This work was supported by grants from the National Institute of Food and Agriculture, U.S. Department of Agriculture, award ILLU-698-909 (ZME) and Pfizer, Inc (ZME). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Agriculture.

1. Preparation of RNA-Seq Samples
<ol>
	<li>Cell Culture and Treatment
	<ol>
		<li>Culture the MCF7 cells in Improved MEM (IMEM) plus phenol-red medium with 10% Heat inactive FBS, 1% penicillin/streptomycin at 37 &#176;C in humidified 5% CO2 atmosphere. 
		Note: Same cell line and cell culture condition is used throughout the study.</li>
		<li>Seed 100,000 MCF7 cells per well in 6-well plates. Have triplicates for each treatment. On day 3, remove the medium and add 2 ml fresh medium with or without 10-8 M E2 to each well. Incubate the plates for 24 hr in 37 &#176;C in humidified 5% CO2 atmosphere.</li>
		<li>To harvest the cells, add 1 ml RNA isolation reagent (acid guanidinium thiocyanate-phenol-chloroform) into each well and incubate for 5 min at room temperature (RT). Then collect the cell lysate reagent by pipetting and deposit into 1.5 ml tubes</li>
	</ol>
	</li>
	<li>RNA Isolation
	<ol>
		<li>Add 200 &#181;l chloroform to 1 ml cell lysate reagent (1.1.3) and vortex for 10 sec. Incubate at room temperature for 5 min. Centrifuge samples at 16,000 x g for 15 min at 4 &#176;C. Following centrifugation, transfer the aqueous phase (top) to a new tube without disturbing the other phases.</li>
		<li>Add 500 &#181;l 100% isopropanol to a new tube with the transferred-aqueous phase, and mix by inverting. Incubate at -20 &#176;C overnight. Centrifuge the samples at 16,000 x g for 10 min at 4 &#176;C. Observe a pellet at the bottom of the tube. Discard the supernatant and wash the pellet with 750 &#181;l RNase-free 70% ethanol by brief vortex.</li>
		<li>Centrifuge samples at 6,000 x g for 5 min at 4 &#176;C. Remove the supernatant, and centrifuge the sample at 16,000 x g for 1 min at 4 &#176;C to collect all the remaining ethanol. Remove the remaining ethanol by pipetting with a gel-loading tip and set the tubes up-side down to air-dry for 10 min. Dissolve the pellet in 20-50 &#181;l DEPC treated water depending on the size of the pellets.</li>
	</ol>
	</li>
	<li>Purify the RNA using RNA clean-up kit following the protocol4. Measure the RNA concentration using a fluorometry based assay kit following the protocol9. 
	NOTE: OD at 260 nm is taken to determine the RNA concentration and ratio of OD at 260 nm and 280 nm are used to determine the purity of sample. It is recommended checking the RNA quality using Bio-analyzer assay.</li>
	<li>Take about 0.5 to 1 &#956;g purified RNA for sequencing. 
	NOTE: Biotech center prepares the sequencing libraries and performs paired-end read sequencing using methods detailed previously2.</li>
</ol>
2. Preparation of ChIP-Seq Sample
<ol>
	<li>Cell Culture and Treatment
	<ol>
		<li>Seed 500,000 MCF7 cells per 10 cm plate in growth medium. 
		NOTE: For each pull down 16 plates were used. On day 3, remove the medium and add 5 ml fresh medium with or without 10-8 M E2 to each plate. Treat the cells for 45 min (37 &#176;C in humidified 5% CO2 atmosphere).</li>
	</ol>
	</li>
	<li>Crosslink and Cell Harvest
	<ol>
		<li>Add 250 &#181;l 37% Formaldehyde into 5 ml medium to crosslink chromatin, incubate at room temperature for 15 min. Stop crosslinking by washing cells with 5 ml ice cold 1x MEM.</li>
		<li>Harvest the cells in 250 &#181;l Cell Lysis buffer (10 mM EDTA, 50 mM Tri HCl, 1% SDS, 0.5% N,N-dimethyldodecan-1-amine oxide) per plate.</li>
	</ol>
	</li>
	<li>Fragmentation of Chromatin
	<ol>
		<li>Sonicate the cells for 30 sec x 8 at amp of 30 to reach required chromatin sizes. Cool each sample on ice after a sonication of 30 sec.</li>
		<li>Centrifuge at 16, 000 x g for 10 min at 4 &#176;C, and carefully pipette the supernatant without disturbing the pellet at the bottom.</li>
		<li>Take a 5 &#181;l aliquot of the supernatant, and check the DNA size by electrophoresis through 1.5% agarose gel as described previously10. The ideal size of DNA should be between 200-500 bp.</li>
	</ol>
	</li>
	<li>Chromatin Immunoprecipitation
	<ol>
		<li>Save 25 &#181;l of cell lysate as input. In a 50 ml tube, mix 4 ml of cell lysate with 20 ml IP buffer (2 mM EDTA, 100 mM NaCl, 20 mM Tris-HCl, and 0.5% Triton X), ER&#945; antibody (500 &#181;l F10 and 500 &#181;l HC-20), and 500 &#181;l ProtA and ProtG protein coating magnetic beads. Incubate the mixture by rotation at 4 &#176;C overnight to allow the formation of chromatin- antibody complex.</li>
	</ol>
	</li>
	<li>Washes and Reverse-crosslinking
	<ol>
		<li>Use a magnetic stand to collect the magnetic beads on the magnetized side to remove the wash buffer.</li>
		<li>Wash the magnetic beads in 25 ml Wash buffer I (2 mM EDTA, 20 mM Tris HCl, 01% SDS, 1% Triton X, and 150 mM NaCl). To thoroughly wash the magnetic beads without disturbing DNA-Protein complexes, add 25 ml wash buffer slowly to the tube along the side then invert the tubes until all the magnetic beads are mixed well with the wash buffer without appearing as a pellet. As an alternative, use the rotator to invert the tube. Do not vortex the samples. Wash the magnetic beads using the same method in the following steps.</li>
		<li>Wash the magnetic beads in 25 ml Wash-buffer II (2 mM EDTA, 20 mM Tris HCl, 01% SDS, 1% Triton X, and 500 mM NaCl).</li>
		<li>Wash the magnetic beads in 25 ml Wash-buffer III (1.6 mM EDTA, 10 mM Tris HCl, 1% NP-40, 1% Dexycholate and 250 mM LiCl).</li>
		<li>Wash the magnetic beads in 25 ml 1x TE buffer (1 mM EDTA, and 10 mM Tris HCl) twice.</li>
		<li>Elute DNA in 500 &#181;l Elution buffer (1% SDS, and 10 mM NaHCO3) and then allocate the 500 &#181;l DNA elution into 4 tubes. For the DNA from inputs, elute DNA in 125 &#181;l Elution buffer.</li>
		<li>Incubate the tubes at 65 &#176;C overnight on a heating block. Cover the tubes on the heating block with aluminum foil to keep the heat even over all the tubes. Place a weight on the tubes to keep them from opening.</li>
	</ol>
	</li>
	<li>DNA Isolation
	<ol>
		<li>Cool the tubes at RT for 5 min. Isolate DNA from the pull down samples using ChIP DNA isolation kit. Follow the directions from the manufacturer.
		<ol>
			<li>Warm up the Elution buffer at 65 &#176;C. Add 625 &#181;l binding buffer to each tube. If the precipitate forms, add another 250 &#181;l binding buffer until the solution is clear.</li>
			<li>Load the sample onto the column and centrifuge at 16,000 x g for 30 sec. Wash the sample with 250 &#181;l wash buffer. Remember to add ethanol to the wash buffer when first time to use it. Repeat the wash.</li>
			<li>Elute the DNA in 15 &#181;l Elution buffer. Combine the DNA of same pull down from 4 tubes to achieve around 55 &#181;l DNA.</li>
		</ol>
		</li>
		<li>Isolate the Input DNA Using a DNA Purification Kit.
		<ol>
			<li>Warm up the Elution buffer at 65 &#176;C. Add 625 &#181;l binding buffer in each tube and incubate for 10 min. Load the sample onto the column in a 2 ml collection tube.</li>
