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

Podsumowanie

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.

Streszczenie

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.

Wprowadzenie

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 sequencing^12,13. 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 levels^14,15.

The analysis and interpretation of RNA-seq data require complex and powerful computational software packages and processing within bioinformatic pipelines^16,17. 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' splice site, alternative 5' 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. "Inclusion reads" (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. "Skipping reads" (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 event^19,34.

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 µM 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 µM 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 µM 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.

Protokół

1. Characterization of Drug Resistance Profiles through Cytotoxicity Assays

1. Leukemic Cell Line Culture
   1. 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 µM folic acid supplemented with 10% fetal calf serum and 100 units/ml penicillin G and 100 µg/ml streptomycin.
   2. Culture the cells in a humidified atmosphere at 37 °C in a 5% CO₂ incubator.
   3. Allow cell growth to concentrations between 2 - 3 x 10⁶ cell/ml.
   4. Split the cells twice a week at an initial concentration of 0.3 x 10⁶ cells/ml (e.g., if the cell concentration is 3 x 10⁶ cells/ml, transfer 1 ml cell suspension in a new flask containing 9 ml fresh medium). Discard the culture after 20 consecutive passages.
2. Pancreatic Carcinoma Cell Line Culture
   NOTE: Maintain the human pancreatic carcinoma cell line Panc-1 in 75 cm² culture flasks in 10 ml DMEM medium with high glucose and L-Glutamine supplemented with 10% fetal bovine serum and 100 units/ml penicillin G and 100 µg/ml streptomycin. The drug resistant variant, Panc-1R, is cultured in the same culture medium containing 1 µM gemcitabine dissolved in sterile water. Further details are provided in ²⁴.
   1. Culture the cells at 37 °C in a 5% CO₂ incubator.
   2. Split the cells every 2 - 3 days at a ratio of 1:5 when cells reach confluence of about 90%.
   3. To split the cells, wash the twice with phosphate buffered saline (PBS), add 1 ml of Trypsin/EDTA per 75 cm² culture flask and incubate at 37 °C for 3 min.
   4. Add 9 ml medium and harvest the detached cells in a 15 ml tube. Seed 2 ml cell suspension in a new flask containing 8 ml medium. Discard the culture after 20 consecutive passages.
3. MTT Assay for Leukemic Cells
   1. Prepare in advance the MTT solution: dissolve 500 mg of MTT formazan in 10 ml PBS and stir (protected from light) with a magnetic stirrer for approximately 1 hr. Sterilize the solution with a 0.22 µm filter. NOTE: The solution can be stored in 10 ml aliquots at −20 °C. Protect from direct light after thawing.
   2. Prepare in advance the acidified isopropanol: add 50 ml of 2 M HCl to 2.5 L of isopropanol. NOTE: Store the solution for at least one month at room temperature before use. If the isopropanol is not acidified correctly, it might form precipitates with the medium and compromise the spectrophotometric readout.
   3. Prepare a separate 96-well flat bottom "Day 0" (control) plate to ensure more accurate estimation of growth inhibition: dedicate 3 to 6 wells per cell line, add 30 µl of growth medium and 120 µl of cell suspension (8,000 cells) per each well and 150 µl of growth medium to wells corresponding to blanks (no cells). Proceed from step 1.3.9 to step 1.3.13 of this section to measure the optical density (OD). NOTE: Further details are provided in ⁴.
   4. Prepare a 96-well flat bottom experimental plate: dedicate 30 wells to drug concentrations (10 concentrations, each in triplicate), 10 wells to control cells and 10 wells to control medium without cells (blanks) and prepare a drug dilution range of dexamethasone (Dex) using Dimethyl Sulfoxide as a solvent.
      NOTE: For CEM-WT cells the Dex dilution range is between 2 µM and 0.97 nM. For CEM/R30dm, CEM-R5 and CEM-C3 cells the Dex dilution range is between 640 µM and 0.33 nM.
   5. Add 30 µl from each Dex dilution into an appropriate well of the 96-well plate. Make sure to include each concentration in a triplicate.
   6. Add 30 µl of growth medium to wells corresponding to control cells and 150 µl of growth medium to wells corresponding to blanks.
   7. Harvest exponentially growing cells and resuspend at their optimal seeding concentration.
