A mucus production programme promotes classical pancreatic ductal adenocarcinoma.

OBJECTIVE: The optimal therapeutic response in cancer patients is highly dependent upon the differentiation state of their tumours. Pancreatic ductal adenocarcinoma (PDA) is a lethal cancer that harbours distinct phenotypic subtypes with preferential sensitivities to standard therapies. This study aimed to investigate intratumour heterogeneity and plasticity of cancer cell states in PDA in order to reveal cell state-specific regulators. DESIGN: We analysed single-cell expression profiling of mouse PDAs, revealing intratumour heterogeneity and cell plasticity and identified pathways activated in the different cell states. We performed comparative analysis of murine and human expression states and confirmed their phenotypic diversity in specimens by immunolabeling. We assessed the function of phenotypic regulators using mouse models of PDA, organoids, cell lines and orthotopically grafted tumour models. RESULTS: Our expression analysis and immunolabeling analysis show that a mucus production programme regulated by the transcription factor SPDEF is highly active in precancerous lesions and the classical subtype of PDA - the most common differentiation state. SPDEF maintains the classical differentiation and supports PDA transformation in vivo. The SPDEF tumour-promoting function is mediated by its target genes AGR2 and ERN2/IRE1ß that regulate mucus production, and inactivation of the SPDEF programme impairs tumour growth and facilitates subtype interconversion from classical towards basal-like differentiation. CONCLUSIONS: Our findings expand our understanding of the transcriptional programmes active in precancerous lesions and PDAs of classical differentiation, determine the regulators of mucus production as specific vulnerabilities in these cell states and reveal phenotype switching as a response mechanism to inactivation of differentiation states determinants.

Identification of glioblastoma stem cell-associated lncRNAs using single-cell RNA sequencing datasets.

Glioblastoma multiforme (GBM) is an aggressive, heterogeneous brain tumor in which glioblastoma stem cells (GSCs) are known culprits of therapy resistance. Long non-coding RNAs (lncRNAs) have been shown to play a critical role in both cancer and normal biology. A few studies have suggested that aberrant expression of lncRNAs is associated with GSCs. However, a comprehensive single-cell analysis of the GSC-associated lncRNA transcriptome has not been carried out. Here, we analyzed recently published single-cell RNA sequencing datasets of adult GBM tumors, GBM organoids, GSC-enriched GBM tumors, and developing human brain samples to identify lncRNAs highly expressed in GSCs. We further revealed that the GSC-specific lncRNAs GIHCG and LINC01563 promote proliferation, migration, and stemness in the GSC population. Together, this study identified a panel of uncharacterized GSC-enriched lncRNAs and set the stage for future in-depth studies to examine their role in GBM pathology and their potential as biomarkers and/or therapeutic targets in GBM.

Comparative transcriptomics reveals human-specific cortical features.

The cognitive abilities of humans are distinctive among primates, but their molecular and cellular substrates are poorly understood. We used comparative single-nucleus transcriptomics to analyze samples of the middle temporal gyrus (MTG) from adult humans, chimpanzees, gorillas, rhesus macaques, and common marmosets to understand human-specific features of the neocortex. Human, chimpanzee, and gorilla MTG showed highly similar cell-type composition and laminar organization as well as a large shift in proportions of deep-layer intratelencephalic-projecting neurons compared with macaque and marmoset MTG. Microglia, astrocytes, and oligodendrocytes had more-divergent expression across species compared with neurons or oligodendrocyte precursor cells, and neuronal expression diverged more rapidly on the human lineage. Only a few hundred genes showed human-specific patterning, suggesting that relatively few cellular and molecular changes distinctively define adult human cortical structure.

Comparative single-cell transcriptomic analysis of primate brains highlights human-specific regulatory evolution.

Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic innovations remain poorly understood, in part because of the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single-cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell type-specific gene co-expression networks for comparative analysis. Although orthologue expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3,383 out of 14,131), which are also associated with multiple brain disorders. To assess the functional significance of gene expression differences in an evolutionary context, we evaluate changes in network connectivity across meta-analytic co-expression networks from 19 animals. We find that a subset of these genes has deeply conserved co-expression across all non-human animals, and strongly divergent co-expression relationships in humans (139 out of 3,383, <1% of primate orthologues). Genes with human-specific cellular expression and co-expression profiles (such as NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function.

Myo-differentiation reporter screen reveals NF-Y as an activator of PAX3-FOXO1 in rhabdomyosarcoma.

