This work was supported by the Hunan Provincial Hundred Talent Plan (to X.Z.), the Xiangya Hospital Funds for Talent Introduction (to XZ), the National Natural Science Foundation of China (Grant No. 81572278 and 81272798 to XZ), the grants from China &#34;863&#34; Plan Project (Grant No. 2014AA020610-1 to XZ), and the Hunan Provincial Natural Science Foundation of China (Grant No. 14JJ7008 to XZ). X.Z. conceived the concept for the present manuscript, obtained the iTRAQ quantitative proteomics data of mitochondria samples, wrote and revised the manuscript, coordinated the pertinent work, and was responsible for the financial support and corresponding work. H.L. prepared mitochondria samples. S.Q. participated in partial work. X.H.Z participated in writing and edited English language. N.L. analyzed the iTRAQ proteomics data. All authors approved the final manuscript.

Ovarian tissue samples including ovarian cancer tissues (n = 7) and normal control ovarian tissues (n = 11) were used for this protocol. The present protocol3,4,5 is approved by the Xiangya Hospital Medical Ethics Committee of Central South University, China.
1. Preparation of mitochondria from human ovarian cancer tissues
<ol>
	<li>Prepare 250 mL of the mitochondrial isolation buffer by mixing 210 mM mannitol, 70 mM sucrose, 100 mM potassium chloride (KCl), 50 mM Tris-HCl, 1 mM diamine tetraacetic acid (EDTA), 0.1 mM ethylene glycol bis(2-aminoethyl ether)tetraacetic acid (EGTA), 1 mM phenylmethanesulfonyl fluoride (PMSF) protease inhibitor, 2 mM sodium orthovanadate (V), and 0.2% bovine serum albumin (BSA), pH 7.4.</li>
	<li>Place ~1.5 g of ovarian cancer tissues in a clean glass dish.</li>
	<li>Add 2 mL of the pre-chilled mitochondrial isolation buffer to lightly wash the blood from the tissue surface 3x.</li>
	<li>Use clean ophthalmic scissors to fully mince the tissue into about 1 mm3 pieces, and transfer the minced tissues into a 50 mL centrifuge tube.</li>
	<li>Add 13.5 mL of mitochondrial isolation buffer containing 0.2 mg/mL nagarse, and then use an electric homogenizer to homogenize (use scale 2, 10 s 6x, interval 10 s) the minced tissues (2 min, 4 &#176;C).</li>
	<li>Add another 3 mL of mitochondrial isolation buffer into the tissue homogenates and mix them well by pipetting.</li>
	<li>Centrifuge the prepared tissue homogenate (1,300 x g, 10 min, 4 &#176;C). Remove the crude nuclear fraction (i.e., the pellet) and keep the supernatant.</li>
	<li>Recentrifuge the supernatant (10,000 x g, 10 min, 4 &#176;C). Remove the microsomes (i.e., the supernatant) and keep the pellet.</li>
	<li>Add 2 mL of mitochondrial isolation buffer and resuspend the pellet well by light pipetting.</li>
	<li>Centrifuge the pellet suspension (7,000 x g, 10 min, 4 &#176;C). Discard the supernatant and keep the crude mitochondria (i.e., the pellet).</li>
	<li>Add 12 mL of 25% density gradient medium (i.e., Nycodenz) to resuspend the extracted crude mitochondria.</li>
	<li>Make a discontinuous density gradient by filling a tube from bottom to top with 5 mL of 34%, 8 mL of 30%, 12 mL of 25% (containing the crude mitochondria from step 1.11), 8 mL of 23%, and 3 mL of 20% density gradient medium, and centrifuge it (52,000 x g, 90 min, 4 &#176;C).</li>
	<li>Use a long and blunt syringe to collect the purified mitochondria at the interface between the 25% and 30% density gradient medium into a clean tube.</li>
	<li>Add mitochondrial isolation buffer into the collected mitochondria to dilute it to a three-fold volume. Centrifuge (15,000 x g, 20 min, 4 &#176;C) and discard the supernatant.</li>
	<li>Add 2 mL of mitochondrial isolation buffer to resuspend the pellet, and centrifuge (15,000 x g, 20 min, 4 &#176;C). Discard the supernatant and keep the pellet.</li>
	<li>Collect the final pellet (i.e., the purified mitochondria) and store it at -20 &#176;C. 
	NOTE: Combine all purified mitochondria from the ovarian cancer tissue as the mitochondria sample for quantitative proteomics analysis.</li>
</ol>
2. Preparation of mitochondria from human control ovarian tissues
<ol>
	<li>Prepare 250 mL of the mitochondrial isolation buffer as described in step 1.1.</li>
	<li>Place ~1.5 g of the normal control ovarian tissues in a clean glass dish.</li>
	<li>Add 2 mL of pre-chilled mitochondrial isolation buffer to lightly wash the blood from the tissue surface 3x.</li>
	<li>Use clean ophthalmic scissors to fully mince the tissue into about 1 mm3 pieces, and transfer the minced tissues into a 50 mL centrifuge tube.</li>
	<li>Add 8 mL of 0.05% trypsin/20 mM EDTA in phosphate-buffered saline (PBS) to the minced control tissues, and digest (30 min, room temperature), which helps to lyse the tissues and cells and release mitochondria. Then centrifuge (200 x g, 5 min). Discard the supernatant, and keep the tissues and cells.</li>
	<li>Add 13.5 mL of mitochondrial isolation buffer containing 0.2 mg/mL nagarse, and then use an electric homogenizer to homogenize (use scale 2, 10 s 6x, interval 10 s) the minced tissues (2 min, 4 &#176;C).</li>
	<li>Add another 3 mL of mitochondrial isolation buffer into the tissue homogenates and mix them well by pipetting.</li>
	<li>Centrifuge the prepared tissue homogenate (1,300 x g, 10 min, 4&#176;C). Remove the crude nuclear fraction (i.e., the pellet) and keep the supernatant.</li>
	<li>Recentrifuge the supernatant (10,000 x g, 10 min, 4&#176;C). Remove the microsomes (i.e., the supernatant) and keep the pellet.</li>
	<li>Add 2 mL of mitochondrial isolation buffer to resuspend the pellet well by light pipetting.</li>
	<li>Centrifuge the pellet suspension (7,000 x g, 10 min, 4 &#176;C). Discard the supernatant and keep the crude mitochondria (i.e., the pellet).</li>
	<li>Add 12 mL of 25% density gradient medium to resuspend the extracted crude mitochondria.</li>
	<li>Make a discontinuous density gradient by filling a tube from bottom to top with 8 mL of 38%, 5 mL of 34%, 8 mL of 30%, 12 mL of 25% (containing the crude mitochondria from step 2.12), 8 mL of 23%, and 3 mL of 20% density gradient medium, and centrifuge it (52,000 x g, 90 min, 4 &#176;C).</li>
	<li>Use a long and blunt syringe to collect the purified mitochondria in the range from the interface between 25% and 30% to the interface between 34% and 38% into a clean tube.</li>
	<li>Add mitochondrial isolation buffer into the collected mitochondria to dilute it to a three-fold volume. Centrifuge (15,000 x g, 20 min, 4 &#176;C) and discard the supernatant.</li>
	<li>Add 2 mL of mitochondrial isolation buffer to resuspend the pellet, and centrifuge it (15,000 x g, 20 min, 4 &#176;C). Discard the supernatant and keep the pellet.</li>
	<li>Collect the final pellet (i.e., the purified mitochondria) and store it at -20 &#176;C. 
	NOTE: Combine all purified mitochondria from the control ovarian tissue as the mitochondrial samples for quantitative proteomics analysis.</li>
</ol>
3. Verification of the quality of purified tissue mitochondrial samples
<ol>
	<li>Take a tube of purified mitochondrial samples (i.e., the pellets from the ovarian cancer tissues from step 1.16 and the control ovarian tissues from step 2.17) for verification with electron microscopy (EM). 
	NOTE: The detailed protocol was described previously15,16.</li>
	<li>Prepare the mitochondrial protein extraction buffer by mixing 8 M urea, 2 M thiourea, 40 mM Tris, 1 mM EDTA, 130 mM dithiothreitol (DTT), and 4% (w:v) 3-((3-cholamidopropyl) dimethylammonio)-1-propanesulfonate (CHAPS). Adjust the pH to 8.52.</li>
	<li>Take a tube of purified mitochondrial samples (i.e., the pellets from the ovarian cancer tissues in step 1.16 and the control ovarian tissues in step 2.17) and add mitochondrial protein extraction buffer (mitochondrial samples to protein extraction buffer, 1:5) to resuspend the pellet. Freeze the samples in liquid nitrogen 3x and store at room temperature for 2 h.</li>
	<li>Centrifuge at 12,000 x g, 30 min, 4 &#176;C, collect the supernatant (i.e., the extracted mitochondrial protein sample), and measure the protein content with a bicinchoninic acid (BCA) protein assay kit.</li>
	<li>Prepare 50 mL of 1.5 M Tris-HCl (pH 8.8) by mixing 9.08 g of Tris and 40 mL of ddH2O, adjusting the pH to 8.8 using HCl, and adding ddH2O to a final volume of 50 mL. Store at 4 &#176;C.</li>
	<li>Prepare 50 mL of 1 M Tris-HCl (pH 6.8) by mixing 6.06 g of Tris and 40 mL of ddH2O, adjusting the pH to 6.8 using HCl, and adding ddH2O to a final volume of 50 mL. Store at 4 &#176;C.</li>
	<li>Prepare 100 mL of 30% acrylamide-bis solution by mixing 29 g of acrylamide, 1 g of bis-acrylamide, and 80 mL of ddH2O, and adding ddH2O to a final volume of 100 mL. Store it in a brown bottle at 4 &#176;C.</li>
	<li>Prepare 1,000 mL of 10x tris-glycine electrophoresis buffer by mixing 29 g of glycine, 58 g of Tris, 3.7 g of sodium dodecyl sulfate (SDS), and 800 mL of ddH2O, and then adding ddH2O to a final volume of 1,000 mL.</li>
	<li>Prepare 10 mL of loading buffer by mixing 2 mL of glycerin, 0.02 g of bromophenol blue, 0.4 g of SDS, 2 mL of Tris-HCl pH 6.8, and 7 mL of ddH2O, and then adding ddH2O to a final volume of 10 mL.</li>
	<li>Prepare 10 mL of 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) resolving gel by mixing 4 mL of ddH2O, 3.3 mL of 30% acrylamide-Bis solution, 2.5 mL of 1.5 M Tris-HCl (pH 8.8), 0.1 mL of 10% SDS, 0.1 mL of 10% ammonium persulfate, and 0.004 mL of tetramethylethylenediamine (TEMED). Pour the SDS-PAGE resolving gel solution into the plate between the glass plates to the level of the bottom of the comb. Add 2 mL of isopropyl alcohol onto the top. Leave for 30 min.</li>
	<li>Remove the isopropyl alcohol and wash the top with ddH2O 3x. Pour the 5% concentration gel solution containing 4.1 mL of ddH2O, 1.0 mL of 30% acylamide-bis solution, 0.75 mL of 1 M Tris-HCl (pH 6.8), 0.06 mL of 10% SDS, 0.06 mL of 10% ammonium persulfate, and 0.006 mL of TEMED. Immediately insert the comb into the concentration gel solution, leave for 30 min, and then take out the comb.</li>
