ol class="lroman">
 <li>The Z-score transformation is calculated as follows: Z = (AQUA score - average of all scores for a sample)/standard deviation of scores for the sample. This translates the distribution of scores into a distribution of deviations around the central value of zero deviation.</li>
 </ol>
 </li>
 <li>Bin these Z-score values into six groups (&lt;-2, -2 - -1, -1 - 0, 0 - 1, 1 - 2, &gt;2).</li>
 <li>Calculate the number of values allocated into each bin and sum for each bin. Use this value, along with the total number of fields, to calculate a value for each of the six bins used for the index.</li>
 <li>Sum these values and subtract from one to produce the index of diversity.</li>
 <li>The diversity index provides an indicator of the spread of scores around the mean for a particular sample. Thus, higher indices indicate a broader spread of scores, or increased heterogeneity of expression. Conversely, lower indices, indicate less variation in expression measurement and homogeneous expression in the sample. This is illustrated in Figures 2 and 3.</li>
</ol>
6. Representative Results:
Targeted therapy with relatively low toxicity agents, such as tamoxifen and trastuzumab, are not standard of care for ovarian cancer patients. There is good evidence that platinum-taxane first-line chemotherapy is superior to other chemotherapy regimens for ovarian cancer9-13, and while 70-80% of patients initially respond, most will relapse and die of their disease. Measurement of biomarkers which might predict response to alternative therapies would be useful in selecting individualized therapy for each patient, and since chemotherapy exerts selection pressure on a tumor, surviving tumor cells may possess different characteristics and represent a subpopulation of the tumor before therapy; quantifying this change might therefore be useful. We applied our heterogeneity mapping approach to ER and HER2 expression in ovarian cancer specimens before and after chemotherapy, in order to measure quantitative changes in expression, and assess the relationship between biomarker expression before and after therapy. Both ER and HER2 demonstrate marked heterogeneity within individual tumors, and in some tumors, the Simpson's index changes after therapy from relatively high heterogeneity to low heterogeneity, represented by decreasing Simpson's index scores (see Figures 2 and 3). This supports the notion that clonal selection occurs in response to cytotoxic chemotherapy; more importantly, this may be exploited using targeted therapy against the residual population, in order to better personalize therapy for cancer patients.
<img alt="Figure 1" src="/files/ftp_upload/3334/3334fig1.jpg" /> 
Figure 1. Hematoxylin and eosin stained photomicrographs of different areas of the same ovarian cancer showing variable morphology. (A) x40 photomicrograph shows two adjacent areas of tumor with varying morphology. High power views (x200) reveal cells with cytoplasmic clearing and a solid pattern of growth in the upper part of the tumor (B), while the lower part of the tissue (C) has more homogeneous eosinophilic cytoplasm and a papillary growth pattern. This tumor would, however, be classified as serous papillary histological subtype for the purposes of further management.
<img alt="Figure 2" src="/files/ftp_upload/3334/3334fig2.jpg" /> 
Figure 2. Heterogeneity heatmaps of ER protein expression in cytokeratin-positive ovarian cancer tissue, before (upper panel) and after (lower panel) therapy.
<img alt="Figure 3" src="/files/ftp_upload/3334/3334fig3.jpg" /> 
Figure 3. Heterogeneity heatmaps of HER2 protein expression in cytokeratin-positive ovarian cancer tissue, before (upper panel) and after (lower panel) therapy.

