Special Issue – Mini Review
Editor-in-Chief’s choice
2000
Seeing Is Believing: On the Use of Image Databases
for Visually Exploring Plant Organelle Dynamics
Shoji Mano1,2, Tomoki Miwa3, Shuh-ichi Nishikawa4, Tetsuro Mimura5 and Mikio Nishimura1,2,∗
1Department
of Cell Biology, National Institute for Basic Biology, Okazaki, 444-8585 Japan
of Basic Biology, School of Life Science, Graduate University for Advanced Studies, Okazaki, 444-8585 Japan
3Computer Laboratory, National Institute for Basic Biology, Okazaki, 444-8585 Japan
4Graduate School of Science, Nagoya University, Nagoya, 464-8602 Japan
5Department of Biology, Graduate School of Science, Kobe University, Kobe, 657-8501 Japan
2Department
Organelle dynamics vary dramatically depending on cell
type, developmental stage and environmental stimuli,
so that various parameters, such as size, number and
behavior, are required for the description of the dynamics
of each organelle. Imaging techniques are superior to
other techniques for describing organelle dynamics
because these parameters are visually exhibited. Therefore,
as the results can be seen immediately, investigators
can more easily grasp organelle dynamics. At present,
imaging techniques are emerging as fundamental tools in
plant organelle research, and the development of new
methodologies to visualize organelles and the improvement
of analytical tools and equipment have allowed the largescale generation of image and movie data. Accordingly,
image databases that accumulate information on organelle
dynamics are an increasingly indispensable part of modern
plant organelle research. In addition, image databases are
potentially rich data sources for computational analyses,
as image and movie data reposited in the databases
contain valuable and significant information, such as size,
number, length and velocity. Computational analytical
tools support image-based data mining, such as
segmentation, quantification and statistical analyses, to
extract biologically meaningful information from each
database and combine them to construct models. In this
review, we outline the image databases that are dedicated
to plant organelle research and present their potential as
resources for image-based computational analyses.
Keywords: Arabidopsis thaliana • Image database • Imaging
• Organelle dynamics • Quantification • Systems biology.
∗Corresponding
Abbreviations:
BiFC,
bimolecular
fluorescence
complementation; CLSM, confocal laser scanning
microscope; ER, endoplasmic reticulum; EST, expressedsequence tag; FRAP, fluorescence recovery after
photobleaching; FRET, fluorescence resonance energy
transfer; GFP, green fluorescent protein; MRI, magnetic
resonance imaging; RFP, red fluorescent protein; SNARE,
soluble N-ethylmaleimide-sensitive factor attachment
protein receptor.
Introduction
Technical advances in molecular biological procedures and
the development of equipment have allowed the generation
of a variety of information on a large-scale basis. In the field
of plant biology, the groundwork of sequencing the whole
genome of several plant species (Kaul et al. 2000, Goff et al.
2002, Yu et al. 2002, Tuskan et al. 2006, Jaillon et al. 2007,
Nozaki et al. 2007) led to the accumulation of large data sets
for these plants. These large data sets are deposited in databases that were constructed for storage, and used in complex analyses aimed at gene identification, annotation of
protein function and prediction of protein localization. The
development of analytical equipment and computational
analytical tools has accelerated the implementation of new
methodologies that use large amounts of data gleaned from
databases. Therefore, the efficient extraction of biologically
meaningful information from each database, as well as the
integration of individual data, have become important for
research purposes.
author: E-mail, [email protected]
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128, available FREE online at www.pcp.oxfordjournals.org
© The Author 2009. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.
All rights reserved. For permissions, please email: [email protected]
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
Image databases for plant organelle research
Currently, a variety of public databases for plant biology
are available. Table 1 displays parts of public databases for
plant research. They are broadly categorized into four groups:
(i) databases dedicated to specific plant species, (ii) databases for specific phenomena, (iii) organelle databases and
(iv) integrative databases that compile several types of information from different organisms. These databases are mainly
constructed based on compilations of genome and/or cDNA
sequences and proteomic data.
Most databases house text-based data, including nucleotide and/or amino acid sequences, which are used for further analyses, such as identification of genetic variation and
gene clustering among several organisms; in contrast, some
databases reposite numerical values. For example, databases
of gene expression data from microarray analyses contain
numerical information on the expression of each gene under
various conditions, and databases that deal with results from
proteomic analyses also contain numerical values (e.g., units
of mass). In addition, as shown in Table 2, other databases
gather image data that reveal protein localization and/or
visualized organelles (Fig. 1A, Cutler et al. 2000, Damme
et al. 2004, Brown et al. 2005, Koroleva et al. 2005, Li et al.
2006, Mano et al. 2008). Although these databases deal with
static image data, some databases also collect movie data
(Fig. 1B, Supplementary Movies 1, 2). Compared with textbased databases, these image databases exhibit an ability to
display the dynamics of proteins, organelles and cells, which
include their localization, size, number, morphology, movement and spatial distribution, because the data are immediately visible and accessible. Therefore, researchers can find
information on the dynamics of a molecule/organelle more
easily.
Various technologies have been used to acquire image
data from plant organelles. Several specific dyes, including
Neutral red, MitoTracker and ER tracker, allow the effective
staining of vacuoles (Carter et al. 2004), mitochondria
(Arimura and Tsutsumi 2002) and the endoplasmic reticulum (ER) (Liu et al. 2007), respectively, and new specific dyes
are currently being developed to cope with different excitation and/or emission wavelengths and to improve membrane permeability. In addition, fluorescent proteins, such as
green fluorescent protein (GFP) and its derivatives, have
contributed greatly to the field of imaging analysis, which
includes visualization of plant organelles (Haseloff and
Siemering 1998, Chudakov et al. 2005). Currently, almost all
organelles in plant cells are visualized using GFP and its
derivatives, and the dynamic pattern of subcellular structure
is highlighted (Fig. 2). The improvement of existing fluorescent proteins, the discovery of new fluorescent proteins with
different characteristics (Shaner et al. 2004, Kogure et al.
2006) and the development of new applications [such as
fluorescence resonance energy transfer (FRET) and bimolecular fluorescence complementation (BiFC)] and of new
vectors suitable for expression in plant cells (Nakagawa et al.
2007, Nelson et al. 2007) have revolutionized image analysis
by enabling easier observation of dynamic subcellular processes in plant living cells. More detailed information on GFP
is provided in other review articles (Haseloff and Siemering
1998, Chudakov et al. 2005, Berg and Beachy 2008).
Image data contain rich information, such as size, length,
morphology and velocity; thus, image databases represent
potentially rich data sources for computational analyses
aimed at extracting biologically meaningful information.
Currently, various applications of computational analytical
tools are used for image-based data mining and provide statistical information on organelle dynamics. The information
contained in the image databases, together with other information such as expression data, can be incorporated into
the construction of biological models. In other words, the
use of image databases can represent a gateway resource for
systems biology. Advanced imaging methodologies and
equipment, such as microscopes, have become more easily
applicable to the large-scale generation of image data; thus,
image databases will become useful resources for the comprehensive understanding of plants. In this review, we outline the image databases dedicated to plant organelles and
discuss their potential as image-based computational analysis tools for systems biology. Other public databases available for plant biology are well described in a recent review by
Brady and Provart (2009).
Visual representation of plant organelle
dynamics
Advantages of image databases
The comprehensive understanding of individual plants
requires the study of various biological aspects during their
life cycle, which have been examined experimentally and/or
via the use of databases. Each database has its advantages
and limitations. Text-based descriptions are not necessarily
the most adequate vehicle to address organelle dynamics, as
organelle dynamics can vary dramatically depending on cell
type, developmental stage and environmental stimuli. The
description of organelle dynamics involves various parameters, such as size, length, velocity, direction and time. As
a result, simple phenotypic patterns cannot be assumed in
organelle dynamics, unlike genome sequence data; therefore, organelle dynamics information can be interpreted at
various levels. In contrast, image and movie data are more
adequate descriptors of these dynamics, as various parameters are visually exhibited in a single representation. Therefore, researchers can easily grasp organelle dynamics, as the
results may be seen immediately.
