This work was supported by the Clinical Research Award of the First Affiliated Hospital of Xi&#39;an Jiaotong University, China (XJTU1AF-2018-017, XJTU1AF-CRF-2019-002), the Major Basic Research Project of Natural Science of Shaanxi Provincial Science and Technology Department (2018JM7073, 2017ZDJC-11), the Key Research and Development Project of Shaanxi Provincial Science and Technology Department (2017ZDXM-SF-068, 2019QYPY-138), the Shaanxi Provincial Collaborative Technology Innovation Project (2017XT-026, 2018XT-002), and the Medical Research Project of Xi&#39;an Social Development Guidance Plan (2017117SF/YX011-3). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We thank the colleagues in the Department of Gynecology of First Affiliated Hospital of Xi&#39;an Jiaotong University for their contributions to collecting samples.

This study was approved by the Medical Institutional Ethics Committee of the First Affiliated Hospital of Xi&#39;an Jiaotong University (No. XJTUIAF2018LSK-139). Informed consent was obtained from all enrolled patients.
1. Criteria for entering the cancer group and the control group
<ol>
	<li>For the cancer group, enroll patients who are primarily diagnosed with ovarian cancer, and after laparotomy, they are proven to have serous ovarian cancer by pathological findings.</li>
	<li>For the control group, enroll patients that are primarily diagnosed with uterine myoma or uterine adenomyosis, without presenting any ovarian condition, and who have undergone hysterectomy and salpingo-oophorectomy. 
	NOTE: This standard is not definite. Patients with diseases not affecting the ovaries who undergo hysterectomy and salpingo-oophorectomy can also be enrolled.</li>
	<li>Exclude patients with one or more of the following criteria: 
	Pregnant or breast-feeding women. 
	Taking antibiotics 2 months prior to the surgery. 
	Having fever or elevated inflammatory markers. 
	Having inflammation of any kind. 
	Having undergone neoadjuvant chemotherapy.</li>
</ol>
2. Gather samples
<ol>
	<li>During the surgery, place the resected ovaries into a sterile tube and place the tube in liquid nitrogen for transport. Avoid touching anything else throughout the whole procedure.</li>
	<li>Separate the ovaries into approximately 1-cm thick tissue samples with a pair of new sterile tweezers under a laminar flow cabinet. After separation, preserve samples at -80 &#176;C. 
	&#8203;NOTE: All the procedures for gathering samples are aseptic, including separating the ovaries.</li>
</ol>
3. Sequence the 16S rRNA gene
<ol>
	<li>Extract DNA.
	<ol>
		<li>Add 1.2 mL of inhibit EX buffer into a 2 mL centrifuge tube. Then, add 180-220 mg of samples into the tube. Let the sample fully mix (70 &#176;C water bath for 5 min and then vortex for 15 s).</li>
		<li>Centrifuge the tube for 1 min at 600 x g.</li>
		<li>Place 550 &#181;L of the supernatant into a new 1.5 mL tube, and centrifuge for 1 min at 600 x g.</li>
		<li>Transfer 400 &#181;L of the supernatant with 30 &#181;L of proteinase K into another 1.5 mL tube.</li>
		<li>Add 400 &#181;L of buffer AL and use a vortex mixer for 15 s.</li>
		<li>Incubate at 70 &#176;C for 10 min.</li>
		<li>Add 400 &#181;L of 96-100% alcohol. Use a vortex mixer for 15 s.</li>
		<li>Transfer 600 &#181;L of mixture into an absorption column and centrifuge for 1 min at 13700 g. Exchange the lower tube. Repeat this step 11 times.</li>
		<li>Add 500 &#181;L of buffer AW1, centrifuge for 1 min at 13,700 x g, and change the lower tube.</li>
		<li>Add 500 &#181;L of buffer AW2, centrifuge for 3 min at 13,700 x g, and change the lower tube.</li>
		<li>Centrifuge for 3 min at 13,700 x g.</li>
		<li>Transfer the mixture into a new 1.5 mL tube, add 200 &#181;L of buffer ATE, incubate at room temperature for 5 min and centrifuge for 1 min at 13,700 x g.</li>
	</ol>
	</li>
	<li>Quality testing. Use 1% Sepharose gel electrophoresis to test the quality. Add 400 ng of sample, 120 V, 30 mins. Ideal result: DNA concentration: &#8805; 10 ng/&#181;L, DNA purity: A260/A280 = 1.8-2.0, gross DNA: &#8805; 300 ng.</li>
