One important goal of genomics is to explore the extent of alternative splicing in the transcriptome and generate a comprehensive catalog of splice forms. New computational and experimental approaches have led to an increase in the number of predicted alternatively spliced transcripts; however, validation of these predictions has not kept pace. In this work, we systematically explore different methods for the validation of cassette exons predicted by computational methods or tiling microarrays. Our goal was to find a procedure that is cost effective, sensitive and specific. We examined three ways of priming the reverse transcription (RT) reaction-poly-dT priming, random priming and pooled exon-specific priming. We also examined two strategies for PCR amplification-flanking PCR, which uses primers that hybridize to the constitutive exons flanking the predicted exon, and a semi-nested PCR with a primer that targets the predicted exon. We found that the combination of RT using a pool of gene-specific primers followed by semi-nested PCR resulted in a significant increase in sensitivity over the most commonly used methodology (97% of the test set was detected versus 14%). Our method was also highly specific-no false positives were detected using a test set of true negatives. Finally, we demonstrate that this method is able to detect alternative exons with a high sensitivity from whole-organism RNA, allowing all tissues to be sampled in a single experiment. The protocol developed here is an accurate and cost-effective way to validate predictions of alternative splicing.

Recruitment and activation of thermogenic adipocytes have received increasing attention as a strategy to improve systemic metabolic control. The analysis of brown and brite adipocytes is complicated by the complexity of adipose tissue biopsies. Here, we provide an in-depth analysis of pure brown, brite, and white adipocyte transcriptomes. By combining mouse and human transcriptome data, we identify a gene signature that can classify brown and white adipocytes in mice and men. Using a machine-learning-based cell deconvolution approach, we develop an algorithm proficient in calculating the brown adipocyte content in complex human and mouse biopsies. Applying this algorithm, we can show in a human weight loss study that brown adipose tissue (BAT) content is associated with energy expenditure and the propensity to lose weight. This online available tool can be used for in-depth characterization of complex adipose tissue samples and may support the development of therapeutic strategies to increase energy expenditure in humans.