Event-based vision sensor enables fast and dense single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) is often hampered by the fixed frame rate of the acquisition. Here, we present an alternative new approach to data acquisition and processing based on an affordable event-based sensor. This type of sensor reacts to light intensity changes rather than in...

Full description

Saved in:
Bibliographic Details
Published inbioRxiv
Main Authors Clement Cabriel, Specht, Christian G, Izeddin, Ignacio
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 22.10.2022
Subjects
Cameras
Data acquisition
Light intensity
Localization
Microscopy
Photons
Sensors
Spatial discrimination
Online AccessGet full text

Cover

More Information
Summary:Single-molecule localization microscopy (SMLM) is often hampered by the fixed frame rate of the acquisition. Here, we present an alternative new approach to data acquisition and processing based on an affordable event-based sensor. This type of sensor reacts to light intensity changes rather than integrating photons during each frame exposure time. This makes it particularly suited to SMLM, where the ability to surpass the diffraction-limited resolution is provided by blinking events. Each pixel works independently and returns a signal only when an intensity change is detected. Since the output is a list containing only useful data rather than a series of frames, the temporal resolution is significantly better than typical scientific cameras. We demonstrate event-based SMLM super-resolution imaging on biological samples with spatial resolution on par with EMCCD or sCMOS performance. Furthermore, taking advantage of its unique properties, we use event-based SMLM to perform very dense single-molecule imaging, where frame-based cameras experience significant limitations. Competing Interest Statement The authors have declared no competing interest. Footnotes * New data has been added with DNA-PAINT labels. Text has been changed accordingly. * https://github.com/Clement-Cabriel/Evb-SMLM.git
DOI:10.1101/2022.07.22.501162