Semi-automatic procedure for the determination of the cell...

[Frontiers in Bioscience, Elite, 5, 533-545, January 1, 2013]

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Semi-automatic determination of cell surface areas used in systems biology

Volker Morath1,2, Margret Keuper1,3, Marta Rodriguez-Franco4, Sumit Deswal1,2,5, Gina Fiala1,2,5, Britta Blumenthal1,2,Daniel Kaschek1,6, Jens Timmer1,6, Gunther Neuhaus4, Stephan Ehl7,8, Olaf Ronneberger1,3, Wolfgang Werner A.Schamel1,2,7

1BIOSS Centre for Biological Signaling Studies, University of Freiburg, Germany, 2Department of Molecular Immunology, MaxPlanck-Institute of Immunobiology and Institute of Biology III, Faculty of Biology, University of Freiburg, Germany, 3ComputerScience Department, Technical Faculty, University of Freiburg, Germany, 4Department of Cell Biology, Faculty of Biology,University of Freiburg, Germany, 5Spemann Graduate School of Biology and Medicine, SGBM, University of Freiburg,Germany, 6Physics Institute, University of Freiburg, Germany, 7Centre for Chronic Immunodeficiency CCI, University ClinicsFreiburg and Medical Faculty, University of Freiburg, Germany, 8Centre for Pediatrics and Adolescent Medicine, UniversityMedical Center Freiburg

TABLE OF CONTENTS

1. Abstract2. Introduction3. Materials and methods

3.1 Cells3.2 Flow cytometry3.3 Confocal microscopy3.4 Electron microscopy3.5 Data processing

4. Results4.1 Design of the study4.2. Determination of the cellular and nuclear radii distributions4.3. Acquisition of high resolution 2D images4.4. Calculation of the stereological 3D parameters

5. Discussion6. Acknowledgements7. References

1. ABSTRACT

Quantitative biology requires high precisionmeasurement of cellular parameters such as surface areas orvolumes. Here, we have developed an integrated approachin which the data from 3D confocal microscopy and 2Dhigh-resolution transmission electron microscopy werecombined. The volumes and diameters of the cells withinone population were automatically measured from theconfocal data sets. The perimeter of the cell slices wasmeasured in the TEM images using a semi-automatedsegmentation into background, cytoplasm and nucleus.These data in conjunction with approaches from stereologyallowed for an unbiased estimate of surface areas with highaccuracy. We have determined the volumes and surfaceareas of the cells and nuclei of six different immune celltypes. In mast cells for example, the resulting cell surfacewas 3.5 times larger than the theoretical surface assumingthe cell was a sphere with the same volume. Thus, ouraccurate data can now serve as inputs in modelingapproaches in systems immunology.

2. INTRODUCTION

A mechanistic understanding of biologicalprocesses requires the generation of quantitative data setsand their description in mathematical terms. This approachhas been extensively used in the last years for a detailedunderstanding of signal transduction pathways . As manymathematical models are based on ordinary differentialequations, their calibration requires accurate data of asmany reaction network components as possible. Theexperimental data that serve as an input to the modelsinclude kinetics or dose responses of proteinphosphorylations (measured by intracellular flowcytometry, beads based assays, mass spectroscopy orWestern blotting) or protein-protein interactions (quantifiedby immunoprecipitations followed by Western blotting,flow cytometry or mass spectroscopy) . Commonparameters to be measured are reaction rates (measured byenzyme assays), association constants (determined bysurface plasmon resonance or flow cytometry) and initialprotein concentrations.

Semi-automatic parameter determination

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In modeling, it is common practice to determinesome parameters by fitting the mathematical model to theexperimental data , which is a reasonable approach in manycases. However, the more parameters are determinedexperimentally the better are the models. In addition, oftenrelative changes rather than absolute values are measured,requiring scaling factors. Thus, it is important to determinethe concentration of molecules in molar values, such asmoles of a receptor per area of the cell surface or moles ofa signaling molecule per volume of cytoplasm. In the caseof proteins, one can use quantitative Western blotting orflow cytometry in combination with appropriatestandards to determine the total number of a protein onthe cell surface or inside the cell . However, the surfacearea is not easy to measure and its measurement thusconstitutes a severe error source in the determination ofmembrane protein concentrations.