	<li>Prepare the libraries using a 16S metagenomic sequencing kit according to the manufacturer&#39;s protocol.
	<ol>
		<li>Perform PCR. Briefly, each 25 &#181;L PCR reaction contains 12.5 ng of sample DNA as input, 12.5 &#181;L of 2x KAPA HiFi HotStart ReadyMix and 5 &#181;L of each primer at 1 &#181;M.</li>
		<li>Carry out PCR using the following protocol: an initial denaturation step performed at 95&#176;C for 3 min followed by 25 cycles of denaturation (95&#176;C, 30 s), annealing (55&#176;C, 30 s) and extension (72&#176;C, 30 s), and a final elongation of 5 min at 72&#176;C.</li>
		<li>Clean up the PCR product from the reaction mix with magnetic beads using the manufacturer&#39;s instructions.</li>
		<li>Repeat steps 3.3.1 and 3.3.2.</li>
		<li>Quality testing. Please refer to step 3.2.</li>
		<li>Repeat step 3.3.3.</li>
		<li>Quality testing. Use 1% Sepharose gel electrophoresis to test impurity, a spectrophotometer to test purity, a fluorometer to test the concentration, and an RNA assay kit to test integrity. Follow the manufacturer&#39;s protocol. Normalize and pool the libraries; then sequence (2 x 300 bp paired-end read setting) using 600 cycle V3 standard flow cells, producing approximately 100,000 paired-end 2 x 300 base reads. 
		&#8203;NOTE: The full-length primer sequences: 16S Amplicon polymerase chain reaction (PCR) Forward primer: 5&#39; TCGTCGGCAGCGTCAGATGTGTATAAGA GACAG-[CCTACGGGNGGCWGCAG] and 16S Amplicon PCR Reverse primer: 5&#39; GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-[GACTACHVGGGTATCTAATCC].</li>
	</ol>
	</li>
</ol>
4. Analyze 16S rRNA gene sequencing data
<ol>
	<li>Filter the raw reads of every sample based on sequencing quality with the software package QIIME 2-20180220.
	<ol>
		<li>Copy three files into the directory: emp-paired-end-sequences_01 
		one forward.fastq.gz file that contains the forward sequence reads, 
		one reverse.fastq.gz file that contains the reverse sequence reads, 
		one barcodes.fastq.gz file that contains the associated barcode reads</li>
		<li>Execute 
		qiime tools import \ 
		--type EMPPairedEndSequences \ 
		--input-path emp-paired-end-sequences_01 \--output-path emp-paired-end-sequences_02.qza</li>
	</ol>
	</li>
	<li>Remove the primer and adaptor sequences. 
	qiime cutadapt trim-paired \ 
	--i-demultiplexed-sequences demultiplexed-seqs_02.qza \ 
	--p-front-f GCTACGGGGGG \ 
	--p-front-r GCTACGGGGGG \ 
	--p-error-rate 0 \ 
	--quality-cutoff 25 \ 
	--o-trimmed-sequences trimmed-seqs_03.qza \ 
	--&#8203;verbose</li>
	<li>Shorten sequence reads in which both paired-end qualities are lower than 25. See above --quality-cutoff 25</li>
	<li>Analyze the sequencing data.
	<ol>
		<li>Gather sequences to form operational taxonomic units (OTUs) with a similarity cutoff at 97%. 
		qiime vsearch dereplicate-sequences \ 
		--i-sequences trimmed-seqs_03.qza \ 
		--o-dereplicated-table table_04.qza \ 
		--o-dereplicated-sequences rep-seqs_04.qza 
		&#8203; 
		qiime vsearch cluster-features-closed-reference \ 
		--i-table table_04.qza \ 
		--i-sequences rep-seqs_04.qza \ 
		--i-reference-sequences 97_otus.qza \ 
		--p-perc-identity 0.97 \ 
		--o-clustered-table table-cr-97.qza \ 
		--o-clustered-sequences rep-seqs-cr-97.qza \ 
		--o-unmatched-sequences unmatched-cr-97.qza</li>
		<li>For the OTUs, calculate the relative abundance in each sample. Abundance information is in table-cr-97.qza</li>
	</ol>
	</li>
	<li>Employ a native Bayesian classifier, which aims at the RDP training set (version 9;&#160;http://sourceforge.net/projects/rdp-classifier/), to sort all of the sequences. Mapped taxon information is in table-cr-97.qza</li>
	<li>Within the given OTU, assign a classification that reflects the major coherence of the sequences to OTUs. Then, align the OTUs. See table-cr-97.qza and rep-seqs-cr-97.qza</li>
	<li>Based on the sample group information, perform alpha diversity (including the Chao 1, ACE, Shannon, Simpson and Evenness indexes) and the UniFrac-based principal coordinates analysis (PCoA). 
	qiime tools export \ 
	--input-path table-cr-97.qza \ 
	--output-path exported-feature-table 
	exported-feature-table 
	 
