This study was supported by an Australian Research Council (ARC) Future Fellowship (FT16010043) and ANU Futures Scheme.

1. Installation of tools and R packages used in the analysis
<ol>
	<li>Conda is a popular and flexible package manager that allows convenient installation of packages with their dependencies across all platforms. Use &#39;Anaconda&#39; (conda package manager) to install &#39;conda&#39; which can be used to install the tools/packages required for the analysis.</li>
	<li>Download &#39;Anaconda&#39; according to the system requirements from https://www.anaconda.com/products/individual#Downloads and install ...

<ol>
	<li>Katz, Y., Wang, E. T., Airoldi, E. M., Burge, C. 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L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alternative+polyadenylation+of+mRNA+precursors.">Alternative polyadenylation of mRNA precursors.</a> Nature Reviews Molecular Cell Biology. 18 (1), 18-30 (2017).</li><li>Herrmann, C. J., 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=PolyASite+2.0:+a+consolidated+atlas+of+polyadenylation+sites+from+3'+end+sequencing.">PolyASite 2.0: a consolidated atlas of polyadenylation sites from 3' end sequencing.</a> Nucleic Acids Research. 48 (1), 174-179 (2020).</li><li>Liu, R., Loraine, A. E., Dickerson, J. 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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 (12), 550 (2014).</li><li>Sznajder, L. J., 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=Loss+of+MBNL1+induces+RNA+misprocessing+in+the+thymus+and+peripheral+blood.">Loss of MBNL1 induces RNA misprocessing in the thymus and peripheral blood.</a> Nature Communications. 11, 1-11 (2020).</li><li>Batra, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Loss+of+MBNL+leads+to+disruption+of+developmentally+regulated+alternative+polyadenylation+in+RNA-mediated+disease.">Loss of MBNL leads to disruption of developmentally regulated alternative polyadenylation in RNA-mediated disease.</a> Molecular Cell. 56 (2), 311-322 (2014).</li><li>Leinonen, R., Sugawara, H., Shumway, 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=The+sequence+read+archive.">The sequence read archive.</a> Nucleic acids research. 39, 19-21 (2010).</li><li>Tange, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> GNU parallel-the command-line power tool. 36, 42-47 (2011).</li><li>Martin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cutadapt+removes+adapter+sequences+from+high-throughput+sequencing+reads.">Cutadapt removes adapter sequences from high-throughput sequencing reads.</a> EMBnet journal. 17 (1), 10-12 (2011).</li><li>Bolger, A. M., Lohse, M., Usadel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trimmomatic:+a+flexible+trimmer+for+Illumina+sequence+data.">Trimmomatic: a flexible trimmer for Illumina sequence data.</a> Bioinformatics. 30 (15), 2114-2120 (2014).</li><li>Dobin, 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=STAR:+ultrafast+universal+RNA-seq+aligner.">STAR: ultrafast universal RNA-seq aligner.</a> Bioinformatics. 29 (1), 15-21 (2013).</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 (1), 139-140 (2010).</li><li>Robinson, M. D., Oshlack, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+scaling+normalization+method+for+differential+expression+analysis+of+RNA-seq+data.">A scaling normalization method for differential expression analysis of RNA-seq data.</a> Genome Biology. 11 (3), 25 (2010).</li><li>Veiga, D. F. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=maser:+Mapping+Alternative+Splicing+Events+to+pRoteins.">maser: Mapping Alternative Splicing Events to pRoteins.</a> R package version 1.4.0. , (2019).</li><li>Langmead, B., Trapnell, C., Pop, M., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrafast+and+memory-efficient+alignment+of+short+DNA+sequences+to+the+human+genome.">Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.</a> Genome Biology. 10 (13), 25 (2009).</li><li>Quinlan, A. R., Hall, I. 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In this study, we evaluated exon-based and event-based approaches to detect AS and APA in bulk RNA-Seq and 3&#39; end sequencing data. The exon-based AS approaches produce both a list of differentially expressed exons and a gene-level ranking ordered by the statistical significance of overall gene-level differential splicing activity (Tables 1-2, 4-5). For the diffSplice package, differential usage is determined by fitting weighted linear models at an exon-level to estimate the differential log fold-chan...

