quantitative analysis of keratin filament networks in scanning

© 2005 The Royal Microscopical Society

Journal of Microscopy, Vol. 220, Pt 2 November 2005, pp. 84–95

Received 25 March 2005; accepted 16 June 2005

Blackwell Publishing, Ltd.

Quantitative analysis of keratin filament networks in scanning electron microscopy images of cancer cells

M . B E I L , H . B R A X M E I E R , * F. F L E I S C H E R , * V. S C H M I D T † & P. WA LT H E R ‡

Department of Internal Medicine I, University Hospital Ulm, D-89070 Ulm, Germany

*

Department of Applied Information Processing & Department of Stochastics,

†

Department of Stochastics, and

‡

Electron Microscopy Facility, University Ulm, D-89069 Ulm, Germany

Key words.

Anisotropy, cytoskeleton, filaments, image analysis, keratin, scanning electron microscopy, skeletonization, statistical estimation.

Summary

The keratin filament network is an important part of thecytoskeleton. It is involved in the regulation of shape andviscoelasticity of epithelial cells. The morphology of keratinnetworks depends on post-translational modifications of keratinmonomers.

In-vitro

studies indicated that network charac-teristics, such as filament crosslink density, determines thebiophysical properties of the filament network. This reportpresents a quantitative method for the morphological analysisof keratin filament networks. Visualization of filaments wasbased on prefixation extraction of epithelial cells and scanningelectron microscopy (SEM). SEM images were processed bya skeletonization algorithm to obtain a graph structure thatrepresents individual filaments as well as their connections.This method was applied to investigate the effects of trans-forming growth factor

α

(TGF

α

) on the morphology of keratinnetworks in pancreatic cancer cells. TGF

α

contributes topancreatic cancer progression and activates signallingpathways phosphorylating keratin monomers. Using thisnew method, a significant alteration to the keratin networkmorphology could be detected in response to TGF

α

.

Received 25 March 2005;

accepted 16 June 2005

1. Introduction

The filamentous cytoskeleton of eukaryotic cells consists ofthree polymer systems: microfilaments, microtubules and inter-mediate filaments. The intermediate filaments in epithelialcells are composed of keratins. Simple epithelia mainly expresskeratin 8 and 18 (Fuchs & Weber, 1994). The major role ofthe intermediate filament cytoskeleton is the formation ofa scaffold defining the shape and mechanical properties

of cells (Herrmann

et al

., 2003). However, the structure ofthis scaffold needs to be modulated dynamically to meet thechanging needs of cells, e.g. during migration. The structureof keratin networks is regulated by post-translational modifica-tions of keratin monomers, such as phosphorylation (Cou-lombe & Omary, 2002; Strnad

et al

., 2002). These processesrepresent an important interface between biochemical signal-ling pathways and the keratin filament system.

In-vitro

studieswith keratin suspensions have demonstrated that spatialarrangement patterns of filaments influence the mechanicalproperties of keratin networks (Coulombe & Omary, 2002). Ina previous study, we were able to establish a link between kera-tin phosphorylation, network architecture, viscoelastic prop-erties and migration of cancer cells (Beil

et al

., 2003).Morphological analysis of the filamentous cytoskeleton by light

microscopy is based on the fact that the width of the filamentsand the interfilament distances are smaller than the diffraction-limited resolution of optical microscopy. By combining electronmicroscopy with a prefixation extraction method, individualfilaments can be visualized within cells, and the network archi-tecture can thus be analysed at the level of its basic components(Ris, 1985; Svitkina & Borisy, 1998). We have modified thismethod to image keratin filaments by scanning electron micro-scopy (SEM). Because keratin filaments are characterized by a highdegree of biochemical stability (Zackroff & Goldman, 1979),the specificity of this method for keratin filaments is ensuredby high detergent concentrations during the extraction process.The next step was to develop a quantitative method to analysethe filament network architecture and its alterations in responseto biochemical signals. In contrast to the approach chosen byVerkhovsky

et al

. (2003) restricting the analysis to the orienta-tional order of filament networks, the image analysis methodproposed in the present paper provides information about lengthand orientation of filaments as well as about the interconnectionswithin the network. In particular, the method consists of a

Correspondence to: Professor Paul Walther. Tel.: +49 731 50 23441; fax: +49

731 50 23383; e-mail: [email protected]

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skeletonization of binarized images with a subsequent processingof the skeleton to remove artefacts. Finally, the skeleton structureis transformed into a graph to provide a quantitative analysisof the resulting line structure.