      NOTE: In order to determine optimal starting cell concentrations, it is recommended to assess the growth profile of each cell line cell line in a 96-well plate by seeding cells at several concentrations and measuring it daily for at least 4 days. Choose a seeding concentration which prevents overgrowth of cells after 72 hr, as this will influence the experiment by saturating the OD values. For CEM-WT, CEM-C5 and CEM-R5 the optimal seeding concentration is 8,000 cells/well, while for CEM/R30dm it is 5,000 cells/well.
   8. Add 120 µl of cell suspension to each well containing either the drug solution or the growth medium (wells corresponding to control cells). Fill the empty outer wells of the plate with 150 µl PBS to ensure good humidity in the plate and incubate the plates for 72 hr at 37 °C with 5% CO₂ in a cell culture incubator.
   9. Add 15 µl of the MTT solution to each well and shake the plate for 5 min with a plate-shaker up to a maximum of 900 shakes/min.
   10. Place the plates back at 37 °C with 5% CO₂ in a cell culture incubator and incubate for another 4 - 6 hr.
   11. Add 150 µl of the acidified isopropanol to each well and mix well with a multichannel pipette to thoroughly resuspend all the formazan crystals. Start with the blank wells and make sure to rinse the tips well before proceeding to another row of the plate.
   12. Incubate the plate at room temperature (RT) for 10 min.
   13. Using a microplate reader, determine the OD at 540 and 720 nm to ensure an accurate measurement by correcting for background OD. Then save data in a spreadsheet file and analyze it⁴.
4. SRB Assay for Pancreatic Carcinoma Cells
   1. Dissolve SRB reagent in 1% acetic acid at final concentration of 0.4% (w/v).
   2. Dissolve trichloroacetic acid (TCA) in ultrapure water at final concentration of 50% (w/v).
   3. Dissolve Tris(hydroxymethyl)-aminomethane in ultrapure water at final concentration of 10 mM.
   4. Prepare a separate 96-well flat bottom "Day 0" control plate to ensure more accurate estimation of growth inhibition: seed 6 wells with cells growing in exponential phase in 100 µl medium at appropriate seeding concentration and add 100 µl medium only to wells corresponding to blanks. Incubate overnight at 37 °C with 5% CO₂ to ensure proper adhesion of the cells to the plate. Then add 100 µl medium to all the wells and proceed to steps 1.4.8 - 1.4.16 of this section.
   5. Prepare a 96-well flat bottom experimental plate: seed cells growing in exponential phase in triplicate in 96 wells flat bottom plates at the appropriate density in 100 µl of medium by using a multichannel pipet.
      NOTE: In order to determine optimal starting cell concentrations, it is recommended to assess the growth profile of each cell line cell line in a 96-well plate by seeding cells at several concentrations and measuring it daily for at least 4 days. Choose a seeding concentration which prevents overgrowth of cells after 72 hr, as this will influence the experiment by saturating the OD values. For Panc-1 and Panc-1R cells, the optimal seeding concentration is 8000 cells/well.
   6. Add 100 µl medium to medium-only wells and incubate overnight at 37 °C with 5% CO₂ to ensure proper adhesion of the cells to the plate.
   7. Prepare a drug dilution range of gemcitabine between 1 µM and 10 nM for Panc-1 and 1 mM and 100 nM for Panc-1R cells. Add 100 µl from each dilution into an appropriate well of the 96-well plate by using a multichannel pipet. Make sure to have each concentration in triplicate. In addition, add 100 µl medium to the medium-only wells and the control cells. Incubate at 37 °C with 5% CO₂ for 72 hr.
   8. Add 25 µl cold TCA solution to the wells by using a multichannel pipet and incubate the plates for at least 60 min at 4 °C in order to precipitate and fix the proteins at the bottom of the wells.
   9. Empty the plate by removing the medium and dry briefly on a tissue.
   10. Wash 5 times with tap water, then empty the plate and let dry at room temperature.
   11. Add 50 µl SRB solution per well by using a repeat-pipet and stain for 15 min at room temperature.
   12. Empty the plate by removing the SRB stain.
   13. Wash 4 times with 1% acetic acid, then empty the plate and let it dry at room temperature.
   14. Add 150 µl Tris solution per well by using a multichannel pipet and mix for 3 min on a plate-shaker up to a maximum of 900 shakes/min.
   15. Read the optical density at 540 nm (or 492 nm if the OD values are too high).
   16. Analyze the data.
5. Data Analysis for MTT and SRB Assays
   1. Calculate the OD values of cells at "Day 0" using the following formula: OD_{Day 0} = OD_{control cells} - Average OD_{blank wells}
   2. Calculate the percentage of surviving cells for each drug concentration according to the following formula: % Treated cells = Average [OD_{treated cells} - Average OD_{blank wells} - OD_{Day 0}]/[OD_{control cells} - Average OD_{blank wells} - OD_Day0]*100
   3. Plot the dose-response curve (drug concentration vs. growth inhibition in %).
   4. Calculate the concentration of the drug which inhibits the growth of cells by 50% (IC₅₀) using the dose-response curve.