RNA-seq has been widely used over the years typically for estimating differential gene expression and gene discovery1. In addition, it can also be utilized to estimate varying exon level usage due to gene expressing different isoforms, hence contributing to a better understanding of gene regulation at the post-transcriptional level. The majority of eukaryotic genes generate different isoforms by alternative splicing (AS) to increase the diversity of mRNA expression. AS events can be divided into different patterns: skipping of complete exons (SE) where a (&#34;cassette&#34;) exon is completely removed out of the transcript along with its flanking introns; alternative (donor) 5&#39; splice site selection (A5SS) and alternative 3&#39; (acceptor) splice site selection (A3SS) when two or more splice sites are present on either end of an exon; retention of introns (RI) when an intron is retained within the mature mRNA transcript and mutual exclusion of exon usage (MXE) where only one of the two available exons can be retained at a time2,3. Alternative polyadenylation (APA) also plays an important role in regulating gene expression using alternative poly (A) sites to generate multiple mRNA isoforms from a single transcript4. Most polyadenylation sites (pAs) are located in the 3&#39; untranslated region (3&#39; UTRs), generating mRNA isoforms with diverse 3&#39; UTR lengths. As the 3&#39; UTR is the central hub for recognizing regulatory elements, different 3&#39; UTR lengths can affect mRNA localization, stability and translation5. There are a class of 3&#39; end sequencing assays optimized to detect APA that differ in the details of the protocol6. The pipeline described here is designed for PolyA-seq, but can be adapted for other protocols as described.
In this study, we present a pipeline of differential exon analysis methods7,8 (Figure 1), which can be divided into two broad categories: exon-based (DEXSeq9, diffSplice10) and event-based (replicate Multivariate Analysis of Transcript Splicing (rMATS)11). The exon-based methods compare the fold change across conditions of individual exons, against a measure of overall gene fold change to call differentially expressed exon usage, and from that compute a gene-level measure of AS activity. Event-based methods use exon-intron-spanning junction reads to detect and classify specific splicing events such as exon skipping or retention of introns, and distinguish these AS types in the output3. Thus, these methods provide complementary views for a complete analysis of AS12,13. We selected DEXSeq (based on the DESeq214 DGE package) and diffSplice (based on the Limma10 DGE package) for the study as they are amongst the most widely used packages for differential splicing analysis. rMATS was chosen as a popular method for event-based analysis. Another popular event-based method is MISO (Mixture of Isoforms)1. For APA we adapt the exon-based approach.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62636/62636fig01.jpg" /> 
Figure 1.&#160;Analysis pipeline. Flowchart of the steps used in the analysis. Steps include: obtaining the data, performing quality checks and read alignment followed by counting reads using annotations for known exons, introns and pA sites, filtering to remove low counts and normalization. PolyA-seq data was analysed for alternative pA sites using diffSplice/DEXSeq methods, bulk RNA-Seq was analysed for alternative splicing at the exon level with diffSplice/DEXseq methods, and AS events analysed with rMATS. <a href="https://www.jove.com/files/ftp_upload/62636/62636fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The RNA-seq data used in this survey was acquired from Gene Expression Omnibus (GEO) (GSE138691)15. We used mouse RNA-seq data from this study with two condition groups: wild-type (WT) and Muscleblind-like type 1 knockout (Mbnl1 KO) with three replicates each. To demonstrate differential polyadenylation site usage analysis, we obtained mouse embryo fibroblasts (MEFs) PolyA-seq data (GEO Accession GSE60487)16. The data has four condition groups: Wild-type (WT), Muscleblind-like type1/type 2 double knockout (Mbnl1/2 DKO), Mbnl 1/2 DKO with Mbnl3 knockdown (KD) and Mbnl1/2 DKO with Mbnl3 control (Ctrl). Each condition group consists of two replicates.
<table border="1" fo:font-size="5px" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>GEO Accession</td>
			<td>SRA Run number</td>
			<td>Sample name</td>
			<td>Condition</td>
			<td>Replicate</td>
			<td>Tissue</td>
			<td>Sequencing</td>
			<td>Read length</td>
		</tr>
		<tr>
			<td rowspan="6">RNA-Seq</td>
			<td>GSM4116218</td>
			<td>SRR10261601</td>
			<td>Mbnl1KO_Thymus_1</td>
			<td>Mbnl1 knockout</td>
			<td>Rep 1</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116219</td>
			<td>SRR10261602</td>
			<td>Mbnl1KO_Thymus_2</td>
			<td>Mbnl1 knockout</td>
			<td>Rep 2</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116220</td>
			<td>SRR10261603</td>
			<td>Mbnl1KO_Thymus_3</td>
			<td>Mbnl1 knockout</td>
			<td>Rep 3</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116221</td>
			<td>SRR10261604</td>
			<td>WT_Thymus_1</td>
			<td>Wild type</td>
			<td>Rep 1</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116222</td>
			<td>SRR10261605</td>
			<td>WT_Thymus_2</td>
			<td>Wild type</td>
			<td>Rep 2</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116223</td>
			<td>SRR10261606</td>
			<td>WT_Thymus_3</td>
			<td>Wild type</td>
			<td>Rep 3</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td rowspan="8">3P-Seq</td>
			<td>GSM1480973</td>
			<td>SRR1553129</td>
			<td>WT_1</td>
			<td>Wild type (WT)</td>
			<td>Rep 1</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480974</td>
			<td>SRR1553130</td>
			<td>WT_2</td>
			<td>Wild type (WT)</td>
			<td>Rep 2</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480975</td>
			<td>SRR1553131</td>
			<td>DKO_1</td>
			<td>Mbnl 1/2 double knockout (DKO)</td>
			<td>Rep 1</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480976</td>
			<td>SRR1553132</td>
			<td>DKO_2</td>
			<td>Mbnl 1/2 double knockout (DKO)</td>
			<td>Rep 2</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480977</td>
			<td>SRR1553133</td>
			<td>DKOsiRNA_1</td>
			<td>Mbnl 1/2 double knockout with Mbnl 3 siRNA (KD)</td>
			<td>Rep 1</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480978</td>
			<td>SRR1553134</td>
			<td>DKOsiRNA_2</td>
			<td>Mbnl 1/2 double knockout with Mbnl 3 siRNA (KD)</td>
			<td>Rep 2</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>36 bp</td>
		</tr>
		<tr>
			<td>GSM1480979</td>
			<td>SRR1553135</td>
			<td>DKONTsiRNA_1</td>
			<td>Mbnl 1/2 double knockout with non-targeting siRNA (Ctrl)</td>
			<td>Rep 1</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480980</td>
			<td>SRR1553136</td>
			<td>DKONTsiRNA_2</td>
			<td>Mbnl 1/2 double knockout with non-targeting siRNA (Ctrl)</td>
			<td>Rep 2</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
	</tbody>
</table>
Table 1. Summary of RNA-Seq and PolyA-seq datasets used for the analysis.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Not relevent for computational study</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

