Removal of Out-of-Plane Fluorescence for Single Cell Visualization and Quantification in Cryo-Imaging

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 micronscale-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 [μT: 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.

Quantitative detection of drug dose and spatial distribution in the lung revealed by Cryoslicing Imaging

“…The sequence of corrected block face images was inverted into single slice images by subtracting out-of-plane fluorescence from the block face images according to an empirical algorithm described by [18]. Briefly, if F n and F n−1 represent consecutive corrected block face images (Eq.…”

“…As the lung has to be excised Cryoslicing Imaging is a time-consuming, terminal procedure, but it provides much better spatial resolution and can serve as a quantitative verification of the in vivo imaging results [14]. Similar systems of varying measurement capabilities, spatial resolution and sensitivity have been used in the past for validation of novel imaging modalities like near-field thermo-acoustic imaging [15] and fluorescence molecular tomography [10], for anatomical visualization of intramural coronary vasculature [16], to study bio-distribution of molecular probes [17] or GFP-labeled cancer cells [14], and for single cell visualization and quantification [18].…”

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging

“…Numbers of cells connected at the interface between any two chunks was subtracted from the total count to eliminate double counting. Optionally, one can use next image processing technique [24] as part of the workflow to reduce effects of subsurface fluorescent in the event of very bright cells.…”

Parallel Simulation for Parameter Estimation of Optical Tissue Properties

“…the detection of bacterial pathogens in living hosts, the measurement of neoplasmic tumour growth, the pharmacokinetic study of drug effects or photo-dynamic cancer therapy [3]. A crucial factor in these treatments is the optical tissue properties, which vary throughout the body [4] and which determine how laser light is scattered, diffused and attenuated. As a consequence of varying optical properties, tissues are exposed to varying energy levels of incident light [5] and the treatment must avoid extended exposures to high energy levels.…”