Reconstructing protein networks of epithelial differentiation from histological sections

Vol. 23 no. 23 2007, pages 3200–3208BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm504

Systems biology

Reconstructing protein networks of epithelial differentiation

from histological sectionsNiels Grabe1,2,*, Thora Pommerencke1,2, Thorsten Steinberg1,3, Hartmut Dickhaus1,2 andPascal Tomakidi1,31Hamamatsu Tissue Imaging and Analysis (TIGA) Center, BIOQUANT, University Heidelberg, BQ0010,Im Neuenheimer Feld 267, 2Institute for Medical Biometry and Informatics, University Hospital Heidelberg,Im Neuenheimer Feld 305 and 3Department of Orthodontics and Dentofacial Orthopedics, Dental School Universityof Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Received on July 9, 2007; revised on September 14, 2007; accepted on October 3, 2007

Associate Editor: Limsoon Wong

ABSTRACT

Motivation: For systems biology of complex stratified epithelia like

human epidermis, it will be of particular importance to reconstruct

the spatiotemporal gene and protein networks regulating keratino-

cyte differentiation and homeostasis.

Results: Inside the epidermis, the differentiation state of individual

keratinocytes is correlated with their respective distance from the

connective tissue. We here present a novel method to profile this

correlation for multiple epithelial protein biomarkers in the form of

quantitative spatial profiles. Profiles were computed by applying

image processing algorithms to histological sections stained with

tri-color indirect immunofluorescence. From the quantitative spatial

profiles, reflecting the spatiotemporal changes of protein expression

during cellular differentiation, graphs of protein networks were

reconstructed.

Conclusion: Spatiotemporal networks can be used as a means for

comparing and interpreting quantitative spatial protein expression

profiles obtained from different tissue samples. In combination with

automated microscopes, our new method supports the large-scale

systems biological analysis of stratified epithelial tissues.

Contact: [email protected]

1 INTRODUCTION

Systems biology aims at the analysis and in silico modeling and

simulation of large and complex biological systems. Currently,

most publications in this field are focused on the analysis and

modeling of intracellular networks (Hahn and Weinberg, 2002;

Ideker et al., 2001; Kitano, 2002), or are based on static tissue

structures (Noble, 2004). For systems biology of complex

stratified epithelia, it will be of particular importance to

reconstruct the spatiotemporal gene and protein networks

regulating cellular differentiation, a pivotal cornerstone of

epithelial homeostasis (Grabe and Neuber, 2005, 2007). Hence,

the expression changes of multiple epithelial biomarkers have

to be profiled in parallel during cell migration through the

tissue and furthermore, networks of directly or indirectly

interacting proteins (protein networks) have to be deduced

from these profiles. The prominent omics-technologies like

microarrays (Cole et al., 2001; Koria et al., 2003) and 2D/1MS

proteomics (Huang et al., 2003) have been applied for example

on skin, but employ homogenized, structurally destroyed tissue.

Therefore, these methods are only hardly applicable for

this purpose. Tissue profiling, using mass spectrometry

(Caldwell and Caprioli, 2005) or laser capture microdissection

(Emmert-Buck et al., 1996) represents considerable solutions.

However, these methods are cost-intensive, and can currently

only provide a limited spatial resolution in comparison to

immunohistology. Moreover, they may fail in developing a

profile for a certain biomarker of interest. Instead, antibody-

based proteomics aims at the systematic generation and use of

protein-specific antibodies to functionally explore the proteome

(Uhlen and Ponten, 2005). Based on this spirit, we set out to

develop a method for the quantitative spatial analysis of

immunofluorescence-stained stratified epithelial tissue sections.

With the advent of fully automatic microscopic scanning robots,

it will be possible to scan fluorescence-stained tissue sections in

high throughput. A method which, based on such microscopic

high-throughput data of tissue slides can reconstruct spatio-

temporal networks of differentiation from histological sections

could therefore bring important insights into the dynamics of

differentiation and homeostasis of epithelial tissues.At the example of the human epidermis, we here show how

such a method can be established in principle. We quantita-

tively study the differentiation of five key proteins of epidermal

homeostasis: integrin �6, desmoplakin, involucrin, filaggrin and

keratin K1/10. The functional of individual proteins in

epidermal homeostasis is explained in the following.The epidermis as a squamous, keratinized epithelium consists

of basal, spinous, granular and cornified cell layers (Candi

et al., 2005). In this strata model, each layer is defined by

position, morphology, polarity and stage of differentiation of

keratinocytes. Being basal lamina-adhesion sites, hemidesmo-

somes are linked to the intermediate filament system, and

contain a specific set of proteins including �6�4 integrin.

