Rod-shaped microglia interact with neuronal dendrites to regulate cortical excitability in TDP-43 related neurodegeneration.

Motor cortical hyperexcitability is well-documented in the presymptomatic stage of amyotrophic lateral sclerosis (ALS). However, the mechanisms underlying this early dysregulation are not fully understood. Microglia, as the principal immune cells of the central nervous system, have emerged as important players in sensing and regulating neuronal activity. Here we investigated the role of microglia in the motor cortical circuits in a mouse model of TDP-43 neurodegeneration (rNLS8). Utilizing multichannel probe recording and longitudinal in vivo calcium imaging in awake mice, we observed neuronal hyperactivity at the initial stage of disease progression. Spatial and single-cell RNA sequencing revealed that microglia are the primary responders to motor cortical hyperactivity. We further identified a unique subpopulation of microglia, rod-shaped microglia, which are characterized by a distinct morphology and transcriptional profile. Notably, rod-shaped microglia predominantly interact with neuronal dendrites and excitatory synaptic inputs to attenuate motor cortical hyperactivity. The elimination of rod-shaped microglia through TREM2 deficiency increased neuronal hyperactivity, exacerbated motor deficits, and further decreased survival rates of rNLS8 mice. Together, our results suggest that rod-shaped microglia play a neuroprotective role by attenuating cortical hyperexcitability in the mouse model of TDP-43 related neurodegeneration.

Rainwater Charitable Foundation criteria for the neuropathologic diagnosis of progressive supranuclear palsy.

Neuropathologic criteria for progressive supranuclear palsy (PSP) proposed by a National Institute of Neurological Disorders and Stroke (NINDS) working group were published in 1994 and based on the presence of neurofibrillary tangles in basal ganglia and brainstem. These criteria did not stipulate detection methods or incorporate glial tau pathology. In this study, a group of 14 expert neuropathologists scored digital slides from 10 brain regions stained with hematoxylin and eosin (H&E) and phosphorylated tau (AT8) immunohistochemistry. The cases included 15 typical and atypical PSP cases and 10 other tauopathies. Blinded to clinical and neuropathological information, raters provided a categorical diagnosis (PSP or not-PSP) based upon provisional criteria that required neurofibrillary tangles or pretangles in two of three regions (substantia nigra, subthalamic nucleus, globus pallidus) and tufted astrocytes in one of two regions (peri-Rolandic cortices, putamen). The criteria showed high sensitivity (0.97) and specificity (0.91), as well as almost perfect inter-rater reliability for diagnosing PSP and differentiating it from other tauopathies (Fleiss kappa 0.826). Most cases (17/25) had 100% agreement across all 14 raters. The Rainwater Charitable Foundation criteria for the neuropathologic diagnosis of PSP feature a simplified diagnostic algorithm based on phosphorylated tau immunohistochemistry and incorporate tufted astrocytes as an essential diagnostic feature.

Deep Learning-Based Image Classification in Differentiating Tufted Astrocytes, Astrocytic Plaques, and Neuritic Plaques.

This study aimed to develop a deep learning-based image classification model that can differentiate tufted astrocytes (TA), astrocytic plaques (AP), and neuritic plaques (NP) based on images of tissue sections stained with phospho-tau immunohistochemistry. Phospho-tau-immunostained slides from the motor cortex were scanned at 20× magnification. An automated deep learning platform, Google AutoML, was used to create a model for distinguishing TA in progressive supranuclear palsy (PSP) from AP in corticobasal degeneration (CBD) and NP in Alzheimer disease (AD). A total of 1500 images of representative tau lesions were captured from 35 PSP, 27 CBD, and 33 AD patients. Of those, 1332 images were used for training, and 168 images for cross-validation. We tested the model using 100 additional test images taken from 20 patients of each disease. In cross-validation, precision and recall for each individual lesion type were 100% and 98.0% for TA, 98.5% and 98.5% for AP, and 98.0% and 100% for NP, respectively. In a test set, all images of TA and NP were correctly predicted. Only eleven images of AP were predicted to be TA or NP. Our data indicate the potential usefulness of deep learning-based image classification methods to assist in differential diagnosis of tauopathies.