The new method was used to study the association betweenphosphorylation of keratin 8 and the keratin network archi-tecture in pancreatic cancer cells. Keratin 8 is phosphorylatedat Ser 431 by extracellular signal-regulated kinases (ERKs)(Omary

et al

., 1998). ERKs can be activated by incubatingcells with transforming growth factor

α

(TGF

α

), which playsa crucial role in pancreatic cancer progression (Seufferlein

et al

., 1999; Wagner

et al

., 2001).

2. Materials and methods

2.1. Cell culture

Panc1 human pancreatic cancer cells (American Type CultureCollection, Manassas, VA, U.S.A.) were grown on glass chamberslides. Cells were washed and subsequently incubated in serum-free medium for 1 h before being treated with 100 ng mL

−

1

TGF

α

(R&D Systems, Minneapolis, MN, U.S.A.) for 30 min.

2.2. Electron microscopy

The preparation of cells to visualize single keratin filaments byhigh-resolution SEM by a prefixation extraction method (Ris,1985) was partially based on the protocol of Svitkina & Borisy(1998). After washing the cells with phosphate-buffered saline(PBS), an extraction solution [1% Triton X-100, 2.2% PEG(molecular weight, 35 kDa), 50 m

m

imidazole, 50 m

m

potas-sium chloride, 0.5 m

m

magnesium chloride, 0.1 m

m

EDTAand 1 m

m

EGTA, pH 6.9] was added for 20 min at 4

°

C. Cellswere rinsed four times with PBS and fixed with 4.0% formal-dehyde (ultrapure EM grade, methanol free, Polysciences,Eppelheim, Germany) in 0.1

m

cacodylate buffer (pH 7.3)for 10 min at room temperature. After rinsing with PBS,the slides were gradually dehydrated in propanol (30%,50%, 70%, 90% and twice in 100% for 5 min at each step) andfinally subjected to critical-point drying using carbon dioxideas transitional medium (Critical Point Dryer CPD 030, Bal-Tec,

Principality of Liechtenstein). Care was taken to preventaccidental air drying when changing the fluids. After drying,the cover slips were carefully cut to fit onto the Hitachi S-5200specimen holders (5

×

8 mm) using a home-made diamondcutter. The cut glass slides were then mounted on the Hitachiholders using double-sided tape and liquid silver paint to improveelectrical conductivity. Samples were subsequently rotary coatedwith 3 nm of platinum–carbon by electron beam evaporationusing a Bal-Tec Baf 300 freeze etching device (Bal-Tec) andimaged with a Hitachi S-5200 in-lens SEM (Tokyo, Japan) atan accelerating voltage of 4 kV using the secondary electronsignal. It was previously shown by immunoelectron micros-copy that this method depletes actin filaments and microtu-bules and preserves keratin filaments (Beil

et al

., 2003).To analyse the filamentous structure of the keratin network,

sample regions were imaged at a primary magnification of35000

×

(pixel size 2.63 nm). These regions were chosen fromthe subcortical compartment of the cells directly adjacent tothe cytoplasmic membrane. This procedure resulted in imagescontaining a thin layer of filaments. In total we analysed 15Panc1 cells (seven cells after incubation with TGF

α

and eightuntreated cells). Figure 1 depicts typical Panc1 cells from theTGF

α

-treated group and untreated controls.

2.3. Image segmentation

As the skeletonization algorithm we used skeletonizationby morphological thinning. The original structure is thinnedby the use of a structuring element, e.g. a 3

×

3 matrix, that isrotated. This method is repeated until the structure remainsunchanged for a whole cycle of rotated matrices. Homotopy,i.e. connectivity properties between different objects in thestructure, is thereby preserved. For definitions of the basicmorphological image operators, such as erosion, dilation,opening and closing, as well as algorithms for skeletoniza-tion of binary images see Jähne (2001), Serra (1988), Soille(2003) and references therein.

