Plant and Cell Physiology Advance Access published September 14, 2009
Running Title: Image databases for plant organelle research
Corresponding Author:
Dr. Mikio Nishimura
Department of Cell Biology,
National Institute for Basic Biology,
Tel: +81-564-55-7500
Fax: +81-564-537400
Email: [email protected]
Subject Areas:
(10) genomics, systems biology and evolution
(11) new methodology
Number of black and white figures, color figures and tables:
0 black and white figures, 5 color figures, and 3 tables
© The Author 2009. Published by Oxford University Press on behalf of
Japanese Society of Plant Physiologists. All rights reserved. For Permissions,
please e-mail: [email protected]
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Okazaki 444-8585, Japan.
Title: Seeing Is Believing: On the Use of Image Databases for Visually Exploring
Plant Organelle Dynamics
Authors: Shoji Mano1,2, Tomoki Miwa3, Shuh-ichi Nishikawa4, Tetsuro Mimura5 and
Mikio Nishimura1, 2
1
Department of Cell Biology, National Institute for Basic Biology, Okazaki 444-8585,
Japan.
2
Department of Basic Biology, School of Life Science, The Graduate University for
Advanced Studies, Okazaki 444-8585, Japan.
3
Computer Laboratory, National Institute for Basic Biology, Okazaki 444-8585, Japan.
4
Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan.
5
Department of Biology, Graduate School of Science, Kobe University, Kobe 657-8501,
Japan.
Abbreviations: BiFC; bimolecular fluorescence complementation, CLSM, confocal
laser scanning microscope; ER; endoplasmic reticulum, EST, expressed-sequence 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
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Author’s Addresses:
Abstract
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
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 large-scale 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
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parameters are visually exhibited. Therefore, as the results can be seen immediately,
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;
these plants. These large data sets are deposited in databases that were constructed for
the 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.
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: (1) databases dedicated to specific plant species, (2) databases for specific
phenomena, (3) organelle databases, and (4) 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
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Jaillon et al. 2007; Nozaki et al. 2007) led to the accumulation of large data sets for
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
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, Supplemental Movies 1, 2).
Compared with text-based 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 is
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 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
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organelles (Fig. 1A, Cutler et al. 2000; Damme et al. 2004; Brown et al. 2005; Koroleva
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 FRET and BiFC)
and of new vectors suitable for expression in plant cells (Nakagawa et al. 2007; Nelson
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
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et al. 2007) have revolutionized image analysis by enabling easier observation of
databases available for plant biology are well described in a recent review by Brady and
Provart (Brady and Provart 2009).
Visual representation of plant organelle dynamics
Advantages of image databases
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, 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
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The comprehensive understanding of individual plants requires the study of various
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 for its analysis
(Supplemental Movie 1, Jedd and Chua 2002; Mano et al. 2002; Mathur et al. 2002).
number of reports containing movie data as supplemental 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, Supplemental Movie
1). In addition, as shown in Supplemental 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).
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Similar live-cell imaging approaches are applied to other plant organelles; therefore, the
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 more than 200 proteins identified via a proteomic analysis using
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 Arabidopsis
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
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localization images of full-length cDNA–GFP fusions (Fig. 4A, Brown et al. 2005). 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
Arabidopsis full-length cDNAs fused to GFP were transiently expressed in Arabidopsis
suspension-cultured cells for acquisition of image data. One hundred fifty-five image
data are collected in this database at present. The localization of each fusion protein is
categorized into five main categories, based on localization image data: cytoplasm,
nucleus, nucleolus, organelles, and endomembrane compartments.
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 Arabidopsis 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 with the GFP database, in its current
configuration, SUBA II places more weight on text-based information than on
image-based information. The increase of image data will be expected in the future.
The PODB2 houses data of various visualized organelles in different tissues
and developmental stages in several plant species (Fig. 1, Fig. 4E, Mano et al. 2008).
Unlike the five databases described above, which are compilations of localization data
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localization of Arabidopsis proteins (Fig. 4D, Koroleva et al. 2005). In this project,
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
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
Arabidopsis 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, Supplemental
Movie 1, 2).
In addition to the databases described above, a wealth of image data on plant
organelles are uploaded onto the websites of individual laboratory pages. Although
these are not large-scale data, visitors can obtain valuable information (such as
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as of the positions of chloroplasts in Arabidopsis mesophyll cells in response to changes
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, which includes 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
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 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 FRAP and GFP-tagged plastid proteins revealed that stromule 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).
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experimentally by the contributors.
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
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 focus planes convey the
spatial information of visualized organelles in cells. In other words, quantification based
on image data yields a statistical power that allows showing changes in organelle
dynamics. 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
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visualization with GFP, the image-based comparison through image databases will
detection of segmented regions, calculation of each region, and registration that align
calculated images. The process of tracking objects is included in the analyses of movie
data. Each step is affected on several factors, especially on the resolution of data.
