Drosophila embryos and their effect on nuclear size.bio.biologists.org/content/biolopen/early/2017/01/18/[email protected] Key words: Drosophila, live imaging, maternal haploid,

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Quantitative microscopy uncovers ploidy changes during mitosis in live

Drosophila embryos and their effect on nuclear size.

Wee Choo Puah1, Rambabu Chinta1 & Martin Wasser1, 2

Author affiliations

1Imaging Informatics Division, Bioinformatics Institute (BII), Agency for Science, Technology

and Research (A*STAR), Singapore Republic of Singapore

2Present address: BioImagingMW, Block 28D Dover Crescent, #31-73, Singapore 134028,

Republic of Singapore

Correspondence to

Martin Wasser

BioImagingMW

Block 28D Dover Crescent, #31-73

Singapore 134028

Phone: (65) 67733242

[email protected]

Key words: Drosophila, live imaging, maternal haploid, Spartan, quantitative microscopy, mitosis Summary statement: 3D time-lapse microscopy revealed karyotype changes during mitosis of live maternal-haploid Drosophila embryos. Image quantification demonstrated that DNA content and nuclear crowding determine nuclear size.

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ABSTRACT

Time-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, 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.

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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. However, transforming multi-dimensional images

into valuable information requires smart software for data quantification and visualization. In

developmental biology, dedicated software tools have been developed for cell lineage

analysis using fluorescently labeled nuclei in models like Drosophila (Amat et al., 2014;

Minden et al., 1989), C. elegans (Bao et al., 2006; Long et al., 2009) and zebrafish (Keller et

al., 2008; Lou et al., 2011). In the cell cycle field, automated image analysis systems have

been developed for genome wide image-based screens in culture cells for genes involved in

cell divisions (Neumann et al., 2010). Less research has been directed at developing

methods for the study of cell divisions in multi-cellular organisms such as Drosophila (Chinta

and Wasser, 2012; Yau and Wakefield, 2007). Most image analysis pipelines can be divided

into four major steps: segmentation, tracking, annotation and statistical analysis. 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; Gul-Mohammed et al., 2014;

Li et al., 2007). In time series microscopy, tracking establishes associations between objects

in different frames and is required to measure temporal features and reconstruct cell

lineages (Meijering et al., 2009). Annotation assigns biological meaning to detected objects.

For high-throughput analysis, machine learning is essential for automated phenotypic

classification (Sommer and Gerlich, 2013). Besides customized software, many of the image

analysis steps can be performed by generic open source or commercial packages (Eliceiri et

al., 2012).

Drosophila embryogenesis is a useful model to study the cell cycle in the context of a

developing multi-cellular organism (Foe and Alberts, 1983; Garcia et al., 2007). Genetics

allow the analysis of homologues of human genes that are relevant for diseases like cancer

(Bier, 2005; Halder and Mills, 2011). Upon fertilization, the 2 haploid gametes fuse to give

rise to diploid zygote. Nuclei divide synchronously 13 times in a common cytoplasm or

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syncytium. After completion of the syncytial blastoderm, nuclei are engulfed by a plasma

membrane to generate cells (Lecuit and Wieschaus, 2000). Once cellularization is complete,

cells, concurrent with gastrulation, resume divisions in so-called mitotic domains (Foe and

Alberts, 1983). The transparency of embryos enables live cell imaging using fluorescent

proteins. While early studies relied on the injection of fluorescently labeled antibodies or

proteins (Minden et al., 1989; Warn et al., 1987), modern approaches are based on readily

available genetically encoded fluorescent fusions such as histone tagged to green

fluorescent protein (GFP) or its variants (Shaner et al., 2005). In Drosophila, haploidy can

result from the failure of pronuclear fusion during fertilization. The mutation maternal haploid

(mh) was isolated in a screen for female sterile mutations (Gans et al., 1975). mh leads to

gynogenetic development due to the elimination of the male pronucleus (Santamaria, 1983)

which has been proposed to be caused by defects in sperm chromatin remodeling, DNA

replication or chromosome condensation (Loppin et al., 2001). The mh gene encodes the fly

ortholog of the recently identified human Spartan protein, a conserved regulator of DNA

damage tolerance (Delabaere et al., 2014).

The discovery that the sizes of cytoplasm and nucleus are positively correlated dates

back over a century. However, little is known about the mechanisms that establish and

maintain the nuclear-cytoplasmic (NC) ratio (Jevtić et al., 2014; Webster et al., 2009).

Understanding the mechanism is relevant because NC ratio is correlated with cancer and

ageing. The nucleoskeletal theory proposes that the amount and compaction of DNA

influences the size of the nucleus which in turn determines the size of the cell (Cavalier-

Smith, 1978). Conflicting with this idea are more recent data obtained in fission and budding

yeast that suggest that cell size determines nuclear size (Jorgensen et al., 2007; Neumann

and Nurse, 2007). Studies in Drosophila and Xenopus demonstrated the role of the nuclear

envelope in controlling nuclear size (Brandt et al., 2006; Levy and Heald, 2010).

Previously, we developed a 3D level-set based segmentation method for interphase

nuclei and mitotic chromosomes in Drosophila embryogenesis (Chinta and Wasser, 2012).

Here we report the extension of this work to detect and characterize phenotypic effects of

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cell cycle defects in selected lineages. To validate our approach in studying karyotype

changes resulting from mitotic division defects, we applied our method to time-lapse data of

mh embryos. We discovered that haploid nuclei in mh embryos can collide during the

syncytial blastoderm to give rise to diploid and polyploid nuclei that persist until gastrulation

and resume mitosis. Being able to track synchronized nuclei of different karyotypes side-by-

side in the same embryo allowed us to investigate the contributions of DNA content and NC

ratio to nuclear volume. In early interphase after exit from mitosis, nuclear growth is

proportional to DNA content, while in late interphase, the nuclear density imposes

constraints on volume expansion in the later stages of interphase. In addition, we

demonstrate that fluorescently tagged Mh-protein localizes to nuclear speckles and is able to

rescue female sterility of the mh1 mutation.

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RESULTS

Image analysis pipeline for the study of division cycles in live Drosophila

embryos

To track nuclei in early Drosophila, we acquired 3D time-lapse images of embryos

expressing the live chromosome reporter Histone H2Av-GFP, hereafter referred to as

histone-GFP (Clarkson and Saint, 1999). Image recording was carried out using a Zeiss 5

Live confocal laser scanning microscope from the syncytial blastoderm until gastrulation for

up to 4 hours at 55 to 60 second intervals. 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 (editing and integration of Segmentation, Tracking

and Annotation) tool performs tracking and quantification of dynamic features. (E) The

DyVis3D module visualizes selected lineages by iso-surface volume rendering. While steps

B and C use images as inputs to detect ROIs and compute their features, steps D and E use

ROIs as inputs to perform tracking, quantification of cellular dynamics and 3D visualization.

The 3D nuclear segmentation method takes advantage of multiple level sets to adapt

to local variations in histone-GFP intensity due to cell cycle dependent variations in

chromatin compaction (Chinta and Wasser, 2012). For each detected nucleus, the tool

produces a primary segmentation output consisting of a 3D surface (represented as a stack

of 2D contours), a silhouette contour of the 3D shape, an optimal intensity threshold, and a

set of intensity and shape features (Fig. S1). einSTA performs time-series analysis of

detected ROIs which are represented by silhouette contours and centroids that are projected

onto maximum intensity projections (MIP) of image stacks. The visual cues help to spot

segmentation errors such as merged or fragmented ROIs (Fig. S2). In proliferating tissues,

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the size, shape, texture and brightness of biological structures such as chromosomes can

vary depending cell cycle phase or genotype. To improve robustness of image analysis

towards this phenotypic heterogeneity, we introduced the concept of composite

segmentation. Each frame in a time-series dataset is associated with one or more

segmentation layers (Fig. S2A, S2B), each of which represents a set of ROIs that was

produced by a different segmentation method and/or set of parameters applied to the same

image stack. An additional composite layer is then created to be populated with

combinations of ROIs derived from different segmentation layers. In our case study, we

initialized the composite layer with all ROIs detected by the MLS method using a parameter

set optimized for a set of ground truth images. The second layer was produced by applying a

shape-split operation to the ROIs of the primary layer (Fig. S3). Incorrectly segmented

objects in the composite layer were removed and substituted in an interactive fashion with

objects from the second layer.

Tracking creates temporal associations between ROIs of adjacent frames and is

required to determine dynamic features. We applied a bilateral nearest neighbor (NN) search

to find assignments between ROI centroids of subsequent frames. This automated step was

followed by manual correction of assignment errors. Based on 27818 ground truth

assignments with a mean displacement of 1.41 µm ± 1.61 µm (median 0.90) in 121 pairs of

frames in an embryo imaged from nuclear cycle 12 until cell cycles in gastrulation, recall and

precision rates of the NN search were estimated as 94.6% and 91.8%, respectively (Fig.

S4). Once the accuracy of selected tracks was verified, time-series data such as volume and

intensity changes were exported for further statistical analysis (see next chapters).