	qiime diversity alpha \ 
	--i-table table-cr-97.qza \ 
	--p-metric observed_otus \ 
	--o-alpha-diversity observed_otus_vector.qza 
	 
	qiime diversity beta \ 
	--i-table table-cr-97.qza \ 
	--p-metric braycurtis \ 
	--o-distance-matrix unweighted_unifrac_distance_matrix.qza</li>
</ol>
5. Predict bacterial function
<ol>
	<li>To predict the related representation of the characteristics of the bacteria, use BugBase21. The input OTU table for BugBase is prepared using the following commands. 
	biom convert -i otu_table.biom -o otu_table.txt --to-tsv 
	biom convert -i otu_table.txt -o otu_table_json.biom --table-type=&#34;OTU table&#34; --to-json 
	NOTE: The prediction is based on six phenotype categories (Ward&#160;et al. unpublished) (https://bugbase.cs.umn.edu/): Gram staining, oxygen tolerance, ability to form biofilms, mobile element content, pathogenicity, and oxidative stress tolerance.</li>
	<li>Predict the functional composition of a metagenome by PICRUSt with the usage of marker gene data and a database containing reference genomes22. 
	make_otu_table.py -i microbiome_97/uclust_ref_picked_otus/test_paired_otus.txt -t /mnt/nas_bioinfo/ref/qiime2_ref/97_otu_taxonomy.txt -o otu_table.biom &#38; 
	normalize_by_copy_number.py -i otu_table.biom -o normalized_otus.biom 
	predict_metagenomes.py -i normalized_otus.biom -o metagenome_predictions.biom 
	categorize_by_function.py -i metagenome_predictions.biom -c &#34;KEGG_Pathways&#34; -l 1 -o picrust_L1.biom 
	categorize_by_function.py -f -i metagenome_predictions.biom -c KEGG_Pathways -l 1 -o metagenome_predictions.L1.txt</li>
	<li>Analyze the differences in functions among each group with the help of STAMP23,24. Please refer to the citations to operate the software.</li>
</ol>
6. Data
<ol>
	<li>Use statistical software to calculate the significance of the findings. The indication of statistical significance should be set as P &#60; 0.05.</li>
	<li>Assess differences in age and parity by Student&#39;s t-test. Assess differences in menopausal status, history of hypertension and diabetes by the chi-square test. Assess differences in the number of ovarian bacterial taxa by the Mann-Whitney U test.</li>
</ol>

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S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translocation+of+a+gut+pathobiont+drives+autoimmunity+in+mice+and+humans.">Translocation of a gut pathobiont drives autoimmunity in mice and humans.</a> Science. 359 (6380), 1156-1161 (2018).</li><li>Brunelli, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Globular+structure+of+human+ovulatory+cervical+mucus.">Globular structure of human ovulatory cervical mucus.</a> FASEB J. 21 (14), 3872-3876 (2007).</li><li>Chen, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+microbiota+continuum+along+the+female+reproductive+tract+and+its+relation+to+uterine-related+diseases.">The microbiota continuum along the female reproductive tract and its relation to uterine-related diseases.</a> Nature Communications. 8 (1), 875 (2017).</li><li>Zervomanolakis, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physiology+of+upward+transport+in+the+human+female+genital+tract.">Physiology of upward transport in the human female genital tract.</a> Annals of the New York Academy of Sciences. 1101, 1-20 (2007).</li><li>Verstraelen, 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=Characterisation+of+the+human+uterine+microbiome+in+non-pregnant+women+through+deep+sequencing+of+the+V1-2+region+of+the+16S+rRNA+gene.">Characterisation of the human uterine microbiome in non-pregnant women through deep sequencing of the V1-2 region of the 16S rRNA gene.</a> PeerJ. 4, 1602 (2016).</li><li>Fang, R. 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The authors have nothing to disclose.

Ovarian cancer has a notable influence on women&#39;s fertility25. Most ovarian cancer patients are diagnosed at late stages, and the 5-year survival rate is less than 30%18. Confirmation of bacteria in the abdominal solid viscera, including the liver, pancreas and spleen, has been published. The existence of bacteria in the upper female reproductive tract occurs because the cervix is not enclosed2,3,<sup...