Live-cell imaging offers a wealth of information on the
dynamics of proteins and organelles, such as movement,
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
2001
S. Mano et al.
Table 1 Vairous databases for plant science
Name
URL
Comments
TAIRa
http://www.arabidopsis.org/
Arabidopsis thaliana
RAP-DBb
http://rapdb.dna.affrc.go.jp/
Oryza sativa
Oryzabasec
http://www.shigen.nig.ac.jp/rice/oryzabase/top/top.jsp
Oryza sativa
Specific plant-species database
v1.1d
http://genome.jgi-psf.org/Poptr1_1/Poptr1_1.home.html
Populus trichocarpa
Genoscopee
http://www.genoscope.cns.fr/spip/Vitis-vinifera-e.html
Vitis vinifera
PHYSCObasef
http://moss.nibb.ac.jp/
Physcomitrella patens
LegumeBaseg
http://www.shigen.nig.ac.jp/bean/lotusjaponicus/top/top.jsp
Lotus japonicus
miyakogusa.jp
http://www.kazusa.or.jp/lotus/
Lotus japonicus
Cyanidioschyzon merolae Genome Projecth
http://merolae.biol.s.u-tokyo.ac.jp/
Cyanidioschyzon merolae
SoyBasei
http://soybase.org/
Glycine max
http://rsoy.psc.riken.jp/
Glycine max
http://genbank.vurv.cz/ewdb/default.htm
Titicum L.
Plantpromoter db 2.0l
http://ppdb.gene.nagoya-u.ac.jp/cgi-bin/index.cgi
Cis-element sequences
PLACEm
http://www.dna.affrc.go.jp/PLACE/
Cis-element sequences
PlantsPn
http://plantsp.genomics.purdue.edu/html/
Phosphorylation site
PhosPhAt 2.2o
http://phosphat.mpimp-golm.mpg.de/
Phosphorylation site
TarBase v5cp
http://diana.cslab.ece.ntua.gr/tarbase/
miRNA
PlnTFDBq
http://plntfdb.bio.uni-potsdam.de/v2.0/
Transcription factor
TOBFACr
http://compsysbio.achs.virginia.edu/tobfac/
Transcription factor
PthoPlants
http://www.pathoplant.de/
Plant-pathogen
interactions
Arabidopsis Mitochondrial Protein Databaset
http://www.plantenergy.uwa.edu.au/applications/ampdb/index.html
Mitochondria
GOBASEu
http://gobase.bcm.umontreal.ca/
Mitochondria,
Chloroplast
Chloroplast Genome Databasev
http://chloroplast.cbio.psu.edu/
Chloroplast
PeroxisomeDBw
http://www.peroxisomedb.org/
Peroxisome
AraPerox 1.2x
http://www.araperox.uni-goettingen.de/
Peroxisome
Populus trichocarpa
Soybean full-length cDNA databasej
The ECPGR Wheat
Databasek
Specific-aspcets database
Organelle database
Naviagatory
http://bioweb.ucr.edu/Cellwall/index.pl
Cell wall
eSLDBz
http://gpcr2.biocomp.unibo.it/esldb/index.htm
Eukaryotic subcellular
localization
Organelle Genome Resources
http://www.ncbi.nlm.nih.gov/genomes/GenomesHome.
cgi?taxid=2759&hopt=html
Chloroplasts,
Mitochondria
Cell Wall
Integrative database
NCBI
http://www.ncbi.nlm.nih.gov/
DDBJ1
http://www.ddbj.nig.ac.jp/
TIGR
http://www.tigr.org/db.shtml
NASC
http://arabidopsis.info/
PEDANT32
http://pedant.gsf.de/
Continued
2002
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
Image databases for plant organelle research
Table 1 Continued
Name
URL
KEGG3
http://www.kegg.jp/ja/
Comments
RIKEN
https://database.riken.jp/sw/view#CRIB00158S000016?view=panel
KAZUSA
http://www.kazusa.or.jp/j/resources/database.html
http://www.nias.affrc.go.jp/database/index.html
NIAS
aSwabreck
bTanaka
cKurata
et al. (2008);
et al. (2008);
and Yamazaki (2006); dTuskan et al. (2006); eJaillon et al. (2007); fNishiyama et al. (2003); gSato et al. (2008); hNozaki
et al. (2007); iSakata et al; (2009); jUmezawa et al. (2008); kRöder et al. (2002); lYamamoto and Obokata (2008); mHigo et al. (1999); nNühse et al. (2004); oHeazlewood et al.
(2008); pPapadopoulos et al. (2009); qRiaño-Pachón et al. (2007); rRushton et al. (2008); sBülow et al. (2007); tHeazlewood et al. (2004); uO’Brien et al. (2009); vCui et al. (2006);
wSchlüter et al. (2007); xReumann et al. (2004); yGirke et al. (2004); zPierleoni et al. (2007); 1Sugawara et al. (2009); 2Walter et al. (2009); 3Kanehisa et al. (2008)
Table 2 Databases for dealing with image data in plant science
Name
URL
Comments
AtNoPDBa
http://bioinf.scri.sari.ac.uk/cgi-bin/atnopdb/home
Images of GFP-tagged proteins in nucleus in
Arabidopsis thaliana
FTFLPdbb, c
http://gfp.stanford.edu/
Images of GFP-tagged proteins in various organelles
in Arabidopsis thaliana
Plant Cell Imagingb, c
http://deepgreen.stanford.edu/
Images and movies of GFP-tagged proteins in various
organelles in Arabidopsis thaliana
GFP localisome databased
http://www.psb.ugent.be/papers/cellbiol/
Images of GFP-tagged proteins involved in cytokinesis and cell plate formation in Arabidopsis thaliana
and BY-2 cells
GFP databasee
http://data.jic.bbsrc.ac.uk/cgi-bin/gfp/
Images of GFP-tagged proteins in various organelles
in Arabidopsis thaliana
PODB2f
http://podb.nibb.ac.jp/Organellome/
Images and movies of various visualized organelles at
differet stages in several plant species
SUBA IIg
http://www.plantenergy.uwa.edu.au/applications/suba/index.php Images of GFP-tagged proteins in various organelles
in Arabidopsis thaliana
The Illuminated Plant Cellh
http://www.illuminatedcell.com/
aBrown
Images and movies of various visualized organelles in
several plant species
et al. (2005); bCutler et al. (2000); cLi et al. (2006); dDamme et al. (2004); eKoroleva et al. (2005); fMano et al. (2008); gHeazlewood et al. (2007); hMathur (2007).
division and fusion. Live-cell imaging data are deposited as
movie files, which offer valuable information that is not
extractable from static image data. For example, peroxisomes are visualized by electron microscopy exclusively
(Nishimura et al. 1986), because, unlike mitochondria, there
is no specific dye for these organelles and because, unlike
chloroplasts, peroxisomes are not autofluorescent. Peroxisomes were recognized as motile organelles that move
dynamically in cells, until real-time imaging using GFP was
applied to their analysis (Supplementary Movie 1; Jedd
and Chua 2002, Mano et al. 2002, Mathur et al. 2002).
Similar live-cell imaging approaches are applied to other
plant organelles; therefore, the number of reports containing movie data as supplementary data continues to
grow steadily. For example, the Plant Organelles Database
2 (PODB2) collects movie data that show the dynamic
movement of various organelles (Fig. 1B, Supplementary
Movie 1). In addition, as shown in Supplementary Movie 2
for the ER network in tobacco suspension-cultured cells
(Mitsuhashi et al. 2000), movies for the rotation of 3D structures, which are constructed from serial images derived from
different focal planes, enable a clear understanding of the
stereological arrangement of organelles. This type of movie
favors the elucidation of the spatial structure of the ER in
a cell. Recently, many individual researchers have set up
websites to upload their movie data.