Fusions of nuclei in mh1 syncytial embryos lead to karyotype changes

Cell division defects can lead to alterations of chromosome number. To test if our

analysis system could detect variations in ploidy, we decided to compare maternal haploid

(mh1) with diploid wildtype embryos. Consistent with haploidy, histone-GFP labeled mitotic

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chromosomes of mh1 embryos occupied a discernibly smaller regions than in diploid

embryos (Fig. 2A, 2B) and the presence of 5 chromosome arms in haploids (one arm for the

X, 2 arms each 2nd and 3rd) instead of 10 in diploids could often be confirmed visually in

images of anaphase nuclei (Fig. S5). Volume measurements showed that mitotic

chromosomes occupied approximately half the space in haploid embryos compared to their

diploid counterparts (Fig. 2A, 2B). Furthermore, chromosomal volume statistics of mh1 and

control embryos showed that measurements during metaphase and anaphase were

proportional to DNA content (Fig. S6A). To assess the influence of variations in histone-GFP

reporter gene expression on volume measurements, we analyzed the fluorescence intensity

of detected objects. We noticed that mean histone-GFP intensity increased consistently

between subsequent nuclear cycles (29.5% ± 5.7%, p=0.000, mean mu>0, n=8). In contrast,

we did not observe a corresponding increase in measured volumes (-5.6% ± 10.6%,

p=0.179, µ ≠ 0), indicating that our image segmentation method was robust towards

variations in fluorescence intensity (Fig. S6B).

Unexpectedly, images of some post-blastoderm mh1 embryos showed nuclei of

variable sizes and chromosome numbers, indicating a mixture of karyotypes (Fig.2C).

Volume measurements of mitotic chromosomes in metaphase, anaphase and telophase

suggested that a fraction of cells contained between two to four times more DNA than

haploid cells in the same embryo. To understand the diversity in karyotypes, that had also

been reported in previous studies (Loppin et al., 2001; Santamaría and Gans, 1980), we

traced individual nuclei from the post-blastoderm (gastrula) back to the syncytial blastoderm

stage using the einSTA tool. Time-lapse image analysis revealed that increased ploidy arose

from collisions of mitotic chromosomes during the syncytial blastoderm (Fig. 2D, 2E; Movie

1, 2, 3). Collisions led to volume changes proportional to chromosome numbers. Diploid

nuclei in mutant mh1 embryos gave rise to mitotic chromosomes that occupied twice the

volumes of haploid chromosome sets in the same embryo in metaphase and anaphase, and

comparable volumes to diploid chromosome sets in control embryos. 3D visualization of

tracks in 3D helped to rule out segmentation errors as explanations for karyotype changes

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(Movie 3). All observed collisions (n=87) happened during metaphase (25%) or anaphase

(75%) of the syncytial blastoderm (Fig. 2D-E). Chromosome collisions were rare events. The

estimated proportion of nuclei per nuclear cycle involved in collisions was 4.5% ± 3.8%

(1399 nuclei in 9 nuclear cycles) (Fig. S7). As previously reported, mh1 embryos undergo 14

instead of 13 nuclear divisions prior to cellularization (Edgar et al., 1986). Hence it seemed

conceivable that chromosome fusions could arise from the increased density of nuclei during

nuclear cycle 14. However, we detected several fusions during the earlier nuclear cycles 12

and 13, indicating that physical crowding in the periphery of embryos was unlikely to be the

cause of nuclear collisions (Fig. S7). We did not observe any chromosome collisions during

the syncytial blastoderm of wildtype embryos (1015 nuclei during NC11-13 of 3 embryos).

Collisions in anaphase did not involve daughter nuclei from the same mother nucleus,

arguing against defects in chromosome segregation as an explanation for karyotype

changes. Moreover, we never observed fusions of interphase nuclei during syncytial

blastoderm or later in embryogenesis after cellularization. The majority of cases involved two

haploid nuclei merging to become diploid. In one case, three haploid genomes gave rise to

one haploid, diploid and triploid nucleus by colliding in anaphase (Fig. 2D). In another case,

we saw a simultaneous fusion of the three haploid genomes in metaphase (Fig. 2E). Our

tracking data did not provide any evidence that diploidy in haploid embryos resulted from

endoreplication during the cellular blastoderm since we did not to detect any track that

changed its ploidy between the last syncytial and the first non-synchronized division in

gastrulation. Another explanation of nuclear collision could be that haploidy could make

chromosomes more prone to collisions. However, we also observed diploid nuclei in mh1

embryos that fused to become tetraploid (Movie 2, 3). In summary, the combination of 3D

time-lapse imaging and image analysis enabled the discovery that chromosome collision

lead to karyotype changes in mh1 embryos.

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Diversity of nuclear densities in mh embryos at mid-blastula transition

Mitotic defects during the syncytial blastoderm stage are known to lead to an

elimination of surface nuclei (Philp et al., 1993). To quantify nuclear proliferation and

elimination, we implemented a method to estimate the density of surface nuclei (Fig. 3A-D).

The method computes a convex hull using the x-y components of nuclear centroids as

inputs. Nuclear density (ND) was determined as the number of nuclei divided by their

enclosing hull area (#nuclei/1000 µm2). We analyzed the time-lapse datasets of three control

and nine mh1 embryos labeled with histone-GFP. ND values determined 5.5 minutes after

start of anaphase of the final synchronized metaphase showed a greater variability in mh1

(mean=28.7 ± 11.7; ranging from 12.2 to 42.1) than in control embryos (26.5 ± 1.8; ranging

from 24.5 to 27.6) (Fig. 3E). Consistent with previous reports that mh1 embryos undergo an

additional nuclear cycle before entering mid-blastula transition (MBT, here defined at as the

period following the last synchronized division cycle), four mh1 embryos showed up to 60%

higher nuclear densities than controls after the exit from the last syncytial mitosis. Time

series plots revealed that ND decreased during the cellular blastoderm (Fig. 3F). As a result,

after 40 minutes, the increased ND over controls narrowed to less than 30%. The decreasing

ND was reflected by an elimination of surface nuclei that could be observed in the time-lapse

images of live mh1 embryos (Movie 1). Unexpectedly, we encountered a second group of mh

embryos that entered MBT with nuclear densities lower than (3 embryos) or similar to (2

embryos) controls (Fig. 3E, 3F). In summary, the quantification of nuclear density and its

developmental dynamics added new phenotypic insights about mh1 mutants.

Control of interphase nuclear volume during the cellular blastoderm

Published data contain conflicting theories on the factors that determine the size of

the nucleus. On one hand, the nucleoskeletal theory proposes that the amount of DNA is the

major determining factor (Cavalier-Smith, 1978), while on the other hand, experimental data

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in yeast suggest that cytoplasmic size dictates the volume of the nucleus independent of

ploidy (Jorgensen et al., 2007; Neumann and Nurse, 2007). The diversity in nuclear

densities and karyotypes at the onset of MBT in mh1 embryos opened the opportunity to

analyze the influence of DNA content and NC-ratio on nuclear growth rate and volume. We

focused our analysis on cellularization which begins after the exit from the last syncytial

mitosis and takes approximately one hour to complete (Lecuit, 2004). Time-series plots

comparing the volumes of individual nuclei of different karyotypes in the same embryo

showed that volumes were proportional to the amount of DNA (Fig. 4A). In addition, we

noticed two distinct phases of nuclear growth during the cellular blastoderm stage, an initial

rapid growth lasting 8-10 minutes (phase1) and a subsequent slow growth phase (phase2)

that lasts until the next mitosis (Fig. 4B). To estimate and compare nuclear growth rates

within and between embryos, we determined the slopes (µm3/minute) of the linear

regressions (volume versus time) that were computed for the first 10 frames (9.1 minutes)

after anaphase and the subsequent 30 frames (27.5 minutes) (Fig. 4B). We performed the

quantification of 89 nuclear tracks derived from 7 embryos (5 mh1 and 2 control) that were

recorded from the syncytial nuclear divisions until the post-blastoderm cell divisions (Table

S1). The mean R2 values of the tracks were 0.941±0.046 and 0.850±0.175 for phases 1 and

2, respectively, indicating that the linear growth model represented a good approximation.

Ploidies of the analyzed nuclei ranged from 1 to 4, while nuclear densities after exit from the

last syncytial mitosis ranged from 12.2 to 42.1 nuclei per 1000 µm2. The deceleration in

nuclear growth between early phase 1 and late phase 2 ranged from 2.9 to 9.6. When

comparing synchronized nuclei of different karyotypes within the same embryos, growth

rates (Fig. 4C, 4D) and volumes (Fig. S8) were always proportional to DNA content

throughout the cellular blastoderm stage, supporting the nucleoskeletal theory. Meanwhile,

we noticed that sizes of nuclei of the same ploidy showed increasing divergence between

embryos as interphase progressed (Fig. 5A-B). Plotting the mean volumes versus ND of

haploid and diploid nuclei at anaphase (Fig. 5C) and 36 minutes later in interphase (Fig. 5D)

further helped to visualize the negative effect of ND on nuclear size.