In many reports, the cell is considered as a sphere ora cylinder and the volume or surface area are calculated usingthe according formula. But as evidenced form electronmicroscopy (EM) images, the surface of many cell types is notsmooth and the cell is not a perfect sphere or cylinder. Manycell surfaces contain protrusions that make the total surfacearea larger than calculated from the assumption of a perfectsphere or cylinder. Therefore, the surface density of membraneproteins calculated by supposing a spherical cell is higher thanit is in reality.

Besides the measurement of relative increases in cellvolume, for example upon cell growth , several technologieswere used to estimate the volume of cells. These includeCoulter counting, flow cytometry, radioisotope labeling,impedance measurements and light-microscopy as well asstereological techniques (see below). One of the currently usedtechniques to estimate cell surface area and volume is confocallight microscopy . In this method, image sets consisting of verythin, serial optical sections across the cell are obtained and athree-dimensional (3D) model of an individual cell isconstructed using digital image processing techniques. Toobtain the average of a cell population a large number of cellshave to be processed. Further, scanning ion conductancemicroscopy is also used for measuring cell volumes . However,light microscopy-based methods have a limited spatialresolution of approx. 0.2 µm, which might not be enough tovisualize finer cell protrusions.

A high-resolution technique to visualize even smallcellular structures is transmission EM (TEM). Indeed, TEMhas been used to determine the nuclear and cytoplasmicvolumes and the surface area of cells .

In stereology a quantitative analysis of 3D structuresis undertaken based on the evaluation of 2D images .Stereological approaches are applied in physiology, neuro-sciences or immunology where complex tissues are sampledand also for single cell suspensions .

In stereology unbiased sampling strategies havebeen developed to give every point in a biological samplethe same probability to be observed . This allows to directly

compute parameters of the 3D tissue from measurementswithin random 2D sections.

The standard procedure in stereology allowsfor computing the total surface area or the totalcytoplasm volume within a certain reference volumefrom the random 2D sections analyzed by TEM. As wewere interested in the surface area per cell, we wouldneed the exact number of cells within the referencevolume, which cannot be obtained from randomsections. A possible solution would be to use alignedserial sections, but this puts much higher demands onthe preparation and recording and requires to assume ahomogeneous cell density within the sample. Morecomplex measurements based on 2D slices, such as thecell size distribution within a population are even morechallenging, and would require additional assumptions.

So we propose to use pure random sections forthe TEM and obtain the missing parameters (cell sizedistribution within a given population) by fullyautomated 3D confocal microscopy.

3. MATERIALS AND METHODS

3.1. CellsThe human T cell line Jurkat , the murine B cell

line J558L and murine bone marrow derived-mast cellswere grown using 5% or 10% fetal calf serum. The bonemarrow derived mast cells were a gift of Michael Huber,Aachen, and Marina Freudenberg, Freiburg. Primary mouseB cells were obtained from the spleen of C57BL/6 miceand purified using a CD43 MicroBead MACS Cellseparations Kit in an AutoMACS separator (MiltenyiBiotech) according to the manufacturer’s protocol .

Human peripheral blood mononuclear cells(PBMCs) were obtained from anticoagulated peripheralblood of healthy donors by Ficoll density gradientcentrifugation (PAN-Biotec). For T cell blasts generationPBMCs (1.5x106 cells/ml) were stimulated with 1.25mg/ml phytohemagglutinin (PHA) and IL-2 (100 U/ml) for3 days and with IL-2 only (100 U/ml) for another 2 days.Primary human T cells were isolated from PBMCs usingthe Pan T cell isolation kit II from Miltenyi Biotecaccording to the manufacturer’s instructions. In a secondpurification step, the MACS-sorted T cells were stainedwith APC-labelled anti-CD14, APC-labelled anti-CD19APC and APC-labelled anti-CD56 and further purified byFACS sorting. The purity of the isolated primary human Tcell fraction was determined by anti-CD3 staining and was96%.