Suprabasal spinous cells are more oval than the flattened*To whom correspondence should be addressed.

3200 � The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]

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spinous cells located adjacent to the granular cell zone, which

are joined by the name-giving desmosomes (Kottke et al.,

2006). Tethering of the desmosomes to the cells’ cytoplasmic

intermediate filaments is provided by desmoplakin (DPK).

Moreover, the spinous differentiation stage of keratinocytes

following the basal stage is characterized by an induction of the

keratin K1/K10 couple (Lechler and Fuchs, 2005). Involucrin

(Rice and Green, 1979) is cross-linked early in cornified

envelope formation, and forms a scaffold for incorporation of

other precursors. Its expression is initiated in the early spinous

layer, and maintained in the granular layer (Eckert et al., 2002).

During terminal differentiation of keratinocytes, profilaggrin is

cleaved to yield filaggrin, which temporally coincides with the

transition of the nucleated granular cell to the anucleated

cornified cell. The final layer of the epidermis is the stratum

corneum consisting of terminally differentiated keratinocytes

embedded in an extracellular lipid matrix.

With respect to squamous epithelia, the existence of a spatial

and functional heterogeneity of biomarker expression between

the horizontally arranged layers is of particular importance.

However, up till now, biomarker expression during keratino-

cyte differentiation has been rather described in a qualitative

fashion. For example with the human protein atlas (Uhlen and

Ponten, 2005; Uhlen et al., 2005), such an endeavor is pursued

in a large scale. Also binary vectors of protein expression have

been successfully measured (Schubert, 2006; Schubert et al.,

2006). Unfortunately, the traditional qualitative mode of

description is lacking a quantification of the spatial expression

of a biomarker and a correlation of this quantification with the

time-dependent dynamics of progressive keratinocyte differ-

entiation as well. For applying the visionary concepts of

systems biology onto this spatiotemporal expression of multiple

biomarkers, which as a whole constitute epithelial organization,

an extension of the qualitative description of keratinocyte

biomarkers into a quantitative mode is indispensable.

Observing the change of protein expression in relation to the

distance to the connective tissue yields profiles (or gradients) of

biomarker expressions. The present study describes a new

method to generate such profiles in a quantitative way

(Quantitative Spatial Profiles or QSPs) for certain epithelial

biomarkers. We conceive such profiles as time-series data of

cellular differentiation from which we then can deduce the

topology of a protein network. The resulting outline of a

network should not be understood as directly describing

functional regulation like it occurs when a transcription factor

interacts with a promoter. Instead, it is a general description of

the spatiotemporal coregulation of the studied protein biomar-

kers during differentiation. Such spatiotemporal networks,

describing the changes of protein expressions during progressive

keratinocyte maturation, render a framework for the future in

silico modeling and simulation of epithelial differentiation.

2 METHODS

2.1 Generation of triple-stained tissue sections by

indirect immunofluorescence (IIF)

Three human epidermal tissue samples derived from different head and

neck regions were obtained from healthy patients with their informed

consent according to the Helsinki Declaration, and the protocol was

approved by the institutional ethics committee. For the generation of

the triple stains, 10�m serial frozen sections were mounted on glass

slides (Histobond, Marienfeld, Germany), preceding freezing of the

epidermal tissue in liquid nitrogen vapor. About five sections were used

for each of the five protein biomarkers in each of the three tissues,

thereby leading to a total of 75 sections. To facilitate computational

image analysis, a triple staining, based on double IIF was applied to

each tissue section. For each biomarker studied in each tissue sample,

an exemplary triple-stained tissue section is given in Figure 1.

Triple staining comprises of staining the according marker together

with extracellular matrix collagen type-I, as well as a counterstain of cell

nuclei (DAPI). Collagen type-I marks the dermal connective tissue for

the later applied computational image analysis. IIF was performed

according to previous protocols (Tomakidi et al., 2003) with foregoing

optimization of the working dilutions (wd) of primary and secondary

antibodies applied to the skin specimens (antibody working dilution

and manufacturer are described below). Briefly, after fixation with

methanol and acetone, slides were incubated with respective primary

antibodies overnight at room temperature in a humid chamber. Then,

slides were rinsed in PBS for three times and incubated with

fluorochrome-conjugated secondary antibodies for 1 h, followed by

three washes in PBS.

