BATLAS: Deconvoluting Brown Adipose Tissue.

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.

Model-based probe set optimization for high-performance microarrays.

A major challenge in microarray design is the selection of highly specific oligonucleotide probes for all targeted genes of interest, while maintaining thermodynamic uniformity at the hybridization temperature. We introduce a novel microarray design framework (Thermodynamic Model-based Oligo Design Optimizer, TherMODO) that for the first time incorporates a number of advanced modelling features: (i) A model of position-dependent labelling effects that is quantitatively derived from experiment. (ii) Multi-state thermodynamic hybridization models of probe binding behaviour, including potential cross-hybridization reactions. (iii) A fast calibrated sequence-similarity-based heuristic for cross-hybridization prediction supporting large-scale designs. (iv) A novel compound score formulation for the integrated assessment of multiple probe design objectives. In contrast to a greedy search for probes meeting parameter thresholds, this approach permits an optimization at the probe set level and facilitates the selection of highly specific probe candidates while maintaining probe set uniformity. (v) Lastly, a flexible target grouping structure allows easy adaptation of the pipeline to a variety of microarray application scenarios. The algorithm and features are discussed and demonstrated on actual design runs. Source code is available on request.

A sensitive procedure to detect alternatively spliced mRNA in pooled-tissue samples.

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.

Non-EST-based prediction of novel alternatively spliced cassette exons with cell signaling function in Caenorhabditis elegans and human.

To better understand the complex role that alternative splicing plays in intracellular signaling, it is important to catalog the numerous splice variants involved in signal transduction. Therefore, we developed PASE (Prediction of Alternative Signaling Exons), a computational tool to identify novel alternative cassette exons that code for kinase phosphorylation or signaling protein-binding sites. We first applied PASE to the Caenorhabditis elegans genome. In this organism, our algorithm had an overall specificity of > or =76.4%, including 33 novel cassette exons that we experimentally verified. We then used PASE to analyze the human genome and made 804 predictions, of which 308 were found as alternative exons in the transcript database. We experimentally tested 384 of the remaining unobserved predictions and discovered 26 novel human exons for a total specificity of > or =41.5% in human. By using a test set of known alternatively spliced signaling exons, we determined that the sensitivity of PASE is approximately 70%. GO term analysis revealed that our exon predictions were found in the introns of known signal transduction genes more often than expected by chance, indicating PASE enriches for splice variants that function in signaling pathways. Overall, PASE was able to uncover 59 novel alternative cassette exons in C. elegans and humans through a genome-wide ab initio prediction method that enriches for exons involved in signaling.