Noh Jungsik, Wong Wen Mai, Danuser Gaudenz, Meeks Julian P
Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Graduate Program in Neuroscience, Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA.
bioRxiv. 2024 Sep 24:2024.09.24.614764. doi: 10.1101/2024.09.24.614764.
Calcium fluorescence imaging enables us to investigate how individual neurons of live animals encode sensory input or drive specific behaviors. Extracting and interpreting large-scale neuronal activity from imaging data are crucial steps in harnessing this information. A significant challenge arises from uncorrectable tissue deformation, which disrupts the effectiveness of existing neuron segmentation methods. Here, we propose an open-source software, DynamicNeuronTracker (DyNT), which generates dynamic neuron masks for deforming and/or incompletely registered 3D calcium imaging data using patch-matching iterations. We demonstrate that DyNT accurately tracks densely populated neurons, whereas a widely used static segmentation method often produces erroneous masks. DyNT also includes automated statistical analyses for interpreting neuronal responses to multiple sequential stimuli. We applied DyNT to analyze the responses of pheromone-sensing neurons in mice to controlled stimulation. We found that four bile acids and four sulfated steroids activated 15 subpopulations of sensory neurons with distinct combinatorial response profiles, revealing a strong bias toward detecting sulfated estrogen and pregnanolone.
钙荧光成像使我们能够研究活体动物的单个神经元如何编码感觉输入或驱动特定行为。从成像数据中提取和解释大规模神经元活动是利用这些信息的关键步骤。不可纠正的组织变形带来了一个重大挑战，它破坏了现有神经元分割方法的有效性。在这里，我们提出了一种开源软件，动态神经元追踪器（DyNT），它使用补丁匹配迭代为变形和/或未完全配准的3D钙成像数据生成动态神经元掩码。我们证明DyNT能够准确跟踪密集分布的神经元，而一种广泛使用的静态分割方法往往会产生错误的掩码。DyNT还包括用于解释神经元对多个连续刺激反应的自动统计分析。我们应用DyNT分析了小鼠中信息素感知神经元对受控刺激的反应。我们发现四种胆汁酸和四种硫酸化类固醇激活了15个感觉神经元亚群，它们具有不同的组合反应谱，揭示了对检测硫酸化雌激素和孕烷醇酮的强烈偏好。