Regarding protein localization, some databases provide
prediction information of protein localization based on
amino acid sequences, such as plastids (Cui et al. 2006,
O’Brien et al. 2009), mitochondria (Heazlewood et al. 2004,
O’Brien et al. 2009) and peroxisomes (Reumann et al. 2004).
Although this information is valuable as a starting point,
often the localization information obtained in this way needs
to be validated experimentally (flowchart in black in Fig. 3).
In such cases, searches of image databases can be a powerful
tool to confirm localization (flowchart in red in Fig. 3).
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
2003
S. Mano et al.
Image databases for plant organelle research
Compared with text-based databases, there are few
image databases for plant organelle research (Table 2). The
Arabidopsis Nucleolar Protein Database (AtNoPDB) provides
information on >200 proteins identified via a proteomic
analysis using localization images of full-length cDNA–GFP
fusions (Fig. 4A; Brown et al. 2005). The localization of each
fusion protein is categorized into five different subnucleolar
categories, based on localization image data: nucleolar,
nucleolus-associated structures, nucleoplasmic, nuclear
bodies and extranuclear (Brown et al. 2005). The GFP localisome database is dedicated to proteins that are implicated in
cytokinesis and division-plane determination (Fig. 4B; Damme
et al. 2004). The images of GFP fusion proteins were obtained
from Arabidopsis thaliana and tobacco suspension-cultured
cells at different developmental stages. These two databases
allow the investigation of the nature of proteins localized at the
nucleus and of cell-plate determination during cytokinesis.
The project aimed at generating pools of independent
transgenic A. thaliana expressing GFP-fused full-length
cDNAs has been launched and the localization of each fusion
gene product was determined in thousands of transgenic
plants. Image data were deposited into the Fluorescent
Tagging of Full-length Proteins database (FTFLPdb), which is
open to the public (Fig. 4C; Cutler et al. 2000, Li et al. 2006).
Additional image and movie data for organelle dynamics are
uploaded on the website of Dr Ehrhardt (http://deepgreen
.stanford.edu/), who is in charge of the construction of
the FTFLPdb. The use of the FTFLPdb is a fast, first-level
method to examine the localization of proteins of interest.
Furthermore, resources such as transgenic seeds are distributed through the Arabidopsis Biological Resource Center
(http://www.arabidopsis.org/).
The GFP database at the John Innes Centre also provides
information on the localization of Arabidopsis proteins
(Fig. 4D; Koroleva et al. 2005). In this project, Arabidopsis
full-length cDNAs fused to GFP were transiently expressed
in Arabidopsis suspension-cultured cells for acquisition of
image data. At present, 155 image data are collected in
this database. The localization of each fusion protein is
categorized into five main categories, based on localization
image data: cytoplasm, nucleus, nucleolus, organelles and
endomembrane compartments.
Fig. 1 Examples of submitted image and movie data in the PODB2.
(A) The thumbnails showing each image were downloaded from the
database and arranged. The PODB2 houses data of various visualized
2004
organelles in different tissues and developmental stages in several
plant species (Mano et al. 2008). (B) Output from the Organelles
Movie Database in the PODB2 is shown. The movie represents
behavior of the ER in the process of cell plate formation during
cytokinesis in tobacco suspension-cultured cells. Green signal
(i.e., GFP) allows the visualization of the ER. The red signal represents
the endosome, which forms the cell plate and is visualized using
FM4-64 (Higaki et al. 2008).
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
Image databases for plant organelle research
Fig. 2 Morphology of visualized organelles in transgenic A. thaliana expressing a GFP fusion gene. Leaf (A–E, G–J, L) or root cells (F, K) of 12-dayold seedlings were observed using confocal scanning microscopy. (A) Cytosolic fluorescence from GFP without an organelle targeting signal
(Mano et al. 2002). (B) Peroxisomes in the GFP–PTS1 plant (Mano et al. 2002). (C) Chloroplasts in the A5-3 plant expressing pt–sGFP (Niwa et al.
1999). (D) Mitochondria in the Mt–GFP plant (Arimura and Tsutsumi 2002). (E) ER and ER bodies in the GFP-h plant expressing SP–GFP–HDEL
(Matsushima et al. 2002). (F) Nucleus in transgenic A. thaliana expressing GFP–puSRp35 (Mano et al. unpublished result). (G) Plasma membrane
in transgenic A. thaliana (stock no. CS84725) that was donated to TAIR (Cutler et al. 2000). (H) Actin filaments in the GFP–hTalin plant (Takemoto
et al. 2003). (I) Chloroplasts (red) and peroxisomes (green) from the GFP–PTS1 plant (Mano et al. 2002). (J) Tubulin in the GFP–TUA6 plant
(Ueda et al. 1999). (K) Vacuoles in transgenic Arabidopsis thaliana expressing SP–GFP–2SC (Tamura et al. 2005). (L) Tonoplast in transgenic
A. thaliana expressing gTIP–GFP (Mitsuhashi et al. 2000). Bars, 20 µm.
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
2005
S. Mano et al.
Fig. 3 Scheme for the application of image database to plant organelle research and systems biology. Flowchart in black: Genome and proteome
databases provide prediction information of protein localization based on amino acid sequences. Image experiments can be performed to validate
the prediction, and their results can be deposited in image databases. Flowchart in red: The data in the image databases, which were derived from
imaging experiments, supplement the information in the genome and proteome databases. Flowchart in green: The image and movie data in the
databases represent valuable and significant resources to obtain biologically meaningful information. Based on information extracted using
image-based quantification, researchers can estimate the number of organelles in a cell, the size of each organelle, their spatial distribution, track
motile organelles, etc. In addition, based on calculated results, researchers can compare their data with deposited data. Flowchart in blue: Data
mining based on image-based quantification provides the basis for mathematical modeling, and imaging can also be a suitable method to verify
the model. Flowchart in open arrows: The large amount of data deposited into various kinds of database must be used to identify and quantify
the various components of organelles, cells, organs and individuals in systems biology. Image databases dedicated to protein localization and/or
organelles are expected to advance for systems biology.
2006
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
Image databases for plant organelle research
Fig. 4 Web interfaces of some image databases. (A–E) Examples of a result screen for AtNoPDB (A, Brown et al. 2005), GFP localisome database
(B, Damme et al. 2004), FTFLPdb (C, Cutler et al. 2000, Li et al. 2006), GFP database (D, Koroleva et al. 2005) and PODB2 (E, Mano et al. 2008).
(F) The main page of the project of ‘The Illuminated Plant Cell’ (Mathur 2007).
Figure details. A: Reproduced/ Brown, J.W.S., Shaw, P. and Marshall, D. F. (2005) Arabidopsis nucleolar protein database (AtNoPDB) Nucleic Acids
Res. 33: D633–D636 with permission from Plant Bioinformatics group, Scottish Crop Research Institute.
B: Reproduced/ Vam Damme, D., Bouget, F.Y., Vam Poucke, K., Inzé D. and Geelen, D. (2004) Molecular dissection of plant cytokinesis and
phragmoplast structure: a survey of GFP-tagged proteins. Plant J. 40: 386–398. with permission from Vam Damme et al.
C: Reproduced/ Li S, Ehrhardt DW, Rhee Sy. (2006) Systematic analysis of Arabidopsis organelles and a protein localisation database for facilitating
flourescent tagging off full-length Arabidopsis proteins. Plant Physiol. 141(2):527–39. with permission from SY Rhee
D: Reproduced/ Koroleva, O.A., Tomlinson, M.L., Leader, D., Shaw, P. and Doonan, J.H. (2005) High-throughput protein localization in Arabidopsis
using Agrobacterium-mediated transient expression of GFP-ORF fusions. Plant J. 41: 162–174. with permission from Wiley- Blackwell Ltd.