2.3.1. Classification of skeleton pixels

. By counting the numberof neighbouring pixels belonging to the skeleton it is possibleto classify the pixels of the skeleton into endpoints (having

Fig. 1. SEM images of Panc1 human pancreatic cancercells after applying a prefixation extraction removingall non-keratin proteins. These overview images wereacquired with a magnification of 3500×. (a) Untreatedcells; (b) cells incubated with TGFα.

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only one neighbour), linepoints (having exactly two neigh-bours) and crosspoints (having more than two neighbours).In the following, using neigbour relationships, the connectionbetween two crosspoints, a crosspoint and an endpoint or twoendpoints is termed a connection path if otherwise only line-points are involved. Note that often a classification using merelythe number of neighbouring pixels might not be satisfactorybecause this would result in very many pixels classified ascrosspoints and hence in very short connection paths betweenthem. Therefore, certain criteria should be used in order toimprove the results (see Fig. 2). In such an improved classifi-cation, homotopy should be preserved and the number ofconnection paths should be minimized. One criterion to decidebetween different possible solutions is to regard the differencein angle for the connections between one pixel and itsneighbours, where angles close to

π

are preferred. In contrastto methods which consider only the local neighbourhood ofpotential branching points (Beil

et al

., 1995), the approachconsidered in the present paper takes the topological propertiesof other line points into account.

2.3.2. Pruning

. After reclassification, endpoints of the skeletonstructure were detected. Because the planar filament networkin our application is a network with closed branches, a pruningprocedure is necessary to remove artefacts created by thepreparation or imaging processes. A pruning procedure onthe skeletons denotes the removal of all connection pathscontaining at least one endpoint and all the pixels of theseconnection paths. After pruning, pixels must be reclassifiedbecause crosspoints may have become linepoints.

2.3.3. Transformation of the skeleton into a graph

. Assuming thatconnections between crosspoints are linear, the pruned skele-ton can be transformed into a graph or, seen from a differentviewpoint, into a line segment structure, in which each pair ofcrosspoints that has a connection path is represented as a linesegment between them (see Fig. 3a,b).

2.3.4. Modification of line segment structure

. To remove artefactsresulting from loops (connection paths where starting pointand endpoint are identical) or from two filaments crossingeach other and enclosing a small angle (see Fig. 3), the linestructure is modified by merging two or more crosspointsinto a single one using the centre of gravity of the involvedcrosspoints as a new crosspoint. This step is performed forcrosspoints a distance of less than

d

max

apart and in an ascendingsequence, meaning that crosspoints a smaller distance apartare merged first.

2.3.5. Algorithm description

. Figure 4 depicts intermediateresults of the algorithms implemented. The greyscale imagesare transformed into binary images by using a threshold

t

=

f

,where

f

represents the mean greyscale value within theimage. On these binary images an opening is performed inorder to reduce noise effects on the filament topology of theimage objects. The binary images are then skeletonized.The resulting skeletons are pruned and transformed into linesegment structures. Finally, these line segment structuresare modified by merging nearby crosspoints (

d

max

=

8 pixels)to obtain line segment structures that can be statisticallyanalysed.

Fig. 2. Classification and its modification, where Cdenotes crosspoints and L denotes linepoints. (a) Originalskeleton; (b) classification by number of neighbours;(c) improved classification.


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2.4. Statistical analysis

The line segments produced by the algorithm described in Section2.3 were analysed statistically with respect to characteristics suchas the number of line segments, their length and orientationdistributions. With regard to orientation distributions, the analysiswas performed on circular cutouts with radii of 300 pixels (

≈

0.79

µ

m) centred at the midpoints of the images. A circular samplingwindow was chosen to avoid any bias with respect to orientationdistributions. For the analysis of segment lengths, samplingwindows were chosen to be quadratic with a side length of 500pixels (

≈

1.32

µ

m) centred at the midpoints of the images. Orienta-tion distributions were tested for uniformity by using Kuiper’stest (Mardia & Jupp, 2000), while statistical comparison of groupswas based on the Wilcoxon–Mann–Whitney test.

