quantitative in vivo microscopy: the return from the ‘omics’

Quantitative in vivo microscopy: the return from the ‘omics’Rodrigo Fernandez-Gonzalez1,2, Arrate Munoz–Barrutia3,Mary Helen Barcellos-Hoff1 and Carlos Ortiz-de-Solorzano3

The confluence of recent advances in microscopy

instrumentation and image analysis, coupled with the

widespread use of GFP-like proteins as reporters of gene

expression, has opened the door to high-throughput in vivo

studies that can provide the morphological and temporal

context to the biochemical pathways regulating cell function.

We are now able to quantify the concentration and three-

dimensional distribution of multiple spectrally resolved GFP-

tagged proteins. Using automatic segmentation and tracking

we can then measure the dynamics of the processes in which

these elements are involved. In this way, parallel studies are

feasible where multiple cell colonies treated with drugs or gene

expression repressors can be monitored and analyzed to study

the dynamics of relevant biological processes.

Addresses1 Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley,

California 94720, USA2 UC Berkeley/UC San Francisco Joint Graduate Group in

Bioengineering, Berkeley, California 94720, USA3 Center for Applied Medical Research (CIMA), University of Navarra,

Pıo XII 55, 31008 Pamplona, Spain

Corresponding author: Ortiz-de-Solorzano, Carlos

([email protected])

Current Opinion in Biotechnology 2006, 17:501–510

This review comes from a themed issue on

Tissue and cell engineering

Edited by James L Sherley

Available online 8th August 2006

0958-1669/$ – see front matter

# 2006 Elsevier Ltd. All rights reserved.

DOI 10.1016/j.copbio.2006.07.005

IntroductionThe end of the 20th century left us with the daunting task

of processing and analyzing the monumental amount of

information compiled by the human genome sequencing

initiatives [1,2]. This fabulous effort, the first of a kind of

what has been termed ‘big biology’, was closely followed

by other large-scale projects aimed at complementing the

‘static’ genomic picture with dynamic clues as to which

genes are expressed in specific cell types (transcrip-

tomics) [3,4] or how different forms of splicing and

post-translational modification give rise to the set of

proteins that determine cell and tissue phenotypes (pro-

teomics) [5,6]. Bionformatic tools are being developed

that use statistical approaches to dig into the genome,

transcriptome and proteome to find patterns of activity

www.sciencedirect.com

and to infer functional networks of interest involved in

both normal and pathological processes. Informative as

they are, these networks are just static pictures of com-

plex dynamic processes, obtained from disaggregated

cellular material. The challenge now is to provide the

morphological and temporal context of the processes that

have been described by the ‘omics’. In summary, a need

exists for a return to the native environment where these

processes occur (i.e. to the living cell). Furthermore, a

correct description of these processes must necessarily be

quantitative, as cell function derives from a delicate

homeostatic balance between all the elements involved

in each specific network.

A proper understanding of dynamic events might be best

achieved by observing them as they occur in vivo. Several

whole animal, non-invasive, low-resolution image mod-

alities exist (e.g. positron emission tomography, com-

puted tomography and magnetic resonance imaging)

that give live valuable information about organs or tissues,

but they lack enough resolving power to image the

intricacies of cellular machineries. Intravital microscopy

(IM) [7,8] is a promising modality that nears cellular

resolution within living animals. However, high cellular

resolution can only be obtained near the skin or the

surface of the organs. Moreover, IM poses important

technical challenges and is constrained by the difficulty

of tagging cellular elements or proteins in living animals.

Therefore, optical fluorescence microscopy of live cell

colonies remains the most informative tool for capturing

at high resolution the interactions between multiple

elements in intact cells. In this review, we will discuss

some recent developments that are pushing microscopy

beyond its traditional applications, by increasing its dis-

criminative capacity and quantitative nature. We will

concentrate on how these methods apply to live cell

imaging of in vitro cultured cells. Figure 1 schematically

describes most of the methods that will be discussed in

the following sections.

Seeing life in colourTo detect in vivo the existence or the activity of proteins or

nucleic acids, to measure the concentration of ions or to

define the extent of compartments within the cell, these

elements must be labelled with fluorescent reporters.

