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Raccoons (Procyon lotor) are worldwide invaders, due to deliberate or accidental releases, and their impacts exceed hundred of billions of Euros in Europe only. In France, raccoons have currently established three separate, expanding populations. Identifying the current spatial genetic structure, dispersal events and phylogeography of these populations is needed to infer the invasion history and identify management units. We used wild and captive individuals sampled in France and Belgium to characterize the genetic diversity and current population genetic structure of French raccoon populations and identify potential genetic connectivity with the Belgium population using both mitochondrial DNA and microsatellite loci. Results confirm that French populations are the result of at least three independent introductions. While the three populations display low genetic diversity and sign of recent bottleneck, they are still expanding, suggesting that in addition to their ecological plasticity, the remaining genetic diversity is sufficient to successfully adapt to their new environment and allow a quick colonization. Particular attention must be given to the North-Eastern population, which shows genetic admixture with the Belgium population, as admixed individuals may exhibit hybrid vigor facilitating their expansion. The comparison of captive and wild individuals did not allow to identify a potential captive origin of the wild populations. The current regulation in France allowing captivity in zoos without enforcement to tighten the biosecurity of detention facilities might dampen any management measure as few introduced founders might be enough to create new populations.

1. The American mink Neogale vison is an invasive alien species in Europe that threatens endemic biodiversity and can transmit zoonotic diseases, including the SARS-CoV-2 virus. The last attempt to map the geographic range of this species in Europe, at continental scale, dates back to 2007. 2. We aimed to update the distribution map of the feral American mink and assess its temporal trends. The information we collected was critically analysed with the aim of improving future monitoring protocols and data collection. 3. We gathered and standardised data from 34 databases, covering 32 countries. Through 3 five-year periods from 2007 to 2021, changes in range size, hunting bags and capture statistics were analysed. We also reviewed the current situation of mink farming in the different European countries and recorded population control schemes. 4. The American mink is now widespread in the Baltic States, France, Germany, Iceland, Ireland, Poland, Scandinavia, Spain and the UK. The species is reported to be absent in some areas (e.g. parts of the UK, Iceland and Norway). Data are deficient for several countries, mainly in south-eastern Europe. These findings indicate that, during the last 15 years, the species has continued to spread across the continent, increasing its potential extent of occurrence in most countries. Our effort to collect and harmonise data across international borders highlighted information gaps and heterogeneity in data quality. 5. Updated distribution data on the species provided here will aid risk assessment and risk management policies. These actions require a coordinated effort for population monitoring at continental level. Monitoring effort and data collection should be intensified in south-eastern Europe to improve data on the current distribution of this invasive species.

Detection dogs for wolf scat can assist in conservation-monitoring of wolves. In this pilot project, the first encounters of experienced detection dogs with wolf scat were documented. It was tested how a dog, trained with only a few scat samples (minimally four, maximally six), could generalise towards new unknown wolf scats and discriminate them from distracting odours. Four dogs all showed a behavioural expression of aversion upon first presentation with wolf scat during their initial training to fixate on wolf scat. The dog that showed the least aversion to wolf scats was trained during two weeks. His ability to discriminate wolf scat from other odours and to generalise to new wolf samples was evaluated. He was presented a line-up with new wolf scat, dog scat and other distracting odours in eight boxes, seven trials and random rotations of odours. The test was conducted blindly without the dog handler knowing if or where a positive sample was present. The dog showed partial generalisation of wolf scat odour from only a few known training samples to new wolf samples. The test was evaluated for methodological improvements.