Recurrent chromosomal rearrangements found in rhabdomyosarcoma (RMS) produce the PAX3-FOXO1 fusion protein, which is an oncogenic driver and a dependency in this disease. One important function of PAX3-FOXO1 is to arrest myogenic differentiation, which is linked to the ability of RMS cells to gain an unlimited proliferation potential. Here, we developed a phenotypic screening strategy for identifying factors that collaborate with PAX3-FOXO1 to block myo-differentiation in RMS. Unlike most genes evaluated in our screen, we found that loss of any of the three subunits of the Nuclear Factor Y (NF-Y) complex leads to a myo-differentiation phenotype that resembles the effect of inactivating PAX3-FOXO1. While the transcriptomes of NF-Y- and PAX3-FOXO1-deficient RMS cells bear remarkable similarity to one another, we found that these two transcription factors occupy nonoverlapping sites along the genome: NF-Y preferentially occupies promoters, whereas PAX3-FOXO1 primarily binds to distal enhancers. By integrating multiple functional approaches, we map the PAX3 promoter as the point of intersection between these two regulators. We show that NF-Y occupies CCAAT motifs present upstream of PAX3 to function as a transcriptional activator of PAX3-FOXO1 expression in RMS. These findings reveal a critical upstream role of NF-Y in the oncogenic PAX3-FOXO1 pathway, highlighting how a broadly essential transcription factor can perform tumor-specific roles in governing cellular state.

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector.

Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.

Identification of glioblastoma stem cell-associated lncRNAs using single-cell RNA-sequencing datasets.

Glioblastoma multiforme (GBM) is an aggressive, heterogeneous grade IV brain tumor. Glioblastoma stem cells (GSCs) initiate the tumor and are known culprits of therapy resistance. Mounting evidence has demonstrated a regulatory role of long non-coding RNAs (lncRNAs) in various biological processes, including pluripotency, differentiation, and tumorigenesis. A few studies have suggested that aberrant expression of lncRNAs is associated with GSCs. However, a comprehensive single-cell analysis of the GSC-associated lncRNA transcriptome has not been carried out. Here, we analyzed recently published single-cell RNA-sequencing datasets of adult human GBM tumors, GBM organoids, GSC-enriched GBM tumors, and developing human brains to identify lncRNAs highly expressed in GBM. To categorize GSC populations in the GBM tumors, we used the GSC marker genes SOX2, PROM1, FUT4, and L1CAM. We found three major GSC population clusters: radial glia, oligodendrocyte progenitor cells, and neurons. We found 10â€"100 lncRNAs significantly enriched in different GSC populations. We also validated the level of expression and localization of several GSC-enriched lncRNAs using qRT-PCR, single-molecule RNA FISH, and sub-cellular fractionation. We found that the radial glia GSC-enriched lncRNA PANTR1 is highly expressed in GSC lines and is localized to both the cytoplasmic and nuclear fractions. In contrast, the neuronal GSC-enriched lncRNAs LINC01563 and MALAT1 are highly enriched in the nuclear fraction of GSCs. Together, this study identified a panel of uncharacterized GSC-specific lncRNAs. These findings set the stage for future in-depth studies to examine their role in GBM pathology and their potential as biomarkers and/or therapeutic targets in GBM.

Pan-human consensus genome significantly improves the accuracy of RNA-seq analyses.

The Human Reference Genome serves as the foundation for modern genomic analyses. However, in its present form, it does not adequately represent the vast genetic diversity of the human population. In this study, we explored the consensus genome as a potential successor of the current reference genome and assessed its effect on the accuracy of RNA-seq read alignment. To find the best haploid genome representation, we constructed consensus genomes at the pan-human, superpopulation, and population levels, using variant information from The 1000 Genomes Project Consortium. Using personal haploid genomes as the ground truth, we compared mapping errors for real RNA-seq reads aligned to the consensus genomes versus the reference genome. For reads overlapping homozygous variants, we found that the mapping error decreased by a factor of approximately two to three when the reference was replaced with the pan-human consensus genome. We also found that using more population-specific consensuses resulted in little to no increase over using the pan-human consensus, suggesting a limit in the utility of incorporating a more specific genomic variation. Replacing the reference with consensus genomes impacts functional analyses, such as differential expressions of isoforms, genes, and splice junctions.

A limited set of transcriptional programs define major cell types.

We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been traditionally classified. We identified genes whose expression is specific to these cell types, and from these genes, we estimated the contribution of the major cell types to the composition of human tissues. We found this cellular composition to be a characteristic signature of tissues and to reflect tissue morphological heterogeneity and histology. We identified changes in cellular composition in different tissues associated with age and sex, and found that departures from the normal cellular composition correlate with histological phenotypes associated with disease.

Expanded encyclopaedias of DNA elements in the human and mouse genomes.

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

Management, Analyses, and Distribution of the MaizeCODE Data on the Cloud.

