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Beretta, C.

Publications and source records attributed to Beretta, C..

4 recordsLinked to original sources

Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis

Intravital two-photon microscopy has emerged as a powerful technology to study brain tumor biology and its temporal dynamics, including invasion, proliferation and therapeutic resistance in the superficial layers of the mouse cortex. However, intravital microscopy of deeper cortical layers and especially the subcortical white matter, an important route of glioblastoma invasion and recurrence, has not yet been feasible due to low signal-to-noise ratios, missing spatiotemporal resolution and the inability to delineate myelinated axonal tracts. Here, we present a tailored intravital microscopy and artificial intelligence-based analysis methodology and workflow that enables routine deep imaging of glioblastoma over extended time periods, named Deep3P. We show that three-photon microscopy, adaptive optics, as well as customized deep learning-based denoising and machine learning segmentation together allow for deep brain intravital investigation of tumor biology up to 1.2 mm depth. Leveraging this approach, we find that perivascular invasion is a preferred invasion route into the corpus callosum as compared to intracortical glioblastoma invasion and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we can define an imaging biomarker of white matter disruption during early glioblastoma colonization. Taken together, Deep3P allows for an efficient and non-invasive investigation of brain tumor biology and its tumor microenvironment in unprecedented deep white and gray matter of the living mouse, opening up novel opportunities for studying the neuroscience of brain tumors and other model systems.

cancer biology↗

Loss of secondary motor cortex neurons in chronic neuropathic pain

Chronic neuropathic pain is associated with structural plasticity of the brain on different spatial scales, yet, little is known on the mesoscopic scale of tissue composition. Here, we determined the cellular composition of cortical areas and structural variability of entire neurons during the development of chronic neuropathic pain using longitudinal in vivo two-photon microscopy and behavioral assessment. When monitoring cell type composition in response to spared-nerve injury in 84 cortical volumes containing {bsim}25000 cells each, we found neuronal loss in the secondary motor cortex region M2 immediately adjacent to the cingulate cortex already one week after surgery. Loss of mostly interneurons was also evident when monitoring individual M2 neurons over time. This neuronal loss was preceded by decreased spine density and loss of distal dendritic branches. In conclusion, our work delineates M2 as a novel site and neuronal loss as a so far underappreciated mechanism underlying chronic neuropathic pain states.

neuroscience↗

Comprehensive monitoring of tissue composition using in vivo imaging of cell nuclei and deep learning

Comprehensive analysis of tissue composition has so far been limited to ex-vivo approaches. Here, we introduce NuCLear (Nucleus-instructed tissue composition using deep learning), an approach combining in vivo two-photon imaging of histone 2B-eGFP-labeled cell nuclei with subsequent deep learning-based identification of cell types from structural features of the respective cell nuclei. Using NuCLear, we were able to classify almost all cells per imaging volume in the secondary motor cortex of the mouse brain (0.25 mm3 containing [~]25000 cells) and to identify their position in 3D space in a non-invasive manner using only a single label throughout multiple imaging sessions. Twelve weeks after baseline, cell numbers did not change yet astrocytic nuclei significantly decreased in size. NuCLear opens a window to study changes in relative abundance and location of different cell types in the brains of individual mice over extended time periods, enabling comprehensive studies of changes in cellular composition in physiological and pathophysiological conditions.

neuroscience↗

Grey matter volume changes and corresponding cellular metrics identified in a longitudinal in vivo imaging approach

Magnetic resonance imaging (MRI) of the brain combined with voxel-based morphometry (VBM) has revealed structural changes of grey and white matter in a range of neurological and psychiatric disorders. However, the cellular basis of volume changes observed with VBM has remained unclear. We devised an approach to systematically correlate changes in grey matter volume (GMV) with cellular composition. Mice were alternately examined with structural MRI and two-photon in vivo microscopy at three time points, taking advantage of age-dependent changes in brain structure. We chose to image fluorescently labelled cell nuclei, because these can be readily imaged in large tissue volumes and allow inferences on several structural parameters: (1) the physical volume as determined from a subset of nuclei used to generate a geometrically defined space, (2) the number of cells, (3) the nearest neighbour distance measured between all nuclei as an indicator of cell clustering, and (4) the volume of the cell nuclei. Using this approach, we found that physical volume did not significantly correlate with GMV change, whereas mean nuclear volume was inversely correlated. When focusing on layers within the imaging volume, positive correlations of GMV were found with cell number near the cortical surface and nearest neighbour distance in deeper layers. Thus, the novel approach introduced here provided new insights into the factors underlying grey matter volume changes.

neuroscience↗