Name: using rna-sequencing to detect novel splice variants related to drug resistance in in vitro cancer models
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Description: Watch this Scientific Journal Video about Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models at JoVE.com

The authors would like to acknowledge prof. J.J. McGuire, prof. R. Kofler and dr. K. Quint for providing the resistant cell lines used in this work. The study has been founded by grants from Cancer Center Amsterdam (CCA) Foundation (to JC, EG and RS), the KiKa (Children Cancer-free grant for AW) foundation, the Law Offices of Peter G. Angelos Grant from the Mesothelioma Applied Research Foundation (to VEG and EG), Associazione Italiana per la Ricerca sul Cancro (AIRC), Istituto Toscano Tumori (ITT), and Regione Toscana Bando FAS Salute (to EG).

1. Characterization of Drug Resistance Profiles through Cytotoxicity Assays
<ol>
	<li>Leukemic Cell Line Culture
	<ol>
		<li>Maintain the parental T-cell ALL cell line CCRF-CEM (CEM-WT) as well as its drug resistant sublines, including CEM/R30dm, CEM-R5 and CEM-C3, in 25 cm2 in 10 ml RPMI-1640 medium containing 2.3 &#181;M folic acid supplemented with 10% fetal calf serum and 100 units/ml penicillin G and 100 &#181;g/ml streptomycin.</li>
		<li>Culture the cells in a humidified atmosphere at...

<ol>
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Here we describe a novel approach that combines well-established cytotoxicity screening techniques and powerful NGS-based transcriptomic analyses to identify differential splicing events in relation to drug resistance. Spectrophotometric assays are convenient and robust high-throughput methods to assess drug sensitivity in in vitro cancer models and represent the first choice for many laboratories performing cytotoxicity screenings. Troubleshooting as well as possible variations for this method were extensively ...