<ol>
	<li>Gutierrez, M. L., Sayagues, J. M., Del Mar, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytogenetic+heterogeneity+of+pancreatic+ductal+adenocarcinomas:+identification+of+intratumoral+pathways+of+clonal+evolution.">Cytogenetic heterogeneity of pancreatic ductal adenocarcinomas: identification of intratumoral pathways of clonal evolution.</a> Histopathology. 58, 486-497 (2011).</li><li>Yachida, S., Jones, S., Bozic, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distant+metastasis+occurs+late+during+the+genetic+evolution+of+pancreatic+cancer.">Distant metastasis occurs late during the genetic evolution of pancreatic cancer.</a> Nature. 467, 1114-1117 (2010).</li><li>Gutierrez, M. L., Sayagues, J. M., Del Mar, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytogenetic+heterogeneity+of+pancreatic+ductal+adenocarcinomas:+identification+of+intratumoral+pathways+of+clonal+evolution.">Cytogenetic heterogeneity of pancreatic ductal adenocarcinomas: identification of intratumoral pathways of clonal evolution.</a> Histopathology. 58, 486-497 (2011).</li><li>Yachida, S., Jones, S., Bozic, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distant+metastasis+occurs+late+during+the+genetic+evolution+of+pancreatic+cancer.">Distant metastasis occurs late during the genetic evolution of pancreatic cancer.</a> Nature. 467, 1114-1117 (2010).</li><li>Liu, J., Lau, S. K., Varma, V. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+mapping+of+tumor+heterogeneity+on+clinical+tissue+specimens+with+multiplexed+quantum+dots.">Molecular mapping of tumor heterogeneity on clinical tissue specimens with multiplexed quantum dots.</a> ACS Nano. 4, 2755-2765 (2010).</li><li>Camp, R. L., Chung, G. G., Rimm, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+subcellular+localization+and+quantification+of+protein+expression+in+tissue+microarrays.">Automated subcellular localization and quantification of protein expression in tissue microarrays.</a> Nat. Med. 8, 1323-1327 (2002).</li><li>Simpson, E. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+biodiversity.">Measurement of biodiversity.</a> Nature. 163, 688-68 (1949).</li><li>Gustavson, M. D., Bourke-Martin, B., Reilly, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+an+unsupervised+pixel-based+clustering+algorithm+for+compartmentalization+of+immunohistochemical+expression+using+Automated+Quantitative+Analysis.">Development of an unsupervised pixel-based clustering algorithm for compartmentalization of immunohistochemical expression using Automated Quantitative Analysis.</a> Appl. Immunohistochem. Mol. Morphol. 17, 329-337 (2009).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Paclitaxel+plus+carboplatin+versus+standard+chemotherapy+with+either+single-agent+carboplatin+or+cyclophosphamide,+doxorubicin,+and+cisplatin+in+women+with+ovarian+cancer:+the+ICON3+randomised+trial.">Paclitaxel plus carboplatin versus standard chemotherapy with either single-agent carboplatin or cyclophosphamide, doxorubicin, and cisplatin in women with ovarian cancer: the ICON3 randomised trial.</a> Lancet. 360, 505-515 (2002).</li><li>McGuire, W. P., Hoskins, W. J., Brady, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cyclophosphamide+and+cisplatin+compared+with+paclitaxel+and+cisplatin+in+patients+with+stage+III+and+stage+IV+ovarian+cancer.">Cyclophosphamide and cisplatin compared with paclitaxel and cisplatin in patients with stage III and stage IV ovarian cancer.</a> N. Engl. J. Med. 334, 1-6 (1996).</li><li>Muggia, F. M., Braly, P. S., Brady, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phase+III+randomized+study+of+cisplatin+versus+paclitaxel+versus+cisplatin+and+paclitaxel+in+patients+with+suboptimal+stage+III+or+IV+ovarian+cancer:+a+gynecologic+oncology+group+study.">Phase III randomized study of cisplatin versus paclitaxel versus cisplatin and paclitaxel in patients with suboptimal stage III or IV ovarian cancer: a gynecologic oncology group study.</a> J. Clin. Oncol. 18, 106-115 (2000).</li><li>Piccart, M. J., Bertelsen, K., James, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Randomized+intergroup+trial+of+cisplatin-paclitaxel+versus+cisplatin-cyclophosphamide+in+women+with+advanced+epithelial+ovarian+cancer:+three-year+results.">Randomized intergroup trial of cisplatin-paclitaxel versus cisplatin-cyclophosphamide in women with advanced epithelial ovarian cancer: three-year results.</a> J. Natl. Cancer. Inst. 92, 699-708 (2000).</li><li>Cannistra, S. 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+of+the+ovary.">Cancer of the ovary.</a> N. Engl. J. Med. 351, 2519-2529 (2004).</li><li>Baker, A. F., Dragovich, T., Ihle, N. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stability+of+phosphoprotein+as+a+biological+marker+of+tumor+signaling.">Stability of phosphoprotein as a biological marker of tumor signaling.</a> Clin. Cancer. Res. 11, 4338-4340 (2005).</li><li>Espina, V., Edmiston, K. H., Heiby, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+portrait+of+tissue+phosphoprotein+stability+in+the+clinical+tissue+procurement+process.">A portrait of tissue phosphoprotein stability in the clinical tissue procurement process.</a> Mol. Cell. Proteomics. 7, 1998-2018 (2008).</li><li>Klymkowsky, M. W., Savagner, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epithelial-mesenchymal+transition:+a+cancer+researcher's+conceptual+friend+and+foe.">Epithelial-mesenchymal transition: a cancer researcher's conceptual friend and foe.</a> Am. J. Pathol. 174, 1588-1593 (2009).</li><li>Kurrey, N. K., Jalgaonkar, S. P., Joglekar, A. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Snail+and+slug+mediate+radioresistance+and+chemoresistance+by+antagonizing+p53-mediated+apoptosis+and+acquiring+a+stem-like+phenotype+in+ovarian+cancer+cells.">Snail and slug mediate radioresistance and chemoresistance by antagonizing p53-mediated apoptosis and acquiring a stem-like phenotype in ovarian cancer cells.</a> Stem. Cells. 27, 2059-2068 (2009).</li><li>Creighton, C. J., Li, X., Landis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Residual+breast+cancers+after+conventional+therapy+display+mesenchymal+as+well+as+tumor-initiating+features.">Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features.</a> Proc. Natl. Acad. Sci. U. S. A. 106, 13820-13825 (2009).</li><li>Aitken, S. J., Thomas, J. S., Langdon, S. P., Harrison, D. J., Faratian, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+analysis+of+changes+in+ER,+PR+and+HER2+expression+in+primary+breast+cancer+and+paired+nodal+metastases.">Quantitative analysis of changes in ER, PR and HER2 expression in primary breast cancer and paired nodal metastases.</a> Ann. Oncol. 21, 1254-1261 (2010).</li><li>Sayagues, J. M., Abad, M. M., Melchor, H. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intratumoural+cytogenetic+heterogeneity+of+sporadic+colorectal+carcinomas+suggests+several+pathways+to+liver+metastasis.">Intratumoural cytogenetic heterogeneity of sporadic colorectal carcinomas suggests several pathways to liver metastasis.</a> J. Pathol. 221, 308-319 (2010).</li></ol>

