Name: three differential expression analysis methods for rna sequencing: limma, edger, deseq2
Uploaded: 2021-09-18T14:00:00
Description: Watch this Scientific Journal Video about Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2 at JoVE.com

This work was supported by the&#8194;National&#8194;Natural&#8194;Science&#8194;Foundation&#8194;of&#8194;China&#8194;(Grant&#8194;No. 81860276) and Key Special Fund Projects of National Key R&#38;D Program (Grant&#8194;No. 2018YFC1003200).

NOTE: Open the R-studio program and load R file &#34;DEGs.R&#34;, the file can be acquired from Supplementary files/Scripts.
1. Downloading and pre-processing of data
<ol>
	<li>Download the high-throughput sequencing (HTSeq) count data of cholangiocarcinoma (CHOL) from The Cancer Genome Atlas (TCGA). This step can be easily achieved by the following R code.
	<ol>
		<li>Click Run to install R packages.</li>
		<li>Click Run to load R packages. 
		if(!requ...

<ol>
	<li>Tambonis, T., Boareto, M., Leite, V. B. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+Expression+Analysis+in+RNA-seq+Data+Using+a+Geometric+Approach.">Differential Expression Analysis in RNA-seq Data Using a Geometric Approach.</a> Journal of Computational Biology. 25, 1257-1265 (2018).</li><li>Wang, Z., Gerstein, M., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq:+a+revolutionary+tool+for+transcriptomics.">RNA-Seq: a revolutionary tool for transcriptomics.</a> Nature Reviews. Genetics. 10, 57-63 (2009).</li><li>Anders, 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=Count-based+differential+expression+analysis+of+RNA+sequencing+data+using+R+and+Bioconductor.">Count-based differential expression analysis of RNA sequencing data using R and Bioconductor.</a> Nature Protocols. 8, 1765-1786 (2013).</li><li>McDermaid, A., Monier, B., Zhao, J., Liu, B., Ma, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpretation+of+differential+gene+expression+results+of+RNA-seq+data:+review+and+integration.">Interpretation of differential gene expression results of RNA-seq data: review and integration.</a> Briefings in Bioinformatics. 20, 2044-2054 (2019).</li><li>Costa-Silva, J., Domingues, D., Lopes, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq+differential+expression+analysis:+An+extended+review+and+a+software+tool.">RNA-Seq differential expression analysis: An extended review and a software tool.</a> PloS One. 12, 0190152 (2017).</li><li>Law, C. W., 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=RNA-seq+analysis+is+easy+as+1-2-3+with+limma,+Glimma+and+edgeR.">RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR.</a> F1000Research. 5, (2016).</li><li>Varet, H., Brillet-Gu&#233;guen, L., Copp&#233;e, J. Y., Dillies, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SARTools:+A+DESeq2-+and+EdgeR-Based+R+Pipeline+for+Comprehensive+Differential+Analysis+of+RNA-Seq+Data.">SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data.</a> PloS One. 11, 0157022 (2016).</li><li>Ritchie, M. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=limma+powers+differential+expression+analyses+for+RNA-sequencing+and+microarray+studies.">limma powers differential expression analyses for RNA-sequencing and microarray studies.</a> Nucleic Acids Research. 43, 47 (2015).</li><li>Robinson, M. D., McCarthy, D. J., Smyth, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=edgeR:+a+Bioconductor+package+for+differential+expression+analysis+of+digital+gene+expression+data.">edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.</a> Bioinformatics. 26, 139-140 (2010).</li><li>Love, M. I., Huber, W., Anders, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moderated+estimation+of+fold+change+and+dispersion+for+RNA-seq+data+with+DESeq2.">Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.</a> Genome Biology. 15, 550 (2014).</li><li>Gentleman, R. 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=Bioconductor:+open+software+development+for+computational+biology+and+bioinformatics.">Bioconductor: open software development for computational biology and bioinformatics.</a> Genome Biology. 5, 80 (2004).</li><li>Law, C. W., Chen, Y., Shi, W., Smyth, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=voom:+Precision+weights+unlock+linear+model+analysis+tools+for+RNA-seq+read+counts.">voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.</a> Genome Biology. 15, 29 (2014).</li><li>Smyth, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linear+models+and+empirical+bayes+methods+for+assessing+differential+expression+in+microarray+experiments.">Linear models and empirical bayes methods for assessing differential expression in microarray experiments.</a> Statistical Applications in Genetics and Molecular Biology. 3, (2004).</li><li>Lund, S. P., Nettleton, D., McCarthy, D. J., Smyth, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+differential+expression+in+RNA-sequence+data+using+quasi-likelihood+with+shrunken+dispersion+estimates.">Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates.</a> Statistical Applications in Genetics and Molecular Biology. 11, (2012).</li><li>Reeb, P. D., Steibel, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+statistical+analysis+models+for+RNA+sequencing+experiments.">Evaluating statistical analysis models for RNA sequencing experiments.</a> Frontiers in Genetics. 4, 178 (2013).</li><li>Rocke, D. M., 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=Excess+False+Positive+Rates+in+Methods+for+Differential+Gene+Expression+Analysis+using+RNA-Seq+Data.">Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data.</a> bioRxiv. , (2015).</li><li>Agarwal, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+and+calibration+of+transcriptome+data+from+RNA-Seq+and+tiling+arrays.">Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays.</a> BMC genomics. 11, 383 (2010).</li><li>Leng, N., 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=EBSeq:+an+empirical+Bayes+hierarchical+model+for+inference+in+RNA-seq+experiments.">EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.</a> Bioinformatics. 29, 1035-1043 (2013).</li></ol>

