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Super-Resolved Imaging of Early-Stage Dynamics in the Immune Response

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The use of photo-activated fluorescent molecules to create long sequences of low-density, diffraction-limited images gives us the ability to achieve highly-precise molecule localizations. However, this methodology requires lengthy imaging times, resulting in poor temporal resolution. This is particularly problematic when dynamic interactions of live cells on short time scales are of interest. We consider the problem of shortening dramatically the acquisition times in superresolution microscopy down to seconds, in order to image the cellular dynamics during T-cell activation. For this purpose, we apply our newly developed method for super-resolution microscopy, SPARCOM, to real-time imaging of the immune response initiation. Despite the small number of frames used for reconstruction, we are able to achieve high-quality imaging that shows how T-cell receptors re-organize during the activation events, with respect to the cell’s surface. Thus, our work demonstrates the use of SPARCOM for imaging dynamic cellular processes well below the diffraction limit.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages3468-3472
Number of pages5
ISBN (Electronic)9781665441155
DOIs
StatePublished - 19 Sep 2021
Event28th IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 19 Sep 202122 Sep 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September

Conference

Conference28th IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period19/09/2122/09/21

Keywords

  • Fluorescence microscopy
  • High-Resolution imaging
  • SPARCOM
  • Sparse recovery

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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