Name: characterization and functional prediction of bacteria in ovarian tissues
Uploaded: 2021-10-23T13:00:00
Description: Watch this Scientific Journal Video about Characterization and Functional Prediction of Bacteria in Ovarian Tissues at JoVE.com

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>...

<ol>
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D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+potential+role+of+lung+microbiota+in+lung+cancer+attributed+to+household+coal+burning+exposures.">The potential role of lung microbiota in lung cancer attributed to household coal burning exposures.</a> Environmental and Molecular Mutagenesis. 55 (8), 643-651 (2014).</li><li>Kwon, M., Seo, S. S., Kim, M. K., Lee, D. O., Lim, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Compositional+and+Functional+Differences+between+Microbiota+and+Cervical+Carcinogenesis+as+Identified+by+Shotgun+Metagenomic+Sequencing.">Compositional and Functional Differences between Microbiota and Cervical Carcinogenesis as Identified by Shotgun Metagenomic Sequencing.</a> Cancers. 11 (3), 309 (2019).</li><li>Urbaniak, 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+of+Breast+Tissue+and+Its+Association+with+Breast+Cancer.">The Microbiota of Breast Tissue and Its Association with Breast Cancer.</a> Applied and Environmental Microbiology. 82 (16), 5039-5048 (2016).</li><li>Feng, Y., 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=Metagenomic+and+metatranscriptomic+analysis+of+human+prostate+microbiota+from+patients+with+prostate+cancer.">Metagenomic and metatranscriptomic analysis of human prostate microbiota from patients with prostate cancer.</a> BMC Genomics. 20 (1), 146 (2019).</li><li>Walsh, D. 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D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbiome+analyses+of+blood+and+tissues+suggest+cancer+diagnostic+approach.">Microbiome analyses of blood and tissues suggest cancer diagnostic approach.</a> Nature. 579 (7800), 567-574 (2020).</li><li>Bolger, A. M., Lohse, M., Usadel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trimmomatic:+a+flexible+trimmer+for+Illumina+sequence+data.">Trimmomatic: a flexible trimmer for Illumina sequence data.</a> Bioinformatics. 30 (15), 2114-2120 (2014).</li><li>Ward, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BugBase+predicts+organism-level+microbiome+phenotypes.">BugBase predicts organism-level microbiome phenotypes.</a> bioRxiv. , (2017).</li><li>Langille, M. 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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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>2200 TapeStation Software</td>
			<td>Agilgent 
			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&#246;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 ...

Watch this Scientific Journal Video about Characterization and Functional Prediction of Bacteria in Ovarian Tissues at JoVE.com

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, ...