Abundant aberrate transcripts in cancers can be easily identified by RNA-seq differential analysis5. However, the application of RNA-seq differential expression analysis is often restricted as it requires certain skills with R language and the capacity to choose appropriate methods. To address this problem, we provide a detailed introduction to the three most known methods (limma, EdgeR and DESeq2) and tutorials for applying the RNA-seq differential expression analysis. This will facilitate the un...

RNA-sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics with many advantages (e.g., high data reproducibility), and has dramatically increased our understanding of the functions and dynamics of complex biological processes1,2. Identification of aberrate transcripts under different biological context, which are also known as differentially expressed genes (DEGs), is a key step in RNA-seq analysis. RNA-seq makes it possible to get a deep understanding of pathogenesis related molecular mechanisms and biological functions. Therefore, differential analysis has been regarded as valuable for diagnostics, prognostics and therapeutics of tumors3,4,5. Currently, more open-source R/Bioconductor packages have been developed for RNA-seq differential expression analysis, particularly limma, DESeq2 and EdgeR1,6,7. However, differential analysis requires certain skills with R language and the ability to choose the appropriate method, which is lacking in the curriculum of medical education.
In this protocol, based on the cholangiocarcinoma (CHOL) RNA-seq count data extracted from The Cancer Genome Atlas (TCGA), three of the most known methods (limma8, EdgeR9 and DESeq210) were carried out, respectively, by the R program11 to identify the DEGs between CHOL and normal tissues. The three protocols of limma, EdgeR and DESeq2 are similar but have different steps among the processes of the analysis. For example, the normalized RNA-seq count data is necessary for EdgeR and limma8, 9, whereas DESeq2 uses its own library discrepancies to correct data instead of normalization10. Furthermore, edgeR is specifically suitable for RNA-seq data, while the limma is used for microarrays and RNA-seq. A linear model is adopted by limma to assess the DEGs12, while the statistics in edgeR are based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests9.
In summary, we provide the detailed protocols of RNA-seq differential expression analysis by using limma, DESeq2 and EdgeR, respectively. By referring to this article, users can easily perform the RNA-seq differential analysis and choose the appropriate differential analysis methods for their data.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>R</td>
			<td></td>
			<td>version 3.6.2</td>
			<td>free software</td>
		</tr>
		<tr>
			<td>Rstudio</td>
			<td></td>
			<td></td>
			<td>free software</td>
		</tr>
	</tbody>
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three differential expression analysis methods for rna sequencing: limma, edger, deseq2

