Elena De Domenico

The paper profiles canine sensory neurons using FLASH-seq to identify species-specific molecular signatures relevant to pain and itch, revealing both conserved and divergent features compared to human and mouse.

The paper investigates how ischemic injury affects microglial response to Aβ pathology, finding a protective microglial phenotype that promotes benign Aβ plaque formation.

This study shows that IgLON5 autoantibodies cause neuronal hyperactivity and Tau pathology by clustering cell surface proteins, linking them to neurodegeneration.

The paper develops an in vitro platform to screen immune senomodulators using human-derived immune cells, enabling personalized anti-aging treatments through multi-scale molecular analysis.

This study shows that increased somatodendritic Tau interacts with nuclear envelope proteins, causing nuclear damage and altering gene expression related to AD and tauopathies.

This study shows that Satb1 downregulation prevents autoimmunity by maintaining Tfh cell homeostasis and preventing excessive GC formation and auto-antibody secretion.

This study investigates how TREM2 expression levels affect microglial function, metabolism, and response to TREM2 agonists in a mouse model of Alzheimer's disease.

This paper develops a 3D cortical brain tissue model with mature microglia that recapitulates Alzheimer's disease hallmarks and enables study of microglia phenotypes.

The paper introduces searchSTR, a tool for genome-wide STR somatic instability analysis, and finds associations between STR instability and age, sex, brain morphology, and neurodegeneration markers.

This study identifies molecular factors shaping brain-resident CD8+ T cell formation and function in health and disease.

The paper characterizes human CMV-specific CD8+ T cells using a five-layer multi-omics approach and identifies novel T cell receptor sequences for CMV epitopes.

This study shows that Trim71 deficiency during gastrulation disrupts gene expression, leading to defects in erythropoiesis and vascular development, causing embryonic lethality.

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.