Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.

Supplementary data are available at Bioinformatics online.

Expression of miR-223-3p, miR-142-3p, and miR-629-3p is increased in sputum of patients with severe asthma and is linked to neutrophilic airway inflammation, suggesting that these miRNAs contribute to this asthma inflammatory phenotype.

We highlight a role for miR-218-5p in the pathogenesis of COPD.

In recent years, technological advances in transcriptome profiling revealed that the repertoire of human RNA molecules is more diverse and extended than originally thought. This diversity and complexity mainly derive from a large ensemble of noncoding RNAs. Because of their key roles in cellular processes important for normal development and physiology, disruption of noncoding RNA expression is intrinsically linked to human disease, including cancer. Therefore, studying the noncoding portion of the transcriptome offers the prospect of identifying novel therapeutic and diagnostic targets. Although evidence of the relevance of noncoding RNAs in cancer is accumulating, we still face many challenges when it comes to accurately profiling their expression levels. Some of these challenges are inherent to the technologies employed, whereas others are associated with characteristics of the noncoding RNAs themselves. In this review, we discuss the challenges related to long noncoding RNA expression profiling, highlight how cancer long noncoding RNAs provide new opportunities for cancer diagnosis and treatment, and reflect on future developments.

Existing compendia of non-coding RNA (ncRNA) are incomplete, in part because they are derived almost exclusively from small and polyadenylated RNAs. Here we present a more comprehensive atlas of the human transcriptome, which includes small and polyA RNA as well as total RNA from 300 human tissues and cell lines. We report thousands of previously uncharacterized RNAs, increasing the number of documented ncRNAs by approximately 8%. To infer functional regulation by known and newly characterized ncRNAs, we exploited pre-mRNA abundance estimates from total RNA sequencing, revealing 316 microRNAs and 3,310 long non-coding RNAs with multiple lines of evidence for roles in regulating protein-coding genes and pathways. Our study both refines and expands the current catalog of human ncRNAs and their regulatory interactions. All data, analyses and results are available for download and interrogation in the R2 web portal, serving as a basis for future exploration of RNA biology and function.

Separating extracellular vesicles (EV) from blood plasma is challenging and complicates their biological understanding and biomarker development. In this study, we fractionate blood plasma by combining size‐exclusion chromatography (SEC) and OptiPrep density gradient centrifugation to study clinical context‐dependent and time‐dependent variations in the biomolecular landscape of systemically circulating EV. Using pooled blood plasma samples from breast cancer patients, we first demonstrate the technical repeatability of blood plasma fractionation. Using serial blood plasma samples from HIV and ovarian cancer patients (n = 10) we next show that EV carry a clinical context‐dependent and/or time‐dependent protein and small RNA composition, including miRNA and tRNA. In addition, differential analysis of blood plasma fractions provides a catalogue of putative proteins not associated with systemically circulating EV. In conclusion, the implementation of blood plasma fractionation allows to advance the biological understanding and biomarker development of systemically circulating EV.

Chronic obstructive pulmonary disease (COPD) is characterized by a progressive airflow limitation and is associated with a chronic inflammatory response in both airways and lungs. microRNAs (miRNAs) are often highly conserved between species and have an intricate role within homeostatic conditions and immune responses. Also, miRNAs are dysregulated in smoking-associated diseases. We investigated the miRNA profile of 523 miRNAs by stem-loop RT-qPCR in lung tissue and cell-free bronchoalveolar lavage (BAL) supernatant of mice exposed to air or cigarette smoke (CS) for 4 or 24 weeks. After 24 weeks of CS exposure, 31 miRNAs were differentially expressed in lung tissue and 78 in BAL supernatant. Next, we correlated the miRNA profiling data to inflammation in BAL and lung, obtained by flow cytometry or ELISA. In addition, we surveyed for overlap with newly assessed miRNA profiles in bronchial biopsies and with previously assessed miRNA profiles in lung tissue and induced sputum supernatant of smokers with COPD. Several miRNAs showed concordant differential expression between both species including miR-31*, miR-155, miR-218 and let-7c. Thus, investigating miRNA profiling data in different compartments and both species provided accumulating insights in miRNAs that may be relevant in CS-induced inflammation and the pathogenesis of COPD.