Recently, an increasing number of articles have been published that prove the existence of bacteria in abdominal solid viscera, such as the kidney, spleen, liver, and ovary1,2. Geller et al. found bacteria in pancreatic tumors, and these bacteria were resistant to gemcitabine, a chemotherapeutic drug2. S. Manfredo Vieira et al. concluded that Enterococcus gallinarum was portable to the lymph nodes, liver and spleen, and it could drive autoimmunity3.
Since the cervix plays a role as a defender, bacteria in the upper female reproductive tract, which contains the uterus, fallopian tubes, and ovaries, have been minimally researched. However, some new theories have been established in recent years. Bacteria may have access to the uterine cavity during the menstrual cycle due to changes in mucins4,5. Additionally, Zervomanolakis et al. confirmed that the uterus, together with the fallopian tubes, is a peristaltic pump controlled by the endocrine system of the ovaries, and this arrangement enables bacteria to enter the endometrium, fallopian tubes, and ovaries6.
The upper reproductive tract is no longer a mystery anymore thanks to the development of bacterial detection methods. Verstraelen et al. used a barcoded paired-end sequencing method to discover uterine bacteria by targeting at the V1-2 hypervariable region of the 16S RNA gene7. Fang et al. employed barcoded sequencing in patients with endometrial polyps and revealed the presence of diverse intrauterine bacteria8. Additionally, by using the 16S RNA gene, Miles et al. and Chen et al. found bacteria in the genital system of women who had undergone salpingo-oophorectomy and hysterectomy, respectively5,9.
Bacteria in tumor tissues have gained increasing attention in recent years. Banerjee et al. discovered that the microbiome signature differed between ovarian cancer patients and controls10. Anoxynatronum sibiricum was associated with tumor stage, and Methanosarcina vacuolata&#160;might be used to diagnose ovarian cancer11. In addition to ovarian cancer, other cancers, such as stomach, lung, prostate, breast, cervix, and endometrium, have been proven to be associated with bacteria12,13,14,15,16,17,18. Poore et al. proposed a new class of microbial-based oncology diagnostics, foreseeing early-stage cancer screening19. In this protocol, we investigated the differences between cancerous and normal ovarian tissues by comparing the composition and function of bacteria in these two tissues.
Immunohistochemistry staining and 16S rRNA gene sequencing were performed to confirm the presence of bacteria in the ovaries. The differences and predicted functions of the ovarian bacteria in cancerous and noncancerous ovarian tissues were studied. The results showed the existence of bacteria in ovarian tissues. Anoxynatronum sibiricum and Methanosarcina vacuolata were related to the stage and the diagnosis of ovarian cancer, respectively. Forty-six significantly different KEGG pathways that were present in both groups were compared.

<table><tbody><tr><td>2200 TapeStation Software</td><td>Agilgent&lt;br/&gt; United States</td><td></td><td></td></tr><tr><td>AmpliSeq for Illumina Library Prep, Indexes, and Accessories</td><td>Illumina</td><td></td><td></td></tr><tr><td>Image-pro plus 7</td><td>Media Cybernetics</td><td></td><td></td></tr><tr><td>Leica ASP 300S</td><td>Leica Biosystems Division of Leica Microsystems</td><td></td><td></td></tr><tr><td>Leica EG 1150</td><td>Leica Biosystems Division of Leica Microsystems</td><td></td><td></td></tr><tr><td>Leica RM2235</td><td>Leica Biosystems Division of Leica Microsystems</td><td></td><td></td></tr><tr><td>LPS Core monoclonal antibody, clone WN1 222-5</td><td>Hycult Biotech</td><td></td><td></td></tr><tr><td>Mag-Bind RxnPure Plus magnetic beads</td><td>Omega Biotek</td><td>M1386-00</td><td></td></tr><tr><td>Mag-Bind Universal Pathogen 96 Kit</td><td>Omega Biotek</td><td>M4029-01</td><td></td></tr><tr><td>MiSeq</td><td>Illumina</td><td>SY-410-1003</td><td></td></tr><tr><td>Silva database</td><td>Max Planck Institute for Marine Microbiology and Jacobs University</td><td></td><td></td></tr><tr><td>the QuantiFluor dsDNA System</td><td>Promega</td><td>E2670</td><td></td></tr><tr><td>Trimmomatic</td><td>Bj&amp;ouml;rn Usadel</td><td></td><td></td></tr><tr><td>ZytoChem Plus (HRP) Anti-Rabbit (DAB) Kit</td><td>Zytomed Systems</td><td>HRP008DAB-RB</td><td></td></tr></tbody></table>

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.

Patients 
A total of 16 qualified patients were included in the study. The control group included 10 women with a diagnosis of benign uterine tumor (among them, 3 patients were diagnosed with uterine myoma, and 7 patients were diagnosed with uterine adenomyosis). Meanwhile, the cancer group contained 6 women with a diagnosis of serous ovarian cancer (among them, 2 patients were diagnosed with stage II, and 2 of them were diagnosed with stage III). The following characteristics showed no differences ...