For cell nuclei labeling, specimens were incubated with 1�g/ml DAPI

for 20min and supplied by three final PBS washes before embedding in

mounting medium (Linaris, Wertheim, Germany). For the biomarker

stainings, primary monoclonal antibodies were the following: integrin

�6 (wd 1:12.5, Chemicon, Hofheim/Taunus, Germany), desmoplakin

(wd 1:10, Progen, Heidelberg, Germany), keratin 1/10 (wd 1:40,

Progen), involucrin (wd 1:50, abcam, Hiddenhausen, Germany) and

filaggrin (wd 1:20, TeBu-Bio, Frankfurt, Germany). For double IIF,

each of these markers was incubated together with a rabbit polyclonal

collagen type-I antibody (1:50, Biodesign/Dunn, Asbach, Germany).

Secondary antibodies were also applied to the sections as a cocktail,

consisting of goat anti-mouse Alexa Fluor� 488 (1:50) and goat anti-

rabbit Alexa Fluor� 594 (1:100, both MoBiTec, Gottingen, Germany).

All antibodies were adjusted to their final working dilution in PBS

containing 0.5% Tween 20, and as further additives 0.5% BSA and

0.02% sodium acid.

2.2 Imaging of triple-stained tissue sections

Triple-stained multifluorescence slides were scanned using a Nikon

Eclipse 90i upright automated microscope (Nikon GmbH, Dusseldorf)

equipped with a Nikon DS-1QM high-resolution CCD camera.

By applying the ‘large image’ function of the NIC-elements software

controlling the microscope, several large stitched images of individual

microscopic images with 10� objective were acquired from each tissue

section. For each image, distinct monochromatic pictures of the red,

blue, green and phase contrast channel were taken. For further image

analysis, own software was developed using MATLAB 7.1 including the

image processing toolbox. In all large images, the epithelium was

segmented computationally using nuclear DAPI in conjunction with the

collagen staining and the phase contrast. Each large image was

then split computationally into a set of subimages where each subimage

had approximately equal horizontal width. Figure 2a shows a

composite example of the three monochromatic large images taken

from an epidermal tissue section. In Figure 2b, the according phase

contrast image shows the upper tissue boundary. By the special

fluorescent staining used, the individual components of the skin

(connective tissue, epithelium and stratum corneum) are labeled in

different colors and could thus be captured in different monochromatic

channels.

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2.3 Computation of quantitative spatial profiles (QSPs)

from each subimage

The following algorithm was applied to each subimage to determine the

differentiation profiles. The individual concepts of the algorithm are

depicted in Figure 3a, while the calculation of the distance-based

quantifications of the degree of differentiation of a cell inside the

epithelium is explained in Figure 3b. A graphical overview of the QSP

computation is given in Figure 5a.

Detecting the epithelium E: the upper tissue border (B), characterized

by the stratum corneum, was detected using the phase-contrast image

PC. The estimated basal lamina (L) of the tissue sections was found by

the dense band of basal keratinocytes visible in the blue DAPI channel.

As an estimate of the epithelial tissue, an initial mask (E) was

determined as the region between L and B and having a low collagen

staining (indicating connective tissue CT) in the red channel near L.

To remove the stratum corneum from E, regions not carrying nuclear

DAPI stain were further excluded. Due to the 3D epidermal–dermal

interdigitations, vertical sections of epidermal specimen occasionally

yield collagen islands (Fig. 2a). These cut papillae are characterized by a

strong collagen stain inside E. Such regions in E were excluded by

removing regions of strong red stain inside E. Finally, the tissue area E

so far detected is slightly extended by X towards the connective tissue

CT. Biomarkers like integrin �6 directly interact with the connective

tissue and are thus located in its close proximity. Therefore, estimating

Fig. 1. Tri-color stains of the studied differentiation markers. Clippings of large of images of indirect immunofluorescences (IIF) of human epidermis

(Staining: red¼ collagen, blue¼ cell nuclei (DAPI) and green¼ respective biomarker protein). The red collagen stain is indicating the connective

tissue, while the DAPI stain above the collagen stain indicates the epithelium.

Fig. 2. Exemplary fluorescent three color image of human epidermis

with the according phase-contrast image. (a) SC¼ stratum corneum,

CT¼ connective tissue with collagen stained with in fluorescent red and

the epithelium E. The green biomarker BM shows filaggrin expression.

BL¼ strongly stained basal layer of E used to estimate the basal lamina

towards CT. Due to 3D epidermal–dermal interdigitations, collagen

islands (CI) may appear in E. (b) Phase contrast image used for

determining the upper tissue outline at the stratum corneum SC.

