Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.
Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance 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.
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' 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 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 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.
1. Characterization of Drug Resistance Profiles through Cytotoxicity Assays
2. RNA Isolation and Library Preparation for RNA-sequencing
3. Detection of Differential Splicing from Sequencing Reads
4. Validation of the Results by RT-PCR and qRT-PCR Assays
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 – 0.97 nM) and the Dex resistant cell lines (640 µM – 0.33 nM). The MTT assay clearly showed Dex resistance in CEM/R30dm (IC50 = 456 ± 49 µM), CEM-R5 (IC50 > 640 µM) and CEM-C3 (IC50 = 386 ± 98 µM) as compared to the CEM-WT cells (IC50 = 0.028 ± 0.003 µM). Similarly the SRB assay demonstrated high gemcitabine resistance in Panc-1R cell line (IC50 = 3.16 ± 0.01 µM) compared to the parental Panc-1 cells (IC50 = 0.077 ± 0.03 µM) as showed in Figure 2.
Following confirmation of drug resistance in all considered cell lines, we next proceeded to mRNA isolation, library preparation and RNA-sequencing. Extraction of total mRNA by using silica membrane spin columns is the preferred choice over other methods since it avoids phenol and protein contamination and provides high purity of the sample with 260 nm / 280 nm absorbance ratio well above 1.8. This is a crucial requirement for complex downstream applications such as deep sequencing. The method used to check the integrity of RNA samples by agarose 1% is particularly suitable for freshly isolated cells (Figure 3). Since the purpose of this protocol is to detect alternative splice variants aberrantly expressed in drug resistant cell lines, we choose positive selection of polyadenylated mRNA for the sequencing library preparation, by using stranded mRNA kit. Electropherograms of single and pooled libraries are depicted in Figure 4, showing an average fragment size of about 300 bp, consistent with sequencing system requirements. The prepared libraries were then sequenced using a chip with Single Read 100 bp mode. The choice of sequencing reads of 100 bp is necessary to detect alternative splicing through downstream bioinformatic pipelines.
After initial processing steps and quality control, the clean reads aligned to human genome (hg19) were subjected to differential splicing analysis using MATS. In this analysis we made comparisons between the drug sensitive parental cell line and each of its drug resistant sublines separately (i.e., CEM WT vs. CEM/R30dm, CEM WT vs. CEM-C3, etc.). MATS relies on a flexible and precise statistical model used to detect differential splicing between samples. By using default analysis options and a FDR value < 10% as a cut-off (depicted in Figure 5B), we were able to identify 38 ± 12 significant differentially spliced gene candidates per comparison ordered by type of alternative splicing event, with most hits classified as exon skipping. Figure 6 illustrates a typical analysis output for the comparison Panc-1 vs. Panc-1R.
We further focused our study on two most common types of alternative splicing events: exon skipping and mutually exclusive exon events with one representative candidate per category described below. DDX5 (DEAD-box helicase 5) has been detected by MATS analysis as statistically significant in the comparison CEM-WT vs. CEM-C3 and CEM-R5, but not significant in the comparison CEM-WT vs. CEM/R30dm. Given its putative role in leukemia25,26, we choose this candidate for further validation. It is highly recommended to visualize RNA-seq data in a genome browser-like tool. IGV provides a versatile and user-friendly interface for this purpose, as shown in Figure 7 for the gene candidate DDX5. PKM (pyruvate kinase muscle isozyme) is a statistically significant mutually exclusive exon event in the comparison CEM-WT vs. all Dex resistant sublines, but not in the comparison Panc-1 vs. Panc-1R. Given the relevance of this enzyme in solid tumor metabolism27,28 and the emerging role of cell metabolism in glucocorticoid resistance in T-ALL29, we choose this candidate for further validation using RT-PCR.
Primer design must be conducted with extreme care in order to amplify the correct amplicon, especially when primers anneal to exon-exon boundaries (in case of the reverse primer detecting DDX5 ΔEx12 variant) or when they anneal to mutually exclusive exons with high sequence homology (exon 9 and exon 10 of PKM). Figure 8 shows the primer design strategy, while Figure 9 shows the results of an RT-PCR for DDX5 gene candidate. DDX5 ΔEx12 is detected in the sample CEM-WT and CEM/R30dm but not in CEM-C3 and CEM-R5, thus confirming MATS data in a qualitative fashion. Cyanine green qRT-PCR assay accurately quantifies the mRNA expression levels of the DDX5 and PKM splice variants, as shown in Figure 10 and Figure 11, respectively.