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Interphase of the nuclear cycles 1-13 only consists of S-phase and lacks the gap

phases G1 and G2, while interphase of the cellular blastoderm comprises S- and G2-phases

(Farrell and O’Farrell, 2014). To examine the influence of G2 on nuclear growth, we

quantified nuclear size changes during the last two synchronized cycles of control and mh1

embryos. During the penultimate nuclear cycles, we observed that a linear and rapid nuclear

growth was followed by DNA condensation (Fig. S9A), suggesting that the rapid nuclear

growth coincides with DNA replication and slow growth with G2. The comparison of nuclei of

different ploidy in mh1 embryos confirmed that, in the syncytial blastoderm, nuclear size (Fig.

S9B) and growth (Fig. S9C) scaled with ploidy.

To explore if a limited pool of maternally supplied nuclear assembly factors may

impose a collective constraint on nuclear growth, we determined the sum of nuclear volumes

relative to the area they occupy on the embryonic surface. We refer to the cumulative

nuclear volume as the volume to hull ratio (VHR). Time series plots indicate that VHR values

of different embryos converged to a narrow range from 4.2 to 5.9 (5.3 ± 0.6) despite the

diversity of karyotypes and nuclear densities (28.4 ± 9.4), supporting the idea that the NC-

ratio limits nuclear growth (Fig. S10). The collective volume analysis does not require

tracking of individual nuclei and is tolerant to segmentation errors like fusion or splitting of

ROIs, thus offering a more efficient way to analyze time-lapse data. In summary, we

demonstrate that nuclear growth rates are determined by both DNA content and the amount

of cytoplasm available for each nucleus.

MH-protein localizes to nuclear speckles in interphase nuclei

To better understand the nuclear fusion phenotype in mh embryos, we identified

CG9203 as the gene causing female sterility, as described below. The same gene was

independently isolated by the Loppin group using a different strategy (Delabaere et al.,

2014). We mapped the mh mutation to a 52.2 kbp region on the X-chromosome by finding

two overlapping duplications in the P[acman] BAC (bacterial artificial chromosome) collection

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(Venken et al., 2010) that could rescue female sterility of the mh1 allele. Upon genomic

sequencing and complementation tests (see Methods for details), we selected CG9203, the

Drosophila ortholog of human Spartan which is involved in DNA repair as a candidate gene

(Centore et al., 2012; Ghosal et al., 2012; Juhasz et al., 2012). CG9203 is expressed

maternally in ovaries (Gelbart and Emmert, 2013) and contains 5 non-synonymous

mutations, one of which involves a conserved methionine in the conserved Sprt domain.

Similar to the BAC clones, a 4432 bp genomic fragment containing the 2421 bp ORF, 1553

bp upstream and 282 bp downstream regions of CG9203 could rescue female sterility.

Rescue of mh1 sterility using the P[acman] clones and genomic DNA was partial.

Approximately 10% of embryos produced by mh1/mh1 females hatched into larvae (32 of 323

eggs laid by mh1/mh1; PBac{DC300}VK00033/+), compared to 0% for females without

CG9203 containing transgenes. The rescued larvae developed into adults without

discernible morphological abnormalities that gave rise to stable mh1 homozygous stocks.

The genomic duplication was able to rescue haploidy. 84.6% (22/26) of mh1;PBac{DC300}/+

fixed syncytial blastoderm embryos in meta- or anaphase were diploid, while the remaining

15.4% were haploid. In contrast, 100% (9/9) of mh1 embryos were haploid. Among the 22

rescued diploid mh1 embryos, none contained polyploidy nuclei, supporting the notion that

loss of mh is responsible for chromosome collisions.

To study the sub-cellular localization of the Mh/CG9203 protein, we fused the

CG9203 ORF including 5 predicted introns in frame with the ORF of the orange fluorescent

protein mKO2 (monomeric kusabira orange) and cloned it into the pUASp vector for

maternal expression using the GAL4 system. Overexpression of mh-mKO2 using the

maternal nanos-VP16.GAL4 driver rescued mh1 sterility and allowed establishment of a

fertile stock, showing that the fusion protein retained Mh functionality. Co-expression of

histone-GFP allowed us to study cell-cycle dependence of Mh-mKO2 localization in live

embryos (Fig. 6; Movie 4). In early interphase, Mh-mKO2 accumulated in nuclei and showed

uniform localization. As interphase progressed, the fusion protein showed concentration in

speckles that increased in size and number. Mh-mKO2 clustering in the speckles began

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after 3-4 minutes of interphase, suggesting that speckle formation may correlate with the end

of DNA replication. Localization to nuclear speckles was also reported for human Spartan

(Machida et al., 2012). Once nuclei entered mitosis, the fusion protein was rapidly depleted

and did not co-localize with condensed chromosomes. Therefore, the localization in mitosis

does not provide a clue on how mh deficiency may contribute to chromosome fusion during

metaphase and anaphase.

In adult ovaries, the localization of overexpressed MH-mKO2 showed developmental

changes in oocytes (Fig. 7), while a speckled pattern was observed in nurse cells of all

stages. In oocytes of early stages, Mh-mKO2 was distributed diffusely throughout the

nucleus (Fig. 7A). The intensity of the fusion protein was higher in non-chromosomal region

of the nucleus. As oocytes grew, Mh-mKO2 became concentrated to 5-8 speckles of

different sizes that did not co-localize with DNA staining (Fig. 7B). The largest speckles were

doughnut-shaped with diameters of up to 2.5 microns (Fig. 7B, inset). The quantity of

nuclear speckles increased dramatically with egg chamber maturation and growth of the

oocyte nucleus (Fig. 7C). When ectopically expressed in larval salivary glands, Mh-mKO2

showed distribution to speckles that did not co-localize with polytene chromosomes, further

supporting the notion that protein does not have a strong affinity to chromatin (Fig. S11A).

Intriguingly, the germline UASp-mh-mKO2 and somatic UAS-mh-mKO2 Gal4 effectors

showed expression in larval and pupal testes (but not ovaries) without the presence of a

Gal4 driver (Fig. S11B). Meanwhile, driverless fluorescence did not occur using other mKO2

fusion constructs, suggesting that the genomic mh fragment, possibly the introns, contain

regulatory sequences activated in the male germline. Previous studies correlated speckle

formation with DNA damage repair. We show that it also occurs in response to nuclear

growth in development and cell cycle.

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DISCUSSION

Quantitative microscopy of mitotic division cycles in embryogenesis

We developed a modular image analysis system for 3D time-lapse microscopy and

applied it to the phenotypic characterization of the maternal haploid mutation in Drosophila

embryos. Tracking and volume measurements demonstrated that nuclei of different ploidies

in mh embryos could be traced back to collisions of mitotic chromosomes during the

syncytial blastoderm stage. Nuclear collisions happened at metaphase or anaphase and

involved chromosomes of different nuclei. We did not encounter a single case where the

failure of sister chromatid segregation lead to increased ploidy. Fusions were observed at

nuclear densities seen in wildtype embryos during nuclear cycles 12 and 13, ruling out the

possibility that nuclear fusions resulted from crowding of nuclei. A previous study showed

that haploid nuclei from mh donor embryos injected into diploid host embryos could give rise

to haploid as well as diploid cell clones in adult flies (Santamaria, 1983). Our results provide

an explanation for these observations.

The mechanism of nuclear fusion and its relationship to the mh gene function remain

to be elucidated. Spartan, the homolog of the mh gene product is implicated in translesion

synthesis, a system that helps replication forks bypass sites of DNA damage (Delabaere et

al., 2014). As such, mh deficiency may lead to incomplete replication of the paternal

pronucleus and its elimination from the zygote. Four models may explain chromosome

collisions in syncytial embryos. First, loss of mh may affect the formation of actin-containing

metaphase furrow that shields neighboring spindles from colliding in the absence of plasma

membranes (Rikhy et al., 2015). Second, nuclear fusions in Centrosomin deficient embryos

were attributed to centrosomal malfunction and reduced spacing of mitotic spindles (Megraw

et al., 1999). Similarly, mh1 embryos may be more prone to centrosomal or spindles

malfunction. Third, the fusion phenotype may be an indirect consequence of losing the

paternal genome during fertilization, which may affect the formation of the metaphase furrow

or centrosome function. Fourth, a second mutation on the mh1 chromosome, such as the

female sterile mutation fs(1)RK4 (Loppin et al., 2001), could be responsible for the

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phenotype. However, we did not find polyploid nuclei in mh1 homozygous embryos whose

haploidy was rescued by a duplication of the mh gene, suggesting that haploidy and

chromosome collisions are linked. Future research should focus on possible replication-

independent functions since Mh-mKO2 accumulated in non-chromosomal nuclear speckles

in late interphase, presumably after completion of replication. One known function of nuclear

speckles is to serve as storage sites of RNA splicing factors (Lamond and Spector, 2003).