identification of alternative splicing and polyadenylation in rna-seq data

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also be utilized to explore other complex regulatory mechanisms at the exon level. Alternative splicing and polyadenylation play a crucial role in the functional diversity of a gene by generating different isoforms to regulate gene expression at the post-transcriptional level, and limiting analyses to the whole gene level can miss this important regulatory layer. Here, we demonstrate detailed step by step analyses for identification and visualization of differential exon and polyadenylation site usage across conditions, using Bioconductor and other packages and functions, including DEXSeq, diffSplice from the Limma package, and rMATS.

After running the above step-by-step workflow, the AS and APA&#160;analysis outputs and representative results are in the form of tables and data plots, generated as follows.
AS: 
The main output of the AS analysis (Supplementary Table 1 for diffSplice; Table 2 for DEXSeq) is a list of exons showing differential usage across conditions, and a list of genes showing significant overall splicing activity of one or more of its constituent exo...

Watch this Scientific Journal Video about Identification of Alternative Splicing and Polyadenylation in RNA-seq Data at JoVE.com

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Alternative splicing (AS) and alternative polyadenylation (APA) expand the diversity of transcript isoforms and their products. Here, we describe bioinformatic protocols to analyze bulk RNA-seq and 3' end sequencing assays to detect and visualize AS and APA varying across experimental conditions.

