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A major challenge in the analysis of RNA-seq data at the transcript-level is accounting for the variability introduced during quantification of RNA sequencing reads. This variability is due to the high levels of sequence similarity among transcripts annotated to the same genomic locus and the mapping ambiguity resulting from the assignment of sequence reads to such transcripts. The quantification uncertainty associated with transcript-level estimated counts is intractable to measure analytically but represents an extra source of variation that seriously compromises differential transcript expression (DTE) analyses if standard statistical methods developed for gene-level analyses are used. Bootstrap counts, as provided by popular RNA-seq quantification tools, allow one to estimate the quantification uncertainty and account for such an effect in DTE analyses. We presentcatchSalmonandcatchKallisto, two functions included in the R/Bioconductor packageedgeR, that estimate the transcript-level quantification uncertainty, here termed mapping ambiguity overdispersion, using bootstrap counts. We discuss how the mapping ambiguity overdispersion can be effectively removed from the data in transcript-level analyses via count scaling, an approach that reduces the size of the estimated counts obtained from quantification tools to effective count sizes that reflect their true precision. The presented count scaling approach allows users to perform efficient DTE analyses within the efficientedgeRframework. A comprehensive simulation study and a DTE analysis of human lung adenocarcinoma cell lines are presented to illustrate the benefits of accounting for the mapping ambiguity overdispersion in transcript-level RNA-seq data analyses.

Mutations in genes encoding general transcription factors cause neurological disorders. Despite clinical prominence, the consequences of defects in the basal transcription machinery during brain development are unclear. We found that loss of the TATA-box binding protein-associated factor TAF8, a component of the general transcription factor TFIID, in the developing central nervous system affected the expression of many, but notably not all genes. Taf8 deletion caused apoptosis, unexpectedly restricted to forebrain regions. Nuclear levels of the transcription factor p53 were elevated in the absence of TAF8, as were the mRNAs of the pro-apoptotic p53 target genes Noxa, Puma and Bax. The cell death in Taf8 forebrain regions was completely rescued by additional loss of p53, but Taf8 and p53 brains failed to initiate a neuronal expression program. Taf8 deletion caused aberrant transcription of promoter regions and splicing anomalies. We propose that TAF8 supports the directionality of transcription and co-transcriptional splicing, and that failure of these processes causes p53-induced apoptosis of neuronal cells in the developing mouse embryo. [Image: see text]

The current lack of benchmark datasets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (“sequins”). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we createdin silicomixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that,StringTie2andbambuoutperformed other tools from the 6 isoform detection tools tested,DESeq2, edgeRandlimma-voomwere best amongst the 5 differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the 5 tools compared, which suggests further methods development is needed for this application.

Breast cancer is a common and highly heterogeneous disease. Understanding the cellular diversity in the mammary gland and its surrounding micro-environment across different states can provide insight into the cancer development in human breast. Recently, a large-scale single-cell RNA expression atlas was constructed of the human breast spanning normal, preneoplastic and tumorigenic states. Single-cell expression profiles of nearly 430,000 cells were obtained from 69 distinct surgical tissue specimens from 55 patients. This article extends the study by providing downstream processed R data objects, complete cell annotation and R code to reproduce all the analyses. Details of all the bioinformatic analyses that produced the results described in the study are provided.

Bone marrow is a preferred metastatic site for multiple solid tumours and is associated with poor prognosis and significant morbidity. Accumulating evidence indicates that cancer cells colonise specialised niches within the bone marrow to support their long-term propagation, but the precise location and mechanisms that mediate niche interactions are unknown. Using breast cancer as a model of solid tumour metastasis to the bone marrow, we applied large-scale quantitative three-dimensional imaging to characterise temporal changes in the bone marrow microenvironment during disease progression. We show that mouse mammary tumour cells preferentially home to a pre-existing metaphyseal domain enriched for type H vessels. Metastatic lesion outgrowth rapidly remodelled the local vasculature through extensive sprouting to establish a tumour-supportive microenvironment. The evolution of this tumour microenvironment reflects direct remodelling of the vascular endothelium through tumour-derived granulocyte-colony stimulating factor (G-CSF) in a hematopoietic cell-independent manner. Therapeutic targeting of the metastatic niche by blocking G-CSF receptor inhibited pathological blood vessel remodelling and reduced bone metastasis burden. These findings elucidate a mechanism of ‘host’ microenvironment hijacking by mammary tumour cells to subvert the local microvasculature to form a specialised, pro-tumorigenic niche., The visualisation of the bone metastasis process in a spatial temporal manner is lacking. Here, the authors use three-dimensional quantitative imaging and show that mouse mammary tumour cells preferentially home to endothelial subtype type H vessels within the bone marrow and remodel this vasculature by producing granulocyte-colony stimulating factor.