propeller: testing for differences in cell type proportions in single cell data

Single cell RNA Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. To date, there are more than a thousand software packages that have been developed to analyse scRNA-seq data. These focus predominantly on visualization, dimensionality reduction and cell type identification. Single cell technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments which has not been possible to address with bulk RNA-seq data is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportions estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. We present propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. The propeller method is publicly available in the open source speckle R package (https://github.com/Oshlack/speckle).

Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream-associated vertebrate species

Fast, reliable, and comprehensive biodiversity monitoring data are needed for environmental decision making and management. Recent work on fish environmental DNA (eDNA) metabarcoding shows that aquatic diversity can be captured fast, reliably, and non-invasively at moderate costs. Because freshwater ecosystems act as sinks in the landscape, they also collect traces of terrestrial species via surface runoff or when specimens come into direct contact with water (e.g., for drinking purposes). Thus, fish eDNA metabarcoding data can provide information on fish but also on other, even terrestrial vertebrate species that live in riparian habitats. This data become available and may offer a much more comprehensive approach for assessing vertebrate diversity at no additional costs. Studies on how the sampling strategy affects species detection especially of stream-associated communities, however, are scarce. We therefore performed an analysis on the effects of biological replication on both fish as well as (semi-)terrestrial species detection. Along a 2-km stretch of the river Mulde (Germany), we collected 18 1L water samples and analyzed the relation of detected species richness and quantity of biological replicates taken. We detected 58 vertebrate species, of which 25 were fish and lamprey, 18 mammals, and 15 birds, which account for 50%, 24% and 7% of all native species to the German federal state of Saxony-Anhalt. However, while increasing the number of biological replicates resulted in only 25 % more detected fish and lamprey species, mammal and bird species richness increased disproportionately by 69 % and 84 %, respectively. Contrary, PCR replicates showed little stochasticity. We thus emphasize to increase the number of biological replicates when the aim is to improve general species detections. This holds especially true, when the focus is on rare aquatic taxa or on (semi-)terrestrial species, the so-called ‘bycatch’. As a clear advantage, this information can be obtained without any additional sampling or laboratory effort when the sampling strategy regarding biological replication is chosen carefully. With the consideration of frequent eDNA metabarcoding as part of national biomonitoring programs, the additional information provided by the bycatch can be used to further investigate the state of the environment and its biodiversity on a much broader scale.

Methods for discovering genomic loci exhibiting complex patterns of differential methylation

Cytosine methylation is widespread in most eukaryotic genomes and is known to play a substantial role in various regulatory pathways. Unmethylated cytosines may be converted to uracil through the addition of sodium bisulphite, allowing genome-wide quantification of cytosine methylation via high-throughput sequencing. The data thus acquired allows the discovery of methylation ‘loci’; contiguous regions of methylation consistently methylated across biological replicates. The mapping of these loci allows for associations with other genomic factors to be identified, and for analyses of differential methylation to take place.The segmentSeq R package is extended to identify methylation loci from high-throughput sequencing data from multiple experimental conditions. A statistical model is then developed that accounts for biological replication and variable rates of non-conversion of cytosines in each sample to compute posterior likelihoods of methylation at each locus within an empirical Bayesian framework. The same model is used as a basis for analysis of differential methylation between multiple experimental conditions with the baySeq R package. We demonstrate this method through an analysis of data derived from Dicer-like mutants in Arabidopsis that reveals complex interactions between the different Dicer-like mutants and their methylation pathways. We also show in simulation studies that this approach can be significantly more powerful in the detection of differential methylation than existing methods.

Replication is Recursion; or, Lambda: the Biological Imperative

There is a striking visual symmetry between the 'paradoxical combinator' (which implements recursion in the mathematical theory of computation) and the biological replication fork (which implements reproduction in the living cell). Can this mean anything? Living organisms are information processors – the genome encodes instructions for the process of life much as computer programs encode instructions for a computer. Replication in biological systems is intuitively similar to recursion in computational systems. The following discussion will present enough of the mathematics to allow biologists to make sense of the symmetries in the logo. Mathematicians will learn nothing new about biology, but may be encouraged to look at biological processes from a new perspective.

Gestation Related Gene Expression of the Endocannabinoid Pathway in Rat Placenta

Mammalian placentation is a vital facet of the development of a healthy and viable offspring. Throughout gestation the placenta changes to accommodate, provide for, and meet the demands of a growing fetus. Gestational gene expression is a crucial part of placenta development. The endocannabinoid pathway is activated in the placenta and decidual tissues throughout pregnancy and aberrant endocannabinoid signaling during the period of placental development has been associated with pregnancy disorders. In this study, the gene expression of eight endocannabinoid system enzymes was investigated throughout gestation. Rat placentae were obtained at E14.25, E15.25, E17.25, and E20, RNA was extracted, and microarray was performed. Gene expression of enzymesFaah, Mgll, Plcd4, Pld1, Nat1, Daglα, andPtgs2was studied (cohort 1, microarray). Biological replication of the results was performed by qPCR (cohort 2). Four genes showed differential expression (Mgll, Plcd4, Ptgs2, and Pld1), from mid to late gestation. Genes positively associated with gestational age werePtgs2, Mgll, and Pld1, whilePlcd4was downregulated. This is the first comprehensive study that has investigated endocannabinoid pathway gene expression during rat pregnancy. This study provides the framework for future studies that investigate the role of endocannabinoid system during pregnancy.