Although slow scanning and/or application of averaging techniques generate
high-resolution data for enhanced identification of the objects, these approaches may
the focus 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 its velocity of movement of targets. Recent instrument developments
produced new microscopes and cameras, which 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 (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 REANT-light
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 SNARE, SYP123 (Enami et al. 2009). These results revealed the
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cause photobleaching and objects, such as proteins and organelles, may disappear from
general-purpose use of this software for several organelles. The “Organelle View”
device of the Organelle DB (Wiwatwattana et al. 2007), which is mainly derived 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
applications,
such
as
Metamorph
(Fig.
http://www.moleculardevices.com/pages/software/metamorph.html),
5B,
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.
(http://hasezawa.ib.k.u-tokyo.ac.jp/zp/hlab),
Hasezawa’s
Dr.
(http://www.plantsci.cam.ac.uk/Haseloff/index.htm),
Haseloff’s
and
Dr.
Hawe’s
(http://www.brookes.ac.uk/lifesci/research/molcell/hawes/gfp.htm!).
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laboratory
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incorporated into the microscope system (Fig. 5A). In addition, various commercial
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 entity, such as genes, proteins,
organelles, metabolites etc. Therefore, various data deposited into databases, which
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 Arabidopsis 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 coexpression 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 auxin-transport-mediated patterning of the developing meristem (Berleth
et al. 2007). As stated above, the reconstitution of the vacuolar membrane at different
stages of tobacco suspension-cultured cells (Kutsuna and Hasezawa 2005) and of the
plasma membrane in Arabidopsis root hair cells (Enami et al. 2009) was performed
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were generated using several “omics” approaches, are used to extract hidden patterns,
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
To extract information on protein–protein interactions in mammal 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 possibility
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 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 U.S., the “iPlant
Collaborative” project was launched to give plant scientists new conceptual advances
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localization of metabolic enzymes is considered, albeit based on the prediction program.
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
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 (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 on dealing with image data regarding the
accuracy of the data. For example, the expression of GFP fusion gene 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
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research project in one of this “iPlant Collaborative” project. These image analyses
fluorescent protein fusions is reviewed extensively by Moore and Murphy (Morre and
Murphy 2009), and Millar et al. (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 CLSM. In this case, the intensity of fluorescent and/or
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 integrating 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
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nonfluorescent probes in cells are affected by various factors, such as gene expression,
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
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.
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, the knowledge on plant organelle dynamics has been
accumulating at a rapid pace in the form of image data. Additionally, the equipments for
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accessible platforms and will accelerate the interest of younger generations in plant
acquisition of images have evolved and are now applied to other fields. For example,
microscale 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
representation and visualization, improvement of common platforms with other
databases, and development of computational analytical tools. Computational biologists,
mathematicians, and software engineers will play active parts in this field. Although the
utilization of image databases as resources for systems biology is still much developing
research areas, new methodologies are currently being developed and tested. The
increase of the image data available and the use of image databases will open new
perspectives in all fields of plant biology, including plant organelle dynamics. Let image
databases valuable as one of infrastructures.
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).
21
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image databases will be improved by including exploitation of better languages of data
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, Yasuo Niwa from The University of Shizuoka, Takashi Hashimoto from the
Nara Institute of Science and Technology, and Daigo Takemoto from Nagoya University
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.
22
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for kindly providing seeds of transgenic Arabidopsis. We would like to thank Ms. Yuko
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Figure legends
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 organelles in different tissues and developmental
stages in several plant species (Mano et al. 2007). (B) Output from the Organelles
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).
Fig. 2 Morphology of visualized organelles in transgenic Arabidopsis thaliana
expressing a GFP fusion gene. Leaf (A–E, G–J, L) or root cells (F, K) of 12-day-old
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 Arabidopsis
thaliana expressing GFP–puSRp35 (Mano et al. unpublished result). (G) Plasma
membrane in transgenic Arabidopsis thaliana (stock no. CS84725) that was donated to
TAIR (Cutler et al. 2000). (H) actin filaments in the GFP-hTalin plant (Takemoto et al.
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Movie Database in the PODB2 is shown. The movie represents behavior of ER in the
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 Arabidopsis thaliana expressing γTIP–GFP (Mitsuhashi et al.
2000). Bars = 20 µm.
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, and 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
databases must be used to identify and quantify the various components of organelles,
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Fig. 3 Scheme for the application of image database to plant organelle research and
cells, organs and individuals in systems biology. Image databases dedicated to protein
localization and/or organelles are expected to advance for systems biology.
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),
(E, Mano et al. 2008). (F) The main page of the project of “The Illuminated Plant Cell”
(Mathur 2007).
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 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 Arabidopsis 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 Arabidopsis thaliana peroxisome
biogenesis mutant that shows various sizes of peroxisomes 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.
