Biology subjects

Patir, A.

Publications and source records attributed to Patir, A..

6 recordsLinked to original sources

bioRxiv2023

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, behaviour, 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 signatures and thereby provide potential functional annotations for as yet poorly characterized genes. 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 15 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 co-expression 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, 9 were related to the nervous system, 7 to the fat body, 14 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 of at the cellular level.

genomics↗

bioRxiv2023

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

Acute stroke causes substantial mortality and morbidity and provokes extensive changes to myeloid immune cell populations in the brain that may be targets for limiting brain damage and enhancing brain repair. The most effective immunomodulatory approaches will require precise manipulation of discrete myeloid cell phenotypes in time and space to avoid harmful effects of indiscriminate neuroimmune perturbation. We sought to define how stroke alters the composition and phenotypes of mononuclear myeloid cells with particular attention to how cell ontogeny and spatial organisation combine to expand myeloid cell diversity across the brain after stroke. Multiple reactive microglial states and dual monocyte-derived populations contributed to an extensive repertoire of myeloid cells in post-stroke brain. We identified important overlap and distinctions among different cell types and states that involved ontogeny- and spatial-related properties. Notably, brain connectivity with infarcted tissue underpinned 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-organised brain damage and neuroaxonal cues.

neuroscience↗

bioRxiv2020

Graphia: A platform for the graph-based visualisation and analysis of complex data

Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics, genomics data. The software imports data already defined as a network or a similarity matrix and is designed to rapidly visualise very large graphs in 2D or 3D space, providing a wide range of functionality for graph exploration. An extensive range of analysis algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are also available. Graphias core is extensible through the deployment of plugins, supporting rapid development of additional computational analyses and features necessary for a given analysis task or data source. A plugin for correlation network analysis is distributed with the core application, to support the generation of correlation graphs from any tabular matrix of continuous or discrete values. This provides a powerful analysis solution for the interpretation of high-dimensional data from many sources. Several use cases of Graphia are described, to showcase its wide range of applications. Graphia runs on all major desktop operating systems and is freely available to download from https://graphia.app/.

bioinformatics↗

bioRxiv2019

The transcriptional signature associated with human motile cilia

Cilia are complex microtubule-based organelles implicated in the aetiology of numerous diseases. Accordingly, many cilia-associated proteins have been described, while those distinguishing cilia subtypes are poorly defined. Here, we characterise the gene signature associated with human motile cilia that captures both known and unknown components of this class of cilia. 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. For validation, we examined the literature, databases, single cell RNA-Seq data, and the localisation of mRNA and proteins in motile ciliated cells. To validate some of the many poorly characterised genes, we performed new localisation experiments on ARMC3, EFCAB6, FAM183A, MYCBPAP, RIBC2 and VWA3A. In summary, we report a highly validated set of motile cilia-associated genes that helps shape our understanding of these complex cellular organelles.\n\nSummaryThis work defines a conserved transcriptional signature associated with human motile cilia, including many genes with little or no previous association with these structures. These genes were compared with existing resources and a number of poorly characterised genes validated.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC=\"FIGDIR/small/817072v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (74K):\norg.highwire.dtl.DTLVardef@152f5d6org.highwire.dtl.DTLVardef@189eba6org.highwire.dtl.DTLVardef@1cde94forg.highwire.dtl.DTLVardef@1906450_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2018

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.\n\nHere we describe a model of self-limiting acute brain inflammation optimised 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.\n\nWe 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.

neuroscience↗

bioRxiv2018

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 better 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. Whilst similar in many ways, 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, little consistency was observed between studies. Hence, we set out to define a conserved microglia signature of the human central nervous system (CNS), through a comprehensive meta-analysis of existing transcriptomic resources. Nine datasets derived from cells and tissue, isolated from different 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. Comparison of individual microglia clusters showed 249 genes highly conserved between them. This core gene signature included all known markers and improves upon published microglial signatures. The utility of this signature was demonstrated by its use in detecting qualitative and quantitative region-specific alterations in aging and Alzheimers 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 in the contribution of human microglia towards the CNS in health and disease.\n\nTable of ContentsMain points O_LIPublished microglial transcriptional signatures in mouse and human show poor consensus.\nC_LIO_LIA core transcriptional signature of human microglia with 249 genes was derived and found conserved across brain regions, encompassing the CNS.\nC_LIO_LIThe signature revealed region-dependent microglial alterations in Alzheimers, highlighting susceptible CNS regions and the involvement of TYROBP signaling.\nC_LI

neuroscience↗