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Characterization and Functional Prediction of Bacteria in Ovarian Tissues

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, ...

characterization-functional-prediction-bacteria-ovarian

Research

JoVE Journal

Cancer Research

2.7K Views.  First Affiliated Hospital of Xi’an Jiaotong University. Dit protocol is ontworpen om bacteriën in eierstokweefsels te ontdekken en hun functies te voorspellen. Immunohistochemische kleuring en 16S rRNA-sequencing werden gebruikt om bacteriën in kankerachtige en niet-kankerachtige eierstokweefsels in situ te ontdekken en te onderscheiden. De compenserende en functionele verschillen van de bacteriën worden voorspeld met behulp van BoPs en cellulair-genetisch onderzoek van gemeenschappen door reconstructie van niet-geobserveerde dagen.Ons p...

Video: Characterization and Functional Prediction of Bacteria in Ovarian Tissues

An Orthotopic Model of Serous Ovarian Cancer in Immunocompetent Mice for in vivo Tumor Imaging and Monitoring of Tumor Immune Responses

Background: Ovarian cancer is generally diagnosed at an advanced stage where the case/fatality ratio is high and thus remains the most lethal of all gynecologic malignancies among US women 1,2,3. Serous tumors are the most widespread forms of ovarian cancer and 4,5 the Tg-MISIIR-TAg transgenic represents the only mouse model that spontaneously develops this type of tumors. Tg-MISIIR-TAg mice express SV40 transforming region under control of the Mullerian Inhibitory Substance type II Receptor (MISIIR) gene promoter 6. Additional transgenic lines have been identified that express the SV40 TAg transgene, but do not develop ovarian tumors. Non-tumor prone mice exhibit typical lifespan for C57Bl/6 mice and are fertile. These mice can be used as syngeneic allograft recipients for tumor cells isolated from Tg-MISIIR-TAg-DR26 mice.
Objective: Although tumor imaging is possible 7, early detection of deep tumors is challenging in small living animals. To enable preclinical studies in an immunologically intact animal model for serous ovarian cancer, we describe a syngeneic mouse model for this type of ovarian cancer that permits in vivo imaging, studies of the tumor microenvironment and tumor immune responses.
Methods: We first derived a TAg+ mouse cancer cell line (MOV1) from a spontaneous ovarian tumor harvested in a 26 week-old DR26 Tg-MISIIR-TAg female. Then, we stably transduced MOV1 cells with TurboFP635 Lentivirus mammalian vector that encodes Katushka, a far-red mutant of the red fluorescent protein from sea anemone Entacmaea quadricolor with excitation/emission maxima at 588/635 nm 8,9,10. We orthotopically implanted MOV1Kat in the ovary 11,12,13,14 of non-tumor prone Tg-MISIIR-TAg female mice. Tumor progression was followed by in vivo optical imaging and tumor microenvironment was analyzed by immunohistochemistry.
Results: Orthotopically implanted MOV1Kat cells developed serous ovarian tumors. MOV1Kat tumors could be visualized by in vivo imaging up to three weeks after implantation (fig. 1) and were infiltrated with leukocytes, as observed in human ovarian cancers 15 (fig. 2).
Conclusions: We describe an orthotopic model of ovarian cancer suitable for in vivo imaging of early tumors due to the high pH-stability and photostability of Katushka in deep tissues. We propose the use of this novel syngeneic model of serous ovarian cancer for in vivo imaging studies and monitoring of tumor immune responses and immunotherapies.

An Orthotopic Model of Serous Ovarian Cancer in Immunocompetent Mice for in vivo Tumor Imaging and Monitoring of Tumor Immune Responses

To study in vivo tumor growth and tumor microenvironment, we used a syngeneic and orthotopic mouse model of ovarian cancer in immunocompetent animals. We transduced a mouse tumor cell line (MOV1) with Katushka fluorescent protein (MOV1KAT) and here we show its orthotopic implantation in ovary and in vivo imaging.

Background: Ovarian cancer is generally diagnosed at an advanced stage where the case/fatality ratio is high and thus remains the most lethal of all ...