F: Reproduced/ Mathur J. (2007) The illuminated plant cell. Trends Plant Sci. 12: 506–513. with permission from Dr Jaideep Mathur.
The subcellular location database for Arabidopsis
proteins (SUBA), which was updated to version 2, houses
large-scale proteomic and GFP localization data sets from
various cellular compartments of A. thaliana (Heazlewood
et al. 2007). This database collects information on protein
localization based on proteomic surveys and literature
references from other resources, such as TAIR and Swiss-Prot
annotations. Therefore, compared with the FTFLPdb and
the GFP database, in its current configuration, SUBA II places
more weight on text-based information than on imagebased information. An increase in image data will be expected
in the future.
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
2007
S. Mano et al.
The PODB2 houses data of various visualized organelles in
different tissues and developmental stages in several plant
species (Figs. 1, 4E; Mano et al. 2008). Unlike the five databases described above, which are compilations of localization data of GFP fusion gene products, the PODB2 focuses
on organelle dynamics rather than on the localization of
each gene product. Therefore, even for the same organelle,
this database collects several images that were acquired
under various conditions. For example, the PODB2 provides
a series of images of changes in actin filaments throughout
the progression of the cell cycle in suspension-cultured
tobacco cells, as well as of the positions of chloroplasts in
Arabidopsis mesophyll cells in response to changes in light
conditions. The comparison of images from various conditions allowed the discovery that actin filaments drastically
change in shape, length, thickness, number and position
during the cell cycle and that chloroplasts move away from
the cell surface to avoid photodamage from strong light. The
unique feature of the PODB2 is that it has been populated
by data determined experimentally and submitted directly
by plant researchers. In addition, the databases described
above collect image data mainly for A. thaliana, whereas the
PODB2 deals with images stemming from any plant species.
Therefore, the comparison of images showing the same
organelles among different plant species is available via the
PODB2. In 2008, this database was updated to deal with
movie data, such as time-lapse imaging and the rotation of
3D structures. Therefore, investigators can examine more
easily dynamic changes in organelles, such as movement,
division and their spatial arrangement in cells (Fig. 1B,
Supplementary Movies 1, 2).
In addition to the databases described above, a wealth of
image data on plant organelles is uploaded onto the websites of individual laboratories. Although these are not largescale data, visitors can obtain valuable information (such as
sophisticated protocols for image detection) and view image
data. For example, the website of ‘The Illuminated Plant Cell’
provides not only image and movie data for several organelles, but also additional information, including the collection of vectors used for the visualization of organelles
(Fig. 4F; Mathur 2007). Similarly to the PODB2, the data
deposited in this website are composed of results determined experimentally by the contributors.
The potential of image databases as resources
for plant organelle research
Platforms as search tools for unidentified subcellular
structures
Visualization of organelles sometimes leads to the discovery
of new subcellular structures. The stromule (stroma-filled
tubules) (Köhler et al. 1997) and the ER body (Matsushima
2008
et al. 2002) are good examples of subcellular structures that
were identified using visualization with GFP in living cells.
Stromules are motile, thin protrusions that emanate from
the plastid surface into the cytosol via the expression of GFP
targeted to the plastid stroma in tobacco and petunia cells
(Köhler et al. 1997). Analyses using fluorescence recovery
after photobleaching (FRAP) and GFP-tagged plastid proteins revealed that stromules have the capacity to form a
link between individual plastids and exchange macromolecules (Kwok and Hanson 2004). ER bodies were originally
described as mystery organelles that were derived from
plastids (Gunning 1998); however, the morphology of the
unidentified GFP-labeled structures triggered their identification as ER bodies (Hayashi et al. 2001).
The deposition of a significant amount of additional visualized subcellular structure data into image databases is
expected to render them useful platforms for the comparison of unidentified organelles with the deposited image data
(flowchart in green in Fig. 3). As new subcellular structures,
such as Rubisco-containing bodies (Ishida et al. 2008) and
secretory vesicle clusters (Toyooka et al. 2009), were found
via visualization with GFP, the image-based comparison
through image databases will allow investigators to shorten
the time-consuming work of searching for references relevant to unidentified subcellular structures.
Quantification analysis based on image data
The image and movie data collected in the databases represent valuable and significant resources to obtain biologically
meaningful information, as image data provide a wealth of
information on size, length and morphology. Additionally,
information on the velocity and direction of organelles can
be estimated using time-lapse data. In addition, the 3D
structures constructed from serial images from different
focal planes convey the spatial information of visualized
organelles in cells. In other words, quantification based on
image data yields a statistical power that allows changes in
organelle dynamics to be shown. Based on information
extracted using image-based quantification, researchers can
estimate the number of organelles in a cell, the size of each
organelle, their spatial distribution and track motile organelles, etc. (flowchart in green in Fig. 3).
The quantification of image data comprises several steps,
which include segmentation (which outlines and identifies
particular regions in an image), edge detection of segmented
regions, calculation of each region and registration, which
aligns calculated images. The process of tracking objects is
included in the analyses of movie data. Each step is affected
by several factors, especially by the resolution of data.
Although slow scanning and/or application of averaging
techniques generates high-resolution data for enhanced
identification of the objects, these approaches may cause
photobleaching and objects such as proteins and organelles
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
Image databases for plant organelle research
may disappear from the focal plane. In contrast, the reduction
of scanning time may cause poor resolution. Therefore, the
duration of the scanning time depends on the intensity of the
visualized object and the velocity of movement of targets.
Recent instrument developments produced new microscopes
and cameras that can be used to obtain high resolution and/or
fast shutter speeds. Furthermore, new fluorescent proteins
were exploited that exhibit higher fluorescence intensity.
These new technologies and tools will assist the quantification
aspect of image analysis. A good overview of quantification
of image data is provided by Hamilton (2009).
Unfortunately, at present there are no general protocols
for image-based quantification that cover all image data,
although computational analytical tools are techniques that
are increasingly indispensable for these analyses. The REANTlight software is suitable for reconstitution of 3D structures
of organelles from serial optical sections, and the volume
and surface areas of protoplasts and vacuoles were measured
using this software (Kutsuna and Hasezawa 2005). Recently,
this software was applied to the reconstruction of the 3D
structure of the plasma membrane in root hair cells, using
plasma-localized soluble N-ethylmaleimide-sensitive factor
attachment protein receptor (SNARE), SYP123 (Enami et al.
2009). These results revealed the general-purpose use of this
software for several organelles. The ‘Organelle View’ device
of the Organelle DB (Wiwatwattana et al. 2007), which is
derived mainly from yeast data at present, allows the animation of rotation in the window to show views from different
angles (Wiwatwattana et al. 2007). These systems are excellent as visual aids for quantification and representation.
Some imaging analysis applications are incorporated into
the microscope system (Fig. 5A). In addition, various commercial applications, such as Metamorph (Fig. 5B; http://
www.moleculardevices.com/pages/software/metamorph
.html), Imaris (http://www.bitplane.com/) and Volocity
(http://www.improvision.com/) are now available. In addition, other analytical tools for the processing of image data,
such as ImageJ and Profiler (Carpenter et al. 2006), are also
available as open sources (Table 3). In particular, ImageJ is
widely used for image analysis (Fig. 5C, D), as it is a free
downloadable application and many plug-ins and macros
(which enable users to perform new methods easily) have
been developed and distributed via their respective websites. Therefore, these open sources provide a common
foundation for the sharing of methods, tools and information regarding image analysis. In addition, several protocols,
applications and tips for image analysis are provided
through the websites of individual laboratories, such as
Dr Hasezawa’s laboratory (http://hasezawa.ib.k.u-tokyo.ac
.jp/zp/hlab), Dr Haseloff’s laboratory (http://www.plantsci
.cam.ac.uk/Haseloff/index.htm) and Dr Hawe’s laboratory
(http://www.brookes.ac.uk/lifesci/research/molcell/hawes/
gfp.htm!).