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also ...

This protocol provides a comprehensive understanding of gene isoforms generated by alternative splicing and polyadenylation by providing a step-by-step workflow to identify differential splicing sites, differentially-expressed exons, and poly(A)sites. The main advantage of this protocol is that it evaluates both exon-based and event-based methods for studying alternative splicing. It also applies exon-based method to study alternative polyadenylation.The R Markdown files that include the codes and notes for AS and AP analysis have been provided. It would be advisable to follow the steps in the R Markdown file and reach the note for each step carefully. To identify differential splicing using diffSplice from limma, follow the R notebook file.Prepare the input files as described in the text manuscript. Ensure steps one through three in the manuscript have been followed sequentially to prepare input files before proceeding further. Begin by loading the necessary libraries.To perform non-specific filtering, first extract the matrix of read counts obtained previously and create a list of features using the DGEList function from the edgeR package, where rows represent genes and columns represent samples. Then, transform the data from raw scale to counts per million using the CPM function from the edgeR package, and keep exons with counts greater than a settable threshold. This data set contains six samples.Hence, the CPM is set at greater than one and at least three samples out of six. Normalize the counts across samples with the calcNormFactors function from the edgeR package using Trimmed Mean of M values. This function will compute scaling factors to adjust library sizes.Use the previously-generated sample table to create the design matrix to define the experimental conditions for each sample. Run the voom function of the limma package to process RNA sequencing data to estimate the variance. This function will generate precision weights to correct for Poisson count noise and transform the exon level counts to log two counts per million or logCPM.Run the lmfit function to fit linear models to the expression data for each exon. Then run the eBayes function to compute empirical-based statistics for the fitted model to detect differential exon expression. Define a contrast matrix for the experimental comparisons of interest.Use the contrasts. fit function to obtain coefficients and standard errors for each pair of comparisons. Run diffSplice on the fitted model to test the differences in exon usage of genes between wild type and knockout.Explore the top-ranked results using the topSplice function where a test equal to t gives a ranking of AS exons and test equal to simes gives a ranking of genes. Run the plotSplice function to plot the results. In putting the gene of interest in the gene ID argument, the red points show the differentially-expressed exons.Generate a volcano plot using EnhancedVolcano bioconductor package to exhibit the differentially-expressed exons. To use rMATS, ensure the latest version of rMATS version 4.1.1 is installed either using conda or GitHub in the working directory. Go to the folder containing bam files obtained after mapping.Prepare text files as required by rMATS for the two conditions of copying the name of bam files and their path separated by a comma. Run rmas. py using the two generated input text files describing the path of the bam files and the annotation.gtf file obtained previously. This generates an output folder rmats_out containing text files describing statistics including P-values and inclusion levels for each splicing event separately. Use the bioconductor package maser to explore the rMATS results.Load the junction and exon counts text files with the extension JCEC into the maser object and include at least five average reads per splicing event to filter the result based on coverage. To visualize the rMATS results, first run the topEvents function from the maser package, selecting the significant splicing events at a false discovery rate of 10%and a minimum 10%change in percent spliced in or PSI. Check the gene events for individual genes of interest and plot PSI values for each splicing event of that gene.Generate a volcano plot by specifying the event type. Use the splicing events results obtained with rMATS in the form of text files to generate sashimi plots using the rmats2sashimiplot package. The sashimi plot shows a skipped exon event in the Wnk1 gene.Each row represents an RNA-seq sample, three replicates of wild type and Mbnl1 knockout. The height shows read coverage in RPKM and the connecting arcs depict junction reads across exons. The bottom part shows annotated gene model alternative isoforms.A substantial fold change and strong statistical evidence of genuine differences can be observed in the genes located at the top left or right quadrants of the volcano plots obtained using diffSplice and DEXSeq. A cassette exon was found to be varying between different conditions for the gene Wnk1. The differential exon usage plot showed evidence of differential splicing at five exon sites near Wnk1.6.45, with the exons highlighted in pink likely to be spliced out in Mbnl1 knockout samples compared to wild type.The volcano plot of genes that are alternatively spliced helped to distinguish between the genes that were excluded from wild type and those that were included in wild type. The types of splicing events SE, A5SS, A3SS, MXE, and RI were visualized using sashimi plots of the top significant genes of those events. The differential APA activity in three prime untranslated regions of genes was observed using volcano plots.The significantly-differential PA site usage results acquired from different pipelines were visualized using event plot. A significant distal to proximal shift of PA site usage in double knockouts can be observed in both genes FOSL1 and Papola. The mean coverage in flanking regions anchored at known PA cleavage sites on the genome-wide level was determined using a diagnostic plot.Ensure the parameters such as transpecific information and allow multi overlap are correctly used when generating count metrics. Linear model fitting and generating contrast pairs is important for proper comparison. For rMATS, ensure all the parameters are set correctly according to your data before running the command.The genes obtained from differential splicing activity could be used to perform gene set enrichment analysis. Another tool called MISO could be used for further event-based analysis.

