Ined by qPCR using 41 (FF) and 37 (FFPE) shared miRNA transcripts. We found that for FF samples, the miRNA-Seq platform exhibited the highest correlation with the qPCR assay (Table 2), followed closely by the Affymetrix platform. Though the FF correlations were relatively low, they were significantly higher than those of the FFPE comparison. However, the apparent low overall correlation between each tested platform and qPCR could also be affected by the specificity and robustness of the qPCR assays. In this regard it is interesting to note that recent evidence indicates wide spread editing of miRNA molecules, even within the seed region, that may have affected the target of the ABI miRNA qPCR assays employed in this study [35]. The absence of a method to accurately measure the true miRNA expression in a given sample continues to make cross platform comparative studies such as this difficult. Indeed, others have compared miRNA expression profiling methods, UKI-1 chemical information although their platform assessments were not as comprehensive as was the current study [26,28,29,36]. These studies also found substantial inter-platform differences. However, our analysis of transcripts that were commonly interrogated demonstrated general similarities in the level of expression across platforms. Particularly for the most abundantly expression miRNA genes, we observed that a significant fraction were consistently detected by all or most of the tested platforms (Table S4 A ). Therefore, with few exceptions, the choice of platform for miRNA expression profiling will be heavily dependent upon the primary objective of the study. If the purpose of the study is to determine the relative expression of miRNA genes already present in the database, any one of the tested platforms would be adequate and the overall cost of the assay, turn-around-time, and ease of data analysis would be critical factors for consideration. However, if the primary objective is the discovery of novel miRNA transcripts, miRNA-Seq would be the preferred method. Currently, methods for miRNA-Seq-based analyses readily allow forthe concurrent multiplexing of up to 48 samples. Together with improved sequencing chemistries and optimized flow cell capacities, miRNA-Seq has become much more cost competitive with 24195657 array-based technologies. However, the data pre-processing steps, such as de-multiplexing and read mapping remain complex, often requiring substantial informatics and programming support not readily available to individual laboratories. This too is rapidly evolving with the development of off-the-shelf software packages that employ relatively common computing power to obtain differential expression patterns.Materials and Methods Sample Collection and ProcessingTissue samples were retrieved from sample archives, according to a protocol that was approved by the Mayo Clinic Institutional Review Board with written informed consent, and were deidentified for this work. In order to compare the various miRNA expression profiling platforms, replicates from three types of samples were AZ-876 site utilized (a total of six samples); 1) fresh frozen (FF); 2) formalin-fixed paraffin embedded (FFPE) tissue from normal human lung and lung tumors, and 3) lung carcinoma cell lines (Figure 1). Total RNA was extracted in duplicate from one FF tissue sample, designated FF1 and FF2, by using the Qiagen miRNeasy kit (Valencia, CA). Likewise, total RNA from matched FFPE samples were also extracted in duplicate, using the RecoverAll kit.Ined by qPCR using 41 (FF) and 37 (FFPE) shared miRNA transcripts. We found that for FF samples, the miRNA-Seq platform exhibited the highest correlation with the qPCR assay (Table 2), followed closely by the Affymetrix platform. Though the FF correlations were relatively low, they were significantly higher than those of the FFPE comparison. However, the apparent low overall correlation between each tested platform and qPCR could also be affected by the specificity and robustness of the qPCR assays. In this regard it is interesting to note that recent evidence indicates wide spread editing of miRNA molecules, even within the seed region, that may have affected the target of the ABI miRNA qPCR assays employed in this study [35]. The absence of a method to accurately measure the true miRNA expression in a given sample continues to make cross platform comparative studies such as this difficult. Indeed, others have compared miRNA expression profiling methods, although their platform assessments were not as comprehensive as was the current study [26,28,29,36]. These studies also found substantial inter-platform differences. However, our analysis of transcripts that were commonly interrogated demonstrated general similarities in the level of expression across platforms. Particularly for the most abundantly expression miRNA genes, we observed that a significant fraction were consistently detected by all or most of the tested platforms (Table S4 A ). Therefore, with few exceptions, the choice of platform for miRNA expression profiling will be heavily dependent upon the primary objective of the study. If the purpose of the study is to determine the relative expression of miRNA genes already present in the database, any one of the tested platforms would be adequate and the overall cost of the assay, turn-around-time, and ease of data analysis would be critical factors for consideration. However, if the primary objective is the discovery of novel miRNA transcripts, miRNA-Seq would be the preferred method. Currently, methods for miRNA-Seq-based analyses readily allow forthe concurrent multiplexing of up to 48 samples. Together with improved sequencing chemistries and optimized flow cell capacities, miRNA-Seq has become much more cost competitive with 24195657 array-based technologies. However, the data pre-processing steps, such as de-multiplexing and read mapping remain complex, often requiring substantial informatics and programming support not readily available to individual laboratories. This too is rapidly evolving with the development of off-the-shelf software packages that employ relatively common computing power to obtain differential expression patterns.Materials and Methods Sample Collection and ProcessingTissue samples were retrieved from sample archives, according to a protocol that was approved by the Mayo Clinic Institutional Review Board with written informed consent, and were deidentified for this work. In order to compare the various miRNA expression profiling platforms, replicates from three types of samples were utilized (a total of six samples); 1) fresh frozen (FF); 2) formalin-fixed paraffin embedded (FFPE) tissue from normal human lung and lung tumors, and 3) lung carcinoma cell lines (Figure 1). Total RNA was extracted in duplicate from one FF tissue sample, designated FF1 and FF2, by using the Qiagen miRNeasy kit (Valencia, CA). Likewise, total RNA from matched FFPE samples were also extracted in duplicate, using the RecoverAll kit.