One way in which invasive alien species affect their environment is by acting as pathogen hosts. Pathogens limited by the availability of the native host species can profit from the presence of additional hosts. The muskrat (Ondatra zibethicus) is known to act as an intermediate host for the fox tapeworm (Echinococcus multilocularis). From 2009 to 2017, 15,402 muskrats caught in Flanders and across the border with Wallonia and France were collected and dissected with the aim of understanding the prevalence of this parasite in muskrats. Visual examination of the livers revealed 202 infected animals (1.31%). Out of the 9421 animals caught in Flanders, we found 82 individuals (0.87%) infected with E. multilocularis. No increase in prevalence was observed during this study. All of the infected animals in Flanders were found in municipalities along the Walloon border. We did not observe a northward spread of E. multilocularis infection from Wallonia to Flanders. We hypothesise that the low prevalence is the result of the reduced availability of intermediate hosts and the successful control programme which is keeping muskrat densities in the centre of the region at low levels and is preventing influx from other areas. Our results illustrate that muskrats are good sentinels for E. multilocularis and regular screening can gain valuable insight into the spread of this zoonosis.

Ecosystem services are the contributions that natural ecosystems make to people. Mammalian carnivores can provide both positive and negative ecosystem services by consuming pest or game species, respectively. In this study, we determined the consumption of certain game and pest species for a guild of sympatric small- and medium-sized carnivores (European badger, red fox, European polecat, stone marten, stoat and least weasel). We found that game and pest species comprise a notable proportion of the diet, ranging from 0.0% to 12.6% and from 2.7% to 46.3%, respectively. Furthermore, for all carnivores we found that the proportion of pest species such as rats and voles (i.e., positive ecosystem service) was higher than the proportion of game species such as pheasants, partridges and hares, i.e., negative ecosystem service). Moreover, stoat and weasel did not consume any game species. This study gives a first overview of the potential ecosystem services through consumption of pest and game species provided by small- and medium-sized carnivores.

Technological developments have led to an explosion of high-throughput single cell data, which are revealing unprecedented perspectives on cell identity. Recently, significant attention has focused on investigating, from single-cell RNA-sequencing (scRNA-seq) data, cellular dynamic processes, such as cell differentiation, cell cycle and cell (de)activation. Trajectory inference methods estimate a trajectory, a collection of differentiation paths of a dynamic system, by ordering cells along the paths of such a dynamic process. While trajectory inference tools typically work with gene expression levels, common scRNA-seq protocols allow the identification and quantification of unspliced pre-mRNAs and mature spliced mRNAs, for each gene. By exploiting the abundance of unspliced and spliced mRNA, one can infer the RNA velocity of individual cells, i.e., the time derivative of the gene expression state of cells. Whereas traditional trajectory inference methods reconstruct cellular dynamics given a population of cells of varying maturity, RNA velocity relies on a dynamical model describing splicing dynamics. Here, we initially discuss conceptual and theoretical aspects of both approaches, then illustrate how they can be combined together, and finally present an example use-case on real data.

The salinity gradient separating marine and freshwater environments represents a major ecological divide for microbiota, yet the mechanisms by which marine microbes have adapted to and ultimately diversified in freshwater environments are poorly understood. Here, we take advantage of a natural evolutionary experiment: the colonization of the brackish Baltic Sea by the ancestrally marine diatom Skeletonema marinoi. To understand how diatoms respond to low salinity, we characterized transcriptomic responses of S. marinoi grown in a common garden. Our experiment included eight genotypes from source populations spanning the Baltic Sea salinity cline. Changes in gene expression revealed a shared response to salinity across genotypes, where low salinities induced profound changes in cellular metabolism, including upregulation of carbon fixation and storage compound biosynthesis, and increased nutrient demand and oxidative stress. Nevertheless, the genotype effect overshadowed the salinity effect, as genotypes differed significantly in their response, both in the magnitude and direction of gene expression. Intraspecific differences included regulation of transcription and translation, nitrogen metabolism, cell signaling, and aerobic respiration. The high degree of intraspecific variation in gene expression observed here highlights an important but often overlooked source of biological variation associated with how diatoms respond and adapt to environmental change.

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present a method for the estimation and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-seq dataset.While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations.In this manuscript, we present a method for the estimation and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

Modern assays have enabled high-throughput studies of epigenetic regulation of gene expression using DNA sequencing. In particular, the assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of chromatin configuration for an entire genome. Despite the gain in popularity of the assay, there have been limited studies investigating the analytical challenges related to ATAC-seq data, and most studies leverage tools developed for bulk transcriptome sequencing (RNA-seq). Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample-specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins, and evaluate it together with eleven different normalization procedures on eight public ATAC-seq datasets. Our work clearly shows that accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, leading to improved accuracy and interpretability of the results. Using two case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

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.

