ABSTRACT Summary: NucleusJ is a simple and user-friendly ImageJ plugin dedicated to the characterization of nuclear morphology and chromatin organization in 3D. Starting from image stacks, the nuclear boundary is delimited by combining the Otsu segmentation method with optimization of nuclear sphericity. Chromatin domains are segmented by partitioning the nucleus using a 3D watershed algorithm and by thresholding a contrast measure over the resulting regions. As output, NucleusJ quantifies 15 parameters including shape and size of nuclei as well as intra-nuclear objects and their position within the nucleus. A step-by-step documentation is available for selftraining, together with data sets of nuclei with different nuclear organization. Availability: Data set of nuclei is available at https://www.gredclermont.fr/media/WorkDirectory.zip. NucleusJ is available at http://imagejdocu.tudor.lu/doku.php?id=plugin:stacks:nuclear_analysis_plugin:start

Aline V Probst

Axel Poulet

Christophe Tatout

David Legland

Ignacio Arganda-Carreras

Philippe Andrey

CiteSeerX

1 
Bioimage informatics 
NucleusJ: an ImageJ plugin for quantifying 3D images of inter-
phase nuclei 
Axel Poulet1, Ignacio Arganda-Carreras2, David Legland2, 3, Aline V. Probst1, Philippe 
Andrey2, 3, 4, * and Christophe Tatout1, *  
1 
UMR CNRS 6293 INSERM U 1103, GRED, Clermont Université, Aubière, France 
2 
UMR INRA-AgroParisTech 1318 ERL CNRS 3559, IJPB, Versailles, France 
3
 UMR INRA-AgroParisTech 782, Food Process Engineering and Microbiology, Thiverval-Grignon, France 
4
 Sorbonne Universités UPMC Univ Paris 06 UFR 927, Paris, France 
 
ABSTRACT 
Summary: NucleusJ is a simple and user-friendly ImageJ plugin 
dedicated to the characterization of nuclear morphology and chro-
matin organization in 3D. Starting from image stacks, the nuclear 
boundary is delimited by combining the Otsu segmentation method 
with optimization of nuclear sphericity. Chromatin domains are seg-
mented by partitioning the nucleus using a 3D watershed algorithm 
and by thresholding a contrast measure over the resulting regions. 
As output, NucleusJ quantifies 15 parameters including shape and 
size of nuclei as well as intra-nuclear objects and their position with-
in the nucleus. A step-by-step documentation is available for self-
training, together with data sets of nuclei with different nuclear or-
ganization.  
 
Availability: Data set of nuclei is available at https://www.gred-
clermont.fr/media/WorkDirectory.zip. NucleusJ is available at 
http://imagejdocu.tudor.lu/doku.php?id=plugin:stacks:nuclear_analysis_plugin:start.  
 
Contact: philippe.andrey@versailles.inra.fr and chris-
tophe.tatout@univ-bpclermont.fr  
 
Supplementary information: available at Bioinformatics on line. 
1 INTRODUCTION  
The nucleus is a compartmentalized organelle containing distinct 
chromosomal domains and a variety of nuclear bodies. Organiza-
tion and positioning of chromatin domains within the nuclear 
space, such as clusters of heterochromatin termed chromocenters 
(Fransz et al., 2002), impact gene expression (Bickmore and van 
Steensel, 2013). To link nuclear structure and function, imaging 
  
