Biology subjects

Marioni, J. C.

Publications and source records attributed to Marioni, J. C..

At least 37 records · Page 2Linked to original sources

bioRxiv2020

Milo: differential abundance testing on single-cell data using k-NN graphs

Single-cell omic protocols applied to disease, development or mechanistic studies can reveal the emergence of aberrant cell states or changes in differentiation. These perturbations can manifest as a shift in the abundance of cells associated with a biological condition. Current computational workflows for comparative analyses typically use discrete clusters as input when testing for differential abundance between experimental conditions. However, clusters are not always an optimal representation of the biological manifold on which cells lie, especially in the context of continuous differentiation trajectories. To overcome these barriers to discovery, we present Milo, a flexible and scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighbourhoods on a k-nearest neighbour graph. Our method samples and refines neighbourhoods across the graph and leverages the flexibility of generalized linear models, making it applicable to a wide range of experimental settings. Using simulations, we show that Milo is both robust and sensitive, and can reveal subtle but important cell state perturbations that are obscured by discretizing cells into clusters. We illustrate the power of Milo by identifying the perturbed differentiation during ageing of a lineage-biased thymic epithelial precursor state and by uncovering extensive perturbation to multiple lineages in human cirrhotic liver. Milo is provided as an open-source R software package with documentation and tutorials at https://github.com/MarioniLab/miloR.

bioinformatics↗

bioRxiv2020

Highly multiplexed spatially resolved gene expression profiling of mouse organogenesis

Transcriptional and epigenetic profiling of single-cells has advanced our knowledge of the molecular bases of gastrulation and early organogenesis. However, current approaches rely on dissociating cells from tissues, thereby losing the crucial spatial context that is necessary for understanding cell and tissue interactions during development. Here, we apply an image-based single-cell transcriptomics method, seqFISH, to simultaneously and precisely detect mRNA molecules for 387 selected target genes in 8-12 somite stage mouse embryo tissue sections. By integrating spatial context and highly multiplexed transcriptional measurements with two single-cell transcriptome atlases we accurately characterize cell types across the embryo and demonstrate how spatially-resolved expression of genes not profiled by seqFISH can be imputed. We use this high-resolution spatial map to characterize fundamental steps in the patterning of the midbrain-hindbrain boundary and the developing gut tube. Our spatial atlas uncovers axes of resolution that are not apparent from single-cell RNA sequencing data - for example, in the gut tube we observe early dorsal-ventral separation of esophageal and tracheal progenitor populations. In sum, by computationally integrating high-resolution spatially-resolved gene expression maps with single-cell genomics data, we provide a powerful new approach for studying how and when cell fate decisions are made during early mammalian development.

developmental biology↗

bioRxiv2020

Transposable element expression at unique loci in single cells with CELLO-seq

The role of Transposable Elements (TEs) in regulating diverse biological processes, from early development to cancer, is becoming increasing appreciated. However, unlike other biological processes, next generation single-cell sequencing technologies are ill-suited for assaying TE expression: in particular, their highly repetitive nature means that short cDNA reads cannot be unambiguously mapped to a specific locus. Consequently, it is extremely challenging to understand the mechanisms by which TE expression is regulated and how they might themselves regulate other protein coding genes. To resolve this, we introduce CELLO-seq, a novel method and computational framework for performing long-read RNA sequencing at single cell resolution. CELLO-seq allows for full-length RNA sequencing and enables measurement of allelic, isoform and TE expression at unique loci. We use CELLO-seq to assess the widespread expression of TEs in 2-cell mouse blastomeres as well as human induced pluripotent stem cells (hiPSCs). Across both species, old and young TEs showed evidence of locus-specific expression, with simulations demonstrating that only a small number of very young elements in the mouse could not be mapped back to with high confidence. Exploring the relationship between the expression of individual elements and putative regulators revealed surprising heterogeneity, with TEs within a class showing different patterns of correlation, suggesting distinct regulatory mechanisms.

molecular biology↗

bioRxiv2020

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

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.

genetics↗

bioRxiv2020

Ageing compromises mouse thymus function and remodels epithelial cell differentiation