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the position of the basal lamina only by the dense basal band of cell

nuclei as described above would not be sufficient. Instead the dermal–

epidermal interaction zone has to be included in E also and thus E was

slightly extended by X.

QSP computation: from the final mask of the epithelial region E,

a normalized distance image (N) was created with a distance of d¼ 0%

at the basal lamina including the above-described extended region, and

d¼ 100% at the border of E to the stratum corneum. A detailed

explanation of the computation is given in Figure 3b. From N and E the

quantitative spatial profiles (QSPs) were computed: for all sliding

distance intervals corresponding regions in E were chosen and the

respective average marker intensity was determined from the green

channel. The signal intensities were corrected for background signal

averaged from the mask of the connective tissue CT. The collected

marker intensities were normalized to a maximum of 100% fluorescence

intensity to account for variations in staining efficiency.

2.4 Computation of protein networks

From the QSPs, an individual spatiotemporal protein network was

computed for each of the three epidermal tissue samples as follows

(Figure 4). For illustration purposes, a graphical representation of the

process flow in protein network computation is given in Figure 5b.

Correlating QSPs: simplified, such a network shows an observed

coregulation of a set of proteins during epidermal differentiation.

Figure 4 illustrates how such networks were computed from QSPs at the

example of two hypothetical tissue samples (Fig. 4a,b). All proteins were

compared pairwise by computing the linear correlation coefficient of

their QSPs, i.e. the QSPs of two proteins are considered to be 100%

correlated during a certain period of time, if their QSPs can be mapped

onto each other by a linear scaling factor. The first peak of two QSPs to

be compared (in the example denoted M1, M2) determines the time

period of mutual increase, while the second peak determines the period

of mutual decrease (Fig. 4a,b). Through normalization, we generally

observed exactly one peak in the QSP of all our tissue samples. This one-

peak-property is a prerequisite for our method as it ensures that all

graphs must intersect at one point as long as the profiles do not have

mutually exclusive flanks. For each phase of mutual increase or decrease,

we required a minimum length of 10% of the total QSP. Also, only

protein abundances above 20% were considered, eliminating back-

ground signals. Finally, the linear correlation coefficient was computed

and stored for each such mutual phase of two protein biomarkers.

Determination of spatiotemporal ‘leadership’ patterns between two

QSPs: for each pair of proteins, we evaluated if one protein

spatiotemporally leads during the whole phase of mutual increase

(Fig. 4c: lead pattern, unidirectional green arrow) or if both proteins

alternate in their leadership (Fig. 4d: alternating pattern, bidirectional

green arrow). This was repeated for the mutual decrease phase yielding

the red arrows. For accepting a ‘lead’, the algorithm required a

leadership of a minimum length of 10% of the total QSP length.

Consensus network: from all three individual networks a consensus

network was derived. At least two arrows with sufficient correlation

(450%) in individual networks substantiated an arrow in the consensus

network. For this calculation, bidirectional arrows were treated as two

single arrows (Fig. 4e).

3 RESULTS

We analyzed the spatiotemporal expression patterns of fiveprotein biomarkers of epidermal differentiation: integrin �6,keratin 1/10, involucrin and filaggrin. To give the reader animpression of the staining patterns obtained, clippings of some

of the large images of the triple-stained tissue sections are givenin Figure 1. As mentioned before, quantitative spatial profiles

(QSPs) were computed not from large images but, from

individual subimages. As the QSPs were derived from a totalof 50 subimages in the average, the shown clippings can only

give a punctual impression of the average profiles we obtainedcomputationally. The average QSPs of all those profiles for

each biomarker for the three epidermal samples are given inFigure 6a. The profiles reflect the normalized quantity of

expression of a biomarker in relation to the quantified degree of

differentiation. Thus, we quantified cellular differentiation bymeasuring the distance of any biomarker-derived pixel in any

location of the epithelial compartment to the epithelial–dermalinterface. Averaging over the profiles of all subimages resulted

in the depicted spatial expression profiles of each biomarkerwith the given SDs.