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration and complex biological processes and has great value in diagnostics, prognostics, and therapeutics of tumors. Differential analysis of RNA-seq data is crucial to identify aberrant transcriptions, and limma, EdgeR and DESeq2 are efficient tools for differential analysis. However, RNA-seq differential analysis requires certain skills with R language and the ability to choose an appropriate method, which is lacking in the curriculum of medical education.
Herein, we provide the detailed protocol to identify differentially expressed genes (DEGs) between cholangiocarcinoma (CHOL) and normal tissues through limma, DESeq2 and EdgeR, respectively, and the results are shown in volcano plots and Venn diagrams. The three protocols of limma, DESeq2 and EdgeR are similar but have different steps among the processes of the analysis. For example, a linear model is used for statistics in limma, while the negative binomial distribution is used in edgeR and DESeq2. Additionally, the normalized RNA-seq count data is necessary for EdgeR and limma but is not necessary for DESeq2.
Here, we provide a detailed protocol for three differential analysis methods: limma, EdgeR and DESeq2. The results of the three methods are partly overlapping. All three methods have their own advantages, and the choice of method only depends on the data.

There are various approaches to visualize the result of differential expression analysis, among which the volcano plot and Venn diagram are particularly used. limma identified 3323 DEGs between the CHOL and normal tissues with the |logFC|&#8805;2 and adj.P.Val &#60;0.05 as thresholds, among which 1880 were down-regulated in CHOL tissues and 1443 were up-regulated (Figure 1a). Meanwhile, edgeR identified the 1578 down-regulated DEGs and 3121 up-regulated DEGs (Figure 1b</...

Watch this Scientific Journal Video about Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2 at JoVE.com

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

A detailed protocol of differential expression analysis methods for RNA sequencing was provided: limma, EdgeR, DESeq2.

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration ...