Immunology and Infection

Heterotypic Three-dimensional In Vitro Modeling of Stromal-Epithelial Interactions During Ovarian Cancer Initiation and Progression

Epithelial ovarian cancers (EOCs) are the leading cause of death from gynecological malignancy in Western societies. Despite advances in surgical treatments and improved platinum-based chemotherapies, there has been little improvement in EOC survival rates for more than four decades 1,2. Whilst stage I tumors have 5-year survival rates &gt;85%, survival rates for stage III/IV disease are &lt;40%. Thus, the high rates of mortality for EOC could be significantly decreased if tumors were detected at earlier, more treatable, stages 3-5. At present, the molecular genetic and biological basis of early stage disease development is poorly understood. More specifically, little is known about the role of the microenvironment during tumor initiation; but known risk factors for EOCs (e.g. age and parity) suggest that the microenvironment plays a key role in the early genesis of EOCs. We therefore developed three-dimensional heterotypic models of both the normal ovary and of early stage ovarian cancers. For the normal ovary, we co-cultured normal ovarian surface epithelial (IOSE) and normal stromal fibroblast (INOF)&nbsp;cells, immortalized by retrovrial transduction of the catalytic subunit of human telomerase holoenzyme (hTERT) to extend the lifespan of these cells in culture. To model the earliest stages of ovarian epithelial cell transformation, overexpression of the CMYC oncogene in IOSE cells, again co-cultured with INOF cells. These heterotypic models were used to investigate the effects of aging and senescence on the transformation and invasion of epithelial cells. Here we describe the methodological steps in development of these three-dimensional model; these methodologies aren't specific to the development of normal ovary and ovarian cancer tissues, and could be used to study other tissue types where stromal and epithelial cell interactions are a fundamental aspect of the tissue maintenance and disease development.

Heterotypic Three-dimensional In Vitro Modeling of Stromal-Epithelial Interactions During Ovarian Cancer Initiation and Progression

We describe methodologies for establishing in vitro heterotypic three-dimensional models comprising ovarian fibroblasts and normal ovarian surface or ovarian cancer epithelial cells. We discuss the use of these models to study stromal-epithelial interactions that occur during ovarian cancer development.

Epithelial ovarian cancers (EOCs) are the leading cause of death from gynecological malignancy in Western societies. Despite advances in surgical ...

Medicine

Ovarian Tissue Culture to Visualize Phenomena in Mouse Ovary

Mammalian females periodically ovulate an almost constant number of oocytes during each estrus cycle. To sustain such regularity and periodicity, regulation occurs at the hypothalamic-pituitary-gonadal axis level and on developing follicles in the ovary. Despite active studies, follicle development mechanisms are not clear because of the several steps involved from the dormant primordial follicle activation to ovulation, and because of the regulation complexity that differs at each follicular stage. To investigate the mechanisms of follicle development, and the dynamics of follicles throughout the estrus cycle, we developed a mouse ovarian tissue culture model that can be used to observe follicle development using a microscope. Systematic follicle development, periodical ovulation, and follicle atresia can all be reproduced in the cultured ovary model, and the culture conditions can be experimentally modulated. Here, we demonstrate the usefulness of this method in the study of the regulatory mechanisms of follicle development and other ovarian phenomena.

Ovarian tissue cultures can be used as models of follicle development, ovulation, and follicle atresia and indicate regulatory mechanisms of dynamic ovarian processes.

Mammalian females periodically ovulate an almost constant number of oocytes during each estrus cycle. To sustain such regularity and periodicity, ...

Developmental Biology

Assessment of Ovarian Cancer Spheroid Attachment and Invasion of Mesothelial Cells in Real Time

Ovarian cancers metastasize by shedding into the peritoneal fluid and dispersing to distal sites within the peritoneum. Monolayer cultures do not accurately model the behaviors of cancer cells within a nonadherent environment, as cancer cells inherently aggregate into multicellular structures which contribute to the metastatic process by attaching to and invading the peritoneal lining to form secondary tumors. To model this important stage of ovarian cancer metastasis, multicellular aggregates, or spheroids, can be generated from established ovarian cancer cell lines maintained under nonadherent conditions. To mimic the peritoneal microenvironment encountered by tumor cells in vivo, a spheroid-mesothelial co-culture model was established in which preformed spheroids are plated on top of a human mesothelial cell monolayer, formed over an extracellular matrix barrier. Methods were then developed using a real-time cell analyzer to conduct quantitative real time measurements of the invasive capacity of different ovarian cancer cell lines grown as spheroids. This approach allows for the continuous measurement of invasion over long periods of time, which has several advantages over traditional endpoint assays and more laborious real time microscopy image analyses. In short, this method enables a rapid, determination of factors which regulate the interactions between ovarian cancer spheroid cells invading through mesothelial and matrix barriers over time.

Ovarian cancer cell invasion into the mesothelial lining of the peritoneum is a dynamic process over time. Utilizing a real time analyzer, the invasive capacity of ovarian cancer cells in a spheroid-mesothelial cell co-culture model can be quantified over prolonged time periods, providing insights into factors regulating the metastatic process.