Figure 6a shows the panel of all QSPs for the three tissuesamples. The x axis describes the relative distance of an

epithelial pixel to the collagen type-I-stained connective tissue.A distance of 0% refers to the marker intensity measured

closest to the connective tissue interface. A distance of 100%

refers to the marker intensity at the stratum corneum. They axis shows the normalized fluorescence intensity for the

respective marker, relative to the background signal intensitiesmeasured in the connective tissue. The SD of the mean value is

given for each data-point. The maximum value of eachbiomarker profile is normalized to 100%. Since we correct

only for background signals measured in the connective tissue,

we still encounter an epithelium-specific fluorescence back-ground noise. From the unspecific background signal of

integrin �6 in the suprabasal layers, this can be estimated to

Fig. 3. Central concepts for generating the quantitative profiles.

(a) Schematic tissue section: the microscopic slide image I, geometric

borders of the analyzed tissue sections B, dense band of the basal

compartment D, connective tissue CT, the basal lamina L, the extended

region X and the resulting epithelium E. During differentiation, cells

migrate according to the arrows from d¼ 0% up to d¼ 100% below the

stratum corneum SC. (b) Generation of the normalized distance

d¼ dAbsD/(dAbsUþ dAbsD), where dAbsU is the absolute distance upwards

of a cell to the stratum corneum, and dAbsD is the absolute distance of a

cell downwards to the epithelial–connective tissue interface. For any cell

in the tissue, dAbsDþ dAbsU is equal to 100% of the spatial distance the

cell migrates from the basal lamina to the stratum corneum.


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lie below 20%. For biomarkers having signal intensities above

this threshold, the proteins can be considered as expressed.

3.1 Results of individual epithelial biomarkers

Integrin �6 (Figs 1 and 6a): in relation to the analyzedbiomarkers, integrin �6 was expressed first in the epithelial

compartment. It was visible as a narrow band at theepithelium–connective tissue interface. To measure the intensityof this band, the image processing algorithm extended the

computationally determined epithelium to include the zone ofepithelial–connective tissue interaction. Thus, we were able

to measure integrin �6 as a peak at the beginning ofdifferentiation.Desmoplakin (Figs 1 and 6a): in all analyzed tissues,

desmoplakin (DPK) was already baso-laterally expressed asreported in literature (Wan et al., 2004). Desmoplakin isconsidered to become embedded in the highly cross-linked

cornified envelope structures during the process of keratinocyteterminal differentiation like other cytoskeletal and desmosomal

components (Haftek et al., 1991). In the QSPs, the samplesderived from epidermal tissues exhibited an initially lowamount of DPK, which continually increased, forming

a steep gradient. The observed profile was bell-shaped withits peak at �40% differentiation. With respect to the tested

biomarkers, DPK was the next to be expressed followingintegrin �6.Keratins K1/10 (Figs 1 and 6a): K1/10 forms a set of

intermediate filaments which are among the first proteins in

cornification of keratinized epithelia, therefore indicating their

early differentiation (Candi et al., 2005). It is suprabasally

expressed (Chu and Weiss, 2002) with a strong expression in the

stratum corneum (Stoler et al., 1988). K1/10 showed a medium

expression directly after the onset of DPK which in all samples

then increased to reach its highest expression level in the

stratum corneum or just below.

Involucrin (Figs 1 and 6a): together with the switch from

K5/K14 to K1/K10, the expression of involucrin as a precursor

of the cornified envelope is a hallmark of terminal keratinocyte

differentiation (Watt et al., 1987). In epidermis, literature

describes the protein to be expressed from three to four layers

above the basal layer at a zone of enlarged cell size (Banks-

Schlegel and Green, 1981; Watt et al., 1987). We detected the

beginning of the presence of involucrin at approximately half of

the width of the epidermis. Consistent with literature (Walts

et al., 1985), involucrin then increasingly accumulated in the

epidermis until it reached its highest expression level below the

stratum corneum.

Filaggrin (Figs 1 and 6a): Filaggrin has the task of finally

bundling and thus collapsing the keratin intermediate filaments

(Candi et al., 2005). In the epidermis, the filaggrin protein

appeared later than involucrin, therefore following involucrin

in its expression profile at a small distance (Figs 1 and 6a).

Filaggrin protein expression is the terminal step of keratinocyte

differentiation that we were able to observe with our markers,

leading to the final flat cell shape characterizing the stratum

corneum.

