Segmentation of subcellular structures from nanoscopy data plays a pivotal role in the understanding of subcellular biological mechanisms. In comparison to regular microscopy images, the data from a Nobel Prize winning nanoscopy technique, namely, Single Molecule Localization Microscopy (SMLM), is in the form of just a point cloud in 3D space. The segmentation problem becomes complex due to varying shapes of the subcellular structures, and presence of various types of noise in the input. Lack of ground-truth further adds to the challenge. In this paper, we propose a Label modulated Dynamic Graph Convolution Network (LDGCN) to detect two different subcellular structures, namely, microtubules and vesicles from noisy 3D point cloud data. We first build graphs dynamically in two different layers of our GCN. On-the-fly, two segmentation class labels (a subcellular structure or noise) are used to induce more dynamism in the proposed solution. In order to deal with the absence of ground truth, we generate synthetic noisy 3D point clouds for microtubules and vesicles with a custom simulator. The performance of our proposed algorithm was tested on these simulated datasets and qualitatively assessed on both simulated and real experimental microscopy data. Qualitative and quantitative comparisons clearly illustrate the efficacy of our solution.