			<li>Wash the sample with 750 &#181;l wash buffer, centrifuge at 16,000 x g for 1 min. Repeat centrifuging to remove any remaining buffer. Elute the DNA into 30 &#181;l of EB.</li>
		</ol>
		</li>
		<li>Measure the DNA Concentration Using a Commercial Fluorochrome Assay with Modification11.
		<ol>
			<li>Dilute the 20x TE buffer into 1x TE as the assay buffer for diluting the reagent and DNA samples. Take 5 &#181;l isolated DNA and dilute it into 50 &#181;l in 1x TE.</li>
			<li>From 2 &#181;g/ml DNA stock solution make a DNA standard ranging from blank to 1, 10, 100, 1000 &#181;g/ml. In a 96 well plate, allocate 25 &#181;l of each standard as well as DNA dilution in duplicate. (Triplicate for standard is recommended.)</li>
			<li>Prepare the working reagent by making a 200-fold dilution from dsDNA reagent in 1x TE buffer in plastic tube. Cover the working reagent solution with aluminum foil to avoid light. Add 200 &#181;l working reagent into each sample. Incubate at room temperature for 5 min. Cover the plate with aluminum foil to avoid light.</li>
			<li>Measure the florescence of the samples using a fluorescence plate reader (excitation at 480 nm, emission 520 nm). Generate the DNA standard curve of florescence versus DNA concentration. Determine the concentrations of the samples based on the generated DNA standard curve.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Verification of ChIP Experiment Using qPCR
	<ol>
		<li>Take 2 &#181;l isolated DNA and diluted into 20 &#181;l in water. Set up the qPCR reaction in triplicate by mixing 2.5 &#181;l DNA dilution, 5 &#181;l Green I PCR master mix, 1 &#181;l primer, and 1.5 &#181;l nuclease free water.</li>
		<li>Run the qPCR with the Fast Real-Time PCR system using the following program: Step 1: 95 &#176;C for 60 sec, Step 2: 95 &#176;C for 10 sec, 60 &#176;C for 60 sec, repeat Step 2 39 times.</li>
	</ol>
	</li>
	<li>Send 10 ng DNA sample to Biotech center for sequencing. 
	NOTE: The biotech center prepares the ChIP DNA into libraries according to instruction, and performs single-read sequencing using methods detailed previously2.</li>
</ol>
3. Metabolomics Assay Sample Preparation
<ol>
	<li>Cell Culture and Treatment
	<ol>
		<li>Seed 400,000 MCF7 cells per 10 cm plate in growth medium. For each treatment prepare three samples. Use two plates in each sample to receive enough cells for the detection. On day 3, remove the medium and add 5 ml fresh medium with or without 10-8 M E2 to the cells. Treat the cells for 24 hr (37 &#176;C in humidified 5% CO2 atmosphere).</li>
	</ol>
	</li>
	<li>Sample Collection
	<ol>
		<li>Add 5 ml ice cold 1x PBS to the plate, aspirate the PBS after briefly tilting the plate several times. Repeat twice, and remove as much PBS as possible after the last wash.</li>
		<li>Add 750 &#181;l of pre-cold acetone to each plate and scrape the cells. Combine the cells in acetone from two plates into a 2 ml tube on ice.</li>
		<li>Cell count for normalization
		<ol>
			<li>For each treatment, use two extra plates for cell quantification. To de-attach the cells from plate, add 2 ml of HE buffer (1x HBSS, 10 mM HEPES, 0.075% NaHCO3, and 1 mM EDTA) into each plate and incubate for 5 min.</li>
			<li>Collect and mix well the cells by pipetting cells in the HE buffer. Use total of 20 &#181;l cell solution to count the cell number using a hemocytometer.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Store the samples at -80 &#176;C before sending to the Metabolomics center to identify and quantify the metabolites using Gas chromatography mass spectrometry (GC-MS) analysis.</li>
</ol>
4. Integrative Analysis
<ol>
	<li>RNA-Seq Data Analysis:
	<ol>
		<li>Perform Quality check of FASTQ files using FastQC:Read QC (http://galaxy.illinois.edu)
		<ol>
			<li>Select FastQC:Read under the tool of NGS:QC and manipulation. Upload the raw data file from current history. 
			NOTE: It is recommended to give a title for the output file to remind you what the job was for since there might be multiple outputs.</li>
		</ol>
		</li>
		<li>Execute and the output file will appear under the History. Repeat the same procedure for each sequence file. Trim adapters using Clip under NGS:FASTX Toolkit.</li>
		<li>Align reads to hg19 using Tophat2 with RefSeq genome reference annotation12 (default values).
		<ol>
			<li>Select Tophat2 under tool of NGS:RNA analysis. Indicate the library paired type (single-end vs paired-end).</li>
			<li>Upload the input FASTQ file from the current history. Select the reference genome. Choose defaults settings. Or use the full parameter list to modify.</li>
			<li>Execute and it will produce two output files: one is UCSC BED track of junctions; another one is a list of read alignments in BAM format.</li>
		</ol>
		</li>
		<li>Upload BAM files to sequencing data analysis tool. Calculate expression values using Quantification Tool.</li>
		<li>Identify differentially up-regulated genes using Expression Analysis Tool using default values and fold change &#62;2 and p-value &#60;0.05. Likewise, identify differentially down-regulated genes using Expression Analysis Tool using default values and fold change &#60;0.5 and p-value &#60;0.05.</li>
	</ol>
	</li>
	<li>ChIP-Seq Data Analysis:
	<ol>
		<li>Align FASTQ Reads to hg19 using Bowtie213 (Default Values) in Galaxy.
		<ol>
			<li>Select Bowtie2 under the tool of NSC:Mapping. Verify if the library is mate-paired (single-end vs paired-end). Upload the FASTQ file from current history.</li>
			<li>Select the reference genome. Use the default parameter settings. Execute and it will produce output file with mapped sequence reads as BAM file. Repeat the procedure for every sequence file.</li>
		</ol>
		</li>
		<li>Identify peaks using MACS14 a p-value cutoff of 6.0e-7 and FDR of 0.01, as previously described2.</li>
	</ol>
	</li>
	<li>Metabolomics Data Analysis:
	<ol>
		<li>Convert chemical names of metabolites to KEGG_IDs.</li>
		<li>Identify differentially regulated metabolites by comparing levels detected in Vehicle vs. E2 treated samples (a 2 fold cut-off was used in this study).</li>
	</ol>
	</li>
	<li>Integrative Analysis:
	<ol>
		<li>Identify genes that have ER&#945; binding sites using sequencing data analysis tool. Translate Regions to genes function using 20 kb as distance cut-off.</li>
		<li>Identify differentially up- and down-regulated genes from RNA-Seq data analysis using fold change &#62;2 and fold change &#60;0.5, respectively with the p-value &#60;0.05 as cut off. Compare to the list of ER&#945; binding site containing genes using Venn diagram comparison.</li>
		<li>Use this list to identify E2 modulated metabolic pathways using Metscape15 module of Cytoscape.
		<ol>
			<li>By selecting Metscape module from apps, choose Build network and then pathway-based option.</li>
			<li>Select the Organism (Human). Select data file of E2 unregulated compound list (KEGG ID) as well E2 up-regulated gene list.</li>
			<li>Choose Compound -Reaction -Enzyme-Gene as the Network type. Choose compound/genes as the query.</li>
			<li>Build network. Repeat using the E2 down-regulated compound and genes.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

