Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light microscopy (LM) and electron microscopy (EM). Here we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100-nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.

神经元网络成像为理解神经系统提供了基础，但解析大体积的致密纳米级结构对于光学显微镜 (LM) 和电子显微镜 (EM) 来说仍然具有挑战性。在这里，我们展示了 X 射线全息纳米断层扫描 (XNH) 可以以亚 100 nm 的分辨率对毫米级体积进行成像，从而能够重建果蝇和小鼠神经组织中的密集布线。我们进行了相关的 XNH 和 EM 来重建数百个皮质锥体细胞，并表明越表层的细胞对其顶端树突受到更强的突触抑制。通过结合多次 XNH 扫描，我们以足够的分辨率对成年果蝇腿进行了成像，以全面对机械感觉神经元进行分类，并追踪从肌肉到中枢神经系统的单个运动轴突。为了加速神经元重建，我们训练了一个卷积神经网络来自动从 XNH 体积中分割神经元。因此，XNH 弥合了 LM 和 EM 之间的关键差距，为神经回路发现提供了新途径。