JC, MG, CJ, and CS are employees of HistoRx, Inc.

The method described herein permits quantification of molecular heterogeneity in standard formalin-fixed, paraffin-embedded histologic sections of tumor material. The method permits a value to be assigned to the degree of heterogeneity in a tissue section, so that this may then be taken into account as a variable in biomarker development and analysis. While in general it is preferable that tissue biomarkers are homogeneous with respect to expression, so that the assay is less susceptible to sampling error or bias, it might under some circumstances be useful to quantify heterogeneity as a parameter. For instance, as shown in the illustrative example for ER and HER2, common biomarkers show considerable heterogeneity of expression and it is currently unknown whether this represents an independent prognostic or predictive factor with respect to clinical outcome or response to therapy, respectively. Likewise, as illustrated, therapy itself might dynamically alter the constituent populations of cells, and therefore measurement of target enrichment might be useful in guiding treatment decisions.
However, the technique is limited by the same factors which limit traditional histopathological or biomarker (such as immunohistochemical) analyses. The result is dependent on the type, size, and quality of the tissue being analyzed, and therefore a single tissue section may not be representative of the entire tumor. Indeed, the Simpson's index may be unduly biased by size of the tissue (number of frames captured and AQUA scores measured). The immunogenicity of the epitopes being measured may be subject to uncontrollable pre-analytical factors which artifactually alter their expression across the tissue section (such as cold ischemia time, or the length of fixation, and edge artifact). This is particularly a problem for phospho-epitopes, which are notoriously labile14, 15. Therefore the technique might be better suited to resection specimens rather than small biopsies, and performed on multiple sections rather than just one. Ultimately, 3D imaging techniques may offer an opportunity to better quantify this parameter.
The technique as presented may also be limited by biological factors, such as variability in expression of the cytokeratin masking epitope. This might be of particular concern in those cancers, including ovarian and breast cancer, which undergo epithelial-to-mesenchymal transition (EMT) or are enriched for non-epithelial components or stem cells16, as has recently been shown to occur in chemotherapy-treated ovarian cancer in vitro17, and in breast cancer patients in response to therapy18. This limitation may be overcome using alternative masking antibodies (or cocktails of antibodies), such as cytokeratin plus vimentin, which is upregulated in EMT16. In addition, since other components of the tissue (the microenvironment), such as fibroblasts or vascular endothelial cells are also increasingly being targeted by biological therapies, the technique may also be expanded to to score these components, using specific masks for compartments of interest, such as PECAM/ CD31 to stain for vascular endothelial cells.
Although the adaptation of the Simpson's index has been used as a measure of heterogeneity in the current protocol due to its simplicity, other measures of heterogeneity could be used, such as simple measurements of central tendency (mean, variance, median, etc). Additionally, the Simpson's index calculations could be modified to better suit a particular test population. The use of Z-score transformations allows the scoring to be more applicable for multiple markers since the heterogeneity is based on deviation around the mean for a data set. However, the overall range of scoring can be limited in this type of transformation. Some designs may be better suited to simply bin the actual AQUA scores for all samples of a particular marker set. The selection of the number of bins and cutoffs is also a limitation in the range of values which can result and may be optimized for alternative experimental designs.
We have used ER and HER2 in the current study as candidate biomarkers, since they are already used in the clinic, help answer relevant clinical questions with respect to the efficacy of targeted therapies, and are known to exhibit considerable heterogeneity and change in primary and distant disease19. However, the optimal marker of heterogeneity remains to be determined. Other possibilities might include cytogenetic markers of clonality, such as those previously used in interphase FISH studies20. The approach requires further validation in large, well annotated clinical cohorts or clinical trials in order to further establish the relevance of this parameter in cancer biology.

