Advances in volumetric super-resolution microscopy and single-particle tracking (associated codes and datasets)

Overview

This Zenodo repository contains datasets and code relating to the thesis entitled "Advances in volumetric super-resolution microscopy and single-particle tracking" by Sam G. Daly (Yusuf Hamied Department of Chemistry, University of Cambridge).

Managed/updated versions my be avalible at https://github.com/TheLeeLab.

The Excel Workbook 'MicrolensRelayCalculator' is designed to help in the design of MLAs for SMLFM.

Available Datasets

Chapter 4

Simulated localisation data for various PSFs: standard, astigmatism, double helix, SMLFM, and tetrapod; 4000 detected photons, 20 emitters per frame, 200 frames.

Microtubule imaging in a fixed HeLa cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames.

Chapter 5

B cell receptor imaging on a fixed B cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.

SPT of the B cell receptor on a live B cell (PALM); 20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.

Membrane imaging on a fixed Jurkat T cell embedded in agarose (resPAINT); 20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.

PD-1 imaging on a fixed T cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.

Membrane imaging on a fixed T cell (resPAINT); 20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.

Chapter 6

SPT of ACBD3 in a live HeLa cell (PALM); 20 ms exposure, 640 and 405 nm excitation, 200 frames.

SPT of TMD mutant (length: 27) in a live HeLa cell (PALM); 20 ms exposure, 640 and 405 nm excitation, 200 frames.

Available Code

Autofocus (BeanShell): Counteracts axial drift in SMLFM experiments.

Calibration (BeanShell): Controls the piezo scanner for axial calibrations in 3D-SMLM.

3D Reconstruction (Matlab): Reconstructs 2D-localised SMLFM data in 3D. Maintained version available on GitHub.

Fiducial correction (Matlab): Removes focal drift artifacts from 3D localisation data.

Temporal grouping (Python): Removes multiple single-molecule blinking events.

3D tracking (Matlab): Converts 3D localisations into tracks and calculates diffusion quantities.

Matching (Matlab): Determines PPV, sensitivity, and Jaccard index from localisation data.

Membrane curvature (Python): Determines the frequency of 3D localisations at a given membrane curvature.

Supported by The Royal Society (RGF\EA\181021)