Biology subjects

Clark, R. A.

Publications and source records attributed to Clark, R. A..

4 recordsLinked to original sources

bioRxiv2021

Arthritis Flares Mediated by Tissue Resident Memory T Cells in the Joint

Although rheumatoid arthritis is a systemic disease, flares typically occur in a subset of joints that is distinctive for each patient. Pursuing this intriguing pattern, we show that arthritis recurrence is mediated by long-lived synovial resident memory T cells (TRM). In three murine models, CD8+ cells bearing TRM markers remain in previously inflamed joints during remission. These cells are bona fide TRM, exhibiting failure to migrate from joint to joint, preferential uptake of fatty acids, and long-term residency. Disease flares result from TRM activation by antigen, leading to CCL5-mediated recruitment of circulating effector cells. Correspondingly, TRM depletion ameliorates recurrence in a site-specific manner. Human rheumatoid arthritis joint tissues contain a comparable CD8+-predominant TRM population, most evident in late-stage non-inflamed synovium, exhibiting limited T cell receptor diversity and a pro-inflammatory transcriptomic signature. Together, these findings establish synovial TRM cells as a targetable mediator of disease chronicity in autoimmune arthritis.

immunology↗

bioRxiv2020

Spatially and cell-type resolved quantitative proteomic atlas of healthy human skin

Human skin provides both physical integrity and immunological protection from the external environment using functionally distinct layers, cell types and extracellular matrix. Despite its central role in human health and disease, the constituent proteins of skin have not been systematically characterized. Here, we combined advanced tissue dissection methods, flow cytometry and state-of-the-art proteomics to describe a spatially-resolved quantitative proteomic atlas of human skin. We quantified 10,701 proteins as a function of their spatial location and cellular origin. The resulting protein atlas and our initial data analyses demonstrate the value of proteomics for understanding cell-type diversity within the skin. We describe the quantitative distribution of structural proteins, known and novel proteins specific to cellular subsets and those with specialized immunological functions such as cytokines and chemokines. We anticipate that this proteomic atlas of human skin will become an essential community resource for basic and translational research (www.skin.science).

cell biology↗

bioRxiv2020

Eigenvector alignment: assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease

Eigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimers disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the networks dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brains functional networks develop and adapt when challenged by disease processes such as AD.

neuroscience↗

bioRxiv2018

Eigenvector-based community detection for identifying information hubs in neuronal networks

Eigenvectors of networked systems are known to reveal central, well-connected, network vertices. Here we expand upon the known applications of eigenvectors to define well-connected communities where each is associated with a prominent vertex. This form of community detection provides an analytical approach for analysing the dynamics of information flow in a network. When applied to the neuronal network of the nematode Caenorhabditis elegans, known circuitry can be identified as separate eigenvector-based communities. For the macaques neuronal network, community detection can expose the hippocampus as an information hub; this result contradicts current thinking that the analysis of static graphs cannot reveal such insights. The application of community detection on a large scale human connectome (~1.8 million vertices) reveals the most prominent information carrying pathways present during a magnetic resonance imaging scan. We demonstrate that these pathways can act as an effective unique identifier for a subjects brain by assessing the number of matching pathways present in any two connectomes.\n\nAuthor summaryThe dynamic response of a network to stimulus can be understood by investigating that systems eigenvectors. The eigenvectors highlight the most prominent nodes; those that are either a major source or destination for information in the network. Moreover by defining a coordinate system based on multiple eigenvectors, the most prominent communities can be detected with the most prominent node detected alongside those in the community that funnel information towards it. These methods are applied to a variety of brain networks to highlight the circuitry present in a flatworm (Caenorhabditis elegans), the macaque and human subjects. Static graphs representing the connectomes are analysed to provide insights that were previously believed to only be detectable by numerically modelling information flow. Finally, we discovered that brain networks created for human subjects at different times can be identified as belonging to the same subject by investigating the similarity of the prominent communities.

neuroscience↗