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University of Illinois received an Investigator Initiated grant from Pfizer Inc. ZME and YCZ received salary support from this grant.

In this paper, we described generation and integrative analysis of RNA-Seq, ChIP-Seq and metabolomics data from MCF-7 cells that are treated with E2. We developed a set of protocols strategizing to utilize the most efficient methods and user friendly soft wares which produce biologically relevant discovery. To our knowledge, ours is the first study to integrate -omics data from three different analysis and identified new metabolic pathways that ER&#945; directly regulated in breast cancer cells.
Transcriptomics 
It is critical to start with high quality RNAs for a successful transcriptomic analysis. Good practice will eliminate the chance of RNA degradation, including creating RNase-free bench, changing gloves often, storing in -80 &#176;C and keeping on ice in use. Addition of overnight incubation at -20 &#186;C improves RNA recovery. To avoid freezing and thawing RNA repeatedly, one can allocate the RNAs for downstream procedures upon the isolation and purification. For the existence of sequencing errors, qRT-PCR using the cDNA from the samples is often performed to verify the differentially expressed genes identified in RNA-Seq. Therefore, enough RNA should be reserved for this purpose. We perform paired-end-sequencing that enables us to analyze splice variants, non-coding RNAs and miRNAs. To avoid high-sequencing costs, single-end sequencing can be performed as well. Compared to microarrays, RNA-Seq is more sensitive and is not limited to the probes provided on the chip. We obtain information not only on coding sequences, but also on non-coding genes and differentially spliced transcripts. We are currently using this technique to analyze transcriptomes of various cell lines after ligand treatments as well as tissues obtained from mouse experiments.
Cistromics 
The ultra-high-throughput sequencing method requires minimal 5 ng ChIP DNA for library construction. In standard ChIP-Seq protocol18, it is recommended to have at least 10 ng. Quantitation with fluorometry is critical. In this protocol we used double strand DNA detection assay to determine the DNA concentration. One can also use fluorometer assay for precise DNA quantification. For the differences in equipment and materials, it is very important for each lab to establish their own sonication protocol, which generates desirable DNA fragments sizes. Over-shearing the ChIP DNA might cause loss of enrichment of target DNAs while ChIP DNA of large sizes create problem for efficient sequencing. Before submitting the DNA to sequencing, it is always important to verify enrichment of some known regions by qPCR. The technique is still limited by the efficiency and specificity of the antibody used for immunoprecipitations. To obtain binding site information that could be reliably used in comparative analyses, we validate our reagents following ENCODE ChIP-Seq standards,19 and use standardized protocols that provide us with consistent data sets. Coupled with the post transcription factor binding site enrichment analysis, ChIP-Seq is still one of the best methods to determine direct binding of transcription factors to chromatin. We are currently using this protocol to study recruitment kinases, coregulators and other transcription factors to chromatin in other cell lines as well as estrogen responsive mouse tissues.
Metabolomics 
For a successful metabolic profiling in human breast cancer cells, a careful sample preparation is critical. The amount of cells in use and cell culture condition in this protocol is optimal for MCF7 cells. Other cell lines might require more or less cells for detection of a significant number of metabolites. We obtain around 100 metabolites in each experiment. This method is limited for detection of short lived metabolites or metabolites that are present at low concentrations. This method offers the ease of using a single sample prep to identify multiple metabolites. We are currently using this technique to identify estrogen regulated metabolites in serum and various tissues from mouse.
Data Integration 
Numerous transcriptomics and cistromics studies were done to investigate estrogen signaling and gene regulation in breast cancer cells, including MCF7 cells2,5-7,20. In this study, for the first time, we added metabolomics data and tried to infer metabolic networks that are directly regulated by ER&#945; upon ligand addition. This novel approach enabled us to pin point molecular circuitries regulated by estrogens that impact cellular metabolism. Overall, our findings support the role of ER&#945; as a master regulator of breast cancer biology.
In summary, we provided a detailed compendium of protocols for generation of RNA-Seq, ChIP-Seq and metabolomics samples from breast cancer cells after estrogen treatments and integrative analysis of the data obtained from these -omics approaches. These protocols will enable researchers to do similar analysis for other transcription factors, nuclear receptor ligands and nutrient conditions.