<table border="1">
	<tbody>
		<tr>
			<td>Name of the reagent</td>
			<td>Company</td>
			<td>Catalogue number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>ScanScope FL</td>
			<td>Aperio</td>
			<td>ScanScope FL</td>
			<td>Used as equipped from vendor</td>
		</tr>
		<tr>
			<td>Spectrum</td>
			<td>Aperio</td>
			<td>Spectrum</td>
			<td>Configuration and catalog numbers will vary on how local institutions set up their database and IT structure.</td>
		</tr>
		<tr>
			<td>AQUAnalysis, in conjunction with AQUAjustment</td>
			<td>HistoRx</td>
			<td>V2.3</td>
			<td>The AQUAjustment package allows AQUAnalysis to use images stored in Spectrum.</td>
		</tr>
		<tr>
			<td>HER2</td>
			<td>Dako</td>
			<td>A0485</td>
			<td>1 in 400 dilution</td>
		</tr>
		<tr>
			<td>mouse anti-pan cytokeratin</td>
			<td>Dako</td>
			<td>M3515</td>
			<td>1 in 50 dilution</td>
		</tr>
		<tr>
			<td>Antibody diluent</td>
			<td>Dako</td>
			<td>S0809</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Envision goat-rabbit HRP</td>
			<td>Dako</td>
			<td>K4003</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Protein block</td>
			<td>Dako</td>
			<td>X0909</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Goat anti-mouse Alexa555</td>
			<td>Invitrogen</td>
			<td>A21422</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>DAPI mounting medium</td>
			<td>Invitrogen</td>
			<td>P36931</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Cy5 Tyramide</td>
			<td>HistoRx</td>
			<td>AQ-EMR1-00001</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Sequenza Immunostaining Center</td>
			<td>Thermo Scientific Shandon</td>
			<td>73300001</td>
			<td>Used here for bench-top immunofluorescence staining</td>
		</tr>
	</tbody>
</table>

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.

Watch this Scientific Journal Video about Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence at JoVE.com

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

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 ...

heterogeneity-mapping-protein-expression-tumors-using-quantitative

Research

JoVE Journal

Medicine

Establishment and Propagation of Human Retinoblastoma Tumors in Immune Deficient Mice

Culturing retinoblastoma tumor cells in defined stem cell media gives rise to primary tumorspheres that can be grown and maintained for only a limited time. These cultured tumorspheres may exhibit markedly different cellular phenotypes when compared to the original tumors. Demonstration that cultured cells have the capability of forming new tumors is important to ensure that cultured cells model the biology of the original tumor. 
Here we present a protocol for propagating human retinoblastoma tumors in vivo using Rag2-/- immune deficient mice. Cultured human retinoblastoma tumorspheres of low passage or cells obtained from freshly harvested human retinoblastoma tumors injected directly into the vitreous cavity of murine eyes form tumors within 2-4 weeks. These tumors can be harvested and either further passaged into murine eyes in vivo or grown as tumorspheres in vitro. Propagation has been successfully carried out for at least three passages thus establishing a continuing source of human retinoblastoma tissue for further experimentation. 
Wesley S. Bond and Lalita Wadhwa are co-first authors.