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FTFLPdb (C, Cutler et al. 2000), GFP database (D, Koroleva et al. 2005), and PODB2
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Figure 1
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Figure 2
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Figure 3
43
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Figure 4
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Figure 5
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Table 1 Vairous databases for plant science
Name
URL
Specific plant-species database
a
Comments
TAIR
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
Populus trichocarpa v1.1 d
http://genome.jgi-psf.org/Poptr1/Poptr1.home.html
Populus trichocarpa
Genoscope e
http://www.genoscope.cns.fr/spip/Vitis-vinifera-e.html
Vitis vinifera
PHYSCObasef
http://moss.nibb.ac.jp/
Physcomitrella patens
LegumeBaseg
miyakogusa.jp
http://www.shigen.nig.ac.jp/bean/lotusjaponicus/top/top.jsp
http://www.kazusa.or.jp/lotus/
Lotus japonicus
Lotus japonicus
Cyanidioschyzon merolae Genome Projecth
http://merolae.biol.s.u-tokyo.ac.jp/
Cyanidioschyzon merolae
SoyBasei
http://soybase.org/
Glycine max
Soybean full-length cDNA database
http://rsoy.psc.riken.jp/
Glycine max
The ECPGR Wheat Databasek
http://genbank.vurv.cz/ewdb/default.htm
Titicum L.
j
Specific-aspcets database
http://ppdb.gene.nagoya-u.ac.jp/cgi-bin/index.cgi
Cis -element sequences
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
r
TOBFAC
http://compsysbio.achs.virginia.edu/tobfac/
Transcription factor
PthoPlants
http://www.pathoplant.de/
Plant-pathogen interactions
Organelle database
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
Cell Wall Naviagatory
http://bioweb.ucr.edu/Cellwall/index.pl
Cell wall
eSLDB
Organelle Genome Resources
http://gpcr2.biocomp.unibo.it/esldb/index.htm
http://www.ncbi.nlm.nih.gov/genomes/GenomesHome.cgi?taxid=2759&hopt=html
Eukaryotic subcellular localization
Chloroplasts, Mitochondria
Integrative database
NCBI
DDBJ
TIGR
NASC
http://www.ncbi.nlm.nih.gov/
http://www.ddbj.nig.ac.jp/
http://www.tigr.org/db.shtml
http://arabidopsis.info/
PEDANT1
http://pedant.gsf.de/
KEGG2
RIKEN
KAZUSA
NIAS
http://www.kegg.jp/ja/
http://omicspace.riken.jp/PosMed/search?keyword=riken&objectSet=database&actionType=searchexec&size=200
http://www.kazusa.or.jp/j/resources/database.html
http://www.nias.affrc.go.jp/database/index.html
z
a
Swabreck et al. (2008); bTanaka et al. (2008); cKurata et al. (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);
j
Umezawa et al. (2008);kRöder et al. (2002); lYamamoto et al. (2008); mHigo et al. (1999); nNühse et al. (2004); oHeazlewood et al. (2008); pPapadopoulos et al. (2009); qRiaño-Pachón et al. (2007);
r
Rushton 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);
1
Walter et al. (2009);2Kanehisa et al. 2008)
Downloaded from http://pcp.oxfordjournals.org/ at Pennsylvania State University on September 17, 2016
Plantpromoter db 2.0 l
PLACEm
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 inArabidopsis thaliana
FTFLPdbb, c
http://gfp.stanford.edu/
Images of GFP-tagged proteins in various organelles inArabidopsis thaliana
Plant Cell Imagingb, c
http://deepgreen.stanford.edu/
Images and movies of GFP-tagged proteins in various organelles inArabidopsis thaliana
GFP localisome databased
http://www.psb.ugent.be/papers/cellbiol/
Images of GFP-tagged proteins involved in cytokinesis and cell plate formation inArabidopsis thaliana and BY-2 cells
GFP databasee
http://data.jic.bbsrc.ac.uk/cgi-bin/gfp/
Images of GFP-tagged proteins in various organelles inArabidopsis 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 inArabidopsis thaliana
The Illuminated Plant Cellh
http://www.illuminatedcell.com/
Images and movies of various visualized organelles in several plant species
a
Brown 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); Mathur (2007).
Downloaded from http://pcp.oxfordjournals.org/ at Pennsylvania State University on September 17, 2016
Table 3 A selection of open source application tools for image analysis
Name
URL
BioImage Suite
http://www.bioimagesuite.org/
http://www.bioimage.ucsb.edu/
http://www.cellprofiler.org/index.htm
http://dbkgroup.org/celltracker/index.php
http://cshprotocols.cshlp.org/
http://icluster.imb.uq.edu.au/
http://rsb.info.nih.gov/ij/
http://imagesurfer.med.unc.edu/
http://mayavi.sourceforge.net/
MSIb
http://www.molsci.org/about/index.html
c
OME
Paraview
http://www.openmicroscopy.org/site
http://www.paraview.org/
SCMDc
http://scmd.gi.k.u-tokyo.ac.jp/datamine/
a
BISQUE: Bisque - Bio-Image Semantic Query User Environment; bMSI: Molecular Sciences Institute
d
SCMD: Saccharomyces Cerevisiae Morphological Database; cOME: Open Microscopy Environment.
Downloaded from http://pcp.oxfordjournals.org/ at Pennsylvania State University on September 17, 2016
BISQUEa
CellProfiler
CellTracker
Cold Spring Harbor Protocols
iCluster
ImageJ
ImageSurfer
MayaVi Data Visualizer