3.2. Flow cytometryFlow cytometric analyses were performed using a

LSRII Flow Cytometer (Becton Dickinson). In order tocompare the different cell types, the voltage for the forwardscatter (FSC) and side scatter (SSC) were fixed to 350 Vand 287 V, respectively. A minimum of 10000 cells wererecorded. Data were analysed with the FlowJo software(Tree Star, Inc.) as before .


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3.3. Confocal microscopyCells were stained in 2 ml culture media with 2

µg Calcein AM (Mill Valley) and 32 mM DAPI(Invitrogen) at 37°C in 5% CO2 for 45 minutes. Cells werewashed once and resuspending in culture media. Subsequently,cells were passed through a 41 µm polyamide mesh (Reichelt)to obtain a single cell suspension. Calcein AM diffuses intothe cells where it is hydrolyzed by intracellular esterases andemits a strong fluorescence and thus stains the whole cell.DAPI binds to the minor groove of the DNA and thus stainsthe nucleus. Confocal microscopy was performed with a ZeissAX10 Imager equipped with a CSU-X1 spinning disk(Yokogawa), an MRM camera and a LCI Plan-Neofluar 63xwater immersion objective (both Zeiss) with an exposure timeof 200 ms. The cytoplasmic dye Calcein AM was excited at488 nm and measured through the emission filter BP 525/50SFP. For the nuclear dye DAPI an excitation wavelength of405 nm was used in combination with the emission filter BP450/50 DAPI.

3.4. Electron microscopyFor prefixation glutaraldehyde (Sigma) was

added to the culture media to a final concentration of 2.5%and incubated for 30 minutes. After harvesting, cells werefixed for 3 h with 2.5% glutaraldehyde in 50 mMcacodylate buffer and post-fixed with 1% OsO4 at 4°C.Dehydration through a graded series of ethanol wasperformed before embedding in Epon 812 resin. Ultra-thin(approx. 90 nm) sections were stained with uranyl acetate(UAc) and lead citrate (PbCit) and examined in aPhilips/FEI CM10 (80 kV) electron microscope equippedwith a Bio-scan Camera Model 792. Images were recordedwith Digital Micrograph software (Gatan).

3.5. Data processingAll programs for the processing of the confocal

data were written in C++. The manual corrections weredone using ImageJ. For the processing of the TEM images,the Matlab-based Berkley segmentation engine (BSE) wasused . The software for the data evaluation was written inMatlab.

4. RESULTS

We determined first order stereologicalparameters, such as surface and volume, by combining 3Dconfocal with 2D electron microscopical methods and(semi-) automated pattern recognition (Figure 1).

4.1. Design of the studyThe 3D images of the cells and the nuclei were

obtained by spinning disk confocal microscopy (Figure1B). Automatic edge detection and expert control (Figure1C and D) allowed determining the average volumes, lowerbounds for the surfaces and the size distribution of the cellsas well as the nuclei. The latter was important forcalculating the surface area, since the 2D TEM images donot allow us to obtain the size distribution of the cells, dueto the fact that one does not know at which height each cellwas cut for image acquisition. For example, the same TEMimage could be obtained from a small cell sectioned at the“equator” or a large cell sectioned at its ”pole”.

To obtain high resolution data allowing for thedetermination of the exact path of nuclear- and cellmembranes, the cells were fixed, sectioned and 2D imageswere recorded by TEM (Figure 1E). TEM images weresemi-automatically processed (Figure 1F and G) andanalyzed for the section area and the boundary length of thecell and nucleus surfaces. The size distribution of the cellsobtained by confocal microscopy and the section area andboundary length distribution from the high resolutionimages by TEM, allowed us to calculate the averagesurface area of the cells and the nuclei (Figure 1H). Theaverage cell and nucleus radii were calculated from the 3Dconfocal microscopy areas using the circle formula.