Table 3 A selection of open source application tools for image
analysis
Name
URL
BioImage Suite
http://www.bioimagesuite.org/
BISQUEa
http://www.bioimage.ucsb.edu/
CellProfiler
http://www.cellprofiler.org/index.htm
CellTracker
http://dbkgroup.org/celltracker/index.php
Cold Spring Harbor
Protocols
http://cshprotocols.cshlp.org/
iCluster
http://icluster.imb.uq.edu.au/
ImageJ
http://rsb.info.nih.gov/ij/
ImageSurfer
http://cismm.cs.unc.edu/downloads/
MayaVi Data Visualizer
http://mayavi.sourceforge.net/
MSIb
http://www.molsci.org/about/index.html
OMEc
http://www.openmicroscopy.org/site
Paraview
http://www.paraview.org/
SCMDd
http://scmd.gi.k.u-tokyo.ac.jp/datamine/
aBISQUE:
Bisque - Bio-Image Semantic Query User Environment; bMSI: Molecular
Sciences Institute; cOME: Open Microscopy Environment; dSCMD: Saccharomyces
Cerevisiae Morphological Database.
Image databases as resources for systems biology
Systems biology is a comprehensive study of the dynamics
and structure of complete biological systems. The achievement of this aim requires the integration of information
on a large numbers of elements, which include entities such
as genes, proteins, organelles, metabolites, etc. Therefore,
various data deposited into databases, which were generated using several ‘omics’ approaches, are used to extract
hidden patterns, interactions and mechanisms that underlie
biological processes (flowchart in open arrows in Fig. 3).
For example, the interaction of carbon with nitrogen or
carbon with light in A. thaliana were systematically addressed
and qualitative multinetwork models were constructed
(Gutiérrez et al. 2007, Thum et al. 2008). Moreover, metabolomics data were integrated with the data from pathway
and co-expression databases to investigate the metabolite
correlation networks (Kusano et al. 2007).
As described above, image and movie data contain
valuable biological information on protein and organelle
dynamics. Therefore, image databases dedicated to protein
localization and/or organelles contribute to systems biology
as potentially rich data sources to investigate the behavior
and localization of gene products and the behavior of the
organelles that house these proteins. For example, the fluorescence pattern of the GFP–PIN1 fusion protein is used
as a computed-simulation model of phyllotaxis via auxintransport-mediated patterning of the developing meristem
(Berleth et al. 2007). As stated above, the reconstitution
of the vacuolar membrane at different stages of tobacco
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
2009
S. Mano et al.
Fig. 5 Application of computational analytical tools in image analysis. (A) The fusion of GFP with one peroxisomal membrane protein was
transiently expressed in onion cells with red fluorescent protein (RFP)–PTS1, which was a peroxisomal matrix marker. The intensity of both GFP
and RFP signals in red line, which is shown in image data, were analyzed using the LSM5 software version 3.2 (Carl Zeiss, Jena, Germany). (B) GFP
fluorescence of root cells in transgenic A. thaliana expressing GFP without any targeting signals were observed, and the intensity of signals was
calculated and visualized with pseudocolors by MetaMorph (Nihon Molecular Devices, Tokyo, Japan). (C) The image of peroxisomes visualized by
GFP in leaf cells of an A. thaliana peroxisome biogenesis mutant that shows various sizes of peroxisome was acquired using CLSM. Edge detection
of peroxisome was implemented in ImageJ. (D) The number, area, intensity and circularity of each peroxiosme in (C) were calculated.
2010
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
Image databases for plant organelle research
suspension-cultured cells (Kutsuna and Hasezawa 2005) and
of the plasma membrane in Arabidopsis root hair cells
(Enami et al. 2009) was performed based on image data of
GFP fusion proteins. The addition of additional data, such as
expression data, proteomics data, and interactome data, to
these results will extend our knowledge at the subcellular
and cellular levels. The metabolic network of primary metabolisms in Chlamydomonas reinhardtii was reconstructed using
genomic and biochemical information (Boyle and Morgan
2009). In this analysis, the subcellular localization of metabolic
enzymes is considered, albeit based on the prediction program.
To extract information on protein–protein interactions in
mammalian and yeast cells, the subcellular localization of
proteins was evaluated using several databases (mainly
text-based databases) (Shin et al. 2009). If the information in
image databases is incorporated into the analyses for prediction of subcellular localization, the reconstruction of networks
or simulation models will become more confirmatory and
will refine our understanding of biological systems. As the
PODB2 contains image and movie data of proteins and organelles at different stages and in the presence/absence of environmental stimuli, such as light (Mano et al. 2008), this database
has the potential to play an important role, as it verifies
simulation models and provides information on the reconstruction of simulation models (flowchart in blue in Fig. 3).
In the UK, the Centre for Plant Integrative Biology (CPIB)
at the University of Nottingham aims to create a virtual root
as an example of the use of integrative systems biology
to model multicellular systems (http://www.cpib.info/). To
accomplish this purpose, imaging approaches are incorporated with experimental, innovative mathematical, engineering and computer science research. In the USA, the
‘iPlant Collaborative’ project was launched to give plant scientists new conceptual advances through integrative and
computational thinking via the construction of cyberinfrastructures, which consist of a comprehensive combination
of hardware, software, statistical measurements, data mining,
modeling and simulation, and bioinformatics (Ledford 2009,
http://www.iplantcollaborative.org/). Image analyses at the
subcellular, cellular and individual levels are proposed as
morphodynamics research projects in this ‘iPlant Collaborative’ project. These image analyses include acquisition of
image data, the development of modeling software and the
incorporation of the systems developed into image databases. Thus, image databases will continue to be important
for the enhancement of systems biology. The several imaging techniques available and their utility in systems biology
are reviewed by Kherlopian et al. (2008).
Notes on handling image database
We should keep in mind that image techniques have their
limitations, as do all technologies. Therefore, we should be
careful in dealing with image data regarding the accuracy of
the data. For example, the expression of GFP fusion genes in
cells or application of dyes has the potential to generate
experimental artifacts. Investigators may have to examine
individually whether deposited data are evaluated for
sufficient quality using appropriate measurements. In the
case of protein localization, besides imaging data (such
as double staining of organellar proteins with a predetermined localization), other approaches such as biochemical
analysis and complementation of mutant phenotypes
would be convincing. The validation of the localization of
fluorescent protein fusions is reviewed extensively by Moore
and Murphy (2009), and Millar et al. (2009).
Image data provide quantitative and semi-quantitative
insights into plant organelle research. To gain detailed information based on image data, one needs tools with finer resolution by confocal laser scanning microscope (CLSM). In this
case, the intensity of fluorescent and/or non-fluorescent
probes in cells are affected by various factors, such as gene
expression, permeability of dyes and transport efficiency of
gene products. Although it is instructive to keep the limitations of imaging analyses in mind when drawing conclusions
about cellular events based on this type of analysis, image
databases have the potential to take advantage of direct
comparison of control and experimentally manipulated cells
to determine whether the different image is true or false
(e.g., sampling error). However, the careful application of
imaging approaches to other techniques, such as genetic
and biochemical studies, and their integration will allow the
demonstration of the significant impact of imaging technology on plant organelle dynamics.