Ovarian cancers metastasize by shedding into the peritoneal fluid and dispersing to distal sites within the peritoneum. Monolayer cultures do not ...

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 ...

Microdissection and Dissociation of the Murine Oviduct: Individual Segment Identification and Single Cell Isolation

Mouse model systems are unmatched for the analysis of disease processes because of their genetic manipulability and the low cost of experimental treatments. However, because of their small body size, some structures, such as the oviduct with a diameter of 200-400 &#956;m, have proven to be relatively difficult to study except by immunohistochemistry. Recently, immunohistochemical studies have uncovered more complex differences in oviduct segments than were previously recognized; thus, the oviduct is divided into four functional segments with different ratios of seven distinct epithelial cell types. The different embryological origins and ratios of the epithelial cell types likely make the four functional regions differentially susceptible to disease. For example, precursor lesions to serous intraepithelial carcinomas arise from the infundibulum in mouse models and from the corresponding fimbrial region in the human fallopian tube. The protocol described here details a method for microdissection to subdivide the oviduct in such a way to yield a sufficient amount and purity of RNA necessary for downstream analysis such as reverse transcription-quantitative PCR (RT-qPCR) and RNA sequencing (RNAseq). Also described is a mostly non-enzymatic tissue dissociation method appropriate for flow cytometry or single cell RNAseq analysis of fully differentiated oviductal cells. The methods described will facilitate further research utilizing the murine oviduct in the field of reproduction, fertility, cancer, and immunology.

A method for microdissection of the mouse oviduct that allows collection of the individual segments while maintaining RNA integrity is presented. In addition, non-enzymatic oviductal cell dissociation procedure is described. The methods are appropriate for subsequent gene and protein analysis of the functionally different oviductal segments and dissociated oviductal cells.

Mouse model systems are unmatched for the analysis of disease processes because of their genetic manipulability and the low cost of experimental ...

Modeling the Early Steps of Ovarian Cancer Dissemination in an Organotypic Culture of the Human Peritoneal Cavity

The pattern of ovarian cancer metastasis is markedly different from that of most other epithelial tumors, because it rarely spreads hematogenously. Instead, ovarian cancer cells exfoliated from the primary tumor are carried by peritoneal fluid to metastatic sites within the peritoneal cavity. These sites, most notably the abdominal peritoneum and omentum, are organs covered by a mesothelium-lined surface. To investigate the processes of ovarian cancer dissemination, we assembled a complex three-dimensional culture system that reconstructs the lining of the peritoneal cavity in vitro. Primary human fibroblasts and mesothelial cells were isolated from human omentum. The fibroblasts were then mixed with extracellular matrix and covered with a layer of the primary human mesothelial cells to mimic the peritoneal and omental surfaces encountered by metastasizing ovarian cancer cells. The resulting organotypic model is, as shown, used to examine the early steps of ovarian cancer dissemination, including cancer cell adhesion, invasion, and proliferation. This model has been used in a number of studies to investigate the role of the microenvironment (cellular and acellular) in early ovarian cancer dissemination. It has also been successfully adapted to high throughput screening and used to identify and test inhibitors of ovarian cancer metastasis.

Here, we present a protocol to construct a three-dimensional in vitro model of the lining of the peritoneal cavity, composed of primary human mesothelial cells and fibroblasts layered with extracellular matrix, as a tool to investigate ovarian cancer cell adhesion, invasion, and proliferation.

The pattern of ovarian cancer metastasis is markedly different from that of most other epithelial tumors, because it rarely spreads hematogenously. ...

Human Ovarian Epithelium Organoids: A Model for Tissue Repair and Drug Testing

Human Ovarian Surface Epithelium Organoids as a Platform to Study Tissue Regeneration

The ovarian surface epithelium (OSE), the outermost layer of the ovary, undergoes rupture during each ovulation and plays a crucial role in ovarian wound healing while restoring ovarian integrity. Additionally, the OSE may serve as the source of epithelial ovarian cancers. Although the OSE regenerative properties have been well studied in mice, understanding the precise mechanism of tissue repair in the human ovary remains hampered by limited access to human ovaries and suitable in vitro culture protocols. Tissue-specific organoids, miniaturized in vitro models replicating both structural and functional aspects of the original organ, offer new opportunities for studying organ physiology, disease modeling, and drug testing. 
Here, we describe a method to isolate primary human OSE (hOSE) from whole ovaries and establish hOSE organoids. We include a morphological and cellular characterization showing heterogeneity between donors. Additionally, we demonstrate the capacity of this culture method to evaluate hormonal effects on OSE-organoid growth over a 2-week period. This method may enable the discovery of factors contributing to OSE regeneration and facilitate patient-specific drug screenings for malignant OSE.