*To whom correspondence should be addressed.  
tools that quantify nuclear morphology, positioning and organiza-
tion of chromatin domains in three dimensions (3D) are needed. 
Although proprietary softwares or open source programs such as 
NEMO and TANGO are available (Iannuccelli et al., 2010; Ollion 
et al., 2013), they either require financial investment or minimal 
knowledge of image processing algorithms. Our NucleusJ plugin 
assembles a complete set of methods in a coherent strategy, quanti-
fies 15 different nuclear and chromatin parameters and does not 
require expertise in image analysis. 
2 METHODS 
The NucleusJ plugin processes 3D images of nuclei either acquired by live 
imaging with fluorescent reporters, from fixed tissues or isolated nuclei 
stained with DNA dyes. NucleusJ uses the MorphoLibJ library 
(https://github.com/ijpb/MorphoLibJ) available for ImageJ and Fiji. The 
implemented 3D image processing and analysis pipeline can be run either 
completely or partially, on single images or in batch mode, thus offering 
flexibility to accommodate the diversity of practical needs and application 
purposes [Fig. 1]. In the course of the analysis, transformed images are 
generated, automatically organized in sub-directories, and results of quanti-
tative analyses are arranged in four output files [Fig. 1].  
Segmentation is an essential procedure in image analysis as it separates 
objects from each other and from the background. NucleusJ includes two 
segmentation steps that extract first the nucleus and then chromatin do-
mains such as chromocenters. 3D nucleus segmentation is performed using 
an adaptation of the Otsu thresholding method (Otsu, 1979), here combined 
with optimization of a shape parameter called sphericity (36π × Volume2 / 
Surface Area3). The threshold value provided by the standard Otsu method 
is used as a starting point to test a range of thresholds, which eventually 
leads to the selection of the value for which the sphericity is maximal [Fig. 
1 and Supplementary Fig. S1, S2]. Chromocenter segmentation is per-
formed using the segmented nucleus image as mask. We implemented the 
watershed immersion algorithm (Vincent and Soille, 1991) in 3D to parti-
tion the nucleus mask into a number of regions. To overcome the low con-
trast between chromocenters and the background intensity of the nucleus, 
Associate Editor: Prof. Robert F. Murphy
© The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: 
journals.permissions@oup.com 
 Bioinformatics Advance Access published November 20, 2014
 at Pennsylvania State U
niversity on O
ctober 7, 2016
http://bioinform
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2 
the resulting image is analyzed to create a map of intensity contrasts be-
tween neighbouring regions (Andrey et al., 2010). Because semi-automatic 
or manual approaches are more accurate than fully automatic procedures, 
we kept the final thresholding of the intensity contrast to obtain the seg-
mented image of chromocenters as a manual step.  
 
 
 
 
 
 
 
 
 
 
 
Fig. 1.  NucleusJ combines tools that can be run in single or batch mode 
and along different scenarios (1 to 4). Raw images are stored in A by the 
user. Transformed images are automatically organized in directories (B-D) 
and quantitative parameters exported as output files (I-IV). Nucleus Seg-
mentation (1) and Nucleus Segmentation & Analysis (2) produce segmented 
images automatically stored in B. Quantitative parameters are exported as 
files I and II only when (2) is chosen. Chromocenter Segmentation (3) 
applies the 3D watershed transformation to raw images in A using seg-
mented nuclei stored in B. Contrast images are then stored in C. Chromo-
centers are defined by manual thresholding and images stored in D. Chro-
mocenter parameters are computed by Chromocenter analysis (4) and 
exported as file III. Finally, a compilation of all parameters is exported as 
file IV. Images of Arabidopsis nuclei were acquired with a structured illu-
mination microscope (Leica microsystems) using a water 63x objective. 
Resolution: xy = 0.103µm and z = 0.200µm. Scale bar: 2µm.  
3 RESULTS 
Following segmentation, a morphometric analysis is applied to measure 
nuclear volume and 3D shape descriptors (flatness, elongation, sphericity). 
NucleusJ also computes chromatin organization parameters such as num-
ber, total volume and intensity of chromocenters relative to the entire nu-
cleus (Tessadori et al., 2007) as well as individual chromocenter parame-
ters including volume and distance to the nuclear periphery. Overall, the 
nucleus is characterized by 15 quantitative parameters [Fig. 1 and supple-
mental table 1], which are automatically saved as output files for further 
statistical analysis. NucleusJ was validated using an Arabidopsis mutant 
line lacking CROWDED NUCLEI (CRWN) proteins, which are putative 
components of the plant nucleoskeleton (Wang et al., 2013). Analysis of a 
small set of 38 (wild type) and 39 (crwn1 crwn2 mutant) nuclei revealed 
reduced nuclear volume, increased sphericity, and fewer chromocenters of 
increased size in the mutant compared to WT, in perfect agreement with 
Wang et al., 2013 [Fig. 2]. 
 
