Quantitative microscopy uncovers ploidy changes during mitosis in live Drosophila embryos and their effect on nuclear size.

Wee Choo Puah,Rambabu Chinta,Martin Wasser

doi:10.1242/bio.022079

Abstract

ABSTRACTTime-lapse microscopy is a powerful tool to investigate cellular and developmental dynamics. In Drosophila melanogaster, it can be used to study division cycles in embryogenesis. To obtain quantitative information from 3D time-lapse data and track proliferating nuclei from the syncytial stage until gastrulation, we developed an image analysis pipeline consisting of nuclear segmentation, tracking, annotation and quantification. Image analysis of maternal-haploid (mh) embryos revealed that a fraction of haploid syncytial nuclei fused to give rise to nuclei of higher ploidy (2n, 3n, 4n). Moreover, nuclear densities in mh embryos at the mid-blastula transition varied over threefold. By tracking synchronized nuclei of different karyotypes side-by-side, we show that DNA content determines nuclear growth rate and size in early interphase, while the nuclear to cytoplasmic ratio constrains nuclear growth during late interphase. mh encodes the Drosophila ortholog of human Spartan, a protein involved in DNA damage tolerance. To explore the link between mh and chromosome instability, we fluorescently tagged Mh protein to study its subcellular localization. We show Mh-mKO2 localizes to nuclear speckles that increase in numbers as nuclei expand in interphase. In summary, quantitative microscopy can provide new insights into well-studied genes and biological processes.

Highlights

Time-lapse microscopy of living tissues and cells in three dimensions provides new insights into the dynamics of biological systems and promises the discovery of new gene functions and biomolecular mechanisms
To measure and visualize the morphological changes of interphase nuclei and mitotic chromosomes, we built a semi-automated image analysis system that comprises five standalone modules (Fig. 1). (A) A batch file converter (TLM-converter) fragments large multi-dimensional datasets for easier downstream processing into smaller portions (Puah et al, 2011). (B) The batch 3D image segmentation tool (Chinta and Wasser, 2012) detects the regions of interest (ROIs) that correspond to nuclei. (C) The optional post-processing module extracts additional data from the primary segmentation outputs. (D) The einSTA tool performs tracking and quantification of dynamic features. (E) The DyVis3D module visualizes selected lineages by iso-surface volume rendering
The 3D nuclear segmentation method takes advantage of multiple level sets to adapt to local variations in histone-green fluorescent protein (GFP) intensity due to cell cycle dependent variations in chromatin compaction (Chinta and Wasser, 2012)

Summary

Introduction

Time-lapse microscopy of living tissues and cells in three dimensions provides new insights into the dynamics of biological systems and promises the discovery of new gene functions and biomolecular mechanisms. In the cell cycle field, automated image analysis systems have been developed for Imaging Informatics Division, Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Republic of Singapore. *Present address: BioImagingMW, Block 28D Dover Crescent, #31-73, Singapore 134028, Republic of Singapore Image segmentation detects regions of interest as areas (2D) or surfaces (3D) that enclose biological objects, such as cells or nuclei (Coelho et al, 2009; GulMohammed et al, 2014; Li et al, 2007). Many of the image analysis steps can be performed by generic open source or commercial packages (Eliceiri et al, 2012)

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Conclusion