identification-alternative-splicing-polyadenylation-rna-seq

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5.1K ビュー.  The Australian National University. このプロトコルは、差分スプライシング部位、発現差のあるエクソン、およびポリ(A)部位を特定するための段階的なワークフローを提供することにより、選択的スプライシングおよびポリアデニル化によって生成された遺伝子アイソフォームの包括的な理解を提供します。このプロトコルの主な利点は、選択的スプライシングを研究するためのエクソンベースの方法と?...

ビデオ: Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of protein complexes with quantitative proteomics in S. cerevisiae

Lipids are the building blocks of cellular membranes that function as barriers and in compartmentalization of cellular processes, and recently, as important intracellular signalling molecules. However, unlike proteins, lipids are small hydrophobic molecules that traffic primarily by poorly described nonvesicular routes, which are hypothesized to occur at membrane contact sites (MCSs). MCSs are regions where the endoplasmic reticulum (ER) makes direct physical contact with a partnering organelle, e.g., plasma membrane (PM). The ER portion of ER-PM MCSs is enriched in lipid-synthesizing enzymes, suggesting that lipid synthesis is directed to these sites and implying that MCSs are important for lipid traffic. Yeast is an ideal model to study ER-PM MCSs because of their abundance, with over 1000 contacts per cell, and their conserved nature in all eukaryotes. Uncovering the proteins that constitute MCSs is critical to understanding how lipids traffic is accomplished in cells, and how they act as signaling molecules. We have found that an ER called Scs2p localize to ER-PM MCSs and is important for their formation. We are focused on uncovering the molecular partners of Scs2p. Identification of protein complexes traditionally relies on first resolving purified protein samples by gel electrophoresis, followed by in-gel digestion of protein bands and analysis of peptides by mass spectrometry. This often limits the study to a small subset of proteins. Also, protein complexes are exposed to denaturing or non-physiological conditions during the procedure. To circumvent these problems, we have implemented a large-scale quantitative proteomics technique to extract unbiased and quantified data. We use stable isotope labeling with amino acids in cell culture (SILAC) to incorporate staple isotope nuclei in proteins in an untagged control strain. Equal volumes of tagged culture and untagged, SILAC-labeled culture are mixed together and lysed by grinding in liquid nitrogen. We then carry out an affinity purification procedure to pull down protein complexes. Finally, we precipitate the protein sample, which is ready for analysis by high-performance liquid chromatography/ tandem mass spectrometry. Most importantly, proteins in the control strain are labeled by the heavy isotope and will produce a mass/ charge shift that can be quantified against the unlabeled proteins in the bait strain. Therefore, contaminants, or unspecific binding can be easily eliminated. By using this approach, we have identified several novel proteins that localize to ER-PM MCSs. Here we present a detailed description of our approach. 

Here we describe a new quantitative proteomics technique for identifying protein complexes in Saccharomyces cerevisiae. In this study, we have used the SILAC method together with affinity purification followed by tandem mass spectrometry to identify with high specificity the binding partners of an ER protein, Scs2p.