	<li>Prepare the 1x Tris-glycine electrophoresis buffer by diluting 100 mL of 10x Tris-glycine electrophoresis buffer with 900 mL of ddH2O and pour into the electrophoretic tank.</li>
	<li>Mix 30 &#181;g of the mitochondrial proteins from the ovarian cancer and control ovarian tissues from step 3.4 with loading buffer to reach a final volume of 20 &#181;L. Boil the mixture for 5 min, then separate it with the 10% SDS-PAGE resolving gel using a constant voltage of 100 V. When the bromphenol blue reaches the bottom, stop the electrophoresis.</li>
	<li>Prepare 1.5 L of electrophoretic transfer buffer by mixing 150 mL of 10x Tris-glycine electrophoresis buffer, 1,050 mL of ddH2O, and 300 mL of methanol.</li>
	<li>Soak the PVDF membrane in 100% methanol for 10 min and in electrophoretic transfer buffer for at least 5 min. Soak five sheets of filter paper in 1x elecrophoretic transfer buffer for at least 5 min.</li>
	<li>Take out the SDS-PAGE gel containing the proteins, and soak it in electrophoretic transfer buffer for 10 min.</li>
	<li>Make a transfer cassette by placing a wet sponge on the white plate, three sheets of wet filter paper, the PVDF membrane, the SDS-PAGE gel with the proteins, two sheets of wet filter paper, and the wet sponge from the bottom to top. Avoid any bubbles.</li>
	<li>Place the transfer cassette into the electrophoretic tank, pour in the electrophoretic transfer buffer, and then transfer for 2 h under a constant 200 mA current.</li>
	<li>Prepare 10x Tris-buffered saline (TBS) stock solution by mixing 12.114 g of Tris, 29.22 g of NaCl, and adding ddH2O to a final volume of 500 mL. Prepare 2 L of 1x TBST by mixing 200 mL of 10x TBS, 2 mL of Tween-20, and 1,798 mL of ddH2O. Prepare 100 mL of blocking solution by mixing 100 mL of TBST and 5 g of BSA.</li>
	<li>After transfer, take out the PVDF membrane, wash lightly in TBST for 5 min, then block it in blocking solution for 1 h at room temperature.</li>
	<li>Incubate the PVDF membrane (4 &#176;C overnight) with different primary antibodies specific to different subcellular organelles, including lamin B (cell nucleus; goat anti-human antibody 1: 1,000 blocking solution), flotillin-1 (cytomembrane; rabbit anti-human antibody 1:500 blocking solution), COX4I1 (mitochondrion; rabbit anti-human 1:1,000 blocking solution), GM130 (Golgi apparatus; mouse anti-human antibody 1:1,000 blocking solution), catalase (peroxisome; rabbit anti-human antibody 1:1,000 blocking solution), and cathepsin B (lysosome; rabbit anti-human antibody 1:1,000 blocking solution). Lightly wash in TBST for 5 min 3x.</li>
	<li>Incubate the PVDF membrane for 1 h at room temperature with the corresponding secondary antibodies (rabbit anti-goat, goat anti-rabbit, or goat anti-mouse). Lightly wash in TBST for 5 min 3x. Visualize it with electrochemiluminescence (ECL).</li>
</ol>
4. iTRAQ-SCX-LC-MS/MS analysis
NOTE: The detailed procedures for section 4 refer to the iTRAQ instructions (Table of Materials).
<ol>
	<li>Add SDT buffer (4% SDS, 100 mM Tris-HCl pH 7.6, and 100 mM DTT) to the purified mitochondrial samples (i.e., the pellets from the ovarian cancer tissues from step 1.16 and the control ovarian tissues from step 2.17). Vortex the sample until there is no visible precipitate. 
	NOTE: The mitochondrial sample to SDT buffer ratio should be 1:5.</li>
	<li>Boil the SDT-treated sample for 5 min, cool down the sample on ice, and then centrifuge (2,000 x g, 2 min).</li>
	<li>Collect the supernatant as the extracted mitochondrial protein samples and measure the protein content with a BCA protein assay kit.</li>
	<li>Take SDT-extracted mitochondrial proteins (200 &#181;g of each sample) for reduction, alkylation, digestion with trypsin, desalination, and lyophilization3,4. Do three replicates for each sample.</li>
	<li>Treat the tryptic peptides (100 &#181;g of each sample) from step 4.4 with 100 mM tetraethyl ammonium bromide solution (pH 8.5), and label the tryptic peptides with one of the 6-plex iTRAQ reagents according to its instructions3,4. Label each sample 3x.</li>
	<li>Mix equally 6 labeled tryptic peptide samples (three from ovarian cancer tissues and three from control ovarian tissues), and dry with a vacuum concentrator.</li>
	<li>Fractionate the mixed iTRAQ-labeled peptides with SCX chromatography into 60 fractions (one fraction per 1 min), then combine every two fractions as a SCX-fractionated sample (n = 30).</li>
	<li>Subject each SCX-fractionated sample to LC-MS/MS analysis on a mass spectrometer3,4 coupled with a nano LC system within a 60-min LC separation gradient to obtain MS/MS data.</li>
	<li>Search the MS/MS data to identify proteins with the search engine (Table of Materials).</li>
	<li>Use the iTRAQ reporter-ion intensities to quantify each protein and determine mitochondrial differentially expressed proteins (mtDEPs) with a change of &#62;1.5 or &#60;-1.5 fold, and p &#60; 0.05.</li>
</ol>