2. RNA Isolation and Library Preparation for RNA-sequencing

1. Sample Collection and RNA Isolation
   1. For CEM cells: harvest 10⁶ cells directly from culture medium.
   2. For Panc-1 and Panc-1R: remove medium, wash the cells twice with PBS and detach by adding trypsin-EDTA and incubating at 37 °C for 3 min. Add culture medium and harvest 10⁶ cells.
   3. Spin down samples at 300 x g for 3 min, remove supernatant and extract total mRNA using commercially available silica membrane spin columns, by following the manufacturer`s protocol.
   4. Determine the concentration and purity of total RNA by using a UV-Vis spectrophotometer.
      NOTE: RNA is considered of high purity if the 260 nm / 280 nm absorbance ratio is above 1.8. The samples can be stored at - 80 °C.
   5. Assess total RNA integrity by electrophoresis of 200 ng of sample on 1% agarose gel stained with ethidium bromide.
      NOTE: The detection of two intact bands corresponding to mammalian 28S and 18S rRNAs at approximately 2 kb and 1 kb in size is indicative of good total RNA integrity.
2. Sequencing Library Preparation
   1. Use 2 µg of total mRNA per each sample. Follow mRNA library preparation protocol according to manufacturer's instructions.
   2. Determine quality and concentration of each library by using a bioanalyzer system. Pool the libraries in a single sample up to a final concentration of 10 nmol/L and measure it with the bioanalyzer.
   3. Use high-throughput sequencing system with Single Read 100 bp mode.
      NOTE: Read length of > 80 bp is necessary for identifying transcriptional isoforms.

3. Detection of Differential Splicing from Sequencing Reads

1. Alignment of Reads to Reference Genome and Quality Check
   1. Compile a gene annotation (.gtf file) from the NCBI refGene table using UCSC table browser (25,963 genes).
   2. Perform annotation-aware gapped alignment of sequencing reads to the reference genome (GRCh37) using STAR.
   3. Sort and index the resulting alignment files with picard tools.
   4. Perform post-mapping quality control by using RSeQC and samtools.
2. Differential Splicing Detection between Sample Groups
   1. Install Python and corresponding versions of NumPy and SciPy. Download and install samtools. Download and install bowtie and tophat,
   2. Add the Python, bowtie, tophat and samtools directories to the $PATH environment variable. Download pre-built bowtie indexes (hg19). Download rMATS version 3.0.9.
      NOTE: Further details about rMATS are provided in ¹⁹ and ³⁴.
   3. Detect alternative splicing events by comparing each Dex-resistant cell line to CEM-WT and Panc-1 to Panc-1R in separate MATS runs according to Figure 5A.
   4. To detect differential splicing events from previously aligned sequencing reads (.bam files), run MATS using the commands from Figure 5B for CEM-WT vs. CEM- C3, CEM-WT vs. CEM-R5, CEM-WT vs. CEM/R30dm and Panc1 to Panc-1R comparisons.
      NOTE: MATS will create an output folder with two .txt files with results per type of splicing event analyzed (SE - exon skipping, A5SS - alternative 5' splice site, A3SS - alternative 3' splice site, MEX - mutually exclusive exons and RI - intron retention): one file containing results based on junction counts only and a second file with results based on junction counts and reads on target. Moreover, an additional .txt file with a result overview is generated in the same folder.
   5. For SE, A5SS, A3SS and RI import to spreadsheets the .txt files based on junction counts and reads on target. For MEX import the .txt files based on junction counts only.
      NOTE: MATS output file is sorted by ascending P-values and contains several parameters: gene ID, gene symbol, chromosome and strand position, genomic coordinates of the alternatively spliced fragments, counts as well as the length of inclusion and skipping forms for both analyzed samples, the length of inclusion and skipping form used for normalization, p-value, false discovery rate (FDR), inclusion level for each sample based on normalized counts and the differential inclusion score (average(IncLevel1) - average(IncLevel2)).
   6. Select statistically significant candidate genes with an FDR<10% for further validation (e.g., DDX5 and PKM2).
   7. Visualize alternative splicing events with Integrative Genomics Viewer (IGV, https://www.broadinstitute.org/igv/), as reported in Figure 5.

