Dataset Information

Intensify3D: Normalizing signal intensity in large heterogenic image stacks.

ABSTRACT: Three-dimensional structures in biological systems are routinely evaluated using large image stacks acquired from fluorescence microscopy; however, analysis of such data is muddled by variability in the signal across and between samples. Here, we present Intensify3D: a user-guided normalization algorithm tailored for overcoming common heterogeneities in large image stacks. We demonstrate the use of Intensify3D for analyzing cholinergic interneurons of adult murine brains in 2-Photon and Light-Sheet fluorescence microscopy, as well as of mammary gland and heart tissues. Beyond enhancement in 3D visualization in all samples tested, in 2-Photon in vivo images, this tool corrected errors in feature extraction of cortical interneurons; and in Light-Sheet microscopy, it enabled identification of individual cortical barrel fields and quantification of somata in cleared adult brains. Furthermore, Intensify3D enhanced the ability to separate signal from noise. Overall, the universal applicability of our method can facilitate detection and quantification of 3D structures and may add value to a wide range of imaging experiments.

SUBMITTER: Yayon N

PROVIDER: S-EPMC5844907 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Intensify3D: Normalizing signal intensity in large heterogenic image stacks.

Yayon Nadav N Dudai Amir A Vrieler Nora N Amsalem Oren O London Michael M Soreq Hermona H

Scientific reports 20180309 1

Three-dimensional structures in biological systems are routinely evaluated using large image stacks acquired from fluorescence microscopy; however, analysis of such data is muddled by variability in the signal across and between samples. Here, we present Intensify3D: a user-guided normalization algorithm tailored for overcoming common heterogeneities in large image stacks. We demonstrate the use of Intensify3D for analyzing cholinergic interneurons of adult murine brains in 2-Photon and Light-Sh ...[more]

PMID: 29523815

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Project description:Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.

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Dataset Information

Intensify3D: Normalizing signal intensity in large heterogenic image stacks.

Publications

Intensify3D: Normalizing signal intensity in large heterogenic image stacks.

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