Interestingly, a link between chromosome instability and localization to nuclear speckles was

found for poly(ADP-ribose) polymerase-1 (PARP-1), which localizes to nuclear speckles in

mouse oocytes and predisposes female gametes to genome instability (Yang et al., 2009).

Nuclear size control

Taking advantage of the fact that we could track synchronized nuclei of different

karyotypes in the same cytoplasm, we investigated the control of nuclear size in the context

of Drosophila embryogenesis. Two opposing models explain the size of nuclei. While the

evolutionarily conserved scaling of nuclear volume with genome size supports the intuitive

model that DNA content is the main determinant of nuclear size, more recent evidence from

various experimental systems favors the idea that cytoplasmic volume and components

dictate nuclear volume (Edens et al., 2013). Our data show that both theories can be

reconciled (Fig. 8). During mitosis, condensed chromatin occupies a space that is

proportional to ploidy. In early interphase after exit from mitosis, nuclear volumes expand

rapidly and growth rates and absolute sizes remain correlated with ploidy, indicating that

some nuclear assembly factors are associated with chromatin. As interphase progresses,

nuclear growth slows and crowding exerts a negative effect on nuclear growth, indicating

that nuclei share a finite amount of other nuclear assembly factors that are deposited

maternally into the egg chambers. As a result, nuclear volumes of the same karyotype can

diverge between embryos. The conclusion that finite supplies of cytosolic factors are

recruited for nuclear assembly are consistent with the ‘limited flat membrane hypothesis’ that

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proposes that growth of the nucleus is limited by the amount of ER membranes available in

the cytoplasm for incorporation into the NE (Webster et al., 2009).

Based on our experiments, the variations of nuclear size and growth in mh embryos

can be explained by variations in ploidy and nuclear density. However, to fully rule out

genetic effects of the mh mutation and strengthen the argument of generality, we will need to

extend our analysis to other genetic perturbations. One example is the mutant daughterless-

abo-like (dal) where embryos, due to nuclear division defects, enter cellularization at half the

nuclear density of wildtype embryos (Sullivan et al., 1990). Although no quantification was

carried out, nuclei and cells were reported to be much larger than in wildtype embryos,

indicating that the inverse correlation between nuclear size and density in mh embryos may

reflect a general response of nuclei to increased cytoplasmic volume. While the literature

reports that mh embryos undergo 14 nuclear cycles, live imaging helped to uncover that a

subset of embryos undergo their final synchronized mitosis at nuclear densities similar to

wildtype embryos in nuclear cycles 12 or 13. Since nuclear density depends on the number

of division cycles and the depletion of nuclei, a key experiment would be to perform live

imaging of all nuclear cycles from the zygote onwards. However, the single photon CLSM

used in this study has limited depth penetration and could only image the cortical cycles 10-

13. Multi-photon confocal microscopy or light sheet microscopy (Keller et al., 2010) appear

suitable to overcome this optical limitation and record nuclear cycles 1-9 in the interior of the

egg.

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Materials and methods

Drosophila Stocks

Nuclei and mitotic chromosomes in live maternal haploid (mh1) and control

Drosophila embryos were visualized using the His2Av-GFP reporter gene on the 3rd

chromosome that expresses Histone-H2Av fused to green fluorescent protein (GFP)

(Clarkson and Saint, 1999). The reporter gene was combined with the X-chromosome y1 wa

mh1/FM7a (Bloomington stock no. B-7130). Fly stocks were obtained from the Bloomington

Drosophila stock center. Female sterility associated with mh1 could be rescued with two

overlapping BAC X-chromosomal duplications X: 15453241-15544202 and X: 15397371-

15505482 of the stocks w1118; Dp(1;3), PBac{DC300}VK00033 (B-30414) and w1118;

Dp(1;3), PBac{DC521}VK00033 (B-32322), respectively. The nanos:Gal4:Vp16 driver (B-

4937) was used for maternal overexpression, Smid-Gal4 (B-27893) for expression in salivary

glands. P-element mediated germline transformation was carried out by an external service

provider (BestGene, Chino Hill, CA). Stocks newly generated for this study were deposited

at Vienna Drosophila Resource Center (VDRC): http://stockcenter.vdrc.at.

Molecular Biology and identification of the mh gene

Genomic DNA of mh1 homozyogous adults was isolated using the Wizard Genomic

DNA purification kit (Promega, Madison, WI). Genomic DNA of the mutant X-chromosome

was re-sequenced by a service provider (BGI, Shenzhen, China). To rescue female sterility

linked to the mh1 chromosome, we amplified by PCR 4432 bp of genomic DNA from wildtype

Canton-S flies containing the CG9203 gene. The genomic DNA fragment (X: 15,476,953-

15,472,522) contained the 2432 bp ORF with 5 exons and 4 introns, a 1637 bp upstream

and 282 bp downstream region. The PCR primers included XbaI and XhoI restriction

enzyme (RE) sites that were used to insert the genomic fragment into the pCasper4

transformation vector. To study protein localization of the CG9203 gene product, the

CG9203 ORF was fused in frame with the cDNA of monomeric Kusabira orange 2 (mKO2)

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(Sakaue-Sawano et al., 2008) and cloned into pUASp and pUAST derived expression

vectors using the Gateway recombination system (Invitrogen, USA) as previously described

(Puah and Wasser, 2016). In short, the CG9203 ORF was amplified by PCR disabling the

predicted stop codon and cloned directionally by topoisomerase I to create a Gateway entry

vector. Recombination between entry clone and the pUASp-GC-mKO2/pUAST-GC-mKO2

destination vectors created the pUASp-gc9203-mKO2 and pUAST-cg9203-mKO2 P-element

germline transformation vectors.

Time-lapse microscopy

Sample preparation and in vivo imaging were performed as previously described

(Chinta and Wasser, 2012). 0 to 2 hour old embryos were dechorionated for two minutes

with a 50% bleach solution, washed with PBS, transferred to an uncoated 32 mm glass

bottom dish (MatTek, Ashland, Massachusetts) and covered with a solidified layer of 0.8%

low melting point agarose in PBS to prevent dehydration during imaging. Time-lapse imaging

was carried out at room temperature (21-23° C) using a high-speed, line-scanning Zeiss 5

Live confocal laser scanning inverted microscope (Carl Zeiss, Jena, Germany) and a 63x/1.4

oil DIC Plan-Apochromat objective. For single-channel imaging of histone-GFP, we used an

excitation wavelength of 489 nm at 0.5% to 1% laser power (max. output 100 mW). We

selected a 505 nm emission long pass filter and set the slit size to 10 µm. The optimal

spacing between z slices of 0.44 µm was suggested by the manufacturer's acquisition

software. We recorded the 3D image stacks, consisting of 66-70 slices of 1024x1024 pixels

(zoom 1.0, 8 bits), at 50 to 60 second intervals for up to 4 hours. Image acquisition focused

on the head region to limit the number of cells migrating out of the field of view due to

gastrulation. The voxel dimensions in x, y and z were 0.1x0.1x0.44 µm. Dual-channel

recording of histone-GFP and Mh-mKO2 was carried out at 50-second intervals using 488

nm and 532 nm lasers at 2.8% power (band pass filter NFT 535, long pass filter 550 nm). 3D

image stacks contained 37 optical sections with voxel dimensions of 0.1x0.1x0.71 µm

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(x,y,z). The imaging setup did not result in photo-toxicity as control embryos developed to

become larvae. Images were saved as LSM files using the Zeiss acquisition software.

Image analysis pipeline

Our image analysis pipeline consisted of five main steps: (A) image data

reorganization, (B) 3D image segmentation, (C) optional post-processing of segmentation

outputs, (D) time-series analysis, and (E) optional 3D visualization (Fig. 1). (A) 3D time-lapse

imaging generated sizeable datasets of 180-240 time points which were saved as LSM files

containing between 13 to 18 Gigabytes (GB) of data. To facilitate downstream processing,

we fragmented the 4D CLSM datasets into portions of 10 time points and saved them as

files of the open source Image cytometry standard (ICS) format (Dean et al., 1990) using our

custom-built TLM-converter software (Puah et al., 2011). The newly reported tools of the

pipeline (C-E) were implemented in C++ using the Open CV computer vision library

(http://opencv.org/), the libics v1.5 reference library (http://libics.sourceforge.net/) for reading

and writing multi-dimensional ICS files and the Visualization tool kit (Vtk) (Schroeder et al.,

2006). The graphical user interfaces were implemented in the .NET (Microsoft) environment.

Software, manuals and samples datasets are available for download at following link:

https://figshare.com/s/e030e8dec869d8095f53.

3D Image Segmentation

To detect and extract features of interphase nuclei and mitotic chromosomes, we

applied a fully automated 3D image segmentation tool (Chinta and Wasser, 2012). The

segmentation method is based on multiple level sets (MLS) and adapts to differences in

intensity, shape and size as chromosomes progress through the cell cycle phases. We used

the batch processing module of our tool to segment up to 8 datasets, containing 10 time

points each, in parallel. On a desktop PC with an Intel i7 core CPU, 80 time points were

processed in approximately 2 hours. We selected the same parameters for all 4D datasets

used in this study: 𝛥k0=0.5, 𝛥kM=0.5 and 𝛥kRG=0.2 are tuning factors for the surface

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evolution during the object detection, seed detection and region growing steps, respectively.