Three differential expression analysis methods for RNA sequencing:limma, EdgeR, and DESeq2. Open the RStudio program and then load R file, DEGs. The file can be acquired from supplementary files.One.Downloading and pre-processing of data.1.1. Download the high-throughput sequencing count data of Cholangiocarcinoma from the Cancer Genome Atlas. This tab can easily achieved by the following code.Click run to install the R package. Click run to load R package. Set working directory.Choose the cancer type. Run R code from the GDCquery file to download the data. File GDCquery can be acquired from supplementary files/scripts.After execution, the Cholangiocarcinoma RNA sequencing count data can be downloaded and named CNT, where rows represent ensemble gene IDs and columns represent symbols IDs. Please notice the numbers at position 14 to 15 in the symbols IDs. Numbers range from 01 to 09 indicate tumors and 10 to 19 indicate normal tissues.1.2.Conversation of ensemble gene IDs to gene symbols. Import the annotation file into R, according to its'storage path. The annotation file can be acquired from supplementary files.Run the R code from the gtf v22 file. Which can be acquired from supplementary files/scripts. Apply inn"function and to convert the ensemble gene IDs to gene symbols.1.3.Filter low-expressed genes. Click run to install package edgeR"Click run to load the R package edgeR"Run following R code to keep genes with counts per million values greater than one in at least two samples.Two. Differential expression analysis through limma"Click Run to install R package limma"Click Run to load R package limma"edgeR"Run the following R code to create design matrix.Extract group information. Set 01"as tumor tissue. Set 11"as normal tissue.Create design matrix. Create the DGEList object. Normalize the data.Run the following R code to perform the limma-trend method based differential expression analysis. Calculate the CPM value. Click Run to fit a linear model to predict the data or infer the relationship between variables.Calculate T value, F value and the log-odds based on Bayesian. Extract the results table. The results of differential expression analysis is saved in res_limma"which includes the log2 fold change value.The average log2 expression level of the gene in the experiment. The modified T statistic, P value, the false discovery rate corrected p value and the log-odds of differentially expressed genes. Identify the differentially expressed genes.So the adjusted P value less than 0.05, and absolute value of log false change greater than or equal to two are thresholds to screen the differentially expressed genes. The results res limma shows that comparing with the normal tissues, 1, 443 genes are up-regulated, and 1, 880 genes are down-regulated in Cholangiocarcinoma tissues. Output the result table to a file.Click Run to install R package ggplot2"Click Run to load R package ggplot2"Run R code from the volcano file to create the volcano plot and the file volcano can be acquired from supplementary files. Genes can be mapped to different positions according to their log2 fold change and adjusted P values. So up-regulated differentially expressed genes are colored in red.and the down-regulated differentially expressed genes are colored in green. Click export"to save the volcano plot.Three. Differential expression analysis through edgeR"Click Run to load R package edgeR"Run the following R code to create design matrix.Click Run to create the DGEList object and normalize the data. Click Run to estimate the dispersion of gene expression value. Click Run to fit model to count data.Conduct statistical test. Extract the result table. The result is saved in res edgeR"which includes the log fold change value, logCPM, F, p value and the false discovery rate corrected p value.Identify the differentially expressed genes. The result res edgeR"shows that comparing with the normal tissues, 3, 121 genes are up-regulated, and 1, 578 genes are down-regulated in Cholangiocarcinoma tissues. Output the result table to a file.Create the volcano plot. Click export to save the volcano plot.Four. Differential expression analysis through DESeq2.Click Run to install R package DESeq2"Click Run to load R package DESeq2"Run the following R code to determine the groping factor. Create the DESeq2 data set object. Perform analysis.Generate the result table. The result is saved in res DESeq2, which includes the mean of the normalized read count, log fold change value, log fold change standard arrow, the weld statistic, original P value and the corrected P value. Identify DEGs.The result res DESeq2 shows that comparing with the normal tissues, two thousand nine hundred and thirty eight genes are up-regulated, and one thousand six hundred and sixteen genes are down-regulated in Cholangiocarcinoma tissues. Output the result table to a file. Create the volcano plot.Click export to save the volcano plot.Five. Venn diagram. Click Run to install the R package venn diagram.Click Run to load the R package venn diagram. Make a venn diagram of up-regulated differentially expressed genes. Click export to save the van diagram, Make a venn diagram of down-regulated differentially expressed genes.Click export to save the venn diagram.Six. Representative results. Figure one shows the volcano plots of all genes acquired by limma, edgeR and DESeq2.Negative log p value is plotted against the log fold change. Red points represent the up-regulated differentially expressed genes, and the green points represent the down-regulated differentially expressed genes. Limma identifies the one thousand eight hundred and eighty down-regulated differentially expressed the genes, and the one thousand four hundred and forty three up-regulated differentially expressed genes in Cholangiocarcinoma tissues.EdgeR identify the one thousand five hundred seventy eight down-regulated differentially expressed genes, and three thousand one hundred and twenty one up-regulated differentially expressed genes. DESeq2 identify one thousand six hundred and sixteen down-regulated differentially expressed genes, and two thousand nine hundred and thirty eight up-regulated differentially expressed genes. Figure two, venn diagrams show overlap among the results divide from limma edgeR and DESeq2.Compare the results of these three methods, One thousand four hundred and thirty one up-regulated differentially expressed genes, and one thousand five hundred and thirty one down-regulated differentially expressed genes are overlapping.Seven.Conclusion. In this protocol, we provided here a detailed protocol of different types of measure analysis for a high sequence of count data by using R packages, limma, edgeR, and DESeq2. Three methods have similar and staffs among their process of their analysis.And then their from those three medicines are partly overlapping. All three medicines have their own advantages. And the choice is just depends on time of your data.If there is my current data, limma should be given with priority, but generation sequencing data, in edgeR, and DESeq2 are preferred.