Ageing is characterised by cellular senescence, leading to imbalanced tissue maintenance, cell death and compromised organ function. This is first observed in the thymus, the primary lymphoid organ that generates and selects T cells. However, the molecular and cellular mechanisms underpinning these ageing processes remain unclear. Here, we show that mouse ageing leads to less efficient T cell selection, decreased self-antigen representation and increased T cell receptor repertoire diversity. Using a combination of single-cell RNA-seq and lineage-tracing, we find that progenitor cells are the principal targets of ageing, whereas the function of mature thymic epithelial cells is compromised only modestly. Specifically, an early-life precursor cell population, retained in the mouse cortex postnatally, is virtually extinguished at puberty. Concomitantly, a medullary precursor cell quiesces, thereby impairing maintenance of the medullary epithelium. Thus, ageing disrupts thymic progenitor differentiation and impairs the core immunological functions of the thymus.

immunology↗

bioRxiv2019

Investigating higher order interactions in single cell data with scHOT

Single-cell RNA-sequencing has transformed our ability to examine cell fate choice. For example, in the context of development and differentiation, computational ordering of cells along pseudotime enables the expression profiles of individual genes, including key transcription factors, to be examined at fine scale temporal resolution. However, while cell fate decisions are typically marked by profound changes in expression, many such changes are observed in genes downstream of the initial cell fate decision. By contrast, the genes directly involved in the cell fate decision process are likely to interact in subtle ways, potentially resulting in observed changes in patterns of correlation and variation rather than mean expression prior to cell fate commitment. Herein, we describe a novel approach, scHOT - single cell Higher Order Testing - which provides a flexible and statistically robust framework for identifying changes in higher order interactions among genes. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. We demonstrate the utility of scHOT by studying embryonic development of the liver, where we find coordinated changes in higher order interactions of programs related to differentiation and liver function. We also demonstrate its ability to find subtle changes in gene-gene correlation patterns across space using spatially-resolved expression data from the mouse olfactory bulb. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.

bioinformatics↗

bioRxiv2019

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

Technological advances have enabled the joint analysis of multiple molecular layers at single cell resolution. At the same time, increased experimental throughput has facilitated the study of larger numbers of experimental conditions. While methods for analysing single-cell data that model the resulting structure of either of these dimensions are beginning to emerge, current methods do not account for complex experimental designs that include both multiple views (modalities or assays) and groups (conditions or experiments). Here we present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of structured single cell multi-modal data. MOFA+ builds upon a Bayesian Factor Analysis framework combined with fast GPU-accelerated stochastic variational inference. Similar to existing factor models, MOFA+ allows for interpreting variation in single-cell datasets by pooling information across cells and features to reconstruct a low-dimensional representation of the data. Uniquely, the model supports flexible group-level sparsity constraints that allow joint modelling of variation across multiple groups and views.\n\nTo illustrate MOFA+, we applied it to single-cell data sets of different scales and designs, demonstrating practical advantages when analyzing datasets with complex group and/or view structure. In a multi-omics analysis of mouse gastrulation this joint modelling reveals coordinated changes between gene expression and epigenetic variation associated with cell fate commitment.

bioinformatics↗

bioRxiv2019

CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets

Droplet-based single-cell assays, including scRNA-seq, snRNA-seq, and CITE-seq, produce a significant amount of background noise counts, the hallmark of which is non-zero counts in cell-free droplets and off-target gene expression in unexpected cell types. The presence of such systematic background noise is a potential source of batch effect and spurious differential gene expression. Here we develop a deep generative model for noise-contaminated data that is structured to reflect the phenomenology of background noise generation in droplet-based single-cell assays. The proposed model successfully distinguishes cell-containing from cell-free droplets without supervision, learns the profile of background noise, and retrieves a noise-free quantification in an end-to-end fashion. We present a scalable and robust implementation of our method as a module in the open-source software package CellBender. We show that CellBender operates close to the theoretically optimal denoising limit in simulated datasets, and present extensive evaluations using real datasets and experimental benchmarks drawn from different tissues, protocols, and modalities to show that CellBender significantly improves the agreement of droplet-based single-cell data with established gene expression patterns, and that the learned background noise profile provides evidence for degraded or uncaptured cell types.