This protocol is designed to discover bacteria in ovarian tissues and predict their functions. Immunohistochemistry staining and 16S rRNA sequencing, were used in order to discover and distinguish bacteria in cancerous and non-cancerous ovarian tissues in situ. The compensational and the functional differences of the bacteria is predicted by using BoPs and cellular-genetic investigation of communities by reconstruction of unobserved days.Our protocol aims at extracting bacteria from ovarian tissues in situ. It can also be used to discover bacteria in tumors of any kind. The main advantages of our protocol are that it's easy and convenient, that it can be realized in almost every lab.It's also rapid to get the results. During practice in the protocol, it's important to exclude any bacteria off-site tumor tissues. Thus, all procedures in this protocol needs to be sterile.During the surgery, separate resected ovaries into roughly one centimeter thick tissue samples with a pair of new sterile tweezers. Avoid touching anything else during the whole procedure. After separation, remove the sample into a sterile tube and put it in liquid nitrogen for transmission.Preserve samples in minus 80 degrees Celsius. Fix tissues by formalin, and then embed them by paraffin. This protocol can be paused here.The tissues can be kept in room temperature for longer preservation. In order to operate further steps, cut the samples in five micrometer serial sections. Then, dry the samples.Do de-paraffin and rehydration to the samples. To retrieve antigen, a 10 minute microwave treatment in EDTA buffer is needed. Dip the samples in PBS which contains 0.3%hydrogen.Peroxidize for 20 minutes to stop endogenous peroxidase activity. Perform the antibody EMA at LPS core, at a concentration of 1 over 300. And maintain it for one night at four degrees Celsius.In order to discover the bacteria, employ a DAP substrate kit to detect existence of HRP. Basing on a manufacturer's instructions, use HRP polymer, anti-rabbit antibody. Use table concentrator to let antibody fully combine.Use PBS to eliminate uncombined antibodies. Do your chemical staining according to the manufacturer's instructions, then flush the samples under water. Do dehydration to the samples.Suit samples by signing. And enter as a sample using imaging Pro Plus Prepare library based on the manufacturer's protocol. Practice the primer pattern, which consists of the gene-specific sequences and the Illumina adapter overhang nucleotide sequences.To amplify the birth rates for V4 region of bacterial 16s on rRNA gene sequences, amplify the template from the DNA sample input by using amplicon PCR. Use Mag-Bind RxNPure Plus magnetic beads to remove the reaction mix from the PCR product. Perform indexed PCR amplification for a second time.Use Agilent 2200 TapeStation to check the library. Use QuantiFluor dsDNA System to quantify the library. With a 600 cycle, v3 standard flow cell produce approximately 100, 000 paired ends.2 times 300 base 3. Libraries are denormalized, pooled, and sequenced. The raw rates of every sample are filtrated on the basis of sequencing quality by Trimmomatic.The primer and adapter sequences should be both removed. Sequence reads with both pair and qualities lower than 25 should be shortened. Analyse the 16s rRNA by the software package QIIME.Gather sequences to form OTUs with a similarity cutoff at 97%For OTUs, the relative abundance should be calculated in each sample. Employ a Bayesian classifier, which is in the RDP training set to sort all the sequences. With given OTU, a classification which have the major coherence of the sequences is assigned to OTUs.The OTUs are aligned to Silva database, basing on a sample group information. Perform alpha diversity and the UniFrac-based principal coordinates analysis. To predict relating presentation of the characteristics of the bacteria use BugBase.Predict the functional composition of a metagenome at PICRUSt. With the usage of monitoring data and a database containing reference genomes, analyze the different functions among each group with the help of STAMP software. Use one statistic software to calculate the result.The indication of statistical significance should be set as p less than 0.05. Calculate confounding factors, which include age and parity, by Student's t-test. Calculate menopausal status, history of hypertension and diabetes by the Chi-square test.Calculate number of ovary bacteria taxa by the Mann-Whitney U test. 16 patients were enrolled into this study. BugBase was used to practice analysis of predicted metagenomes.The potentially pathogenetic and immunohistochemistry of ovaries using anti-bacterial LPS antibody. This figure shows bacterial richness and diversity in the cancer and control groups. Revealed by 16S rRNA sequencing.The figures are observed species index. Chao1 index, ACE index, Shannon index, Evenness index, Simpson index, respectively. The relative abundance of phyla and the 12 most abundant bacterial species in the ovarian samples is shown in this figure.Figure A, B, C and D.Means the relative abundance of the panel in the ovaries of the patients in the control group and the ovarian cancer group. The relative abundances of the twelve most abundant bacteria species in the ovaries of the control patients and ovarian cancer group. This is commonests, clustered using PCoA and the relative abundance of Anoxynatronum sibiricum and Methanosarcina vacuolata.Figure A shows the commonests, which were clustered using PCoA. PC1 and PC2 are plotted on x and the y-axis. The red block is equal to the sample in the ovarian cancer group.The blue circle is equal to a sample in control group. The samples from the ovarian cancer group can be separated from other samples in the control group. Figure B shows commonests which were clustered using PCoA.PC1 and PC2 are plotted on the x and y axis. The red block is equal to a sample in the ovarian cancer group. The blue solid circle is equal to a sample from the patient with uterine myoma.And the blue hollow circle is equal to a sample of a patient with uterine adenomyosis. Figure C shows the relative abundance of Anoxynatronum sibiricum. Figure D is Methanosarcina vacuolata.This figure is the BugBase analysis of predicted metagenomics. The potentially pathogenetic and oxidative stress-tolerance phenotype of the ovaries in the cancer group were stronger than that of the control group. This figure is significantly different KEGG path space between the cancer and control groups by PICRUSt analysis.When we get our samples, a pair of new sterile tweezers is needed. Pay attention to potential contamination to the samples. That bacteria off-site tumor tissues may affect the resolve greatly.Setting control group to every step is preferred. In order to reduce the effect of potential contamination.