Image databases as resources for educational use
The image databases may serve as an educational aid that
engages both biology students and members of the general
public who are interested in biology, via a visually ‘fun’
interface. In addition to image databases for plant organelles,
different image databases provide image data information,
such as individual organisms, including plants. For example,
ImageBank (http://bio.ltsn.ac.uk/imagebank/) contains image
data for various organisms (including plant species). The Plants
Database (http://plants.usda.gov/) and the Protist Information
Server (http://protist.i.hosei.ac.jp/) are dedicated to the collection of image and movie data from various plant species and
protists, respectively. In most cases, images may only be freely
downloaded and used for educational purposes, such as
learning and teaching. It is, therefore, expected that image
databases will be easily accessible platforms and will accelerate
the interest of younger generations in plant biology.
Finally, we should acknowledge the contributor and the
copyright holder of the images and movies from the public
database every time these are used. Accurate credit leads to
improvement of the status and better management of
databases.
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
2011
S. Mano et al.
Conclusions and prospects
Direct visual analysis is a persuasive approach—imaging
techniques have been the ‘eyes’ of science. The development
of imaging techniques in the field of plant organelle research
expands our current insights and contributes to the solidification of old concepts and the replacement of others with
new concepts. In the past, several approaches, such as electron microscopy, provided some snapshot (or steady-state)
information on organelles, but not organelle dynamics, such
as movement, division and fission. Now that almost all plant
organelles are easily visualized and their dynamics are investigated in living cells, knowledge of plant organelle dynamics
has been accumulating at a rapid pace in the form of image
data. Additionally, the equipment for acquisition of images
has evolved and is now applied to other fields. For example,
microscale magnetic resonance imaging (MRI), which is
mainly used for clinical imaging, was used to acquire images
of chloroplasts in Spirogyra alga and the reconstructed 3D
view displayed the spiral-shaped array of chloroplasts on the
inner surface of the cell wall (Ciobanu and Pennington 2004).
To use these image data as valuable resources, the infrastructure of image databases will be improved by including exploitation of better languages of data representation and
visualization, improvement of common platforms with
other databases and development of computational analytical tools. Computational biologists, mathematicians and
software engineers will play an active part in this field.
Although the utilization of image databases as resources for
systems biology is still very much a developing research area,
new methodologies are currently being developed and
tested. The increase in image data available and the use of
image databases will open new perspectives in all fields of
plant biology, including plant organelle dynamics. Image
databases will be as valuable as those of infrastructures.
Supplementary data
Supplementary data are available at PCP Online.
Funding
The Ministry of Education, Sports, Culture, Science, and
Technology, the Grant-in-Aid for Scientific Research of
Priority Areas ‘Organelle Differentiation as the Strategy for
Environmental Adaptation in Plants’ (No. 16085101); the
Japan Society for the Promotion of Science Grant-in-Aid for
Publication of Scientific Research Results (No. 218060).
Acknowledgments
We are grateful to Drs Shin-ichi Arimura from the University
of Tokyo, Hirokazu Kobayashi from the University of
Shizuoka, Ikuko Hara-Nishimura from Kyoto University,
2012
Yasuo Niwa from the University of Shizuoka, Takashi
Hashimoto from the Nara Institute of Science and Technology, and Daigo Takemoto from Nagoya University for kindly
providing seeds of transgenic A. thaliana. We would like to
thank Ms Yuko Kuboki and Chizuru Ueda from the Department of Cell Biology and the staff at the Strategic Planning
Department of the National Institute for Basic Biology for
their assistance in English proofreading and for maintaining
the PODB2 website.
References
Arimura, S. and Tsutsumi, N. (2002) A dynamin-like protein (ADL2b),
rather than FtsZ, is involved in Arabidopsis mitochondrial division.
Proc. Natl Acad. Sci. USA 99: 5727–5731.
Berg, R.H. and Beachy, R.N. (2008) Fluorescent protein application in
plants. Methods Cell Biol. 85: 153–177.
Berleth, T., Scarpella, E. and Prusinkiewicz, P. (2007) Towards the
systems biology of auxin-transport-mediated patterning. Trends
Plant Sci. 12: 151–159.
Boyle, N.R. and Morgan, J.A. (2009) Flux balance analysis of primary
metabolism in Chlamydomonas reinhardtii. BMC Syst. Biol. 3: 4.
Brady, S.M. and Provart, N.J. (2009) Web-queryable large-scale data sets
for hypothesis generation in plant biology. Plant Cell 21: 1034–1051.
Brown, J.W.S., Shaw, P.J., Shaw, P. and Marshall, D.F. (2005) Arabidopsis
nucleolar protein database (AtNoPDB). Nucleic Acids Res. 33:
D633–D636.
Bülow, L., Schindler, M. and Hehl, R. (2007) PathoPlant®: a platform for
microarray expression data to analyze co-regulated genes involved in
plant defense responses. Nucleic Acids Res. 35: D841–D845.
Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H.,
Friman, O., et al. (2006) CellProfiler: image analysis software for
identifying and quantifying cell phenotypes. Genome Biol. 7: R100.
Carter, C., Pan, S., Zouhar, J., Avila, E.L., Girke, T. and Raikhel, N.V. (2004)
The vegetative vacuole proteome of Arabidopsis thaliana reveals
predicted and unexpected proteins. Plant Cell 16: 3285–3303.
Chudakov, D.M., Lukyanov, S. and Lukyanov, K.A. (2005) Fluorescent
proteins as a toolkit for in vivo imaging. Trends Biotechnol. 23:
605–613.
Ciobanu, L. and Pennington, C.H. (2004) 3D micron-scale MRI of single
biological cells. Solid State Nucl. Magn. Reson. 25: 138–141.
Cui, L., Veeraraghavan, N., Richter, A., Wall, K., Jansen, R.K., LeebensMack, J., et al. (2006) ChloroplastDB: the chloroplast genome
database. Nucleic Acids Res. 34: D568–D571.
Cutler, S.R., Ehrhardt, D.W., Griffitts, J.S. and Somerville, C.R. (2000)
Randam GFP::cDNA fusions enable visualization of subcellular
structures in cells of Arabidopsis at a high frequency. Proc. Natl
Acad. Sci. USA 97: 3718–3723.
Damme, D.V., Bouget, F.Y., Poucke, K.V., Inzé, D. and Geelen, D. (2004)
Molecular dissection of plant cytokinesis and phragmoplast
structure: a survey of GFP-tagged proteins. Plant J. 40: 386–398.
Enami, K., Ichikawa, M., Uemura, T., Kutsuna, N., Hasezawa, S.,
Nakagawa, T., et al. (2009) Differential expression control and
polarized distribution of plasma membrane-resident SYP1 SNAREs
in Arabidopsis thaliana. Plant Cell Physiol. 50: 280–289.
Girke, T., Lauricha, J., Tran, H., Keegstra, K. and Raikhel, N. (2004)
The cell wall navigator database. A systems-based approach to
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
Image databases for plant organelle research
organism-unrestricted mining of protein families involved in cell
wall metabolism. Plant Physiol. 136: 3003–3008.
Goff, S.A., Ricke, D., Lan, T.H., Presting, G., Wang, R., Glazebrook, J., et al.
(2002) A draft sequence of the rice genome (Oryza sativa L. ssp.
japonica). Science 296: 92–100.
Gunning, B.E.S. (1998) The identity of mystery organelles in Arabidopsis
plants expessing GFP. Trends Plant Sci. 3: 417–417.
Gutiérrez, R.A., Lejay, L.V., Dean, A., Chiaromonte, F., Shasha, D.E. and
Coruzzi, G.M. (2007) Qualitative network models and genome-wide
expression data define carbon/nitrogen-responsive molecular
machines in Arabidopsis. Genome Biol. 8: R7.
Hamilton, N. (2009) Quantification and its applications in fluorescent
micoscopy imaging. Traffic 10: 951–961.
Haseloff, J. and Siemering, K.R. (1998) The uses of green fluorescent
protein in plants. Edited by Chalfie, M. and Kain, S. pp. 191–220.
A John Wiley & Sons, Inc., New York.