Minimum seed size was set to 1 µm3 and minimum detected object size to 5 µm3 to filter out

non-nuclear objects. The Batch 3D MLS segmentation tool exports four output files per

image stack (Fig. S1). (1) The master segmentation project file (*.SGPJ) stores information

about input images, segmentation parameters and the names of three associated output

files, which contain the contours and features of detected objects. (2) The segmented

objects file (.CSV format) contains a table of detected 3D objects and their features,

including a unique object ID, centroid coordinates, shape and intensity parameters. (3) 3D

surface files (.cont) store 3D ROIs as sets of overlapping 2D contours that can be used to

restore and modify segmentation projects during the post-processing step. (4) Silhouette

contour files (.cont) contain one silhouette contour per 3D object. Silhouette contours are

extracted from the projection of all polygons belonging to a 3D object and are used to

evaluate the boundaries of detected objects in MIP images.

Segmentation post-processing

As an optional step in the image analysis pipeline, the post-processing module

extracts additional features from segmentation outputs for the following purposes. (1) It

computes additional shape, intensity and texture features that not required for all

applications. Here, we obtained Mh-mKO2 intensity parameters within the regions of

histone-GFP labeled nuclei (Fig. 6B, 6C). In a previous study, we obtained 3D shape and

texture features required for cell cycle classification (Du et al., 2011). (2) It can save cropped

3D regions of 3D stacks for iso-surface rendering. (3) It helps to separate merged nuclei, a

segmentation error particularly affecting image stacks of the early cellular blastoderm stage.

We implemented a simple 3D shape-split algorithm that was applied to all 2D polygons of 3D

objects within a user-defined range of volumes (default 10 to 1000 µm3). The algorithm finds

the shortest cutting line between two contour points that fulfills three constraints (Fig. S3): (1)

a minimum area of the input polygon (default 700 pixels or 7 µm2), (2) minimum perimeter of

the smaller output polygon (default 70 pixels or 7 µm) and (3) a maximum ratio between

cutting distance and the perimeter of smaller output polygon (default 0.3). If any of the

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constraints are violated, no splitting occurs. Splitting is repeated until no input polygon is left

that meets all constraints. 3D objects are reconstructed from cut polygons based on a

minimum overlap between polygons in consecutive focal planes (default 50% with regards to

the smaller polygon).

Time-series analysis

Time-series analysis, such as tracking nuclei and quantifying morphological changes

during development, was performed using the einSTA (integration and editing of

Segmentation, Tracking and Annotation) custom software. einSTA saves computational

resources by only using processed data without the need to load large multi-dimensional

image data. The workflow of constructing and quantifying tracks of proliferating nuclei

consists of four main steps.

(1) Edit initial tracking project file. The user constructs an initial tracking project file

(.TRPJ) using a text editor (Fig. S1). The header lists file type (here "Tracking-Project 2"),

number of frames, image dimensions, and the names and types (integer, double, text) of

features. The current prototype uses up to 98 shape, intensity and texture features to

describe biological objects. The header is followed by a list of MIP images (1 per frame). The

third mandatory component is a list of segmented object files. Each row corresponds to a

single frame and can contain one or more comma separated ROI files. This way,

quantification can be performed with ROIs derived from the outputs of different segmentation

methods.

(2) Create Composite Segmentations. Biological objects, such as chromosomal

DNA in different cell cycle phases, can be heterogeneous in terms of intensity, shape,

texture and location, resulting in variable segmentation quality within the same image stack.

To improve segmentation quality, we introduced the concept of composite segmentation.

The composite segmentation contains a combination of ROIs derived from different

segmentation outputs. We evaluated segmentation quality using silhouette contours and

centroids drawn on MIP images (Fig. S2). Initially, we populated the composite with all ROIs

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from the most accurate segmentation. Later, errors such as fused, split or missing objects

were rectified by a range of editing functions. In this study, we created a second layer of

ROIs by applying a shape split method in the post-processing module to the original

segmentation (Fig. S3, see above).

(3) Tracking of nuclei. Tracking creates associations between objects in

subsequent frames and was performed in a semi-automated fashion. An automated bilateral

nearest neighbor search was combined with manual error correction. The nearest neighbor

search worked well during interphase when displacements were small (Fig. S4A). However,

this approach failed during anaphase of mitosis when mean displacements could be as high

as 15 µm microns per 55 seconds (Fig. S4C). A set of visualization techniques, such as

overlaying subsequent frames and displaying links between centroid (Fig. S4 A-C), helped to

validate tracks. Missing or wrong links were edited manually. Selected tracks and their

features were exported as comma separated text files for further statistical analysis.

(4) Quantification of nuclear densities. To monitor the proliferation and elimination

of nuclei on the cortex of embryos, we used the x and y components of the centroid

coordinates as inputs to determine convex hulls using the cvConvexHull2 function of the

OpenCV library. Dividing the number of ROIs by the hull area produced an estimate of

nuclear density. Prior to calculating the convex hull, we applied a set of constraints such as

ranges for volume (10 to 1000 µm3) and depth (0 to 25 µm). Nuclear hull statistics were

exported as flat files for further statistical analysis.

Dynamic iso-surface visualization (E)

We developed the custom tool DyVis3D (dynamic visualization in 3D) to generate

and view interactive animations of selected tracks using three steps. (1) Tracks of nuclei

created in einSTA are exported as flat files. (2) The post-processing module imports these

tracks to extract foreground regions of 3D ROIs within their bounding boxes, while

background regions are masked in black. Cropped image stacks are saved along with a

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time-lapse 3D (.TL3D) file describing the tracks. (3) In DyVis3D, the TL3D file is opened to

reconstruct a dynamic scene composed of wireframe models representing the nuclei. The

track information enables the selective rendering (color, transparency, level of detail, style)

of individual tracks. The optimal thresholds obtained during the MLS surface evolution were

used as initial intensity thresholds for iso-surface extraction.

Statistical Analysis

Statistical data analysis was performed using Excel (Microsoft), Minitab 16 (Minitab

Inc.), R statistical software and Python. R was used to compute Student’s t-tests, confidence

intervals of means and proportions. Minitab was applied to produce interval and box-and-

whisker plots (boxplots). Excel was applied to calculate basic statistics, perform regression

analysis and plot line and bar charts. The Python library matplotlib was used to generate

some of the 2D plots (Fig. 6B, 6C).

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Acknowledgements M.W. and W.C.P. were affiliated with the Bioinformatics Institute (BII) until 2013, R.C. until

2012. We thank the Bloomington Drosophila stock center for providing fly stocks. We thank

the IMCB (ACC and SC labs) for the use of Drosophila facilities.

Competing Interests The authors declare no competing interests.

Author contributions W.C.P. performed the experiments and analyzed data. R.C. developed the 3D image

segmentation software. M.W. conceived the study, developed the einSTA and DyVis3D

software, performed experiments, analyzed the data and wrote the manuscript.

Funding The study and publication costs were funded by the BII, Agency for Science, Technology

and Research (A*STAR), Singapore.

Data availability The previously published software tools “TLM-converter” and “3D Cell Nuclei Detection” are

available for download at http://web.bii.a-star.edu.sg/archive/TLM-

Converter/User_License_DownloadInstructions_TLM_rev01.htm and http://web.bii.a-

star.edu.sg/archive/MLSCellNucleiDetection/, respectively. The movies, the software tools

einSTA and DyVis3D, and sample datasets are available for download at

https://figshare.com/s/e030e8dec869d8095f53. Drosophila stocks generated for this study

have been deposited at the Vienna Drosophila Resource Center (VDRC):

http://stockcenter.vdrc.at.

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http://web.bii.a-star.edu.sg/archive/TLM-Converter/User_License_DownloadInstructions_TLM_rev01.htm

http://web.bii.a-star.edu.sg/archive/TLM-Converter/User_License_DownloadInstructions_TLM_rev01.htm

http://web.bii.a-star.edu.sg/archive/MLSCellNucleiDetection/

http://web.bii.a-star.edu.sg/archive/MLSCellNucleiDetection/

https://figshare.com/s/e030e8dec869d8095f53


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Figures

Fig. 1. Image analysis workflow for 3D time-lapse microscopy. 3D time-lapse datasets

were processed using a pipeline of five standalone software modules. (A) The TLM-

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Converter fragments the 3D image data to facilitate downstream processing tasks, such as

image segmentation. For this study, datasets were split into portions of 10 time-points. (B)

Automated 3D segmentation detected nuclei and saved their features and surfaces. (C) The

optional post-processing module split merged ROIs and calculated additional 3D features.

(D) The einSTA (editing and integration of Segmentation, Tracking and Annotation) tool

performs tracking and time-series analysis. Objects can be manually labeled and exported

for machine learning tasks. (E) The dynamic visualization module DyVis3D renders and

animates selected tracks in 3D.