Despite considerable advances in cancer treatment, resistance of malignant cells to chemotherapy, either intrinsic or acquired upon prolonged drug exposure, is the major reason for treatment failure in a wide range of leukemia and solid tumors1.
In order to delineate the mechanisms underlying drug resistance, in vitro cell line models are developed by stepwise selection of cancer cells resistant to chemotherapeutic agents. This procedure mimics the regimes used in the clinical settings and therefore allows in depth investigation of relevant resistance mechanisms. Resistant cells which survive the treatment are then distinguished from parental sensitive cells by using cell viability/cytotoxicity assays2. In vitro drug resistance profiles of primary cells have been shown to be significantly related to clinical response to chemotherapy3.
High-throughput cytotoxicity assays constitute a convenient method to determine drug sensitivity in vitro. Herein, the viability of cells is assessed by for instance the 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide - MTT assay4, which is based on metabolic conversion of certain substrates (i.e., tetrazolium salts) into colored products, thereby reflecting the mitochondrial activity of cells. Alternatively, the cellular protein content can be quantified using the sulforhodamine B (SRB) assay5. Here, the number of viable cells is proportional to the optical density (OD) measured at an appropriate wavelength in a spectrophotometer, with no need of extensive and time-consuming cell counting procedures. The growth inhibition induced by a certain chemotherapeutic drug can be calculated based on the OD of the wells in which cells were treated with a test agent and compared with the OD of untreated control cells. A dose-response curve is obtained by plotting drug concentrations versus percentages of viable cells relative to control cells. Finally, drug sensitivity can be reported as the concentration that results in 50% of cell growth inhibition as compared to untreated cells (IC50).
The mechanisms underlying drug resistance include many different abnormalities, such as alterations affecting gene expression of determinants of drug activity and cellular metabolism. These molecular lesions, including mutations, aberrations at a transcriptional and post-transcriptional level as well as disturbed epigenetic regulation often affect genes involved either in drug metabolism or apoptosis6.
Alternative pre-mRNA splicing and its intricate regulation have recently received considerable attention as a novel entity that may dictate drug resistance of cancer cells7. Up to 95% of human genes are alternatively spliced in normal cells by means of this tightly regulated process which produces many different protein isoforms from the same gene. Alternative splicing is often deregulated in cancer and several tumors are characterized by altered splicing of a growing number of genes involved in drug metabolism (i.e., deoxycytidine kinase, folylpolyglutamate synthetase, or Multidrug resistance proteins)6,8. However, comprehensive analysis of splicing profiles of drug resistant cells is painfully lacking. Therefore, it is imperative to develop high throughput methods for alternative splicing analysis. This could help to develop more effective therapeutic approaches.
During the last decade, the rapid development of next generation sequencing (NGS) technologies has enriched biomedical research with new insights into the molecular mechanisms governing regulation of genome expression and their role in various biological processes9. RNA-sequencing (RNA-seq) is a powerful sub-application of NGS in the field of transcriptomics. It allows a genome-wide profiling (both qualitatively and quantitatively) of the expression patterns of thousands of genes simultaneously and is well suited for the characterization of novel coding mRNAs as well as long non-coding RNA, miRNA, siRNA, and other small RNA classes (e.g., snRNA and piRNA)10,11.
RNA-Seq has many advantages over previous technologies for transcriptome characterization (e.g., Sanger sequencing and expression microarrays). It is not based on existing genome annotation, it has a single-nucleotide level of resolution and it has a broader dynamic range for expression level estimation. Briefly, the basic experimental workflow of RNA-seq experiments consists of polyadenylated transcript (mRNA) selection and fragmentation, followed by conversion into cDNA, library construction and finally, massively parallel deep sequencing12,13. Due to rapid drop of sequencing costs over the last few years, RNA-seq is gradually replacing other technologies and significant efforts are being made to improve the library preparation protocols. For instance, it is now possible to retain the strand information of mRNA transcripts by marking the second strand cDNA with deoxyuridine triphosphate (dUTP) and, prior to PCR amplification, digesting the marked strand with uracil-DNA-glycosilase (UDG). This process enhances the accuracy of gene annotation and estimation of the expression levels14,15.