<ol>
	<li>Sakhuja, S., Yun, H., Pisu, M., Akinyemiju, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Availability+of+healthcare+resources+and+epithelial+ovarian+cancer+stage+of+diagnosis+and+mortality+among+Blacks+and+Whites.">Availability of healthcare resources and epithelial ovarian cancer stage of diagnosis and mortality among Blacks and Whites.</a> Journal of Ovarian Research. 10, 57 (2017).</li><li>Gadducci, A., 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=Surveillance+procedures+for+patients+treated+for+epithelial+ovarian+cancer:+a+review+of+the+literature.">Surveillance procedures for patients treated for epithelial ovarian cancer: a review of the literature.</a> International Journal of Gynecological Cancer. 17, 21-31 (2007).</li><li>Li, N., Li, H., Cao, L., Zhan, X. <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+the+mitochondrial+proteome+in+human+ovarian+carcinomas.">Quantitative analysis of the mitochondrial proteome in human ovarian carcinomas.</a> Endocrine-Related Cancer. 25, 909-931 (2018).</li><li>Li, N., Zhan, X., Zhan, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+lncRNA+SNHG3+regulates+energy+metabolism+of+ovarian+cancer+by+an+analysis+of+mitochondrial+proteomes.">The lncRNA SNHG3 regulates energy metabolism of ovarian cancer by an analysis of mitochondrial proteomes.</a> Gynecologic Oncology. 150, 343-354 (2018).</li><li>Li, N., Zhan, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Signaling+pathway+network+alterations+in+human+ovarian+cancers+identified+with+quantitative+mitochondrial+proteomics.">Signaling pathway network alterations in human ovarian cancers identified with quantitative mitochondrial proteomics.</a> EPMA Journal. 10, 153-172 (2019).</li><li>Deng, P., Haynes, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mitochondrial+dysfunction+in+cancer:+Potential+roles+of+ATF5+and+the+mitochondrial+UPR.">Mitochondrial dysfunction in cancer: Potential roles of ATF5 and the mitochondrial UPR.</a> Semin Cancer Biology. 47, 43-49 (2017).</li><li>Georgieva, 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=Mitochondrial+dysfunction+and+redox+imbalance+as+a+diagnostic+marker+of+"free+radical+diseases".">Mitochondrial dysfunction and redox imbalance as a diagnostic marker of "free radical diseases".</a> Anticancer Research. 37, 5373-5381 (2017).</li><li>Sotgia, F., 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+mitochondrial+based+oncology+platform+for+targeting+cancer+stem+cells+(CSCs):+MITO-ONC-RX.">A mitochondrial based oncology platform for targeting cancer stem cells (CSCs): MITO-ONC-RX.</a> Cell Cycle. 17, 2091-2100 (2018).</li><li>Lee, J. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Individualized+metabolic+profiling+stratifies+pancreatic+and+biliary+tract+cancer:+a+useful+tool+for+innovative+screening+programs+and+predictive+strategies+in+healthcare.">Individualized metabolic profiling stratifies pancreatic and biliary tract cancer: a useful tool for innovative screening programs and predictive strategies in healthcare.</a> EPMA Journal. 9, 287-297 (2018).</li><li>Zhan, X., Long, Y., Lu, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploration+of+variations+in+proteome+and+metabolome+for+predictive+diagnostics+and+personalized+treatment+algorithms:+Innovative+approach+and+examples+for+potential+clinical+application.">Exploration of variations in proteome and metabolome for predictive diagnostics and personalized treatment algorithms: Innovative approach and examples for potential clinical application.</a> Journal of Proteomics. 188, 30-40 (2018).</li><li>Lu, M., Zhan, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+crucial+role+of+multiomic+approach+in+cancer+research+and+clinically+relevant+outcomes.">The crucial role of multiomic approach in cancer research and clinically relevant outcomes.</a> EPMA Journal. 9, 77-102 (2018).</li><li>Hu, R., Wang, X., Zhan, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-parameter+systematic+strategies+for+predictive,+preventive+and+personalised+medicine+in+cancer.">Multi-parameter systematic strategies for predictive, preventive and personalised medicine in cancer.</a> EPMA Journal. 4, 2 (2013).</li><li>Cheng, T., Zhan, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pattern+recognition+for+predictive,+preventive,+and+personalized+medicine+in+cancer.">Pattern recognition for predictive, preventive, and personalized medicine in cancer.</a> EPMA Journal. 8, 51-60 (2017).</li><li>Zhan, X., Zhou, T., Li, N., Li, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+differentially+mitochondrial+proteomic+dataset+in+human+ovarian+cancer+relative+to+control+tissues.">The differentially mitochondrial proteomic dataset in human ovarian cancer relative to control tissues.</a> Data in Brief. 20, 459-462 (2018).</li><li>McMillan, J. D., Eisenback, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transmission+electron+microscopy+for+analysis+of+mitochondria+in+mouse+skeletal+muscle.">Transmission electron microscopy for analysis of mitochondria in mouse skeletal muscle.</a> Bio-protocol. 8, e2455 (2018).</li><li>Paul, P. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeted+ablation+of+TRAF6+inhibits+skeletal+muscle+wasting+in+mice.">Targeted ablation of TRAF6 inhibits skeletal muscle wasting in mice.</a> Journal of Cell Biology. 191, 1395-1411 (2010).</li><li>Pascual-Ahuir, A., Manzanares-Estreder, S., Proft, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pro-+and+antioxidant+functions+of+the+peroxisome-mitochondria+connection+and+its+impact+on+aging+and+disease.">Pro- and antioxidant functions of the peroxisome-mitochondria connection and its impact on aging and disease.</a> Oxidative Medicine and Cellular Longevity. 2017, 9860841 (2017).</li><li>Schrader, M., Costello, J., Godinho, L. F., Islinger, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peroxisome-mitochondria+interplay+and+disease.">Peroxisome-mitochondria interplay and disease.</a> Journal of Inherited Metabolic Disease. 38, 681-702 (2015).</li><li>Bartol&#225;k-Suki, E., Imsirovic, J., Nishibori, Y., Krishnan, R., Suki, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+mitochondrial+structure+and+dynamics+by+the+cytoskeleton+and+mechanical+factors.">Regulation of mitochondrial structure and dynamics by the cytoskeleton and mechanical factors.</a> International Journal of Molecular Sciences. 18, 1812 (2017).</li><li>Rezaul, K., Wu, L., Mayya, V., Hwang, S., Han, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+systematic+characterization+of+mitochondrial+proteome+from+human+T+leukemia+cells.">A systematic characterization of mitochondrial proteome from human T leukemia cells.</a> Molecular &amp; Cellular Proteomics. 4, 169-181 (2005).</li><li>Zhan, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+many+proteins+can+be+identified+in+a+2DE+gel+spot+within+an+analysis+of+a+complex+human+cancer+tissue+proteome?.">How many proteins can be identified in a 2DE gel spot within an analysis of a complex human cancer tissue proteome?.</a> Electrophoresis. 39, 965-980 (2018).</li><li>Zhan, X., Li, N., Zhan, X., Qian, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revival+of+2DE-LC/MS+in+proteomics+and+its+potential+for+large-scale+study+of+human+proteoforms.">Revival of 2DE-LC/MS in proteomics and its potential for large-scale study of human proteoforms.</a> Med One. 3, e180008 (2018).</li></ol>