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Fig. 2. Collisions of mitotic chromosomes in maternal haploid (mh1) syncytial

blastoderm embryos lead to changes in ploidy. (A) Diploid (2n) karyotypes of control can

be visually distinguished from (B) haploid (1n) karyotypes of mh1 embryos in mitotic

chromosomes labeled with histone-GFP (green). Time stamps in (min:sec). Left panels show

maximum intensity projections of live mitotic chromosomes that were recorded at 55 second

intervals in metaphase, anaphase and telophase, while the right panels show the same

images with three annotations obtained from nuclear segmentation. The silhouette contours

around the segmented nuclei and the centroids (small circle) are drawn in white. The volume

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measurements (µm3, yellow) are proportional to DNA content. (C) In vivo imaging during

gastrulation revealed that mh1 embryos contained nuclei of different ploidy. Volume

measurements indicate that mutant embryos contained haploid (arrow) as well as diploid

nuclei (arrow heads). (D, E) Time-lapse image analysis revealed that mitotic chromosomes

in mh1 syncytial blastoderm embryos could collide during metaphase (D) or anaphase (E),

giving rise to changes in ploidy. Yellow lines connect centroids of previous (cyan) and

current (yellow) frame. (D) At 1:50, three haploid nuclei underwent anaphase. At 2:45, sister

chromatids collided to create one haploid, one diploid and one triploid instead of 6 haploid

daughter nuclei. All three daughters persisted into post-cellular blastoderm to resume non-

synchronized mitotic divisions. The haploid cell divided at 97:10 and the triploid cell at

106:20. (E) In this sequence, chromosome of three haploid nuclei (0:00) fused during

metaphase (0:55). The subsequent anaphase (2:45) divided the chromatids into two triploid

instead of 6 haploid daughter nuclei.

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Fig. 3.Variations of nuclear density at midblastula transition (MBT) of mh1 embryos.

Nuclear density or ND [#N/1000 µm2] was used to monitor proliferation and depletion of

nuclei. ND was calculated by dividing the number of detected nuclei (centroids marked by

dots) with the area of the convex hull (red curve). Panels A to D show one control (wt-24)

and three mh1 embryos after completion of the final synchronized mitosis. Compared to 13

nuclear cycles in wildtype (A), the majority of mh1 embryos undergo 14 nuclear cycles (B)

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that lead to higher ND values. A smaller proportion of mh1 embryos, enter MBT with NDs

similar to (C) or lower (D) than control. (E) mh1 embryos show a broader ND distribution at

the onset of MBT. Error bars indicate 95% confidence intervals (CI). (F) Plotting ND against

time reveals proliferation and depletion of surface nuclei in one wildtype and four mh1

embryos (see Table I) during the last 1-3 synchronous nuclear division cycles and

subsequent cellularization. All plots are aligned with respect to the last synchronized mitosis

(arrow). Anaphase (*) of the synchronous nuclear cycles (NC) is accompanied by a doubling

of ND. Consistent with one additional NC in mh1 mutants, the embryos mh-39 and mh-42

showed higher ND readouts at MBT. Due to elimination of cortical nuclei, ND values

declined after anaphase, while remaining constant in the control embryo wt-24. A small

proportion of mh1 embryos entered MBT at nuclear densities similar to (mh-21) or lower (mh-

13) compared to controls.

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Fig. 4.The sizes of interphase nuclei and their growth rates positively correlate with

DNA content. (A) Mitotic chromosomes of three syncytial haploid nuclei (s1-s3) collided

during anaphase of NC14 (inset, red arrow) to give rise to synchronized daughter nuclei of

different ploides (1n, 2n, 3n). See Fig. 2D and Movie 1. The line plots indicate that nuclear

growth and size positively correlate with DNA content. Blue arrows indicate anaphase, the

peaks during syncytial blastoderm correspond to late interphase of the haploid nuclei from

NC12-14. (B) To quantify nuclear volume expansion following the last synchronous mitosis,

we divided interphase into a rapid early phase of 7 minutes and a late slow growth phase of

37 minutes and determined linear regression. This plot compares the volume changes of

diploid (2n) and tetraploid (4n) nuclei after exit from mitosis in embryo mh-13. Nuclear

growth rates g derived from linear regression are positively correlated with ploidy. R2 values

in brackets. (C, D) Bar charts compare early and late growth rates (mean ± 95% c.i.) of 2

control and 4 mh1 embryos (see Table S1). Within embryos, nuclear growth rates scale with

DNA, i.e. diploid grow significantly faster than haploid nuclei in the same syncytium. t-tests *

P ≤0.05, ** P ≤0.01, **** P≤0.0001.

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Fig. 5. The sizes of interphase nuclei are inversely correlated with nuclear density.

The volumes of (A) haploid and (B) diploid nuclei and their growth rates differ between

embryos and are inversely correlated with nuclear density. The scatter plots show mean

volume measurements for the initial 40 minutes after exit from the last synchronized mitosis.

Numbers next to the right ends of the lines indicate ND at the beginning of MBT. (Sample

sizes in brackets) (C, D) Nuclear volume depends on both DNA content and ND. Scatter

plots show the relationship between ND and nuclear volumes (mean ± 95% c.i.) at anaphase

of the last synchronized mitosis (C) and during interphase 37 minutes later (D). Diploid

nuclei grow faster than haploid ones in the same neighborhood. Sample size: control

embryos wt(2n) were wt-24 and wt-26 (n=10), mh1 embryos (n haploid, n diploid nuclei) were

mh-13 (0,10), mh-21 (10,6), mh-39 (10,6) and mh-42 (10,7).

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Fig. 6. Cell-cycle dependent changes of Mh-mKO2 localization. (A) Mh-mKO2 (red)

changes in intensity and localization during nuclear cycles of syncytial blastoderm embryos.

Nuclear cycle phases were visualized using histone-GFP (green). Mh-mKO2 shows a

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uniform distribution in early interphase. As interphase progresses, Mh-mKO2 localizes to

nuclear speckles that increase in number and brightness. The speckles are degraded during

the progression from prophase to metaphase (see Movie 4). UASp-mh-mKO2 was

expressed using the nos-Gal4:VP16 driver. Frames are MIPs of 3D stacks recorded using

CLSM. (B) Fluctuations of Mh-mKO2 fluorescence in (A) between end of NC 12 and early

interphase of NC14 were quantified in segmented nuclei. Mh-mKO2 intensity parameters

(sum, mean, standard deviation) and nuclear volume are shown in relative units (mean ±

95% c.i.). Arrows labeled AP12/13 indicate anaphase of nuclear cycles 12 and 13. Speckle

formation correlates with increased standard deviation. (C) The fluctuations of histone-GFP

intensity are shown for comparison. n=4 (until metaphase 12), n=8 (AP12 to metaphase 13),

n=16 (from AP13).

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Fig. 7. Localization of Mh-mKO2 in the oocyte nucleus during oogenesis. (A, B, C) Mh-

mKO2 (red) expressed using maternal nos-VP16:Gal4 driver localizes to nuclear speckles in

nurse cells (NC) and oocytes (OC). DNA (cyan) was stained with Hoechst dye. Panels to the

right show separate Mh-mKO2 (A’-C’) and DNA staining (A’’-C’’) of the oocyte nuclei at

higher magnifications. Arrows in A’-C’ indicate the positions of Hoechst-stained DNA in A’’-

C’’. (A’) In early stages of oogenesis, Mh-mKO2 shows a uniform distribution in the oocyte

nucleus. (B’) As egg chambers grow (stage 9), Mh-mKO2 clusters in 6 nuclear bodies, some

of which display a doughnut-morphology (arrow head, inset). (C’) In later stages, the quantity

of speckles increases dramatically coinciding with an over 100 fold increase of size nuclear

size compared to stage 7. Ovaries were dissected in PBS and fixed in 4% formaldehyde in

PBS.

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Fig. 8 Nuclear growth during cellularization is controlled by both DNA content and

nuclear to cytoplasmic ratio. mh embryos display variations in nuclear density and ploidy.

During the syncytial blastoderm, ploidy changes result from collisions of mitotic

chromosomes. In early interphase of the cellular blastoderm, rapid nuclear volume

expansion depends mainly on ploidy (1n=haploid, 2n=diploid), suggesting that chromatin

associated components recruit nuclear assembly factors (NAFs) from the cytoplasm. As

NAFs become depleted in late interphase, nuclear growth slows down. Due to competition

for a limited supply of cytoplasmic NAFs, nuclear growth displays an inverse correlation with

nuclear density.

