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>

<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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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. 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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 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, ...

characterization-functional-prediction-bacteria-ovarian

Research

JoVE Journal

Cancer Research

2.7K Views.  First Affiliated Hospital of Xi’an Jiaotong University. 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).

Video: Characterization and Functional Prediction of Bacteria in Ovarian Tissues

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 ...

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 ...

Risk Prediction Model of Gastric Low-Grade Intraepithelial Neoplasia

Establishment and Evaluation of a Risk Prediction Model for Pathological Escalation of Gastric Low-Grade Intraepithelial Neoplasia

The study aims to explore the risk factors for pathological escalation after endoscopic surgery for gastric low-grade intraepithelial neoplasia (LGIN) and to establish and evaluate a risk prediction model for LGIN. A total of 120 patients diagnosed with gastric LGIN by biopsy and endoscopic submucosal dissection (ESD) between November 2020 and June 2022 were retrospectively analyzed. Gender, age, Helicobacter pylori (HP) infection, lesion size, lesion location, morphology, gastric mucosal congestion, nodules status, surface ulceration and erosion, and ME-observation of all patients were collected and divided into upgraded and non-upgraded groups according to the biopsy and ESD postoperative pathological diagnosis results. Independent risk factors for pathological escalation after ESD surgical treatment were screened by logistic regression analysis, and a risk prediction model was established. Among the 120 patients with gastric LGIN, 49 patients developed postoperative pathological upgrading; the rate of pathological upgrading was 40.83%. Among them, 42 patients were upgraded to high-grade intraepithelial neoplasia (HGIN), 1 case was upgraded to advanced gastric cancer, and 6 cases were upgraded to early gastric carcinoma (EGC). Univariate analysis showed that age, lesion size, gastric mucosal congestion, surface ulcers, and erosion were significantly different between the groups (p &lt; 0.05). Multivariate Logistic regression analysis revealed that age &#8805;60 years, focal length &#8805;2 cm, gastric mucosal congestion, and surface ulceration and erosion were independent risk factors for postoperative pathological escalation in patients with gastric LGIN. Final joint probability forecasting model for P = 1/[1 + e(26.515-0.161 x &#946;1-0.357 x &#946;2+0.039 x &#946;3-0.269 x &#946;4)]. Age, lesion size &#8805;2 cm, gastric mucosal congestion, and lesion surface ulceration and erosion are risk factors for postoperative pathological upgrading in patients with gastric LGIN. The risk prediction model established in this study based on risk factors has predictive value and can provide a scientific reference for the clinical treatment of patients with gastric LGIN.

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Here, we performed a systematic evaluation of patients diagnosed with gastric low-grade intraepithelial neoplasia by previous endoscopic biopsy and obtained a pathological diagnosis by complete resection of the lesion by endoscopic submucosal dissection (ESD), analyzing factors that potentially increase the risk of pathological escalation.

The study aims to explore the risk factors for pathological escalation after endoscopic surgery for gastric low-grade intraepithelial neoplasia (LGIN) and ...

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 ...