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Research

JoVE Journal

Cancer Research

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for immunotherapies. Recent clinical advances in immunotherapy highlight the need for reproducible methods to accurately and thoroughly characterize the tumor and its associated immune infiltrate. Tumor enzymatic digestion and flow cytometric analysis allow broad characterization of numerous immune cell subsets and phenotypes; however, depth of analysis is often limited by fluorophore restrictions on panel design and the need to acquire large tumor samples to observe rare immune populations of interest. Thus, we have developed an effective and high throughput method for separating and enriching the tumor immune infiltrate from the non-immune tumor components. The described tumor digestion and centrifugal density-based separation technique allows separate characterization of tumor and tumor immune infiltrate fractions and preserves cellular viability, and thus, provides a broad characterization of the tumor immunologic state. This method was used to characterize the extensive spatial immune heterogeneity in solid tumors, which further demonstrates the need for consistent whole tumor immunologic profiling techniques. Overall, this method provides an effective and adaptable technique for the immunologic characterization of subcutaneous solid murine tumors; as such, this tool can be used to better characterize the tumoral immunologic features and in the preclinical evaluation of novel immunotherapeutic strategies.

Here we describe a method to separate and enrich components of the tumor immune and non-immune microenvironment in established subcutaneous tumors. This technique allows for the separate analysis of tumor immune infiltrate and non-immune tumor fractions which can permit comprehensive characterization of the tumor immune microenvironment.

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for ...

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 ...

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.

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 ...

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to interference with proper development and may even result in initiation or progression of cancers in certain cases. Because this pathway serves complex and versatile functions, it can be studied extensively through many different approaches. Of these, bioinformatics provides an undeniably cost-efficient, approachable, and user-friendly method of study. Bioinformatics is a useful way to extract smaller pieces of information from large-scale datasets. Through the implementation of various bioinformatics approaches, researchers can quickly, reliably, and efficiently interpret these large datasets, yielding insightful applications and scientific discoveries. Here, a protocol is presented for integration of bioinformatics approaches to investigate the role of Notch signaling in ovarian cancer. Furthermore, bioinformatics findings are validated through experimentation.

Bioinformatics is a useful way to process large-scale datasets. Through the implementation of bioinformatics approaches, researchers can quickly, reliably, and efficiently obtain insightful applications and scientific discoveries. This article demonstrates the utilization of bioinformatics in ovarian cancer research. It also successfully validates bioinformatics findings through experimentation.

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to ...

Circulating Tumor Cell Lines: an Innovative Tool for Fundamental and Translational Research

Metastasis is a leading cause of cancer death. Despite improvements in treatment strategies, metastatic cancer has a poor prognosis. We thus face an urgent need to understand the mechanisms behind metastasis development, and thus to propose efficient treatments for advanced cancer. Metastatic cancers are hard to treat, as biopsies are invasive and inaccessible. Recently, there has been considerable interest in liquid biopsies including both cell-free circulating deoxyribonucleic acid (DNA) and circulating tumor cells from peripheral blood and we have established several circulating tumor cell lines from metastatic colorectal cancer patients to participate in their characterization. Indeed, to functionally characterize these rare and poorly described cells, the crucial step is to expand them. Once established, circulating tumor cell (CTC) lines can then be cultured in suspension or adherent conditions. At the molecular level, CTC lines can be further used to assess the expression of specific markers of interest (such as differentiation, epithelial or cancer stem cells) by immunofluorescence or cytometry analysis. In addition, CTC lines can be used to assess drug sensitivity to gold-standard chemotherapies as well as to targeted therapies. The ability of CTC lines to initiate tumors can also be tested by subcutaneous injection of CTCs in immunodeficient mice.
Finally, it is possible to test the role of specific genes of interest that might be involved in cancer dissemination by editing CTC genes, by short hairpin ribonucleic acid (shRNA) or Crispr/Cas9. Modified CTCs can thus be injected into immunodeficient mouse spleens, to experimentally mimic part of the metastatic development process in vivo.
In conclusion, CTC lines are a precious tool for future research and for personalized medicine, where they will allow prediction of treatment efficiency using the very cells that are originally responsible for metastasis.