bioinformatics↗

bioRxiv2019

Rora regulates activated T helper cells during inflammation

The transcription factor Rora has been shown to be important for the development of ILC2 and the regulation of ILC3, macrophages and Treg cells. Here we investigate the role of Rora across CD4+ T cells, both in vitro as well as in the context of several in vivo type 2 infection models. We dissect the function of Rora using overexpression and a CD4-conditional Rora-knockout mouse, as well as a RORA-reporter mouse. We establish the importance of Rora in CD4+ T cells for controlling lung inflammation induced by Nippostrongylus brasiliensis infection, and have measured the effect on downstream genes using RNA-seq. Using a systematic stimulation screen of CD4+ T cells, coupled with RNA-seq, we identify upstream regulators of Rora, most importantly IL-33 and CCL7. Our data suggest that Rora is a negative regulator of the immune system, possibly through several downstream pathways, and is under control of the local microenvironment.

immunology↗

bioRxiv2019

A high-content RNAi screen reveals multiple roles for long noncoding RNAs in cell division

Genome stability relies on proper coordination of mitosis and cytokinesis, where dynamic microtubules capture and faithfully segregate chromosomes into daughter cells. The role of long noncoding RNAs (lncRNAs) in controlling these processes however remains largely unexplored. To identify lncRNAs with mitotic functions, we performed a high-content RNAi imaging screen targeting more than 2,000 human lncRNAs. By investigating major hallmarks of cell division such as chromosome segregation, mitotic duration and cytokinesis, we discovered numerous lncRNAs with functions in each of these processes. The chromatin-associated lncRNA, linc00899, was selected for in-depth studies due to the robust mitotic delay observed upon its depletion. Transcriptome analysis of linc00899-depleted cells together with gain-of-function and rescue experiments across multiple cell types identified the neuronal microtubule-binding protein, TPPP/p25, as a target of linc00899. Linc00899 binds the genomic locus of TPPP/p25 and suppresses its transcription through a cis-acting mechanism. In cells depleted of linc00899, the consequent upregulation of TPPP/p25 alters microtubule dynamics and is necessary and sufficient to delay mitosis. Overall, our comprehensive screen identified several lncRNAs with roles in genome stability and revealed a new lncRNA that controls microtubule behaviour with functional implications beyond cell division.

cell biology↗

bioRxiv2019

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

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, 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.

genomics↗

bioRxiv2019

Single cell multi-omics profiling reveals a hierarchical epigenetic landscape during mammalian germ layer specification

Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan. Recent studies employing single cell RNA-sequencing have identified major transcriptional changes associated with germ layer specification. Global epigenetic reprogramming accompanies these changes, but the role of the epigenome in regulating early cell fate choice remains unresolved, and the coordination between different epigenetic layers is unclear. Here we describe the first single cell triple-omics map of chromatin accessibility, DNA methylation and RNA expression during the exit from pluripotency and the onset of gastrulation in mouse embryos. We find dynamic dependencies between the different molecular layers, with evidence for distinct modes of epigenetic regulation. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of local lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements, driven by loss of methylation in enhancer marks and a concomitant increase of chromatin accessibility. In striking contrast, the epigenetic landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or epigenetically remodelled prior to overt cell fate decisions during gastrulation, providing the molecular logic for a hierarchical emergence of the primary germ layers.\n\nHighlightsO_LIFirst map of mouse gastrulation using comprehensive single cell triple-omic analysis.\nC_LIO_LIExit from pluripotency is associated with a global repressive epigenetic landscape, driven by a sharp gain of DNA methylation and a gradual decrease of chromatin accessibility.\nC_LIO_LIDNA methylation and chromatin accessibility changes in enhancers, but not in promoters, are associated with germ layer formation.\nC_LIO_LIMesoderm and endoderm enhancers become open and demethylated upon lineage commitment.\nC_LIO_LIEctoderm enhancers are primed in the early epiblast and protected from the global repressive dynamics, supporting a default model of ectoderm commitment in vivo.\nC_LI

developmental biology↗

bioRxiv2018

Altered composition of the γδ T cell pool in lymph nodes during ageing enhances tumour growth

