Biology subjects

Bullmann, T.

Publications and source records attributed to Bullmann, T..

3 recordsLinked to original sources

bioRxiv2022

NMDA-receptor-Fc-fusion constructs neutralize anti- NMDA receptor antibodies

N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common subtype of autoimmune encephalitis characterized by a complex neuropsychiatric syndrome ranging from memory impairment and psychosis to coma. Patients develop an intrathecal immune response against NMDARs with antibodies that presumably bind to the amino-terminal domain (ATD) of the GluN1 subunit. The therapeutic response to immunotherapy is often delayed and does not directly interfere with intrathecal synthesis of pathogenic antibodies. Therefore, new therapeutic approaches for fast neutralization of NMDAR antibodies are needed. Here, we developed fusion constructs consisting of the Fc part of immunoglobulin G and the ATDs of either GluN1 or GluN2B or both, GluN1 and GluN2B, subunits. Surprisingly, both subunits were required to generate high-affinity epitopes. The construct with both subunits efficiently prevented NMDAR binding of patient-derived monoclonal antibodies and of patient cerebrospinal fluid containing high-titer NMDAR antibodies. Furthermore, it inhibited the internalization of NMDARs in rodent dissociated neurons and human induced pluripotent stem cells (iPSC)-derived neurons. Finally, the construct stabilized NMDAR currents recorded in rodent neurons. Our results demonstrate that both GluN1 and GluN2B subunits contribute to the main immunogenic region of the NMDAR and provide a promising strategy for fast and specific treatment of NMDAR encephalitis, which can complement immunotherapy.

neuroscience↗

bioRxiv2019

UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

Recently, there has been a rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from a stack of two-dimensional (2D) electron microscopic (EM) images. The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for DNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, that enables 2D- and 3D-DNN-based segmentation for non-computer experts. UNI-EM is a software collection for DNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM comes with a set of 2D DNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D DNN-based neuron segmentation algorithm. The 2D- and 3D-DNNs are known to show state-of-the-art level segmentation performance. We then provided two-example workflows: mitochondria segmentation using a 2D DNN as well as neuron segmentation using FFNs. Following these example workflows, users can benefit from DNN-based segmentation without any knowledge of Python programming or DNN frameworks.

neuroscience↗

bioRxiv2017

Network Analysis Of High-Density Microelectrode Recordings

A high-density microelectrode arrays (HDMEA) with 3,150 electrodes per square millimetre was used to capture neuronal activity across various scales, including axons, dendrites, and networks. We present a new method for high-throughput segmentation of axons based on the spatial smoothness of signal delays. Comparison with both ground truth and receiver operator characteristics shows that the new segmentation method outperforms previous methods based on the signal-amplitude-to-noise ratio. Structural and functional neuronal network connectivity were reconstructed using a common extension of \"Peters rule\" and a inter-spike histogram method, respectively. Approximately one third of these connections are putative chemical synapses. We evaluated the spike patterns but did not find evidence for \"polychronisation\" (non-synchronous but precisely timed spike sequences). The developed framework can be used to investigate the relationship between the topology of neuronal connections and emerging temporal spike patterns observed in dissociated neuronal cultures.

neuroscience↗