Except for easily accessible cell-surface proteins and lipids,

this calls for the internalization of a fluorescent tagging

molecule in the cells, which can be done through micro-

injection [9–11], viral or chemically mediated transfection

[12,13] or by spontaneous endocytosis [14,15]. Labour-

intensive and hazardous viral transfection is still the most


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http://dx.doi.org/10.1016/j.copbio.2006.07.005

502 Tissue and cell engineering

Figure 1

Tasks for the in vivo imaging and quantization of live cells. An example of in vivo imaging and tracking that summarizes most of the tasks

discussed in the text. (a) Protein tagging. Labeling a given protein (Protein i) with a fluorochrome (FPi) can be achieved through co-expression

of the gene coding the protein (cPi) and the DNA sequence coding the fluorescent protein (cFPi). The fusion of cPi and cFPi coexpresses a fully

functional Protein i with the fluorescent reporter FPi. The fusion construct can be inserted into the cellular genome by transfecting the cells using

viral vectors. The cellular cytoplasm and/or nucleus can also be labelled by internalization of a label that stains the cytosol or the chromatin

respectively, while not interfering with normal cellular function. (b) Acquisition and analysis. Using a fast confocal microscope sequences of

Current Opinion in Biotechnology 2006, 17:501–510 www.sciencedirect.com

Quantitative in vivo microscopy Fernandez-Gonzalez et al. 503

commonly used labelling method because of its high

efficiency and specificity. However, a great deal of research

is being carried out to develop polymeric substrates coated

with active biological surfaces that could improve the

efficiency and specificity of the internalization of carrier

vehicle particles in the cell or nucleus [16,17].

The direct labelling of proteins with fluorescent dyes has

important drawbacks: dilution of the dye upon cell divi-

sion, uneven cellular delivery and, more importantly,

interference of the dye with the activity of the protein.

Thus, labelling is today being replaced by co-expression

of the protein of interest with a tagging fluorescent

molecule, mediated by the endogenous promoter or by

an exogenous viral promoter. This is done by transfecting

cells with a DNA sequence coding the fluorescent mole-

cule fused to the coding sequence of the protein of

interest. The early sixties discovery of the green fluores-

cence protein (GFP) as the causing principle for the green

bioluminescence of the jellyfish Aequorea victoria [18], its

molecular cloning [19] and, more importantly, the demon-

stration of its in vivo functional expression in bacteria and

nematodes [20], opened the door to a generalized use of

GFP as a powerful fluorescent reporter in mammalian

cellular biology.

Because of the low brightness of the native GFP, and the

need for additional reporter colours in multiple labelling

experiments, new fluorescent variants have been purified

from other species or produced by directed evolution [21–

27]. A recent review [28��] provides assistance on how to

choose among the rainbow of available fluorescent proteins

(FPs). Five qualities are discussed that one should look for

when choosing a FP: brightness, photostability, robustness

against oligomerization, stability versus environmental

changes, and minimum crosstalk between fluorochrome

emissions. The authors offer their recommendations for

each spectral range and discuss in detail the benefits and

problems of the most common variants.

The possibility of combining different fluorescent repor-

ters is one of the strengths of fluorescence microscopy that

is now being applied to live-cell imaging. However,

spectral overlap between fluorochrome emissions limits

the number of molecules that can be simultaneously

detected. To overcome this, new algorithmic methods

can be used to reduce the crosstalk between fluorescent

emissions [29��]. The state-of-the-art approach combines

the acquisition of spectral scans with analysis methods

inherited from satellite imaging and remote sensing. To

obtain spectral scans one can use Fourier spectroscopy.

Namely, interferometric measurements of the samples

(Figure 1 Legend Continued) three-dimensional image stacks are acquired

elements being tracked. Spectal unmixing is then used to discriminate the e

labelled cellular structures. Finally, each spectral channel of the four-dimens

of each cellular element being tracked.


are obtained to generate an interferogram, from which the

spectral distribution of the image can be recovered using

the Fourier transform. The first interferometric device —

a combination of spectroscopy, CCD (charge-coupled

device) imaging and conventional microscopy — was

extensively used for chromosome karyotyping [30,31]

and, more recently, for multicolour analysis of immuno-

fluorescence-stained tissue samples [32]. Alternatively,

continuous narrow-band sequential spectral scans can be

obtained using computer programmed liquid crystal tun-

able filters (LCTF) or acousto-optic tunable filters

(AOTF) inserted at a barrier filter position in the micro-

scope [33,34]. These sequential spectral imagers are not

appropriate for four-dimensional analysis of fast cellular

processes, because sequential acquisition is slow and

causes high fluorophore bleaching rates. To address these

issues, Dickinson et al. [35] developed a spectral imager

based on a special grating as a dispersive spectral element,

coupled to a 32-channel photon-multiplier tube (PMT)

array. This allows fast parallel acquisition of up to 32

spectral channels, thus reducing acquisition time and

photobleaching [34,35].

