Biology subjects

Dann, E.

Publications and source records attributed to Dann, E..

4 recordsLinked to original sources

bioRxiv2021

Intrinsic and extrinsic regulation of human fetal bone marrow haematopoiesis and perturbations in Down syndrome

Throughout postnatal life, haematopoiesis in the bone marrow (BM) maintains blood and immune cell production. Haematopoiesis first emerges in human BM at 12 post conception weeks while fetal liver (FL) haematopoiesis is still expanding. Yet, almost nothing is known about how fetal BM evolves to meet the highly specialised needs of the fetus and newborn infant. Here, we detail the development of fetal BM including stroma using single cell RNA-sequencing. We find that the full blood and immune cell repertoire is established in fetal BM in a short time window of 6-7 weeks early in the second trimester. Fetal BM promotes rapid and extensive diversification of myeloid cells, with granulocytes, eosinophils and dendritic cell (DC) subsets emerging for the first time. B-lymphocyte expansion occurs, in contrast with erythroid predominance in FL at the same gestational age. We identify transcriptional and functional differences that underlie tissue-specific identity and cellular diversification in fetal BM and FL. Finally, we reveal selective disruption of B-lymphocyte, erythroid and myeloid development due to cell intrinsic differentiation bias as well as extrinsic regulation through an altered microenvironment in the fetal BM from constitutional chromosome anomaly Down syndrome during this crucial developmental time window.

developmental biology↗

bioRxiv2021

Cells of the human intestinal tract mapped across space and time

The cellular landscape of the human intestinal tract is dynamic throughout life, developing in utero and changing in response to functional requirements and environmental exposures. To comprehensively map cell lineages in the healthy developing, pediatric and adult human gut from ten distinct anatomical regions, as well as draining lymph nodes, we used singlecell RNA-seq and VDJ analysis of roughly one third of a million cells. This reveals the presence of BEST4+ absorptive cells throughout the human intestinal tract, demonstrating the existence of this cell type beyond the colon for the first time. Furthermore, we implicate IgG sensing as a novel function of intestinal tuft cells, and link these cells to the pathogenesis of inflammatory bowel disease. We define novel glial and neuronal cell populations in the developing enteric nervous system, and predict cell-type specific expression of Hirschsprungs disease-associated genes. Finally, using a systems approach, we identify key cell players across multiple cell lineages driving secondary lymphoid tissue formation in early human development. We show that these programs are adopted in inflammatory bowel disease to recruit and retain immune cells at the site of inflammation. These data provide an unprecedented catalogue of intestinal cells, and new insights into cellular programs in development, homeostasis and disease.

cell biology↗

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

Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics

The spatial organization of cell types in tissues fundamentally shapes cellular interactions and function, but the high-throughput spatial mapping of complex tissues remains a challenge. We present [c]ell2location, a principled and versatile Bayesian model that integrates single-cell and spatial transcriptomics to map cell types in situ in a comprehensive manner. We show that [c]ell2location outperforms existing tools in accuracy and comprehensiveness and we demonstrate its utility by mapping two complex tissues. In the mouse brain, we use a new paired single nucleus and spatial RNA-sequencing dataset to map dozens of cell types and identify tissue regions in an automated manner. We discover novel regional astrocyte subtypes including fine subpopulations in the thalamus and hypothalamus. In the human lymph node, we resolve spatially interlaced immune cell states and identify co-located groups of cells underlying tissue organisation. We spatially map a rare pre-germinal centre B-cell population and predict putative cellular interactions relevant to the interferon response. Collectively our results demonstrate how [c]ell2location can serve as a versatile first-line analysis tool to map tissue architectures in a high-throughput manner.

genomics↗