Three-dimensional structured illumination microscopy with enhanced axial resolution

Release date: 11-08-2022 (MM-DD-YYYY)
Contacts: Xuesong Li (lix3@janelia.hhmi.org), Hari Shroff (shroffh@janelia.hhmi.org)
Organization: National Institutes of Health (NIH) & Janelia Research Campus, Howard Hughes Medical Institute (HHMI)

These are representative test datasets for paper "Three-dimensional structured illumination microscopy with enhanced axial resolution".

1. "bac_3DSIM(1P35NA).tif", "bac_4BSIM(1P35NA).tif", "U2OS_Tomm20_3DSIM(1P35NA).tif" and "U2OS_Tomm20_4BSIM(1P35NA).tif" are raw 3D SIM or 4-beam SIM images for Wiener reconstruction. The Matlab-based reconstruction code and software can be downloaded here.

2. "DeepLearning_models(EMTB).zip" contains 2 deep learning models for image denoising and 1 model for axial resolution improvement. "DeepLearning_test_dataset(EMTB).zip" contains 15 low-SNR raw 3D SIM images as input and 1 final result after the 3-step deep learning process. Both the models and test datasets are based on live Jurkat T cells with a microtubule marker labeled by EMTB 3x GFP and must be used together. The Python-based deep learning code to improve axial resolution of 3D SIM images can be downloaded here.

All the others are representative source data for the main figures (Figs 1d, 2a, 2e, 2h, 3a, 3c, 3h, 3b, 3e, 4b, 4e, 5b, 5e, 6) in paper "Three-dimensional structured illumination microscopy with enhanced axial resolution".