Differentiation

Mutualincrease

Mutualdecrease

Relative concentration

M1

M2

M1 leads M2 trails

M1 leads M2 trails

Intersection

Peaks

QSPs of hypothetical tissue 1(a)

t1

Mutualincrease

Relative concentration

Mutualdecrease

Alternatinglead

M2

M1

M1 leadsM2 trails

Differentiation

Intersection

Peaks

QSPs of hypothetical tissue 2

t2

t3

M1 M2

Spatiotemporal network of hypothetical tissue 1

M1 M2

Spatiotemporal network of hypothetical tissue 2

Confidence (M1�M2) = t1 = 100%

Confidence (M1�M2) = t2 =70%Confidence (M2�M1) = t3 =30%

M1 M2

Consensusnetwork of both tissues

(b)(c)

(d)

(e)

Fig. 4. Regulatory networks generalize spatiotemporal multi-biomarker profiles. The QSPs of two proteins of two hypothetical tissue samples (a, b)

are shown. (a) In the hypothetical tissue 1, the protein biomarker M1 precedes the protein biomarker M2 during the phase of mutual increase and

mutual decease. The mutual increase phase ends at the first peak of the protein biomarkers (here the peak of M1). The mutual decrease phase starts at

the second peak (here the peak of M2). The two profiles necessarily intersect at one point. (c) The resulting network illustrates that M1 leads during

both phases of increase and decrease. (b) In the hypothetical tissue sample 2, the protein biomarkers M1 and M2 alternate in their leadership before

the first peak is reached. Therefore, two intersections points occur. From the second peak on, the mutual decrease phase starts, which is lead by M1.

(d) In the resulting network, the alternating lead during the mutual increase phase is depicted by a bidirectional green arrow. (e) From the individual

spatiotemporal network, the consensus network is derived. Only the green arrow from M1 to M2 and not the reverse from M2 to M1 has been

detected as well in (c) as in (d) and is therefore the present. The confidence, which is here only illustrated for the increase is defined as the relative

amount of time one protein leads during a phase of mutual increase or decrease.

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3.2 Reconstructed consensus network

From all QSPs of all protein biomarkers we computed a

spatiotemporal consensus network, as described in the Methods

section. This consensus network (Fig. 6b) describes the

central spatiotemporal expression patterns common to all

protein biomarkers of the three epidermal tissue samples.

The increase of DPK is correlated with INV and K1/10. The

rising flanks of INV/FIL/K1/10 constitute a subnetwork.

A well-known sequence of keratinocyte differentiation is

DPK! INV!FIL, which is clearly revealed by the green

and red arrows. K1/10 forms a feedback-loop to INV and

weakly to FIL. Integrin �6 is expressed only subbasally and

therefore, lacks shared flanks with other proteins, and is

excluded from the remaining network; it here serves as a

negative example. The high correlation values computed for all

shown arrows suggest a robust network structure.

3.3 Calculated confidences

To assess in how far the derived network arrows can be trusted,

we calculated a confidence value for each green (Fig. 6c) or red

(Fig. 6d) arrow in each tissue sample. This confidence is

calculated as the relative amount of time a protein leads during

a phase of mutual increase or decrease. For example Figure 6cshows that in epidermis 1, DPK is mutually increasing togetherwith K1/10, and during the whole period DPK is in the lead,

yielding a confidence of 100%. This confidence value holds truefor each tissue sample and is accordingly marked in dark green.Only slight differences in the quantitative amounts of the

confidence values can be observed between the tissue samples inFigure 6c. The confidence values for the falling flanks (Fig. 6d)appear a little less stable, although this is compensated by

averaging over all tissue samples. This higher variability has tobe attributed to the fact that the falling flanks constitute a muchshorter part of each QSP than the rising flanks. Generally, the

computed confidences strongly support the derived consensusnetwork structure.

4 DISCUSSION

For systems biology of complex stratified epithelia, it is of

particular importance to reconstruct the spatiotemporal geneand protein networks regulating cellular differentiation, apivotal cornerstone of epithelial homeostasis. To facilitate this

reconstruction, we here have presented a new method based onthe quantitative spatial analysis of immunofluorescence stainedstratified epithelial tissue sections.

The expression of biomarkers in squamous epithelia, includ-ing those previously mentioned has been described on aqualitative basis for decades. For example, alterations in

protein expression profiles have been described when studyingthe effects of retinoic acid on epidermal morphogenesis

(Asselineau et al., 1989). However, up till now, no quantitativespace- and time-resolving measurements of known epithelialbiomarkers have been published. In current literature, qualita-

tive textual descriptions of individual markers like ‘the studiedmarker is expressed in the suprabasal layers’ still prevail inconjunction with immunohistological images. We argue here,

that such qualitative descriptions do not appear sufficient forreconstructing networks of differentiation and thus answeringthe question of how epidermal homeostasis is achieved by the

collective behavior of the individual cells of the epithelial tissue.However, even after having acquired quantitative spatialprofiles of protein expression it has to be discussed how

network patterns could be derived from such data. Generally,although the epidermal samples exhibited a varying thickness,i.e. total number of epithelial cell layers, the biomarker profiles

were well comparable. This high level of comparability was dueto the normalization of the distance, reflecting differentiation