Estrogens are important regulators of many physiological processes in both females and males including reproductive tissues, metabolic tissues, brain and bone1. In addition to beneficial effects in these tissues, estrogens also drive cancers that arise from mammary and reproductive tissues. Estrogens mainly work through ERs to induce cell type specific effects. Deep sequencing of transcripts regulated by ER&#945; using RNA-Seq and genome-wide ER&#945; DNA binding sites analysis using ChIP-Seq proved to be useful to understand how ER&#945; works in different tissues and cancers that arise from them. We and others have published gene expression profiles associated with different receptors (ER&#945; v.s. ER&#946;)2,3, different ligands3-5 and different coregulators2,4,6,7.
RNA-Seq is the main method to examine the transcriptome, offering higher precision and efficiency compared to microarray based gene expression analysis8. RNA obtained from cell lines2-4,7, tissues or tumor samples are sequenced, mapped to available genomic assemblies and differentially regulated genes are identified. Chromatin Immunoprecipitation (ChIP) is employed to dissect the transcription factor and coregulator chromatin binding to known regulatory binding sites. ChIP-Seq (ChIP followed by high throughput sequencing) provides unbiased detection of global binding sites. Metabolomics is another increasingly used system biology approach, which quantitatively measures, dynamic multiparametric response of living systems to various stimuli including chemicals and genetic perturbations.
By performing global metabolic profiling, a functional readout can be obtained from cells, tissues, and blood. In addition, information from transcriptome experiments do not always reflect actual changes in the level of enzymes that contribute to biochemical pathways. Combined analysis of transcriptome and metabolome data enables us to identify and correlate changes in gene expression with actual metabolite changes. Harnessing the information from all these large scale datasets provide the mechanistic details to understand the role of transcription factors regulating complex biological systems, especially ones that pertain to human development and diseases like cancer and diabetes.
The complex nature of the mammalian genome makes it challenging to integrate and fully interpret the data obtained from the transcriptome, cistrome and metabolome experiments. Identifying functional binding events that would lead to changes in expression of target genes is important because once functional binding sites are identified; ensuing analysis, including transcription binding (TF) motif analysis, could be performed with higher accuracy. This leads to the identification of biologically meaningful TF cascades and mechanisms. Also direct comparison of RNA-Seq and ChIP-Seq experiments are not always possible since the data from each experiment have differing scales and noises and in some cases meaningful signals are obscured by population noise. We are not aware of any study that integrated information from these three independent but related approaches to understand the direct metabolic regulation by ER&#945; in breast cancer. Therefore, our overall goal in this paper is to relate productive binding events to gene expression and metabolite changes. In order to achieve this goal, we integrated data from RNA-Seq, ChIP-Seq and metabolomics experiments and identified those estrogen induced ER&#945; binding events that would lead to gene expression changes in metabolic pathways. For the first time, we provide a complete set of protocols (Figure 1) for generating ChIP-Seq, RNA-Seq and metabolomics profiling and performing integrative analysis of the data to uncover novel gene circuits regulating metabolism of breast cancer cell lines.

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			<td>Each tablet is sufficient for 10ml lysis buffer</td>
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systems biology of metabolic regulation by estrogen receptor signaling in breast cancer

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective mining of the information in the large-scale datasets that are obtained from gene expression microarrays, deep sequencing experiments or metabolic profiling is essential to uncover and then effectively target the critical regulators of diseased cell phenotypes. Estrogen Receptor &#945; (ER&#945;) is one of the master transcription factors regulating the gene programs that are important for estrogen responsive breast cancers. In order to understand to role of ER&#945; signaling in breast cancer metabolism we utilized transcriptomic, cistromic and metabolomic data from MCF-7 cells treated with estradiol. In this report we described generation of samples for RNA-Seq, ChIP-Seq and metabolomics experiments and the integrative computational analysis of the obtained data. This approach is useful in delineating novel molecular mechanisms and gene regulatory circuits that are regulated by a particular transcription factor which impacts metabolism of normal or diseased cells.

