Dataset supporting Speiser, Mueller et al Nature Methods 2021, Deep learning enables fast and dense single-molecule localization with high accuracy
datasetposted on 14.06.2021, 23:40 authored by Artur Speiser, Lucas-Raphael Muller, Philipp Hoess, Ulf Matti, Christopher J Obara, Wesley R. Legant, Anna Kreshuk, Jakob H. Macke, Jonas Ries, Srinivas TuragaSrinivas Turaga
Data supporting Speiser, Mueller et al Nature Methods 2021, Deep learning enables fast and dense single-molecule localization with high accuracy.
Software can be found at https://github.com/TuragaLab/DECODE
Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but standard analysis algorithms necessitate the activation of only single isolated emitters which limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take fast dynamic live-cell SMLM data with reduced light exposure and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.
The datasets included here are used to extensively test and compare the performance of DECODE for a wide variety of different recording techniques and conditions.
Includes several sets of simulated data that can be used to evaluate the performance of localization algorithms.
Metrics_Density_X are simulations with different emitter densities (Fig. 2c,d,e) and Signal to Noise Ratios (SNR, Fig. 2c).
CRLB_SNR are simulations of isolated emitters used for comparisons with the Cramér–Rao bound (Fig. 2a)
RMSE_Sigma_corr are simulations of repeated samples of the camera model for identical emitter parameters to test the uncertainty estimates of DECODE (Fig. 2b).
All localizations and evaluations are collected in Fig2_collect_5.hdf5 and can be accessed as shown in Fig_draft_upload.ipynb.
The same sample of microtubules, labeled with anti-α tubulin primary and AF647 secondary antibodies, imaged with different UV activation intensities to resulting in different emitter densities per frame and acquisition times between 93 and 1120 s, while keeping the total number of localizations the same. Used to evaluate the performance of localization algorithms across emitter densities (Fig. 4a,b,c).
Fast live-cell SMLM on the Golgi apparatus labeled withα-mannosidase II-mEos3.2. Used to evaluate the ability to process fast dynamic live-cell SMLM data with reduced light exposure. (Fig. 4d)
Fast live-cell SMLM on the endoplasmic reticulum labeled with calnexin-mEos3.2. (Fig. 4e)
Fast live-cell SMLM on the nuclear pore complex proteinNup96-mMaple acquired in 3 seconds.(Fig. 4f)
Microtubules labeled with a high concentration of anti-α and anti-β tubulin primary and Alexa Fluor 647 secondary antibodies.Used to evaluate the performance of localization algorithms at ultra-high labeling densities. (Fig. 4g,h).
Localizations inferred with DECODE for a COS-7 cell imaged with LLS-PAINT microscopy (Legant at. al 2016).
Fast live-cell SMLM on the endoplasmic reticulum labeled with calnexin-mEos3.2 (Supplementary Fig. 3)
Howard Hughes Medical Institute
European Molecular Biology Laboratory
European Research Council CoG-724489
Excellence Strategy (EXC-Number 2064/1, Project number 390727645)
German Federal Ministry of Education and Research (BMBF, project `ADMIMEM', FKZ 01IS18052)
Searle Scholars Program
Beckman Young Investigator Program
NIH New Innovator Award (DP2GM136653)
Packard Fellows Program
Connecting the dots between single molecule dynamics and cell differentiation
National Institute of General Medical SciencesFind out more...