4. Validation of the Results by RT-PCR and qRT-PCR Assays

1. Primer Design
   NOTE: In order to provide a reliable validation of results obtained using the bioinformatic pipeline, the mRNA transcripts resulting from alternative splicing events are amplified by using RT-PCR and visualized by agarose gel electrophoresis of the PCR products. Cyanine green dye such as SYBR green qRT-PCR assay is used to quantify specific splice variants relative to a housekeeping gene. Figure 7 depicts the strategy applied to visualize the exon 12 skipping event of the DDX5 gene and to quantify the mutually exclusive exon 9 and exon 10 of the PKM gene.
   1. For RT-PCR assay, design primer pairs which anneal to constitutive exons (exon 10 and exon 13) located upstream and downstream the alternative splicing sites (Figure 7A).
      NOTE: The amplicon size should cover between 100 and 800 bp in order to ensure a clear separation of the predicted PCR products on agarose gel. The annealing temperature of the primers should be between 55 and 65 °C and the GC content should not exceed 60%.
   2. Cyanine green assay (Figure 7B).
      1. Check the sequence homology of the two mutually exclusive exons and design two primer pairs each of which specifically and exclusively detects only one of the two splice variants.
         NOTE: For PKM, the reverse primer anneals to exon 11 common to both variants, while the forward primers are variant-specific and anneal to exon 9 (PKM1) or exon 10 (PKM2).
      2. In order to detect DDX5 full-length gene, anneal the reverse primer within the skipped exon (exon 12).
         NOTE: For the specific detection of the DDX5 ΔEx12 variant, the reverse primer spans the exon11/exon13 boundary. Use the same forward primer which anneals to constitutive exon 10 for both reactions.
         NOTE: The amplicon size should be between 80 and 200 bp.
2. First strand cDNA Synthesis
   1. Set up reverse transcription of 1 µg of the isolated RNA to cDNA by using 200 U/µl of Moloney Murine Leukemia Virus (M-MLV) reverse transcriptase in its reaction buffer diluted 1:5 with sterile water. Add DTT 1 µM, 0.05 µg of random hexamers, deoxynucleotide mix (dNTP) 1 mM, and 40 U/µl of ribonuclease inhibitor.
   2. Vortex briefly and incubate the reaction mix at 37 °C for 2 hr.
   3. Incubate the reaction mix at 70 °C for 5 min to inactivate the reverse transcriptase, transfer the mix on ice and spin down by using a microcentrifuge. Samples can be used immediately or stored at - 20 °C.
3. RT-PCR Reaction and Agarose Gel
   1. Set up the PCR reaction in a PCR tube per each sample by mixing 12.5 µl of 2x concentrated PCR mastermix, 1.25 µl of 10 µM forward primer and the same amount for the reverse primer up to a final volume of 25 µl with sterile water.
   2. Add 1 µl of cDNA to the mix and place the tubes in the thermocycler. Run the program as follows: initial denaturation: 95 °C for 2 min. Reiterate the following steps for 35 cycles: denaturation at 95 °C for 25 sec; annealing at 52 °C for 35 sec; extension at 72 °C for 1 min. Set final extension at 72 °C for 5 min.
   3. Prepare 1% agarose gel by dissolving 1 g of agarose in 100 ml 1x TBE buffer. Add 2.5 µl of ethidium bromide to the solution. CAUTION: ethidium bromide is carcinogenic, handle with care by using a chemical fume hood.
   4. Load the samples and run the gel in 1x TBE buffer at 100 V for approximately 30 min in an electrophoresis system.
   5. Reveal the gel with a digital UV camera and save the image.
4. qRT-PCR and Data Analysis
   1. Set up the Cyanine green PCR reaction per each sample by mixing 12.5 µl of 2x green PCR Mastermix, 2 µl of 5 µM forward primer and the same amount for the reverse primer up to a final volume of 15 µl with sterile water in a PCR tube. Prepare a mix for each specific splice variant to be detected (PKM1, PKM2, DDX5 full length, DDX5 ΔEx12) and for the housekeeping gene (GUS).
   2. Load the mastermix on a white 96-well plate and prepare duplicates per each sample.
   3. Dilute the cDNA 10x in water, add 5 µl to each mix and place the plate in the thermocycler. Run the program as follows: initial denaturation: 95 °C for 5 min. Reiterate the following steps for 45 cycles: denaturation at 95 °C for 10 sec; annealing at 58 °C (for DDX5 and DDX5 ΔEx12 mixes) or 60 °C (for PKM1 and PKM2 mixes) for 20 sec. Set final extension at 72 °C for 20 sec. Set melting curve by applying a gradient from 65 to 97 °C.
   4. In order to assess the specificity of the primer sets to their target, check the melting curves and verify that a single peak is formed per each primer set.
   5. Calculate the second derivative values of the amplification curves and export the cycle threshold values (Ct).
   6. Calculate the relative expression levels (REL) of the mRNA splice variants compared to the GUS housekeeping (reference) gene in each sample by using the "delta Ct (ΔCt)" method. The formula is: REL = 2^-ΔCt, where ΔCt = Ct _{target splice variant} - Ct _{reference gene}
   7. In order to quantify the relative abundance of splice variants, calculate the REL ratio by using the formula: Ratio REL ratio = REL_{splice variant 1}/ REL_{splice variant 2}.