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Supplementary Information

Supplementary Tables

Embryo ND

[#nuclei/

1000 µm2]

Ploidy n Early growth

[m3/min]

± 95% CI

RSQ

early growth

Late growth

[m3/min]

± 95% CI

RSQ

late

growth

wt-24 24.0 2n 10 15.03 ± 0.45 0.965 1.56 ± 0.24 0.790

wt-26 25.7 2n 10 11.70 ± 0.74 0.954 0.97 ± 0.30 0.609

mh-13

12.9 2n 10 16.63 ± 0.99 0.978 5.64 ± 0.65 0.944

4n 2 25.06 ± 15.12 0.962 7.21 ± 0.11 0.925

mh-21

20.8 1n 10 10.22 ± 0.63 0.941 2.68 ± 0.704

2n 6 16.96 ± 3.77 0.910 5.04 ± 1.34 0.898

mh-27

27.4 1n 5 10.39 ± 0.83 0.983 3.42 ± 0.47 0.957

2n 5 17.63 ± 2.01 0.897 4.35 ± 0.79 0.890

mh-39

39.3 1n 10 7.73 ± 0.63 0.919 1.98 ± 0.32 0.890

2n 6 14.13 ± 1.70 0.914 4.22 ± 0.46 0.908

3n 4 15.48 ± 0.45 0.873 4.19 ± 0.94 0.849

mh-42 42.1 1n 10 7.56 ± 0.74 0.930 1.25 ± 0.27 0.745

2n 7 11.48 ± 1.91 0.922 2.10 ± 0.74 0.641

mh 28.9 ± 8.7 1n 35 8.77 ± 0.54 0.938 2.18 ± 0.38 0.805

mh 2n 34 15.33 ± 1.06 0.943 4.36 ± 0.52 0.867

wildtype 25.7 ± 4.1 2n 20 13.36 ± 0.89 0.959 1.27 ± 0.22 0.699

Table S1. Positive correlation between ploidy and nuclear growth rates.

Nuclear volumes and growth rates were determined for 89 synchronized nuclei in 5 mh1 and

2 wildtype embryos that were tracked after exit from the last synchronized mitosis. Early and

late nuclear growth rates (mean ± 95% c.i.) were calculated for the indicated number of

tracks n from linear regression of the first 9 and the subsequent 30 minutes, respectively

(see Figure 4B). RSQ or squared Pearson product-moment correlation coefficient measures

the linear correlation between time and volume.

Biology Open (2017): doi:10.1242/bio.022079: Supplementary information

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Supplementary Figures

Fig. S1. Data management of nuclear image segmentation and tracking projects.

The automated 3D nuclear segmentation method uses 3D+t data sets as inputs and

produces four types of output files (grey box). For each frame (time point) and set of

segmentation parameters, it generates a .sgpj master file, which is associated with a file

containing a list of detected objects and their extracted features. Each list of objects is

further linked to two contour files (.cont) that represent 3D surfaces as sets of polygons and

2D silhouette contours. The tracking project files (.trpj) handles the time-series analysis of all

nuclear lineages associated with one embryonic sample. Each time point is represented by

one or more segmentation outputs, which can be combined in a mix-and-match strategy

called composite segmentation. Tracking project files store associations between nuclei in

consecutive frames and annotations, such as cell cycle phases.

Segmentation output files

3D+t Image data

(1 ics file per sample)

Segmentation Data

(1 sgpj file per frame) • Raw Image data• Segmentation parameters• Detected objects

Detected Objects

(1 csv file per frame) • Object features• 3D surfaces• Silhouette contours

3D surfaces

(1 cont file per frame)

Silhouette Contours

(1 cont file per frame)

Tracking Project

(1 trpj file per sample) • List of segmented objects• List of MIP files• Object annotations• Associations between objects

MIP Image

1 tif file per frame


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Fig. S2. Evaluation of segmentation accuracy.

(A, B) The einSTA (editing and integration of segmentation, tracking and annotation) tool

facilitates the evaluation of segmentation through projection of silhouette contours and

centroids (2nd and 3rd column) on MIP images (1st column). Wrong segmentations like

merged interphase nuclei (A) or a split nucleus in prometaphase (B) can be easily detected

and substituted by correct outputs of an alternative segmentation. (C) Using iso-surface

reconstruction, the quality of segmentation can inspected using the DyVis3D tool. The scale

bar in (B) represents 10 µm and also applies to (A).


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Fig. S3. Automated splitting method of fused segmented nuclei.

(A) 3D objects are represented by overlapping polygons in successive optical sections (z1-

z11). The first column shows the contours of detected interphase nuclei in different optical

sections. Yellow lines in the second and third column indicate polygons that were cut by the


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splitting algorithm. Note that polygons in z5 to z9 split twice. (B) Splitting of merged objects

is achieved by iteratively searching for the shortest cutting line that fulfils a set of constraints

(see Material & Methods).


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0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0

2

4

6

8

10

12

14

16

0.0 20.0 60.0 80.0 100.0

Re

call/

Pre

cisi

on

Dis

pla

cem

en

t [u

m]

40.0

Displacement [um] Recall Precision

Time [min]

AP12

AP13

D

AP14


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Fig. S4. Automated tracking performance depends on the cell cycle phase.

(A-C) show histone-GFP labeled nuclei in nuclear cycle 12 (NC12) of a wildtype embryo.

Nuclei in current and previous frames are colorized green and red, respectively. White lines

in the second column between previous (blue) and current (white) object centroids indicate

the displacements during 55 second intervals. The three examples illustrate that the recall

rate R of a nearest neighbor tracking method correlates inversely with average displacement

d which is low during interphase (A) and high during anaphase (C). (D) The chart

corresponds to the same embryo and shows the relationship between average nuclear

displacement and the recall/precision rates of automated tracking from NC12 until

gastrulation. AP12/13= anaphase of NC12/13; AP14= start of non-synchronized post-

syncytial blastoderm mitosis.


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Fig. S5. Ploidy differences can be visually validated during anaphase.

In projections of anaphase chromosomes labeled with histone H2Av-GFP, (A) 7 of the 10

major chromosome arms (2 sets of X, 2L, 2R, 3L, 3R) can be seen in diploid daughter nuclei

of a control embryo, while (B) 3 to 5 of 5 expected arms can be distinguished in haploid

nuclei of mh1 embryos.

wildtype diploid

mh1

haploid

A

B

5 mm

Histone H2Av-GFP


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A

mh-

42

Ana

m14

h

-42

eta

m14

h

-42

naM

-

m13

h

-42

eta

A

m13

h

-39

naM

-

m14

h

-39

eta

A

m14

h

-39

Ana

M

m13

h

-39

eta 1

w3

t

-26

AnM

a

w1

t3

-

26

eta

w1

t3

-26

Ana

M

w1

t2

-

26

eta

w1

t2

-24

Ana

M

w1

t3

-

24

eta

w1

t3

-

24

Meta

A

1n

2a

M

w1

t2

-24

100

80

60

40

20

0

B

Dataset Phase Penultimate cycle Ultimate cycle change Intensity

Change volume Volume [µm3] Intensity n Volume [µm3] Intensity n

wt-24 Meta 67.3 ± 11.0 66.7 ± 5.6 128 68.5 ± 9.5 85.0 ± 6.1 244 27.4% 1.7%

wt-24 Ana 30.5 ± 5.9 65.9 ± 29.7 128 29.7 ± 3.9 85.2 ± 8.9 227 29.2% -2.7%

wt-26 Meta 68.6 ± 5.2 542.3 ± 27.2

21 65.6 ± 3.9 669.8 ± 26.1

21 23.5% -4.3%

wt-26 Ana 41.5 ± 2.8 472.1 ± 26.5

33 34.4 ± 1.8 596.6 ± 25.7

36 26.4% -17.3%

mh-39 Meta 37.6 ± 5.5 82.1 ± 7.8 145 36.0 ± 3.9 105.2 ± 7.6 135 28.1% -4.3%

mh-39 Ana 21.3 ± 5.1 65.9 ± 8.9 111 16.4 ± 1.9 92.6 ± 9.5 95 40.6% -22.8%

mh-42 Meta 31.2 ± 2.5 90.4 ± 5.8 30 34.8 ± 2.6 113.3 ± 6.1 26 25.3% 11.3%

mh-42 Ana 17.9 ± 1.7 83.6 ± 4.0 20 16.8 ± 1.3 113.2 ± 6.4 20 35.5% -6.4%

Volume

[m

3]


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Fig. S6. Volumes of mitotic chromosomes are proportional to DNA content.

(A) Boxplots of the volumes of detected nuclei of 2 wildtype and 2 mh1 embryos in

metaphase and anaphase of the 2 last nuclear cycles show that measurements are

proportional to DNA. Volume estimates at anaphase are approximately half of those at

metaphase, while haploid nuclei in mh1 embryos show approximately half the volume of their

diploid counterparts in controls. (B) Increased fluorescence intensity does not result in

increased volume measurements. While intensity consistently increased from penultimate to

final to nuclear cycles, we did not observe a proportional increase of nuclear volumes. Note

that embryo wt-26 was recorded in 16-bit grayscale while the other 3 were recorded in 8 bit.


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Fig. S7. Nuclear collisions are not related to increased nuclear densities in mh

syncytial embryos.

The scatter plot shows collision rates of 4 mh1 embryos in up to 3 final synchronized division

cycles. ND was determined during metaphase. Collision rate is defined as the proportion of

mother or daughter nuclei involved in collision during metaphase or anaphase, respectively.