Once the spectral scan is obtained, the common approach

for true-colour separation is to use a constrained least-

squares algorithm to estimate the proportion of each pure

fluorochrome in every pixel of the image [29��,36,37].

This is the method of choice in time-lapse imaging (for

co-localization studies and to resolve fast processes

[29��,38,39]) and in FRET microscopy (to separate fluor-

escence of FRET pairs [40�,41,42,43�]). More general,

blind linear unmixing methods can be very helpful when

resolving individual components of unknown fluorescent

signals or when the mixed fluorochrome spectra, or the

levels of cross-talk between them, are expected to change

during the acquisition. As an example, Chorvat et al. [44]

successfully applied principal component analysis to

identify major spectral components in images of highly

autofluorescent cardiomyocytes. Other methods based on

independent component analysis have also been pro-

posed for blind spectral unmixing in other imaging areas

that could also benefit the spectral separation of fluor-

escent emissions [45,46].

Counting, not just seeingMeasuring absolute protein concentrations of FP–protein

fusions in vivo is challenging even in delicately controlled

experiments, as the actual number of fluorescent mole-

cules per protein molecule, as well as their photon flux

and extinction coefficients, vary with changes in the

chemical environment. External calibration strategies

have been proposed; for example, by comparing the

at predefined time points to create four-dimensional movies of the

missions of the different fluorochromes used as reporters for the

ional sequence is segmented separately to detect the dynamics



intensity of the fluorescence captured from the cells with

standard fluorescence curves generated using dilution

series of recombinant versions of the same FP–protein

fusion [47]. When counting the number of cell-surface

proteins, a similar strategy would be to compare the

fluorescence intensities measured on the cell surface with

those measured on transparent beads coated with known

surface densities of the fusion protein being quantified

[48]. Both these methods use the unstable underlying

condition that the properties of the fluorophores are

insensitive to conformational differences between the

native and recombinant protein or to changes in the

environmental conditions. This concern can be addressed

using internal controls. For example, Dundr et al. [49]

used medium-seeded GFP-tagged rotavirus-derived

virus-like particles (VLP) to calibrate other GFP-tagged

proteins. As a proof-of-principle, they added GFP–VLPs

to culture medium of cells expressing a GFP–fusion

construct of pol I. From the known number of molecules

per GFP–VLP and their measured average fluorescence

intensity, the authors estimated the number of molecules

of the target protein imaged in a different part of the

sample. This approach is hampered by the different

chemical environments inside and outside the cells that

could affect the levels of autofluorescence, the folding

properties of the fluorochromes, and so on. It could also be

argued that the excitation and emission properties of the

fluorochromes might be different when fused to VLP

than when fused to other proteins. To address this issue,

Wu et al. [50��] used an external calibration approach,

which cleverly demonstrated its accuracy and general-

ization. The authors inserted the coding sequence for

monomeric yellow fluorescent protein (mYFP) into the

yeast genome, at the N or C termini of 27 proteins. They

then calculated the fluorescence per fusion protein in

their samples as the per-cell ratio between the average

fluorescence intensity measured by microscopy and the

average number of mYFP fusion proteins calculated by

quantitative immunoblotting, normalized using known

loading sequences of a control protein–mYFP fusion.

The ratios for all 27 fusion proteins aligned (r = 0.99),

showing a linear relationship between fluorescence inten-

sity and average fusion protein concentration. This linear

calibration table was then used to estimate concentrations

of other mYFP-tagged proteins.

Seeing in three and four dimensionsLife happens in four dimensions. Therefore, interpreting a

dynamic process is greatly facilitated by capturing all four

dimensions. Confocal microscopy [51] revolutionized ima-

ging by increasing the limited depth of focus inherent to

conventional optical systems. By using pinhole restriction,

light is limited to that coming from the focal plane, allowing

the acquisition of sharp in-focus images at sequential

depths that can be combined in three-dimensional image

stacks. Traditional confocal systems rely on slow sequen-

tial (pixel by pixel) laser scanning of the sample in three


dimensions. This compromises their ability to image fast

cellular processes. Furthermore, these systems cause high

photobleaching and phototoxicity because they illuminate

the cells with a focalized laser beam. A multiphoton

microscope produces pinhole-less confocality using addi-

tive confluence of photons at the focal plane of the micro-

scope. This eliminates bleaching of out of focus planes,

increasing detection sensitivity. However, the multipho-

ton microscope is still a sequential scanning device that can

only be used to image slow processes.