Wyniki

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 µM - ...

Dyskusje

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 ...

Materiały

Name	Company	Catalog Number	Comments
Sulforhodamine B	Sigma-Aldrich	230162
Trichloroacetic acid	Sigma-Aldrich	251399
CCRF-CEM	ATCC, Manassas, VA, USA	ATCC CCL-119
Panc-1	ATCC, Manassas, VA, USA	ATCC CRL-1469
DMEM high glucose	Lonza, Basel, Switzerland	12-604F
RPMI-1640	Gibco, Carlsbad, CA, USA	11875093
Fetal bovine and calf serum	Greiner Bio-One, Frickenhausen, Germany	758093
penicillin G streptomycin sulphate	Gibco, Carlsbad, CA, USA	15140122
Tris(hydroxymethyl)-aminomethane	Sigma Aldrich	252859
MTT formazan	Sigma Aldrich	M2003
Anthos-Elisa-reader 2001	Labtec, Heerhugowaard, Netherlands		UV-Vis 96-well plate spectrophotometer
Greiner CELLSTAR 96 well plates	Greiner/Sigma	M0812-100EA
Trypsin/EDTA Solution 100 ml	Lonza, Basel, Switzerland	CC-5012
CELLSTAR Cell Culture Flasks 25 cm2	Greiner Bio-One, Frickenhausen, Germany	82051-074
CELLSTAR Cell Culture Flasks 75 cm3	Greiner Bio-One, Frickenhausen, Germany	82050-856
Phosphate Buffered Saline (NaCl 0.9%)	B.Braun Melsungen AG, Germany	362 3140