ND values of a control embryo (wt-24) without collisions are indicated at the bottom. Error

bars indicate 95% confidence intervals of the proportions. Numbers of nuclei scored per

embryo starting from earliest to latest nuclear cycles are: mh-13 (85), mh-20 (140), mh-39

(52, 103, 204), mh-42 (96, 173, 283) and wt-24 (40, 76, 147).

0%

5%

10%

15%

20%

25%

0 5 10 15 20 25

mh-13 mh-20 mh-39

mh-42 wt24

NC11 NC12 NC13

ND [nuclei/

1000 mm2]

Collision Rate [%]


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Fig. S8. Volumes of interphase nuclei correlate with DNA content.

The bar charts compare nuclear volumes (mean ± 95% c.i.) of six embryos (see Table S1) at

(A) anaphase and (B) interphase 36 minutes later. Within mh1 embryos, the differences

between ploidies are maintained as nuclei grow. In contrast, these differences are not

preserved between nuclei of different embryos. For instance, anaphase volumes in wildtype

embryos are not significantly different from diploid counterparts in mh1 embryos, whereas

the same nuclei are significantly smaller at interphase. The statistical significance p was

determined using the Students’ t-test: * p ≤ 0.05, ** p ≤ 0.01, **** p ≤ 0.0001, n.s. not

significant p>0.05

0 20 40 60

wt-24

wt-26

mh-13 (2n)

mh-13 (4n)

mh-21 (1n)

mh-21 (2n)

mh-39 (1n)

mh-39 (2n)

mh-39 (3n)

mh-42 (1n)

mh-42 (2n)

wt

mh (1n)

mh (2n)

0 200 400 600

wt-24

wt-26

mh-13 (2n)

mh-13 (4n)

mh-21 (1n)

mh-21 (2n)

mh-39 (1n)

mh-39 (2n)

mh-39 (3n)

mh-42 (1n)

mh-42 (2n)

wt

mh (1n)

mh (2n)

B Interphase Nuclear volume (36 min) A Nuclear volume at anaphase (0 min)

n.s.

****

****

****

**

****

****

****

**

**

**** **** [µm3] [µm3]

(n=34) (n=35) (n=20) (n=7) (n=10) (n=4) (n=6) (n=10) (n=6) (n=10) (n=2) (n=10) (n=10) (n=10)


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0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

0 5 10 15 20 25 30 35 40wt-24 wt-26 mh-39 (2n) mh-42 (2n)

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

0 5 10 15 20 25 30 35 40mh-39 (1n) mh-39 (2n) mh-42 (1n) mh-42 (2n)

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

wt-24 NC12

wt-24 NC13

wt-26 NC12

wt-26 NC13

mh-39 (1n) NC13

mh-39 (1n) NC14

mh-39 (2n) NC14

mh-42 (1n) NC13

mh-42 (1n) NC14

mh-42 (2n) NC14

n=18

n=18

n=6

n=6

n=4

n=3

n=2

n=11

n=6

n=6

A

Nu

cle

ar

vo

lum

e [m

m3]

Nu

cle

ar

vo

lum

e [m

m3]

B

C

Time [min]

Time [min]

Nu

cle

ar

gro

wth

[m

m3/m

in]

NC13

NC14

anaphase

anaphase

NC13 (wt), NC14 (mh)


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Fig. S9. Changes of nuclear volume during the syncytial blastoderm.

(A, B) We analyzed the last two synchronized nuclear cycles in two wildtype and two mh1

embryos. The time-series plots show time[min] versus nuclear volume [m3] (mean ± s.d.)

and were aligned relative to anaphase of the penultimate nuclear cycle (blue arrow). Number

of tracks (n) is shown in (C), except for one (n=1) diploid nucleus of embryo mh-39 in NC12.

(A) Nuclear growth during of NC12 of wildtype embryos shows a single rapid phase from

anaphase (blue arrow) to late interphase (red arrows), giving rise to a triangular curve. In

NC13, a longer interphase is associated with slower nuclear growth. Nuclear growth rates

and sizes are similar in diploid mh1 and control embryos. (B) Nuclear sizes in mh1 embryos

during the syncytial blastoderm are proportional to ploidy. The line plots compare haploid

(1n) and diploid (2n) nuclei of two embryos. (C) The bar chart compares nuclear growth

rates (mean ± s.d.) in the first 6 minutes of interphase of the control and mh1 embryos.


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Fig. S10. The normalized sums of nuclear volumes converge during cellularization.

(A) To obtain volume to hull ratios (VHR), we divided the sum of volumes of all detected

ROIs by the convex hull area defined by their centroids. ROIs were filtered by depth (<= 25

µm) and size (> 10 µm3). Time point 0 min refers to the exit from the final synchronized

mitosis. (B) Despite a close to threefold difference between minimum (14.2) and maximum

(41.5) nuclear density among embryos (28.4 ± 9.4), nuclear expansion after MBT led to

significantly smaller variations in maximum VHR values (5.3 ± 0.6).

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 time [min]

wt-24

mh-13

mh-21

mh-42

mh-39

mh-27

Nuclear Volume to hull-area ratio (VHR)

Embryo Nuclear

Density

[nuclei/1000

mm2]

Max. VHR Duration to

max VHR [min]

Duration to

first cellular

mitosis

wt-24 24.0 5.9 49 66

mh-13 13.3 5.7 88 105

mh-21 20.8 5.2 41 92

mh-27 27.4 5.1 50 74

mh-39 39.3 5.5 59 72

mh-42 42.1 4.2 28 61

A

B


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Fig. S11. Mh-mKO2 reporter gene expression in salivary glands and testes.

(A) In salivary glands, ectopically expressed Mh-mKO2 predominantly localizes to speckle-

like structure in non-chromosomal regions. DNA was labeled with Hoechst dye. (B)

Expression of Mh-mKO2 in testes does not require Gal4. The UAS-mh-mKO2 fusion gene

inserted into the pUASp and pUAST transformation (not shown) vectors is expressed in

larval and pupal testes (arrow, magnified in right panel) in the absence of the Gal4

transcription factor.


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Movies

To play a movie in a web browser, navigate to

https://figshare.com/s/e030e8dec869d8095f53 and click on the “View” button below the

thumbnail of the selected movie.

Movie 1. Fusion of nuclei during mitosis of nuclear cycles in maternal haploid (mh)

embryos leads changes in ploidy. Embryonic nuclei labeled with histone-GFP were

imaged by confocal microscopy from NC12 until gastrulation at 55 second intervals. During

anaphase of NC14, the haploid daughter nuclei of 3 haploid nuclei (white circles mark the

centroids, while blue circles indicate position of previous time point) collide to give rise to 1

haploid, diploid and triploid progenitor. Tracking confirmed that the 3 nuclei resumed mitotic

division at gastrulation. Another group of 3 haploid nuclei (arrow further right) merged during

metaphase of NC to give rise to two triploid daughter nuclei. Time is indicated in min:sec.


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http://movie.biologists.com/video/10.1242/bio.022079/video-1


Movie 2. Nuclear fusion in mh embryos during the syncytial blastoderm stage does

not depend on crowding or haploidy. White circles mark the centroids of diploid (2n)

nuclei that fuse during anaphase of the final nuclear division cycle to give rise to tetraploid

(4n) nuclei. Nuclear migration is visualized by lines connecting previous (blue circle) and

current centroids (white circle). The final syncytial division cycle prior to MBT occurred at a

nuclear density consistent with the penultimate nuclear cycle in wildtype embryos,

suggesting that fusion does not depend on the crowding or haploidy. After completion of

cellularization (100 minutes) tetraploid cells divide normally. A haploid cell (1n) is marked for

reference in bottom right region of the frame.


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Movie 3. Dynamic 3D visualization of nuclei during early embryogenesis. Selected

nuclei of a mh1 embryo (same as in movie 2) were tracked using einSTA from the syncytial

blastoderm until gastrulation. Subsequently, isosurfaces of tracks were generated using

DyVis3D, which were rendered from two different perspectives. Two fusion events of diploid

nuclei becoming tetraploid during anaphase are indicated by arrows at 11:55 [min:sec]. The

two resulting tetraploid in the isosurface views are colored in yellow.


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Movie 4. Mh-mKO2 localizes to nuclear speckles in interphase nuclei of Drosophila

syncytial blastoderm embryos. UASp-mh-mKO2 (red) was expressed using the nos-

Gal4:VP16 driver. Histone H2Av-GFP (green) was co-expressed to monitor progression of

the nuclear cycles. Video frames show projections of 3D stacks image stacks that were

recorded at 50 second intervals. In early interphase (4:10-7:30 min:sec), Mh-mKO2 is

uniformly distributed in nuclei. In later interphase (8:20-17:30), Mh-mKO2 clusters in nuclear

speckles (8:20-17:30), that rapidly disappear as nuclei enter mitosis (18:20-21:40).


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Documents

Drosophila embryos and their effect on nuclear size.bio.biologists.org/content/biolopen/early/2017/01/18/[email protected] Key words: Drosophila, live imaging, maternal haploid,