ORIGINAL ARTICLE
Automated Organelle-Based Colocalization in
Whole-Cell Imaging
Ben J. Woodcroft,1 Luke Hammond,1 Jennifer L. Stow,1 Nicholas A. Hamilton1,2*
1
Institute of Molecular Bioscience, The
University of Queensland, Brisbane,
Queensland 4072, Australia
2
ARC Centre of Excellence in
Bioinformatics, The University of
Queensland, Brisbane, Queensland
4072, Australia
Received 18 December 2008; Revision
Received 27 July 2009; Accepted 5
August 2009
Additional Supporting Information may be
found in the online version of this article
Grant sponsors: National Health and
Medical Research Council;
Australian Research Council.
B. J. Woodcroft and L. Hammond
contributed equally to the work.
*Correspondence to: Nicholas Hamilton,
Institute for Molecular Bioscience, The
University of Queensland, Brisbane,
Queensland 4072, Australia
Abstract
The use of fluorescence microscopy to investigate protein colocalization is an invaluable
tool for understanding subcellular structures and their associated proteins. However,
current techniques are largely limited to two-dimensional (2D) imaging and often
require manual segmentation. Here, we present OBCOL, a methodology to automatically segment and quantify protein colocalization not within an image as a whole but
on all individual punctuate organelles within a 3D multichannel image. A wide variety
of colocalization statistics may then be calculated on the objects found, and features
reported for each such as position, degree of overlap between channels, and number of
component objects. OBCOL was validated on imaging of two fluorescent markers
(Dextran, EGF) in 3D microscopy imaging. OBCOL’s application was then exemplified
by investigating the colocalization of three fluorescently tagged proteins (VAMP3,
Rab11, and transferrin) on recycling endosomes in mammalian cells. The methodology
showed for the first time the diversity of endosomes labeled with one or more of these
proteins and quantitatively demonstrated the degree of overlap among these proteins in
individual recycling endosomes. The consistent segregation of these markers provides
novel evidence for the subcompartmentalization of recycling endosomes. OBCOL is a
flexible methodology for 3D multifluorophore image analysis. This study clearly
demonstrated its value for investigating subcellular structures and their constituent
proteins. ' 2009 International Society for Advancement of Cytometry
Key terms
colocalization; confocal microscopy; fluorescent imaging; image analysis; image segmentation; organelle; cell biology
Email: [email protected]
Published online 10 September 2009 in
Wiley InterScience (www.interscience.
wiley.com)
DOI: 10.1002/cyto.a.20786
© 2009 International Society for
Advancement of Cytometry
Cytometry Part A 75A: 941 950, 2009
COLOCALIZATION
between fluorescently labeled molecules is one of the most
widely used techniques to assess the degree of spatial coincidence, and hence potential interactions, among subcellular species such as proteins. To date, the analysis of
colocalization has been largely qualitative via the ‘‘dye-overlay’’ method (1). This
method images two proteins and their subcellular localizations in red and green
channels and uses the merged image to visually assess the degree of colocalization.
Pixel fluorograms or scattergrams in which a plot is made of the pair of intensities
for each pixel are also often used to provide more detailed colocalization (2).
Recently, there has been much interest in quantitative approaches to measure colocalization. Broadly, such methods can be divided into threshold based or intensity
based. In threshold-based methods, a threshold is set for each channel. At its simplest, the percentage of above-threshold pixels in one channel that are in regions
above the threshold in the other channel is recorded. For intensity-based approaches,
pixel values are taken into account. For instance, Pearson’s correlation coefficient calculates the correlation between pixel intensities in each channel, and Mander’s coefficient (3) gives a ratio of intensities in one channel relative to above-threshold regions
in the other. Methods have also been developed to remove bias in using visual inspection to set thresholds (4). An excellent survey of colocalization analysis methods,
ORIGINAL ARTICLE
including analysis and implementations of the main
approaches can be found in Ref. 5.
Our own research interests focus on recycling endosomes,
which are highly dynamic, multifaceted, and multifunctional
organelles, found in most cells (6). As with other endosomes,
each type of recycling endosome is characterized by various
resident membrane proteins, by the more transient populations of membrane-associated proteins, and by cohorts of
cargo proteins that pass dynamically through these organelles
(7). While endosomes are best known for handling protein
cargo being internalized from and recycled back to the cell
surface—such as transferrin—these endosomes are now also
recognized as stations for exocytic cargo moving to the cell
surface in secretory pathways (6–9). The multiple trafficking
pathways feeding into endosomes and the selective egress of
proteins leaving endosomes require sophisticated mechanisms
for protein sorting and handling. Indeed, there is now evidence from live-cell imaging that different resident proteins
and different cargo in transit can be segregated within endosomes, suggesting endosome subcompartmentalization as a
possible mechanism for sorting. Further analysis of recycling
endosome substructure and quantitative analysis of protein
distribution within individual endosomes is now required—
and on a large scale—to fully document the form and function
of these organelles.