The analysis and interpretation of RNA-seq data require complex and powerful computational software packages and processing within bioinformatic pipelines16,17. First, the raw reads undergo quality control by removing technical and biological artefacts and discarding (trimming) the sequences which do not reach stringent quality requirements. Subsequently the reads for each sample are mapped to a reference genome and indexed into gene-level, exon-level, or transcript-level, in order to determine the abundance of each category. Depending on the application, refined data are then computed through statistical models for the identification of allele-specific expression, alternative splicing, gene fusions and single nucleotide polymorphisms (SNPs)12. Finally, differential analysis on selected level (i.e., gene expression or alternative splicing) can be used to compare samples obtained under different conditions.
Differential splicing analysis describes the differences in splice site usage between two samples. An increasing number of software packages devoted to this purpose are available based on different statistical models, performances and user interface18. Among these, MATS (Multivariate Analysis of Transcript Splicing) emerges as a freely available and precise computational tool based on a Bayesian statistical framework and designed to detect differential splicing events from either single or paired end RNA-seq data. Starting from the aligned (.bam) files, MATS can detect all major types of alternative splicing events (exon skipping, alternative 3&#39; splice site, alternative 5&#39; splice site, mutually exclusive exons and intron retention - also see Figure 1).
First, the software identifies reads which support a certain splice event, for instance exon skipping, and classifies them into two types. &#34;Inclusion reads&#34; (for the canonical splice event) map within the investigated exon and span the junctions between that specific exon and the two upstream and downstream flanking exons. &#34;Skipping reads&#34; (for the alternative splice event) span the junction between the two flanking exons. Subsequently, MATS returns the normalized inclusion level for both the canonical and alternative events and compares values between samples or conditions. Ultimately, it calculates P-value and false discovery rate (FDR) assuming that the difference in the variant ratio of a gene between two conditions exceeds a given user-defined threshold for each splicing event19,34.
Following differential splicing analysis in conjunction with RNA-seq, an extensive experimental validation is warranted in order to identify true-positive gene candidates18. Quantitative reverse transcribed-polymerase chain reaction (qRT-PCR) is the most commonly used and optimal method in validation of candidates obtained from RNA-Seq analysis20. The aim of this paper is to provide a robust methodology to investigate drug resistance-related splicing profiles in solid tumors and hematological malignancies. Our approach utilizes RNA-seq-based transcriptome profiling of selected cell line models of drug resistant cancers in combination with an established qRT-PCR method for the validation of candidate genes implicated in drug resistance.
The human leukemia cell line models used in this study included pediatric T-cell acute lymphoblastic leukemia (T-ALL) cell line CCRF-CEM (CEM-WT), its two glucocorticoid (GC)-resistant subclones CEM-C7H2-R5C3 (CEM-C3) and CEM-C7R5 (CEM-R5)21,22 and the methotrexate (MTX)-resistant subline CEM/R30dm23. Although current therapies based on GCs and MTX establish clinical benefit in about 90% of cases, the emergence of GC-resistance still represents an unsolved problem with an unclear molecular mechanism. To isolate GC-resistant sub-clones, CEM-WT cells were cultured in 1 &#181;M dexamethasone (Dex) for 2 to 3 weeks. MTX-resistant subline CEM/R30dm was developed through repeated short- term (24 hr) exposure of CEM-WT cells to 30 &#181;M MTX as a mimic of clinical protocols. Interestingly, this cell line also displayed cross-resistance to Dex (unpublished results) for which the mechanism is not fully understood.
The solid tumor model investigated in the present study is pancreatic ductal adenocarcinoma, notorious for its extraordinary refractoriness to chemotherapy. To this end, we selected Panc-1 cell line and its gemcitabine-resistant sub-clone Panc-1R obtained by continuous incubation with 1 &#181;M of the drug24. Here we describe an approach to discover novel mechanisms underlying in-vitro drug resistance by combining three protocols: colorimetric cytotoxicity assays to assess drug sensitivity in leukemic cells and cancer cells from solid tumors, RNA-seq-based pipeline to identify novel splice variants related to drug sensitivity/resistance and RT-PCR and qRT-PCR analysis to validate potential candidates.