New, faster hardware variants have been developed that

parallelize pinhole-based confocal image acquisition by

either using line detectors or a spinning (Nipkow) disk

[52] with a spiral pattern of pinholes that collect and

project light onto a rectangular CCD. These systems

sacrifice axial resolution because of cross-talk between

neighbouring pinholes, but are swift enough to image

very fast cellular processes. A parallelized multiphoton

microscope has also been proposed [53] that breaks the

traditional trade-off between axial sectioning and paral-

lelization by using time-multiplexing, thus avoiding

simultaneous illumination of — and therefore cross-talk

between — neighbouring pinholes. The result is better

axial resolution and less phototoxicity than Nipkow disk

based systems. Their high cost is a clear disadvantage, as

they use expensive pulsed (Ti:sapphire) lasers compared

with the more affordable continuous wave Argon-Krypton

lasers used in Nipkow-based systems.

Volumetric renderings of four-dimensional stacks can

produce impressive visual reconstructions of live cellular

structures. These can be used for the qualitative assess-

ment of cellular processes. However, being quantitative

calls for the segmentation of nuclei or cells from a nuclear

or cell membrane marker to delineate their surfaces

before quantification and tracking of other elements

within them. A great deal of research is being carried

out on three-dimensional cellular segmentation [54–59],

although most existing methods are computationally

expensive, rendering them appropriate only for slow invivo processes or for off-line analysis. Faster, fully auto-

mated algorithms are required for real-time quantification

of four-dimensional cellular events in vivo.

Once the cells and/or elements of interest have been

correctly segmented, cell or organelle dynamics can be

measured using algorithmic tracking. Several commonly

used tracking algorithms are described and compared by

Cheezum an colleagues [60]. Figure 2 illustrates a simple

example of tracking of mitochondria in neuronal cells.

Most of these methods use either local spatial, static or

temporal information. More sophisticated schemes that

use both space and time, and which integrate high-level

knowledge of the dynamics of the populations being

studied, might produce more relevant results that those

currently obtained. Along these lines, Dufour et al. [61��]



Figure 2

Tracking mitochondria in plated neurons. (a,b) First and last time point of a short (5 min) two-dimensional time-lapse movie capture from plated

neurons (in green) labelled with a mitochondrial marker (in red). (c) Reconstruction of the mitochondrial paths (yellow tubes) as seen from the

three-dimensional segmentation of a stack made using all the images of the time-lapse sequence. The image shows the reconstructed paths

intersecting one of the images of the time-lapse series. (d) Same as in (c), superimposed with volume rendering of the neuron to allow better

visual localization of the mitochondria. (e,f) Examples of mitochondrial tracks that can be analyzed to extract the dynamics (speed, trajectory)

of the organelle.

describe an intelligent algorithmic integration of segmen-

tation and tracking consisting of a full four-dimensional

segmentation plus tracking algorithm that uses active

contours to detect cells and follow their evolution with

time. There are many other considerations to be made, for

which we refer the interested reader to an excellent

review by Kozubek et al. [62] that discusses both hardware

and software options for fully automated four-dimen-

sional experiments.


Some interesting four-dimensional application studies

have been recently published that show as a proof-of-

principle what could be done using fully integrated

approaches. For instance, Moleenar et al. [15] measured

the dynamics of telomeres in human osteosarcoma cells

and Gerlich et al. [63] used a fully automated four-dimen-

sional approach to track the positioning of chromosomes

during mitosis in normal rat kidney cells. The limit to the

widespread application these methods is the complexity



of acquiring and handling vast amounts of image data, as

discussed below.

Making sense of what you seeSingle quantitative cell measurements, either static

(three-dimensional) or dynamic (four-dimensional) are

not very informative. To produce more general informa-

tion, patterns of spatial or temporal data must be extracted

from multiple cells that can, in turn, be used to identify

subsets of proteins or cells in heterogeneous populations.

The goal is to incorporate the behavior of a single element

or of several measured elements into that of complex,

heterogeneous populations of cells as they occur in living

organisms.