(Fig. 3b). Therefore, it can be expected that a robust regulatorynetwork controls this differentiation behavior. As there is anobvious relation between cellular differentiation and cell

distance to the connective tissue in one direction and to thestratum corneum in the other direction, this ‘distance’ isobviously incorporated indirectly into such a regulatory

network. Therefore, we try a first approach of interpretingthe obtained QSPs in the context of the upper and lower spatialboundary of the epithelium. Integrin �6 displayed a common

starting point for all profiles, since its expression starts at theepithelium–connective tissue interface. Therefore, the onset ofthe spatial expression of integrin �6 is naturally linked to the

basal lamina. Due to its restriction to the basal pole of basal cell

Fig. 5. Overview of both workflows for (a) computation of quantitative

spatial profiles (QSPs) from each subimage as described in Section 2.3

and (b) computation of protein networks described in Section 2.4.

(a) The mask of the epithelium E is calculated by combining the distinct

images of the phase-contrast image (PC) of the tissue section, the red

collagen and the blue cell nuclei. The epithelial mask E is converted into

a distance image having in each pixel p an intensity equal to the

minimum distance of p to the outline of E. The normalized distance d of

each pixel in E relative to the lower border of E is calculated as

described in Figure 2. The resulting quantitative spatial profile QSP

then gives for each distance d the average biomarker intensity (imaged

in green). By using distinct filters, all fluorescent images (red, green and

blue) are captured in different spectral channels. (b) For all five

biomarkers studied individual QSPs are determined and then linearly

correlated. Pairs of QSPs with sufficient correlation (450%) are

analyzed for mutual or unidirectional leadership. Accordingly, the

final directed graph describing the desired protein network results.


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Fig. 6. Reconstructing the regulatory network from quantitative spatial profiles. (a) QSPs of the three epidermal biopsies (Epidermis 1–3). Symbols

denote: INT¼ integrin �6, K1/10¼ keratin 1/10, DPK¼desmoplakin, INV¼ involucrin, FIL¼ filaggrin. In each multidimensional profile, the nor-

malized protein abundance is given in relation to the normalized distance between the by the image processing estimated basal lamina, and the stratum

corneum. The shown SD for each biomarker is computed by averaging the profiles of each of the approximately 50 subimages. (b) Spatiotemporal

regulatory consensus network reconstructed from the profiles of the three epidermal samples. Green arrows indicate a correlated mutual increase in

abundance during which the protein at the start of the arrow leads, i.e. it has a higher relative abundance than the other protein. Bidirectional arrows

indicate that both proteins alternate in taking the lead. Red arrows indicate a correlatedmutual decrease. The small numbers annotating all arrows give

the undirected mutual correlation values. Only relative protein abundances above 20% are considered. For example, the increase of DPK is correlated

with INV and K1/10. The high correlation values computed for all shown arrows indicate a robust network structure. ‘NL’ (No Lead) instead of a

correlation value is given, when in the respective tissue sample the leader protein did not precede the trailing protein for a long enough period of time

(resulting in an insufficient confidence). (c) Percent confidencematrices for the epidermal samples 1–3 for all mutually increasing (green) and decreasing

(red) flanks. Confidence is calculated as the relative amount of time a protein leads during a phase of mutual increase or decrease. For example in

epidermis 1, DPK is mutually increasing with K1/10, and during the whole period DPK is in the lead, yielding a confidence of 100%.

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keratinocytes in epidermis, integrin �6 lacks a leading positionin relation to the other biomarkers, neither during decrease norduring increase. Therefore, it is missing in the consensus

network, and not marked in the corresponding confidencediagrams. Desmoplakin was the first protein we observed,which displayed a maximum expression in all of the analyzed

tissues. Its expression profiles were tightly connected to thebasal lamina, although its peak was measured at �40%differentiation, thereby indicating also a correlation to the

stratum corneum. K1/10 expression profiles started also veryearly, but remained then on a medium level of expression toreach its peak in the stratum corneum. Thus, it also seems to be

regulated in dependence of the basal layer as well as independence of the stratum corneum. Involucrin expressionappeared at a half-way distance, therefore strongly suggesting a

correlation more to the stratum corneum. The filaggrin profilesshowed a peak expression close to the maximum distance fromthe epithelium–connective tissue interface. It therefore seems tobe regulated in dependence of the stratum corneum.