The authors have nothing to disclose.

Mitochondrial alterations are a hallmark of ovarian cancer. Preparation of high-quality mitochondrial samples from human ovarian cancer and control tissues for large-scale quantitative proteomics benefit the in-depth understanding of mitochondrial function in ovarian cancer pathogenesis and mitochondrial molecular network changes, and help clarify its molecular mechanism for subsequent discovery of target therapy and effective biomarkers based on mitochondria4,5<...

Ovarian cancer is a common gynecologic cancer with high mortality but unclear molecular mechanism1,2. Most of ovarian cancers are diagnosed in the advanced stage, which seriously hampers therapy. Mitochondrial changes are a hallmark of human ovarian cancers, and mitochondria are the centers of energy metabolism, cell signaling, and oxidative stress3,4,5,6,7. In-depth insights into the changes of the mitochondrial proteome in ovarian cancers compared to control ovarian tissue will benefit in-depth understanding of the molecular mechanisms of ovarian cancer, and the discovery of effective and reliable biomarkers and therapeutic targets. Mitochondrial metabolism has been proposed and recognized as a target for cancer therapy, and antimitochondrial therapy might ultimately be very beneficial for preventing the recurrence and metastasis of cancer8. Individual metabolic profiling is also already practiced as a useful tool for cancer stratification and predictive strategies9,10.
The long-term goal of this research is to develop and use a quantitative mitochondrial proteomics method to study ovarian cancer for clarification of mitochondrial proteome alterations between ovarian cancer and control ovarian tissues, and their molecular network alterations from a systematic multi-omics angle11,12, which will result in the discovery of mitochondria-targeted molecular biomarkers13 for clarification of the molecular mechanisms of ovarian cancer, prediction, and personalized treatment of ovarian cancer patients. Isobaric tags for relative and absolute quantification (iTRAQ) labeling3,4 are an effective method to quantify the mitochondrial protein changes. Preparation of high-quality mitochondrial samples from human ovarian cancer and control ovarian tissues are the prerequisite for iTRAQ quantitative analysis of mitochondrial proteomes3. Mitochondrial preparation coupled with iTRAQ quantitative proteomics has been successfully used in long-term research programs about the human ovarian cancer mitochondrial proteome, including the establishment of mitochondrial proteome reference maps3, the analysis of differentially expressed mitochondrial profiles4,14 and post-translational modifications, including phosphorylation, which has already resulted in the discovery of important signaling pathway network changes in human ovarian cancers5, including alterations in energy metabolism4, lipid metabolism, and mitophagy pathway-systems3.
Previous studies have found that differential-speed centrifugation in combination with density gradient centrifugation is an effective method to isolate and purify mitochondria from human ovarian cancer and control ovarian tissues3,4,5,14. The iTRAQ labeling coupled with strong cation exchange (SCX)-liquid chromatography (LC)-tandem mass spectrometry (MS/MS) is the key technique to detect, identify, and quantify the proteins from the prepared mitochondrial samples.
Here, detailed protocols for mitochondrial preparation coupled with iTRAQ quantitative proteomics are described. These have been successfully used in the analysis of human ovarian cancer tissue mitochondrial proteomes. The protocols include preparation of samples, differential-speed centrifugation, density gradient centrifugation, quality assessment of mitochondrial samples, protein digestion with trypsin, iTRAQ labeling, SCX fractionation, LC, MS/MS, database searching, and quantitative analysis of mitochondrial proteins. Moreover, this protocol easily translates to analyze other human tissue mitochondrial proteomes.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>BCA protein assay kit</td>
			<td>Vazyme</td>
			<td>E112</td>
			<td>BCA protein assay kit is a special 3-component version of our popular BCA reagents, optimized to measure (A562nm) total protein concentration of dilute protein solutions (0.5 to 20 micrograms/ml).</td>
		</tr>
		<tr>
			<td>Bovine serum albumin (BSA)</td>
			<td>Solarbio</td>
			<td>A8020-5G</td>
			<td>Heat shock fraction, Australia origin, protease free, low fatty acid, low IgG, pH 7, &#8805;98%</td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>XiangYi</td>
			<td>TDZ4--WS</td>
			<td></td>
		</tr>
		<tr>
			<td>CHAPS</td>
			<td>Sigma</td>
			<td>C9426-5G</td>
			<td>BioReagent, suitable for electrophoresis, &#8805;98% (HPLC) (Sigma-Aldrich)</td>
		</tr>
		<tr>
			<td>Diamine tetraacetic acid (EDTA)</td>
			<td>Sigma</td>
			<td>798681-100G</td>
			<td>Anhydrous, free-flowing, Redi-Dri, &#8805;98%</td>
		</tr>
		<tr>
			<td>DTT</td>
			<td>Sigma</td>
			<td>10197777001</td>
			<td>1,4-Dithiothreitol</td>
		</tr>
		<tr>
			<td>Easy nLC</td>
			<td>Proxeon Biosystems (now Thermo Fisher Scientific)</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Ethylen glycol bis(2-aminoethyl ether)tetraacetic acid (EGTA)</td>
			<td>Sigma</td>
			<td>E0396-10G</td>
			<td>BioXtra, &#8805;97 .0%</td>
		</tr>
		<tr>
			<td>Homogenizer</td>
			<td>SilentShake</td>
			<td>HYQ-3110</td>
			<td></td>
		</tr>
		<tr>
			<td>iTRAQ reagent kit</td>
			<td>Applied Biosystems</td>
			<td></td>
			<td>Applied Biosystems iTRAQ Reagents&#8211;Chemistry Reference Guide, P/N 4351918A</td>
		</tr>
		<tr>
			<td>Low-temperature super-speed centrifuger</td>
			<td>Eppendorf</td>
			<td>5424R</td>
			<td></td>
		</tr>
		<tr>
			<td>Mannitol</td>
			<td>Macklin</td>
			<td>M813424-100G</td>
			<td>Mannitol is a polyol (polyhydric alcohol) produced from hydrogenation from fructose that functions as a sweetener, humectant, and bulking agent. It has low hygroscopicity and poor oil solvency.</td>
		</tr>
		<tr>
			<td>MASCOT search engine</td>
			<td>Matrix Science, London, UK; version 2.2</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Nagarse</td>
			<td>Solarbio</td>
			<td>P9090</td>
			<td></td>
		</tr>
		<tr>
			<td>N-hydroxysuccinimide (SDT)</td>
			<td>Sigma</td>
			<td>56480-25G</td>
			<td>Purum, &#8805;97.0% (T)</td>
		</tr>
		<tr>
			<td>Nycodenz</td>
			<td>Alere/Axis-Shield</td>
			<td>1002424-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Phenylmethanesulfonyl fluoride (PMSF) protease inhibitor</td>
			<td>Solarbio</td>
			<td>P0100-1ML</td>
			<td>PMSF is a protease inhibitor that reacts with serine residues to inhibit trypsin, chymotrypsin, thrombin, and papain.</td>
		</tr>
		<tr>
			<td>Potassium chloride</td>
			<td>Macklin</td>
			<td>P816354-25G</td>
			<td>Potassium chloride, KCI, also known as potassium muriate and sylvite, is a colorless crystalline solid with a salty taste that melts at 776&#176;C (1420 OF). It is soluble in water, but insoluble in alcohol. Potassium chloride is used in fertilizers, pharmaceuticals, photography, and as a salt substitute.</td>
		</tr>
		<tr>
			<td>Proteome Discover 1.4</td>
			<td>Matrix Science, London, UK</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>PVDF membrane</td>
			<td>Millipore</td>
			<td>05317</td>
			<td>It is 1 roll, 26.5 cm x 1.875 m, 0.45 &#181;m pore size, hydrophobic PVDF transfer membrane with low background fluorescence for western blotting. It is compatible with visible and infrared fluorescent probes.</td>
		</tr>
		<tr>
			<td>Q Exactive mass spectrometer</td>
			<td>Thermo Fisher Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SCX column</td>
			<td>Sigma</td>
			<td>58997</td>
			<td>It is 5-&#956;m particle size, length 5cm &#215; i.d. 4.6mm (Supelco).</td>
		</tr>
		<tr>
			<td>Sodium orthovanadate (V)</td>
			<td>Macklin</td>
			<td>S817660-25G</td>
			<td>Sodium orthovanadate (Vanadate) is a general competitive inhibitor for protein phosphotyrosyl phosphatases. The inhibition by sodium orthovanadate is reversible upon the addition of EDTA or by dilution.</td>
		</tr>
		<tr>
			<td>Sucrose</td>
			<td>Macklin</td>
			<td>S824459-500G</td>
			<td>Vetec reagent grade, 99%</td>
		</tr>
		<tr>
			<td>Thiourea</td>
			<td>Sigma</td>
			<td>62-56-6</td>
			<td>ACS reagent, &#8805;99.0%</td>
		</tr>
		<tr>
			<td>Tris base</td>
			<td>Sigma</td>
			<td>10708976001</td>
			<td>TRIS base is useful in the pH range of 7.0-9.0. It has a pKa of 8.1 at 25&#176;C.</td>
		</tr>
		<tr>
			<td>Trypsin (cell culture use)</td>
			<td>Gibco</td>
			<td>25200-056</td>
			<td>This liquid formulation of trypsin contains EDTA and phenol red. Gibco Trypsin-EDTA is made from trypsin powder, an irradiated mixture of proteases derived from porcine pancreas. Due to its digestive strength, trypsin is widely used for cell dissociation, routine cell culture passaging, and primary tissue dissociation.</td>
		</tr>
		<tr>
			<td>Urea</td>
			<td>Sigma</td>
			<td>U5378-100G</td>
			<td>powder, BioReagent, for molecular biology, suitable for cell culture</td>
		</tr>
	</tbody>
</table>