three-differential-expression-analysis-methods-for-rna-sequencing

Research

JoVE Journal

Cancer Research

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

Two-dimensional Gel Electrophoresis Coupled with Mass Spectrometry Methods for an Analysis of Human Pituitary Adenoma Tissue Proteome

Human pituitary adenoma (PA) is a common tumor that occurs in the human pituitary gland in the hypothalamus-pituitary-targeted organ axis systems, and may be classified as either clinically functional or nonfunctional PA (FPA and NFPA). NFPA is difficult for early stage diagnosis and therapy due to barely elevating hormones in the blood compared to FPA. Our long-term goal is to use proteomics methods to discover reliable biomarkers for clarification of PA molecular mechanisms and recognition of effective diagnostic, prognostic markers and therapeutic targets. Effective two-dimensional gel electrophoresis (2DE) coupled with mass spectrometry (MS) methods were presented here to analyze human PA proteomes, including preparation of samples, 2D gel electrophoresis, protein visualization, image analysis, in-gel trypsin digestion, peptide mass fingerprint (PMF), and tandem mass spectrometry (MS/MS). 2-Dimensional gel electrophoresis matrix-assisted laser desorption/ionization mass spectrometry PMF (2DE-MALDI MS PMF), 2DE-MALDI MS/MS, and 2DE-liquid chromatography (LC) MS/MS procedures have been successfully applied in an analysis of NFPA proteome. With the use of a high-sensitivity mass spectrometer, many proteins were identified with the 2DE-LC-MS/MS method in each 2D gel spot in an analysis of complex PA tissue to maximize the coverage of human PA proteome.

Here, we present a two-dimensional gel electrophoresis (2DE) coupled with mass spectrometry (MS) to separate and identify human pituitary adenoma tissue proteome, which presents a good and reproducible 2DE pattern. Many proteins are observed in each 2DE spot when analyzing complex cancer proteome with the use of high-sensitivity MS.

Human pituitary adenoma (PA) is a common tumor that occurs in the human pituitary gland in the hypothalamus-pituitary-targeted organ axis systems, and may ...

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Ablation of androgen receptor (AR) signaling by androgen deprivation is the goal of the first line of therapy for prostate cancer that initially results in cancer regression. However, in a significant number of cases, the disease progresses to advanced, castration-resistant prostate cancer (CRPC), which has limited therapeutic options and is often aggressive. Distant metastasis is mostly observed at this stage of the aggressive disease. CRPC is treated by a second generation of AR pathway inhibitors that improve survival initially, followed by the emergence of therapy resistance. Neuroendocrine prostate cancer (NEPC) is a rare variant of prostate cancer (PCa) that often develops as a result of therapy resistance via a transdifferentiation process known as neuroendocrine differentiation (NED), wherein PCa cells undergo a lineage switch from adenocarcinomas and show increased expression of neuroendocrine (NE) lineage markers. In addition to the genomic alterations that drive the progression and transdifferentiation to NEPC, epigenetic factors and microenvironmental cues are considered essential players in driving disease progression. This manuscript provides a detailed protocol to identify the epigenetic drivers (i.e., small non-coding RNAs) that are associated with advanced PCa. Using purified microRNAs from formalin-fixed paraffin-embedded (FFPE) metastatic tissues and corresponding serum-derived extracellular vesicles (EVs), the protocol describes how to prepare libraries with appropriate quality control for sequencing microRNAs from these sample sources. Isolating RNA from both FFPE and EVs is often challenging because most of it is either degraded or is limited in quantity. This protocol will elaborate on different methods to optimize the RNA inputs and cDNA libraries to yield most specific reads and high-quality data upon sequencing.

Therapy resistance often develops in patients with advanced prostate cancer, and in some cases, cancer progresses to a lethal subtype called neuroendocrine prostate cancer. Assessing the small non-coding RNA-mediated molecular changes that facilitate this transition would allow better disease stratification and identification of causal mechanisms that lead to development of neuroendocrine prostate cancer.