Transcriptomics 
To analyze differentially expressed genes by E2 treatment, we chose to perform an RNA-Seq experiment. In addition to providing information about mRNA levels, RNA-Seq data can also be used to monitor changes in non-coding RNA (long non-coding RNAs, microRNAs) and alternative splicing events. We did not provide information on the analysis of non-coding RNAs or alternatively transcribed genes, since scope of our study is to identify protein coding genes that are important for metabolic regulation. However, RNA-Seq is an excellent way of obtaining information on differential gene expression events.
In order to obtain high quality reads, it is important to get high purity RNA. After isolating RNA we purified the RNA using a RNA clean up kit. Unfortunately nearly 50% of the RNA is lost during this process and amount of starting RNA should be taken into consideration to obtain required amount (100-500 ng) by the end of purification step.
We quantified the RNA yields using fluorometer RNA HS assay, which is very accurate and is preferred method for quantification of low-abundance RNA. Next, the integrity and overall quality of the RNA samples were examined using bioanlayzer, which is a viable alternative to gel electrophoresis using a minimal amount of RNA. The analysis shows all the samples have sharp and clean bands of 18S and 28S rRNAs indicating the integrity and purity of the samples (Figure 2B). RNA Integrity Number (RIN) was used as a standard for RNA quality control. RIN 10 indicates intact RNA, RIN 6 partially degraded and RIN3 strongly degraded16.
It is recommended that the RIN to be at least 7 for sequencing experiment. All of our RNA samples received RIN value between 9.6-9.9 (Figure 2A). Sequencing libraries were constructed from 500ng of high-quality RNA from each sample using ultra-high-throughput sequencing system such as Illumina 2500. On average 18,000,000 High-quality sequencing reads were obtained for each sample (Table 1), which was ready for downstream analysis. To check the overall quality of the high-throughput sequence raw data, FastQC was run for each sample&#39;s reads. A representative FastQC report for one vehicle RNA-Seq data was shown (Figure 3). The sequence length of the most reads was about 100 bp (Figure 3A). For each read, the quality score was 32-34 for the first 5 bp and 36-38 from 6-100bp (Figure 3B). Among 17,990,863 reads generated from this sample, most of them had quality score higher than 34 (Figure 3C). The GC content per read in the sequence also followed the normal distribution with an average of 48% GC. Overall the quality score of the reads was very high.
Cistromic analysis of ER&#945; binding sites 
To achieve the efficient deep sequencing, obtaining DNA fragments at desirable size is one of the important factors. Current sequencing approaches may have different requirements on the DNA fragments, for example 300-600 bp for ultra-high-throughput sequencing system, 100-200 bp for AB/SOLiD17. In this experiment most of the DNA fragments are between 300 bp and 600 bp as shown on gel (Figure 4). As an alternative, readers might chose to prepare their chromatin using MNase digestion, however we feel our established protocol provides the required samples with high-quality and provides reproducible results. Ten ng of DNA from each treatment and its corresponding input sample was prepared in EB buffer. The ChIP DNA was single-read sequenced using ultra-high-throughput sequencing system. ChIP DNA samples yielded about 20,000,000 reads while the input DNA yielded more than 35.000,000 reads (Table 1).
Metabolomics 
To quantify the metabolites in mammalian cells using GCMS, at least 10 &#181;g of cell mass are required. We started with 400,000 MCF7 cells with the experiments. By the time of harvest, there were about 500,000 cells in each plate. The overview of the metabolites represented by peaks were compared between the control and the Estradiol treated sample (Figure 5). About 100 metabolites were identified, which count for 60% of the total known in mammalians. Here we present a list of the top 10 metabolites, which show significant changes in E2 treatment (Table 2). Overall, E2 upregulated pathways including fructose and mannose metabolism, histidine metabolism, purine metabolism, cholesterol biosynthesis and vitamin B3 metabolism. The downregulated pathways were butanoate metabolism, de novo fatty acid biosynthesis, pentose phosphate pathway, urea cycle and metabolism of arginine (Table 3).
Data analysis and integration 
The data from RNA-Seq, ChIP-Seq and GC-MS were processed and analyzed through a series of steps (Figure 6). After the comparison between genes identified in RNA-Seq and ChIP-Seq, one set of up-regulated and one set of down-regulated genes by Estradiol were extracted. From the metabolic data, we obtained two sets of up- and down-regulated compounds by the Estradiol treatment. Next we used Metscape, an app for Cytoscape, to visualize and interpret the gene expression and metabolic data in the context of human metabolism15. First we chose to include elements of compound, reaction, enzyme, and gene in the network. We selected the compounds/genes as query and two networks of the E2 up-regulation (Figure 7A) and down-regulation (Figure 7B) were shown. The Metscape also provides an option to build networks on selective pathways. For example, using the same set of data as building the down-regulated overall network, a subnetwork of androgen-estrogen biosynthesis and metabolism pathway was generated by switching the query from the compound/gene to specific pathway. The genes or compounds with significant changes were highlighted in brighter color. Besides selecting a specific pathway from the drop-down window as described, one can also create a sub-network by applying &#34;create subnetwork&#34; function to the chosen area. As shown in Figure 6A and B, ER&#945; activation and direct binding to gene regulatory regions impacted many metabolic pathways in breast cancer cells. Our integrative analysis indicated that, ER&#945; directly binds to and regulates expression of FASN gene, which is important for de novo fatty acid biosynthesis pathway (Figure 7C). In addition E2 treatment downregulated androgen-estrogen biosynthesis pathways by repressing expression of CYP4Z1, CYP2E1, CYP4B1, CYP2C8, TPO, GSTA4 and GSTM2 in these pathways.
<img alt="Figure 1" src="/files/ftp_upload/53832/53832fig1.jpg" /> 
Figure 1: Workflow of the preparation of samples of RNA, DNA and cell lysates for the RNA-Seq, ChIP-Seq, and Metabolomic analysis. 
<img alt="Figure 2" src="/files/ftp_upload/53832/53832fig2.jpg" /> 
Figure 2: A representative electropherogram of the RNAs analyzed with Agilent 2100 Bioanalyzer. RNA Integrity Number (RIN), indicator of RNA quality, was given by the analysis as 9.9. (A) On a virtual gel picture of the RNA sample both bands of 26S and 18S are sharp. (B) Fluorescent signals of individual trace in the RNA shows that the intensity of 28S peak is greater than 18S peak and no contamination is seen. <a href="https://www.jove.com/files/ftp_upload/53832/53832fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/53832/53832fig3.jpg" /> 
Figure 3: A representative quality check on the RNA-Seq raw data. One of the vehicle sample&#39;s sequence data was analyzed using FastQC. (A) The sequence length distribution of the library which is 100 bp. (B) The quality score along the position in read. (C) The quality score per sequence showed the universal high quality of all sequences. (D) The GC content per read matched the theoretical distribution. <a href="https://www.jove.com/files/ftp_upload/53832/53832fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/53832/53832fig4.jpg" /> 
Figure 4: Gel electrophoresis of the sonicated chromatin fragments. 5 &#181;l of the cell lysate from each sample was run on a 1.5% agarose gel with a 1,000 bp plus ladder on the first lane. The majority of the fragments are between 300 and 600 bp. <a href="https://www.jove.com/files/ftp_upload/53832/53832fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/53832/53832fig5.jpg" /> 
Figure 5: The overview of peaks representing metabolites from the GC-MS. The upper panel shows the peaks identified in the E2 treated samples while the lower panel from the control. The area enclosed was roomed in to show the exact metabolite represented by each peak. <a href="https://www.jove.com/files/ftp_upload/53832/53832fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/53832/53832fig6.jpg" /> 
Figure 6: Flowchart of the Integrative clustering method. 
<img alt="Figure 7" src="/files/ftp_upload/53832/53832fig7.jpg" /> 
Figure 7: The visualization of the integrated gene-metabolite data in Metscape. (A) The network of pathways up-regulated by E2. (B) The network of pathways down-regulated by E2. (C) E2 stimulated ER&#945; recruitment to the nearby region of FASN, which is found to be regulated in the de novo fatty acid biosynthesis network generated by applying subnetwork function. <a href="https://www.jove.com/files/ftp_upload/53832/53832fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Table 1" src="/files/ftp_upload/53832/53832table1.jpg" /> 
Table 1: Summary of ultra-high-throughput sequencing of RNA-Seq and ChIP-Seq.
<img alt="Table 2" src="/files/ftp_upload/53832/53832table2.jpg" /> 
Table 2: List of top 10 metabolites with significant changes with E2 treatment.
<img alt="Table 3" src="/files/ftp_upload/53832/53832table3.jpg" /> 
Table 3: Pathways up- and down-regulated by E2 treatment.