preparation of mitochondria from ovarian cancer tissues and control ovarian tissues for quantitative proteomics analysis

Ovarian cancer is a common gynecologic cancer with high mortality but unclear molecular mechanism. Most ovarian cancers are diagnosed in the advanced stage, which seriously hampers therapy. Mitochondrial changes are a hallmark of human ovarian cancers, and mitochondria are the centers of energy metabolism, cell signaling, and oxidative stress. In-depth insights into the changes of the mitochondrial proteome in ovarian cancers compared to control ovarian tissue will benefit in-depth understanding of the molecular mechanisms of ovarian cancer, and the discovery of effective and reliable biomarkers and therapeutic targets. An effective mitochondrial preparation method coupled with an isobaric tag for relative and absolute quantification (iTRAQ) quantitative proteomics are presented here to analyze human ovarian cancer and control mitochondrial proteomes, including differential-speed centrifugation, density gradient centrifugation, quality assessment of mitochondrial samples, protein digestion with trypsin, iTRAQ labeling, strong cation exchange fractionation (SCX), liquid chromatography (LC), tandem mass spectrometry (MS/MS), database analysis, and quantitative analysis of mitochondrial proteins. Many proteins have been successfully identified to maximize the coverage of the human ovarian cancer mitochondrial proteome and to achieve the differentially expressed mitochondrial protein profile in human ovarian cancers.

There was a difference in the preparation of the mitochondria from ovarian cancer tissues and control ovarian tissues. This study found that it was much easier to prepare mitochondria from ovarian cancer tissues than from control ovarian tissues3,4. Some improvements had to be made to the protocol for the preparation of mitochondria from control ovarian tissues. First, prior to tissue homogenization, it was necessary to add 8 mL o...

Watch this Scientific Journal Video about Preparation of Mitochondria from Ovarian Cancer Tissues and Control Ovarian Tissues for Quantitative Proteomics Analysis at JoVE.com

Preparation of Mitochondria from Ovarian Cancer Tissues and Control Ovarian Tissues for Quantitative Proteomics Analysis

This article presents a protocol of differential-speed centrifugation in combination with density gradient centrifugation to separate mitochondria from human ovarian cancer tissues and control ovarian tissues for quantitative proteomics analysis, resulting in a high-quality mitochondrial sample and high-throughput and high-reproducibility quantitative proteomics analysis of a human ovarian cancer mitochondrial proteome.

Ovarian cancer is a common gynecologic cancer with high mortality but unclear molecular mechanism. Most ovarian cancers are diagnosed in the advanced ...

preparation-mitochondria-from-ovarian-cancer-tissues-control-ovarian

Research

JoVE Journal

Cancer Research

5.7K Views.  Central South University. This article presents a protocol of differential-speed centrifugation in combination with density gradient centrifugation to separate mitochondria from human ovarian cancer tissues and control ovarian tissues for quantitative proteomics analysis, resulting in a high-quality mitochondrial sample and high-throughput and high-reproducibility quantitative proteomics analysis of a human ovarian cancer mitochondrial proteome.