 
 
 
 
Fig. 2 Application of NucleusJ using plant cell nuclei. A) Top: repre-
sentative epidermis pavement cell nuclei of wild type (wt, left) and mutant 
(crwn1 crwn2, right) cotyledons. Scale bar: 2µm. Bottom: model from the 
quantitative data: nucleus in gray is surrounded by the nuclear envelope 
(NE) in dark gray, chromocenters (Cc) in white. B) Principal Component 
Analysis using the 15 parameters on wt (black) and crwn1 crwn2 (red) 
nuclei. C) Statistical analysis of four selected parameters (***: P<0.0001). 
The analysis of the 77 nuclei took about one hour. The time-limiting step is 
the manual thresholding of contrast images for chromocenter segmentation, 
since it involves visual crosschecking with raw images [Fig. 1]. The statis-
tical analysis of the data validates our method and opens avenues to pheno-
type nuclei in other Arabidopsis mutants, as well as in different cell types 
of other organisms. 
4 CONCLUSION 
Computational image analysis provides precise, objective and re-
producible quantitative data from images. Our plugin generates, 
within a few steps, 3D quantitative measurements from single im-
ages or large data sets, without requiring expertise in image analy-
sis. Nuclear domains visible with DNA dyes, such as chromocen-
ters, are not specific to plants. NucleusJ will therefore be useful for 
a large community of users interested in quantifying size and shape 
of nuclei, nuclear objects or chromatin domains as well as posi-
tioning of the latter in nuclear space. In the future, we expect to 
improve our segmentation methods, to extend NucleusJ to quantify 
in situ hybridization (FISH) and immunochemistry signals and to 
define a new method to automatically segment chromocenters. 
 
Funding: This work was supported by Auvergne and Oxford 
Brookes universities to AP, by the French National Research 
Agency [ANR-11 JSV2 009 01, ANR-12-ISV6-0001 to AVP and 
Infrastructure TEFOR/ANR-11-INBS-0014 to IAC] and by the 
Region Auvergne [Life GRID to CT]. 
 
Conflict of interest: none declared. 
 
REFERENCES 
Andrey,P. et al. (2010) Statistical Analysis of 3D Images Detects Regular Spatial 
Distributions of Centromeres and Chromocenters in Animal and Plant 
Nuclei. PLoS Computational Biology, 6, e1000853. 
Bickmore,W.A. and van Steensel,B. (2013) Genome Architecture: Domain Organiza-
tion of Interphase Chromosomes. Cell, 152, 1270-1284. 
Fransz,P. et al. (2002) Interphase chromosomes in Arabidopsis are organized as well 
defined chromocenters from which euchromatin loops emanate. Proceed-
ings of the National Academy of Sciences U.S.A, 99, 14584-14589. 
Iannuccelli,E. et al. (2010) NEMO: a tool for analyzing gene and chromosome territo-
ry distributions from 3D-FISH experiments. Bioinformatics, 26, 696-697. 
Ollion,J. et al. (2013) TANGO: a generic tool for high-throughput 3D image analysis 
for studying nuclear organization. Bioinformatics, 29, 1840-1841. 
Otsu,N. (1979) A threshold selection method from grey-level histograms. IEEE 
Transactions on Systems, Man and Cybernetics, 9, 62-66. 
Tessadori,F. et al. (2007) Large-scale dissociation and sequential reassembly of peri-
centric heterochromatin in dedifferentiated Arabidopsis cells. Journal of 
Cell Science, 120, 1200-1208. 
Vincent,L. and Soille,P. (1991) Watersheds in digital spaces: an efficient algorithm 
based on immersion simulations. IEEE Transactions on Pattern Analysis 
and Machine Intelligence, 13, 583-598. 
Wang,H. et al. (2013) Arabidopsis CROWDED NUCLEI (CRWN) proteins are 
required for nuclear size control and heterochromatin organization. BMC 
Plant Biology, 13, 200. 
 
 
 at Pennsylvania State U
niversity on O
ctober 7, 2016
http://bioinform
atics.oxfordjournals.org/
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NucleusJ: an ImageJ plugin for quantifying 3D images of inter- phase nuclei

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NucleusJ: an ImageJ plugin for quantifying 3D images of inter- phase nuclei

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