Conserved stromal-immune cell circuits secure B cell homeostasis and function.

B cell zone reticular cells (BRCs) form stable microenvironments that direct efficient humoral immunity with B cell priming and memory maintenance being orchestrated across lymphoid organs. However, a comprehensive understanding of systemic humoral immunity is hampered by the lack of knowledge of global BRC sustenance, function and major pathways controlling BRC-immune cell interactions. Here we dissected the BRC landscape and immune cell interactome in human and murine lymphoid organs. In addition to the major BRC subsets underpinning the follicle, including follicular dendritic cells, PI16+ RCs were present across organs and species. As well as BRC-produced niche factors, immune cell-driven BRC differentiation and activation programs governed the convergence of shared BRC subsets, overwriting tissue-specific gene signatures. Our data reveal that a canonical set of immune cell-provided cues enforce bidirectional signaling programs that sustain functional BRC niches across lymphoid organs and species, thereby securing efficient humoral immunity.

Convergence of multiple RNA-silencing pathways on GW182/TNRC6.

The RNA-binding protein TRIM71/LIN-41 is a phylogenetically conserved developmental regulator that functions in mammalian stem cell reprogramming, brain development, and cancer. TRIM71 recognizes target mRNAs through hairpin motifs and silences them through molecular mechanisms that await identification. Here, we uncover that TRIM71 represses its targets through RNA-supported interaction with TNRC6/GW182, a core component of the miRNA-induced silencing complex (miRISC). We demonstrate that AGO2, TRIM71, and UPF1 each recruit TNRC6 to specific sets of transcripts to silence them. As cellular TNRC6 levels are limiting, competition occurs among the silencing pathways, such that the loss of AGO proteins or of AGO binding to TNRC6 enhances the activities of the other pathways. We conclude that a miRNA-like silencing activity is shared among different mRNA silencing pathways and that the use of TNRC6 as a central hub provides a means to integrate their activities.

The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data.

The rapid growth of high-throughput single-cell and single-nucleus RNA-sequencing (scRNA-seq and snRNA-seq) technologies has produced a wealth of data over the past few years. The size, volume and distinctive characteristics of these data necessitate the development of new computational methods to accurately and efficiently quantify sc/snRNA-seq data into count matrices that constitute the input to downstream analyses. We introduce the alevin-fry framework for quantifying sc/snRNA-seq data. In addition to being faster and more memory frugal than other accurate quantification approaches, alevin-fry ameliorates the memory scalability and false-positive expression issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify sc/snRNA-seq data, and also how the spliced and unspliced molecule quantification required as input for RNA velocity analyses can be seamlessly extracted from the same preprocessed data used to generate normal gene expression count matrices.

A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data.

Interspecies RNA-Seq datasets are increasingly common, and have the potential to answer new questions about the evolution of gene expression. Single-species differential expression analysis is now a well-studied problem that benefits from sound statistical methods. Extensive reviews on biological or synthetic datasets have provided the community with a clear picture on the relative performances of the available methods in various settings. However, synthetic dataset simulation tools are still missing in the interspecies gene expression context. In this work, we develop and implement a new simulation framework. This tool builds on both the RNA-Seq and the phylogenetic comparative methods literatures to generate realistic count datasets, while taking into account the phylogenetic relationships between the samples. We illustrate the usefulness of this new framework through a targeted simulation study, that reproduces the features of a recently published dataset, containing gene expression data in adult eye tissue across blind and sighted freshwater crayfish species. Using our simulated datasets, we perform a fair comparison of several approaches used for differential expression analysis. This benchmark reveals some of the strengths and weaknesses of both the classical and phylogenetic approaches for interspecies differential expression analysis, and allows for a reanalysis of the crayfish dataset. The tool has been integrated in the R package compcodeR, freely available on Bioconductor.

Orchestrating single-cell analysis with Bioconductor.

Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users.

Publisher Correction: Orchestrating single-cell analysis with Bioconductor.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

monaLisa: an R/Bioconductor package for identifying regulatory motifs.