Culturing CTCs allows a deeper functional characterization of cancer, through assaying specific marker expression, and assessing drug resistance and the ability to colonize the liver among other possibilities. Overall, CTC culture could be a promising clinical tool for personalized medicine to improve patient outcome.

Metastasis is a leading cause of cancer death. Despite improvements in treatment strategies, metastatic cancer has a poor prognosis. We thus face an ...

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 ...

Quantifying Replication Stress in Ovarian Cancer Cells Using Single-Stranded DNA Immunofluorescence

Replication stress is a hallmark of several ovarian cancers. Replication stress can emerge from multiple sources, including double-strand breaks, transcription-replication conflicts, or amplified oncogenes, inevitably resulting in the generation of single-stranded DNA (ssDNA). Quantifying ssDNA, therefore, presents an opportunity to assess the level of replication stress in different cell types and under various DNA-damaging conditions or treatments. Emerging evidence also suggests that ssDNA can be a predictor of responses to chemotherapeutic drugs that target DNA repair. Here, we describe a detailed immunofluorescence-based methodology to quantify ssDNA. This methodology involves labeling the genome with a thymidine analog, followed by the antibody-based detection of the analog at the chromatin under non-denaturing conditions. Stretches of ssDNA can be visualized as foci under a fluorescence microscope. The number and intensity of the foci directly co-relate with the level of ssDNA present in the nucleus. We also describe an automated pipeline to quantify the ssDNA signal. The method is rapid and reproducible. Furthermore, the simplicity of this methodology makes it amenable to high-throughput applications such as drug and genetic screens.

Here, we describe an immunofluorescence-based method to quantify the levels of single-stranded DNA in cells. This efficient and reproducible method can be utilized to examine replication stress, a common feature in several ovarian cancers. Additionally, this assay is compatible with an automated analysis pipeline, which further increases its efficiency.

Replication stress is a hallmark of several ovarian cancers. Replication stress can emerge from multiple sources, including double-strand breaks, ...

Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays

Immunofluorescence is one of the most widely used techniques to visualize target antigens with high sensitivity and specificity, allowing for the accurate identification and localization of proteins, glycans, and small molecules. While this technique is well-established in two-dimensional (2D) cell culture, less is known about its use in three-dimensional (3D) cell models. Ovarian cancer organoids are 3D tumor models that recapitulate tumor cell clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Thus, they are superior to cell lines for the evaluation of drug sensitivity and functional biomarkers. Therefore, the ability to utilize immunofluorescence on primary ovarian cancer organoids is extremely beneficial in understanding the biology of this cancer. The current study describes the technique of immunofluorescence to detect DNA damage repair proteins in high-grade serous patient-derived ovarian cancer organoids (PDOs). After exposing the PDOs to ionizing radiation, immunofluorescence is performed on intact organoids to evaluate nuclear proteins as foci. Images are collected using z-stack imaging on confocal microscopy and analyzed using automated foci counting software. The described methods allow for the analysis of temporal and special recruitment of DNA damage repair proteins and colocalization of these proteins with cell-cycle markers.

Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays

The present protocol describes methods for evaluating DNA damage repair proteins in patient-derived ovarian cancer organoids. Included here are comprehensive plating and staining methods, as well as detailed, objective quantification procedures.

Immunofluorescence is one of the most widely used techniques to visualize target antigens with high sensitivity and specificity, allowing for the accurate ...

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 ...