In this study the three main cell types of theadaptive immune system, T, B and mast cells, were used.These were primary human T cells from blood of a healthydonor and phytohemagglutinin/IL-2 expanded human Tcells (T cell blasts), the human T-cell line Jurkat , primarymouse B cells isolated from the spleen, the murine B-cellline J558L and bone marrow derived mast cells . Usingflow cytometry we show that 94%, 70%, 80%, 95%, 91%and 85% of the primary T cells, T cell blasts, Jurkat,primary B cells, J558L and mast cells, respectively, wereviable (Figure 2A). As expected, the primary cells weresmaller than the cultured cell lines, as seen by the lowervalues of the forward scatter. Further, Jurkat cells showedthe highest biological variation concerning the size (FSC,Figure 2A), where approximately 0.5% of the cells weregiant multinucleated cells (Figure 2B).

4.2. Determination of the cellular and nuclear radiidistributions

Confocal microscopy was used to obtain themean radius and the size distribution of the cells and nuclei.Unbiased sampling was assured by randomly recording allz-stacks containing several cells within the image display.In figure 3 we show an example of one z-plane for each celltype. A differential interference contrast (DIC) image(Figure 3A), the Calcein AM (whole cell, Figure 3B) andDAPI (nucleus, Figure 3E) fluorescences are displayed.The two fluorescence images were recorded, in order tofacilitate a semi-automatic segmentation of the cells andtheir nuclei. Grayscale values of the z-stacks werenormalized using a min/max function (Adjust Displayfunction) to set the range of grayscale between the lowestand the highest pixel present in the z-stack. We used aresolution of 0.1 µm in x/y-direction and 1 µm in z-direction. The resolution in z-direction represents a trade-off between accuracy and time requirement for themeasurement as it has been discussed for anotherstereological project .

The raw data exported from the microscope werenoisy and contained structural details that were not desiredfor this evaluation. For this reason a Gaussian smoothingwith standard deviation of 0.3 µm in all three directionswas applied to the data. In order to discriminate betweenforeground and background, an intensity threshold wascomputed based on the edge information in the data. Theedges of cells and nuclei were determined by consideringthe length of the corresponding intensity gradient I, i.e.


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Figure 1. The work flow followed in this report. Firstly, the living cells (A) were stained with Calcein AM (cytoplasm) andDAPI (nucleus) whose fluorescences were used to determine the shape of the cytoplasm and the nucleus in 3D by spinning diskconfocal microscopy (B). The resulting images were segmented with a thresholding method based on the gradient magnitude andafter a manual control (D) allowed to determine the size distribution of the cells (C) and the nuclei (H). Secondly, the cells werefixed, stained with OsO4 and PbCit and 2D images recorded by TEM (E). The images were segmented using the Berkeleysegmentation engine (BSE) (F). The resulting segmentation masks were semi-automatically merged (G) and automaticallyanalyzed for the surface of the section and the boundary length (H). Thirdly, using the mean diameter of the cells obtained byconfocal microscopy and the membrane boundaries drawn into the high resolution TEM images, allowed us to calculate thestructural parameters of the cells and the nuclei (H).

the magnitude of the intensity change between neighboringpixels. For different thresholds, we compared the values ofthe gradient magnitude on the resulting boundaries.The best threshold τ yields cell boundaries lying on theimage edges and thus can be determined by maximizing thegradient magnitude on the resulting contour.

δ is the Dirac function.

This threshold optimization was done for thecells with steps in the intensity range from 10 to 1500 andfor the nucleus with steps in the range from 10 to 400, bothwith a step size of 2. The result was manually controlledusing an overlay of the computed outline and the originalimage in 3D (Figure 3C and F) to exclude incompletelystained cells, cells that are only partially visible on theimage display or cell doublets (i.e. two cells touching oneanother). The resulting binary masks marking the completecells (red) and the nuclei (green) are shown (Figure 3H).

The segmentation masks were least reliable in thez-direction due to data blurring and the lower resolution inthis direction. Thus, we did not use pixel counting of thecomplete volume to compute the average radii of thesegmented masks. Instead, the radii were computed fromthe z-planes with the maximum section area, i.e. theequatorial plane of the cell. The section area in theequatorial plane was measured and the correspondingradius was calculated using the circle formula. Theresulting distribution of the cellular and nuclear radii ineach cell population is displayed (Figure 3D and G,respectively). Here and in further analyses we omitted the

few giant multi-nuclear Jurkat cells (Figure 2B; cells withtwo or three nuclei that were within the size distributionwere included in our analysis).