2.4.1. Estimation of segment lengths and orientation angle

. Segmentlengths were estimated by using the Euclidean distancebetween the two segment endpoints. Segment lengths weretaken into account if the lexicographically smaller endpointof the segment was located inside the sampling window. Thesampling window was chosen to be sufficiently small in orderto avoid edge effects caused by an inability to observe wholelengths of relevant segments. Orientations of segments weredetermined as angles between segments and the

x

-axis, whereonly segments with their lexicographically smaller endpointlying in the sampling window were considered.

2.4.2. Orientation analysis

. The main aim of the orientationanalysis was to determine the degree of anisotropy, i.e. the

degree of non-uniformity, of the orientation distributions ofthe detected line segments for the two different groups. In orderto analyse the orientation distributions, two different approacheswere considered. As a first approach, the circular standarddeviation was computed, whereas in the second approach anindex of dispersion was estimated by using resamplingtechniques. Note that for both approaches we consideredthe typical point method, meaning that each line segment isweighted with the same weight, independent of its length.

2.4.3. Circular variance and circular standard deviation

. For a moredetailed explanation of the characteristics described here, see,for example Batchelet (1981) and Mardia & Jupp (2000). Let

θ

1

, … ,

θ

n

denote a sample of line segment angles with respect tothe

x

-axis and with corresponding unit vectors

u

1

, … ,

u

n

. Thenthe mean direction

8

of

θ

1

, … ,

θ

n

is the direction of the result-ant

u

1

+

…

+

u

n

of

u

1

, … ,

u

n

. It is also the direction of the centreof mass

u

of

u

1

, … ,

u

n

, where

u

can be described in Cartesiancoordinates as

(2.1)

with

(2.2)

and

(2.3)

Fig. 3. Transformation and improvement of the structure.(a) Typical skeleton of two filaments crossing eachother at a small angle; (b) transformation into graphstructure; (c) merging of nearby crossings.

u Ç ( , )= Í

Ç cos ==∑1

1n jj

n

θ

Í sin .==∑1

1n jj

n

θ

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Fig. 4. Intermediate steps in the image segmentation. (a) Greyscale sample image; (b) binary image after thresholding; (c) skeleton of binary image;(d) graph structure after pruning; (e) graph structure after merging nearby crossings.


89



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Hence,

8

is the solution of the system of equations

(2.4)

(provided that

R

>

0), where the mean resultant length

R

isgiven as

(2.5)

The sample circular variance is defined as

(2.6)

and can be used as a measure of dispersion because if the

n

oberveddirections are tightly clustered about the mean direction

8

, thenthe sample circular variance will be nearly zero. On the otherhand, if the directions are widely dispersed then

V

will be almost 1.An analogue for circular data of the standard deviation on

the line is given by the sample circular standard deviation,defined by

(2.7)

and which has similar dispersion measure properties as

V

.Therefore, if denote

m

samples of line segment angles with sizes

n

1

, … ,

nm, respectively,we compute the sample circular standard deviations σ1, … ,σm in order to obtain information regarding the orientationdistributions.

2.4.4. Index of dispersion. As a confirmation for the resultsobtained for the sample circular standard deviation, weregarded a further parameter for the degree of anisotropy ofthe line segments constructed as follows. Let

denote the minimal size of the m samples of angles θ1 = (θ11, … ,. Now for each sample θ1 a subsample

of n* angle values is drawn without repetition, where n* ≤ nmin.This procedure is repeated l times. For each of these l samplesof size n* the index of dispersion Di, j is given by

(2.8)

where s is the number of classes, i.e. the number of directionalsectors considered, xk is the number of angles of the subsamplebelonging to the kth class, and pk is the probability of belongingto the kth class under the assumption of isotropy. The meanindex of dispersion Di is then given by

(2.9)

These mean indices of dispersion can be regarded as anisot-ropy parameters reflecting the test statistic for the chi-squaredgoodness-of-fit test where by a randomized drawing procedureit is ensured that samples are of equal size. Note that incontrast to the circular standard deviation, a small value of Di

indicates that angles are almost uniformly distributed. Henceif Di is small it can be assumed that the angles have a completelyrandom distribution, whereas large values of Di indicate thatthere are preferred directions with respect to the orientation ofthe line segments. For later analysis we used s = 16 classes andl = 1000 iterations.