Lipids are the building blocks of cellular membranes that function as barriers and in compartmentalization of cellular processes, and recently, as ...

RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells

The characterization of gene expression in cells via measurement of mRNA levels is a useful tool in determining how the transcriptional machinery of the cell is affected by external signals (e.g. drug treatment), or how cells differ between a healthy state and a diseased state. With the advent and continuous refinement of next-generation DNA sequencing technology, RNA-sequencing (RNA-seq) has become an increasingly popular method of transcriptome analysis to catalog all species of transcripts, to determine the transcriptional structure of all expressed genes and to quantify the changing expression levels of the total set of transcripts in a given cell, tissue or organism1,2 . RNA-seq is gradually replacing DNA microarrays as a preferred method for transcriptome analysis because it has the advantages of profiling a complete transcriptome, providing a digital type datum (copy number of any transcript) and not relying on any known genomic sequence3.
Here, we present a complete and detailed protocol to apply RNA-seq to profile transcriptomes in human pulmonary microvascular endothelial cells with or without thrombin treatment. This protocol is based on our recent published study entitled "RNA-seq Reveals Novel Transcriptome of Genes and Their Isoforms in Human Pulmonary Microvascular Endothelial Cells Treated with Thrombin,"4 in which we successfully performed the first complete transcriptome analysis of human pulmonary microvascular endothelial cells treated with thrombin using RNA-seq. It yielded unprecedented resources for further experimentation to gain insights into molecular mechanisms underlying thrombin-mediated endothelial dysfunction in the pathogenesis of inflammatory conditions, cancer, diabetes, and coronary heart disease, and provides potential new leads for therapeutic targets to those diseases.
The descriptive text of this protocol is divided into four parts. The first part describes the treatment of human pulmonary microvascular endothelial cells with thrombin and RNA isolation, quality analysis and quantification. The second part describes library construction and sequencing. The third part describes the data analysis. The fourth part describes an RT-PCR validation assay. Representative results of several key steps are displayed. Useful tips or precautions to boost success in key steps are provided in the Discussion section. Although this protocol uses human pulmonary microvascular endothelial cells treated with thrombin, it can be generalized to profile transcriptomes in both mammalian and non-mammalian cells and in tissues treated with different stimuli or inhibitors, or to compare transcriptomes in cells or tissues between a healthy state and a disease state.

This protocol presents a complete and detailed procedure to apply RNA-seq, a powerful next-generation DNA sequencing technology, to profile transcriptomes in human pulmonary microvascular endothelial cells with or without thrombin treatment. This protocol is generalizable to various cells or tissues affected by different reagents or disease states.

The characterization of gene expression in cells via measurement of mRNA levels is a useful tool in determining how the transcriptional machinery of the ...

Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq

The mosquito gut accommodates dynamic microbial communities across different stages of the insect's life cycle. Characterization of the genetic capacity and functionality of the gut community will provide insight into the effects of gut microbiota on mosquito life traits. Metagenomic RNA-Seq has become an important tool to analyze transcriptomes from various microbes present in a microbial community. Messenger RNA usually comprises only 1-3% of total RNA, while rRNA constitutes approximately 90%. It is challenging to enrich messenger RNA from a metagenomic microbial RNA sample because most prokaryotic mRNA species lack stable poly(A) tails. This prevents oligo d(T) mediated mRNA isolation. Here, we describe a protocol that employs sample derived rRNA capture probes to remove rRNA from a metagenomic total RNA sample. To begin, both mosquito and microbial small and large subunit rRNA fragments are amplified from a metagenomic community DNA sample. Then, the community specific biotinylated antisense ribosomal RNA probes are synthesized in vitro using T7 RNA polymerase. The biotinylated rRNA probes are hybridized to the total RNA. The hybrids are captured by streptavidin-coated beads and removed from the total RNA. This subtraction-based protocol efficiently removes both mosquito and microbial rRNA from the total RNA sample. The mRNA enriched sample is further processed for RNA amplification and RNA-Seq.

A ribosomal RNA (rRNA) depletion protocol was developed to enrich messenger RNA (mRNA) for RNA-seq of the mosquito gut metatranscriptome. Sample specific rRNA probes, which were used to remove rRNA via subtraction, were created from the mosquito and its gut microbes. Performance of the protocol can result in the removal of approximately 90-99% of rRNA.

