Rbm8a deficiency causes hematopoietic defects by modulating Wnt/PCP signaling.

Defects in blood development frequently occur among syndromic congenital anomalies. Thrombocytopenia-Absent Radius (TAR) syndrome is a rare congenital condition with reduced platelets (hypomegakaryocytic thrombocytopenia) and forelimb anomalies, concurrent with more variable heart and kidney defects. TAR syndrome associates with hypomorphic gene function for RBM8A/Y14 that encodes a component of the exon junction complex involved in mRNA splicing, transport, and nonsense-mediated decay. How perturbing a general mRNA-processing factor causes the selective TAR Syndrome phenotypes remains unknown. Here, we connect zebrafish rbm8a perturbation to early hematopoietic defects via attenuated non-canonical Wnt/Planar Cell Polarity (PCP) signaling that controls developmental cell re-arrangements. In hypomorphic rbm8a zebrafish, we observe a significant reduction of cd41-positive thrombocytes. rbm8a-mutant zebrafish embryos accumulate mRNAs with individual retained introns, a hallmark of defective nonsense-mediated decay; affected mRNAs include transcripts for non-canonical Wnt/PCP pathway components. We establish that rbm8a-mutant embryos show convergent extension defects and that reduced rbm8a function interacts with perturbations in non-canonical Wnt/PCP pathway genes wnt5b, wnt11f2, fzd7a, and vangl2. Using live-imaging, we found reduced rbm8a function impairs the architecture of the lateral plate mesoderm (LPM) that forms hematopoietic, cardiovascular, kidney, and forelimb skeleton progenitors as affected in TAR Syndrome. Both mutants for rbm8a and for the PCP gene vangl2 feature impaired expression of early hematopoietic/endothelial genes including runx1 and the megakaryocyte regulator gfi1aa. Together, our data propose aberrant LPM patterning and hematopoietic defects as consequence of attenuated non-canonical Wnt/PCP signaling upon reduced rbm8a function. These results also link TAR Syndrome to a potential LPM origin and a developmental mechanism.

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

Motivation: Although transcriptomics data is typically used to analyse 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, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Results: 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, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. Availability and implementation: DifferentialRegulation is distributed as a Bioconductor R package.

mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data.

Multiplexed assays of variant effect (MAVE) experimentally measure the effect of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. The core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan .

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.

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability.

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, and neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.

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.

The shaky foundations of simulating single-cell RNA sequencing data.

BACKGROUND: With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyze aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant-on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task and often use simulated data that provide a ground truth for evaluations, thus demanding a high quality standard results credible and transferable to real data. RESULTS: Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity. CONCLUSIONS: Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects, they yield over-optimistic performance of integration and potentially unreliable ranking of clustering methods, and it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.

Understanding and evaluating ambiguity in single-cell and single-nucleus RNA-sequencing.

Recently, a new modification has been proposed by Hjörleifsson and Sullivan et al. to the model used to classify the splicing status of reads (as spliced (mature), unspliced (nascent), or ambiguous) in single-cell and single-nucleus RNA-seq data. Here, we evaluate both the theoretical basis and practical implementation of the proposed method. The proposed method is highly-conservative, and therefore, unlikely to mischaracterize reads as spliced (mature) or unspliced (nascent) when they are not. However, we find that it leaves a large fraction of reads classified as ambiguous, and, in practice, allocates these ambiguous reads in an all-or-nothing manner, and differently between single-cell and single-nucleus RNA-seq data. Further, as implemented in practice, the ambiguous classification is implicit and based on the index against which the reads are mapped, which leads to several drawbacks compared to methods that consider both spliced (mature) and unspliced (nascent) mapping targets simultaneously - for example, the ability to use confidently assigned reads to rescue ambiguous reads based on shared UMIs and gene targets. Nonetheless, we show that these conservative assignment rules can be obtained directly in existing approaches simply by altering the set of targets that are indexed. To this end, we introduce the spliceu reference and show that its use with alevin-fry recapitulates the more conservative proposed classification. We also observe that, on experimental data, and under the proposed allocation rules for ambiguous UMIs, the difference between the proposed classification scheme and existing conventions appears much smaller than previously reported. We demonstrate the use of the new piscem index for mapping simultaneously against spliced (mature) and unspliced (nascent) targets, allowing classification against the full nascent and mature transcriptome in human or mouse in <3GB of memory. Finally, we discuss the potential of incorporating probabilistic evidence into the inference of splicing status, and suggest that it may provide benefits beyond what can be obtained from discrete classification of UMIs as splicing-ambiguous.

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.

Hand2 delineates mesothelium progenitors and is reactivated in mesothelioma.

The mesothelium lines body cavities and surrounds internal organs, widely contributing to homeostasis and regeneration. Mesothelium disruptions cause visceral anomalies and mesothelioma tumors. Nonetheless, the embryonic emergence of mesothelia remains incompletely understood. Here, we track mesothelial origins in the lateral plate mesoderm (LPM) using zebrafish. Single-cell transcriptomics uncovers a post-gastrulation gene expression signature centered on hand2 in distinct LPM progenitor cells. We map mesothelial progenitors to lateral-most, hand2-expressing LPM and confirm conservation in mouse. Time-lapse imaging of zebrafish hand2 reporter embryos captures mesothelium formation including pericardium, visceral, and parietal peritoneum. We find primordial germ cells migrate with the forming mesothelium as ventral migration boundary. Functionally, hand2 loss disrupts mesothelium formation with reduced progenitor cells and perturbed migration. In mouse and human mesothelioma, we document expression of LPM-associated transcription factors including Hand2, suggesting re-initiation of a developmental program. Our data connects mesothelium development to Hand2, expanding our understanding of mesothelial pathologies.

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.

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.

Mass cytometric and transcriptomic profiling of epithelial-mesenchymal transitions in human mammary cell lines.

Epithelial-mesenchymal transition (EMT) equips breast cancer cells for metastasis and treatment resistance. However, detection, inhibition, and elimination of EMT-undergoing cells is challenging due to the intrinsic heterogeneity of cancer cells and the phenotypic diversity of EMT programs. We comprehensively profiled EMT transition phenotypes in four non-cancerous human mammary epithelial cell lines using a flow cytometry surface marker screen, RNA sequencing, and mass cytometry. EMT was induced in the HMLE and MCF10A cell lines and in the HMLE-Twist-ER and HMLE-Snail-ER cell lines by prolonged exposure to TGFß1 or 4-hydroxytamoxifen, respectively. Each cell line exhibited a spectrum of EMT transition phenotypes, which we compared to the steady-state phenotypes of fifteen luminal, HER2-positive, and basal breast cancer cell lines. Our data provide multiparametric insights at single-cell level into the phenotypic diversity of EMT at different time points and in four human cellular models. These insights are valuable to better understand the complexity of EMT, to compare EMT transitions between the cellular models used here, and for the design of EMT time course experiments.

treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses.

treeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data.

A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although there are various tools and methods to correct for batch effects, their performance can vary. Therefore, it is important to understand how batch effects manifest to adjust for them. Here, we systematically explore batch effects across various scRNA-seq datasets according to magnitude, cell type specificity, and complexity. We developed a cell-specific mixing score (cms) that quantifies mixing of cells from multiple batches. By considering distance distributions, the score is able to detect local batch bias as well as differentiate between unbalanced batches and systematic differences between cells of the same cell type. We compare metrics in scRNA-seq data using real and synthetic datasets and whereas these metrics target the same question and are used interchangeably, we find differences in scalability, sensitivity, and ability to handle differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.

A unique bipartite Polycomb signature regulates stimulus-response transcription during development.

Rapid cellular responses to environmental stimuli are fundamental for development and maturation. Immediate early genes can be transcriptionally induced within minutes in response to a variety of signals. How their induction levels are regulated and their untimely activation by spurious signals prevented during development is poorly understood. We found that in developing sensory neurons, before perinatal sensory-activity-dependent induction, immediate early genes are embedded into a unique bipartite Polycomb chromatin signature, carrying active H3K27ac on promoters but repressive Ezh2-dependent H3K27me3 on gene bodies. This bipartite signature is widely present in developing cell types, including embryonic stem cells. Polycomb marking of gene bodies inhibits mRNA elongation, dampening productive transcription, while still allowing for fast stimulus-dependent mark removal and bipartite gene induction. We reveal a developmental epigenetic mechanism regulating the rapidity and amplitude of the transcriptional response to relevant stimuli, while preventing inappropriate activation of stimulus-response genes.

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.

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.

muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data.

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.

ExploreModelMatrix: Interactive exploration for improved understanding of design matrices and linear models in R.

Linear and generalized linear models are used extensively in many scientific fields, to model observed data and as the basis for hypothesis tests. The use of such models requires specification of a design matrix, and subsequent formulation of contrasts representing scientific hypotheses of interest. Proper execution of these steps requires a thorough understanding of the meaning of the individual coefficients, and is a frequent source of uncertainty for end users. Here, we present an R/Bioconductor package, ExploreModelMatrix, which enables interactive exploration of design matrices and linear model diagnostics. Given a sample data table and a desired design formula, the package displays how the model coefficients are combined to give the fitted values for each combination of predictor variables, which allows users to both extract the interpretation of each individual coefficient, and formulate desired linear contrasts. In addition, the interactive interface displays informative characteristics for the regular linear model corresponding to the provided design, such as variance inflation factors and the pseudoinverse of the design matrix. We envision the package and the built-in collection of common types of linear model designs to be useful for teaching and self-learning purposes, as well as for assisting more experienced users in the interpretation of complex model designs.