The mammalian liver is a central hub for systemic metabolic homeostasis. Liver tissue is spatially structured, with hepatocytes operating in repeating lobules, and sub-lobule zones performing distinct functions. The liver is also subject to extensive temporal regulation, orchestrated by the interplay of the circadian clock, systemic signals and feeding rhythms. However, liver zonation was previously analyzed as a static phenomenon, and liver chronobiology at tissue level resolution. Here, we use single-cell RNA-seq to investigate the interplay between gene regulation in space and time. Using mixed-effect models of mRNA expression and smFISH validations, we find that many genes in the liver are both zonated and rhythmic, most of them showing multiplicative space-time effects. Such dually regulated genes cover key hepatic functions such as lipid, carbohydrate and amino acid metabolism, but also previously unassociated genes, such as protein chaperones. Our data also suggest that rhythmic and localized expression of Wnt targets could be explained by rhythmically expressed Wnt ligands from non-parenchymal cells near the central vein. Core circadian clock genes are expressed in a non-zonated manner, indicating that the liver clock is robust to zonation. Together, our scRNA-seq analysis reveals how liver function is compartmentalized spatio-temporally at the sub-lobular scale.
Neurogenesis in the vertebrate brain comprises many steps ranging from the proliferation of progenitors to the differentiation and maturation of neurons. Although these processes are highly regulated, the landscape of transcriptional changes and progenitor identities underlying brain development are poorly characterized. Here, we describe the first developmental single-cell RNAseq catalog of more than 200,000 zebrafish brain cells encompassing 12 stages from 12 hours post-fertilization to 15 days post-fertilization. We characterize known and novel gene markers for more than 800 clusters across these timepoints. Our results capture the temporal dynamics of multiple neurogenic waves from embryo to larva that expand neuronal diversity from ~20 cell types at 12 hpf to ~100 cell types at 15 dpf. We find that most embryonic neural progenitor states are transient and transcriptionally distinct from long-lasting neural progenitors of post-embryonic stages. Furthermore, we reconstruct cell specification trajectories for the retina and hypothalamus, and identify gene expression cascades and novel markers. Our analysis reveal that late-stage retinal neural progenitors transcriptionally overlap cell states observed in the embryo, while hypothalamic neural progenitors become progressively distinct with developmental time. These data provide the first comprehensive single-cell transcriptomic time course for vertebrate brain development and suggest distinct neurogenic regulatory paradigms between different stages and tissues.
Advances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. It has become possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data. It can impute temporal gene expression profiles along the reconstructed tree. We show MERLoT’s capabilities on various real cases and hundreds of simulated datasets.
20The mammalian liver performs key physiological functions for maintaining energy and 21 metabolic homeostasis. Liver tissue is both spatially structured and temporally 22 orchestrated. Hepatocytes operate in repeating anatomical units termed lobules and 23 different lobule zones perform distinct functions. The liver is also subject to extensive 24 temporal regulation, orchestrated by the interplay of the circadian clock, systemic signals 25 and feeding rhythms. Liver zonation was previously analyzed as a static phenomenon and 26 liver chronobiology at the tissue level. Here, we use single-cell RNA-seq to investigate 27 the interplay between gene regulation in space and time. Categorizing mRNA expression 28 profiles using mixed-effect models and smFISH validations, we find that many genes in 29 the liver are both zonated and rhythmic, most of them showing multiplicative space-time 30 effects. Such dually regulated genes cover key hepatic functions such as lipid, 31 carbohydrate and amino acid metabolism. In particular, our data suggest that rhythmic 32 and localized expression of Wnt targets may be explained by rhythmic Wnt signaling 33 from endothelial cells near the central vein. Core circadian clock genes are expressed in 34 a non-zonated manner, indicating that the liver clock is robust to zonation. Together, our 35 comprehensive data reveal how liver function is compartmentalized spatio-temporally at 36 the sub-lobular scale. 37Recently, we combined single-cell RNA-sequencing (scRNA-seq) of dissociated 53 hepatocytes and single-molecule RNA fluorescence in situ hybridization (smFISH) to 54 reconstruct spatial mRNA expression profiles along the porto-central axis 7 . This analysis 55 revealed an unexpected breadth of spatial heterogeneity, with ~50% of genes showing 56 spatially non-uniform patterns. Among them, functions related to ammonia clearance, 57 carbohydrate catabolic and anabolic processes, xenobiotics detoxification, bile acid and 58 cholesterol synthesis, fatty acid metabolism, targets of the Wnt and Ras pathways, and 59 hypoxia-induced genes were strongly zonated. 60In addition to its spatial heterogeneity, the liver is also highly dynamic 61 temporally. Chronobiology studies showed that temporally gated physiological and 62 metabolic programs in the liver result from the complex interplay between the 63 endogenous circadian liver oscillator, rhythmic systemic signals, and feeding/fasting 64 cycles 8,9,10 . An intact circadian clock has repeatedly been demonstrated as key for healthy 65 metabolism, also in humans 11 . Temporal compartmentalization can prevent two opposite 66 and incompatible processes from simultaneously occurring, for example, glucose is 67 stored as glycogen following a meal and is later released into the blood circulation during 68 fasting period to maintain homeostasis in plasma glucose levels. Functional genomics 69 studies of the circadian liver were typically performed on bulk liver tissue 12 . 70In particular, we and others showed how both the circadian clock and the fee...
Advances in single-cell transcriptomics techniques are revolutionising studies of cellular differentiation and heterogeneity. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a tool to reconstruct complex lineage trees from single-cell transcriptomics data. We demonstrate MERLoT's robustness, flexibility and capabilities on various real and simulated datasets.Recent advances in single-cell sequencing techniques [1, 2, 3] permit to measure the expression profiles of tens of thousands of cells making ambitious projects like the single-cell transcriptional profiling of a whole organism [4] or the human cell atlas [5] possible. These efforts will better characterize the different cell types in multicellular organisms and their lineage relationships [6]. These advances also put within reach the question of how single cells develop into tissues, organs or entire organisms, one of the most fascinating and ambitious goals in biology that would also have wide-ranging consequences for the study of many human diseases.A critical step is to develop methods that can reliably reconstruct cellular lineage trees that reflect the process by which mature cell types differentiate from progenitor cells. This is challenging due to the inherently high statistical noise levels in single cell transcriptomes, the high-dimensionality of gene expression space, and the strong non-linearities [6]. A cellular lineage tree does not only give us insight into the temporal order of branching and emergence of intermediary cell types. It can also substantially reduce the high noise levels by model-based averaging: Since many cells contribute to the idealized, smoothly varying expected gene expression rates that are imputed for every point on the tree, noise can be averaged out, and accuracy increases with the number of cells in the dataset.A few methods exist for the reconstruction of lineage trees [7, 8, 9, 10]. All but the first start by embedding the set of high-dimensional gene expression vectors into a low-dimensional space, by which much of the noise is filtered out: SLICER uses local linear embedding [8], Monocle2 uses Reverse Graph Embedding (DDRtree) [9] and Destiny [10] uses diffusion maps [11].We present MERLoT, a tool that can robustly reconstruct complex lineage trees. We first demonstrate MERLoT's performance on real datasets using different low-dimensional embedding methods. Our results on simulated data show that MERLoT's lineage tree reconstructions retrieve 18-95% more of the mutual information between predicted and known tree branch assignments, compared to the best next tool, Monocle2. Additionally, MERLoT can impute pseudotime courses [12] of gene expression levels along each trajectory on the lineage tree. This information can facilitate the analysis of gene regulatory networks controlling cell fate decisions and development.
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