The mosquito gut accommodates dynamic microbial communities across different stages of the insect's life cycle. Characterization of the genetic capacity ...

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Alternative splicing plays a critical role in the epithelial-mesenchymal transition (EMT), an essential cellular program that occurs in various physiological and pathological processes. Here we describe a strategy to detect alternative splicing during EMT using an inducible EMT model by expressing the transcription repressor Twist. EMT is monitored by changes in cell morphology, loss of E-cadherin localization at cell-cell junctions, and the switched expression of EMT markers, such as loss of epithelial markers E-cadherin and &#947;-catenin and gain of mesenchymal markers N-cadherin and vimentin. Using isoform-specific primer sets, the alternative splicing of interested mRNAs are analyzed by quantitative RT-PCR. The production of corresponding protein isoforms is validated by immunoblotting assays. The method of detecting splice isoforms described here is also suitable for the study of alternative splicing in other biological processes.

Alternative splicing regulation has been shown to contribute to the epithelial-mesenchymal transition (EMT), an essential cellular program in various physiological and pathological processes. Here we describe a method utilizing an inducible EMT model for the detection of alternative splicing during EMT.

Alternative splicing plays a critical role in the epithelial-mesenchymal transition (EMT), an essential cellular program that occurs in various ...

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, most notably developmental arrest and reduced metabolism. Global gene expression profiling via RNA-Seq can provide important insights into the transcriptional mechanisms of photoperiodic diapause. The Asian tiger mosquito, Aedes albopictus, is an outstanding organism for studying the transcriptional bases of diapause due to its ease of rearing, easily induced diapause, and the genomic resources available. This manuscript presents a general experimental workflow for identifying diapause-induced transcriptional differences in A. albopictus. Rearing techniques, conditions necessary to induce diapause and non-diapause development, methods to estimate percent diapause in a population, and RNA extraction and integrity assessment for mosquitoes are documented. A workflow to process RNA-Seq data from Illumina sequencers culminates in a list of differentially expressed genes. The representative results demonstrate that this protocol can be used to effectively identify genes differentially regulated at the transcriptional level in A. albopictus due to photoperiodic differences. With modest adjustments, this workflow can be readily adapted to study the transcriptional bases of diapause or other important life history traits in other mosquitoes.

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

RNA-Seq analyses are becoming increasingly important for identifying the molecular underpinnings of adaptive traits in non-model organisms. Here, a protocol to identify differentially expressed genes between diapause and non-diapause Aedes albopictus mosquitoes is described, from mosquito rearing, to RNA sequencing and bioinformatics analyses of RNA-Seq data.

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, ...

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. Saccharomyces cerevisiae, or budding yeast, is a model eukaryote for which a complete collection of ~6,000 gene deletion mutants and hypomorphic essential gene mutants are commercially available. These collections of mutants can be used to systematically detect chemical-gene interactions, i.e. genes necessary to tolerate a chemical. This information, in turn, reports on the likely mode of action of the compound. Here we describe a protocol for the rapid identification of chemical-genetic interactions in budding yeast. We demonstrate the method using the chemotherapeutic agent 5-fluorouracil (5-FU), which has a well-defined mechanism of action. Our results show that the nuclear TRAMP RNA exosome and DNA repair enzymes are needed for proliferation in the presence of 5-FU, which is consistent with previous microarray based bar-coding chemical genetic approaches and the knowledge that 5-FU adversely affects both RNA and DNA metabolism. The required validation protocols of these high-throughput screens are also described.

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Here we present a cost-effective method for defining chemical-genetic interactions in budding yeast. The approach is built on fundamental techniques in yeast molecular biology and is well suited for the mechanistic interrogation of small to medium collections of chemicals and other media environments.

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. ...