Ablation of androgen receptor (AR) signaling by androgen deprivation is the goal of the first line of therapy for prostate cancer that initially results ...

Evaluating Cell Death Using Cell-Free Supernatant of Probiotics in Three-Dimensional Spheroid Cultures of Colorectal Cancer Cells

This manuscript describes a protocol to evaluate cancer cell deaths in three dimensional (3D) spheroids of multicellular types of cancer cells using supernatants from Lactobacillus fermentum cell culture, considered as probiotics cultures. The use of 3D cultures to test Lactobacillus cell-free supernatant (LCFS) are a better option than testing in 2D monolayers, especially as L. fermentum can produce anti-cancer effects within the gut. L. fermentum supernatant was identified to possess increased anti-proliferative effects against several colorectal cancer (CRC) cells in 3D culture conditions. Interestingly, these effects were strongly related to the culture model, demonstrating the notable ability of L. fermentum to induce cancer cell death. Stable spheroids were generated from diverse CRCs (colorectal cancer cells) using the protocol presented below. This protocol of generating 3D spheroid is time saving and cost effective. This system was developed to easily investigate the anti-cancer effects of LCFS in multiple types of CRC spheroids. As expected, CRC spheroids treated with LCFS strongly induced cell death during the experiment and expressed specific apoptosis molecular markers as analyzed by qRT-PCR, western blotting, and FACS analysis. Therefore, this method is valuable for exploring cell viability and evaluating the efficacy of anti-cancer drugs.

Here methods are presented to understand anti-cancer effects of Lactobacillus cell-free supernatant (LCFS). Colorectal cancer cell lines show cell deaths when treated with LCFS in 3D cultures. The process of generating spheroids can be optimized depending on the scaffold and the analysis methods presented are useful for evaluating the involved signaling pathways.

This manuscript describes a protocol to evaluate cancer cell deaths in three dimensional (3D) spheroids of multicellular types of cancer cells using ...

An Automated Differential Nuclear Staining Assay for Accurate Determination of Mitocan Cytotoxicity

The contribution of mitochondria to oncogenic transformation is a subject of wide interest and active study. As the field of cancer metabolism becomes more complex, the goal of targeting mitochondria using various compounds that inflict mitochondrial damage (so-called mitocans) is becoming quite popular. Unfortunately, many existing cytotoxicity assays, such as those based on tetrazolium salts or resazurin require functional mitochondrial enzymes for their performance. The damage inflicted by compounds that target mitochondria often compromises the accuracy of these assays. Here, we describe a modified protocol based on differential staining with two fluorescent dyes, one of which is cell-permeant (Hoechst 33342) and the other of which is not (propidium iodide). The difference in staining allows living and dead cells to be discriminated. The assay is amenable to automated microscopy and image analysis, which increases throughput and reduces bias. This also allows the assay to be used in high-throughput fashion using 96-well plates, making it a viable option for drug discovery efforts, particularly when the drugs in question have some level of mitotoxicity. Importantly, results obtained by Hoechst/PI staining assay show increased consistency, both with trypan blue exclusion results and between biological replicates when the assay is compared to other methods.

The protocol describes a rapid, high-throughput, reliable, inexpensive, and unbiased assay for efficiently determining cellular viability. This assay is particularly useful when cells&#39; mitochondria have been damaged, which interferes with other assays. The assay uses automated counting of cells stained with two nuclear dyes &#8211; Hoechst 33342 and propidium iodide.

The contribution of mitochondria to oncogenic transformation is a subject of wide interest and active study. As the field of cancer metabolism becomes ...