Watch this Scientific Journal Video about Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer at JoVE.com

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Integration of data from genome-wide sequencing experiments and metabolomics experiments is a challenge. In this paper we report, for the first time, generation, analysis and integration of transcriptome, cistrome and metabolome data from breast cancer cells treated with estradiol.

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective ...

systems-biology-metabolic-regulation-estrogen-receptor-signaling

Nanyang Technological University

Research

JoVE Journal

Medicine

10.3K Views.  University of Illinois, Urbana-Champaign. Integration of data from genome-wide sequencing experiments and metabolomics experiments is a challenge. In this paper we report, for the first time, generation, analysis and integration of transcriptome, cistrome and metabolome data from breast cancer cells treated with estradiol. 

Video: Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

Angiogenesis is an essential feature of cancer growth and metastasis formation. In bone metastasis, angiogenic factors are pivotal for tumor cell proliferation in the bone marrow cavity as well as for interaction of tumor and bone cells resulting in local bone destruction. Our aim was to develop a model of experimental bone metastasis that allows in vivo assessment of angiogenesis in skeletal lesions using non-invasive imaging techniques.
For this purpose, we injected 105 MDA-MB-231 human breast cancer cells into the superficial epigastric artery, which precludes the growth of metastases in body areas other than the respective hind leg1. Following 25-30 days after tumor cell inoculation, site-specific bone metastases develop, restricted to the distal femur, proximal tibia and proximal fibula1. Morphological and functional aspects of angiogenesis can be investigated longitudinally in bone metastases using magnetic resonance imaging (MRI), volumetric computed tomography (VCT) and ultrasound (US).
MRI displays morphologic information on the soft tissue part of bone metastases that is initially confined to the bone marrow cavity and subsequently exceeds cortical bone while progressing. Using dynamic contrast-enhanced MRI (DCE-MRI) functional data including regional blood volume, perfusion and vessel permeability can be obtained and quantified2-4. Bone destruction is captured in high resolution using morphological VCT imaging. Complementary to MRI findings, osteolytic lesions can be located adjacent to sites of intramedullary tumor growth. After contrast agent application, VCT angiography reveals the macrovessel architecture in bone metastases in high resolution, and DCE-VCT enables insight in the microcirculation of these lesions5,6. US is applicable to assess morphological and functional features from skeletal lesions due to local osteolysis of cortical bone. Using B-mode and Doppler techniques, structure and perfusion of the soft tissue metastases can be evaluated, respectively. DCE-US allows for real-time imaging of vascularization in bone metastases after injection of microbubbles7.
In conclusion, in a model of site-specific breast cancer bone metastases multi-modal imaging techniques including MRI, VCT and US offer complementary information on morphology and functional parameters of angiogenesis in these skeletal lesions.

In the pathogenesis of bone metastasis, angiogenesis is a crucial process and therefore represents a target for imaging and therapy. Here, we present a rat model of site-specific breast cancer bone metastasis and describe strategies to non-invasively image angiogenesis in vivo using magnetic resonance imaging, volumetric computed tomography and ultrasound.

Angiogenesis is an essential feature of cancer growth and metastasis formation. In bone metastasis, angiogenic factors are pivotal for tumor cell ...

Cell Death Associated with Abnormal Mitosis Observed by Confocal Imaging in Live Cancer Cells

Phenanthrene derivatives acting as potent PARP1 inhibitors prevented the bi-focal clustering of supernumerary centrosomes in multi-centrosomal human cancer cells in mitosis. The phenanthridine PJ-34 was the most potent molecule. Declustering of extra-centrosomes causes mitotic failure and cell death in multi-centrosomal cells. Most solid human cancers have high occurrence of extra-centrosomes. The activity of PJ-34 was documented in real-time by confocal imaging of live human breast cancer MDA-MB-231 cells transfected with vectors encoding for fluorescent &gamma;-tubulin, which is highly abundant in the centrosomes and for fluorescent histone H2b present in the chromosomes. Aberrant chromosomes arrangements and de-clustered &gamma;-tubulin foci representing declustered centrosomes were detected in the transfected MDA-MB-231 cells after treatment with PJ-34. Un-clustered extra-centrosomes in the two spindle poles preceded their cell death. These results linked for the first time the recently detected exclusive cytotoxic activity of PJ-34 in human cancer cells with extra-centrosomes de-clustering in mitosis, and mitotic failure leading to cell death. According to previous findings observed by confocal imaging of fixed cells, PJ-34 exclusively eradicated cancer cells with multi-centrosomes without impairing normal cells undergoing mitosis with two centrosomes and bi-focal spindles. This cytotoxic activity of PJ-34 was not shared by other potent PARP1 inhibitors, and was observed in PARP1 deficient MEF harboring extracentrosomes, suggesting its independency of PARP1 inhibition. Live confocal imaging offered a useful tool for identifying new molecules eradicating cells during mitosis.

The cytotoxic activity of the phenanthridine PJ-34 in cancer cells undergoing mitosis was documented in real time by live confocal imaging. PJ-34 eradicated human breast cancer MDA-MB-231 cells harboring extra-centrosomes in mitosis. Unlike normal bi-focal mitosis, the extra-centrosomes were not clustered in the two spindle poles in the presence of PJ-34.