Hayashi, Y., Yamada, K., Shimada, T., Matsushima, R., Nishizawa, N.K.,
Nishimura, M., et al. (2001) A proteinase-storing body that prepares
for cell death or stresses in the epidermal cells of Arabidopsis. Plant
Cell Physiol. 42: 894–899.
Heazlewood, J.L., Durek, P., Hummel, J., Selbig, J., Weckwerth, W.,
Walther, D., et al. (2008) PhosPhAt: a database of phosphorylation
sites in Arabidopsis thaliana and a plant-specific phosphorylation
site predictor. Nucleic Acids Res. 36: D1015–D1021.
Heazlewood, J.L., Tonti-Filippini, J.S., Gout, A.M., Day, D.A., Whelan, J.
and Millar, A.H. (2004) Experimental analysis of the Arabidopsis
mitochondrial proteome highlights signaling and regulatory
components, provides assessment of targeting prediction programs,
and indicates plant-specific mitochondrial proteins. Plant Cell 16:
241–256.
Heazlewood, J.L., Verboom, R.E., Tonti-Filippini, J., Small, I. and Millar, A.H.
(2007) SUBA: the Arabidopsis subcellular database. Nucleic Acids Res.
35: D213–D218.
Higaki, T., Kutsuna, N., Sano, T. and Hasezawa, S. (2008) Quantitative
analysis of changes in actin microfilament contribution to cell plate
development in plant cytokinesis. BMC Plant Biol. 8: 80.
Higo, K., Ugawa, Y., Iwamoto, M. and Korenaga, T. (1999) Plant cisacting regulatory DNA elements (PLACE) database: 1999. Nucleic
Acids Res. 27: 297–300.
Ishida, H., Yoshimoto, K., Izumi, M., Reisen, D., Yano, Y., Makino, A.,
et al. (2008) Mobilization of Rubisco and stroma-localized
fluorescent proteins of chloroplasts to the vacuole by an ATG
gene-dependent autophagic process. Plant Physiol. 148: 142–155.
Jaillon, O., Aury, J., Noel, B., Policriti, A., Clepet, C., Casagrande, A., et al.
(2007) The grapevine genome sequence suggests ancestral
hexaploidization in major angiosperm phyla. Nature 449: 463–467.
Jedd, G. and Chua, N.H. (2002) Visualization of peroxisomes in living
plant cells reveals acto-myosin-dependent cytoplasmic streaming
and peroxisome budding. Plant Cell Physiol. 43: 384–392.
Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M.,
et al. (2008) KEGG for linking genomes to life and the environment.
Nucleic Acids Res. 36: D480–D484.
Kaul, S., Koo, H.L., Jenkins, J., Rizzo, M., Rooney, T., Tallon, L.J., et al.
(2000) Analysis of the genome sequence of the flowering plant
Arabidopsis thaliana. Nature 408: 796–815.
Kherlopian, A.R., Song, T., Duan, Q., Neimark, M.A., Po, M.J., Gohagan, J.K.,
et al. (2008) A review of imaging techniques for systems biology. BMC
Syst. Biol. 2: 74.
Kogure, T., Karasawa, S., Araki, T., Saito, K., Kinjyo, M. and Miyawaki, A.
(2006) A fluorescent variant of a protein from the stony coral
Montipora facilitates dual-color single-laser fluorescence crosscorrelation spectroscopy. Nat. Biotechnol. 24: 577–581.
Köhler, R.H., Cao, J., Zipfel, W.R., Webb, W.W. and Hanson, M.R. (1997)
Exchange of protein molecules through connections between
higher plant plastids. Science 276: 2039–2042.
Koroleva, O.A., Tomlinson, M.L., Leader, D., Shaw, P. and Doonan, J.H.
(2005) High-throughput protein localization in Arabidopsis using
Agrobacterium-mediated transient expression of GFP-ORF fusions.
Plant J. 41: 162–174.
Kurata, N. and Yamazaki, Y. (2006) Oryzabase. An integrated biological
and genome information database for rice. Plant Physiol. 140: 12–17.
Kusano, M., Fukushima, A., Arita, M., Jonsson, P., Moritz, T., Kobayashi, M.,
et al. (2007) Unbiased characterization of genotype-dependent
metabolic regulations by metabolomic approach in Arabidopsis
thaliana. BMC Syst. Biol. 1: 53.
Kutsuna, N. and Hasezawa, S. (2005) Morphometrical study of
plant vacuolar dynamics in single cells using three-dimensional
reconstruction from optical sections. Microsc. Res. Tech. 68: 296–306.
Kwok, E.Y. and Hanson, M.R. (2004) GFP-labelled Rubisco and aspartate
aminotransferase are present in plastid stromules and traffic
between plastids. J. Exp. Bot. 55: 595–604.
Ledford, H. (2009) Cyberinfrastructure: Feed me data. Nature 459:
1047–1049.
Li, S., Ehrhardt, D.W. and Rhee, S.Y. (2006) Systematic analysis of
Arabidopsis organelles and a protein localization database for
facilitating fluorescent tagging of full-length Arabidopsis proteins.
Plant Physiol. 141: 527–539.
Liu, J.X., Srivastava, R., Che, P. and Howell, S.H. (2007) An endoplasmic
reticulum stress response in Arabidopsis is mediated by proteolytic
processing and nuclear relocation of a membrane-associated
transcription factor, bZIP28. Plant Cell 19: 4111–4119.
Mano, S., Miwa, T., Nishikawa, S., Mimura, T. and Nishimura, M. (2008)
The plant organelles database (PODB): a collection of visualized
plant organelles and protocols for plant organelle research. Nucleic
Acids Res. 36: D929–D937.
Mano, S., Nakamori, C., Hayashi, M., Kato, A., Kondo, M. and
Nishimura, M. (2002) Distribution and characterization of
peroxisomes in Arabidopsis by visualization with GFP: Dynamic
morphology and actin-dependent movement. Plant Cell Physiol. 43:
331–341.
Mathur, J. (2007) The illuminated plant cell. Trends Plant Sci. 12: 506–513.
Mathur, J., Mathur, N. and Hülskamp, M. (2002) Simultaneous
visualization of peroxisomes and cytoskeletal elements reveals actin
and not microtubule-based peroxisome motility in plants. Plant
Physiol. 128: 1031–1045.
Matsushima, R., Hayashi, Y., Kondo, M., Shimada, T., Nishimura, M. and
Hara-Nishimura, I. (2002) An endoplasmic reticulum-derived
structure that is induced under stress conditions in Arabidopsis.
Plant Physiol. 130: 1807–1814.
Millar, A.H., Carrie, C., Pogson, B. and Whelan, J. (2009) Exploring the
function-location nexus: using multiple lines of evidence in defining
the subcellular location of plant proteins. Plant Cell 21: 1625–1631.
Mitsuhashi, N., Shimada, T., Mano, S., Nishimura, M. and HaraNishimura, I. (2000) Characterization of organelles in the vacuolarsorting pathway by visualization with GFP in tobacco BY-2 cells.
Plant Cell Physiol. 41: 993–1001.
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
2013
S. Mano et al.
Moore, I. and Murphy, A. (2009) Validating the location of fluorescent
protein fusions in the endomembrane system. Plant Cell 21:
1632–1636.
Nakagawa, T., Suzuki, T., Murata, S., Nakamura, S., Hino, T., Maeo, K.,
et al. (2007) Improved gateway binary vectors: high-performance
vectors for creation of fusion constructs in transgenic analysis of
plants. Biosci. Biotechnol. Biochem. 71: 2095–2100.
Nelson, B.K., Cai, X. and Nebenführ, A. (2007) A multicolored set of
in vivo organelle markers for co-localization studies in Arabidopsis
and other plants. Plant J. 51: 1126–1136.
Nishimura, M., Yamaguchi, J., Mori, H., Akazawa, T. and Yokota, S.