SUMMARY: Proteins binding to specific nucleotide sequences, such as transcription factors, play key roles in the regulation of gene expression. Their binding can be indirectly observed via associated changes in transcription, chromatin accessibility, DNA methylation and histone modifications. Identifying candidate factors that are responsible for these observed experimental changes is critical to understand the underlying biological processes. Here, we present monaLisa, an R/Bioconductor package that implements approaches to identify relevant transcription factors from experimental data. The package can be easily integrated with other Bioconductor packages and enables seamless motif analyses without any software dependencies outside of R. AVAILABILITY AND IMPLEMENTATION: monaLisa is implemented in R and available on Bioconductor at https://bioconductor.org/packages/monaLisa with the development version hosted on GitHub at https://github.com/fmicompbio/monaLisa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MiR-CLIP reveals iso-miR selective regulation in the miR-124 targetome.

Many microRNAs regulate gene expression via atypical mechanisms, which are difficult to discern using native cross-linking methods. To ascertain the scope of non-canonical miRNA targeting, methods are needed that identify all targets of a given miRNA. We designed a new class of miR-CLIP probe, whereby psoralen is conjugated to the 3p arm of a pre-microRNA to capture targetomes of miR-124 and miR-132 in HEK293T cells. Processing of pre-miR-124 yields miR-124 and a 5'-extended isoform, iso-miR-124. Using miR-CLIP, we identified overlapping targetomes from both isoforms. From a set of 16 targets, 13 were differently inhibited at mRNA/protein levels by the isoforms. Moreover, delivery of pre-miR-124 into cells repressed these targets more strongly than individual treatments with miR-124 and iso-miR-124, suggesting that isomirs from one pre-miRNA may function synergistically. By mining the miR-CLIP targetome, we identified nine G-bulged target-sites that are regulated at the protein level by miR-124 but not isomiR-124. Using structural data, we propose a model involving AGO2 helix-7 that suggests why only miR-124 can engage these sites. In summary, access to the miR-124 targetome via miR-CLIP revealed for the first time how heterogeneous processing of miRNAs combined with non-canonical targeting mechanisms expand the regulatory range of a miRNA.

Preprocessing choices affect RNA velocity results for droplet scRNA-seq data.

Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a 'direction of change' and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare five widely used quantification tools, in total yielding thirteen different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in five experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration.

Bias, robustness and scalability in single-cell differential expression analysis.

Many methods have been used to determine differential gene expression from single-cell RNA (scRNA)-seq data. We evaluated 36 approaches using experimental and synthetic data and found considerable differences in the number and characteristics of the genes that are called differentially expressed. Prefiltering of lowly expressed genes has important effects, particularly for some of the methods developed for bulk RNA-seq data analysis. However, we found that bulk RNA-seq analysis methods do not generally perform worse than those developed specifically for scRNA-seq. We also present conquer, a repository of consistently processed, analysis-ready public scRNA-seq data sets that is aimed at simplifying method evaluation and reanalysis of published results. Each data set provides abundance estimates for both genes and transcripts, as well as quality control and exploratory analysis reports.

Tximeta: Reference sequence checksums for provenance identification in RNA-seq.

Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets. We provide a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility. The tximeta package is available at https://bioconductor.org/packages/tximeta.

Faithful mRNA splicing depends on the Prp19 complex subunit faint sausage and is required for tracheal branching morphogenesis in Drosophila.

Morphogenesis requires the dynamic regulation of gene expression, including transcription, mRNA maturation and translation. Dysfunction of the general mRNA splicing machinery can cause surprisingly specific cellular phenotypes, but the basis for these effects is not clear. Here, we show that the Drosophila faint sausage (fas) locus, which is implicated in epithelial morphogenesis and has previously been reported to encode a secreted immunoglobulin domain protein, in fact encodes a subunit of the spliceosome-activating Prp19 complex, which is essential for efficient pre-mRNA splicing. Loss of zygotic fas function globally impairs the efficiency of splicing, and is associated with widespread retention of introns in mRNAs and dramatic changes in gene expression. Surprisingly, despite these general effects, zygotic fas mutants show specific defects in tracheal cell migration during mid-embryogenesis when maternally supplied splicing factors have declined. We propose that tracheal branching, which relies on dynamic changes in gene expression, is particularly sensitive for efficient spliceosome function. Our results reveal an entry point to study requirements of the splicing machinery during organogenesis and provide a better understanding of disease phenotypes associated with mutations in general splicing factors.

