Evaluation of bovine milk extracellular vesicles for the delivery of locked nucleic acid antisense oligonucleotides.

The natural capacity of extracellular vesicles (EVs) to transport their payload to recipient cells has raised big interest to repurpose EVs as delivery vehicles for xenobiotics. In the present study, bovine milk-derived EVs (BMEVs) were investigated for their potential to shuttle locked nucleic acid-modified antisense oligonucleotides (LNA ASOs) into the systemic circulation after oral administration. To this end, a broad array of analytical methods including proteomics and lipidomics were used to thoroughly characterize BMEVs. We found that additional purification by density gradients efficiently reduced levels of non-EV associated proteins. The potential of BMEVs to functionally transfer LNA ASOs was tested using advanced in vitro systems (i.e. hPSC-derived neurons and primary human cells). A slight increase in cellular LNA ASO internalization and target gene reduction was observed when LNA ASOs were delivered using BMEVs. When dosed orally in mice, only a small fraction (about 1% of total administered dose) of LNA ASOs was recovered in the peripheral tissues liver and kidney, however, no significant reduction in target gene expression (i.e. functional knockdown) was observed.

MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures.

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.