Figure 1: Schematic Representation of Alternative Splicing Events. Schematic representation of the possible patterns of alternative splicing of a gene. Boxes are discrete exons that can be independently included or excluded from the mRNA transcript. Please click here to view a larger version of this figure.
Figure 2: Results of Cytotoxicity Assays. (A) MTT assay for leukemic cell lines shows high levels of dexamethasone resistance in CEM-C3 (IC50 = 386 ± 98), CEM-R5 (IC50 > 640 µM) and CEM/R30dm (IC50 = 456 ± 49 µM) as compared to the parental CEM-WT (IC50 = 0.028 ± 0.003 µM). (B) SRB assays for pancreatic carcinoma cell lines reveal high levels of gemcitabine resistance in Panc-1R (IC50 = 3.16 ± 0.01 µM) as compared to the parental Panc-1 (IC50 = 77.22 ± 2.76 nM). The graphs report mean cell growth % ± SEM of three independent experiments. Please click here to view a larger version of this figure.
Figure 3: RNA Quality Assessment using an Agarose Gel. Two hundred ng of total mRNA were run on 1% agarose gel stained with ethidium bromide. The presence of intact bands corresponding to ribosomal RNA (rRNA) species 18S and 28S and the absence of smears at lower molecular weights are indicative of good quality RNA. Please click here to view a larger version of this figure.
Figure 4: Bioanalyzer Traces of Sequencing Libraries. (A) Electropherograms of sequencing libraries show peaks at approximately 300 bp, which is indicative of good quality. Sample 1 to 6 = CEM-WT, CEM-C3, CEM-R5, CEM/R30dm, Panc-1 and Panc-1R. (B) Electropherogram of the pooled samples (FU, Fluorescence Units).
Figure 5: Detection of Differential Splicing with MATS. (A) Group comparisons and (B) scripts used to run MATS. Please click here to view a larger version of this figure.
Figure 6: MATS Output List. The figure depicts a typical output of MATS analysis in a software with spreadsheets: here are reported the differentially spliced candidates in the comparison Panc-1 vs. Panc-1R for exon skipping events (FDR < 10%). Please click here to view a larger version of this figure.
Figure 7: Visualization of Differential Splicing of DDX5 Candidate Gene through IGV Genome Browser. Files with aligned reads (.bam) corresponding to leukemic cells have been uploaded on IGV genome browser and visualized by using Sashimi plots (minimum junction counts value = 10 to visualize significant splicing events). Splice junction counts are represented by connecting lines and a number corresponding to the RNA-seq reads spanning the exons. CEM-WT and CEM/R30dm show skipping counts spanning exon 11 to exon 13 compared to CEM-C3 and CEM-R5 which do not show any exon 12 skipping. Please click here to view a larger version of this figure.
Figure 8: Primer Design for RT-PCR and Cyanine Green qRT-PCR. (A) RT-PCR: primer pairs detecting differential splicing of DDX5 anneal to constitutive exons (exon 10 and exon 13) located upstream and downstream from the alternatively spliced exons. (B) qRT-PCR assay: for the relative quantification of transcripts resulting from exon skipping events compared to the canonical transcripts, the reverse primer anneals either within the skipped exon (alternative variant) or to the exon11/exon13 boundary (canonical variant) of DDX5 gene. For the quantification of mutually exclusive exons, the reverse primer anneals to exon 11 common to both isoforms, while the forward primer anneal either to exon 9 (PKM1) or exon 10 (PKM2). Please click here to view a larger version of this figure.
Figure 9: RT-PCR Validation of DDX5 Differential Splicing. The 1% agarose gel shows differential splicing of the DDX5 gene in leukemic cells. The fragment corresponding to DDX5 full length amplicon (650 bp) is amplified in all samples while DDX5 ΔEx12 (430 bp) variant is amplified in CEM-WT and CEM/R30dm samples and the size corresponds to Exon 12 skipping. This is not detected in CEM-C3 and CEM-R5 cells, as determined by MATS analysis. Please click here to view a larger version of this figure.
Figure 10: mRNA Expression Levels of DDX5 Splice Variants in CEM Cells. (A) qRT-PCR assay. Mean relative expression levels and standard error of the mean (REL ± SEM) of two independent experiments. (B) Ratio of REL (± SEM) of the splice variants. Please click here to view a larger version of this figure.