2.4.5. Software. All software developed and used for imageanalysis as well as for statistical analysis was included in theGeoStoch library system. GeoStoch is a Java-based open-library system developed by the Department of AppliedInformation Processing and the Department of Stochasticsof the University of Ulm, and which can be used for stochastic–geometric modelling and spatial statistical analysis of imagedata (Mayer, 2003; Mayer et al., 2004; http://www.geostoch.de).

3. Results

3.1. Visual results

Figure 5 shows the results of the segmentation algorithm forthree different sample images that have an ascending degreeof structural complexity. Problems occurred in cases wherespaces between filaments were extremely narrow, i.e. filamentswere touching each other, or if filament connections couldnot be further differentiated even by a human observer. Theseexamples also indicated that the assumption of piecewiselinearity of connections between crosspoints might be areasonable choice because most of these connections werequite small and longer connections tended to be approximatelylinear. Moreover, most but not all branching points representedinterconnections of keratin filaments. An inspection of thesegmentation results revealed that 9.8% of all branchingpoints (10.3% in the control group, 9.5% in the TGFα-treatedgroup) resulted from cross-over of long filaments located indifferent planes.

3.2. Number of segments and segment lengths

Figure 6 shows for both groups (untreated controls, cells treatedwith TGFα) a sample histogram of segment lengths for a specificimage, as well as a cumulated histogram for all segment lengthswith respect to all images of a group. Although the shape of thelength distribution seems to be quite similar, the histogramsindicate that mean segment lengths are reduced in the case ofthe TGFα-treated group compared with the control group.Note that due to the applied algorithm the minimal length ofa detected segment equals dmax = 21 nm and that the mean

Ç R R cos , sin = =8 Í 8

R Ç ( ) ./= +2 2 1 2Í

V = −1 R

σ ( log( )) ./= − −2 1 1 2V

θ θ θ θ θ θ1 11 1 11 ( , ... , ), , ( , ... , )= … =n m m mn m

n ni m

imin, ,

min == …1

θ θ θ θ1 11n m m mnm), ... , ( , ... , )=

Ds

x n p i m j li j k kk s

,, ,

( * ) , , ... , , , ... , ,= − = == …∑1

1 12

1

Dl

Di i jj l

.,, ,

== …∑1

1

http://www.geostoch

90 M . B E I L E T A L .

© 2005 The Royal Microscopical Society, Journal of Microscopy, 220, 84–95

number of detected segments is thereby reduced. In Fig. 7 boxplotsfor the total number of segments and the mean segmentlength per sampling region are displayed for both the TGFα-incubated group of cells and the control group. Statistical tests

for the two groups on the equality of the mean number ofsegments and on the equality of mean segment length, respec-tively, were performed and both were rejected at a test level ofγ = 0.05. Note that there is no principle difference in regarding

Fig. 5. Results of the segmentation algorithm.

Q UA N T I TAT I V E A NA LYS I S O F F I L A M E N T N E T WO R K S 91


total mean numbers instead of densities, i.e. mean numbers perunit area, because the sampling windows were of a fixed size.Furthermore, note that the filament number and density arecorrelated strongly with crosslink density.

3.3. Orientation distributions and anisotropy

The orientation of a line segment can be described by its angleθ to the x-axis with θ ∈ [–π/2, π/2]. Note that we have axial data

Fig. 6. Histograms of segment length.

Fig. 7. Boxplots of the total number of segments and mean segment length (the median is depicted as the line in the middle of the box, the first and thirdquartiles as the upper and lower border of the box, and the minimum and maximum value as the upper and lower hook).