MaizeCODE is a project aimed at identifying and analyzing functional elements in the maize genome. In its initial phase, MaizeCODE assayed up to five tissues from four maize strains (B73, NC350, W22, TIL11) by RNA-Seq, Chip-Seq, RAMPAGE, and small RNA sequencing. To facilitate reproducible science and provide both human and machine access to the MaizeCODE data, we enhanced SciApps, a cloud-based portal, for analysis and distribution of both raw data and analysis results. Based on the SciApps workflow platform, we generated new components to support the complete cycle of MaizeCODE data management. These include publicly accessible scientific workflows for the reproducible and shareable analysis of various functional data, a RESTful API for batch processing and distribution of data and metadata, a searchable data page that lists each MaizeCODE experiment as a reproducible workflow, and integrated JBrowse genome browser tracks linked with workflows and metadata. The SciApps portal is a flexible platform that allows the integration of new analysis tools, workflows, and genomic data from multiple projects. Through metadata and a ready-to-compute cloud-based platform, the portal experience improves access to the MaizeCODE data and facilitates its analysis.

Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods.

BACKGROUND: Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly. RESULTS: We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes. CONCLUSION: The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.

Is it time to change the reference genome?

The use of the human reference genome has shaped methods and data across modern genomics. This has offered many benefits while creating a few constraints. In the following opinion, we outline the history, properties, and pitfalls of the current human reference genome. In a few illustrative analyses, we focus on its use for variant-calling, highlighting its nearness to a 'type specimen'. We suggest that switching to a consensus reference would offer important advantages over the continued use of the current reference with few disadvantages.

The long noncoding RNA ROCKI regulates inflammatory gene expression.

Long noncoding RNAs (lncRNAs) can regulate target gene expression by acting in cis (locally) or in trans (non-locally). Here, we performed genome-wide expression analysis of Toll-like receptor (TLR)-stimulated human macrophages to identify pairs of cis-acting lncRNAs and protein-coding genes involved in innate immunity. A total of 229 gene pairs were identified, many of which were commonly regulated by signaling through multiple TLRs and were involved in the cytokine responses to infection by group B Streptococcus We focused on elucidating the function of one lncRNA, named lnc-MARCKS or ROCKI (Regulator of Cytokines and Inflammation), which was induced by multiple TLR stimuli and acted as a master regulator of inflammatory responses. ROCKI interacted with APEX1 (apurinic/apyrimidinic endodeoxyribonuclease 1) to form a ribonucleoprotein complex at the MARCKS promoter. In turn, ROCKI-APEX1 recruited the histone deacetylase HDAC1, which removed the H3K27ac modification from the promoter, thus reducing MARCKS transcription and subsequent Ca2+ signaling and inflammatory gene expression. Finally, genetic variants affecting ROCKI expression were linked to a reduced risk of certain inflammatory and infectious disease in humans, including inflammatory bowel disease and tuberculosis. Collectively, these data highlight the importance of cis-acting lncRNAs in TLR signaling, innate immunity, and pathophysiological inflammation.

The fractured landscape of RNA-seq alignment: the default in our STARs.

Many tools are available for RNA-seq alignment and expression quantification, with comparative value being hard to establish. Benchmarking assessments often highlight methods' good performance, but are focused on either model data or fail to explain variation in performance. This leaves us to ask, what is the most meaningful way to assess different alignment choices? And importantly, where is there room for progress? In this work, we explore the answers to these two questions by performing an exhaustive assessment of the STAR aligner. We assess STAR's performance across a range of alignment parameters using common metrics, and then on biologically focused tasks. We find technical metrics such as fraction mapping or expression profile correlation to be uninformative, capturing properties unlikely to have any role in biological discovery. Surprisingly, we find that changes in alignment parameters within a wide range have little impact on both technical and biological performance. Yet, when performance finally does break, it happens in difficult regions, such as X-Y paralogs and MHC genes. We believe improved reporting by developers will help establish where results are likely to be robust or fragile, providing a better baseline to establish where methodological progress can still occur.

Optimizing RNA-Seq Mapping with STAR.

Recent advances in high-throughput sequencing technology made it possible to probe the cell transcriptomes by generating hundreds of millions of short reads which represent the fragments of the transcribed RNA molecules. The first and the most crucial task in the RNA-seq data analysis is mapping of the reads to the reference genome. STAR (Spliced Transcripts Alignment to a Reference) is an RNA-seq mapper that performs highly accurate spliced sequence alignment at an ultrafast speed. STAR alignment algorithm can be controlled by many user-defined parameters. Here, we describe the most important STAR options and parameters, as well as best practices for achieving the maximum mapping accuracy and speed.

A benchmark for RNA-seq quantification pipelines.

Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package ( http://bioconductor.org/packages/rnaseqcomp ). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.