Comparative analysis of mesenchymal stem cells cultivated in serum free media

Differential expression analysis of mRNAs and proteins showed that the expression of genes related with apoptosis, immune response, and inflammatory response were significantly up-regulated in ADSCs cultivated in FBS containing media. Overall design: Total RNA was isolated using Trizol reagent (Invitrogen). RNA quality was assessed using Agilent 2100 bioanalyzer with the RNA 6000 Nano Chip (Agilent Technologies, Amstelveen, Netherlands). RNA was quantified using ND-2000 spectrophotometer (Thermo Fisher Scientific). An RNA sequencing library was constructed from the control and test RNAs using QuantSeq 3' mRNA-Seq Library Prep Kit (Lexogen, Inc., Austria) according to the manufacturer's instructions. Briefly, each 500ng of total RNA was prepared, an oligo-dT primer containing an Illumina-compatible sequence at its 5' end was hybridized to the RNA and reverse transcription was performed. After degradation of the RNA template, second strand synthesis was initiated by a random primer containing an Illumina-compatible linker sequence at its 5' end. The double-stranded library was purified by using magnetic beads to remove all reaction components. The library was amplified to add the complete adapter sequences required for cluster generation. The finished library was purified from PCR components. High-throughput sequencing was performed as single-end 75-bp sequencing using NextSeq 500 (Illumina, Inc., USA). QuantSeq 3' mRNA-Seq reads were aligned using Bowtie2 [25]. Bowtie2 indices were either generated from the genome assembly sequence or the representative transcript sequences for aligning to the genome and transcriptome. The alignment file was used for assembling transcripts, estimating their abundances and determining the differential expression of genes. Differentially expressed genes (DEGs) were determined based on counts from unique and multiple alignments using BEDtools [26]. The read count data were processed based on quantile normalization method using EdgeR within R (R development Core Team, 2016) using Bioconductor (Gentleman et al.,2004). Gene classification was based on searches done by DAVID (http://david.abcc.ncifcrf.gov/) and Medline databases (http://www.ncbi.nlm.nih.gov/).