Despite an enormous interest in the role of extracellular vesicles, including exosomes, in cancer and their use as biomarkers for diagnosis, prognosis, drug response and recurrence, there is no consensus on dependable isolation protocols. We provide a comparative evaluation of 4 exosome isolation protocols for their usability, yield and purity, and their impact on downstream omics approaches for biomarker discovery. OptiPrep density gradient centrifugation outperforms ultracentrifugation and ExoQuick and Total Exosome Isolation precipitation in terms of purity, as illustrated by the highest number of CD63-positive nanovesicles, the highest enrichment in exosomal marker proteins and a lack of contaminating proteins such as extracellular Argonaute-2 complexes. The purest exosome fractions reveal a unique mRNA profile enriched for translation, ribosome, mitochondrion and nuclear lumen function. Our results demonstrate that implementation of high purification techniques is a prerequisite to obtain reliable omics data and identify exosome-specific functions and biomarkers.

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Recent years have seen an increase of extracellular vesicle (EV) research geared towards biological understanding, diagnostics and therapy. However, EV data interpretation remains challenging owing to complexity of biofluids and technical variation introduced during sample preparation and analysis. To understand and mitigate these limitations, we generated trackable recombinant EV (rEV) as a biological reference material. Employing complementary characterization methods, we demonstrate that rEV are stable and bear physical and biochemical traits characteristic of sample EV. Furthermore, rEV can be quantified using fluorescence-, RNA- and protein-based technologies available in routine laboratories. Spiking rEV in biofluids allows recovery efficiencies of commonly implemented EV separation methods to be identified, intra-method and inter-user variability induced by sample handling to be defined, and to normalize and improve sensitivity of EV enumerations. We anticipate that rEV will aid EV-based sample preparation and analysis, data normalization, method development and instrument calibration in various research and biomedical applications.

Digital PCR (dPCR) has developed considerably since the publication of the Minimum Information for Publication of Digital PCR Experiments (dMIQE) guidelines in 2013, with advances in instrumentation, software, applications, and our understanding of its technological potential. Yet these developments also have associated challenges; data analysis steps, including threshold setting, can be difficult and preanalytical steps required to purify, concentrate, and modify nucleic acids can lead to measurement error. To assist independent corroboration of conclusions, comprehensive disclosure of all relevant experimental details is required. To support the community and reflect the growing use of dPCR, we present an update to dMIQE, dMIQE2020, including a simplified dMIQE table format to assist researchers in providing key experimental information and understanding of the associated experimental process. Adoption of dMIQE2020 by the scientific community will assist in standardizing experimental protocols, maximize efficient utilization of resources, and further enhance the impact of this powerful technology.

A reliable and robust method for measuring the expression of alternatively spliced transcripts is an important step in investigating the significance of each variant. So far, accurate quantification of splice variants has been laborious and difficult due to the intrinsic limitations of conventional methods. The many advantages of real-time PCR have made this technique attractive to study its application in quantification of splice isoforms. We use skipping of exon 37 in the NF1 gene as a model to compare and evaluate the different strategies for quantitating splice variants using real-time PCR. An overview of three different possibilities for detecting alternative transcripts is given. We propose the use of a boundary-spanning primer to quantify isoforms that differ greatly in abundance. We describe here a novel method for creating a reliable standard curve using one plasmid containing both alternative transcripts. In addition, we validate the use of an absolute standard curve based on a dilution series of fluorometrically quantified PCR products.

Reference genes have become the method of choice for normalization of qPCR data. It has been demonstrated in many studies that reference gene validation is essential to ensure accurate and reliable results. This chapter describes how a pilot study can be set up to identify the best set of reference genes to be used for normalization of qPCR data. The data from such a pilot study should be analyzed with dedicated algorithms such as geNorm to rank genes according to their stability--a measure for how well they are suited for normalization. geNorm also provides insights into the optimal number of reference genes and the overall quality of the selected set of reference genes. Importantly, these results are always in function of the sample type being studied. Guidelines are provided on the interpretation of the results from geNorm pilot studies as well as for the continued monitoring of reference gene quality in subsequent studies. For screening studies including a large, unbiased set of genes (e.g., complete miRNome) an alternative normalization method can be used: global mean normalization. This chapter also describes how the data from such studies can be used to identify reference genes for subsequent validation studies on smaller sets of selected genes.

The XML-based Real-Time PCR Data Markup Language (RDML) has been developed by the RDML consortium (http://www.rdml.org) to enable straightforward exchange of qPCR data and related information between qPCR instruments and third party data analysis software, between colleagues and collaborators and between experimenters and journals or public repositories. We here also propose data related guidelines as a subset of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to guarantee inclusion of key data information when reporting experimental results.

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality. RNAcentral is freely available at https://rnacentral.org.