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein and RNA multiplexing by leveraging photo-cleavable oligo-tagged antibodies and probes, respectively. Oligos are cleaved and quantified from specific regions across the tissue to elucidate the underlying biology. Here, the study demonstrates that automated custom antibody visualization protocols can be utilized to guide ROI selection in conjunction with spatial proteomics assays. This specific method did not show acceptable performance with spatial transcriptomics assays. The protocol describes the development of a 3-plex immunofluorescent (IF) assay for marker visualization on an automated platform, using tyramide signal amplification (TSA) to amplify the fluorescent signal from a given protein target and increase the antibody pool to choose from. The visualization protocol was automated using a thoroughly validated 3-plex assay to ensure quality and reproducibility. In addition, the exchange of DAPI for SYTO dyes was evaluated to allow imaging of TSA-based IF assays on the spatial profiling platform. Additionally, we tested the ability of selecting small ROIs using the spatial transcriptomics assay to allow the investigation of highly-specific areas of interest (e.g., areas enriched for a given cell type). ROIs of 50 &#181;m and 300 &#181;m diameter were collected, which corresponds to approximately 15 cells and 100 cells, respectively. Samples were made into libraries and sequenced to investigate the capability to detect signals from small ROIs and profile-specific regions of the tissue. We determined that spatial proteomics technologies highly benefit from automated, standardized protocols to guide ROI selection. While this automated visualization protocol was not compatible with spatial transcriptomics assays, we were able to test and confirm that specific cell populations can successfully be detected even in small ROIs with the standard manual visualization protocol.

Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 &#181;m.

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein ...

Transmitochondrial Cybrid Generation From Suspension-Growing Cancer Cells

Transmitochondrial Cybrid Generation Using Cancer Cell Lines

In recent years, the number of studies dedicated to ascertaining the connection between mitochondria and cancer has significantly risen. However, more efforts are still needed to fully understand the link involving alterations in mitochondria and tumorigenesis, as well as to identify tumor-associated mitochondrial phenotypes. For instance, to evaluate the contribution of mitochondria in tumorigenesis and metastasis processes, it is essential to understand the influence of mitochondria from tumor cells in different nuclear environments. For this purpose, one possible approach consists of transferring mitochondria into a different nuclear background to obtain the so-called cybrid cells. In the traditional cybridization techniques, a cell line lacking mtDNA (&#961;0, nuclear donor cell) is repopulated with mitochondria derived from either enucleated cells or platelets. However, the enucleation process requires good cell adhesion to the culture plate, a feature that is partially or completely lost in many cases in invasive cells. In addition, another difficulty found in the traditional methods is achieving complete removal of the endogenous mtDNA from the mitochondrial-recipient cell line to obtain pure nuclear and mitochondrial DNA backgrounds, avoiding the presence of two different mtDNA species in the generated cybrid. In this work, we present a mitochondrial exchange protocol applied to suspension-growing cancer cells based on the repopulation of rhodamine 6G-pretreated cells with isolated mitochondria. This methodology allows us to overcome the limitations of the traditional approaches, and thus can be used as a tool to expand the comprehension of the mitochondrial role in cancer progression and metastasis.

Author Spotlight: Transmitochondrial Cybrid Generation Using Cancer Cell Lines  

This protocol describes a technique for cybrid generation from suspension-growing cancer cells as a tool to study the role of mitochondria in the tumorigenic process.

In recent years, the number of studies dedicated to ascertaining the connection between mitochondria and cancer has significantly risen. However, more ...

FISH for Long Non-Coding RNA Localization in Human Osteosarcoma Cells

RNA Fluorescence In Situ Hybridization for Long Non-Coding RNA Localization in Human Osteosarcoma Cells

The important roles of long non-coding RNAs (lncRNAs) in cancer have been studied, such as regulating the proliferation, epithelial-mesenchymal transition (EMT), migration, infiltration, and autophagy of cancer cells. Localization detection of lncRNAs in cells can provide insight into their functions. By designing the lncRNA-specific antisense chain sequence followed by labeling with fluorescent dyes, RNA fluorescence in situ hybridization (FISH) can be applied to detect the cellular localization of lncRNAs. Together with the development of microscopy, the RNA FISH techniques now even allow for visualization of the poorly expressed lncRNAs. This method can not only detect the localization of lncRNAs alone, but also detect the colocalization of other RNAs, DNA, or proteins by using double-color or multicolor immunofluorescence. Here, we have included the detailed experimental operation procedure and precautions of RNA FISH by using lncRNA small nucleolar RNA host gene 6 (SNHG6) in human osteosarcoma cells (143B) as an example, to provide a reference for researchers who want to perform RNA FISH experiments, especially lncRNA FISH.

Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells 

The present protocol describes a method of RNA fluorescence in situ hybridization to localize the lncRNAs in human osteosarcoma cells.

The important roles of long non-coding RNAs (lncRNAs) in cancer have been studied, such as regulating the proliferation, epithelial-mesenchymal transition ...

Diagnosis of Vitreoretinal Lymphoma Using Cell-Free DNA

Cell-Free DNA Extraction of Vitreous and Aqueous Humor Specimens for Diagnosis and Monitoring of Vitreoretinal Lymphoma

Vitreoretinal lymphoma (VRL) represents an aggressive lymphoma, often categorized as primary central nervous system diffuse large B-cell lymphoma. To diagnose VRL, specimens such as vitreous humor and, more recently, aqueous humor are collected. Diagnostic testing for VRL on these specimens includes cytology, flow cytometry, and molecular testing. However, both cytopathology and flow cytometry, along with molecular testing using cellular DNA, necessitate intact whole cells. The challenge lies in the fact that vitreous and aqueous humor typically have low cellularity, and many cells get destroyed during collection, storage, and processing. Moreover, these specimens pose additional difficulties for molecular testing due to the high viscosity of vitreous humor and the low volume of both vitreous and aqueous humor. This study proposes a method for extracting cell-free DNA from vitreous and aqueous specimens. This approach complements the extraction of cellular DNA or allows the cellular component of these specimens to be utilized for other diagnostic methods, including cytology and flow cytometry.

Author Spotlight: Advancing VRL Diagnosis Using Cell-Free DNA Extraction from Vitreous Humor 

A procedure to extract cell-free DNA from vitreous and aqueous humor to perform molecular studies for diagnosing vitreoretinal lymphoma is established here. The method offers the ability to concurrently extract DNA from the cellular component of the sample or to reserve it for ancillary testing.

Vitreoretinal lymphoma (VRL) represents an aggressive lymphoma, often categorized as primary central nervous system diffuse large B-cell lymphoma. To ...

Nuclei Isolation from Human Pancreatic Tumor Cells for Multiome Sequencing

Begin by transferring previously thawed pancreatic tumor cell suspension to a two-milliliter microcentrifuge tube and add PBS to reach a final volume of two milliliters. Then, use a hemocytometer to evaluate the quantity and quality of the cells. Once the cell pellet is obtained through centrifugation, using progressively smaller pipette tips, carefully decant the supernatant to minimize pellet distribution while maximizing the fluid decanted.Prepare 1X cell lysis buffer, then add 100 microliters of it to 900 microliters of previously prepared cell lysis dilution buffer to obtain 0.1X cell lysis buffer. Transfer 100 microliters of chilled 0.1X cell lysis buffer to the cell pellet and gently pipette five times to ensure complete resuspension. After incubation on ice for three minutes, add one milliliter of chilled wash buffer to the resuspended pellet and gently pipette it five times.Following centrifugation, discard the supernatant as demonstrated, and repeat this washing process two more times. Load trypan blue-stained suspension into a hemocytometer to count the number of nuclei. Resuspend the nuclei pellet in 1X nuclei buffer based on goal-targeted nuclei recovery.Carefully pass the resuspended nuclei through a 40-micrometer pipette tip cell strainer, and then maintain it on ice. Now recount the nuclei. The nuclei obtained using this method exhibited appropriate size and shape.Occasional mild stippling of the nuclear envelope can be observed and should be monitored at the final nuclear evaluation via hemocytometer.