Author Spotlight: Advancing Human Ovarian Repair and Cancer Studies with Surface Epithelium Organoids

This protocol describes establishing three-dimensional (3D) tissue organoids from primary human ovarian surface epithelium (hOSE) cells. The protocol includes isolation of hOSE from freshly collected ovaries, cellular expansion of the hOSE, cryopreservation-thawing procedures, and organoid derivation. Immunofluorescence, quantitative analysis, and showcasing utility as a screening platform are included.

The ovarian surface epithelium (OSE), the outermost layer of the ovary, undergoes rupture during each ovulation and plays a crucial role in ovarian wound ...

Models of Murine Vaginal Colonization by Anaerobically Grown Bacteria

The mammalian vagina can be colonized by many bacterial taxa. The human vaginal microbiome is often dominated by Lactobacillus species, but one-in-four women experience bacterial vaginosis, in which a low level of lactobacilli is accompanied by an overgrowth of diverse anaerobic bacteria. This condition has been associated with many health complications, including risks to reproductive and sexual health. While there is growing evidence showing the complex nature of microbial interactions in human vaginal health, the individual roles of these different anaerobic bacteria are not fully understood. This is complicated by the lack of adequate models to study anaerobically grown vaginal bacteria. Mouse models allow us to investigate the biology and virulence of these organisms in vivo. Other mouse models of vaginal bacterial inoculation have previously been described. Here, we describe methods for the inoculation of anaerobically grown bacteria and their viable recovery in conventionally raised C57Bl/6 mice. A new, less stressful procedural method for vaginal inoculation and washing is also described. Inoculation and viable recovery of Gardnerella are outlined in detail, and strategies for additional anaerobes such as Prevotella bivia and Fusobacterium nucleatum are discussed.

The protocol presents a mouse model of vaginal colonization with anaerobically cultured human vaginal bacteria. We focus on Gardnerella vaginalis,&#160;while including suggestions for Prevotella bivia and Fusobacterium nucleatum. This protocol can also be used as a guide for vaginal inoculations and viable recovery of other anaerobically grown bacteria.

The mammalian vagina can be colonized by many bacterial taxa. The human vaginal microbiome is often dominated by Lactobacillus species, but one-in-four ...

Bovine Ovarian Cortex Tissue Culture

Follicle development from the primordial to antral stage is a dynamic process within the ovarian cortex, which includes endocrine and paracrine factors from somatic cells and cumulus cell-oocyte communication. Little is known about the ovarian microenvironment and how the cytokines and steroids produced in the surrounding milieu affect follicle progression or arrest. In vitro culture of ovarian cortex enables follicles to develop in a normalized environment that remains supported by adjacent stroma. Our objective was to determine the effect of nutritional Stair-Step diet on the ovarian microenvironment (follicle development, steroid, and cytokine production) through in vitro culture of bovine ovarian cortex. To accomplish this, ovarian cortical pieces were removed from heifers undergoing two different nutritionally developed schemes prior to puberty: Control (traditional nutrition development) and Stair-Step (feeding and restriction during development) that were cut into approximately 0.5-1 mm3 pieces. These pieces were subsequently passed through a series of washes and positioned on a tissue culture insert that is set into a well containing Waymouth&#39;s culture medium. Ovarian cortex was cultured for 7 days with daily culture media changes. Histological sectioning was performed to determine follicle stage changes before and after the culture to determine effects of nutrition and impact of culture without additional treatment. Cortex culture medium was pooled over days to measure steroids, steroid metabolites, and cytokines. There were tendencies for increased steroid hormones in ovarian microenvironment that allowed for follicle progression in the Stair-Step versus Control ovarian cortex cultures. The ovarian cortex culture technique allows for a better understanding of the ovarian microenvironment, and how alterations in endocrine secretion may affect follicle progression and growth from both in vivo and in vitro treatments. This culture method may also prove beneficial for testing potential therapeutics that may improve follicle progression in women to promote fertility.

In vitro culture of bovine ovarian cortex and the effect of nutritional Stair-step diet on ovarian microenvironment is presented. Ovarian cortex pieces were cultured for seven days and steroids, cytokines, and follicle stages were evaluated. The Stair-Step diet treatment had increased steroidogenesis resulting in follicle progression in culture.

Follicle development from the primordial to antral stage is a dynamic process within the ovarian cortex, which includes endocrine and paracrine factors ...

Characterization and Functional Prediction of Bacteria in Ovarian Tissues

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