Human induced pluripotent stem cell-derived microglia-like cells harboring TREM2 missense mutations show specific deficits in phagocytosis

Dysfunction of microglia, the brain’s immune cells, is linked to neurodegeneration. Homozygous missense mutations in TREM2 cause Nasu-Hakola disease (NHD), an early-onset dementia. To study the consequences of these TREM2 variants, we generated induced pluripotent stem cell-derived microglia-like cells (iPSC-MGLCs) from patients with NHD caused by homozygous T66M or W50C missense mutations. iPSC-MGLCs expressed microglial markers and secreted higher levels of TREM2 than primary macrophages. TREM2 expression and secretion were reduced in variant lines. LPS-mediated cytokine secretion was comparable between control and TREM2 variant iPSC-MGLCs, whereas survival was markedly reduced in cells harboring missense mutations when compared with controls. Furthermore, TREM2 missense mutations caused a marked impairment in the phagocytosis of apoptotic bodies, but not in Escherichia coli or zymosan substrates. Coupled with changes in apoptotic cell-induced cytokine release and migration, these data identify specific deficits in the ability of iPSC-MGLCs harboring TREM2 missense mutations to respond to specific pathogenic signals

Contour Segmentation with Recurrent Neural Networks of Pulse-Coding Neurons

. The performance of technical and biological vision systems crucially relies on powerful processing capabilities. Robust object recognition must be based on representations of segmented object candidates which are kept stable and sparse despite the highly variable nature of the environment. Here, we propose a network of pulse-coding neurons based on biological principles which establishs such representations using contour information. The system solves the task of grouping and figureground segregation by creating flexible temporal correlations among contour extracting units. In contrast to similar previous approaches, we explicitly address the problem of processing grey value images. In our multi-layer architecture, the extracted contour features are edges, line endings and vertices which interact by introducing facilatory and inhibitory couplings among feature extracting neurons. As the result of the network dynamics, individual mutually occluding objects become defined by temporally..

Stimulus-dependent correlations in stochastic networks

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