Graphia: A platform for the graph-based visualisation and analysis of high dimensional data.

Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to Graphia's functionality is support for the calculation of correlation matrices from any tabular matrix of continuous or discrete values, whereupon the software is designed to rapidly visualise the often very large graphs that result in 2D or 3D space. Following graph construction, an extensive range of measurement algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are available, for graph exploration and analysis. Combined, these provide a powerful solution for the interpretation of high-dimensional data from many sources, or data already in the form of a network or equivalent adjacency matrix. Several use cases of Graphia are described, to showcase its wide range of applications in the analysis biological data. Graphia runs on all major desktop operating systems, is extensible through the deployment of plugins and is freely available to download from https://graphia.app/.

A core transcriptional signature of human microglia: Derivation and utility in describing region-dependent alterations associated with Alzheimer's disease.

Growing recognition of the pivotal role microglia play in neurodegenerative and neuroinflammatory disorders has accentuated the need to characterize their function in health and disease. Studies in mouse have applied transcriptome-wide profiling of microglia to reveal key features of microglial ontogeny, functional profile, and phenotypic diversity. While similar, human microglia exhibit clear differences to their mouse counterparts, underlining the need to develop a better understanding of the human microglial profile. On examining published microglia gene signatures, limited consistency was observed between studies. Hence, we sought to derive a core microglia signature of the human central nervous system (CNS), through a comprehensive analysis of existing transcriptomic datasets. Nine datasets derived from cells and tissues, isolated from various regions of the CNS across numerous donors, were subjected independently to an unbiased correlation network analysis. From each dataset, a list of coexpressing genes corresponding to microglia was identified, with 249 genes highly conserved between them. This core signature included known microglial markers, and compared with other signatures provides a gene set specific to microglia in the context of the CNS. The utility of this signature was demonstrated by its use in detecting qualitative and quantitative region-specific alterations in aging and Alzheimer's disease. These analyses highlighted the reactive response of microglia in vulnerable brain regions such as the entorhinal cortex and hippocampus, additionally implicating pathways associated with disease progression. We believe this resource and the analyses described here, will support further investigations to the contribution of human microglia in CNS health and disease.

Phenotypic and spatial heterogeneity of brain myeloid cells after stroke is associated with cell ontogeny, tissue damage, and brain connectivity.

Acute stroke triggers extensive changes to myeloid immune cell populations in the brain that may be targets for limiting brain damage and enhancing repair. Immunomodulatory approaches will be most effective with precise manipulation of discrete myeloid cell phenotypes in time and space. Here, we investigate how stroke alters mononuclear myeloid cell composition and phenotypes at single-cell resolution and key spatial patterns. Our results show that multiple reactive microglial states and monocyte-derived populations contribute to an extensive myeloid cell repertoire in post-stroke brains. We identify important overlaps and distinctions among different cell types/states that involve ontogeny- and spatial-related properties. Notably, brain connectivity with infarcted tissue underpins the pattern of local and remote altered cell accumulation and reactivity. Our discoveries suggest a global but anatomically governed brain myeloid cell response to stroke that comprises diverse phenotypes arising through intrinsic cell ontogeny factors interacting with exposure to spatially organized brain damage and neuro-axonal cues.

Cellular heterogeneity of the developing worker honey bee (Apis mellifera) pupa: a single cell transcriptomics analysis.

It is estimated that animals pollinate 87.5% of flowering plants worldwide and that managed honey bees (Apis mellifera) account for 30-50% of this ecosystem service to agriculture. In addition to their important role as pollinators, honey bees are well-established insect models for studying learning and memory, behavior, caste differentiation, epigenetic mechanisms, olfactory biology, sex determination, and eusociality. Despite their importance to agriculture, knowledge of honey bee biology lags behind many other livestock species. In this study, we have used scRNA-Seq to map cell types to different developmental stages of the worker honey bee (prepupa at day 11 and pupa at day 15) and sought to determine their gene expression signatures. To identify cell-type populations, we examined the cell-to-cell network based on the similarity of the single-cells transcriptomic profiles. Grouping similar cells together we identified 63 different cell clusters of which 17 clusters were identifiable at both stages. To determine genes associated with specific cell populations or with a particular biological process involved in honey bee development, we used gene coexpression analysis. We combined this analysis with literature mining, the honey bee protein atlas, and gene ontology analysis to determine cell cluster identity. Of the cell clusters identified, 17 were related to the nervous system and sensory organs, 7 to the fat body, 19 to the cuticle, 5 to muscle, 4 to compound eye, 2 to midgut, 2 to hemocytes, and 1 to malpighian tubule/pericardial nephrocyte. To our knowledge, this is the first whole single-cell atlas of honey bees at any stage of development and demonstrates the potential for further work to investigate their biology at the cellular level.

Single-cell RNA-seq reveals CD16- monocytes as key regulators of human monocyte transcriptional response to Toxoplasma.