Since endosomes can best be studied as dynamic, intact
structures in living cells, their detailed analysis ideally includes
the ability to label them with multiple markers or tagged proteins, and to observe and analyze them in three-dimensional
(3D) or 4D. Localization and colocalization analysis is then
needed to provide information on the interactions, functions,
and dynamics of proteins within the organelle.
Available image analysis programs typically quantify fluorescence from different channels in whole images or whole
cells, but the increasing requirement is for fluorescence from
different channels to be read and compared within organelles.
While software is available to segment and count punctuate
structures (10–12), and there is no shortage of colocalization
software (5), current software offers no easy or sufficient way
to perform colocalization on individual structures from
whole-cell imaging.
We thus developed a program, OBCOL (Object Based
Colocalization), as a method for quantifying colocalization of
proteins within specific organelles in whole-cell images.
OBCOL segments 2D, 3D, and 4D multichannel confocal
microscope images into discrete structures and allows a variety
of statistical measurements to be calculated on each. For ease
of use, OBCOL is implemented as an ImageJ (13) plug-in, a
tool widely used within the biological image processing community.
Using OBCOL, we examined single-, double-, and triplelabeled endosomes within a cell as separate populations. This
approach allowed a more detailed and broader view of protein
localizations within recycling endosomes. The detail was provided by localizing multiple proteins and their compartmentalization within individual endosomes, while the breadth of
view was ensured by obtaining information on the whole
942
population of endosomes within a single cell. Herein, we
describe the OBCOL methodology and demonstrate how it
has been used to analyze and define sets of proteins within
recycling endosomes of living cells.
OBCOL is open source and distributed under the GNU
General Public License. It is available for download from
http://obcol.imb.uq.edu.au/. A user manual is included within
the distribution.
MATERIALS AND METHODS
Cell Culture and Transfection
The 3T3 mouse fibroblast and COS-1 green monkey kidney cell lines were (separately) cultured in Dulbecco’s modified Eagle’s medium (Invitrogen) supplemented with 10% fetal bovine serum (FBS) and 2 mM glutamine (BioWhittaker).
For transfection, LipofectAMINE 2000 (Invitrogen) was used
according to the manufacturer’s instructions.
Uptake of Tfn in 3T3 Cells and EGF and Dextran
in COS-1 Cells
For uptake of fluorescent transferrin (Tfn-647), 3T3 cells
were incubated with 10 lg/ml AlexaFluor647-conjugated Tfn
in medis for 15–60 min at 378C before imaging. For uptake of
fluorescent EGF (EGF-488) and Dextran (Dextran-568), 1 lg/
ml AlexaFluor568-conjugated Dextran 10,000 MW (Invitrogen) was internalized to COS-1 cells, which have been serum-starved for 60 min at 378C, in serum-free media for
90 min at 378C. AlexaFluor488-conjugated EGF (1 lg/ml;
Invitrogen) was then internalized to the cells in serum-free
media with 1 lg/ml Dextran-568 for 5 min at 378C and chased
in serum-free media with 1 lg/ml Dextran-568 for 55 min at
378C.
Live-Cell Imaging of 3T3 Cells
Confocal imaging of live cells was conducted using Carl
Ziess LSM510 confocal microscopes. For imaging, cells were
cultured on 35-mm glass-bottom dishes (MatTek Corporation) and immersed in CO2-independent growth medium
containing 2% l-glutamine and 10% FBS before viewing. Temperatures during imaging were maintained at 37 0.28C using
a Carl Zeiss stage-mounted heating chamber. The 488-, 543-,
and 633-nm laser lines were used to excite the GFP, mCherry,
and Alexa-647 fluorophores, respectively. Imaging was performed with a plan APO 3 100 at a resolution of 10243 1024.
Pixel resolution in the imaging is 0.09 lm 3 0.09 lm with 42
slices imaged at 0.40 lm intervals. Volocity v3.7 was used to
visualize and surface-render acquired images.
Immunofluorescence Microscopy of COS-1 Cells
Cells were fixed with 4% paraformaldehyde in PBS for
30 min at room temperature, washed twice with PBS, and
then mounted with Aqueous Mounting Medium PERMAFLUORTM (Beckman). Confocal imaging was performed using
Carl Zeiss LSM 5 PASCAL confocal microscope with a 63 3
1.4 plan-Apochromat oil immersion lens. Excitation was performed with a 30 mV argon laser emitting at 488 nm and with
Organelle-Based Colocalization
ORIGINAL ARTICLE
a 1.0 mW helium/neon laser emitting at 543 nm. Emissions
were collected using a 505–530 nm band-pass filter for
Alexa488 and a LP560 filter for Alexa568. Pixel resolution in
the imaging is 0.11 lm 3 0.11 lm with 25 slices imaged at
0.20 lm intervals. See Ref. 14 for further details.