<table border="0" cellpadding="0" cellspacing="0" width="732">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Sulforhodamine B</td>
			<td style="width:195px;">Sigma-Aldrich</td>
			<td style="width:119px;">230162</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Trichloroacetic acid</td>
			<td style="width:195px;">Sigma-Aldrich</td>
			<td style="width:119px;">251399</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">CCRF-CEM</td>
			<td style="width:195px;">ATCC, Manassas, VA, USA</td>
			<td style="width:119px;">ATCC CCL-119</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Panc-1</td>
			<td style="width:195px;">ATCC, Manassas, VA, USA</td>
			<td style="width:119px;">ATCC CRL-1469</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">DMEM high glucose</td>
			<td style="width:195px;">Lonza, Basel, Switzerland</td>
			<td style="width:119px;">12-604F</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">RPMI-1640&#160;</td>
			<td style="width:195px;">Gibco, Carlsbad, CA, USA</td>
			<td style="width:119px;">11875093</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:252px;">Fetal bovine and calf serum</td>
			<td style="width:195px;">Greiner Bio-One, Frickenhausen, Germany</td>
			<td style="width:119px;">758093</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">penicillin G streptomycin sulphate</td>
			<td style="width:195px;">Gibco, Carlsbad, CA, USA</td>
			<td style="width:119px;">15140122</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Tris(hydroxymethyl)-aminomethane</td>
			<td style="width:195px;">Sigma Aldrich</td>
			<td style="width:119px;">252859</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">MTT formazan</td>
			<td style="width:195px;">Sigma Aldrich</td>
			<td style="width:119px;">M2003</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:252px;">Anthos-Elisa-reader 2001</td>
			<td style="width:195px;">Labtec, Heerhugowaard, Netherlands</td>
			<td></td>
			<td>UV-Vis 96-well plate spectrophotometer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Greiner CELLSTAR 96 well plates</td>
			<td style="width:195px;">Greiner/Sigma</td>
			<td>M0812-100EA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:252px;">Trypsin/EDTA Solution 100 ml</td>
			<td style="width:195px;">Lonza, Basel, Switzerland</td>
			<td>CC-5012</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">CELLSTAR Cell Culture Flasks 25 cm2</td>
			<td style="width:195px;">Greiner Bio-One, Frickenhausen, Germany</td>
			<td>82051-074</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">CELLSTAR Cell Culture Flasks 75 cm3</td>
			<td style="width:195px;">Greiner Bio-One, Frickenhausen, Germany</td>
			<td>82050-856</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Phosphate Buffered Saline (NaCl 0.9%)</td>
			<td>B.Braun Melsungen AG, Germany</td>
			<td>362 3140</td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