92 M . B E I L E T A L .


given, meaning that a specific line segment has two possibledirections. Figure 8 displays sample histograms for thetwo groups. Although there is a strong variability within eachgroup, the orientation distribution in the case of the TGFα-incubated cells tends to be more uniform than in the untreatedcells. Statistical tests for uniform orientation distributionshow that for the control group 7/8 sample images lead to arejection of the hypothesis of uniform orientation distribution,whereas for the TGFα-incubated cells only 4/7 sample imagescaused a rejection (γ = 0.05). In Fig. 9(a,b) boxplots of thecircular standard deviation described in Eq. (2.7) as well as

boxplots of the index of dispersion described in Eq. (2.9) asanisotropy parameters are displayed for the two groups.Statistical tests for equality of expected circular standarddeviations and for equality of expected indices of dispersionlead in both cases to rejection (γ = 0.05), meaning that theexpected circular standard deviation is greater for theTGFα-treated group than for the control group. This result isconfirmed by the fact that the opposite is true for the expectedindices of dispersion. Hence, filaments of the control groupseem to be more uniformly orientated than those of theTGFα-treated group.

Fig. 8. Histograms of segment orientations as depicted by its angle with the x-axis.

Fig. 9. Boxplots of circular standard deviation andboxplots of indices of dispersion (the median is depictedas the line in the middle of the box, the first and thirdquartiles as the upper and lower border of the box,and the minimum and maximum value as the upperand lower hook).



4. Discussion

The cytoskeleton is involved in essentially all structural anddynamic aspects of living cells (Ramaekers & Bosman, 2004).Morphological features of the intermediate filament cytoskeletonwere shown to determine the biomechanical properties offilament networks, which control cell shape and motility aswell as tissue stability (Goldman et al., 1996; Bruel et al., 2001;Ma et al., 2001; Coulombe & Omary, 2002). We have shown ina previous report that extensive remodelling of the keratinnetwork in response to sphingosylphosphorylcholine changesthe viscoelastic modulus, i.e. the force required to stretch acell a set distance, of human pancreatic cancer cells, overridingeven the impact of microfilament reorganization and possiblyresulting in an enhanced metastatic activity of pancreatic cancercells (Beil et al., 2003). TGFα was shown to be also involvedin pancreatic cancer progression (Wagner et al., 2001). In thepresent study, we therefore investigated the role of TGFα in thestructure of the keratin filament network by using a keratin-specific prefixation extraction method and SEM.

We observed distinct changes of the keratin networkarchitecture in Panc1 cells in response to TGFα. The focus of ourinvestigation was then to quantify the structural alterations ofthe keratin network at the cell periphery. This region appearsto be an important centre of spatially defined keratin organiza-tion given that de novo filament formation was observed inthis compartment (Windoffer et al., 2004). Thus, stimulatedchanges of the network architecture might become visible firstin this cytoplasmic region. Furthermore, the subcortical com-partment represents an interface connecting the cytoskeletonand intracellular signalling pathways with transmembranereceptors mediating contact to other cells and the extracellularmatrix. Thus, this region plays an important role in cell–cellinteractions and cell migration (Wehrle-Haller & Imhof, 2003).A better understanding of the modulation of these aspects ofcancer cell biology by TGFα could thus reveal new therapeutictargets to prevent progression of the disease. Because thestructural changes after a short incubation with TGFα wereexpected to be subtle, the morphological investigation requireda sensitive method for the analysis of keratin filaments and thecharacteristics of their spatial distribution.

The biochemical stability of keratin filaments permitted theapplication of high detergent concentrations during the extrac-tion phase washing out almost all non-keratin molecules fromthe cytoplasm. We cannot exclude that this approach also removedsome of the keratin filaments with a decreased stability duringfilament turnover. However, experiments with lower detergentconcentrations resulted in a very similar filament density butwith a large number of protein precipitates composed ofother abundant cellular proteins such as actin. After applyingcritical-point drying and platinum coating, SEM produced high-contrast images of the keratin cytoskeleton. The availability ofwhole cells provided the opportunity to select specifically sub-cortical regions for further analysis.

