Explore the vast range of titles available.

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. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.

RNA-sequencing has become the gold standard for whole-transcriptome gene expression quantification. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at accurately quantifying gene expression levels from RNA-sequencing reads. We performed an independent benchmarking study using RNA-sequencing data from the well established MAQCA and MAQCB reference samples. RNA-sequencing reads were processed using five workflows (Tophat-HTSeq, Tophat-Cufflinks, STAR-HTSeq, Kallisto and Salmon) and resulting gene expression measurements were compared to expression data generated by wet-lab validated qPCR assays for all protein coding genes. All methods showed high gene expression correlations with qPCR data. When comparing gene expression fold changes between MAQCA and MAQCB samples, about 85% of the genes showed consistent results between RNA-sequencing and qPCR data. Of note, each method revealed a small but specific gene set with inconsistent expression measurements. A significant proportion of these method-specific inconsistent genes were reproducibly identified in independent datasets. These genes were typically smaller, had fewer exons, and were lower expressed compared to genes with consistent expression measurements. We propose that careful validation is warranted when evaluating RNA-seq based expression profiles for this specific gene set.

A known oncogene, MITF , resides in a region of chromosome 3 that is amplified in melanomas and associated with poor prognosis; now, a long non-coding RNA gene, SAMMSON , is shown to also lie in this region, to also act as a melanoma-specific survival oncogene, and to be a promising therapeutic target for anti-melanoma therapy. An oncogenic non-coding RNA The known oncogene MITF is found in the 3p13–3p14 region of chromosome 3 that is amplified in melanomas and associated with poor prognosis. This study shows that a long non-coding RNA, SAMMSON , also lies in this region and is co-gained with MITF . SAMMSON interacts with p32 and thereby affects mitochondrial function in a pro-oncogenic manner. SAMMSON depletion sensitizes melanoma cells to MAPK-targeting therapeutics in vivo and in patient-derived xenograft models. These results point to SAMMSON as a potentially useful biomarker for malignancy and as an anti-melanoma therapeutic target. Focal amplifications of chromosome 3p13–3p14 occur in about 10% of melanomas and are associated with a poor prognosis. The melanoma-specific oncogene MITF resides at the epicentre of this amplicon 1 . However, whether other loci present in this amplicon also contribute to melanomagenesis is unknown. Here we show that the recently annotated long non-coding RNA (lncRNA) gene SAMMSON is consistently co-gained with MITF . In addition, SAMMSON is a target of the lineage-specific transcription factor SOX10 and its expression is detectable in more than 90% of human melanomas. Whereas exogenous SAMMSON increases the clonogenic potential in trans , SAMMSON knockdown drastically decreases the viability of melanoma cells irrespective of their transcriptional cell state and BRAF , NRAS or TP53 mutational status. Moreover, SAMMSON targeting sensitizes melanoma to MAPK-targeting therapeutics both in vitro and in patient-derived xenograft models. Mechanistically, SAMMSON interacts with p32, a master regulator of mitochondrial homeostasis and metabolism, to increase its mitochondrial targeting and pro-oncogenic function. Our results indicate that silencing of the lineage addiction oncogene SAMMSON disrupts vital mitochondrial functions in a cancer-cell-specific manner; this silencing is therefore expected to deliver highly effective and tissue-restricted anti-melanoma therapeutic responses.

To increase the throughput, lower the cost, and save scarce test reagents, laboratories can pool patient samples before SARS-CoV-2 RT-qPCR testing. While different sample pooling methods have been proposed and effectively implemented in some laboratories, no systematic and large-scale evaluations exist using real-life quantitative data gathered throughout the different epidemiological stages. Here, we use anonymous data from 9673 positive cases to model, simulate and compare 1D and 2D pooling strategies. We show that the optimal choice of pooling method and pool size is an intricate decision with a testing population-dependent efficiency-sensitivity trade-off and present an online tool to provide the reader with custom real-time 1D pooling strategy recommendations.