Fission yeast Cactin restricts telomere transcription and elongation by controlling Rap1 levels.

The telomeric transcriptome comprises multiple long non-coding RNAs generated by transcription of linear chromosome ends. In a screening performed in Schizosaccharomyces pombe, we identified factors modulating the cellular levels of the telomeric transcriptome. Among these factors, Cay1 is the fission yeast member of the conserved family of Cactins, uncharacterized proteins crucial for cell growth and survival. In cay1∆ mutants, the cellular levels of the telomeric factor Rap1 are drastically diminished due to defects in rap1+ pre-mRNA splicing and Rap1 protein stability. cay1∆ cells accumulate histone H3 acetylated at lysine 9 at telomeres, which become transcriptionally desilenced, are over-elongated by telomerase and cause chromosomal aberrations in the cold. Overexpressing Rap1 in cay1+ deleted cells significantly reverts all telomeric defects. Additionally, cay1∆ mutants accumulate unprocessed Tf2 retrotransposon RNA through Rap1-independent mechanisms. Thus, Cay1 plays crucial roles in cells by ultimately harmonizing expression of transcripts originating from seemingly unrelated genomic loci.

Human cactin interacts with DHX8 and SRRM2 to assure efficient pre-mRNA splicing and sister chromatid cohesion.

Cactins constitute a family of eukaryotic proteins broadly conserved from yeast to human and required for fundamental processes such as cell proliferation, genome stability maintenance, organismal development and immune response. Cactin proteins have been found to associate with the spliceosome in several model organisms, nevertheless their molecular functions await elucidation. Here we show that depletion of human cactin leads to premature sister chromatid separation, genome instability and cell proliferation arrest. Moreover, cactin is essential for efficient splicing of thousands of pre-mRNAs, and incomplete splicing of the pre-mRNA of sororin (also known as CDCA5), a cohesin-associated factor, is largely responsible for the aberrant chromatid separation in cactin-depleted cells. Lastly, cactin physically and functionally interacts with the spliceosome-associated factors DHX8 and SRRM2. We propose that cellular complexes comprising cactin, DHX8 and SRRM2 sustain precise chromosome segregation, genome stability and cell proliferation by allowing faithful splicing of specific pre-mRNAs. Our data point to novel pathways of gene expression regulation dependent on cactin, and provide an explanation for the pleiotropic dysfunctions deriving from cactin inactivation in distant eukaryotes.

Towards unified quality verification of synthetic count data with countsimQC.

Summary: Statistical tools for biological data analysis are often evaluated using synthetic data, designed to mimic the features of a specific type of experimental data. The generalizability of such evaluations depends on how well the synthetic data reproduce the main characteristics of the experimental data, and we argue that an assessment of this similarity should accompany any synthetic dataset used for method evaluation. We describe countsimQC, which provides a straightforward way to generate a stand-alone report that shows the main characteristics of (e.g. RNA-seq) count data and can be provided alongside a publication as verification of the appropriateness of any utilized synthetic data. Availability and implementation: countsimQC is implemented as an R package (for R versions ≥ 3.4) and is available from https://github.com/csoneson/countsimQC under a GPL (≥2) license. Contact: charlotte.soneson@uzh.ch or mark.robinson@imls.uzh.ch.

Increased ex vivo antigen presentation profile of B cells in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is thought to be T cell mediated but the mechanisms eliciting such a dysregulated adaptative immune response remain enigmatic. OBJECTIVE: To examine the activation profile of antigen-presenting cells (APCs) in MS. METHODS: A total of 98 study subjects were enrolled including patients suffering from relapsing-remitting, secondary- and primary-progressive (PP) MS, other inflammatory neurological diseases, and healthy controls. Blood monocytes and B cells were stimulated using specific ligands of toll-like receptors (TLRs) or inflammasomes or Epstein-Barr virus (EBV) particles. Their activation profile was determined before or after stimulation by flow cytometry (CD40, CD80, CD83, CD86, and human leukocyte antigen-antigen D related (HLA-DR)) and Luminex assay, measuring the concentration of eight cytokines in culture supernatants. Differences among groups were assessed in a linear model framework. RESULTS: We demonstrate that relapsing MS patients exhibit an increased expression of HLA-DR and CD40 ex vivo, mostly at the surface of B cells. Specific stimulations of TLR or inflammasomes enhance the expression of components of the immunological synapse and the cytokine secretion but without differences between categories of study subjects. CONCLUSION: These data suggest that the activation profile of B cells is increased in MS. However, the perception of the danger signal by B lymphocytes and monocytes does not seem to be different in MS patients as compared to control subjects.