In conclusion, spinning disk confocal microscopeimages were used to record the biological variability of thedifferent cell populations concerning their cellular andnuclear radii.

4.3. Acquisition of high resolution 2D imagesNext, TEM was used to obtain images of the cells

with a resolution that allows unequivocal detection of allmembrane protrusions. For each cell type, a magnificationwas chosen that allowed recording all cell sections in oneimage. The magnifications used were 4600x, 5800x and13500x, yielding pixel sizes in the x- and y-directions of0.0196 mm, 0.015 mm and 0.0067 mm. Since the cells donot have a preferential orientation within the cellsuspension, this results in a systematic random samplingstrategy that gives every position within a cell the sameprobability to be sampled.

The precise segmentation of the 2D TEM images(Figure 4A and 5A) was based on hierarchical regionscomputed with the Berkley segmentation engine (BSE) . Ina first step, the image pixels were hierarchically groupedinto regions depending on their grayscale and texture(Figure 4B and 5B). The BSE tool provides a Matlab-basedgraphical user interface for the interactive generation offinal segmentation masks from region hierarchies. Herein,the region hierarchies are merged by the user drawing dotsand lines (Figure 4C and 5C), such that very little manualinteraction is needed compared to a fully manualsegmentation. The graphical user interface displays theresulting binary masks (Figure 4F and 5F) and an overlayover the original data (Figure 4D and 5D). A slightmodification of the input-script was necessary in order to


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Figure 2. Flow cytometric and confocal analysis of the cells used in this study. (A) Primary human T cells, human T cell blastsand the human T cell line Jurkat as well as primary mouse B cells, the mouse B cell line J558L and mouse bone marrow-derivedmast cells were analyzed by flow cytometry using the forward and side scatter (FSC and SSC). (B) The Jurkat cell line contains asmall proportion of giant cells. These cells contain several nuclei. One example is shown using the cytoplasmic dye Calcein AM(left), nucleic dye DAPI (middle) and a combined 3D representation (right).

automatically load, process, and save one TEMimage after another. We needed, depending on the image’scomplexity, one to two minutes to generate a segmentationmask for a cell and the corresponding nucleus, compared to10 to 20 minutes in a fully manual segmentation.

These binary masks were used to compute theEuclidean contour length shown in figure 4D and 5D. Sincethe contour length is also dependent on the magnificationused, a smoothing was performed. This smoothing had tobe performed in a range that it conserved all biologically

relevant protrusions but removed noise. The choice of thesmoothing factor was reasoned by the fact that the smallestexpected curvature of the membranes are in the range of 60nm in diameter . Hence, the contour was smoothed with ablurring kernel with a full duration at half maximum of 50nm.

In order to validate the results obtained from theTEM images, the areas of the cell sections were computedto determine the approximate radii of the cells in thecorresponding cutting plane. From the radii distribution


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Figure 3. 3D spinning disk confocal fluorescence microscopy. For each cell type representative data from the confocal spinningdisk microscope are shown using the z-stack from the equatorial plane (largest radius). DIC (A), Calcein AM (B) and DAPI (E)fluorescence images are displayed. An overlay of the automatically determined boundaries and the fluorescence images isdisplayed for the cells (C) and the nuclei (F). Finally the binary masks that mark the cells and nuclei section areas are shown (H).From the equatorial areas the corresponding radii were calculated and the radii distributions for the cells and the nuclei areplotted in the histograms (D and G, respectively).


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Figure 4. High resolution 2D TEM images for the human cells. One representative image from TEM is shown for each cell typeas indicated. The original TEM image (A) was used to calculate the pre-segmentation (B). Using the BSE tool, region hierarchieswere merged manually by drawing dots and lines (C). This semi-automated process resulted in binary masks that fit to theoriginal images as shown in an overlay (D). The distribution of the boundary length of the cells is plotted in the histograms (E).The binary masks are shown for the cells (red) and the nuclei (green, F).