Background Advances in high-throughput technologies and bioinformatics have transformed gene expression profiling methodologies. The results of microarray experiments are often validated using reverse transcription quantitative PCR (RT-qPCR), which is the most sensitive and reproducible method to quantify gene expression. Appropriate normalisation of RT-qPCR data using stably expressed reference genes is critical to ensure accurate and reliable results. Mi(cro)RNA expression profiles have been shown to be more accurate in disease classification than mRNA expression profiles. However, few reports detailed a robust identification and validation strategy for suitable reference genes for normalisation in miRNA RT-qPCR studies. Methods We adopt and report a systematic approach to identify the most stable reference genes for miRNA expression studies by RT-qPCR in colorectal cancer (CRC). High-throughput miRNA profiling was performed on ten pairs of CRC and normal tissues. By using the mean expression value of all expressed miRNAs, we identified the most stable candidate reference genes for subsequent validation. As such the stability of a panel of miRNAs was examined on 35 tumour and 39 normal tissues. The effects of normalisers on the relative quantity of established oncogenic ( miR-21 and miR-31 ) and tumour suppressor ( miR-143 and miR-145 ) target miRNAs were assessed. Results In the array experiment, miR-26a , miR-345 , miR-425 and miR-454 were identified as having expression profiles closest to the global mean. From a panel of six miRNAs ( let-7a , miR-16 , miR-26a , miR-345 , miR-425 and miR-454 ) and two small nucleolar RNA genes ( RNU48 and Z30 ), miR-16 and miR-345 were identified as the most stably expressed reference genes. The combined use of miR-16 and miR-345 to normalise expression data enabled detection of a significant dysregulation of all four target miRNAs between tumour and normal colorectal tissue. Conclusions Our study demonstrates that the top six most stably expressed miRNAs ( let-7a , miR-16 , miR-26a , miR-345 , miR-425 and miR-454 ) described herein should be validated as suitable reference genes in both high-throughput and lower throughput RT-qPCR colorectal miRNA studies.

12 microRNA expression profiling platforms are compared for their reproducibility, sensitivity, accuracy and specificity, and the strengths and weaknesses of each platform are discussed. MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription–quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 12 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from microRNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals.

miRNAs can both promote and repress tumorigenesis, and directly control epithelial–mesenchymal transition (EMT). miR-9 (which is upregulated in breast cancer cells) is activated by MYC and MYCN, and regulates EMT and metastasis through direct control of E-cadherin. In contrast, tumour angiogenesis is controlled indirectly through effects on vascular endothelial growth factor (VEGF) expression. MicroRNAs (miRNAs) are increasingly implicated in regulating the malignant progression of cancer. Here we show that miR-9, which is upregulated in breast cancer cells, directly targets CDH1 , the E-cadherin-encoding messenger RNA, leading to increased cell motility and invasiveness. miR-9-mediated E-cadherin downregulation results in the activation of β -catenin signalling, which contributes to upregulated expression of the gene encoding vascular endothelial growth factor (VEGF); this leads, in turn, to increased tumour angiogenesis. Overexpression of miR-9 in otherwise non-metastatic breast tumour cells enables these cells to form pulmonary micrometastases in mice. Conversely, inhibiting miR-9 by using a 'miRNA sponge' in highly malignant cells inhibits metastasis formation. Expression of miR-9 is activated by MYC and MYCN, both of which directly bind to the mir-9-3 locus. Significantly, in human cancers, miR-9 levels correlate with MYCN amplification, tumour grade and metastatic status. These findings uncover a regulatory and signalling pathway involving a metastasis-promoting miRNA that is predicted to directly target expression of the key metastasis-suppressing protein E-cadherin.

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. There is no universal reference material to develop extracellular vesicle (EV) separation methods and carry out calibration and normalization. Here the authors use HIV-derived gag proteins to assemble recombinant fluorescent EV as a trackable reference material resembling the physical and biochemical properties of sample EV.

Synchronization of mitochondrial and cytoplasmic translation rates is critical for the maintenance of cellular fitness, with cancer cells being especially vulnerable to translational uncoupling. Although alterations of cytosolic protein synthesis are common in human cancer, compensating mechanisms in mitochondrial translation remain elusive. Here we show that the malignant long non-coding RNA (lncRNA) SAMMSON promotes a balanced increase in ribosomal RNA (rRNA) maturation and protein synthesis in the cytosol and mitochondria by modulating the localization of CARF, an RNA-binding protein that sequesters the exo-ribonuclease XRN2 in the nucleoplasm, which under normal circumstances limits nucleolar rRNA maturation. SAMMSON interferes with XRN2 binding to CARF in the nucleus by favoring the formation of an aberrant cytoplasmic RNA–protein complex containing CARF and p32, a mitochondrial protein required for the processing of the mitochondrial rRNAs. These data highlight how a single oncogenic lncRNA can simultaneously modulate RNA–protein complex formation in two distinct cellular compartments to promote cell growth.

Accumulating evidence highlights the role of long non-coding RNAs (lncRNAs) in cellular homeostasis, and their dysregulation in disease settings. Most lncRNAs function by interacting with proteins or protein complexes. While several orthogonal methods have been developed to identify these proteins, each method has its inherent strengths and limitations. Here, we combine two RNA-centric methods ChIRP-MS and RNA-BioID to obtain a comprehensive list of proteins that interact with the well-known lncRNA HOTAIR. Overexpression of HOTAIR has been associated with a metastasis-promoting phenotype in various cancers. Although HOTAIR is known to bind with PRC2 and LSD1 protein complexes, only very limited unbiased comprehensive approaches to map its interactome have been performed. Both ChIRP-MS and RNA-BioID data sets show an association of HOTAIR with mitoribosomes, suggesting that HOTAIR has functions independent of its (post-)transcriptional mode-of-action.