Removal of out-of-plane fluorescence for single cell visualization and quantification in cryo-imaging
GJ Steyer, D Roy, O Salvado, ME Stone… - Annals of Biomedical …, 2009 - Springer
We developed a cryo-imaging system, which alternates between sectioning (10–40 μ m)
and imaging bright field and fluorescence block-face image volumes with micron-scale-
resolution. For applications requiring single-cell detection of fluorescently labeled cells
anywhere in a mouse, we are developing software for reduction of out-of-plane
fluorescence. In mouse experiments, we imaged GFP-labeled cancer and stem cells, and
cell-sized fluorescent microspheres. To remove out-of-plane fluorescence, we used a …
and imaging bright field and fluorescence block-face image volumes with micron-scale-
resolution. For applications requiring single-cell detection of fluorescently labeled cells
anywhere in a mouse, we are developing software for reduction of out-of-plane
fluorescence. In mouse experiments, we imaged GFP-labeled cancer and stem cells, and
cell-sized fluorescent microspheres. To remove out-of-plane fluorescence, we used a …
Abstract
We developed a cryo-imaging system, which alternates between sectioning (10–40 μm) and imaging bright field and fluorescence block-face image volumes with micron-scale-resolution. For applications requiring single-cell detection of fluorescently labeled cells anywhere in a mouse, we are developing software for reduction of out-of-plane fluorescence. In mouse experiments, we imaged GFP-labeled cancer and stem cells, and cell-sized fluorescent microspheres. To remove out-of-plane fluorescence, we used a simplified model of light-tissue interaction whereby the next-image was scaled, blurred, and subtracted from the current image. We estimated scaling and blurring parameters by minimizing an objective function on subtracted images. Tissue-specific attenuation parameters [μ T: heart (267 ± 47.6 cm−1), liver (218 ± 27.1 cm−1), brain (161 ± 27.4 cm−1)] were found to be within the range of estimates in the literature. “Next-image” processing removed out-of-plane fluorescence equally well across multiple tissues (brain, kidney, liver, etc.), and analysis of 200 microsphere images gave 97 ± 2% reduction of out-of-plane fluorescence. Next-image processing greatly improved axial-resolution, enabled high quality 3D volume renderings, and improved automated enumeration of single cells by up to 24%. The method has been used to identify metastatic cancer sites, determine homing of stem cells to injury sites, and show microsphere distribution correlated with blood flow patterns.
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