The cytotoxicity assays described in the protocol provide a reliable and robust method to assess the resistance of cancer cells to chemotherapeutic agents in vitro. By means of the MTT assay, sensitivity to Dex was determined in four T-ALL cell lines, including Dex-sensitive parental CEM-WT cells, and three Dex-resistant sublines: CEM/R30dm, CEM-R5 and CEM-C3. Two different concentration ranges had to be used due to the large difference in sensitivity between CEM-WT (2 &#181;M - ...

Watch this Scientific Journal Video about Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models at JoVE.com

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.

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 ...

The overall goal of this protocol is to investigate the association of aberrant splicing with drug resistance profiles in in vitro models of solid tumors and hematological malignancies. This method can help answer key question in the field of molecular pharmacology and chemotherapy resistance such as which are the mechanism underlying drug resistance on post-transcriptional level? The main advantage of this technique is that it combines well established cytotoxicity screening protocol with powerful next generation sequencing based transcriptomic analysis.This technique could lead to a better understanding of the molecular mechanisms underlying chemotherapy failure and it will improve the early detection of drug resistance. This method can provide insight into genome-wide determinants of drug resistance in cell line models. But it can also be applied to other systems such as primary patient samples and xenograft.Demonstrating this procedure will be Johan Fomirno, a technician from our laboratory at the Cancer Center Amsterdam. To perform the MTT assay for leukemic cells, prepare 96-well flat bottom experimental plate. Dedicate wells to drug concentrations to control cells and to control medium without cells.Prepare a drug dilution range of dexamethasone or dex. Add 30 microliters from each dex dilution into an appropriate well of the 96-well plate. Make sure to include each concentration in triplicate.Harvest exponentially growing leukemic cells and re-suspend them at their optimal seeding concentration. Add 120 microliters of the cell suspension to each well containing either the drug solution or the growth medium, and incubate at 37 degrees Celsius with 5%carbon dioxide for 72 hours. After the incubation step, add 15 microliters of MTT reagent to each well by using a repetitive pipette.Shake the plate for five minutes with a plate shaker up to a maximum of 900 shakes per minute. Place the plates back at 37 degrees Celsius with 5%carbon dioxide in a cell culture incubator and incubate for another four to six hours. Following incubation, make sure to thoroughly re-suspend the formazan crystals as the absorbance values depend on proper solubilization of the compound.Add 150 microliters of the acidified isopropanol to each well and mix well with a multichannel pipette to thoroughly re-suspend all the formazan crystals. Using a microplate reader, determine the optical density or OD at 540 and 720 nanometers to ensure an accurate measurement by correcting for background OD.To perform SRB assay for pancreatic carcinoma cells, prepare a 96-well flat bottom experimental plate. Seed pancreatic carcinoma cells growing in exponential phase in triplicate in a 96-well flat bottom plates at the appropriate density in 100 microliters of medium by using a multichannel pipette.Add 100 microliters of medium to medium-only wells and incubate overnight at 37 degrees Celsius with 5%carbon dioxide to ensure proper adhesion of the cells to the plate. Add 100 microliters from each dilution into an appropriate well of the 96-well plate by using a multichannel pipette. Make sure to have each concentration in triplicate.Incubate at 37 degrees Celsius with 5%carbon dioxide for 72 hours. Next, add 25 microliters of cold TCA solution to the wells by using a multichannel pipette. Incubate the plates for at least 60 minutes at four degrees Celsius to precipitate and fix the proteins at the bottom of the wells.Empty the plate by removing the medium and dry briefly on a tissue. Wash five times with tap water before emptying the plate and drying at room temperature. Add 50 microliters of SRB solution per well by using a repetitive pipette and stain for 15 minutes at room temperature.Then, empty the plate by removing the SRB stain. After washing the plate four times with 1%acetic acid, empty the plate and let it dry at room temperature. Add 150 microliters of Tris solution per well by using a multichannel pipette and to mix for three minutes on a plate shaker up to a maximum of 900 shakes per minute.Read the optical density at 540 nanometers. Spin down the samples at 300 times g for three minutes. After removing the supernatant, extract total mRNA using commercially available silica membrane spin columns.Determine the concentration and purity of total RNA by using UV-Vis spectrophotometer. To prepare the sequencing library, use two micrograms of total mRNA per sample. Follow mRNA library preparation protocol according to manufacturer's instructions.After pooling the libraries in a single sample up to a final concentration of 10 nanomoles per liter using a bioanalyzer, use a high throughput sequencing system with single read 100 base pair mode. Design all primers as described in the text protocol. For first strand cDNA synthesis, set up reverse transcription of one microgram of the isolated RNA to cDNA by using 200 units per microliter of Moloney murine leukemia virus reverse transcriptase in its reaction buffer diluted one to five with sterile water.To perform the RT-PCR reaction, add one microliter of cDNA to the PCR reaction mix and place the tubes in the thermocycler. Run the program as described in the text protocol. Following PCR, load the samples into a 1%agarose gel and run the gel in TBE buffer at 100 volts for approximately 30 minutes in an electrophoresis system.To avoid cross contamination, use filter tips while performing the qRT-PCR assay. To perform qRT-PCR, prepare cyanine green PCR reaction mix for each specific splice variant to be detected as well as for the housekeeping gene. Load the master mix on a white 96-well plate and prepare duplicates for each sample.Add five microliters of cDNA diluted 10 times in water to each mix before placing the plate in the thermocycler. Growth inhibition curves as determined by MTT assay for leukemic cells and SRB assay for pancreatic carcinoma cells are depicted here. Curves for dexamethasone resistant and gemcitabine resistant cells are shifted to the right indicating growth inhibition as compared to the parental sensitive cells.The concentration and quality of cDNA libraries for all cell lines is determined with a bioanalyzer DNA chip. Electropherograms show fluorescence peaks at approximately 300 base pairs which are indicative of good quality for the subsequent RNA sequencing procedure. Short reads of the gene candidate DDX5 can be visualized as Sashimi plots where connecting lines represent splice junctions.DDX5 transcript shows differential usage of exon 12 and CEM wild-type and CEM/R30dm cells as compared to glucocorticoid-resistant cell lines, CEM-C3 and CEM-R5. RNA sequencing results are validated by performing RT-PCR on selected genes for which primer pairs and yield to exons located upstream and downstream of the alternatively spliced exon. Differential splicing of the DDX5 gene is shown here.Quantification of differentially-spliced transcripts through qRT-PCR assay using isoform-specific reverse primers confirms the specific down regulation of the DDX5 delta exon 12 splice variant and a specific glucocorticoid-resistant sub-lines. Once mastered, this technique can be done in three to four weeks. While attempting this procedure, it's important to remember to keep RNA samples on ice in order to avoid degradation.Following this procedure, other methods like ectopic overexpression of novel splice variant can be performed. This will answer additional questions like functional impact of alternate splicing profile on drug resistance. This technique can pave the way for researches in the field of molecular pharmacology to explore genome-wide determinants of drug resistance.

Research

JoVE Journal

Cancer Research

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