A glioma classification scheme based on coexpression modules of EGFR and PDGFRA.

We hypothesized that key signaling pathways of glioma genesis might enable the molecular classification of gliomas. Gene coexpression modules around epidermal growth factor receptor (EGFR) (EM, 29 genes) or platelet derived growth factor receptor A (PDGFRA) (PM, 40 genes) in gliomas were identified. Based on EM and PM expression signatures, nonnegative matrix factorization reproducibly clustered 1,369 adult diffuse gliomas WHO grades II-IV from four independent databases generated in three continents, into the subtypes (EM, PM and EM(low)PM(low) gliomas) in a morphology-independent manner. Besides their distinct patterns of genomic alterations, EM gliomas were associated with higher age at diagnosis, poorer prognosis, and stronger expression of neural stem cell and astrogenesis genes. Both PM and EM(low)PM(low) gliomas were associated with younger age at diagnosis and better prognosis. PM gliomas were enriched in the expression of oligodendrogenesis genes, whereas EM(low)PM(low) gliomas were enriched in the signatures of mature neurons and oligodendrocytes. The EM/PM-based molecular classification scheme is applicable to adult low-grade and high-grade diffuse gliomas, and outperforms existing classification schemes in assigning diffuse gliomas to subtypes with distinct transcriptomic and genomic profiles. The majority of the EM/PM classifiers, including regulators of glial fate decisions, have not been extensively studied in glioma biology. Subsets of these classifiers were coexpressed in mouse glial precursor cells, and frequently amplified or lost in an EM/PM glioma subtype-specific manner, resulting in somatic copy number alteration-dependent gene expression that contributes to EM/PM signatures in glioma samples. EM/PM-based molecular classification provides a molecular diagnostic framework to expedite the search for new glioma therapeutic targets.

Serum neurofilament light chain in early relapsing remitting MS is increased and correlates with CSF levels and with MRI measures of disease severity.

BACKGROUND/OBJECTIVES: Neurofilament light chain (NfL) levels in the cerebrospinal fluid (CSF) of multiple sclerosis (MS) patients correlate with the degree of neuronal injury. To date, little is known about NfL concentrations in the serum of relapsing remitting multiple sclerosis (RRMS) patients and their relationship with CSF levels and magnetic resonance imaging (MRI) measures of disease severity. We aimed to validate the quantification of NfL in serum samples of RRMS, as a biofluid source easily accessible for longitudinal studies. METHODS: A total of 31 RRMS patients underwent CSF and serum sampling. After a median time of 3.6 years, 19 of these RRMS patients, 10 newly recruited RRMS patients and 18 healthy controls had a 3T MRI and serum sampling. NfL concentrations were determined by electrochemiluminescence immunoassay. RESULTS: NfL levels in serum were highly correlated to levels in CSF (r = 0.62, p = 0.0002). Concentrations in serum were higher in patients than in controls at baseline (p = 0.004) and follow-up (p = 0.0009) and did not change over time (p = 0.56). Serum NfL levels correlated with white matter (WM) lesion volume (r = 0.68, p < 0.0001), mean T1 (r = 0.40, p = 0.034) and T2* relaxation time (r = 0.49, p = 0.007) and with magnetization transfer ratio in normal appearing WM (r = -0.41, p = 0.029). CONCLUSION: CSF and serum NfL levels were highly correlated, and serum concentrations were increased in RRMS. Serum NfL levels correlated with MRI markers of WM disease severity. Our findings further support longitudinal studies of serum NfL as a potential biomarker of on-going disease progression and as a potential surrogate to quantify effects of neuroprotective drugs in clinical trials.