Abstract

The nuclear lamina is an intermediate filament network that provides a structural framework for the cell nucleus. Changes in lamina structure are found during changes in cell fate such as cell division or cell death and are associated with human diseases. An unbiased method that quantifies changes in lamina shape can provide information on cells undergoing changes in cellular functions. We have developed an image processing methodology that finds and quantifies the 3D structure of the nuclear lamina. We show that measurements on such images can be used for cell classification and provide information concerning protein spatial localization in this structure. To demonstrate the efficacy of this method, we compared the lamina of unmanipulated human mesenchymal stem cells (hMSCs) at passage 4 to cells activated for apoptosis. A statistically significant classification was found between the two populations.

Highlights

  • Quantitative molecular imaging is a relatively recent research field, which is composed of two distinct domains

  • To demonstrate the efficacy of this method, we compared the lamina of unmanipulated human mesenchymal stem cells at passage 4 to cells activated for apoptosis

  • We have developed an imaging method that segments the nuclear lamina resulting in a quantitative description of this structure using two key steps, segmentation and measurement, from which unbiased quantitative spatial information obtained from the lamina structure can be generated and statistically evaluated

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Summary

Introduction

Quantitative molecular imaging is a relatively recent research field, which is composed of two distinct domains. Typical imaging modalities include light microscopy, AFM, and electron microscopy It is in this second domain that we are working. The “bulk visualization” of biomolecules is not new—consider the measurements of DNA content made by Casperssen in the 1930s [1]—modern probe/marker technology has made it possible to make visible specific DNA sequences such as telomeric DNA and proteins such as actin and lamin A. With this possibility to produce images at the true molecular level, the challenges increase. The tools that we present, are appropriate for use in a variety of molecular imaging problems

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