Image Processing and Analysis
OBCOL’s operation is made up of three different units,
shown in Figure 1. Individual 2D images for each channel are
first segmented into discrete structures, which are then aggregated into 3D organelles. Once the boundaries of each discrete
organelle in the image have been defined, visualization and
statistical analysis tools are presented to the user.
Since microscope imaging conditions vary widely, some
preprocessing of images may improve the OBCOL procedure.
For instance, light median filtering or Gaussian blurring and
setting background pixels below a given intensity cut off to
black (intensity 0) is commonly used (15) and was beneficial
here for obtaining good segmentation. Median filtering is to
be preferred over Gaussian blurring as the median filter will in
general preserve edge features. In 2D filtering of a 3D image
stack, each 2D slice of the stack is filtered individually. For 3D
filtering, adjacent slices to each slice are considered in producing the filtered image. However, given that the distance
between adjacent slices in fluorescent imaging is often several
times larger than pixel resolution, it should be carefully considered whether 3D filtering is appropriate for a given data set.
For instance, for the data described in the previous section,
each pixel has dimension 0.09 lm 3 0.09 lm with 0.40 lm
intervals between slices, hence 2D median filtering was chosen
(see later). Several ImageJ filters and plug-ins are available
from the ImageJ web site to filter images in 2D or 3D.
Note that while such preprocessing may allow more accurate object segmentation, intensity-based analysis of objects in
later stages of the OBCOL pipeline can still be performed on
the unprocessed images.
Segmentation. Segmentation involves firstly segmenting each
individual 2D image for each channel in isolation. The aim is
to delineate pieces of the foreground that potentially belong to
different organelles. Specifically, experimentation with a number of techniques revealed that a watershed algorithm (13,16)
yielded optimal segmentation results (data not shown). This
algorithm partitions can image into regions by ‘‘flooding’’
from points of maximum intensity. Two flood regions are
combined into one if there is a path between the corresponding intensity peaks that does not fall below a given intensity,
that intensity being the maximum of the two peaks minus a
user-defined intensity tolerance. By changing the tolerance,
the user controls how ‘‘broken up’’ each image becomes. With
a larger tolerance, object regions will tend to be larger, encompassing more local peaks, whereas a smaller tolerance will
result in those peaks being considered separate objects.
Within OBCOL, regions that have been separated at this
stage of the pipeline may be combined again later in the 3D
aggregation stage (discussed below). This might be the case,
Cytometry Part A 75A: 941 950, 2009
for example, if a single organelle contained two nonoverlapping regions of one protein, but a region of another protein
(imaged in a separate channel) overlapped both regions of the
first protein.
It is worth noting that there is a wide range of segmentation strategies that may be applied to 3D data. Because of the
difference in scales of the pixel resolution to distance between
slices typical in fluorescent imaging we choose to segment
each slice individually, though methods have been developed
to segment anisotropic 3D data (see, for instance (10,12)).
However, other segmentation strategies could readily be
utilized within OBCOL. For instance, a 3D watershedding
algorithm such as is available in ImageJ could be used to segment the image data. Binary masks of each segmented color
channel could then be input to the OBCOL pipeline. Acting
on the masks, the aggregation stage (described below) would
then collect together the discrete objects, which would then be
quantified in terms of the overlap between channels and
position as before. Furthermore, once discrete objects had
been found, intensity-based colocalization statistics could be
calculated using the pipeline on the source images as usual.
Localized thresholding. A further stage of segmentation in
OBCOL is available if required. While median filtering or
Gaussian blurring limits over-segmentation in watershedding, they may lead to an associated diffusion of intensities
which softens the edges of the imaged organelle. Indeed,
image acquisition itself is prone to diffusion, with as much
as 30% of intensity attributable to noise and background
levels (15). Such light diffusion tends to manifest itself
around regions of higher intensity in the image, as opposed
to regions of no signal. A local thresholding technique can
therefore be applied (at the users preference) to the image
data in each of the watershed regions found (Fig. 1C). By
considering each structure and thresholding in isolation, it
is possible to selectively set to background (intensity 0)
pixels from parts of the image where more diffusion exists,
while keeping intact those fainter structures that are less
prone to diffusion. The thresholding algorithm applied here
is based on the isodata algorithm (17,18). It served to
slightly reduce the size of the segmented regions found in
the watershedding stage, thus reducing over-selection due
to diffusion.
Aggregating structures. Once 2D images for each channel
are segmented into regions, the next step is to partition the
set of regions into organelles. A segmented structure in one
2D image is considered directly connected to a second structure if the second structure is in an adjacent plane of the
same channel and they overlap, or if the second structure is
in a corresponding or adjacent plane in another channel and
they overlap (Fig. 1). In other words, a region is directly
connected to those regions that it ‘‘touches’’ or overlaps with
in one of the channels in either the same or adjacent slices.
Two structures can then be indirectly connected through a
chain of direct connections. Thus, structures that are directly
943
ORIGINAL ARTICLE
aggregated between slices 9 and 10, slices 18 and 19, and so
forth. Hence, the OBCOL user may supply the number of
slices per time point, in order that structures are not
aggregated across time points. In this way, 3D movies can be
quantified using a single application of OBCOL.
Object analysis. The pipeline’s output is a collection of organelles, each made up of structures from different planes and
channels. The user is then presented with a number of visualization and statistical analysis options, as shown in Figure 2.
Example images for each may be found in the User Manual.
Statistical analysis. Statistical analysis and quantification are
the main aims of OBCOL, thus several facilities in two broad
categories are provided (Fig. 2). The first does not use intensity information from the original source images, whereas the
second does. In the first, for each object found, the number of
structures in each channel is reported, as well as the number
of pixels from each channel, the intersection sizes, the number
of discrete objects in each channel in the object, and the position of the object (obtained by averaging over the positions of
pixels in the object). Table 1 provides an output example.
The second category of statistics takes into account not
only the spatial representation of the image, but also intensity
information from the original images. Previous colocalization
quantification methods were adapted to operate within the
confined space of an organelle, as opposed to a whole image.
Calculated statistics built into OBCOL include Pearson’s coefficient, overlap coefficient, Mander’s coefficient (3), cytofluorogram parameters, and Li’s intensity coefficient (ICQ) (1).
Each of these statistics is calculated for each organelle, where
the inputs are each pixel in each channel within that organelle.
Figure 1. Steps in the OBCOL pipeline with two channels, each
made up of two slices. (A) Cartoon representing the raw images.
(B) Images after background removal. Planes then undergo
watershed segmentation and local thresholding to become individual structures (C). Structures are then aggregated together to
form final representations of organelles as shown in (D). (D) Similar to the ‘‘View all Objects By Id’’ visualization, except there the
connecting lines between structures are not shown. Each color
represents an object found, with lines connecting the elements of
the object. [Color figure can be viewed in the online issue, which
is available at www.interscience.wiley.com.]
or indirectly connected are considered a single organelle by
the method.
In the interests of flexibility, the user controls what is
considered a direct connection between two structures. Two
overlapping structures are deemed directly connected if they
share some minimal number of overlapping pixels. The
minimum is by default one, but is user-specifiable.
Time. A sequence or stack of images put into to OBCOL may
represent multiple time points. For instance, a stack of 90
images might represent 10 3D stacks of nine slices depth at
consecutive time points. In this case, structures should not be
944
Figure 2. Options in OBCOL. Upon completion of the OBCOL
pipeline, the user is presented with a number of statistical analysis and visualization options. [Color figure can be viewed in the
online issue, which is available at www.interscience.wiley.com.]
Organelle-Based Colocalization
ORIGINAL ARTICLE
Table 1. Example output from OBCOL for the 3T3 cell
OBJECT ID
ROIS R
ROIS
G
ROIS
B
PIXELS
R
PIXELS
G
PIXELS
R1G
PIXELS
B
PIXELS
R1B
PIXELS
G1B
PIXELS
R1G1B
TOTAL
PIXELS
x
y
z
28
30
31
33
36
41
43
47
48
49
51
53
666
27
3,801
1,319
1,353
3
8
4
1
1
9
0
3
2
1
4
4
4
121
3
3
0
7
4
5
0
1
11
3
3
0
2
11
2
6
11
3
0
4
7
27
6
0
1
12
1
0
0
0
11
3
7
130
0
1
5
11
82
38
24
8
53
0
9
16
2
4
43
27
819
36
0
0
58
8
45
0
6
103
43
23
0
37
285
13
83
1,407
42
54
0
5
8
12
0
2
50
0
12
0
6
16
0
1
642
19
0
73
18
720
67
0
2
26
0
0
0
0
30
8
25
468
0
0
0
4
21
9
0
1
23
0
0
0
0
0
8
11
173
0
3
24
38
26
8
0
2
62
5
0
0
0
86
20
43
899
0
3
0
9
12
9
0
4
73
0
0
0
0
8
3
27
1,160
0
0
29
143
877
188
24
25
390
48
44
16
45
429
95
217
5,568
97
0
0
419.5
277.7
232.4
321.0
277.2
194.7
245.4
379.6
409.1
468.8
139.7
230.4
371.9
522.4
370.1
305.6
309.3
112.9
99.1
147.9
56.8
108.6
176.4
91.2
78.7
79.6
123.0
197.0
115.8
181.4
78.5
85.2
68.9
105.1
6.9
14.2
13.6
20.0
13.0
7.4
17.5
7.1
9.2
20.6
7.0
15.3
7.3
23.9
34.3
21.0
7.1
Each row corresponds to a discrete object found by OBCOL and has a unique identifier (Object ID). The R,G, and B ROI’s give the number of objects in the R, G, and B channels, respectively. The next six columns give the number of pixels in the object in each of the channels
and their intersections. The total number of pixels within the object is then given, followed by the coordinates of the object center (x, y, z).
The pixel dimension is 0.09 lm 3 0.09 lm with a slice separation of 0.40 lm. The final five rows correspond to the objects shown in Figures
4E–4I. The complete table of data is given in Supporting Materials.
Implementation of these statistics was achieved by reusing
code from the JACoP ImageJ plug-in (5). Please refer to this
paper for further details.
The user may also filter the objects found by size.
Typically, this will be used to remove small artifacts or noise
from the analysis. For instance, it has been observed that if
you sample at the Nyquist rate, which is below the resolution
limit, background noise will tend to be made up of small patchy regions (10). Filtering may also be used to select those
structures for which a size range is a part of their definition.
Note that size filtering only occurs subsequent to object finding and hence the complete data set of objects found is always
available.
Visualization. OBCOL offers four options for visualization,
accessed by respective buttons in the menu (Fig. 2). In the first
two, organelles are shown as flat areas in an image, without
regarding the intensity information contained in the original
images (Fig. 4C). With the first option, ‘‘View all Objects By
Id,’’ each organelle is visualized using a distinct color, providing an overall view of the segmentation as it applies to the
images being analyzed. The second visualization is similar to
the first, with the exception that coloring is performed on the
basis of each structure’s channel, instead of its organelle. This
gives a result similar to the dye-overlay method mentioned
earlier, with the difference that structures from the first channel are colored red, the second green, and the overlap yellow,
and so on. If four or more channels are visualized, colors are
spread across a spectrum. The third and fourth visualization
methods include intensity information from the original input
Cytometry Part A 75A: 941 950, 2009
images. The third method of visualization is to use the processed organelle regions as masks over the original image.
Again, this is similar to the dye-overlay method, in that the intensity values from each channel are overlaid, except that in
our method, areas deemed background in the image are set to
black. Finally, each object may be viewed individually. Selecting the organelle to be visualized in isolation is achieved
simply by mouse-clicking selection. Importantly, any image
may be selected, including rendered visualizations and even
the original images used to choose the structure. This can be
useful in investigating a specific object of interest.
When visualizing multiple objects with the first three
methods, users are presented with the option of filtering the
objects before display, based on the number of pixels in each
object. The most common use of this function is to avoid
cluttering the visualization with small objects, which might be
artifact.
Software Implementation
OBCOL is a pure Java, open-source ImageJ plug-in. It
was tested on ImageJ version 1.41d, with Java 1.6 (19). The
plug-in and user manual are available for download from
http://obcol.imb.uq.edu.au/.
Computational Expense
Processing of the imaging analysis described in Results
took 3 min for the Tfn/Rab11/Vamp3 dataset on an Intel
Core 2 Duo with 4 GB of RAM running Fedora Linux 10.
The Dextran/EGF image data was processed in 1 min.
945
ORIGINAL ARTICLE
Figure 3. Validation of OBCOL segmentation and structures. (A) A slice from a 3D stack of a COS-1 cell with EGF (green) and Dextran (red)
fluorescently tagged. (B) The corresponding slice from the segmentation of the stack using the default settings in OBCOL and filtering out
objects of less than 5 pixels in size. Each color in (B) corresponds to a distinct object found. See Results for details of the OBCOL objects
found in the segmentation. The white circle in (A) shows a structure not shown in (B) due to its small size. The arrow on the left in (B)
points to an example of a structure (purple) that a detailed view of the source data suggests should have been segmented into two components. The arrow on the right gives an example of a structure (green and purple) that has been segmented into two components when the
source data suggests this should have been segmented as a single structure. Of the 183 structures found by OBCOL, three objects were
found that might have better been segmented into two objects, and three objects were found that were segmented into two components
when there was in fact one. (Inset) A structure from another slice of the stack. OBCOL has correctly identified this as a single object despite
its irregular shape. See Supporting Material for the complete image stack together with the segmentation. Scale bar 10 lm.
The process runs with comparable speed on Macintosh and
Windows XP operating systems.
RESULTS
Validation
To validate the OBCOL approach, imaging of fluorescent
EGF and Dextran was captured in COS-1 Cells (see Methods).
A single slice from the stack of 25 is shown in Figure 3A, and
the complete stack is available in Supporting Material. Before
segmentation in OBCOL, each channel was preprocessed by
median filtering (radius 0.5 pixels) and subtracting background (ball radius 4 pixels). Cut-offs of 15 and 10 were then
applied to the red and green channels, respectively. The
OBCOL plug-in was utilized using the default settings. The
results were then filtered to show only those objects found
comprising of at least 5 pixels (Fig. 3B).
The above led to 183 detected objects, which were then
examined in detail and compared against the experimental
data. The ‘‘Sync Windows’’ utility in ImageJ was used to
enable corresponding points in the experimental and segmented data to be readily seen, and images were viewed at 200%
size to allow small features to be observed. First, the experimental data was examined slice by slice, and a check made as
to whether each object that was visually apparent in the experimental data had a corresponding object in the segmented
data. All but 25 objects in the experimental data were found to
have corresponding segmented objects. Each of the 25 objects
was a relatively faint ‘‘dot’’ of 2 pixels diameter, an example
of which is circled in Figure 3A. Utilizing a less stringent
946
object size filter in OBCOL showed that each of these objects
had in fact been detected, but had been filtered out due to
being less than 5 pixels in size. It is worth noting that the 25
objects were close to being visually undetectable in the experimental imaging and that allowing a lower object size filter
might lead to the introduction of artifacts.
Next, each object detected by OBCOL was compared
against the experimental imaging to ensure that each object
found was ‘‘real’’ and not an artifact of image processing. No
artifacts were found. Finally, all structures in the experimental
and segmented data were examined to detect if objects had
been inappropriately ‘‘stuck together’’ or alternatively inappropriately separated by the segmentation. Of the 183 objects
segmented, three were found of the former class and three of
the later. Hence, while not perfect, the error rate in segmentation is relatively low.
Also shown in Figure 3 is a slice from irregularly shaped
structure in the experimental data (3A, inset). Examination of
the experimental data showed this to be clearly a single structure. The corresponding segmentation (3B, inset) shows that
OBCOL had correctly identified it as a single structure. One of
the advantages of the segmentation approach taken in OBCOL
is that it is not limited to finding approximately spherical
objects.
The effect of varying parameters to OBCOL is now examined by using the parameters described in the previous paragraphs and varying each, one at a time. First, using parameters
as above, but not utilizing the localized thresholding switch
gave rise to 169 discrete objects compared with the 183 found
above when localized thresholding was enabled. Hence, it is
Organelle-Based Colocalization
ORIGINAL ARTICLE
clear that localized thresholding is useful to enable object
separation and prevent structures from being ‘‘stuck together.’’
We next consider the overlap parameters used to join structures between slices. Using overlap parameters of 1, 2, 3, 4,
and 5 pixels gave 176, 166, 183, 205, and 275 objects, respectively. The trend is that a larger overlap parameter leads to
more separation of objects. Watershed tolerances of 3, 5 (the
default), 7, and 9 gave rise to 191, 183, 179, and 175 objects,
respectively. The trend is that a lower tolerance gives a higher
degree of separation of structures and hence a larger number
of discrete objects. It is worth noting that while the default parameters to OBCOL will often produce a good segmentation,
some experimentation may be required to produce optimal
results. Thus, if the structures found need to be better separated, increasing the overlap parameters or decreasing the
watershed tolerance may produce better results. Once the
choice of parameters has been established, then multiple experimental image sets might then be consistently compared
using identical parameters.
Cargo Segregation in Endosomes
To demonstrate OBCOL here on a more complex and
interesting example, we applied it to analyze the recycling
endosome-resident proteins, Rab11 and Vamp3, and the recycling cargo protein, transferrin (Tfn), in 3T3 fibroblasts (see
Methods). Cherry-Rab11 and GFP-Vamp3 were transiently
expressed in cells that had also internalized Tfn-647, all of
which were subsequently localized to recycling endosomes by
confocal microscopy (Fig. 4). Cells were imaged live using the
imaging procedure described earlier to reconstruct cells in 3D.
For segmentation into objects, images were preprocessed using
a median filter of radius of 1 a pixel to smooth out noise
effects. Background cut-offs of 48, 50, and 58 intensity units
were applied for the red, green, and blue channels, respectively. Images were then entered into OBCOL using the default
parameters, with the exception of an overlap of 3 pixels
required to join objects and a minimum object size of 15. This
size was chosen to both remove small size artifacts possibly
due to noise as well as in consideration of the known size of
endosomes within these cells. Images were then segmented to
automatically define discrete labeled objects. Evaluating the
quality of segmentation for subcellular imaging is problematic
because the images are typically relatively low contrast, often
noisy, and there is no gold standard for what constitutes discrete objects. However, visual examination of the segmented
images showed comparable objects and boundaries to those in
the source images. Another factor often encountered is that
some proteins localize to more than one compartment, as in
this case where GFP-VAMP3 was on the plasma membrane as
well as in recycling endosomes—the organelle structure of interest. In this case, the plasma membrane staining was
removed manually using ImageJ before OBCOL analysis.
Figure 4 represents a typical cell imaged in these experiments to demonstrate the features of OBCOL. Figure 4A
shows a single slice of the original 3D-reconstructed image
with all three channels, and Figure 4B depicts a 3D view of the
entire cell with all three channels shown. Figures 4C and 4D
Cytometry Part A 75A: 941 950, 2009
illustrate segmentation of the slice in Figure 4A and a 3D view
of the entire cell segmentation, respectively. Each color in 4C
and 4D corresponds to a distinct disjoint object. Each individual labeled object in the cell can be viewed separately and a
variety of these objects are demonstrated; Figure 4E depicts
the segmentation of a single recycling endosome (labeled with
an arrow in 3C) and the component objects in each channel,
whereas Figures 4F–4H show three endosomes containing
only pairs of markers, and a large structure containing scattered elements of all three markers is depicted in Figure 4I.
In total, 875 endosomes were identified in the selected
3T3 cell by OBCOL. Details of each structure are given in
Table 1 and Supporting Table. Endosomal structures identified
by OBCOL included those containing one, two, or three of the
fluorescent labels. Figure 5A gives a breakdown of the 875
endosomes in these categories. Approximately one quarter
(252) of the endosomes contained all three markers, making
them the primary objects of interest in this study by fitting the
definition of recycling endosomes demarcated by having all
three markers. Of the objects containing only single markers,
147 contained only mCherry-Rab11, 142 had GFP-VAMP3
alone, and 71 contained only Tfn-647. The remainder of the
depicted ‘‘endosomes’’ (263) contained pairs of markers (Figs.
4F–4H), the most populous of which were structures containing both GFP-VAMP3 and Tfn-647. Structures containing one
or two markers might be subpopulations or dynamic stages of
recycling endosomes. They could also represent genuine
labeling of these proteins in post-Golgi carriers or other types
of endosomes that are in pathways leading to and from the
recycling endosomes. A particular power of OBCOL is the opportunity to select any of these categories of structures for
analysis.
Figure 5A also shows the (relative) average sizes of the
endosomes in each class. Recycling endosomes that contained
all three markers were, on average, 10-fold larger in volume
than other structures. Closer examination of the size distribution showed skewing of this statistic by a small population
(23) of particularly large structures (volume [1 lm3). Such
structures each had a large number of component parts, for
instance the structure in Figure 4I (object 27 in Table 1) contained 100 regions in each channel. The marker distribution
in large objects of this class is not typical of endosomes, and
such structures could be protein aggregates or other subcellular structures; we therefore excluded these from further analysis. Without these outliers, the average size of triple-labeled
structures was 0.28 lm3, more in keeping with the expected
size of recycling endosomes in these cells.
A proportional Venn diagram representation of the triple-labeled, nonoutlier recycling endosomes is given in Figure
5B, showing the average proportions and overlaps of the three
markers. A large proportion of the volume occupied by each
marker was clearly not coincident with the other markers. For
instance, 50% of volume occupied by the mCherry-Rab11 was
disjoint from the other two markers. Indeed, the recycling
endosomes seemed to contain primarily domains of single
markers (as 68.3% of their volume) with lesser proportions of
their volume containing, respectively, mixed markers. This
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ORIGINAL ARTICLE
Figure 4. OBCOL automatically identifies and analyzes endosomes. A single slice (A) and a 3D reconstruction (B) of a 3T3 fibroblast transfected with mCherry-Rab11 (Red) and GFP-Vamp3 (Green), and then incubated with fluorescently conjugated tranferrin (Blue). Following
analysis with OBCOL, corresponding images displaying individually colorized endosomes were generated (C,D). The arrow in (C) points to
the single endosome shown in (E) with its composition shown rendered as a combined-channel object, followed by the components in
each channel. (F–H) Examples of double-labeled endosomes rendered in 3D. (I) A large triple-labeled endosome rendered in 3D. The data
corresponding to (E–I) are given in the last five rows of Table 1. Scale bars 5 10 lm.
implied that VAMP3 and Rab11—considered as markers for
this compartment—are indeed only labeling domains of recycling endosomes. Moreover, the cargo protein Tfn also
appeared largely distinct from either of the other markers, suggesting that yet another domain of the recycling endosome
exists. This is strong, quantitative evidence that recycling
endosomes are heterogeneous, compartmentalized entities,
made up of several, possibly overlapping domains. Visual
948
inspection of individual endosomes in the source images confirmed this quantitative observation and rendering of an individual recycling endosome confirmed that each of the three
markers occupied distinct domains of the analyzed structure
and in proportions in keeping with the OBCOL analysis
(Fig. 4E).
Another representation of the triple labeling nonoutlier
data is given in Figure 5C, which shows each circle on the
Organelle-Based Colocalization
ORIGINAL ARTICLE
Figure 5. The distribution of endosomes in the triple-labeled cell imaging. The average size and number of endosomes within each subpopulation found in this cell are displayed using data from the OBCOL analysis (A). The population of triple-labeled endosomes contained 23
outliers larger than 1 lm3, and the average size object shown in (A) for triple labeled does not include these. The average marker composition for the nonoutlier subpopulation of triple-labeled endosomes was also determined, displayed here in a proportional Venn diagram
(B). Nonoutlier endosomes containing all three markers (white quadrant in A) are shown individually, plotted according to their marker
composition (C). A wide range of compositions was seen for these triple-labeled objects. [Color figure can be viewed in the online issue,
which is available at www.interscience.wiley.com.]
color triangle corresponding to a single endosome, with the
size of the circle corresponding to the volume of that
endosome, and placement of the circle within the triangle
showing its relative proportion of each of the markers. It is
immediately apparent from this representation that endosomal composition varied widely across the population of
structures. While there were relatively few endosomes consisting principally of GFP-Vamp3 or Tfn-647, endosomes in
all other regions of the color triangle indicated that composition and degree of coincidence between markers was
highly variable.
The Diversity of Localizations in Endosomes
Each of these data representations has allowed us to
see for the first time the enormous diversity in endosome
composition and the degree of spatial coincidence among
markers. Although the marker proportions varied, most
recycling endosomes maintained clearly distinct domains of
VAMP3, Rab11, and Tfn. This OBCOL-based analysis
quantitatively verifies the novel concept first suggested in
two recent studies—that recycling endosomes have ‘‘subcompartments’’ (6,8)—and in doing so has important functional implications. These subdomains of the endosome
purportedly help to sort cargo and machinery to allow
separate egress of different cargo proteins (9,20). It is also
Cytometry Part A 75A: 941 950, 2009
possible that this wide variation in compositional structure
corresponds to progressive stages in the maturation of recycling endosomes. While the maturation of other endosomes
has been studied (21), recycling endosomes remain to be
characterized in this fashion. Further evidence of a link
between maturation and composition might be found by
investigating the potential correlation of spatial relationships such as distance from the nucleus or cell membrane
with compositional structure. However, while only a snapshot of the life of endosomes in the cell can be taken, such
evidence might remain elusive.
One potential concern with OBCOL is the degree of user
bias in preprocessing images and parameter selection for segmentation. We do not explicitly specify how the images should
be preprocessed before analysis by OBCOL; instead, guidelines
are provided to allow the required flexibility when dealing
with the range of image types needed. Furthermore, the user is
responsible for providing several parameters to OBCOL,
including the number of overlapping pixels required for aggregation structures and whether a local thresholding scheme
should be carried out. See the Validation subsection above for
details on how parameters may be varied to obtain improved
segmentation results. Results are also reproducible and
platform independent, allowing for simple and independent
verification of results.
949
ORIGINAL ARTICLE
CONCLUSIONS
We have described here an integrated pipeline for segmentation and analysis of subcellular organelles from 2D, 3D,
or 4D whole-cell, multiple-fluorophore microscopy imaging.
The output from the pipeline is a variety of statistical measurements that can be queried using multiple visualization and
analysis options. Rather than analyzing the whole image, the
outputs provide the first available analysis of an entire population of individual organelles in a cell and their protein compositions within those images. This enables analysis based on a
statistically significant number of observations rather than
selecting a small number for detailed analysis, as has previously been the case. As a result of the significant compartmentalization found in the endosomes imaged, the colocalization quantification features of OBCOL such as Pearson’s and
Mander’s coefficients have not been demonstrated here,
although these well established measures will provide valuable
quantification for appropriate imaging. As an example, for the
structure shown in Figure 4E, the pair-wise Pearson’s
coefficients for the channels are 0.066 (Rab11-VAMP3), 0.654
(Rab11-Tfn), and 20.229 (VAMP3-Tfn). Hence, while the
Rab11-Tfn Pearson coefficient is quite high, the measures
do not give a meaningful measure when there is significant
compartmentalization.
OBCOL was applied here to study the recycling endosomes. The system enabled a refined analysis based on the
statistical distribution of organelle measurements and the
detection of recycling endosome composition. OBCOL could
similarly be applied to a variety of endosomes or punctuate
subcellular structures, particularly early/late endosomes,
lysosomes, and phagolysomes.
Observed structures containing all three markers comprised a subpopulation of large (volume [1 lm3) outliers
with many components in each channel. This may reflect the
stage of endosome maturation or possibly fusion, although it
is difficult to ascertain the source of the variation with a single
observation of the cell in time. Multifluorophore, live-cell 3D
video microscopy now offers the opportunity to observe the
complete life cycle of endocytic structures. Currently, OBCOL
may be used to segment, quantify, and provide positional information from this new data source by ensuring that structures are not aggregated across time points. The challenge is
then to track the imaged structures to create a complete time
course for each structure and its protein composition. While
automated 3D object tracking in complex environments is in
general difficult, the additional information provided by the
protein composition of each organelle should significantly
reduce the problem of object correspondence between time
points. The aim for future versions of OBCOL is thus to
950
enable tracking and quantification of individual organelles
over their entire cellular life cycle.
ACKNOWLEDGMENTS
The authors like to thank Tomohiko Taguchi and Ryo
Misaki for kindly making available the COS-1 cell imaging.
The imaging is part of a dataset (14) created under a grant of
the Core Research for Evolutional Science and Technology, Japan Science and Technology Agency (CREST, JST). Confocal
microscopy of 3T3 fibroblasts was performed at the Australian
Cancer Research Foundation (ACRF)/Institute for Molecular
Bioscience Dynamic Imaging Facility for Cancer Biology,
which was established with the support of the ACRF. The
authors also like to thank Fiona Wylie for her careful proofreading of the manuscript.
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