Alternative splicing and single-cell RNA-sequencing: a feasibility assessment

We know little about how isoform choice is regulated in individual cells for most spliced genes. In theory, single-cell RNA-sequencing (scRNA-seq) could enable us to investigate isoform choice at cellular resolution. Therefore, scRNA-seq could give insight into the fundamental molecular biology process of how alternative splicing is regulated within cells. However, scRNA-seq is a relatively new technology, and at the start of my PhD it was not clear whether existing bioinformatics approaches would enable accurate splicing analyses. In my PhD I consider what the limitations are when attempting to study alternative splicing using scRNA-seq and what can be done to overcome them. Alternative splicing is commonly analysed using bulk RNA sequencing (bulk RNA-seq) data with isoform quantification software. It was not clear whether isoform quantification software designed for bulk RNA-seq would perform well when run on scRNA-seq data. To address this, I performed a simulation-based benchmark of isoform quantification software developed for bulk RNA-seq when run on scRNA-seq. I made two important findings. Firstly, I found that isoform quantification software performs poorly when run on Drop-seq data, but performs better when run on scRNA-seq data generated using full-length transcript protocols (eg. SMART-seq and SMART-seq2). Secondly, I found that for the most part, isoform quantification software performs almost as well when run on full-length scRNA-seq as it does when run on bulk RNA-seq. Based on these findings, I concluded that software tools to accurately quantify the reads from full-length scRNA-seq experiments exist, theoretically enabling alternative splicing to be analysed using scRNA-seq. Encouraged by this result, I embarked on a series of experiments designed to answer questions such as ‘How many isoforms does a gene typically produce per cell?’. This is a key basic biology question that could in theory be answered using scRNA-seq. Unfortunately, I found that the results of these experiments were largely impossible to interpret because I was unable to distinguish between biological signal and technical noise. I realised that without a solid understanding of the technical noise and confounding factors associated with scRNA-seq, distinguishing biological signal from technical noise would be challenging and might not be possible. To address this, I embarked on a second simulation-based study, this time investigating the impact of technical noise on our ability to study alternative splicing using scRNA-seq. I simulated four situations: a situation where every gene expressed one isoform per cell, a situation where all genes expressed two isoforms per cell, a situation where all genes expressed three isoforms per cell and a situation where all genes expressed four isoforms per cell. Importantly, I explicitly simulated isoform choice, dropouts and quantification errors. The results of the four simulated situations were not trivial to distinguish from each other, raising concerns about the feasibility of resolving the more complex splicing patterns that probably exist in reality using scRNA-seq data. I concluded that attempts to study alternative splicing using scRNA-seq are currently substantially confounded by a high rate of dropouts and a lack of understanding about the mechanism of isoform choice. Importantly, improvements to isoform quantification software accuracy alone were insufficient to correct for confounding effects caused by dropouts. I propose that to enable accurate alternative splicing analyses using scRNA-seq, further research into accurately modelling dropouts is required, or alternatively, scRNA-seq technologies should be improved to increase their capture efficiency. Additionally, research into how isoform choice is regulated at a cellular level is necessary to enable accurate analyses. Overall, I find that it is not currently possible to accurately perform alternative splicing analyses using scRNA-seq. However, I am optimistic that with further research, it may become possible in the future

Gene body methylation evolves during the sustained loss of parental care in the burying beetle

Epigenetic modifications, such as 5-methylcytosine (5mC), can sometimes be transmitted between generations, provoking speculation that epigenetic changes could play a role in adaptation and evolution. Here, we use experimental evolution to investigate how 5mC levels evolve in populations of biparental insect (Nicrophorus vespilloides) derived from a wild source population and maintained independently under different regimes of parental care in the lab. We show that 5mC levels in the transcribed regions of genes (gene bodies) diverge between populations that have been exposed to different levels of care for 30 generations. These changes in 5mC do not reflect changes in the levels of gene expression. However, the accumulation of 5mC within genes between populations is associated with reduced variability in gene expression within populations. Our results suggest that evolved change in 5mC could contribute to phenotypic evolution by influencing variability in gene expression in invertebrates

Obstacles to detecting isoforms using full-length scRNA-seq data

Abstract: Background: Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses. Results: In this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results. Conclusions: To accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq

Obstacles to detecting isoforms using full-length scRNA-seq data

Abstract: Background: Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses. Results: In this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results. Conclusions: To accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq

Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges

Plastic Traits of an Exotic Grass Contribute to Its Abundance but Are Not Always Favourable

In herbaceous ecosystems worldwide, biodiversity has been negatively impacted by changed grazing regimes and nutrient enrichment. Altered disturbance regimes are thought to favour invasive species that have a high phenotypic plasticity, although most studies measure plasticity under controlled conditions in the greenhouse and then assume plasticity is an advantage in the field. Here, we compare trait plasticity between three co-occurring, C4 perennial grass species, an invader Eragrostis curvula, and natives Eragrostis sororia and Aristida personata to grazing and fertilizer in a three-year field trial. We measured abundances and several leaf traits known to correlate with strategies used by plants to fix carbon and acquire resources, i.e. specific leaf area (SLA), leaf dry matter content (LDMC), leaf nutrient concentrations (N, C∶N, P), assimilation rates (Amax) and photosynthetic nitrogen use efficiency (PNUE). In the control treatment (grazed only), trait values for SLA, leaf C∶N ratios, Amax and PNUE differed significantly between the three grass species. When trait values were compared across treatments, E. curvula showed higher trait plasticity than the native grasses, and this correlated with an increase in abundance across all but the grazed/fertilized treatment. The native grasses showed little trait plasticity in response to the treatments. Aristida personata decreased significantly in the treatments where E. curvula increased, and E. sororia abundance increased possibly due to increased rainfall and not in response to treatments or invader abundance. Overall, we found that plasticity did not favour an increase in abundance of E. curvula under the grazed/fertilized treatment likely because leaf nutrient contents increased and subsequently its' palatability to consumers. E. curvula also displayed a higher resource use efficiency than the native grasses. These findings suggest resource conditions and disturbance regimes can be manipulated to disadvantage the success of even plastic exotic species

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Reweaving connections: Evaluation report

In August 2018, Queensland University of Technology (QUT) conducted research into three community-based courses facilitated by Community Praxis Co-op (hereby known as Praxis), and resourced and organised by the Sunshine Coast Council (SCC) and Moreton Bay Regional Council. Praxis has been running these courses, organised as a 20-hour long program run over 4-6 workshop sessions, with the aim of enhancing local leadership for community building, for nearly 20 years. The courses have been run in collaboration with councils and communities across South East Queensland and have usually been called Building Better Communities (BBC). However, the SCC used the terminology of Community Connector Workshops. Although Praxis has captured feedback and evaluation internally over these years, this research collaboration is the first extensive look at the courses and their impact

Gene body methylation evolves during the sustained loss of parental care in the burying beetle

Epigenetic modifications, such as 5-methylcytosine (5mC), can sometimes be transmitted between generations, provoking speculation that epigenetic changes could play a role in adaptation and evolution. Here, we use experimental evolution to investigate how 5mC levels evolve in populations of biparental insect (Nicrophorus vespilloides) derived from a wild source population and maintained independently under different regimes of parental care in the lab. We show that 5mC levels in the transcribed regions of genes (gene bodies) diverge between populations that have been exposed to different levels of care for 30 generations. These changes in 5mC do not reflect changes in the levels of gene expression. However, the accumulation of 5mC within genes between populations is associated with reduced variability in gene expression within populations. Our results suggest that evolved change in 5mC could contribute to phenotypic evolution by influencing variability in gene expression in invertebrates