Search bioRxiv⌕ Search

Biology subjects

Lindquist, A.

Publications and source records attributed to Lindquist, A..

2 recordsLinked to original sources

Tet2 loss suppress α-synuclein pathology by stimulating ciliogenesis

There are no approved treatments that slow Parkinsons disease (PD) progression and therefore it is important to identify novel pathogenic mechanisms that can be targeted. Loss of the epigenetic marker, Tet2 appears to have some beneficial effects in PD models, but the underlying mechanism of action is not well understood. We performed an unbiased transcriptomic analysis of cortical neurons isolated from patients with PD to identify dysregulated pathways and determine their potential contributions to the disease process. We discovered that genes associated with primary cilia, non-synaptic sensory and signaling organelles, are upregulated in both early and late PD patients. Enhancing ciliogenesis in primary cortical neurons via sonic hedgehog signaling suppressed the accumulation of -synuclein pathology in vitro. Interestingly, deletion of Tet2 in mice also enhanced the expression of primary cilia and sonic hedgehog signaling genes and rescued the accumulation of -synuclein pathology and dopamine neuron degeneration in vivo. Our findings demonstrate the crucial role of Tet2 loss in regulating ciliogenesis and potentially affecting the progression of PD pathology.

neuroscience↗

A novel automated morphological analysis of microglia activation using a deep learning assisted model

There is growing evidence for the key role of microglial activation in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements, and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools (Aiforia(R) Cloud (Aifoira Inc., Cambridge, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinsons disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are shared within the Aiforia(R) platform and are available for study-specific adaptation and validation.

neuroscience↗