Guided-deconvolution for correlative light and electron microscopy.
Fengjiao MaRainer KaufmannJaroslaw SedzickiZoltán CseresznyésChristoph DehioStephanie HoeppenerMarc Thilo FiggeRainer HeintzmannPublished in: PloS one (2023)
Correlative light and electron microscopy is a powerful tool to study the internal structure of cells. It combines the mutual benefit of correlating light (LM) and electron (EM) microscopy information. The EM images only contain contrast information. Therefore, some of the detailed structures cannot be specified from these images alone, especially when different cell organelle are contacted. However, the classical approach of overlaying LM onto EM images to assign functional to structural information is hampered by the large discrepancy in structural detail visible in the LM images. This paper aims at investigating an optimized approach which we call EM-guided deconvolution. This applies to living cells structures before fixation as well as previously fixed sample. It attempts to automatically assign fluorescence-labeled structures to structural details visible in the EM image to bridge the gaps in both resolution and specificity between the two imaging modes. We tested our approach on simulations, correlative data of multi-color beads and previously published data of biological samples.
Keyphrases
- electron microscopy
- deep learning
- high resolution
- single molecule
- convolutional neural network
- living cells
- optical coherence tomography
- fluorescent probe
- induced apoptosis
- electronic health record
- health information
- artificial intelligence
- magnetic resonance
- machine learning
- magnetic resonance imaging
- high throughput
- cell therapy
- healthcare
- mass spectrometry
- minimally invasive
- randomized controlled trial
- bone marrow
- oxidative stress
- high speed
- pet imaging
- endoplasmic reticulum stress
- contrast enhanced
- quantum dots
- pet ct