satuRn : Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive scRNA-seq data. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs and scaling to scRNA-seq applications

satuRn: Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive scRNA-seq data. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs and scaling to scRNA-seq applications

Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data

The problem of estimating the structure of a graph from observed data is of growing interest in the context of high-throughput genomic data, and single-cell RNA sequencing in particular. These, however, are challenging applications, since the data consist of high-dimensional counts with high variance and over-abundance of zeros. Here, we present a general framework for learning the structure of a graph from single-cell RNA-seq data, based on the zero-inflated negative binomial distribution. We demonstrate with simulations that our approach is able to retrieve the structure of a graph in a variety of settings and we show the utility of the approach on real data

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene-and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq

Altering the sex pheromone cyclo(L-Pro-L-Pro) of the diatom Seminavis robusta towards a chemical probe

As a major group of algae, diatoms are responsible for a substantial part of the primary production on the planet. Pennate diatoms have a predominantly benthic lifestyle and are the most species-rich diatom group, with members of the raphid clades being motile and generally having heterothallic sexual reproduction. It was recently shown that the model species Seminavis robusta uses multiple sexual cues during mating, including cyclo(l-Pro-l-Pro) as an attraction pheromone. Elaboration of the pheromone-detection system is a key aspect in elucidating pennate diatom life-cycle regulation that could yield novel fundamental insights into diatom speciation. This study reports the synthesis and bio-evaluation of seven novel pheromone analogs containing small structural alterations to the cyclo(l-Pro-l-Pro) pheromone. Toxicity, attraction, and interference assays were applied to assess their potential activity as a pheromone. Most of our analogs show a moderate-to-good bioactivity and low-to-no phytotoxicity. The pheromone activity of azide- and diazirine-containing analogs was unaffected and induced a similar mating behavior as the natural pheromone. These results demonstrate that the introduction of confined structural modifications can be used to develop a chemical probe based on the diazirine- and/or azide-containing analogs to study the pheromone-detection system of S. robusta

Mating type specific transcriptomic response to sex inducing pheromone in the pennate diatom Seminavis robusta

Sexual reproduction is a fundamental phase in the life cycle of most diatoms. Despite its role as a source of genetic variation, it is rarely reported in natural circumstances and its molecular foundations remain largely unknown. Here, we integrate independent transcriptomic datasets to prioritize genes responding to sex inducing pheromones (SIPs) in the pennate diatomSeminavis robusta. We observe marked gene expression changes associated with SIP treatment in both mating types, including an inhibition of S phase progression, chloroplast division, mitosis, and cell wall formation. Meanwhile, meiotic genes are upregulated in response to SIP, including a sexually induced diatom specific cyclin. Our data further suggest an important role for reactive oxygen species, energy metabolism, and cGMP signaling during the early stages of sexual reproduction. In addition, we identify several genes with a mating type specific response to SIP, and link their expression pattern with physiological specialization, such as the production of the attraction pheromone diproline in mating type - (MT-) and mate-searching behavior in mating type + (MT+). Combined, our results provide a model for early sexual reproduction in pennate diatoms and significantly expand the suite of target genes to detect sexual reproduction events in natural diatom populations

Distinctive growth and transcriptional changes of the diatom Seminavis robusta in response to quorum sensing related compounds

In aquatic habitats, diatoms are frequently found in association with Proteobacteria, many members of which employ cell-to-cell communication via N-acyl homoserine lactones (AHLs). It has been suggested that diatoms could distinguish between beneficial and algicidal bacteria in their surroundings by sensing AHLs. Although some microalgae can interfere with AHL signaling, e.g., by releasing AHL mimics or degrading them, molecular responses to AHLs in microalgae are still unclear. Therefore, we tested the effects of short-chained AHLs, i.e., N-hexanoyl homoserine lactone (C6-HSL), N-3-hydroxyhexanoyl homoserine lactone (OH-C6-HSL), and N-3-oxohexanoyl homoserine lactone (oxo-C6-HSL) and long-chained AHLs, i.e., N-tetradecanoyl homoserine lactone (C14-HSL), N-3-hydroxytetradecanoyl homoserine lactone (OH-C14-HSL), and N-3-oxotetradecanoyl homoserine lactone (oxo-C14-HSL), on growth of the benthic diatom Seminavis robusta. All tested short-chained AHLs did not affect diatom growth, while long-chained AHLs promoted (C14-HSL) or inhibited (OH-C14-HSL and oxo-C14-HSL) growth. To investigate the physiological effects of these long-chained AHLs in more detail, an RNA-seq experiment was performed during which S. robusta was treated with the growth-promoting C14-HSL and the growth-inhibiting oxo-C14-HSL. One tetramic acid was also tested (TA14), a structural rearrangement product of oxo-C14-HSL, which also induced growth inhibition in S. robusta. After 3 days of treatment, analysis revealed that 3,410 genes were differentially expressed in response to at least one of the compounds. In the treatment with the growth-promoting C14-HSL many genes involved in intracellular signaling were upregulated. On the other hand, exposure to growth-inhibiting oxo-C14-HSL and TA14 triggered a switch in lipid metabolism towards increased fatty acid degradation. In addition, oxo-C14-HSL led to downregulation of cell cycle genes, which is in agreement with the stagnation of cell growth in this treatment. Combined, our results indicate that bacterial signaling molecules with high structural similarity induce contrasting physiological responses in S. robusta