Biology subjects

Garner, L.

Publications and source records attributed to Garner, L..

6 recordsLinked to original sources

bioRxiv2026

MAIT cells derived ligands signal via VEGFR2 to promote tissue repair and liver regeneration

MAIT are a highly versatile population of innate-like T cells that have been implicated in promoting tissue repair-associated process in a variety of tissue and diseases settings in the last years. While certain specific effector molecules responsible for MAIT-cell mediated have been identified, the mechanisms by which MAIT cells exert repair functions remain incompletely understood. Here, we show that hepatic MAIT cells express VEGFA, VEGFB and vimentin, an alternative ligand for the VEGFA-receptor VEGFR2 in both, regenerating and heathy tissue. Expression and secretion of these factors were induced in vitro by combined T cell receptor and cytokine stimulation. Supernatants of activated MAIT cells were able to promote proliferation of different epithelial and endothelial cells, including a liver sinusoidal endothelial-derived cell line in an VEGFR2-dependent manner. Together, our findings expand our understanding of MAIT cell function, especially in the liver and open new opens avenues for exploring MAIT therapeutic potential in modulating tissue repair.

immunology↗

bioRxiv2025

Using time-series remote sensing to identify and track individual bird nests at large scales

The challenges of monitoring wildlife often limits the scales and intensity of the data that can be collected. New technologies - such as remote sensing using unoccupied aircraft systems (UAS) - can collect information more quickly, over larger areas, and more frequently than is feasible using ground-based methods. While airborne imaging is increasingly used to produce data on the location and counts of individuals, its ability to produce individual-based demographic information is less explored. Repeat airborne imagery to generate an imagery time-series provides the potential to track individuals over time to collect information beyond one-off counts, but doing so necessitates automated approaches to handle the resulting high-frequency large-spatial scale imagery. We develop an automated time-series remote sensing approach to identifying wading bird nests in the Everglades ecosystem of Florida, USA to explore the feasibility and challenges of conducting time-series based remote sensing on mobile animals at large spatial scales. We combine a computer vision model for detecting birds in weekly UAS imagery of colonies with biology-informed algorithmic rules to generate an automated approach that identifies likely nests. Comparing the performance of these automated approaches to human assessment of the same imagery shows that our primary approach identifies nests with comparable performance to human photo assessment, and that a secondary approach designed to find quick-fail nests resulted in high false positive rates. We also assessed the ability of both human photo assessment and our primary algorithm to find ground-verified nests in UAS imagery and again found comparable performance, with the exception of nests that fail quickly. Our results show that automating nest detection, a key first step towards estimating nest success, is possible in complex environments like the Everglades and we discuss a number of challenges and possible uses for these types of approaches.

ecology↗

bioRxiv2024

Near real-time monitoring of long legged wading birds using uncrewed aircraft systems and computer vision

Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real-time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end-to-end workflow for conducting near real-time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual level species data using a Retinanet-50 object detector, post-processed, archived, and presented on a web-based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species-level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post-survey processing, data on the populations of these species is available in near real-time (< 1 week from the date of the survey) providing information at the time-scales needed for ecological forecasting and active management.

ecology↗

bioRxiv2023

Fine needle aspiration of human lymph nodes reveals cell populations and soluble interactors pivotal to immunological priming

Lymph node (LN) fine needle aspiration (LN FNA) represents a powerful technique for minimally invasive sampling of human lymph nodes in vivo and has been used to good effect to directly study aspects of the human germinal center response. However, systematic deep phenotyping of the cellular populations and cell-free proteins recovered by LN FNA has not been performed. Thus, we studied human cervical LN FNAs as a proof-of-concept and used single-cell RNA-sequencing and proteomic analysis to benchmark this compartment, define the purity of LN FNA material, and facilitate future studies into this immunologically pivotal environment. Our data provide evidence that LN FNAs contain bone fide LN-resident innate immune populations, with minimal contamination of cells or proteins from blood. Examination of these populations reveals unique biology not predictable from equivalent blood-derived populations. LN FNA supernatants represent a specific source of lymph- and lymph node-derived proteins, and can, in combination with transcriptomic approaches, identify likely receptor-ligand interactions. This study provides the first description of the types and abundance of immune cell populations and cell-free proteins that can be efficiently studied by LN FNA. These findings are of broad utility for understanding LN physiology both in health and disease, including infectious or autoimmune perturbations, and in the case of cervical nodes, neuroscience.

immunology↗

bioRxiv2022

An unbiased characterization of the HLA-E and CD94/NKG2x peptide repertoire reveals peptide ligands that skew NK cell activation

HLA-E is a non-classical class I MHC protein involved in innate and adaptive immune recognition. While recent studies have shown HLA-E can present diverse peptides to NK cells and T cells, the HLA-E and NK receptor peptide repertoire has remained poorly defined, with only a limited number of peptide ligands identified. Here we screen a yeast-displayed peptide library in the context of HLA-E to identify 500 high-confidence unique peptides that bind both HLA-E and CD94/NKG2A or CD94/NKG2C. Utilizing the sequences identified via yeast display selections, we train prediction algorithms and identify human and cytomegalovirus (CMV) proteome-derived, HLA-E-presented peptides capable of binding and signaling through both CD94/NKG2A and CD94/NKG2C. In addition, we identify peptides which selectively activate NKG2C+ NK cells. Taken together, characterization of the HLA-E-binding peptide repertoire and identification of NK activity-modulating peptides present opportunities for studies of NK cell regulation in health and disease, in addition to vaccine and therapeutic design.

immunology↗

bioRxiv2019

Activation of MAIT cells plays a critical role in viral vector vaccine immunogenicity

Mucosal-associated invariant T (MAIT) cells can be activated by viruses through a cytokine-dependent mechanism, and thereby protect from lethal infection. Given this, we reasoned MAIT cells may have a critical role in the immunogenicity of replication-incompetent adenovirus vectors, which are novel and highly potent vaccine platforms. In vitro, ChAdOx1 (Chimpanzee Adenovirus Ox1) induced potent activation of MAIT cells. Activation required transduction of monocytes and plasmacytoid dendritic cells to produce IL-18 and IFN-, respectively. IFN--induced monocyte-derived TNF- was identified as a novel intermediate in this activation pathway, and activation required combinatorial signaling of all three cytokines. Furthermore, ChAdOx1-induced in vivo MAIT cell activation in both mice and human volunteers. Strikingly, MAIT cell activation was necessary in vivo for development of ChAdOx1-induced HCV-specific CD8 T cell responses. These findings define a novel role for MAIT cells in the immunogenicity of viral vector vaccines, with potential implications for future design.\n\nOne sentence summaryRobust immunogenicity of candidate adenovirus vaccine vectors requires the activation of unconventional T cells.

immunology↗