Monocytes are among the major myeloid cells that respond to Toxoplasma, a ubiquitous foodborne that infects ≥ 1 billion people worldwide, in human peripheral blood. As such, a molecular understanding of human monocyte-Toxoplasma interactions can expedite the development of novel human toxoplasmosis control strategies. Current molecular studies on monocyte-Toxoplasma interactions are based on average cell or parasite responses across bulk cell populations. Although informative, population-level averages of monocyte responses to Toxoplasma have sometimes produced contradictory results, such as whether CCL2 or IL12 define effective monocyte responses to the parasite. Here, we used single-cell dual RNA sequencing (scDual-Seq) to comprehensively define, for the first time, the monocyte and parasite transcriptional responses that underpin human monocyte-Toxoplasma encounters at the single cell level. We report extreme transcriptional variability between individual monocytes. Furthermore, we report that Toxoplasma-exposed and unexposed monocytes are transcriptionally distinguished by a reactive subset of CD14+CD16- monocytes. Functional cytokine assays on sorted monocyte populations show that the infection-distinguishing monocytes secrete high levels of chemokines, such as CCL2 and CXCL5. These findings uncover the Toxoplasma-induced monocyte transcriptional heterogeneity and shed new light on the cell populations that largely define cytokine and chemokine secretion in human monocytes exposed to Toxoplasma.

The transcriptional signature associated with human motile cilia.

Cilia are complex microtubule-based organelles essential to a range of processes associated with embryogenesis and tissue homeostasis. Mutations in components of these organelles or those involved in their assembly may result in a diverse set of diseases collectively known as ciliopathies. Accordingly, many cilia-associated proteins have been described, while those distinguishing cilia subtypes are poorly defined. Here we set out to define genes associated with motile cilia in humans based on their transcriptional signature. To define the signature, we performed network deconvolution of transcriptomics data derived from tissues possessing motile ciliated cell populations. For each tissue, genes coexpressed with the motile cilia-associated transcriptional factor, FOXJ1, were identified. The consensus across tissues provided a transcriptional signature of 248 genes. To validate these, we examined the literature, databases (CilDB, CentrosomeDB, CiliaCarta and SysCilia), single cell RNA-Seq data, and the localisation of mRNA and proteins in motile ciliated cells. In the case of six poorly characterised signature genes, we performed new localisation experiments on ARMC3, EFCAB6, FAM183A, MYCBPAP, RIBC2 and VWA3A. In summary, we report a set of motile cilia-associated genes that helps shape our understanding of these complex cellular organelles.

Myeloid Cell and Transcriptome Signatures Associated With Inflammation Resolution in a Model of Self-Limiting Acute Brain Inflammation.

Inflammation contributes to tissue repair and restoration of function after infection or injury. However, some forms of inflammation can cause tissue damage and disease, particularly if inappropriately activated, excessive, or not resolved adequately. The mechanisms that prevent excessive or chronic inflammation are therefore important to understand. This is particularly important in the central nervous system where some effects of inflammation can have particularly harmful consequences, including irreversible damage. An increasing number of neurological disorders, both acute and chronic, and their complications are associated with aberrant neuroinflammatory activity. Here we describe a model of self-limiting acute brain inflammation optimized to study mechanisms underlying inflammation resolution. Inflammation was induced by intracerebral injection of lipopolysaccharide (LPS) and the temporal profile of key cellular and molecular changes were defined during the progression of the inflammatory response. The kinetics of accumulation and loss of neutrophils in the brain enabled well-demarcated phases of inflammation to be operatively defined, including induction and resolution phases. Microglial reactivity and accumulation of monocyte-derived macrophages were maximal at the onset of and during the resolution phase. We profiled the transcriptome-wide gene expression changes at representative induction and resolution timepoints and used gene coexpression network analysis to identify gene clusters. This revealed a distinct cluster of genes associated with inflammation resolution that were induced selectively or maximally during this phase and indicated an active programming of gene expression that may drive resolution as has been described in other tissues. Induction of gene networks involved in lysosomal function, lipid metabolism, and a comparative switch to MHC-II antigen presentation (relative to MHC-I during induction) were prominent during the resolution phase. The restoration and/or further induction of microglial homeostatic signature genes was notable during the resolution phase. We propose the current model as a tractable reductionist system to complement more complex models for further understanding how inflammation resolution in the brain is regulated and as a platform for in vivo testing/screening of candidate resolution-modifying interventions. Our data highlight how resolution involves active cellular and transcriptome reprogramming and identify candidate gene networks associated with resolution-phase adaptations that warrant further study.

Insights into the role of ribonuclease 4 polymorphisms in amyotrophic lateral sclerosis.

Mutations in certain genes of the Ribonuclease (RNASE) superfamily can cause amyotrophic lateral sclerosis (ALS) through altered RNA processing mechanisms. About 30 of these missense mutations in RNASE5/ANG gene have already been reported in ALS patients. In another gene of the ribonuclease superfamily, ribonuclease 4 (RNASE4), missense mutations and single nucleotide polymorphisms have been identified in patients suffering from ALS. However, their plausible molecular mechanisms of association with ALS are not known. Here, we present the molecular mechanisms of RNASE4 polymorphisms with ALS using all-atom molecular dynamics (MD) simulations followed by functional assay experiments. As most ALS causing mutations in RNASE superfamily proteins affect either the ribonucleolytic or nuclear translocation activity, we examined these functional properties of wild-type and known RNASE4 variants, R10W, A98V, E48D and V75I, using MD simulations. Our simulation predicted that these variants would retain nuclear translocation activity and that E48D would exhibit loss of ribonucleolytic activity, which was subsequently validated by ribonucleolytic assay. Our results give a mechanistic insight into the association of RNASE4 polymorphisms with ALS and show that E48D-RNASE4 would probably be deleterious and cause ALS in individuals harbouring this polymorphism.