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derived from the confocal data (Figure 3D), adistribution was simulated that would result whensectioning these cells. A comparison between thesimulated (based on the confocal data) and theobserved (TEM data) distributions was carried out. Incase of the primary B cells and the mast cells the twodistributions were the same, serving as a validationfor our approach. In contrast, the Jurkat cells used forTEM had a larger radius (1.17 times bigger) than theones used for confocal microscopy, which was doneon a different day than the TEM. Indeed, when wecompared Jurkat cells from different origins, slightlydifferent sizes were seen (flow cytometry data notshown). However, one Jurkat “clone” had a constantsize independent on the cell density, CO2concentration (5% or 7.5%) or fetal calf serumconcentration (0% - 10%) of its culture.

4.4. Calculation of the stereological 3D parametersFrom stereology we used that LA (the boundary

length per unit area) and AV (the surface area per unitvolume) for random cuts are coupled by

With the boundary length B per image area A andthe surface area S per reference volume V, equation (1)reads:

(2)

We were interested in the surface area of anindividual cell (not of a certain volume), so we onlyoutlined one cell in each image, which corresponded to asingle cell per reference volume. We only used images witha complete cell section, so the height of the referencevolume was limited to the diameter of the cell d. Bydefining the boundary length in image i as bi , the totalboundary in n images is

(3)

The width and length of image is denoted as wand l. The area of one image is w*l , thus the total area of nimages is

(4)

The reference volume had the same width andlength as one image multiplied with the height h. Theheight of the volume corresponded to the mean diameter of

a cell d (as determined by confocal microscopy), whichresulted in

(5)

Inserting (3), (4) and (5) into (2) we obtain

Using equation (8), we calculated thesurfaces of the cells and their nuclei (Table 1). Thediameter d was computed from the volumesdetermined by spinning disk confocal microscopy(Table). In the case of the Jurkat cells that hadslightly different sizes on different days of cellculture (see above), we had to correct this diameterwith the factor 1.17 (see above). For furtherexperiments, it is advisable to use the same cellpreparation for the confocal and TEM recordings.

5. DISCUSSION

In this report we present a semi-automatedapproach to determine the exact surface area ofimmune cells and their nuclei. Instead of using the“classical” stereology approach counting points andintersects of the TEM images covered with atransparent sheet bearing a rectangular lattice, weused semi-automated pattern recognition to mark theperimeter of the structures in the 2D images. Toobtain exact values for the average surface areas in agiven population of cells we further used the sizedistributions of the cells or nuclei obtained by 3Dconfocal microscopy and stereological calculations.

We found that the volume of the humanimmortalized Jurkat T cell was 12 times larger andthe one of the T cell blasts 2 times larger than the oneof the primary T cell, being in line with relativelarger diameters as detected by flow cytometry.Indeed, it is known that cultured tumor lines arelarger than primary lymphocytes . As expected theprimary cells showed the smallest cell-to-cellvariation compared to the proliferating Jurkat andmast cells.


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Table 1. Summary of the cellular parametersprimary human T cell human T cell blast Jurkat primary mouse

