Biology subjects

Chen, N.-Y.

Publications and source records attributed to Chen, N.-Y..

3 recordsLinked to original sources

bioRxiv2025

Real-time spike sorting with 3D neural probe and triangulation localization

A key challenge in correlating neuronal activity with brain function is the limited sampling probability of neuronal activity in real time. It is crucial to increase the sampling probability substantially and in real time. We hypothesized that 3-dimensional (3D) neural probes offer faster and stronger prospects for cell yield than 2D electrode arrays. We simulated a 1000-neuron neuronal network, mimicking the granular layer of the barrel cortical column, recorded signals from inserted 384 electrodes (organized in 3D or 2D), and sorted units using Kilosrt or triangulation localization. We demonstrated that 3D electrode arrays converge more space for triangulation spike sorting than 2D probes do. 3D neural probes, together with triangulation, could isolate up to 80% of the simulated 1000 neurons (as ground truth) and have a cell yield of up to 5, which is, to the best of our knowledge, significantly higher than standard 2D electrodes with Kilosort or triangulation. With a signal-to-noise ratio (SNR) of 10, which is close to the real world, the simulation data suggest that 3D electrode arrays in a face-centric cubic (FCC) arrangement provide a better cell yield. However, larger background noise (e.g. an SNR of 1, which can be improved with lower electrode impedance) has a stronger impact on the triangulation spike sorting. Since only the peak value of spikes are required for triangulation localization, the computing loading is much less than spike waveform-based spike sorting approach. Thus, combining 3D electrode arrays with triangulation localization is ideal for real-time spike sorting. Thus, we demonstrated that adding one more dimension in designing neural probes can dramatically increase cell yield and speed up isolating neuronal unit activity. We, for the first time, provide a tool for utilizing computer simulations to optimize the design of neural electrode arrays before time-consuming probe fabrication.

neuroscience↗

bioRxiv2024

Enolase-1 is essential for neutrophil recruitment during acute inflammation

Enolase-1 (ENO1) is a moonlighting protein with multiple functions. When expressed on the cell surface, ENO1 binds plasminogen (PLG) and promotes cell migration by facilitating plasmin (PLM)-mediated extracellular matrix degradation. Here, we observed that inflammatory stimulation significantly upregulated ENO1 expression on the neutrophil surface, both in vitro and in vivo. An anti-ENO1 monoclonal antibody (mAb), 7E5, which blocks the ENO1-PLG interaction, effectively suppressed neutrophil transmigration. In mouse models of acute inflammation, including lipopolysaccharide (LPS)-induced lung injury and necrotic cell challenge, 7E5 treatment markedly reduced neutrophil recruitment and neutrophil extracellular trap (NET) formation. Moreover, 7E5 neutralized the immunostimulatory activity of soluble ENO1, which was significantly elevated in circulation during acute inflammation. These findings highlight ENO1 as a key regulator of inflammation and neutrophil infiltration. Targeting ENO1 with antibodies could be a promising strategy to mitigate tissue damage caused by excessive neutrophilic inflammation.

immunology↗

bioRxiv2024

Automated neuropil segmentation of fluorescent images for Drosophila brains

The brain atlas, which provides information about the distribution of genes, proteins, neurons, or anatomical regions in the brain, plays a crucial role in contemporary neuroscience research. To analyze the spatial distribution of those substances based on images from different brain samples, we often need to warp and register individual brain images to a standard brain template. However, the process of warping and registration often leads to spatial errors, thereby severely reducing the accuracy of the analysis. To address this issue, we develop an automated method for segmenting neuropils in the Drosophila brain using fluorescence images from the FlyCircuit database. This technique allows future brain atlas studies to be conducted accurately at the individual level without warping and aligning to a standard brain template. Our method, LYNSU (Locating by YOLO and Segmenting by U-Net), consists of two stages. In the first stage, we use the YOLOv7 model to quickly locate neuropils and rapidly extract small-scale 3D images as input for the second stage model. This stage achieves a 99.4% accuracy rate in neuropil localization. In the second stage, we employ the 3D U-Net model to segment neuropils. LYNSU can achieve high accuracy in segmentation using a small training set consisting of images from merely 16 brains. We demonstrate LYNSU on six distinct neuropils or structure, achieving a high segmentation accuracy, which was comparable to professional manual annotations with a 3D Intersection-over-Union(IoU) reaching up to 0.869. Most notably, our method takes only about 7 seconds to segment a neuropil while achieving a similar level of performance as the human annotators. The results indicate the potential of the proposed method in high-throughput connectomics construction for Drosophila brain optical imaging.

neuroscience↗