A method is described to propagate human retinoblastoma tumors in mice. Tumor cells are directly injected into the eyes of immune deficient mice. Secondary tumors have been successfully established using both cells directly harvested from human tumors and cultured tumorspheres.

Culturing retinoblastoma tumor cells in defined stem cell media gives rise to primary tumorspheres that can be grown and maintained for only a limited ...

Evaluation of Nanoparticle Uptake in Tumors in Real Time Using Intravital Imaging

Current technologies for tumor imaging, such as ultrasound, MRI, PET and CT, are unable to yield high-resolution images for the assessment of nanoparticle uptake in tumors at the microscopic level1,2,3, highlighting the utility of a suitable xenograft model in which to perform detailed uptake analyses. Here, we use high-resolution intravital imaging to evaluate nanoparticle uptake in human tumor xenografts in a modified, shell-less chicken embryo model. The chicken embryo model is particularly well-suited for these in vivo analyses because it supports the growth of human tumors, is relatively inexpensive and does not require anesthetization or surgery 4,5. Tumor cells form fully vascularized xenografts within 7 days when implanted into the chorioallantoic membrane (CAM) 6. The resulting tumors are visualized by non-invasive real-time, high-resolution imaging that can be maintained for up to 72 hours with little impact on either the host or tumor systems. Nanoparticles with a wide range of sizes and formulations administered distal to the tumor can be visualized and quantified as they flow through the bloodstream, extravasate from leaky tumor vasculature, and accumulate at the tumor site. We describe here the analysis of nanoparticles derived from Cowpea mosaic virus (CPMV) decorated with near-infrared fluorescent dyes and/or polyethylene glycol polymers (PEG) 7, 8, 9,10,11. Upon intravenous administration, these viral nanoparticles are rapidly internalized by endothelial cells, resulting in global labeling of the vasculature both outside and within the tumor7,12. PEGylation of the viral nanoparticles increases their plasma half-life, extends their time in the circulation, and ultimately enhances their accumulation in tumors via the enhanced permeability and retention (EPR) effect 7, 10,11. The rate and extent of accumulation of nanoparticles in a tumor is measured over time using image analysis software. This technique provides a method to both visualize and quantify nanoparticle dynamics in human tumors.

We present a novel approach to quantify nanoparticle localization in the vasculature of human xenografted tumors using dynamic, real-time intravital imaging in an avian embryo model.

Current technologies for tumor imaging, such as ultrasound, MRI, PET and CT, are unable to yield high-resolution images for the assessment of nanoparticle ...

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 ...

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 ...

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 ...

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 ...

Coordinate Mapping of Hyolaryngeal Mechanics in Swallowing

Characterizing hyolaryngeal movement is important to dysphagia research. Prior methods require multiple measurements to obtain one kinematic measurement whereas coordinate mapping of hyolaryngeal mechanics using Modified Barium Swallow (MBS) uses one set of coordinates to calculate multiple variables of interest. For demonstration purposes, ten kinematic measurements were generated from one set of coordinates to determine differences in swallowing two different bolus types. Calculations of hyoid excursion against the vertebrae and mandible are correlated to determine the importance of axes of reference.
To demonstrate coordinate mapping methodology, 40 MBS studies were randomly selected from a dataset of healthy normal subjects with no known swallowing impairment. A 5 ml thin-liquid bolus and a 5 ml pudding swallows were measured from each subject. Nine coordinates, mapping the cranial base, mandible, vertebrae and elements of the hyolaryngeal complex, were recorded at the frames of minimum and maximum hyolaryngeal excursion. Coordinates were mathematically converted into ten variables of hyolaryngeal mechanics.
Inter-rater reliability was evaluated by Intraclass correlation coefficients (ICC). Two-tailed t-tests were used to evaluate differences in kinematics by bolus viscosity. Hyoid excursion measurements against different axes of reference were correlated. Inter-rater reliability among six raters for the 18 coordinates ranged from ICC = 0.90 - 0.97. A slate of ten kinematic measurements was compared by subject between the six raters. One outlier was rejected, and the mean of the remaining reliability scores was ICC = 0.91, 0.84 - 0.96, 95% CI. Two-tailed t-tests with Bonferroni corrections comparing ten kinematic variables (5 ml thin-liquid vs. 5 ml pudding swallows) showed statistically significant differences in hyoid excursion, superior laryngeal movement, and pharyngeal shortening (p &#60; 0.005). Pearson correlations of hyoid excursion measurements from two different axes of reference were: r = 0.62, r2 = 0.38, (thin-liquid); r = 0.52, r2 = 0.27, (pudding).
Obtaining landmark coordinates is a reliable method to generate multiple kinematic variables from video fluoroscopic images useful in dysphagia research.