B cell J558L bone marrowmast cell

A. confocal microscopycell nucleus cell nucleus cell nucleus cell nucleus cell nucleus cell nucleus

number ofsamples 559 559 213 213 118 118 382 382 227 227 175 175

meanradius[µm] *

3.57+/- 0.01

2.91+/- 0.01

4.30+/- 0.04

3.12+/- 0.01

7.99+/- 0.11

6.19+/- 0.09

3.19+/-

0.01

2.81+/-

0.01

6.21+/-

0.04

4.88+/-

0.04

5.33+/-

0.04

3.49+/-

0.03standarddeviation[µm]**

0.23 0.16 0.56 0.2 1.15 0.92 0.26 0.24 0.59 0.53 0.59 0.4

sphericalsurface[µm2] *

160 +/-1

107+/- 1

236+/- 4

123+/- 1

818+/- 21

493+/- 13

129+/- 1

100+/- 1

489+/- 6

303+/- 4

361+/- 6

155+/- 3

meanvolume[µm3] *

192+/- 2

104+/- 1

353+/- 15

129+/- 2

2270+/- 100

1062+/- 45

139+/- 2

95+/- 1

1029+/-20

505+/- 11

658+/-18

184+/- 5

standarddeviation[µm3] **

38 17 220 23 1100 490 37 20 297 170 234 67

B. transmission electron microscopynumber ofsamples 116 102 108 81 135 123 124 123 115 93 43 24

meancontourlength[µm]

32.0 +/-0.8

14.9 +/-0.6 44.9 +/- 2 18.3 +/-

0.868.4 +/-

1.633.9 +/-

1.2

19.3+/-0.3

13.9+/- 0.3

49.5+/-1.3

28.9+/- 1.1

93.1+/-4.7

19.6+/- 1.2

standarddeviation[µm]

8.5 6.0 21 7 19 13.8 3.1 2.9 13.7 10.3 31 5.7

C. calculation of the surface areasmeansurfacearea [µm2]

290+/- 8

110+/- 5

492+/- 27

146+/- 7

1392+/- 51

535+/- 27

157+/- 3

99+/- 2.3

782+/-25

360+/- 16

1264+/-75

174+/- 12

surfacefactor

1.81+/- 0.06

1.03+/- 0.05

2.1+/- 0.15

1.18+/- 0.07

1.7+/- 0.1

1.09+/- 0.08

1.21+/-

0.03

0.99+/-

0.03

1.6+/-

0.07

1.19+/-

0.07

3.5+/-0.3

1.1+/- 0.1

The sizes of the nuclei follow the same order; theprimary cells have the smallest and cultured cells thebiggest nucleus. Most likely this is related to the fact thatprimary B and T cells are resting cells being in the G0 stateof the cell cycle and only minimally transcribe genes. Incontrast, the Jurkat, J558L and mast cells are proliferatingand thus contain a larger amount of the less densely packedeuchromatin that is transcriptionally active. Indeed, in theTEM images the mast cell nucleus contains larger light-stained regions, which represent the euchromatin, andfewer dark-stained heterochromatic regions (Figure 5A).As tumor lines, Jurkat and J558L cells contain a largeamount of chromosomes, thus the nucleus is the largest oneof the cell types analysed.

The mean cellular surface of the resting primaryB and T cells was the smallest, being in line with theirsmall size. The mean surface areas of the Jurkat and mastcells are nearly equal (1390 and 1260 mm2, respectively),although the volume of Jurkat was more than 3 timesbigger than the one of the mast cells. This underscores thenecessity to carefully measure the surface areas, instead ofestimating them from the cell sizes.

We calculated cell surface areas of 157 mm2 and290 mm2 for the primary B and T cells, respectively(table). The first value is in line with similar valuesobtained by using the “classical” stereology approach fromprimary T cells (150 mm2, ) or from total primary

lymphocytes (170 mm2, ). The second value is larger thanthe ones reported. The mean FSC value of the fresh cells asmeasured by flow cytometry was 43000 for the primary Tand 32000 for the B cells (Figure 2A). This indicates thatthe primary T cells in this study indeed were larger than theprimary B cells. The discrepancy with the literature isunknown, but might be related to differences in theisolation procedures of the cells. Interestingly, in ouranalysis the surface area of the primary T cells deviatedmore from the one of a sphere (factor 1.81) than the one ofthe primary B cells (factor 1.21). Indeed, the T cellscontained more protrusions than the B cells (Figure 4 and5).

We observed that within the Jurkat cellpopulation approximately 0.5% of the cells differed in sizefrom the normal population by a factor of 3 to 4 andcontained up to a dozen of nuclei (Figure 2B). These fewcells could alter the resulting mean values significantly.Here, we restricted the analysis to cells that were within thepeak of the size distribution. The two non-tumor cellpopulations did not contain any giant multi-nucleated cells.