However, discussing the regulation of spatial proteinexpression solely in the context of the basal lamina and thestratum corneum appears rather vague, and further, cannot

explain how the expression of the studied set of proteins iscoordinated to achieve controlled differentiation. For such ananalysis, the spatiotemporal patterns of protein coregulation

have to be characterized in more detail. This can better beaccomplished by the creation of a spatiotemporal proteinnetwork illustrating the interdependence of the individual

biomarkers. Such a network serves two purposes: first, it canuncover regulatory relationships between the proteins understudy. Second, the reconstructed single networks can also serve

as generalized descriptions of QSPs. Clearly, due to multipleinfluences, the absolute values of two QSPs may not be easilycomparable, e.g. due to different sites from the head and neck

region from which the epidermal biopsies were taken, differentages, etc. Therefore, reconstructing networks from QSPs can beseen as a potential tool of comparing QSPs obtained from

different tissue samples. The protein consensus network weobtained showed that indeed all the QSPs we obtained for thethree epidermal samples were very well comparable, despite

locoregional differences. High and similar correlation valueswere obtained for most arrows (Fig. 6b). The conductedconfidence analysis further supported the obtained network

topology (Fig. 6c,d). Especially for mutual increases, highconfidences for the increasing flanks could be observed(Fig. 6c). For mutual decreases, the results were not as robust

as for increases because the time periods of decrease showed tobe much shorter than those of increase. Moreover, unspecificstaining in the stratum corneum could have influenced the

QSPs. Here, potential improvements could be made for thenetwork reconstruction algorithm, e.g. through prior splineinterpolation of the QSPs.

An arrow in a protein networks computed by our methoddoes not necessarily imply a direct functional interactionbetween proteins, like it occurs between a transcription factor

and a gene promoter. Rather, it aims at analyzing whichproteins act concertedly or are coregulated in the same way.Therefore, we suggest that based on a highly informative

consensus network, in a second step, further functional studies

concerning the biomarkers could be performed. Clearly, at thecurrent stage we analyzed a selective set of epithelium-specific

molecules, leading to the presented consensus network ofproteins. But the goal of our study was to show that the general

approach is feasible and should be applied further to morecomplicated networks. In addition, the ability to reconstruct a

common spatiotemporal consensus network from three epider-

mal samples derived from different head and neck regionspoints to the potential of the new method to be more generally

applied to squamous epithelial tissues.Taken together, we presented a novel method for obtaining

quantitative spatial profiles of biomarkers reflecting differentstages of epithelial differentiation, and for reconstructing

spatiotemporal networks of epithelial differentiation fromthese profiles. By applying our method to samples of different

patients, we have shown that the reconstructed spatiotemporal

networks may serve as a helpful means for comparing andgeneralizing quantitative spatial profiles of biomarkers. From

our point of view, our approach may therefore serve as avaluable quantitative extension to current qualitative conven-

tional approaches of mapping the proteome expression data.Our method is currently based on images of immunofluor-

escent tissue sections but may also be applied to other sourcesof images. If in the future other imaging techniques like

MALDI-imaging improve substantially in their spatial resolu-

tion, our approach could also be adapted to such sources ofdata. But independent of the used imaging technique, the

spatial resolution of multiple biomarkers and the reconstruc-tion of protein networks from this data, will be a prerequisite

for the prospective in silico modeling and systems biological

analysis of epithelial differentiation and tissue homeostasis.

ACKNOWLEDGEMENTS

The works have been conducted with support from theHAMAMATSU Tissue Imaging and Analysis (TIGA) Center

and the NIKON Imaging Center at the BIOQUANT Facilityof the University Heidelberg. We also wish to thank Dr Claudia

Haag for support with tissue. For assistance and criticalreading of the manuscript we are grateful to Dr Michael Grabe,

Prof. Dr Karsten Neuber (University Hospital Hamburg-

Eppendorf) and Dr Hauke Busch (German Cancer ResearchCenter). BfR/Federal Institute of Risk Assessment, Berlin

(WK 3-1328-192) to N.G. and Dietmar-Hopp-Stiftung GmbH,Fachbereich Medizin, St. Leon Rot to T.S.

Conflict of Interest: A related patent application has been filed.

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Reconstructing protein networks of epithelial differentiation from histological sections

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