(1986) Immunocytochemical analysis shows that glyoxysomes are
directly transformed to leaf peroxisomes during greening of
pumpkin cotyledon. Plant Physiol. 80: 313–316.
Nishiyama, T., Fujita, T., Shin-I, T., Seki, M., Nishide, H., Uchiyama, I.,
et al. (2003) Comparative genomics of Physcomitrella patens
gametophytic transcriptome and Arabidopsis thaliana: Implication
for land plant evolution. Proc. Natl Acad. Sci. USA 100: 8007–8012.
Niwa, Y., Hirano, T., Yoshimoto, K., Shimizu, M. and Kobayashi, H.
(1999) Non-invasive quantitative detection and applications of
non-toxic, S65T-type green fluorescent protein in living plants.
Plant J. 18: 455–463.
Nozaki, H., Takano, H., Misumi, O., Terasawa, K., Matsuzaki, M.,
Maruyama, S., et al. (2007) A 100%-complete sequence reveals
unusually simple genomic features in the hot-spring red alga
Cyanidioschyzon merolae. BMC Biol. 5: 28.
Nühse, T.S., Stensballe, A., Jensen, O.N. and Peck, S.C. (2004)
Phosphoproteomics of the Arabidopsis plasma membrane and
a new phosphorylation site database. Plant Cell 16: 2394–2405.
O’Brien, E.A., Zhang, Y., Wang, E., Marie, V., Badejoko, W., Lang, B.F.,
et al. (2009) GOBASE: an organelle genome database. Nucleic Acids
Res. 37: D946–D950.
Papadopoulos, G.L., Reczko, M., Simossis, V.A., Sethupathy, P. and
Hatzigeorgiou, A.G. (2009) The database of experimentally
supported targets: a functional update of TarBase. Nucleic Acids Res.
37: D155–D158.
Pierleoni, A., Martelli, P.L., Fariselli, P. and Casadio, R. (2007) eSLDB:
eukaryotic subcellular localization database. Nucleic Acids Res. 35:
D208–D212.
Reumann, S., Ma, C., Lemke, S. and Babujee, L. (2004) AraPerox. A
database of putative Arabidopsis proteins from plant peroxisomes.
Plant Physiol. 136: 2587–2608.
Riaño-Pachón, D.M., Ruzicic, S., Dreyer, I. and Mueller-Roeber, B. (2007)
PlnTFDB: an integrative plant transcription factor database. BMC
Bioinformatics 8: 42.
Röder, M.S., Korzun. V., Wendehake, K., Korzun V., Bredemeijer, G.,
Laborie, D., Bertrand, L., et al. (2002) Construction and analysis of
a microsatellite-based database of European wheat varieties. Theor.
Appl. Genet. 106: 67–73.
Rushton, P.J., Bokowiec, M.T., Laudeman, T.W., Brannock, J.F., Chen, X.
and Timko, M.P. (2008) TOBFAC: the database of tobacco
transcription factors. BMC Bioinformatics 9: 53.
Sakata, K., Ohyanagi, H., Nobori, H., Nakamura, T., Hashiguchi, A.,
Nanjo, Y., et al. (2009) Soybean proteome database: a data resource
for plant differential omics. J. Proteome Res. 8: 3539–3548.
Sato, S., Nakamura, Y., Kaneko, T., Asamizu, E., Kato, T., Nakao, M., et al.
(2008) Genome structure of the legume, Lotus japonicus. DNA Res.
15: 227–239.
2014
Schlüter, A., Fourcade, S., Domènech-Estévez, E., Gabaldón, T., HuertaCepas, J., Berthommier, G., et al. (2007) PeroxisomeDB: a database
for the peroxisomal proteome, functional genomics and disease.
Nucleic Acids Res. 35: D815–D822.
Shaner, N.C., Campbell, R.E., Steinbach, P.A., Giepmans, B.N.G., Palmer,
A.E. and Tsien, R.Y. (2004) Improved monomeric red, orange
and yellow fluorescent proteins derived from Discosoma sp. red
fluorescent protein. Nature Biotech. 22: 1567–1572.
Shin, C.J., Wong, S., Davis, M.J. and Ragan, M.A. (2009) Protein-protein
interaction as a predictor of subcellular location. BMC Syst. Biol. 3: 28.
Sugawara, H., Ikeo, K., Fukuchi, S., Gojobori, T. and Tateno, Y. (2009)
DDBJ dealing with mass data produced by the second generation
sequencer. Nucleic Acids Res. 37: D16–D18.
Swarbreck, D., Wilks, C., Lamesch, P., Berardini, T.Z., Garcia-Hernandez, M.,
Foerster, H., et al. (2008) The Arabidopsis Information Resource (TAIR):
gene structure and function annotation. Nucleic Acids Res. 36:
D1009–D1014.
Takemoto, D., Jones, D.A. and Hardham, A.R. (2003) GFP-tagging of
cell components reveals the dynamics of subcellular re-organization
in response to infection of Arabidopsis by oomycete pathogens.
Plant J. 33: 775–792.
Tamura, K., Shimada, T., Kondo, M., Nishimura, M. and Hara-Nishimura, I.
(2005) KATAMARI1/MURUS3 is a novel Golgi membrane protein that
is required for endomembrane organization in Arabidopsis. Plant Cell
17: 1764–1776.
Tanaka, T., Antonio, B.A., Kikuchi, S., Matsumoto, T., Nagamura, Y.,
Numa, H., et al. (2008) The rice Annotation project database (RAPDB): 2008 update. Nucleic Acids Res. 36: D1028–D1033.
Thum, K.E., Shin, M.J., Gutiérrez, R.A., Mukherjee, I., Katari, M.S., Nero, D.,
et al. (2008) An integrated genetic, genomic and systems approach
defines gene networks regulated by the interaction of light and carbon
signaling pathways in Arabidopsis. BMC Syst. Biol. 2: 31.
Toyooka, K., Goto, Y., Asatsuma, S., Koizumi, M., Mitsui, T. and
Matsuoka, K. (2009) A mobile secretory vesicle cluster involved in
mass transport from the Golgi to the plant cell exterior. Plant Cell
21: 1212–1229.
Tuskan, G.A., DiFazio, S., Jansson, S., Bohlmann, J., Grigoriev, I., Hellsten. U.,
et al. (2006) The genome of black cottonwood, Populus trichocarpa.
Science 313: 1596–1604.
Ueda, K., Matsuyama, T. and Hashimoto, T. (1999) Visualization of
microtubules in living cells of transgenic Arabidopsis thaliana.
Protoplasma 206: 201–206.
Umezawa, T., Sakurai, T., Totoki, Y., Toyoda, A., Seki, M., Ishiwata, A.,
et al. (2008) Sequencing and analysis of approximately 40,000
soybean cDNA clones from a full-length-enriched cDNA library.
DNA Res. 15: 333–346.
Walter, M.C., Rattei, T., Arnold, R., Güldener, U., Münsterkötter, M.,
Nenova. K., et al. (2009) PEDANT covers all complete RefSeq
genomes. Nucleic Acids Res. 37: D408–D411.
Wiwatwattana, N., Landau, C.M., Cope, G.J., Harp, G.A. and Kumar, A.
(2007) Organelle DB: an updated resource of eukaryotic protein
localization and function. Nucleic Acids Res. 35: D810–D814.
Yamamoto, Y.Y. and Obokata, J. (2008) ppdb: a plant promoter
database. Nucleic Acids Res. 36: D977–D981.
Yu, J., Hu, S., Wang, J., Wong, G.K.S., Li, S., Liu, B., et al. (2002) A draft
sequence of the rice genome (Oryza sativa L. ssp. indica). Science
296: 79–92.
Plant Cell Physiol. 50(12): 2000–2014 (2009) doi:10.1093/pcp/pcp128 © The Author 2009.
(Received August 3, 2009; Accepted September 8, 2009)