One of the questions addressed in thisproject was how many times bigger the real cellularsurface is when compared to a surface derived fromthe spherical formula using the mean radiusdetermined by light microscopy. This woulddemonstrate how big the deviations from reality were


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Figure 5. High resolution 2D TEM images for the murine cells. For each cell type one representative image from TEM is shown(A). Pre-segmentations and binary masks were done as in figure 4 (B, C, D and F). In E, the distribution of the boundary lengthof the cells is plotted as in figure 4.


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that were used for systems immunology modeling. Theprimary B cells have the smoothest surface and therewiththe smallest factor. In this case the surface was only 1.2times larger than the one of a sphere. In the other cells thesurface is 1.6 to 2.1 times larger and in the mast cells,which have many protrusions, the surface is 3.5 timeslarger than the ones of spheres with the same radii.Whether a factor of 1.2 or 3.5 affects the results of amodeling approach, depends on how robust the result is tochanges in this parameter. If the exact surface area has tobe known, we recommend determining the value with theapproach presented here.

Our volumes and areas can serve as an input intosystems biology approaches. In fact, the exact surfaces andvolumes of most cell types still remain an ill-defined factor.Therefore, it is desirable to determine the cell surface areaand the volumes of the cell and nucleus for the mostcommonly used cell types and cell lines in immunology.However, compartmentalisation of the cellular volume intoorganelles and of the plasma membrane into microdomainshas to be considered, when necessary, and will reduce thevolume or area in which certain proteins can move.

Our semi-automated approach can be the basisfor further advances towards a fully automatedsegmentation of these biological datasets as on the basis ofregion hierarchies. In , we have already presented anapproach for the fully automatic segmentation of bonemarrow-derived mast cells from TEM recordings. Inaddition to cytoplasm and nucleus regions, we also tried toprovide automatic segmentations for mitochondria andother vesicles in this paper. The approach proved to be verypromising with an overall accuracy of 65% for thesegmentation into five classes (background, cytoplasm,nucleus, mitochondria and vesicles). A higher accuracy isto be expected if the very difficult classes mitochondria andvesicles are omitted. Since this automatic approach is basedon learning from training data, the TEM images that wererecorded during the work presented here together with thesemi-automatic segmentations can be used to furtherimprove the method presented in . In order to stimulateprogress in automated pattern recognition research, wemake our TEM images and the according segmentationsavailable as reference data sets: http://lmb.informatik.uni-freiburg.de/resources/datasets/bio/TEM_cells.html.

6. ACKNOWLEDGEMENTS

Volker Morath and Margret Keuper equallycontributed to this article. We thank Kerstin Fehrenbach,Marina Freudenberg and Michael Huber for the generationof bone marrow derived mast cells, Marlena Duchniewiczfor technical instructions, and Felix Popp, Petra Kindle,Rosula Hinnenberg, Volker Speth and Roland Nitschke fortechnical help and supply with reagents. This study wassupported by the Excellence Initiative of the GermanFederal and State Governments (EXC 294, BIOSS andGSC-4, SGBM), FORSYS from the Bundesministerium fürBildung und Forschung, the CRC620 and SCHA 976/2-1from the Deutsche Forschungsgemeinschaft and theSYBILLA project of the EU in FP7.

7. REFERENCES


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Abbreviations: BSE: Berkley segmentation engine; DAPI:4 ,6-Diamidin-2-phenylindol; DIC: differential interferencecontrast; EM: electron microscopy; FSC: forward scatter;PBMC: peripheral blood mononuclear cell; PHA:phytohemagglutinin; SSC: side scatter; TEM: transmissionEM; 2D: two dimensional; 3D: three dimensional

Key Words: Systems biology, Immunology, B cell, Mastcell, T cell, Stereology, Quantification, Surface, Volume,Pattern recognition, Segmentation

Send correspondence to: Wolfgang Werner A. Schamel,BIOSS Centre for Biological Signaling Studies, Universityof Freiburg, Germany, Tel: 49-761-5108-313, Fax: 49-761-5108-423, E-mail: [email protected]

Semi-automatic procedure for the determination of the cell...

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