Protein location patterns have been classified on the basis

of their association with an organelle, such as mitochon-

dria or Golgi, or on the basis of their subcellular

Figure 3

High-throughput screening of cell culture using siRNAs. Workflow of high-th

of the steps, including time lines of each process based on one live-cell mic

et al. [75] with permission.).


distribution [64��,65,66]. Furthermore, mixed patterns

(e.g. proteins that are present in multiple organelles)

can be decomposed into their fundamental constituents,

and the fraction of the protein in each of those constitu-

ents can be automatically monitored under different

conditions [67]. Quantification of cellular properties can

also lead to tissue organization and architecture analysis

and is especially useful to study changes in tissue struc-

ture that occur under different conditions. Although this

review is mainly focused on the analysis of cultured cells,

we will briefly discuss several methods for describing

tissue organization applied to ex vivo samples that could

be extended to the study of cellular organization in vitrofor cell and tissue engineering. For example, in Droso-phila, the frequency of non-hexagonal cells, as well as the

distribution of cellular edge orientations, were shown to

be powerful indicators of the increasing disorder typical of

embryonic development [68�].

roughput RNA interference screening by time-lapse imaging. Flowchart

roarray containing 384 siRNAs. (Figure reproduced from Neumann



Tissue or colony structure can also be analyzed on the

basis of the spatial distribution of their constituent popu-

lations. This can be done by studying the density of cell

populations, as defined by areas of a Voronoi diagram.

(A Voronoi diagram is a decomposition of a metric space,

based on distances between points or particles that belong

to the space. Namely, the lines (in a two-dimensional

space) formed by points equidistant to each pair of

neighbouring points define a polygonal tessellation of

the entire space known as Voronoi diagram or Voronoi

tessellation.) This quantitative method has been used to

demonstrate differences in the distribution of certain

neuronal populations in the brains of wild-type and

mutant mice [69]. In the rat retina [70��], the distribution

of Voronoi areas was compared at local and global levels,

to demonstrate a regular distribution of certain cells

during retinal development. Autocorrelation and cross-

correlation of nearest neighbor distances or distance-to-

neighbor profiles have also been used to investigate the

organization of cell populations in the zebrafish retina

[71,72]. Unfortunately, this approach is not directly

applicable to tissues with more complex topology, as it

assumes a homogeneous cell density in each of the images

under study. To overcome this limitation, we have devel-

oped a method to study the spatial distribution of several

cell populations based on a graph model that embeds

tissue topology [73��]. Using this graph model, a multi-

scale density function is evaluated and the statistical

significance of patterns within a tissue is assessed, locally

and globally. Graphs have also been used to measure the

amount of tissue disorganization in different neoplastic

lesions [74]. Cell centroids (i.e. the cell centre of mass)

were used to build four different types of graphs. Multiple

properties were then measured on these graphs

(edge length, number of edges) and shown to present

statistically significant differences between normal and

malignant samples.

Any of the above-mentioned methods can be used to

characterize global changes in colonies and tissues in

response to different treatments. Such changes might

otherwise be missed using local measurements or global

methods based on averaging of local measurements across

the samples.

Handling the dataAll the tasks described so far can be integrated under

unified automated platforms, allowing high-throughput

studies where hundreds to thousands of differently trea-

ted colonies are periodically imaged and comparatively

analyzed. For instance, this approach is being used by a

European Union funded consortium [75��] to look at

changes in the dynamics of cell division using small

interfering RNAs (siRNAs) that block the transcription

of 49 different genes (see Figure 3 for a brief graphical

explanation of the protocol). These robotic-driven

experiments — the dynamic equivalent of large-scale


microarray-based genome or gene expression studies —

generate an overwhelming amount of image data (tens to

hundreds of Gigabytes) per experiment. This poses

important requirements for data storage and processing

that may be better achieved using distributed databases

and computing across high-speed networks than in single

laboratory settings.

Finally, sharing and validation of the data by the scientific

community necessitates the development of standardized

control and normalization strategies and the integration of

the results in existing distributed protein databases. In

turn, this can only be achieved with proper tools for

navigating and visualizing the data and with tools for

automatically extracting and linking relevant information.

All these areas are still underdeveloped.

ConclusionsImaging and quantifying location, functional status and

abundance in living cells is the key to unravelling the

complex qualitative descriptions of biological processes

provided by large-scale genomic and proteomic

approaches. This puts forth several challenges that are

being addressed by many groups. We have discussed here

some of those challenges, namely: the spectral resolution

of fluorochrome tags that increase the number of ele-

ments being monitored; calibration and normalization

strategies that help absolute protein concentrations to

be estimated from fluorescence measurements; hardware

and software approaches suitable for in vivo tracking of

three-dimensional spaces; and statistical methods for the

characterization of heterogeneous events.