Video: Preparation of Mitochondria from Ovarian Cancer Tissues and Control Ovarian Tissues for Quantitative Proteomics Analysis

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues

The purpose of this study is to introduce a methodology of the immunostaining of human lung tissues, followed by whole-slide digital scanning and image analysis. Digital scanning is a fast way to scan a stack of slides and produce digital images with high quality. It can produce concordant results with conventional light microscopy (CLM) by pathologists. Furthermore, the availability of digital images makes it possible that the same slide can be concurrently observed by multiple people. Moreover, digital images of slides can be stored in a database, which means the long-term deterioration of glass slides is avoided. The limitations of this technique are as follows. First, it needs high-quality prepared tissue and the original immunohistochemistry (IHC) slides without any damage or excess sealant residue. Second, tumor or nontumor areas should be specified by experienced pathologists before the analysis using software, in order to avoid any confusion about the tumor or nontumor areas during scoring. Third, the operator needs to control the color reproduction throughout the digitization process in whole-slide imaging.

ZW10 interacting protein (ZWINT) participates in the mitotic spindle checkpoint and the pathogenesis of carcinoma. Here, we introduce a methodology of the immunostaining of ZWINT in human lung cancer tissues, followed by the digital scanning of whole slides and image analysis. This methodology can provide high-quality digital images and reliable results.

The purpose of this study is to introduce a methodology of the immunostaining of human lung tissues, followed by whole-slide digital scanning and image ...

Characterization and Functional Prediction of Bacteria in Ovarian Tissues

The theory of a "sterile" female upper reproductive tract has been encountering increasing opposition due to advancements in bacterial detection. However, whether ovaries contain bacteria has not yet been confirmed yet. Herein, an experiment to detect bacteria in ovarian tissues was introduced. We chose ovarian cancer patients in the cancer group and noncancerous patients in the control group. 16S rRNA gene sequencing was used to differentiate bacteria in ovarian tissues from the cancer and control groups. Furthermore, we predicted the functional composition of the identified bacteria by using BugBase and PICRUSt. This method can also be used in other viscera and tissues since many organs have been proven to harbor bacteria in recent years. The presence of bacteria in viscera and tissues may help scientists evaluate cancerous and normal tissues and may be aid in the treatment of cancer.

Immunohistochemistry staining and 16S ribosomal RNA gene (16S rRNA gene) sequencing were performed in order to discover and distinguish bacteria in cancerous and noncancerous ovarian tissues in situ. The compositional and functional differences of the bacteria were predicted by using BugBase and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt).

The theory of a "sterile" female upper reproductive tract has been encountering increasing opposition due to advancements in bacterial detection. However, ...

Preparation of Human Tissues Embedded in Optimal Cutting Temperature Compound for Mass Spectrometry Analysis

Sphingolipids are cellular components that have well-established roles in human metabolism and disease. Mass spectrometry can be used to determine whether sphingolipids are altered in a disease and investigate whether sphingolipids can be targeted clinically. However, properly powered prospective studies that acquire tissues directly from the surgical suite can be time consuming, and technically, logistically, and administratively challenging. In contrast, retrospective studies can take advantage of cryopreserved human specimens already available, usually in large numbers, at tissue biorepositories. Other advantages of procuring tissues from biorepositories include access to information associated with the tissue specimens including histology, pathology, and in some instances clinicopathological variables, all of which can be used to examine correlations with lipidomics data. However, technical limitations related to the incompatibility of optimal cutting temperature compound (OCT) used in the cryopreservation and mass spectrometry is a technical barrier for the analysis of lipids. However, we have previously shown that OCT can be easily removed from human biorepository specimens through cycles of washes and centrifugation without altering their sphingolipid content. We have also previously established that sphingolipids in human tissues cryopreserved in OCT are stable for up to 16 years. In this report, we outline the steps and workflow to analyze sphingolipids in human tissue specimens that are embedded in OCT, including washing tissues, weighing tissues for data normalization, the extraction of lipids, preparation of samples for analysis by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS), mass spectrometry data integration, data normalization, and data analysis.

Sphingolipids are bioactive metabolites with well-established roles in human disease. Characterizing alterations in tissues with mass-spectrometry can reveal roles in disease etiology or identify therapeutic targets. However, the OCT-compound used for cryopreservation in biorepositories interferes with mass-spectrometry. We outline methods to analyze sphingolipids in human tissues embedded in OCT with LC-ESI-MS/MS.

Sphingolipids are cellular components that have well-established roles in human metabolism and disease. Mass spectrometry can be used to determine whether ...

Generation and Culturing of High-Grade Serous Ovarian Cancer Patient-Derived Organoids

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the discovery of novel therapeutics and predictive biomarkers for ovarian cancer. These models recapitulate clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Additionally, they have been shown to match the primary tumor morphologically, cytologically, immunohistochemically, and genetically. Thus, organoids facilitate research on tumor cells and the tumor microenvironment and are superior to cell lines. The present protocol describes distinct methods to generate patient-derived ovarian cancer organoids from patient tumors, ascites, and pleural fluid samples with a higher than 97% success rate. The patient samples are separated into cellular suspensions by both mechanical and enzymatic digestion. The cells are then plated utilizing a basement membrane extract (BME) and are supported with optimized growth media containing supplements specific to the culturing of high-grade serous ovarian cancer (HGSOC). After forming initial organoids, the PDOs can sustain long-term culture, including passaging for expansion for subsequent experiments.

Patient-derived organoids (PDO) are a three-dimensional (3D) culture that can mimic the tumor environment in vitro. In high-grade serous ovarian cancer, PDOs represent a model to study novel biomarkers and therapeutics.

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the ...