The image analysis method implemented in this study wasbased on a skeletonization of binarized images with a subsequentpruning of the line structure and reconstruction of branchingpoints. Owing to the sufficiently high contrast of SEM images,the segmentation algorithm yielded a good quality matchbetween the resulting line structure and the filament networkin the original greyscale images. In images with a lower signal-to-noise ratio, methods of greyscale skeletonization mightbe useful (Meyer, 1989; Vincent & Soille, 1991). The morpho-logical analysis of the line structure, i.e. filament network,involved filament number, length distribution and spatialorientation of filaments. During slide processing, partial collapseof the keratin network can occur, inducing displacement ofthe filaments. In this setting, statistical features of filamentdensity and network connectivity are considered to be morestable than features describing the localization of individualfilaments. In experiments in which parallel filaments tendto aggregate, the segmentation method applied for filamentdetection is prone to underestimate the density of filaments.This could introduce a bias into the statistical analysis dependingon network morphology. Misclassification of filament crossoversas branching points represented another potential problem.However, in our experimental setting, misclassifcation rateswere similar between controls and stimulated cells and, thus,did not invalidate the comparison between these two groups.

Using statistical features of network morphology we founda significant difference in the subcortical keratin networkarchitecture between Panc1 exposed to TGFα and controls.Such a remodelling of the peripheral keratin network couldcontribute to an enhanced cell migration induced by TGFα, aswas recently described for ovarian cancer (Sewell et al., 2005).Note that, in addition to the morphological analysis performedin the present paper, an investigation of the detected branch-ing points with respect to their spatial distribution might beof interest. In this context, methods similar to those used inBeil et al. (2005) can be applied. Point process characteristics,such as the pair correlation function or the L-function, bothwith respect to the point pattern generated by the branchingpoints, might be analysed. Such an approach may provideinformation about attraction or repulsion effects betweenpairs of branching points within specific distance regions. Foran introduction into this type of analysis, see, for example,Stoyan et al. (1995).

Several studies have already investigated the intermediatefilament cytoskeleton using light microscopy and quantitativeimage analysis. In a previous report, density and orientationof detectable filaments were found to change during develop-ment of the fetal rat liver (Vassy et al., 1996). The perinuclearorganization of keratins in cancer cells appears to be regulatedby gravity (Vassy et al., 2001). In a study by Helmke et al.(2003), a map of the distribution of intracellular strain wascomputed from the displacement of intermediate filaments inendothelial cells exposed to shear stress. However, the analysisof light microscopy images is confined to thick bundles of

94 M . B E I L E T A L .


filaments and, thus, excludes potentially relevant componentsof the cytoskeleton. This restriction also affects the comparisonof real image data with the results from experiments simu-lating the synthesis of filament networks (Portet et al., 2003).Investigations into model systems of interconnected filamentshave identified the crosslink density as an important parameterdetermining the biophysical properties of networks (Head et al.,2003). In contrast to light microscopy studies, our methodprovides the opportunity to image individual filaments as wellas their crosslinks. By using this approach, data obtained fromreal cells can be compared with the results from simulationexperiments to evaluate the role of structural features in thebiophysical characteristics of cytoskeletal networks. However,SEM-based imaging studies are restricted to experiments inwhich structural changes do not need to be monitored in timefor individual cells. Furthermore, the analysis of networkmorphology can be combined with biochemical data onpost-translational modifications of keratin monomers to gaininsights into the regulation of the keratin cytoskeleton bybiochemical signalling pathways.

In conclusion, we have developed an image analysis methodto investigate the morphology of the keratin cytoskeletonin cultured cells. The visualization of individual filaments wasbased on a prefixation extraction and SEM. Using this newmethod, distinct changes of the keratin network in pancreaticcancer cells could be detected in response to TGFα.

Acknowledgements

We thank Elke Wolff-Hieber and Eberhard Schmid for experttechnical assistance. H.B. is supported by a grant of theGraduiertenkolleg 460 at the University of Ulm. This projectwas partly supported by the Deutsche Forschungsgemeinschaft(SFB 518).

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