Biology subjects

Venturino, A.

Publications and source records attributed to Venturino, A..

3 recordsLinked to original sources

bioRxiv2022

Uncovering brain tissue architecture across scales with super-resolution light microscopy

Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanoscopic synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS leverages fixation-compatible extracellular labeling and advanced optical readout, in particular stimulated-emission depletion and expansion microscopy, to comprehensively delineate cellular structures. It enables 3D-reconstructing single synapses and mapping synaptic connectivity by identification and tailored analysis of putative synaptic cleft regions. Applying CATS to the hippocampal mossy fiber circuitry, we demonstrate its power to reveal the systems molecularly informed ultrastructure across spatial scales and assess local connectivity by reconstructing and quantifying the synaptic input and output structure of identified neurons.

neuroscience↗

bioRxiv2022

A systematic characterization of intrinsically formed microglia-like cells during retinal organoid differentiation.

Brain organoids differentiated from human induced pluripotent stem cells provide a unique opportunity to investigate the development, organization and connectivity of neurons in a complex cellular environment. However, organoids usually lack microglia, brain-resident immune cells which are both present in the early human embryonic brain and participate in neuronal circuit development. Here, we find that microglia innately develop in unguided retinal organoid differentiation between week 3 and 4 in 2.5D culture and appear later in floating, non-pigmented, 3D-cystic compartments. We enriched for cystic structures using a low-dosed BMP4 application and performed mass spectrometry, thus defining the protein composition of microglia-containing compartments. We found that cystic compartments expressed both mesenchymal and epithelial markers with microglia enriched in the mesenchymal region. Interestingly, microglia-like cells started to express the border-associated macrophage marker CD163. The preferential localization of human microglia to a mesenchymal compartment provides insight into the behavior and migration of microglia. The model will ultimately allow detailed study of these enigmatic cells and how they enter and distribute within the human brain.

neuroscience↗

bioRxiv2021

Microglial MorphOMICs unravel region- and sex-dependent morphological phenotypes from postnatal development to degeneration

Microglia contribute to tissue homeostasis in physiological conditions with environmental cues influencing their ever-changing morphology. Strategies to identify these changes usually involve user-selected morphometric features, which, however, have proved ineffective in establishing a spectrum of context-dependent morphological phenotypes. Here, we have developed MorphOMICs, a topological data analysis approach to overcome feature-selection-based biases and biological variability. We extracted a spatially heterogeneous and sexually-dimorphic morphological phenotype for seven adult brain regions, with ovariectomized females forming their own distinct cluster. This sex-specific phenotype declines with maturation but increases over the disease trajectories in two neurodegeneration models, 5xFAD and CK-p25. Females show an earlier morphological shift in the immediately-affected brain regions. Finally, we demonstrate that both the primary- and the short terminal processes provide distinct insights to morphological phenotypes. MorphOMICs maps microglial morphology into a spectrum of cue-dependent phenotypes in a minimally-biased and semi-automatic way.

neuroscience↗