Phenanthrene derivatives acting as potent PARP1 inhibitors prevented the bi-focal clustering of supernumerary centrosomes in multi-centrosomal human ...

Initiation of Metastatic Breast Carcinoma by Targeting of the Ductal Epithelium with Adenovirus-Cre: A Novel Transgenic Mouse Model of Breast Cancer

Breast cancer is a heterogeneous disease involving complex cellular interactions between the developing tumor and immune system, eventually resulting in exponential tumor growth and metastasis to distal tissues and the collapse of anti-tumor immunity. Many useful animal models exist to study breast cancer, but none completely recapitulate the disease progression that occurs in humans. In order to gain a better understanding of the cellular interactions that result in the formation of latent metastasis and decreased survival, we have generated an inducible transgenic mouse model of YFP-expressing ductal carcinoma that develops after sexual maturity in immune-competent mice and is driven by consistent, endocrine-independent oncogene expression. Activation of YFP, ablation of p53, and expression of an oncogenic form of K-ras was achieved by the delivery of an adenovirus expressing Cre-recombinase into the mammary duct of sexually mature, virgin female mice. Tumors begin to appear 6 weeks after the initiation of oncogenic events. After tumors become apparent, they progress slowly for approximately two weeks before they begin to grow exponentially. After 7-8 weeks post-adenovirus injection, vasculature is observed connecting the tumor mass to distal lymph nodes, with eventual lymphovascular invasion of YFP+ tumor cells to the distal axillary lymph nodes. Infiltrating leukocyte populations are similar to those found in human breast carcinomas, including the presence of &#945;&#946; and &#947;&#948; T cells, macrophages and MDSCs. This unique model will facilitate the study of cellular and immunological mechanisms involved in latent metastasis and dormancy in addition to being useful for designing novel immunotherapeutic interventions to treat invasive breast cancer.

Activation of latent mutations with adenovirus-Cre into the mammary ductal system results in a clinically relevant metastatic breast cancer. Incorporation of a YFP promoter allows tracking of distal metastatic tumor cells. This model is useful to study latent metastasis, anti-tumor immunity, and for designing novel immunotherapies to treat breast cancer.

Breast cancer is a heterogeneous disease involving complex cellular interactions between the developing tumor and immune system, eventually resulting in ...

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen receptors (ERs), ER&#945;, and ER&#946;. ER&#945; is frequently upregulated in breast cancer and drives the proliferation of breast cancer cells. The ERs function as transcription factors and regulate gene expression. Whereas ER&#945;&#39;s regulation of protein-coding genes is well established, its regulation of noncoding microRNA (miRNA) is less explored. miRNAs play a major role in the post-transcriptional regulation of genes, inhibiting their translation or degrading their mRNA. miRNAs can function as oncogenes or tumor suppressors&#160;and are also promising biomarkers. Among the miRNA assays available, microarray and quantitative real-time polymerase chain reaction (qPCR) have been extensively used to detect and quantify miRNA levels. To identify miRNAs regulated by estrogen signaling in breast cancer, their expression in ER&#945;-positive breast cancer cell lines were compared before and after estrogen-activation using both the &#181;Paraflo-microfluidic microarrays and Dual Labeled Probes-low density arrays. Results were validated using specific qPCR assays, applying both Cyanine dye-based and Dual Labeled Probes-based chemistry. Furthermore, a time-point assay was used to identify regulations over time. Advantages of the miRNA assay approach used in this study is that it enables a fast screening of mature miRNA regulations in numerous samples, even with limited sample amounts. The layout, including the specific conditions for cell culture and estrogen treatment, biological and technical replicates, and large-scale screening followed by in-depth confirmations using separate techniques, ensures a robust detection of miRNA regulations,&#160;and eliminates false positives and other artifacts. However, mutated or unknown miRNAs, or regulations at the primary and precursor transcript level, will not be detected. The method presented here represents a thorough investigation of estrogen-mediated miRNA regulation.

Molecular signaling through both estrogen and microRNAs are critical in breast cancer development and growth. Estrogen activates the estrogen receptors, which are transcription factors. Many transcription factors can regulate the expression of microRNAs, and estrogen-regulated microRNAs can be profiled using different large-scale techniques.

Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen ...

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model

It is now well known that the cellular and tissue microenvironment are critical regulators influencing tumor initiation and progression. Moreover, the extracellular matrix (ECM) has been demonstrated to be a critical regulator of cell behavior in culture and homeostasis in vivo. The current approach of culturing cells on two-dimensional (2D), plastic surfaces results in the disturbance and loss of complex interactions between cells and their microenvironment. Through the use of three-dimensional (3D) culture assays, the conditions for cell-microenvironment interaction are established resembling the in vivo microenvironment. This article provides a detailed methodology to grow breast cancer cells in a 3D basement membrane protein matrix, exemplifying the potential of 3D culture in the assessment of cell invasion into the surrounding environment. In addition, we discuss how these 3D assays have the potential to examine the loss of signaling molecules that regulate epithelial morphology by immunostaining procedures. These studies aid to identify important mechanistic details into the processes regulating invasion, required for the spread of breast cancer.

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional 3D Model

This article provides detailed methodologies for the use of three-dimensional (3D) assays to quantify breast cancer cell invasion. Specifically, we discuss the procedures required to set up such assays, quantification, and data analysis, as well as methods to examine the loss of membrane integrity that occurs when cells invade.

It is now well known that the cellular and tissue microenvironment are critical regulators influencing tumor initiation and progression. Moreover, the ...

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). 
The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. 
The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

We describe how to obtain parametric and vector maps of the diffusion tensor of the breast using magnetic resonance imaging. The protocol and final output following imaging processing are tailored for tracking breast architectural features and detecting breast malignancy.

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of ...

An Ex vivo Model to Study Hormone Action in the Human Breast

The study of hormone action in the human breast has been hampered by lack of adequate model systems. Upon in vitro culture, primary mammary epithelial cells tend to lose hormone receptor expression. Widely used hormone receptor positive breast cancer cell lines are of limited relevance to the in vivo situation. Here, we describe an ex vivo model to study hormone action in the human breast. Fresh human breast tissue specimens from surgical discard material such as reduction mammoplasties or mammectomies are mechanically and enzymatically digested to obtain tissue fragments containing ducts and lobules and multiple stromal cell types. These tissue microstructures kept in basal medium without growth factors preserve their intercellular contacts, the tissue architecture, and remain hormone responsive for several days. They are readily processed for RNA and protein extraction, histological analysis or stored in freezing medium. Fluorescence activated cell sorting (FACS) can be used to enrich for specific cell populations. This protocol provides a straightforward, standard approach for translational studies with highly complex, varied human specimens.