Coordinate mapping is a method of documenting salient features of hyolaryngeal biomechanics in the pharyngeal phase of swallowing. This methodology uses image analysis software to record coordinates of anatomical landmarks. These coordinates are imported into an excel macro and translated into kinematic variables of interest useful in dysphagia research.

Characterizing hyolaryngeal movement is important to dysphagia research. Prior methods require multiple measurements to obtain one kinematic measurement ...

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 ...

Quantitative Mapping of Specific Ventilation in the Human Lung using Proton Magnetic Resonance Imaging and Oxygen as a Contrast Agent

Specific ventilation imaging (SVI) is a functional magnetic resonance imaging technique capable of quantifying specific ventilation &#8213; the ratio of the fresh gas entering a lung region divided by the region&#8217;s end-expiratory volume &#8213; in the human lung, using only inhaled oxygen as a contrast agent. Regional quantification of specific ventilation has the potential to help identify areas of pathologic lung function. Oxygen in solution in tissue shortens the tissue&#8217;s longitudinal relaxation time (T1), and thus a change in tissue oxygenation can be detected as a change in T1-weighted signal with an inversion recovery acquired image. Following an abrupt change between two concentrations of inspired oxygen, the rate at which lung tissue within a voxel equilibrates to a new steady-state reflects the rate at which resident gas is being replaced by inhaled gas. This rate is determined by specific ventilation. To elicit this sudden change in oxygenation, subjects alternately breathe 20-breath blocks of air (21% oxygen) and 100% oxygen while in the MRI scanner. A stepwise change in inspired oxygen fraction is achieved through use of a custom three-dimensional (3D)-printed flow bypass system with a manual switch during a short end-expiratory breath hold. To detect the corresponding change in T1, a global inversion pulse followed by a single shot fast spin echo sequence was used to acquire two-dimensional T1-weighted images in a 1.5&#160;T MRI scanner, using an eight-element torso coil. Both single slice and multi-slice imaging are possible, with slightly different imaging parameters. Quantification of specific ventilation is achieved by correlating the time-course of signal intensity for each lung voxel with a library of simulated responses to the air/oxygen stimulus. SVI estimations of specific ventilation heterogeneity have been validated against multiple breath washout and proved to accurately determine the heterogeneity of the specific ventilation distribution.

Specific ventilation imaging is a functional magnetic resonance imaging technique that allows for quantification of regional specific ventilation in the human lung, using inhaled oxygen as a contrast agent. Here, we present a protocol to collect and analyze specific ventilation imaging data.

Specific ventilation imaging (SVI) is a functional magnetic resonance imaging technique capable of quantifying specific ventilation &#8213; the ratio of ...

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we present, in a step-by-step guide, the use of ElectroMap for analysis, quantification, and mapping of high-resolution voltage and calcium datasets acquired by optical mapping. ElectroMap analysis options cover a wide variety of key electrophysiological parameters, and the graphical user interface allows straightforward modification of pre-processing and parameter definitions, making ElectroMap applicable to a wide range of experimental models. We show how built-in pacing frequency detection and signal segmentation allows high-throughput analysis of entire experimental recordings, acute responses, and single beat-to-beat variability. Additionally, ElectroMap incorporates automated multi-beat averaging to improve signal quality of noisy datasets, and here we demonstrate how this feature can help elucidate electrophysiological changes that might otherwise go undetected when using single beat analysis. Custom modules are included within the software for detailed investigation of conduction, single file analysis, and alternans, as demonstrated here. This software platform can be used to enable and accelerate the processing, analysis, and mapping of complex cardiac electrophysiology.

This protocol describes the setup and use of ElectroMap, a MATLAB-based open-source software platform for analysis of cardiac optical mapping data. ElectroMap provides a versatile high-throughput tool for analysis of optical mapping voltage and calcium datasets across a wide range of cardiac experimental models.

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we ...