Biology subjects

Angel, P. W.

Publications and source records attributed to Angel, P. W..

3 recordsLinked to original sources

bioRxiv2022

The Human Dendritic Cell Atlas: An integrated transcriptional tool to study human dendritic cell biology

Dendritic cells (DCs) are functionally diverse and are present in most adult tissues, however progress in understanding human DC biology is hampered by a relatively small number of these in circulation and by limited access to human tissues. We built a transcriptional atlas of human DCs by combining samples from 14 expression profiling studies derived from 10 laboratories. We identified significant gene expression variation of DC subset-defining markers across tissue-type and upon viral or bacterial stimulation. We further highlight critical gaps between in vitro-derived DC subsets and their in vivo counterparts and provide evidence that monocytes or cord blood progenitor in vitro-differentiated DCs fail to capture the repertoire of primary DC subsets or behaviours. In constructing a reference DC atlas, we provide an important resource for the community wishing to identify and annotate tissue-specific DC subsets from single-cell datasets, or benchmark new in vitro models of DC biology. Key PointsO_LIA reference atlas of human DC that allows benchmarking of in vitro DC models C_LIO_LIMeta-analysis of 14 integrated studies demonstrate that human conventional dendritic cells have distinct tissue-of-origin phenotypes C_LIO_LIUser uploads allow tissue-relevant annotation of human DC subsets from single cell datasets C_LIO_LIKey subset markers are altered by tissue or activation status C_LIO_LIGaps between in vitro-differentiated DC and in vivo counterparts are partially rescued by humanized mouse models, or coculture with NOTCH-ligands. C_LI

immunology↗

bioRxiv2022

Proliferation is a driver of quorum sensing in an in vitro model of tissue resident macrophages

Macrophages coordinate the initial host inflammatory response to tissue infection, as well as mediating the reparative phase, by producing growth factors that promote tissue repair. One model of this functional dichotomy is that peripherally recruited monocyte-derived macrophages drive acute inflammatory responses to infection, whereas tissue-resident macrophages are responsible for tissue repair. Alternatively, inflammation and repair may be inter-dependent molecular programs, such that both recruited and resident cells have equivalent capacity to contribute. Repeated exposure to pathogenic challenge results in innate tolerance, which may also alter the contributions of discrete macrophage populations to inflammation or repair. In this study a village model of tissue resident and recruited macrophages was created using induced pluripotent stem cell-derived macrophages and peripheral blood monocyte-derived macrophages, respectively. Population responses to repeated exposure to lipopolysaccharide were assessed with single-cell RNA sequencing and donors demultiplexed with Vireo. A subset of genes escaped classical tolerance programs in the iPSC, but not monocyte-derived macrophages, and this was associated with differences in their proliferative capacity. This suggests that targeting the proliferative resident macrophages would be most effective to limit inflammatory signaling.

systems biology↗

bioRxiv2020

A simple, scalable approach to building a cross-platform transcriptome atlas

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows. Graphical abstractRecursive approach to generating a multi-scaled atlas. Top panel: The method integrates data from all cell types in the Stemformatics database, and shows clear division of samples into global categories of stromal, pluripotent or blood (inset) cell types. Bottom panel: Integration of only the blood cell subsets provides a blood atlas. Projection of external samples (green) onto the blood atlas. Samples are coloured by curated annotations derived from the original studies, and can be viewed at Stemformatics.org O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/984468v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@2de08dorg.highwire.dtl.DTLVardef@1f18284org.highwire.dtl.DTLVardef@afd32aorg.highwire.dtl.DTLVardef@1507502_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