How age-associated decline of immune function leads to increased cancer incidence is poorly understood. Here, we have characterized the cellular composition of the{gamma}{delta} T cell pool in peripheral lymph nodes (pLNs) upon ageing. We found that ageing has minimal cell-intrinsic effects on function and global gene expression of{gamma}{delta} T cells, and TCR{gamma}{delta} diversity remained stable. However, ageing altered TCR{delta} chain usage and clonal structure of{gamma}{delta} T cell subsets. Importantly, IL-17-producing{gamma}{delta} 17 T cells dominated the{gamma}{delta} T cell pool of aged mice - mainly due to the selective expansion of V{gamma}6+{gamma}{delta} 17 T cells and augmented{gamma}{delta} 17-polarisation of V{gamma}4+ T cells. Expansion of the{gamma}{delta} 17 T cell compartment was supported by increased Interleukin-7 expression in the T cell zone of old mice. In a Lewis lung cancer model, pro-tumourigenic V{gamma}6+{gamma}{delta}17 T cells were exclusively activated in the tumour-draining LN and their infiltration into the tumour correlated with increased tumour size in aged mice. Thus, upon ageing, substantial compositional changes of{gamma}{delta} T cell pool in a dysregulated pLN microenvironment promote tumour growth.

immunology↗

bioRxiv2018

Staged developmental mapping and X chromosome transcriptional dynamics during mouse spermatogenesis

Understanding male fertility requires an in-depth characterisation of spermatogenesis, the developmental process by which male gametes are generated. Spermatogenesis occurs continuously throughout a males reproductive window and involves a complex sequence of developmental steps, both of which make this process difficult to decipher at the molecular level. To overcome this, we transcriptionally profiled single cells from multiple distinct stages during the first wave of spermatogenesis, where the most mature germ cell type is known. This naturally enriches for spermatogonia and somatic cell types present at very low frequencies in adult testes. Our atlas, available as a shiny app (https://marionilab.cruk.cam.ac.uk/SpermatoShiny), allowed us to reconstruct the three main processes of spermatogenesis: spermatogonial differentiation, meiosis, and spermiogenesis. Additionally, we profiled the chromatin changes associated with meiotic silencing of the X chromosome, revealing a set of genes specifically and strongly repressed by H3K9me3 in the spermatocyte stage, but which escape post-meiotic silencing in spermatids.

developmental biology↗

bioRxiv2017

Robust expression variability testing reveals heterogeneous T cell responses

Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays a crucial role in tissue function and development. Single-cell RNA sequencing can characterise this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinders meaningful comparison of expression variability between cell populations. To address this problem, we introduce a novel analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. Moreover, we introduce a new and robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronisation of the translational machinery in immune cells upon activation. Additionally, our approach identifies new patterns of variability across CD4+ T cell differentiation.

bioinformatics↗

bioRxiv2017

Multi-Omics factor analysis disentangles heterogeneity in blood cancer

Multi-omic studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous datasets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omic datasets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation, and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex-vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multiomics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

bioinformatics↗

bioRxiv2017

Placozoans are eumetazoans related to Cnidaria

The phylogenetic placement of the morphologically simple placozoans is crucial to understanding the evolution of complex animal traits. Here, we examine the influence of adding new genomes from placozoans to a large dataset designed to study the deepest splits in the animal phylogeny. Using site-heterogeneous substitution models, we show that it is possible to obtain strong support, in both amino acid and reduced-alphabet matrices, for either a sister-group relationship between Cnidaria and Placozoa, or for Cnidaria and Bilateria (=Planulozoa), also seen in most published work to date, depending on the orthologues selected to construct the matrix. We demonstrate that a majority of genes show evidence of compositional heterogeneity, and that the support for Planulozoa can be assigned to this source of systematic error. In interpreting this placozoan-cnidarian clade, we caution against a peremptory reading of placozoans as secondarily reduced forms of little relevance to broader discussions of early animal evolution.

zoology↗

bioRxiv2017

Detection and removal of barcode swapping in single-cell RNA-seq data

Barcode swapping results in the mislabeling of sequencing reads between multiplexed samples on the new patterned flow cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays, especially for single-cell studies where many samples are routinely multiplexed together. The severity and consequences of barcode swapping for single-cell transcriptomic studies remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in each of two plate-based single-cell RNA sequencing datasets. We found that approximately 2.5% of reads were mislabeled between samples on the HiSeq 4000 machine, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10X Genomics experiments, exploiting the combinatorial complexity present in the data. This permits the continued use of cutting-edge sequencing machines for droplet-based experiments while avoiding the confounding effects of barcode swapping.

genomics↗