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 diatom Seminavis 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.

Since 2015 wolves more often visit the Netherlands and since 2019 the first pack was a fact including a litter with cubs, the first since 150 years. Although there is already a management plan for wolves, the authorities will start with an update in 2021. This present report contains the answers to 25 questions that were drafted by the authorities. The questions contain five themes: 1: distribution, presence, origin and carrying capacity; 2: behavior and wolf-human relations; 3: policy, damage, monitoring and management, 4: ecology and 5: molecular methods and international exchange of data. The knowledge that was used for answering the questions is based on facts, originating from ongoing monitoring and scientific research. Therefore it can be used to come to motivated policy decisions Vanaf 2015 komen er weer in toenemende mate wolven voor in Nederland en sinds 2019 is er weer sprake van een roedel met jongen, de eerste in circa 150 jaar. De overheid is voornemens om het huidige interprovinciaal Wolvenplan te herzien, waarmee in 2021 zal worden gestart. In dit voorliggende rapport wordt antwoord gegeven op 25 onderzoeksvragen die zijn opgesteld door de overheid. De vragen hebben betrekking op vijf thema’s, te weten (1) verspreiding, voorkomen, herkomst en ecologische draagkracht; (2) gedrag en wolf-mensrelatie; (3) beleid, schade, monitoring en beheer; (4) ecologie; (5) genetische methodieken en uitwisseling van data in Europa. Voor de beantwoording van deze vragen is gebruikgemaakt van de nieuwste inzichten vanuit wetenschappelijk onderzoek en monitoringsprogramma’s. De kennis die in dit rapport is verzameld, kan worden ingezet voor het maken van wetenschappelijk onderbouwde beleidskeuzes ten aanzien van de wolf.

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.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis., The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

Single-cell transcriptome sequencing (scRNA-Seq) has allowed many new types of investigations at unprecedented and unique levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into potentially novel cell types. Many approaches build on the existing clustering literature to develop tools specific to single-cell applications. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters identified. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist to select these tuning parameters, most of them are quite ad hoc. In general, there is little assurance that any given set of parameters will represent an optimal choice in the ever-present trade-off between cluster resolution and replicability. For instance, it may be the case that another set of parameters will result in more clusters that are also more replicable, or in fewer clusters that are also less replicable.Here, we propose a new method calledDunefor optimizing the trade-off between the resolution of the clusters and their replicability across datasets. Our method takes as input a set of clustering results on a single dataset, derived from any set of clustering algorithms and associated tuning parameters, and iteratively merges clusters within partitions in order to maximize their concordance between partitions. As demonstrated on a variety of scRNA-Seq datasets from different platforms,Duneoutperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters. It provides an objective approach for identifying replicable consensus clusters most likely to represent common biological features across multiple datasets.

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.

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.

The problem of community detection in networks is usually formulated as finding a single partition of the network into some “correct” number of communities. We argue that it is more interpretable and in some regimes more accurate to construct a hierarchical tree of communities instead. This can be done with a simple top-down recursive partitioning algorithm, starting with a single community and separating the nodes into two communities by spectral clustering repeatedly, until a stopping rule suggests there are no further communities. This class of algorithms is model-free, computationally efficient, and requires no tuning other than selecting a stopping rule. We show that there are regimes where this approach outperforms K-way spectral clustering, and propose a natural framework for analyzing the algorithm’s theoretical performance, the binary tree stochastic block model. Under this model, we prove that the algorithm correctly recovers the entire community tree under relatively mild assumptions. We apply the algorithm to a gene network based on gene co-occurrence in 1580 research papers on anemia, and identify six clusters of genes in a meaningful hierarchy. We also illustrate the algorithm on a dataset of statistics papers. Supplementary materials for this article are available online.