Published November 17, 2021 | Version v5

Accelerating DNA-PAINT imaging with a deep neural network

  • 1. Heidelberg University
  • 2. Goethe University Frankfurt

Description

(RawFrames_P5-IS_20pM.tif) Raw DNA-PAINT SMLM frames with isolated emitters taken on TOM20 labelled MNTB neuronal rat tissue with P5 imaging strand at 20 pM concentration. 

(Binned_HighDensity_30kpatches.tif and Binned_HighDensity_30kpatches.csv) Artificially summed high-density emitter patches with the corresponding emitter coordinates used for training the DeepSTORM neural network.  

(DeepSTORM_model_metadata.mat and DeepSTORM_model_weights_best.hdf5) The trained model metadata and weights used for all predicted images in the study.

(Figure 4_Large_super-resolution_image.png) The large super-resolution image in Figure 4 and Figure S4.

(High-density-frames_Images 1 - 5 .tif) Five high-emitter density raw frames for alpha-tubulin and TOM20 of 400 frames each.

(Ground-truth-rendered_Image 1 - 5.tif) Five ground truth images rendered in Picasso (drift-corrected, linked localisations, pixel size 13.37 nm/pixel) for alpha-tubulin and TOM20.

(Low density frames for GT images.zip) Five low-emitter density (0.5 nM) raw frames for alpha-tubulin and TOM20 of 10000 frames each which were used to render ground truth images.

(Bassoon_Homer_datasets.zip) Three Bassoon and Homer datasets each with a ground truth image and the corresponding high-density raw frames (5 nM, 800 frames).

Files

Bassoon_Homer_datasets.zip

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