An Ex vivo Model to Study Hormone Action in the Human Breast

We have developed a novel ex vivo model to study hormone action in the human breast. It is based on tissue microstructures isolated from surgical breast tissue specimens which preserve tissue architecture, intercellular interactions, and paracrine signaling.

The study of hormone action in the human breast has been hampered by lack of adequate model systems. Upon in vitro culture, primary mammary epithelial ...

An In Vitro Dormancy Model of Estrogen-sensitive Breast Cancer in the Bone Marrow: A Tool for Molecular Mechanism Studies and Hypothesis Generation

The study of breast cancer dormancy in the bone marrow is an exceptionally difficult undertaking due to the complexity of the interactions of dormant cells with their microenvironment, their rarity and the overwhelming excess of hematopoietic cells. Towards this end, we developed an in vitro 2D clonogenic model of dormancy of estrogen-sensitive breast cancer cells in the bone marrow. The model consists of a few key elements necessary for dormancy. These include 1) the use of estrogen sensitive breast cancer cells, which are the type likely to remain dormant for extended periods, 2) incubation of cells at clonogenic density, where the structural interaction of each cell is primarily with the substratum, 3) fibronectin, a key structural element of the marrow and 4) FGF-2, a growth factor abundantly synthesized by bone marrow stromal cells and heavily deposited in the extracellular matrix. Cells incubated with FGF-2 form dormant clones after 6 days, which consist of 12 or less cells that have a distinct flat appearance, are significantly larger and more spread out than growing cells and have large cytoplasm to nucleus ratios. In contrast, cells incubated without FGF-2 form primarily growing colonies consisting of >30 relatively small cells. Perturbations of the system with antibodies, inhibitors, peptides or nucleic acids on day 3 after incubation can significantly affect various phenotypic and molecular aspects of the dormant cells at 6 days and can be used to assess the roles of membrane-localized or intracellular molecules, factors or signaling pathways on the dormant state or survival of dormant cells. While recognizing the in vitro nature of the assay, it can function as a highly useful tool to glean significant information about the molecular mechanisms necessary for establishment and survival of dormant cells. This data can be used to generate hypotheses to be tested in vivo models.

An In Vitro Dormancy Model of Estrogen-sensitive Breast Cancer in the Bone Marrow: A Tool for Molecular Mechanism Studies and Hypothesis Generation

We developed an in vitro model of dormancy in the bone marrow for estrogen-sensitive breast cancer cells. The goal of this protocol is to demonstrate use of the model for the study of the molecular and cellular biology of dormancy and for generation of hypotheses for subsequent testing in vivo.

The study of breast cancer dormancy in the bone marrow is an exceptionally difficult undertaking due to the complexity of the interactions of dormant ...

In Vivo and Ex Vivo Approaches to Study Ovarian Cancer Metastatic Colonization of Milky Spot Structures in Peritoneal Adipose

High-grade serous ovarian cancer (HGSC), the cause of widespread peritoneal metastases, continues to have an extremely poor prognosis; fewer than 30% of women are alive 5 years after diagnosis. The omentum is a preferred site of HGSC metastasis formation. Despite the clinical importance of this microenvironment, the contribution of omental adipose tissue to ovarian cancer progression remains understudied. Omental adipose is unusual in that it contains structures known as milky spots, which are comprised of B, T, and NK cells, macrophages, and progenitor cells surrounding dense nests of vasculature. Milky spots play a key role in the physiologic functions of the omentum, which are required for peritoneal homeostasis. We have shown that milky spots also promote ovarian cancer metastatic colonization of peritoneal adipose, a key step in the development of peritoneal metastases. Here we describe the approaches we developed to evaluate and quantify milky spots in peritoneal adipose and study their functional contribution to ovarian cancer cell metastatic colonization of omental tissues both in vivo and ex vivo. These approaches are generalizable to additional mouse models and cell lines, thus enabling the study of ovarian cancer metastasis formation from initial localization of cells to milky spot structures to the development of widespread peritoneal metastases.

In Vivo and Ex Vivo Approaches to Study Ovarian Cancer Metastatic Colonization of Milky Spot Structures in Peritoneal Adipose

We outline a protocol that implements both in vivo and ex vivo approaches to study ovarian cancer colonization of peritoneal adipose tissues, particularly the omentum. Furthermore, we present a protocol to quantitate and analyze immune cell-structures in the omentum known as milky spots, which promote metastases of peritoneal adipose.

High-grade serous ovarian cancer (HGSC), the cause of widespread peritoneal metastases, continues to have an extremely poor prognosis; fewer than 30% of ...

Utilization of the Soft Agar Colony Formation Assay to Identify Inhibitors of Tumorigenicity in Breast Cancer Cells

Given the inherent difficulties in investigating the mechanisms of tumor progression in vivo, cell-based assays such as the soft agar colony formation assay (hereafter called soft agar assay), which measures the ability of cells to proliferate in semi-solid matrices, remain a hallmark of cancer research. A key advantage of this technique over conventional 2D monolayer or 3D spheroid cell culture assays is the close mimicry of the 3D cellular environment to that seen in vivo. Importantly, the soft agar assay also provides an ideal tool to rigorously test the effects of novel compounds or treatment conditions on cell proliferation and migration. Additionally, this assay enables the quantitative assessment of cell transformation potential within the context of genetic perturbations. We recently identified peptidylarginine deiminase 2 (PADI2) as a potential breast cancer biomarker and therapeutic target. Here we highlight the utility of the soft agar assay for preclinical anti-cancer studies by testing the effects of the PADI inhibitor, BB-Cl-amidine (BB-CLA), on the tumorigenicity of human ductal carcinoma in situ (MCF10DCIS) cells.

Here, we document the use of the soft agar colony formation assay to test the effects of a peptidylarginine deiminase (PADI) enzyme inhibitor, BB-Cl-amidine, on breast cancer tumorigenicity in vitro.

Given the inherent difficulties in investigating the mechanisms of tumor progression in vivo, cell-based assays such as the soft agar colony formation ...