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A versatile image analysis platform for three-dimensional nuclear reconstruction.

ABSTRACT: Nuclear morphology is indicative of cellular health in many contexts. In order to robustly and quantitatively measure nuclear size and shape, numerous experimental methods leveraging fluorescence microscopy have been developed. While these methods are useful for quantifying two-dimensional morphology, they often fail to accurately represent the three-dimensional structure of the nucleus, thus omitting important spatial and volumetric information. To address the need for a more accurate image analysis modality, we have developed a software platform that faithfully reconstructs membrane surfaces in three dimensions with sub-pixel resolution. Here, we showcase its broad applicability across species and nuclear scale, as well as provide information on how to employ this platform for diverse experimental systems.

SUBMITTER: Williams JF

PROVIDER: S-EPMC6401273 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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A versatile image analysis platform for three-dimensional nuclear reconstruction.

Williams Jessica F JF Mochrie Simon G J SGJ King Megan C MC

Methods (San Diego, Calif.) 20181022

Nuclear morphology is indicative of cellular health in many contexts. In order to robustly and quantitatively measure nuclear size and shape, numerous experimental methods leveraging fluorescence microscopy have been developed. While these methods are useful for quantifying two-dimensional morphology, they often fail to accurately represent the three-dimensional structure of the nucleus, thus omitting important spatial and volumetric information. To address the need for a more accurate image ana ...[more]

PMID: 30359725

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Project description:Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable. After the first stack sweep, we also replace registration operations by operations in the space of transformations, several orders of magnitude faster and less memory-consuming. Implementing TD with operations in the space of transformations produces our Transformation Diffusion Reconstruction (TDR) algorithm, applicable to general transformations that are closed under inversion and composition. In particular, we provide formulas for translation and affine transformations. We also propose an Approximated TDR (ATDR) algorithm that extends the same principles to tensor-product B-spline transformations. Using TDR and ATDR, we reconstruct a full mouse heart at pixel size 0.92µm×0.92µm, cut 10µm thick, spaced 20µm (84G). Our algorithms employ only local information from transformations between neighboring slices, but the TD framework allows theoretical analysis of the refinement as applying a global Gaussian low-pass filter to the unknown stack misalignments. We also show that reconstruction without an external reference produces large shape artifacts in a cardiac specimen while still optimizing slice-to-slice alignment. To overcome this problem, we use a pre-cutting blockface imaging process previously developed by our group that takes advantage of Brewster's angle and a polarizer to capture the outline of only the topmost layer of wax in the block containing embedded tissue for histological sectioning.

Dataset Information

A versatile image analysis platform for three-dimensional nuclear reconstruction.

Publications

A versatile image analysis platform for three-dimensional nuclear reconstruction.

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