PUBLICATION

Image restoration of degraded time-lapse microscopy data mediated by near-infrared imaging

Authors
Gritti, N., Power, R.M., Graves, A., Huisken, J.
ID
ZDB-PUB-240105-32
Date
2024
Source
Nature Methods   21(2): 311-321 (Journal)
Registered Authors
Huisken, Jan
Keywords
none
MeSH Terms
  • Animals
  • Drosophila*
  • Green Fluorescent Proteins/genetics
  • Microscopy, Fluorescence
  • Time-Lapse Imaging/methods
  • Zebrafish*
PubMed
38177507 Full text @ Nat. Methods
Abstract
Time-lapse fluorescence microscopy is key to unraveling biological development and function; however, living systems, by their nature, permit only limited interrogation and contain untapped information that can only be captured by more invasive methods. Deep-tissue live imaging presents a particular challenge owing to the spectral range of live-cell imaging probes/fluorescent proteins, which offer only modest optical penetration into scattering tissues. Herein, we employ convolutional neural networks to augment live-imaging data with deep-tissue images taken on fixed samples. We demonstrate that convolutional neural networks may be used to restore deep-tissue contrast in GFP-based time-lapse imaging using paired final-state datasets acquired using near-infrared dyes, an approach termed InfraRed-mediated Image Restoration (IR2). Notably, the networks are remarkably robust over a wide range of developmental times. We employ IR2 to enhance the information content of green fluorescent protein time-lapse images of zebrafish and Drosophila embryo/larval development and demonstrate its quantitative potential in increasing the fidelity of cell tracking/lineaging in developing pescoids. Thus, IR2 is poised to extend live imaging to depths otherwise inaccessible.
Genes / Markers
Figures
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Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping