Original Article
Three-Dimensional Tissue Cytometer Based
on High-Speed Multiphoton Microscopy
Ki Hean Kim,1 Timothy Ragan,1 Michael J. R. Previte,1 Karsten Bahlmann,1 Brendan A. Harley,1
Dominika M. Wiktor-Brown,2 Molly S. Stitt,2 Carrie A. Hendricks,2 Karen H. Almeida,2
Bevin P. Engelward,2 Peter T. C. So1,2*
1
Department of Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, Massachusetts 02139
2
Biological Engineering Division,
Massachusetts Institute of Technology,
Cambridge, Massachusetts 02139
This manuscript is an expanded version
of a SPIE Proceeding paper [Kim KH, Stitt
MS, Hendricks CA, Almeida KH,
Engelward BP, So PTC. Threedimensional image cytometer based on a
high-speed two-photon scanning
microscope. In: Proceedings of SPIE Vol.
4262, Perisamy A, So PTC, editors; 2001].
It contains parts of results from the
proceeding paper under the permission
of SPIE for republication.
This article contains supplementary
material available via the Internet at
http://www.interscience.wiley.com/
jpages/1552-4922/suppmat.
Karen H. Almeida’s current affiliation is
Department of Chemistry, Rhode Island
College, Providence, RI 02908.
Abstract
Image cytometry technology has been extended to 3D based on high-speed multiphoton microscopy. This technique allows in situ study of tissue specimens preserving important cell–cell and cell–extracellular matrix interactions. The imaging system was
based on high-speed multiphoton microscopy (HSMPM) for 3D deep tissue imaging
with minimal photodamage. Using appropriate fluorescent labels and a specimen translation stage, we could quantify cellular and biochemical states of tissues in a high
throughput manner. This approach could assay tissue structures with subcellular resolution down to a few hundred micrometers deep. Its throughput could be quantified by
the rate of volume imaging: 1.45 mm3/h with high resolution. For a tissue containing
tightly packed, stratified cellular layers, this rate corresponded to sampling about 200
cells/s. We characterized the performance of 3D tissue cytometer by quantifying rare
cell populations in 2D and 3D specimens in vitro. The measured population ratios,
which were obtained by image analysis, agreed well with the expected ratios down to
the ratio of 1/105. This technology was also applied to the detection of rare skin structures based on endogenous fluorophores. Sebaceous glands and a cell cluster at the base
of a hair follicle were identified. Finally, the 3D tissue cytometer was applied to detect
rare cells that had undergone homologous mitotic recombination in a novel transgenic
mouse model, where recombination events could result in the expression of enhanced
yellow fluorescent protein in the cells. 3D tissue cytometry based on HSMPM demonstrated its screening capability with high sensitivity and showed the possibility of studying
cellular and biochemical states in tissues in situ. This technique will significantly expand
the scope of cytometric studies to the biomedical problems where spatial and chemical
relationships between cells and their tissue environments are important.
' 2007
International Society for Analytical Cytology
Key terms
3D image cytometry; tissue cytometry; rare cell detection; multiphoton microscopy
*Correspondence to: Peter T. C. So, 77
Massachusetts Avenue. NE47-279,
Cambridge, MA 02139, USA
Email: [email protected]
Received 13 May 2007; Accepted 21
August 2007
Published online 10 October 2007 in
Wiley InterScience (www.interscience.
wiley.com)
DOI: 10.1002/cyto.a.20470
Cytometry Part A 71A: 9911002, 2007
CYTOMETRY is the quantitative measurement of the physical and biochemical states
of cell populations. Cytometry provides information of the cell population on a cellby-cell basis rather than the population average. Many cytometric approaches are
high-throughput and allow for categorizing large cell populations into subgroups
revealing rare subpopulations. Flow cytometry is a widely used technique in which
cellular specimens are prepared in fluid suspensions and the properties of individual
cells are measured in a narrow fluid stream (1–4). The properties of cells are assayed
based on optical characteristics, such as fluorescence, light scattering, and light
absorption. Flow cytometry has very high throughput reaching a rate up to 10,000
cells/s (3). In combination with a cell sorting apparatus, precise physical separation
of the cellular subpopulations is routinely achieved. Flow cytometry is an indispensa-
ORIGINAL ARTICLE
Grant sponsor: NIH; Grant numbers: R21/R33, CA84740, R01CA79827,
CA112151, 2R01 EB00235, and R33 CA091354; Grant sponsor: NIH/
NIGMS Interdepartmental Biotechnology Training Program Grant;
Grant number: GM008334; Grant sponsor: NIH/NIEHS Training Grant
ble tool in immunology, molecular and cell biology, cytogenesis, and the human genome project.
Image cytometry is a complementary method to flow
cytometry, overcoming some of its limitations such as loss of
cellular morphological information (5–10). In image cytometry, cell specimens are prepared as 2D tissue cultures and are
imaged by either wide field or laser scanning microscopy.
Quantitative image analysis is performed to segment and categorize cellular subpopulations. Although throughput of this
method is relatively low—hundreds to thousands cells per second—it has several advantages: (1) Detailed cellular morphological and structural information are retained. (2) The cells of
interest can be identified and relocated for further analysis.
One key example is the ability to monitor temporal evolution
in live specimens. (3) Image cytometry also provides the spatial information of protein distributions within cells such as
receptor distribution in the membrane versus cytosol. Quantitative image analysis tools have been further developed based
on intensity, morphology, and spectral information of the
image. Image cytometry often utilizes multiple excitation light
sources and detection channels to distinguish multiple labels.
More advanced systems allow hyperspectral analysis of higher
content images acquired using devices such as liquid-crystal
tunable filters (11).
Cells in tissues behave differently from cells in cultures
(12,13). In tissues, cells become specialized, acquiring specific
functions. Cellular behaviors such as proliferation and differentiation are regulated by adjacent cells and their tissue locale,
and cells interact either directly, such as via cell adhesion
molecules (CAM), or indirectly by chemical signaling. Cells
are connected to the extracellular matrix (ECM) via their
membrane receptors such as integrin. ECM is composed of fibrous proteins, such as collagen, and adhesion molecules, such
as fibrin and fibronectin. ECM not only provides physical support but cell–ECM interactions are keys to the collective behaviors in tissues and play a critical role in processes such as
mechanotransduction (14). Therefore, it is important to study
cellular behaviors in the native tissue environment. Those
interactions are either lost or altered when cells are grown in
culture or dissociated from organs (15). The physiology of
pancreatic islets is an example showing different cellular behaviors depending on environments (16,17). Bennett et al. (16)
used multiphoton microscopy (MPM) to monitor redox activity inside intact pancreatic islets based on their NAD(P)H
level. Previous research on the cells grown as 2D cultures
showed significant cell–cell variations in NAD(P)H level as
response to external glucose levels. This 2D culture experiment
resulted in metabolic models of glucose metabolism based on
step-wise recruitment of individual cells. However, when Ben992
in Environmental Toxicology; Grant number: T32-ES07020; Grant
sponsor: Burroughs Wellcome Award.
© 2007 International Society for Analytical Cytology
nett et al. (16) imaged cells within intact islets, they found significantly more homogeneous glucose response of cells in the
islet suggesting that the step-wise model based on 2D culture
results might not be relevant to the actual physiological insulin
response in the pancreas. This difference is clearly important
for the design of pharmaceuticals for diabetes treatment.
Furthermore, in traditional histological tissue examination
with image cytometry, tissue specimens need to be sliced very
thin in order to be imaged. Slicing generates distortion in the
structure of tissue specimens (7). 3D image cytometry can
avoid this artifact by imaging directly inside tissues down to
hundreds of micrometers deep. Tissue structures and gene
expression distributions can be studied based on various labeling techniques such as fluorescence in situ hybridization.
Recently, image cytometry has been extended to 3D for
the quantitative study of histological tissue specimens (8–
10,18–21). It is based on confocal laser scanning microscopy
(CLSM) with multiple excitation light sources. CLSM obtains
3D resolution by rejecting light from out-of-focus regions in
the specimen with a pinhole in front of a detector. Its maximum imaging depth is approximately 100 lm in typical tissues.
Quantitative image analysis to identify individual cell types can
be a challenge, because cells are tightly packed in some tissues.
However, various studies have showed promising results. In the
lymph node, the spatial distribution of leukocytes expressing
different antibodies was mapped in thin tissue slices (9). The
phenotypes and quantities of tissue-infiltrating leukocytes were
further characterized to monitor immune system response to
transplants and therapies in renal tissues. Confocal 3D image
cytometer demonstrated in situ quantification of proliferation
markers and tumor suppressors, and in situ quantification of
apoptosis in tissues (8,20). In the study of Alzheimer’s disease,
neuron subsets with and without cyclin B1 expression were
identified and mapped throughout a 120 lm thick tissue (21).
However, image cytometry based on CLSM is limited to fairly
thin specimens due to tissue turbidity.
We introduce a new 3D tissue cytometric approach based
on high-speed multiphoton microscopy (HSMPM). MPM is a
3D imaging technique, similar to CLSM. However with its features of higher imaging depth in tissues and minimal phototoxicity compared with CLSM, MPM is appropriate for in
vivo tissue studies (22–24). The main obstacle in applying
MPM for tissue cytometry was the limited imaging speed of
conventional MPM systems, but this difficulty has been circumvented by the introduction of HSMPM. We developed
instruments that can assay the tissue volume of 1.45 mm3/h at
\1 lm3 resolution. When the tissue volume contains tightly
packed, stratified cellular layers, these instruments can sample
approximate 100–200 cells/s. Therefore, the 3D tissue cytome3D Tissue Cytometer Based on High-Speed MPM
ORIGINAL ARTICLE
have demonstrated that MPM is very suitable for in-vivo deep
tissue imaging. MPM has become an inevitable tool for biomedical studies, such as neuronal plasticity (26–28), angiogenesis in solid tumors (29), and noninvasive optical biopsy (30).
Figure 1. Schematic of the high-speed multiphoton microscope
based on a polygonal mirror scanner. Excitation beam is drawn
in red and emission beam is in green. Excitation light coming
from a titanium-sapphire laser (Mira 900, Coherent, Palo Alto,
CA) was reflected on the polygonal mirror (Lincoln laser, Phoenix, AZ) and was relayed to a galvanometric mirror scanner
(Cambridge technology, Cambridge, MA). The polygonal mirror
scanned the excitation beam along the fast axis of the sample
plane and the galvanometric mirror scanned along the slow
axis. After the scanners, the excitation beam was directed into
an upright microscope (Axioscope, Zeiss, Thornwood, NY) via a
modified epiluminescence light path. The excitation beam was
expanded by a lens pair and reflected on a dichroic mirror (short
pass, DC700SP, Chroma technology, Brattleboro, VT) toward an
objective. The objective focused excitation light into the specimen generating multiphoton excitation. The excitation focus
scanned in a raster pattern via the polygonal mirror and galvanometric scanner. Emission light was generated at the focus
and was collected by the objective. It was transmitted by the
dichroic mirror and collected by a photomultiplier tube (PMT,
R3896, Hamamatsu, Bridgewater, NJ). The signal from the PMT
was low-pass filtered and amplified via a transimpedance amplifier and measured by an analog-to-digital converter (AD9220EB,
Analog Device, Norwood, MA). A separate laser diode and
photodiode are used to sense the position of facets of the
polygon and to synchronize the other scanner and the detectors.
The schematic shows a single detector for simplicity, but two detectors with a dichroic mirror were used for two channel imaging.
[Color figure can be viewed in the online issue, which is available at
www.interscience.wiley.com.]
ter based on HSMPM can be used to screen a large cell population inside intact tissues where 1 million cells can be quantified within about 3 h.
MATERIALS AND METHODS
Multiphoton Microscopy
MPM is based on nonlinear excitation of fluorophores
(22,25) with two-photon excitation being the most important
and practical case. Nonlinear excitation results in fluorescence
generation only at the focus of excitation light. This localization of excitation volume further results in reduced specimen
photodamage and photobleaching. Most importantly, MPM
allows deeper imaging into tissue specimens than other microscopy technique such as CLSM, because of the reduced
scattering and absorption of excitation light and high collection efficiency of emission light in turbid tissues. Many studies
Cytometry Part A 71A: 9911002, 2007
3D Tissue Cytometry Based on HSMPM
We developed two HSMPM systems that could sustain a
frame rate of about 10 times faster than the conventional
MPM. These HSMPM systems have been reported elsewhere
(31,32) and are explained briefly here. The first method
adapted a polygonal mirror scanner to increase scanning speed
rather than galvanometric scanners, which have a limited
bandwidth (31). The polygonal mirror scanner was a light
weight metal cylinder with mirror facets machined around its
perimeter. Rotation of the cylinder swept the facets and generated line-scans along one axis in the sample plane. It achieved
a higher scanning speed, since the polygon rotated at a constant speed, rather than moving back-and-forth as the galvanometric scanner that requires repeated start-stop. The schematic of the current system is presented in Figure 1 achieving
a frame rate of 13 fps.
The second method increased imaging speed by parallelizing the multiphoton imaging process and it has been called
as multifocal multiphoton microscopy (MMM) (33,34). It
scans a specimen with multiple foci of the excitation light
instead of a single focus and collects emission light from the
multiple foci simultaneously with a spatially-resolved multichannel detector. The imaging speed increases proportional to
the number of excitation foci scanning together. In our implementation, we used 6 3 6 excitation foci for scanning and
multianode photomultiplier tubes (MAPMTs), which have 6
3 6 pixels, for simultaneous signal collection (32) (Fig. 2).
Each MAPMT pixel collected signal from the corresponding
excitation focus in the sample, and the signal collection was
synchronized with scanning of the excitation foci. This
MAPMT-based MMM design was to achieve equivalent imaging depth as the conventional MPM, with higher imaging
speed. The imaging speed increased approximately 30 times.
Since the imaging area was limited by the view field of
microscope objectives on the order of a few hundred micrometers on a side, an automated translation stage was needed
to image a large sample region. A computer-controlled specimen translation stage (H101, Prior Scientific, Rockland, MA)
was used to translate the specimen rapidly and precisely. The
stage was driven by step motors in three axes and its resolution
was 3 lm. The stage communicated with a control computer
via serial port. Its response time was approximately 0.5 s and
was limited by the computer communication speed and its
traveling distances. A significant improvement of the imaging
speed will be possible with a higher bandwidth stage.
The microscope field of view was an important factor
that determined instrument throughput. Since raster scanning
of the excitation light was always faster than the mechanical
translation of the specimen, the throughput was higher with
microscope objectives which had larger view fields. Therefore,
microscope objectives with lower magnifications were preferred. On the other hand, it was also critical to use the objec993
ORIGINAL ARTICLE
Figure 2. Schematic of the multifocal multiphoton microscope based on MAPMT. Excitation light from Titanium-Sapphire laser source
(Tsunami, Spectra-physics, Mountain View, CA) is expanded and illuminated a microlens array (1000-17-S-A, Adaptive Optics, Cambridge,
MA). The microlens array comprise of an array of small lenses of 1 mm 3 1 mm square in size and 17 mm in focal length. The microlens
array generates multiple foci at its focal plane and another lens (L1) collimated these beams from the foci creating 6 3 6 beamlets. Two excitation beam-lets are traced in the schematic. The beam-lets pass through a dichroic mirror and are reflected on x-y galvanometric mirror
scanners (6220, Cambridge Technology, Cambridge, MA). The beam-lets after the scanners are expanded by a combination of an eyepiece
(L2) and a tube lens (L3) to fill the back aperture of an objective. The objective focuses the excitation beam-lets to generate an array of excitation foci in the sample plane. The x-y scanners raster scan the array of excitation foci in sample plane. The excitation foci are separated
from each other by 45 lm in the current instrument. Emission beam-lets from the sample are collected by the objective and traced in green
toward the MAPMT. After reflected on the scanners, emission beamlets are de-scanned and become stationary regardless scanning. The
dichroic mirror reflects the beam-lets toward a MAPMT (H7546, Hamamatsu, Bridgewater, NJ). The MAPMT is a spatially resolved multichannel PMT with a rectilinearly divided anode and had 8 3 8 channels. We use 6 3 6 channels in the middle of the MAPMT for simultaneous signal collection. The outputs from the MAPMT channels are read by a custom built single photon counting circuitry that convert the
photon pulse signals from these channels into digital counts in parallel. The signals are transferred to an acquisition computer via a 32 bit
parallel bus. The schematic shows a single detector for simplicity, but two channel detections were implemented with two MAPMTs and a
dichroic mirror. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]
tives of high NAs to maximize collection of emission light. For
the image cytometer based on the HSMPM using polygonal
scanner, a 253 water immersion, NA 0.8 (LCI Plan-Neofluar
253, NA 0.8, Zeiss, Thornwood, NY) was used and the field
of view was approximately 200 lm 3 200 lm. For the one
based on the multifocal multiphoton microscope, a 203 water
immersion, NA 0.95 objective (XLUMPLFL20XW, Olympus,
Melville, NY) was used and the field of view was 270 lm 3
270 lm.
PROCEDURES AND RESULTS
Rare Cell Detection in 2D and 3D Cell Cultures
The performance of the 3D tissue cytometer was characterized by quantifying rare cell subpopulations in 2D and 3D
specimens in vitro. Two cell populations of different fluorescent labels were mixed at various ratios from 1/1 down to 1/
105. The cell specimens were prepared either as 2D tissue cultures on glass slides or as 3D in collagen scaffolds. The population ratios were measured by 3D HSMPM imaging and subsequent image analyses.
2D and 3D cell culture preparation. Preparation of the 2D
cultures was as described in the literature (35). In brief, mouse
3T3 fibroblast cells were transfected with plasmids containing
994
the coding region for enhanced yellow fluorescent protein
(EYFP) or enhanced cyan fluorescent protein (ECFP) (Clontech, Palo Alto, CA), each under the control of the pCX
(chicken beta actin) promoter (pCX Yellow and pCX Cyan,
respectively). These proteins were chosen with the consideration that both colors could be simultaneously excited at a single excitation wavelength and that the emission spectra of the
two fluorescent proteins could be easily distinguished. 3T3
cells were lipofected with the plasmids using LipofectAMINEPlus (Invitrogen, Carlsbad, CA). EYFP and ECFP cells were
grown separately. EYFP and ECFP cell stocks were trypsinized,
counted, and mixed at various ratios from 1/1 down to 1/105.
The mixed cells were cultured on glass slides for 12 h. The specimens were rinsed with PBS and then sealed with coverslips
before imaging. The cell specimens were imaged live.
For 3D culture, NIH 3T3 mouse fibroblast cells were
grown in collagen scaffolds to mimic tissue environment. Collagen scaffolds have been used extensively as a biomaterial in
tissue engineering (36). Collagen is a natural constituent of
ECM and the scaffolds have porous structures allowing cell
seeding and nutrient diffusion. The collagen scaffolds can
serve as analogs of ECM by providing physical support and
working as insoluble regulators of biological activities that
direct cellular processes such as migration, contraction, and
division. The preparation of the collagen-glycosaminoglycan
3D Tissue Cytometer Based on High-Speed MPM
ORIGINAL ARTICLE
Figure 3. Image analysis for 2D cell cultures. Two cell populations expressing different fluorescent proteins (EYFP, ECFP) were mixed at
various ratios. (a–c) are images of a cell mixture at 1/10 ratio (EYFP/ECFP) in the YC, CC and after the ratio processing respectively. EYFP
expressing cells appears only in YC and these cells appear in yellow color with high ratio values in the ratio image (c). (d) is a ratio image
of a 1/100 mixture ratio (EYFP/ECFP) specimen. Only two EYFP cells are detected. Images of other mixture ratios were analyzed in the
same way and the measured ratios are plotted against the expected ratios down to 1/105 in (e). This figure was published in a SPIE proceeding paper (Kim KH et. al, 2001; Proceedings of SPIE Vol. 4262.) and is republished here with permission of SPIE.
copolymer scaffolds using a freeze-drying method has been
previously described in detail (36–38). Two groups of cells
were prepared, one group labeled with green CellTracker
(C2925, Molecular probes, Eugene, OR) and the other group
not labeled, and both cell populations were nucleus-labeled
with Hoechst. The CellTracker label was chosen because it was
permanent in live cells through multiple cell divisions, and
also because both labels were excited efficiently with single
wavelength excitation light. In the labeling procedure, CellTracker dye was diluted to 10 lM in serum free medium. Culture medium in the culture dishes was replaced with dye solution and the dishes were incubated at 378C for 45 min. Then,
the dye solution was replaced with culture medium and the
dishes were incubated for another 30 min. Afterwards, both labeled and unlabeled cells were trypsinized from the dishes and
cell densities were measured using a hemocytometer (1483,
Hausser scientific, Horsham, PA). The stained cells were mixed
with unstained (control) cells at various ratios from 1/1 down to
1/105. The final concentration of the cell mixtures were adjusted
to 2 million cells/ml, and 25 ll of cell mixtures was seeded onto
each collagen scaffold of 15 mm 3 15 mm. Prior to seeding, collagen scaffolds were incubated in culture medium for 6 h so that
the medium was in equilibrium within the scaffolds. The collagen scaffolds were then dried out slightly for 0.5 h in order to
absorb the additional 25 ll of cell mixture solution. One hour
after seeding, culture medium was added around the collagen
scaffolds and followed by 12 h incubation allowing the seeded
cells to migrate inside scaffolds. The collagen scaffolds containing the cell mixtures were subsequently fixed with buffered zinc
formalin (Z-fix, Anatech LTD, Battle Creek, MI). The cells were
fixed in this experiment to prevent cell division that might occur
Cytometry Part A 71A: 9911002, 2007
at different rates among stained and un-stained cells. After fixation, all the cells were nucleus-labeled with Hoechst (33342, Molecular probes, Eugene, OR). The Hoechst stock (10 mg/ml) was
diluted 1/2,000 times in PBS to a final concentration of 5 lg/ml.
The Hoechst concentration was chosen to approximately equalize the fluorescent intensities of Hoechst and CellTracker at the
photodetectors. The scaffolds were incubated at 378C for 10 min
with Hoechst solution and washed with PBS. The scaffolds were
placed on glass slides and sealed with silicone isolators (JTR20A2-1.0, Grace Bio-Labs, Bend, OR).
Data acquisition and analysis. For the study of 2D specimens,
the 3D tissue cytometer based on the polygon scanning HSMPM
was used. Excitation wavelength was set at 910 nm to excite both
ECFP and EYFP. Although the specimens were monolayer cells,
we imaged 10 layers along axial direction with 2 lm step size.
Since the high NA objective had a narrow depth of focus on the
order of microns, imaging was very sensitive to leveling of the
specimen. Imaging a small volume stack ensured that the cell
layer was captured in focus. To achieve reasonable statistical accuracy, we aimed to detect at least 10 rare cells at each mixture ratio. Since these specimens were composed of monolayer cells, the
throughput rate was much lower as compared to that of 3D samples in which multiple cell layers were present.
To distinguish two emission colors, a dichroic mirror at
495 nm wavelength (495DCXR, Chroma Technology, Brattleboro, NH), a long pass filter at 500 nm (E500LP, Chroma
Technology, Brattleboro, NH), and a short pass filter at 490
nm (E490SP, Chroma Technology, Brattleboro, NH) were
used, dividing emission light into two channels, referred to as
the yellow channel (YC) for EYFP, and the cyan channel (CC)
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ORIGINAL ARTICLE
Figure 4. Image analysis for 3D cell cultures grown in collagen scaffolds. (a) is a 3D reconstructed image of a 1/1 ratio specimen. Cells with
Hoechst label only appear as blue nuclei and the other cells with both Hoechst and CellTracker labels appear as blue nuclei in green cell
bodies. A movie of 3D cell culture in collagen scaffold (1/10 mixture ratio) is available as supplementary material. (b) is a representative
scatter plot for 1:1 ratio specimens. Two subpopulations are clearly distinguished based on the IR. (c) is the result of cell counting analysis
of 3D cell cultures with various mixture ratios down to 1/105. Measured ratios (in x-axis) are plotted against the expected ratios (in y-axis).
for ECFP. Figures 3a and 3b are representative images of a
1/10 mixture ratio (EYFP/ECFP) in YC and CC, respectively.
Both cell types appeared in the YC image (Fig. 3a), since there
was significant bleed through of the ECFP emission spectrum
into YC. On the other hand, only ECFP expressing cells
appeared in the CC image (Fig. 3b). For clearer distinction, ratio images were constructed by combining two channel images
according to the following equation.
9
8
I ICC >
;
: YC
IR ¼ >
IYC þ ICC
ð1Þ
IR represents an intensity value of the ratio image pixels. ICC
and IYC denote pixel values of the CC and YC images, respectively. To remove background noise from the regions not containing cells, ratio images were masked with binary images
obtained by thresholding YC images. The threshold was chosen based on noise level of the images. Pixel values of the ratio
images ranged between 21 and 11 by the definition of Eq.
(1). Given the spectral properties of fluorescent proteins,
EYFP expressing cells had pixel values (IR) around 0.95 in the
ratio images, and ECFP expressing cells had values around 0.5.
These two differently labeled cell populations were easily distinguishable in the ratio images. Figure 3d is a ratio image of a
1/103 mixture ratio specimen where only 2 EYFP cells appear.
Specimens of other mixture ratios were imaged and analyzed
the same way and the results are shown in Figure 3e. Expected
ratios and measured ratios are in horizontal and vertical axes
respectively. The good correlation between the expected and
measured ratio can be seen from the measured slope (1.08)
and the goodness-of-fit (R2 5 0.9855) of the linear regression
line. The linearity of the relationship demonstrates that the
system can detect rare cell populations down to 1/105 ratio.
Similar characterization experiments were performed for the
3D cell blocks grown in collagen scaffolds. The specimens
were imaged with the 3D tissue cytometer based on the multifocal multiphoton microscope operating at 30 frames/s. The
excitation wavelength was 800 nm. Input power was 18 mW
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per excitation focus. The size of image was approximately 270
lm 3 270 lm containing 192 3 192 pixels. For 3D imaging,
50 layers were imaged with 2 lm depth increment. This depth
range covered about 3–4 layers of cells in the specimens. The
two cell populations were distinguished by two-color imaging:
a blue channel (BC) and a green channel (GC) utilizing a
dichroic mirror at 500 nm. No additional barrier filters for the
two channels were used to maximize emission signal collection. The acquired data was analyzed to count the number of
cells in each population. The cell counting algorithm was written in MATLAB (MathWorks, Natick, MA) and is explained in
the next section. Analyzed results are presented in Figure 4.
Figure 4a is a representative 3D image of a 1/1 ratio specimen.
One group of cells which was labeled only with Hoechst
showed blue emission in their nuclei. The other cells, which
were labeled also with CellTracker green, had both blue and
green emissions in their nuclei and green emission only in
their cytoplasms. We focused our analysis on the intensity ratio (IR) of green to BC (IR 5 IGC/IBC) in the nuclei to distinguish the two subpopulations. This method was very effective
for the following reasons. (1) The intensities at nuclei were
high in general so that it was easy to discriminate from background noise. (2) Nuclei were generally round and could be
readily discriminated from other objects based on shape analysis. (3) Nuclei were often well separated each other. Two channel images were analyzed to segment cell nuclei and to quantify each cell populations. Figure 4b is a cell scatter plot from a
specimen of 1/1 population ratio. It was based on average intensity of individual nuclei in two channels (BC intensity in
the horizontal axis and GC intensity in the vertical axis).
Marks in the plot represent individual cells counted in the
analysis. The IRs in the cells with only Hoechst label had a
characteristic ratio of 3/10. The IR values from the cells labeled
with both were all above the IR values from singly labeled cells,
although they varied appreciably. Therefore, a line with the
slope of 0.3 (IR0) from a point (100, 50) in (BC intensity, GC
intensity) distinguished the two cell populations. The analysis
of cell counting and discrimination was performed in the
3D Tissue Cytometer Based on High-Speed MPM
ORIGINAL ARTICLE
Figure 5. Cell counting analysis procedure for the 3D cell cultures grown in collagen scaffolds. Both BC and GC images (a,b) are converted
to BW nucleus images (c,d) based on thresholding. Erosion and dilation remove noise in the images (e,f). Convolution with a cone-shaped
kernel enables cell counting in both channel images (h,i). (j) is a 3D view of BC image after kernel convolution (h). [Color figure can be
viewed in the online issue, which is available at www.interscience.wiley.com.]
images from specimens of different population ratios from 1/1
down to 1/105 and the results are presented in Figure 4c. The
good correlation between the expected and measured ratio
can be seen from the measured slope (0.91) and the goodnessof-fit (R2 5 0.9892) of the linear regression line. However, one
can observe that better correlation is achieved for the 2D as
compared with the 3D case. Significant deviation from the linear regression line is seen for the lowest concentration data
point. This may be due to image analysis limitations. We kept
the cell density high in the scaffold to ensure fast imaging
speed; the image analysis method employed was less reliable
for closely spaced cells since it is based on global intensity
thresholding.
Segmentation and cell counting algorithm. While image
processing was required in all our experiments, we present a
detailed description of the algorithm used for segmenting in
the 3D cell blocks as an example. There have been sophisticated image processing algorithms developed for segmenting
cell nuclei in 3D within real tissue specimens (8,39–49). Here
Cytometry Part A 71A: 9911002, 2007
we use a simple segmentation method, because the cell density
is lower in our specimens and as a demonstration. Its procedure is presented as a flow chart in Figure 5.
Conversion of BC images in each layer to binary black and
white (BW) images by intensity thresholding: BC images
mapped spatial distribution of nuclei in both cell populations
with Hoechst label. Proper thresholding selected the pixels of
nuclei from background noise. The BC images were smoothed
by applying a median filter and then converted to BW images
(IMBW
BC) by intensity thresholding (Ithresh). The average intensity of the nuclei in the focal plane was approximately 160 and
the background noise was less than 10. Ithresh was set at 100 so
that only the nuclei in the focal plane were captured in the
BC-BW images (Ithresh 5 100).
BW
ðir; icÞ ¼ 1; IMBC ðir; icÞ Ithresh
IMBC
BW
IMBC
ðir; icÞ ¼ 0; IMBC ðir; icÞ < Ithresh
ð2Þ
where ir and ic were pixel indices along row and column directions in the image respectively.
997
ORIGINAL ARTICLE
Conversion of GC images to BW images: Valid pixels in the GC
images were selected using the binary images generated in step
(1). The IR of the valid pixels was calculated as the GC intensity to BC intensity. GC images were converted to BW images
(IMBW
GC) based on thresholding of intensity ratio IR0 (Fig. 5b).
These BW images (IMBW
GC) contained the nuclei of the cells
which were double labeled.
BW
ðir; icÞ ¼ 1
For pixels IMBC
IMGC ðir; icÞ
IR0
IMBC ðir; icÞ
IMGC ðir; icÞ
BW
< IR0
IMGC
ðir; icÞ ¼ 0;
IMBC ðir; icÞ
BW
IMGC
ðir; icÞ ¼ 1;
ð3Þ
BW
For pixels IMBC
ðir; icÞ ¼ 0
BW
ðir; icÞ ¼ 0
IMGC
Noise reduction with a series of erosion and dilation operations:
Erosion operation is a logical ‘‘AND’’ operation among a pixel
to be processed and its neighboring pixels. The value of the
processed pixel becomes 1 only if the values of all the neighboring pixels including the processed pixel are 1. Therefore,
erosion operation could remove small structures including
noise in the images. Dilation operation is a logical ‘‘OR’’
operation among the processed pixel and its neighboring pixels. To remove noise in BW images, erosion was performed
with a disk shape element resulting in shrinking the size of
nuclei and removing spurious pixels due to noise. A subsequent dilation operation with the same structure element was
performed to restore the size of the nuclei.
BW
BW
IMBC
¼ dilationðerosionðIMBC
ÞÞ
BW
BW
IMGC
¼ dilationðerosionðIMGC
ÞÞ
ð4Þ
Convolution with a cone shape kernel: To find the locations of
BW
nuclei, the BW images (IMBW
BC, IM GC) were convoluted with a
kernel (K) which had a cone shape intensity distribution with
its peak at the center. It was noted that this convolution operation tended to blur the images. Here we assumed that nuclei
were separated from each other. Also we chose the kernel size
smaller than the average nucleus size in order to reduce the
blurring effect.
BW
BW
IMBC
¼ IMBC
K
BW
BW
¼ IMGC
K
IMGC
ð5Þ
The locations of nuclei were identified by peak finding and masking: After convolution with the kernel image, the images had
peak intensities at the center locations of individual nuclei. A
maximum finding procedure was performed to find the highest peak location, and an area around the peak location was
masked with a masking disk. The area of masking disk was
initially set to be slightly bigger than the average nucleus size
and was adjusted proportional to the peak intensity levels.
Then the next maximum finding operation gave location of
the next highest peak (location of the next nucleus). The
operation of masking with a disk and maximum finding was
998
continued until the peak value became less than a chosen
threshold determined by image noise level.
The above operations (1–5) were performed in each layer
image. The continuity of nuclei in the images of several layers
was checked by measuring the separation distance among their
locations found in the 3D image stack. If two locations were
within a distance, they were assumed to be the same nucleus
and the plane containing the highest intensity value was considered to be the central location of nucleus in 3D.
Counting of both cell groups was performed for each
data set. Each 3D section of 270 lm 3 270 lm 3 100 lm contained approximately 50 cells on average.
Rare Structure Detection in Ex-Vivo Skin
A major strength of this 3D tissue cytometer was to quantify cellular and ECM states in tissue environments. As a demonstration, an ex-vivo human skin specimen was imaged
based on its autofluorescence and second harmonic generation
by the 3D tissue cytometer based on the polygon scanning
HSMPM. The objective used in this experiment was 403
(Fluar, NA 1.3, oil; Zeiss, Thornwood, NY). Since the field of
view of the microscope was limited to 120 lm 3 120 lm, the
large section imaging was performed by translating the specimen with the computer-controlled sample stage, once 3D imaging for each section was completed. Image sections (25 3 25)
were acquired and 50 layers were imaged from surface down
to 67 lm deep for each section. A stitching algorithm was
applied to combine these images stacks together to form montage images of 2.5 mm 3 2.5 mm. Figure 6a shows the montage image at 67 lm deep from the surface. This image shows
tissue structures of collagen, elastin fibers in the dermal layer
which are either autofluorescent or second-harmonic-active.
In addition to the ECM components, the 3D tissue cytometry
could further detect cell clusters at the base of skin hair follicles (Fig. 6b) and a sebaceous gland of the skin (Fig. 6c). This
study represents one of difficult imaging conditions that 3D
tissue cytometry may encounter due to the low endogenous
signal and the high turbidity of the tissue. We expect most
other tissue cytometric studies, which use exogenous fluorophores or fluorescent proteins, to be much easier.
Detection of a Rare Cell Population in Transgenic Mice
As a demonstration of rare cell detection in tissues, we
applied the 3D tissue cytometer to image tissues from the fluorescent yellow direct-repeat (FYDR) mice (50,51), a model
designed to study homologous mitotic recombination.
Homology directed repair (HDR) is a mechanism to repair
double strand breaks but misrepair of those breaks can promote cancer. HDR repairs damaged sequences by copying
sequence information from the sister chromatid or the homologous chromosome and it can be associated with exchange of
DNA sequences. Cells in this mouse model could express
EYFP if mitotic recombination events occurred at a specific
genetic locus. Spontaneous frequency of mitotic recombination was approximate 1/106 for these mice. Flow cytometry
was used to quantify rare fluorescent recombinant cells. Here,
3D Tissue Cytometer Based on High-Speed MPM
ORIGINAL ARTICLE
Figure 6. Large sectional image of ex-vivo
human skin in dermal layer. (a) is a montage
image of 2.5 mm 3 2.5 mm in size. It is constructed by combining 25 3 25 image sections, which were acquired with the HSMPM
based on the polygonal scanner. Collagen and
elastin fibers are shown based on autofluorescence and second harmonic generation. A cell
cluster at the bottom of a hair follicle (b) and a
sebaceous gland (c) appear in marked regions.
we attempted to apply the 3D tissue cytometer to detect these
recombinant cells in tissues.
In this experiment, tissue specimens were excised from
the pancreas of a selected FYDR mouse and were further
stained with Hoechst. After demarcating a region containing a
recombinant fluorescent focus using traditional epifluorescence microscopy, this region of the sample was then analyzed
using the multiphoton imaging described here. The objective
used was 403 water-immersion with 1.2 NA. Two detector
channels were implemented for dual color imaging using a
dichroic mirror at 500 nm. The size of each image stack was
approximately 110 lm 3 110 lm 3 100 lm. The wavelength
of excitation light was 890 nm and approximately 20 mW of
input power was used for imaging. The acquisition speed was
approximately 0.25 frames/s. We found that recombinant cells
expressing EYFP could be identified and a 3D image of the
recombinant cell and its tissue microenviroment is presented
in Figure 7 (note that it is unclear how many cells are present
within the fluorescent region; nuclei appear blue and cyto-
plasm of recombinant cells appears green in this image). This
is the first demonstration that rare recombinant cells can be
imaged in 3D within a tissue specimen. Further studies are
underway to develop methods to both detect and to image
recombinant cells in situ.
DISCUSSION
The 3D tissue cytometer based on HSMPM demonstrated
its ability to screen 2D and 3D tissue cultures containing up to
106 cells. The ex-vivo skin imaging showed that this instrument had a high sensitivity for tissue imaging based on endogenous fluorophores. The detection of rare recombinant cells
showed its potential for in-vivo quantification of rare genetic
events in animal models. Although the 3D tissue cytometer
based on HSMPM showed some promise, it had limitations
and rooms for improvement. HSMPM could excite two different color fluorophores simultaneously using a single excitation
wavelength. Since signals from both fluorophores could be
collected in the nonimaging configuration, two channel spec-
Figure 7. Images of recombinant
cell(s) found in a pancreatic tissue of a FYDR mouse. (a) is a representative 2D plane and (b) is a
3D reconstructed image from the
image stack. Nuclei which are
stained with Hoechst appear as
blue in the image and the recombinant cells express EYFP (green
color) in the cytoplasm.
Cytometry Part A 71A: 9911002, 2007
999
ORIGINAL ARTICLE
tral images were guaranteed to be co-registered. While this
was an advantage, this was also a limitation. Since the excitation wavelength was set to excite both fluorophores, it could
not be optimized to excite both efficiently, and the imaging
speed needed to be slowed down subsequently.
Performance of the 3D tissue cytometer based on the
multifocal multiphoton microscope can be significantly
enhanced by further instrument improvements. First its imaging speed can be increased. An important advantage of the
multiple focal approach is that it can achieve higher imaging
speed while keeping the pixel residence time the same as the
conventional MPM. High-speed single focus systems, such as
the polygon scanning system, necessarily have shorter pixel
residence time to achieve higher frame rate and lower SNR
subsequently. The SNR can be improved by increasing excitation power. However, fluorophore saturation and sample
damage threshold ultimately limit this approach, particularly
for fragile, live samples such as embryos. The multifocal
approach overcomes this limitation and makes high speed
3D tissue cytometry for weakly stained or autofluorescence
samples a viable possibility. Generation of multiple excitation
foci requires more excitation laser power, and currently imaging speed is limited by the maximum power of available
laser sources. With the availability of higher power lasers,
more excitation foci can be generated and, in principle, can
provide higher degree of parallelization and speed. Further,
we used a microlens array to split the excitation beam into
multiple beam-lets. The Gaussian intensity profile of the excitation beam led to uneven excitation intensities in the sample with the center foci producing higher signals than the
surrounding foci. Additionally, since the microlens array was
rectilinear and the excitation beam had a round shape so that
the portion of excitation beam illuminating the outside of
the square profile was not used. Therefore, there was a significantly waste of excitation light (about 30%). In the future,
beam splitting mechanisms, such as ones using multiple
beam splitters or ones using diffractive optical elements, can
be used to generate multiple beam-lets of uniform powers
and improve transmission efficiency (52). The MAPMTs
used in the current system had an acceptable quantum efficiency (QE) of 20%. This was because the photocathode material in MAPMTs was not optimal. Currently, GaAsP photocathode material can achieve higher QE up to 40% and may
be available for the future generations of MAPMT. The current 3D tissue cytometer based on multifocal multiphoton
microscope had only two channels. A new 3D tissue cytometer with multiple channels for spectral imaging can be easily
implemented by modifying the current system. Multiple
color imaging can be implemented in the same configuration
as the one demonstrated with a single excitation focus (53),
but by generating a 1D vector of multiple foci. An improved
system incorporating these improvements is under development in our laboratory.
In this work, we have demonstrated detection of rare cell
populations in 2D and 3D specimens based on automated cell
counting. Very simple image processing algorithms were used
in this pilot study for cell population analysis and these had
1000
limitations. For the 2D specimens, both groups of cells
expressed fluorescence in their cell bodies and it was difficult
to do segmentation in confluent specimens. Sparse specimens
were prepared for this 2D population analysis. For the 3D specimens, the processing algorithm, which was based on nucleus
segmentation, also worked only for the specimens where
nuclei were well separated each other with minimal variations
in their size and shape. Many other cytometric analyses based
on cellular morphology could be performed using data
acquired by the current system, although we have not demonstrated these capabilities here. We believe that the current limitation of high throughput 3D cytometry lies less in the instrument, that can now readily provide 3D image data with sufficient SNR at high speed, but more in the availability of
efficient computation approaches that can efficiently visualize,
segment, and quantify 3D image data sets in large scales. Currently, many advanced algorithms for the segmentation of cell
nuclei and cell bodies have been developed and offer the
potential for more accurate quantification (8,39–49). Some of
these algorithms further incorporate adaptive adjustment of
segmentation parameters for automatic segmentation process.
For future applications, segmentation of more geometrically
complex biological structures in tissues such as blood vessels
and neuronal dendrites will become necessary. Some works
have been done (41,54–58), and more efficient and novel imaging processing routines need to be developed. The high
throughput 3D tissue cytometry will also require high performance computational environments and resources which
can efficiently handle terabyte scale data set.
While the current version of 3D tissue cytometer could
detect rare recombinant cells in FYDR mouse tissues, our current instrument may not be ideal for this application. It is inefficient to image whole organs at high resolution for detecting
few recombinant cells. Since recombination events are rare and
fluorescent recombinant cells appear as readily distinguishable
discrete clusters throughout the tissue, an improved system
may be designed where these cell clusters can be first detected
by low resolution wide field imaging before subsequent multiphoton imaging to provide more informative 3D high resolution assays to determine the composition of these clusters.
3D tissue cytometry is a fairly new technology and its
potential for biomedical research is still far from fully
explored. In the short term, we have identified a number of
immediate applications. We are exploring the application of
3D tissue cytometry to study two aspects of cancer biology.
The first aspect is cancer progression. The process of how cancer cells extravasate through blood vessel walls and expand to
form metastatic cancer at distant organs is far from completely
understood. We are exploiting 3D tissue image cytometry to
explore questions such as the distribution of these cancer cells
on the organ level and the spatial relationship of these metastatic cancer cells with the organ vasculature. This technology
further allows us to study the time course of cancer cell clearance rate from the vascular system and provides temporal information on the growth rate of metastatic tumors. Other interesting areas of application include neural biology where we are
exploring the potential application of this technology to quan3D Tissue Cytometer Based on High-Speed MPM
ORIGINAL ARTICLE
tify neuronal connectivity as a function of animal development,
stem cell research where we are mapping stem cell distribution
in organs and examining the process of adult stem cell division
in animals, and tissue engineering where we are examining cellular differentiation and organ formation in situ.
Finally, with histological quantification, one may envision
that tissue physiology and pathology can be better understood
through modern genomic and proteomic analysis. Combining
high throughput 3D tissue image cytometry with the ability to
map gene and protein expression profiles, physiological models may be developed based on the underlying molecular and
cellular process. These physiological models may allow us to
understand how tissue structure is affected by genetic and protein expression variations. An early example of this type of
analysis may be found in areas such as cancer development.
Cancer is a disease which has a very strong spatial component
to its etiology (59). Cancer cells can invade the stroma of the
surrounding tissue and recruit nonmalignant cells to differentiate and support the growing tumor. The arrangement of
normal tissue boundaries becomes pathogenic as the expression profiles of the surrounding cells are altered by cell signaling from the malignant cells (60–62). The application of 3D
tissue image cytometry to simultaneously map tissue morphology, gene and protein expression patterns may allow us to
better understand this important pathological process.
CONCLUSIONS
The high-speed multiphoton microscope with a computer-controlled specimen stage was adapted as the 3D tissue
cytometer. It still retained the advantages of a standard multiphoton microscope allowing in vivo turbid tissue imaging
with subcellular resolution. Its throughput rate was up to
approximately 200 cells/s. Therefore, it can be used in the studies which need to investigate a large cell population in vivo.
Quantitative analysis of cell populations in 2D and 3D cultures demonstrated capability of this system for detecting rare
cell populations: cell mixtures of various ratios from 1/10 to 1/
105 could be accurately quantified. The wide area human skin
image showed its capability of screening highly turbid tissue
specimens at high speed based on endogenous fluorophores of
tissue components. Using the 3D tissue cytometer, rare recombinant cells were detected in tissue specimens from FYDR
mice which carry fluorescent markers for homologous mitotic
recombination. 3D tissue cytometry based on HSMPM allows
the study of cellular and tissue morphological and biological
states in situ with subcellular resolution. This technique will
significantly expand the scope of cytometric studies to the biomedical problems where cell-tissue interactions are critical.
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