MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription-quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 12 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from microRNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals.

Due to evolutionary conservation of biology, experimental knowledge captured from genetic studies in eukaryotic model organisms provides insight into human cellular pathways and ultimately physiology. Yeast chemogenomic profiling is a powerful approach for annotating cellular responses to small molecules. Using an optimized platform, we provide the relative sensitivities of the heterozygous and homozygous deletion collections for nearly 1800 biologically active compounds. The data quality enables unique insights into pathways that are sensitive and resistant to a given perturbation, as demonstrated with both known and novel compounds. We present examples of novel compounds that inhibit the therapeutically relevant fatty acid synthase and desaturase (Fas1p and Ole1p), and demonstrate how the individual profiles facilitate hypothesis-driven experiments to delineate compound mechanism of action. Importantly, the scale and diversity of tested compounds yields a dataset where the number of modulated pathways approaches saturation. This resource can be used to map novel biological connections, and also identify functions for unannotated genes. We validated hypotheses generated by global two-way hierarchical clustering of profiles for (i) novel compounds with a similar mechanism of action acting upon microtubules or vacuolar ATPases, and (ii) an un-annotated ORF, YIL060w, that plays a role in respiration in the mitochondria. Finally, we identify and characterize background mutations in the widely used yeast deletion collection which should improve the interpretation of past and future screens throughout the community. This comprehensive resource of cellular responses enables the expansion of our understanding of eukaryotic pathway biology.

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In the version of this article initially published, 3D DAOSTORM was erroneously characterized in Table 2 as using least-squares fits. In fact, 3D DAOSTORM uses an implementation of maximum likelihood estimation (MLE). The error has been corrected in the HTML and PDF versions of the article.

Although a number of technical parameters are now being examined to optimize microRNA profiling experiments, it is unknown whether reagent or component changes to the labeling step affect starting RNA requirements or microarray performance. Human brain/lung samples were each labeled in duplicate, at 1.0, 0.5, 0.2, and 0.1 μg of total RNA, by means of two kits that use the same labeling procedure but differ in the reagent composition used to label microRNAs. Statistical measures of reliability and validity were used to evaluate microarray data. Cross-platform confirmation was accomplished using TaqMan microRNA assays. Synthetic microRNA spike-in experiments were also performed to establish the microarray signal dynamic range using the ligation-modified kit. Technical replicate correlations of signal intensity values were high using both kits, but improved with the ligation-modified assay. The drop in detection call sensitivity and miRNA gene list correlations, when using reduced amounts of standard-labeled RNA, was considerably improved with the ligation-modified kit. Microarray signal dynamic range was found to be linear across three orders of magnitude from 4.88 to 5000 attomoles. Thus, optimization of the microRNA labeling reagent can result in at least a 10-fold decrease in microarray total RNA requirements with little compromise to data quality. Clinical investigations bottlenecked by the amount of starting material may use a ligation mix modification strategy to reduce total RNA requirements.