Figure 11: mRNA Expression Levels of PKM Splice Variants in CEM and Panc-1 Cells. (A) qRT-PCR assay. Mean relative expression levels and standard error of the mean (REL ± SEM) of two independent experiments and ratio of REL of the splice variants for CEM cells. (B) qRT-PCR assay. Mean REL ± SEM of two independent experiments and ratio of REL (± SEM) of the splice variants for Panc-1 cells. Please click here to view a larger version of this figure.
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 described elsewhere4,5.
High-throughput genomic analyses currently used to explore drug resistance mechanisms rely mainly on SNPs detection and differential expression estimation of genes associated with a certain drug-resistant phenotype. In this study, we describe the use of RNA-sequencing methods, together with robust bioinformatics pipelines for precise annotation of mRNA transcripts and detection of differential splicing. A particularly important feature of the described protocol is the ability to identify novel splice variants between two sample groups with distinct drug sensitivity profiles. One of the crucial steps for an accurate and unbiased analysis is the isolation of RNA, which must be of high purity and integrity.
MATS is the software we choose among a series of similar bioinformatics tools available (e.g., Cuffdiff 2, DEXseq, DiffSplice and Splicing Compass) for the detection of alternative splicing18. The main key features that make it the preferred option are its superior precision and accuracy as well as the possibility to identify novel events. MATS generates two types of output containing differential splicing analysis: the first is based only on exon junction counts and the second is based on junction counts as well as reads on target. While the latter is preferred for detecting exon skipping events, it is recommended to use the first option for analysis of mutually-exclusive exons as this approach reduces the number of false positive candidates for this particular type of splicing alteration.
Moreover, for analyses focusing on intron retention, alternative 3' and 5' splice site events, a .gtf file with annotated introns should be used. Ultimately, in order to reduce biological and technical variability within sample groups and ensure high true positive rates, it is strongly recommended to sequence at least three replicates34. The selection of differentially spliced gene candidates based on MATS output was combined with a validation step using RT-PCR. This is critically important for the selection of true positive variants among a large list of statistically significant candidates. The key factor for an accurate validation is the design of oligonucleotides and the optimization of the PCR reactions according to molecular biology standards. Particular care should be taken when designing primers spanning exon-exon junctions and additional validation steps, such as sequencing of the amplicons by Sanger method, are warranted in order to confirm their specificity.
The differential splicing of DDX5 and PKM transcripts detected by MATS represent two examples of an aberrant splicing related to drug resistance. DDX5 ΔEx12 was not expressed in the GC-resistant cell lines (CEM-C3 and R5), which have been selected after prolonged Dex exposure. DDX5 ΔEx12 was expressed in the parental cell line but also in the subclone CEM/R30dm, which was selected for resistance to the chemotherapeutic agent MTX rather than Dex. In cancer cells, PKM2 was highly expressed compared to its splice variant PKM1, but the ratio PKM2/PKM1 was higher in Dex-resistant cells, as suggested by NGS results. This was not observed for Panc-1 sample compared to its gemcitabine-resistant counterpart and, indeed, this candidate gene was not among the statistically significant events in the MATS analysis. This might reflect the different cell type and mechanism of drug-resistance induced by gemcitabine exposure.
In conclusion, this protocol constitutes a suitable approach for the discovery of splice variants which may underlie drug resistance and can be applied to either leukemic cells30 or solid tumor cells31. A clear limitation is that tumor cell lines capture only a small part of cancer heterogeneity. Moreover, most cell lines have been maintained for many years in monolayers in growth-promoting media. These conditions affect the cellular characteristics, resulting in the selection of subpopulations that differ dramatically from the cells of the primary tumors from which they originate. However, many of the genes that are involved in drug resistance are also involved in other pivotal cell functions such as cell growth and apoptosis that might be affected by long-term culturing in plastic. Therefore, in order to improve the study of drug resistance, more effort should be directed toward the development of novel preclinical models, such as primary cultures and xenografts, that more closely mimic the in vivo cancer microenvironment so as to avoid relevant changes in cellular characteristics caused by extended periods of cell culture and culture conditions.32 Remarkably, our protocol could be applied also to primary cells, using cytotoxicity assays in order to determine ex vivo IC50 values. Another limitation is that since many mechanisms of resistance exist for each anticancer drug, similar or different mechanisms of resistance could develop in cells exposed to identical but independent treatments. Therefore, a comparative selection strategy should involve parallel selections and analyses, including genetic analyses on splicing variants, of the same parental cells treated with the same chemotherapy agent.
Additional approaches for functional validation should be aimed at overexpressing the splice variant of interest in cell lines or specifically downregulate their expression by using RNA interference or splice-switching oligonucleotides33.
The authors have nothing to disclose.
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).
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 |