Application of AlDeSense to Stratify Ovarian Cancer Cells Based on Aldehyde Dehydrogenase 1A1 Activity

Relapse after cancer treatment is often attributed to the persistence of a subpopulation of tumor cells known as cancer stem cells (CSCs), which are characterized by their remarkable tumor-initiating and self-renewal capacity. Depending on the origin of the tumor (e.g., ovaries), the CSC surface biomarker profile can vary dramatically, making the identification of such cells via immunohistochemical staining a challenging endeavor. On the contrary, aldehyde dehydrogenase 1A1 (ALDH1A1) has emerged as an excellent marker to identify CSCs, owing to its conserved expression profile in nearly all progenitor cells including CSCs. The ALDH1A1 isoform belongs to a superfamily of 19 enzymes that are responsible for the oxidation of various endogenous and xenobiotic aldehydes to the corresponding carboxylic acid products. Chan et al. recently developed AlDeSense, an isoform-selective "turn-on" probe for the detection of ALDH1A1 activity, as well as a non-reactive matching control reagent (Ctrl-AlDeSense) to account for off-target staining. This isoform-selective tool has already been demonstrated to be a versatile chemical tool through the detection of ALDH1A1 activity in K562 myelogenous leukemia cells, mammospheres, and melanoma-derived CSC xenografts. In this article, the utility of the probe was showcased through additional fluorimetry, confocal microscopy, and flow cytometry experiments where the relative ALDH1A1 activity was determined in a panel of five ovarian cancer cell lines.

Methods to measure ALDH1A1 activity in live cells are critical in cancer research due to its status as a biomarker of stemness. In this study, we employed an isoform-selective fluorogenic probe to determine the relative levels of ALDH1A1 activity in a panel of five ovarian cancer cell lines.

Relapse after cancer treatment is often attributed to the persistence of a subpopulation of tumor cells known as cancer stem cells (CSCs), which are ...

Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments is crucial to advancing healthcare and improving patient outcomes. Organoids are in-vitro three-dimensional multicellular miniature organs. Patient-derived organoid (PDO) models of ovarian cancer may be optimal for drug screening because they more accurately recapitulate tissues of interest than two-dimensional cell culture models and are inexpensive compared to patient-derived xenografts. In addition, ovarian cancer PDOs mimic the variable tumor microenvironment and genetic background typically observed in ovarian cancer. Here, a method is described that can be used to test conventional and novel drugs on PDOs derived from ovarian cancer tissue and ascites. A luminescence-based adenosine triphosphate (ATP) assay is used to measure viability, growth rate, and drug sensitivity. Drug screens in PDOs can be completed in 7-10 days, depending on the rate of organoid formation and drug treatments.

We present a protocol that can be used to conduct therapeutic drug testing with patient-derived ovarian cancer organoids.

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments ...

Human Omentum Specimen Processing and Co-Culture with Ovarian Cancer Cells

Establishing a 3D Ex Vivo Model for Ovarian Cancer-Omentum Interaction

An Ex Vivo Model of Ovarian Cancer Peritoneal Metastasis Using Human Omentum

Ovarian cancer is the deadliest gynecologic malignancy. The omentum plays a key role in providing a supportive microenvironment to metastatic ovarian cancer cells as well as immune modulatory signals that allow tumor tolerance. However, we have limited models that closely mimic the interaction between ovarian cancer cells and adipose-rich tissues. To further understand the cellular and molecular mechanisms by which the omentum provides a pro-tumoral microenvironment, we developed a unique 3D ex vivo model of cancer cell-omentum interaction. Using human omentum, we are able to grow ovarian cancer cells within this adipose-rich microenvironment and monitor the factors responsible for tumor growth and immune regulation. In addition to providing a platform for the study of this adipose-rich tumor microenvironment, the model provides an excellent platform for the development and evaluation of novel therapeutic approaches to target metastatic cancer cells in this niche. The proposed model is easy to generate, inexpensive, and applicable to translational investigations.

Author Spotlight: Advanced Ex Vivo Model for Investigating Cancer-Adipose Microenvironment Interaction 

This protocol describes the establishment of a three-dimensional (3D) ex vivo model of cancer cell-omentum interaction. The model provides a platform for elucidating pro-tumor mechanisms within the adipose niche and for testing novel therapies.

Ovarian cancer is the deadliest gynecologic malignancy. The omentum plays a key role in providing a supportive microenvironment to metastatic ovarian ...

Standardizing Ovarian Cancer Organoid Biobanking

Strategy for Biobanking of Ovarian Cancer Organoids: Addressing the Interpatient Heterogeneity across Histological Subtypes and Disease Stages

While the establishment of an ovarian cancer biobank from patient-derived organoids along with their clinical background information promises advances in research and patient care, standardization remains a challenge due to the heterogeneity of this lethal malignancy, combined with the inherent complexity of organoid technology. This adaptable protocol provides a systematic framework to realize the full potential of ovarian cancer organoids considering a patient-specific variability of progenitors. By implementing a structured experimental workflow to select optimal culture conditions and seeding methods, with parallel testing of direct 3D seeding versus a 2D/3D route, we obtain, in most cases, robust long-term expanding lines suitable for a broad range of downstream applications. 
Notably, the protocol has been tested and proven efficient in a great number of cases (N = 120) of highly heterogeneous starting material, including high-grade and low-grade ovarian cancer and stages of the disease with primary debulking, recurrent disease, and post-neoadjuvant surgical specimens. Within a low Wnt, high BMP exogenous signaling environment, we observed progenitors being differently susceptible to the activation of the Heregulin 1 &#223; (HER&#223;-1)-pathway, with HER&#223;-1 promoting organoid formation in some while inhibiting it in others. For a subset of the patient's samples, optimal organoid formation and long-term growth necessitate the addition of fibroblast growth factor 10 and R-Spondin 1 to the medium. 
Further, we highlight the critical steps of tissue digestion and progenitor isolation and point to examples where brief cultivation in 2D on plastic is beneficial for subsequent organoid formation in the Basement Membrane Extract type 2 matrix. Overall, optimal biobanking requires systematic testing of all main conditions in parallel to identify an adequate growth environment for individual lines. The protocol also describes the handling procedure for efficient embedding, sectioning, and staining to obtain high-resolution images of organoids, which is required for comprehensive phenotyping.

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer 

This protocol offers a systematic framework for the establishment of ovarian cancer organoids from different disease stages and addresses the challenges of patient-specific variability to increase yield and enable robust long-term expansion for subsequent applications. It includes detailed steps for tissue processing, seeding, adjusting media requirements, and immunofluorescence staining.

While the establishment of an ovarian cancer biobank from patient-derived organoids along with their clinical background information promises advances in ...