The combination of imaging with the other ‘omics’ can

help interpretation and prediction of the behaviour of

proteins, organelles and cells that will then need to be

confirmed in living tissues and animals. Important work

must be done to integrate all these elements under

unified microscopy platforms and to streamline and

homogenize sample preparation, data acquisition and

data handling to permit high-throughput analysis.

AcknowledgementsRFG is being supported by a predoctoral fellowship from the Departmentof Defense Breast Cancer Research Program (DAMD 17–03–1–0594) and agrant from the National Institute of Environmental Health Sciences(U01ES012801). COS is currently supported by the Spanish Ministry ofScience and Education (MCYT TEC2005–04732), the EU Marie CurieProgram (MIRG–CT–2005–028342) and a Ramon y Cajal Fellowship.

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Reports the delivery of multiple (up to four), multicolour fluorescentprotein constructs and uses four-dimensional, multispectral time-lapseconfocal imaging of cell movements in living chick embryos.

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The authors provide a theoretical analysis on how spectral fingerprintingcan be used to separate the fluorescence of FRET pairs from thatoriginating from unpaired donors and acceptors and how to selectimaging parameters to optimize the signal-to-noise ratio of the estimates.

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A comprehensive attempt to calculate absolute protein concentrations inlive cells using external controls. The authors showed consistent linearrelationships between the intensity of 27 fluorescent protein fusionsmeasured using microscopy and the intensity measured using quantita-tive immunoblotting. From the corresponding known immunoblottingconcentrations, they created a calibration table than can be used tocalculate concentration of other fused proteins.

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First integrated solution for segmenting and tracking cells in three dimen-sions. The authors of this elegant study use multiple implicit activesurfaces to segment and track cells separately, and impose a constraintto avoid merging of neighboring cells.

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Tissue disorder was quantified in Drosophila embryos at different stagesof development using 2D-confocal microscopy images. The cell classi-fication method (automatic or manual) was not mentioned, although theimages shown are amenable to automatic watershed segmentation andclassification based on the number of neighbors.

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Palanza L, Jhaveri S, Donati S, Nuzzi R, Vercelli A: Quantitativespatial analysis of the distribution of NADPH-diaphorase-positive neurons in the developing and mature rat retina.Brain Res Bull 2005, 65:349-360.

This very quantitative approach was also used to validate a computa-tional model of the retina that allowed the authors to gain further insightinto the patterning mechanism.



71. Cameron DA, Carney LH: Cellular patterns in the innerretina of adult zebrafish: quantitative analyses and acomputational model of their formation. J Comp Neurol 2004,471:11-25.

72. Tyler MJ, Carney LH, Cameron DA: Control of cellularpattern formation in the vertebrate inner retina byhomotypic regulation of cell-fate decisions. J Neurosci 2005,25:4565-4576.

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Fernandez-Gonzalez R, Barcellos-Hoff MH, Ortiz de Solorzano C:A tool for the quantitative spatial analysis of complex cellularsystems. IEEE Trans Image Process 2005, 14:1300-1313.

The graph used here (a refined relative neighborhood graph) has the cellnuclei as nodes. The nodes corresponding to neighbor nuclei are auto-matically detected and linked by edges. This graph is then used todetermine the neighborhood between cells and to measure distancesbetween them with a normalized distance unit: the graph edge. Statistical


significance is assessed by comparison to Monte Carlo simulations ofrandom (in the single population case) or independent (for multiplepopulations) distributions.

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75.��

Neumann B, Held M, Liebel U, Erfle H, Rogers P, Pepperkok R,Ellenberg J: High-throughput RNAi screening by time-lapseimaging of live human cells. Nat Methods 2006, 3:385-390.

A beautiful example of integration of automated acquisition and trackingfor high-throughput parallel in vivo genomic analysis. The authors spotted49 different siRNAs in live-cell chambers and periodically capturedimages of each of the cell cultures. They then used image analysis toolsto segment and classify nuclei in each of the sequences and quantifiedthe rate of nuclear division and apoptosis. In this way they could com-paratively assess the role of those 49 genes in the completion anddynamics of cell mitosis.


quantitative in vivo microscopy: the return from the ‘omics’

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