Methods to Discover Alternative Promoter Usage and Transcriptional Regulation of Murine Bcrp1

Gene expression in different tissues is often controlled by alternative promoters that result in the synthesis of mRNA with unique &#8212; usually untranslated &#8212; first exons. Bcrp1 (Abcg2), the murine orthologue of the ABC transporter Breast Cancer Resistance Protein (BCRP, ABCG2), has at least four alternative promoters that are designated by the corresponding four alternative first exons produced: E1U, E1A, E1B, and E1C. Herein, in-silico protocols are presented to predict alternative promoter usage for Bcrp1. Furthermore, reporter assay methods are described to produce reporter constructs for alternative promoters and to determine the functionality of putative promoters upstream of the alternative first exons that are identified.

With the murine ABC transporter Bcrp1 (Abcg2) as an example, in-silico protocols are presented to detect alternative promoter usage in genes expressed in mouse tissues, and to evaluate the functionality of the alternative promoters identified using reporter assays.

Gene expression in different tissues is often controlled by alternative promoters that result in the synthesis of mRNA with unique &#8212; usually ...

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water deficit or pathogens plays a crucial role. Identifying small molecules that regulate stomatal movement can therefore contribute to understanding the physiological basis by which plants adapt to their environment. Large-scale screening approaches that have been used to identify regulators of stomatal movement have potential limitations: some rely heavily on the abscisic acid (ABA) hormone signaling pathway, therefore excluding ABA-independent mechanisms, while others rely on the observation of indirect, long-term physiological effects such as plant growth and development. The screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging. Since evaporation of water through transpiration results in leaf surface cooling, thermal imaging provides a non-invasive approach to investigate changes in stomatal conductance over time. In this protocol, Helianthus annuus seedlings are grown hydroponically and then treated by root feeding, in which the primary root is cut and dipped into the chemical being tested. Thermal imaging followed by statistical analysis of cotyledonary temperature changes over time allows for the identification of bioactive molecules modulating stomatal aperture. Our proof-of-concept experiments demonstrate that a chemical can be carried from the cut root to the cotyledon of the sunflower seedling within 10 minutes. In addition, when plants are treated with ABA as a positive control, an increase in leaf surface temperature can be detected within minutes. Our method thus allows the efficient and rapid identification of novel molecules regulating stomatal aperture.

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

We provide a method for identifying modulators of foliar transpiration by large-scale screening of a compound library.

Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water ...

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus on one or more gene families that are significant to the organism and study. In this protocol, we outline bioinformatic steps to generate a phylogeny and to quantify the expression of genes of interest. Phylogenetic trees can give insight into how genes are evolving within and between species as well as reveal orthology. These results can be enhanced using RNA-seq data to compare the expression of these genes in different individuals or tissues. Studies of molecular evolution and expression can reveal modes of evolution and conservation of gene function between species. The characterization of a gene family can serve as a springboard for future studies and can highlight an important gene family in a new genome or transcriptome paper.

The purpose of this protocol is to investigate the evolution and expression of candidate genes using RNA sequencing data.

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Identification of open reading frames (ORFs), especially those encoding small peptides and being actively translated under specific physiological contexts, is critical for&#160;comprehensive annotations of context-dependent translatomes. Ribosome profiling, a technique for detecting the binding locations and densities of translating ribosomes on RNA, offers an avenue to rapidly discover where translation is occurring at the genome-wide scale. However, it is not a trivial task in bioinformatics to efficiently and comprehensively identify the translating ORFs for ribosome profiling. Described here is an easy-to-use package, named RiboCode, designed to search for actively translating ORFs of any size from distorted and ambiguous signals in ribosome profiling data. Taking our previously published dataset as an example, this article provides step-by-step instructions for the entire RiboCode pipeline, from preprocessing of the raw data to interpretation of the final output result files. Furthermore, for evaluating the translation rates of the annotated ORFs, procedures for visualization and quantification of ribosome densities on each ORF are also described in detail. In summary, the present article is a useful and timely instruction for the research fields related to translation, small ORFs, and peptides.

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Translating ribosomes decode three nucleotides per codon into peptides. Their movement along mRNA, captured by ribosome profiling, produces the footprints exhibiting characteristic triplet periodicity. This protocol describes how to use RiboCode to decipher this prominent feature from ribosome profiling data to identify actively translated open reading frames at the whole-transcriptome level.