@phdthesis{11945,
  abstract     = {G protein-coupled receptors (GPCRs) respond to specific ligands and regulate multiple processes ranging from cell growth and immune responses to neuronal signal transmission. However, ligands for many GPCRs remain unknown, suffer from off-target effects or have poor bioavailability. Additional challenges exist to dissect cell-type specific responses when the same GPCR is expressed on several cell types within the body. Here, we overcome these limitations by engineering DREADD-based GPCR chimeras that selectively bind their agonist clozapine-N-oxide (CNO) and mimic a GPCR-of-interest in a desired cell type.
We validated our approach with β2-adrenergic receptor (β2AR/ADRB2) and show that our chimeric DREADD-β2AR triggers comparable responses on second messenger and kinase activity, post-translational modifications, and protein-protein interactions. Since β2AR is also enriched in microglia, which can drive inflammation in the central nervous system, we expressed chimeric DREADD-β2AR in primary microglia and successfully recapitulate β2AR-mediated filopodia formation through CNO stimulation. To dissect the role of selected GPCRs during microglial inflammation, we additionally generated DREADD-based chimeras for microglia-enriched GPR65 and GPR109A/HCAR2. In a microglia cell line, DREADD-β2AR and DREADD-GPR65 both modulated the inflammatory response with a similar profile as endogenously expressed β2AR, while DREADD-GPR109A showed no impact.
Our DREADD-based approach provides the means to obtain mechanistic and functional insights into GPCR signaling on a cell-type specific level.},
  author       = {Schulz, Rouven},
  issn         = {2663-337X},
  pages        = {133},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Chimeric G protein-coupled receptors mimic distinct signaling pathways and modulate microglia function}},
  doi          = {10.15479/at:ista:11945},
  year         = {2022},
}

@phdthesis{12378,
  abstract     = {Environmental cues influence the highly dynamic morphology of microglia. Strategies to 
characterize these changes usually involve user-selected morphometric features, which 
preclude the identification of a spectrum of context-dependent morphological phenotypes. 
Here, we develop MorphOMICs, a topological data analysis approach, which enables semiautomatic mapping of microglial morphology into an atlas of cue-dependent phenotypes,
overcomes feature-selection bias and minimizes biological variability. 
First, with MorphOMICs we derive the morphological spectrum of microglia across seven 
brain regions during postnatal development and in two distinct Alzheimer’s disease 
degeneration mouse models. We uncover region-specific and sexually dimorphic
morphological trajectories, with females showing an earlier morphological shift than males in 
the degenerating brain. Overall, we demonstrate that both long primary- and short terminal 
processes provide distinct insights to morphological phenotypes. Moreover, using machine 
learning to map novel condition on the spectrum, we observe that microglia morphologies 
reflect a dose-dependent adaptation upon ketamine anesthesia and do not recover to control 
morphologies.
Next, we took advantage of MorphOMICs to build a high-resolution and layer-specific map of 
microglial morphological spectrum in the retina, covering postnatal development and rd10 
degeneration. Here, following photoreceptor death, microglia assume an early developmentlike morphology. Finally, we map microglial morphology following optic nerve crush on the 
retinal spectrum and observe a layer- and sex-dependent response. 
Overall, MorphOMICs opens a new perspective to analyze microglial morphology across 
multiple conditions, and provides a novel tool to characterize microglial morphology beyond 
the traditionally dichotomized view of microglia.},
  author       = {Colombo, Gloria},
  issn         = {2663-337X},
  pages        = {142},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{MorphOMICs, a tool for mapping microglial morphology, reveals brain region- and sex-dependent phenotypes}},
  doi          = {10.15479/at:ista:12378},
  year         = {2022},
}