Daniel D Seaton

Bio: Daniel D Seaton is an academic researcher from European Bioinformatics Institute. The author has contributed to research in topics: Induced pluripotent stem cell & Proteome. The author has an hindex of 14, co-authored 35 publications receiving 686 citations. Previous affiliations of Daniel D Seaton include University of Edinburgh & Imperial College London.

Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

Abstract: Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency of individual lines, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.

Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation

Abstract: Studying the function of common genetic variants in primary human tissues and during development is challenging. To address this, we use an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points. The proportion of neurons produced by each cell line is highly reproducible and is predictable by robust molecular markers expressed in pluripotent cells. Expression quantitative trait loci (eQTL) were characterized at different stages of neuronal development and in response to rotenone-induced oxidative stress. Of these, 1,284 eQTL colocalize with known neurological trait risk loci, and 46% are not found in the Genotype-Tissue Expression (GTEx) catalog. Our study illustrates how coupling scRNA-seq with long-term iPSC differentiation enables mechanistic studies of human trait-associated genetic variants in otherwise inaccessible cell states.

Linked circadian outputs control elongation growth and flowering in response to photoperiod and temperature

Abstract: Clock-regulated pathways coordinate the response of many developmental processes to changes in photoperiod and temperature. We model two of the best-understood clock output pathways in Arabidopsis, which control key regulators of flowering and elongation growth. In flowering, the model predicted regulatory links from the clock to CYCLING DOF FACTOR 1 (CDF1) and FLAVINBINDING, KELCH REPEAT, F-BOX 1 (FKF1) transcription. Physical interaction data support these links, which create threefold feed-forward motifs from two clock components to the floral regulator FT .I n hypocotyl growth, the model described clock-regulated transcription of PHYTOCHROME-INTERACTING FACTOR 4 and 5 (PIF4, PIF5), interacting with post-translational regulation of PIF proteins by phytochrome B (phyB) and other light-activated pathways. The model predicted bimodal and end-of-day PIF activity profiles that are observed across hundreds of PIF-regulated target genes. In the response to temperature, warmth-enhanced PIF4 activity explained the observed hypocotyl growth dynamics but additional, temperature-dependent regulators were implicated in the flowering response. Integrating these two pathways with the clock model highlights the molecular mechanisms that coordinate plant development across changing conditions.

Photoreceptor effects on plant biomass, resource allocation, and metabolic state.

Abstract: Plants sense the light environment through an ensemble of photoreceptors. Members of the phytochrome class of light receptors are known to play a critical role in seedling establishment, and are among the best-characterized plant signaling components. Phytochromes also regulate adult plant growth; however, our knowledge of this process is rather fragmented. This study demonstrates that phytochrome controls carbon allocation and biomass production in the developing plant. Phytochrome mutants have a reduced CO2 uptake, yet overaccumulate daytime sucrose and starch. This finding suggests that even though carbon fixation is impeded, the available carbon resources are not fully used for growth during the day. Supporting this notion, phytochrome depletion alters the proportion of day:night growth. In addition, phytochrome loss leads to sizeable reductions in overall growth, dry weight, total protein levels, and the expression of CELLULOSE SYNTHASE-LIKE genes. Because cellulose and protein are major constituents of plant biomass, our data point to an important role for phytochrome in regulating these fundamental components of plant productivity. We show that phytochrome loss impacts core metabolism, leading to elevated levels of tricarboxylic acid cycle intermediates, amino acids, sugar derivatives, and notably the stress metabolites proline and raffinose. Furthermore, the already growth-retarded phytochrome mutants are less responsive to growth-inhibiting abiotic stresses and have elevated expression of stress marker genes. This coordinated response appears to divert resources from energetically costly biomass production to improve resilience. In nature, this strategy may be activated in phytochrome-disabling, vegetation-dense habitats to enhance survival in potentially resource-limiting conditions.

Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation

Abstract: Common genetic variants can have profound effects on cellular function, but studying these effects in primary human tissue samples and during development is challenging. Human induced pluripotent stem cell (iPSC) technology holds great promise for assessing these effects across different differentiation contexts. Here, we use an efficient pooling strategy to differentiate 215 iPS cell lines towards a midbrain neural fate, including dopaminergic neurons, and profile over 1 million cells sampled across three differentiation timepoints using single cell RNA sequencing. We find that the proportion of neuronal cells produced by each cell line is highly reproducible over different experimental batches, and identify robust molecular markers in pluripotent cells that predict line-to-line differences in cell fate. We identify expression quantitative trait loci (eQTL) that manifest at different stages of neuronal development, and in response to oxidative stress, by exposing cells to rotenone. We find over one thousand eQTL that colocalise with a known risk locus for a neurological trait, nearly half of which are not found in GTEx. Our study illustrates how coupling single cell transcriptomics with long-term iPSC differentiation can profile mechanistic effects of human trait-associated genetic variants in otherwise inaccessible cell states.

Molecular and genetic control of plant thermomorphogenesis

Abstract: Temperature is a major factor governing the distribution and seasonal behaviour of plants. Being sessile, plants are highly responsive to small differences in temperature and adjust their growth and development accordingly. The suite of morphological and architectural changes induced by high ambient temperatures, below the heat-stress range, is collectively called thermomorphogenesis. Understanding the molecular genetic circuitries underlying thermomorphogenesis is particularly relevant in the context of climate change, as this knowledge will be key to rational breeding for thermo-tolerant crop varieties. Until recently, the fundamental mechanisms of temperature perception and signalling remained unknown. Our understanding of temperature signalling is now progressing, mainly by exploiting the model plant Arabidopsis thaliana. The transcription factor PHYTOCHROME INTERACTING FACTOR 4 (PIF4) has emerged as a critical player in regulating phytohormone levels and their activity. To control thermomorphogenesis, multiple regulatory circuits are in place to modulate PIF4 levels, activity and downstream mechanisms. Thermomorphogenesis is integrally governed by various light signalling pathways, the circadian clock, epigenetic mechanisms and chromatin-level regulation. In this Review, we summarize recent progress in the field and discuss how the emerging knowledge in Arabidopsis may be transferred to relevant crop systems.

Integrating circadian dynamics with physiological processes in plants

Abstract: The plant circadian clock coordinates the responses to multiple and often simultaneous environmental challenges that the sessile plant cannot avoid. These responses must be integrated efficiently into dynamic metabolic and physiological networks essential for growth and reproduction. Many of the output pathways regulated by the circadian clock feed back to modulate clock function, leading to the appreciation of the clock as a central hub in a sophisticated regulatory network. In this Review, we discuss the circadian regulation of growth, flowering time, abiotic and biotic stress responses, and metabolism, as well as why temporal 'gating' of these processes is important to plant fitness.

Haplotype-resolved diverse human genomes and integrated analysis of structural variation.

Abstract: Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.

From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases.

Abstract: Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and result in the identification of novel drug targets. However, GWAS have also left a number of outstanding questions. In particular, the majority of disease-associated loci lie in non-coding regions of the genome and, even though they are thought to play a role in gene expression regulation, it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. This has hindered the translation of GWAS findings into clinical interventions. In this review we summarize how these challenges have been addressed over the last decade, with a particular focus on the integration of GWAS results with functional genomics datasets. Firstly, we investigate how the tissues and cell types involved in diseases can be identified using methods that test for enrichment of GWAS variants in genomic annotations. Secondly, we explore how to find the genes regulated by GWAS loci using methods that test for colocalization of GWAS signals with molecular phenotypes such as quantitative trait loci (QTLs). Finally, we highlight potential future research avenues such as integrating GWAS results with single-cell sequencing read-outs, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering. These tools will be crucial to identify new drug targets for common complex diseases.