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Journal of Structural Biology 162 (2008) 380–386

StructuralBiology

Quantitative 3-D imaging of eukaryotic cellsusing soft X-ray tomography

Dilworth Y. Parkinson a, Gerry McDermott b, Laurence D. Etkin c,�,Mark A. Le Gros a, Carolyn A. Larabell a,b,*

a Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 6-2100, Berkeley, CA 94720, USAb Department of Anatomy, University of California-San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143, USA

c Department of Molecular Genetics, University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA

Received 28 November 2007; received in revised form 31 January 2008; accepted 1 February 2008Available online 4 March 2008

Abstract

Imaging has long been one of the principal techniques used in biological and biomedical research. Indeed, the field of cell biology grewout of the first electron microscopy images of organelles in a cell. Since this landmark event, much work has been carried out to imageand classify the organelles in eukaryotic cells using electron microscopy. Fluorescently labeled organelles can now be tracked in live cells,and recently, powerful light microscope techniques have pushed the limit of optical resolution to image single molecules. In this paper, wedescribe the use of soft X-ray tomography, a new tool for quantitative imaging of organelle structure and distribution in whole, fullyhydrated eukaryotic Schizosaccharomyces pombe cells. In addition to imaging intact cells, soft X-ray tomography has the advantageof not requiring the use of any staining or fixation protocols—cells are simply transferred from their growth environment to a sampleholder and immediately cryofixed. In this way the cells can be imaged in a near native state. Soft X-ray tomography is also capable ofimaging relatively large numbers of cells in a short period of time, and is therefore a technique that has the potential to produce infor-mation on organelle morphology from statistically significant numbers of cells.� 2008 Elsevier Inc. All rights reserved.

Keywords: Cellular imaging; Microscopy; Organelle characterization; Schizosaccharomyces pombe; Soft X-ray tomography; Segmentation; Yeast

1. Introduction

Imaging is fundamental in all biological and biomedicalresearch, and the essence of cell biology. In the years sinceGeorge Palade, Keith Porter and Albert Claude (Claude,1949; Palade and Porter, 1954) first used electron micros-copy to visualize the ordered compartments of eukaryoticcells, an enormous amount of effort has been devoted toimaging and classifying organelles (Subramaniam, 2005).Electron microscopy has remained the primary imaging

1047-8477/$ - see front matter � 2008 Elsevier Inc. All rights reserved.

doi:10.1016/j.jsb.2008.02.003

* Corresponding author. Address: Department of Anatomy, Universityof California-San Francisco, 513 Parnassus Avenue, San Francisco, CA94143, USA. Fax: +1 510 486 5664.

E-mail address: Carolyn.Larabell@ucsf.edu (C.A. Larabell).� Deceased.

tool for this type of study. However, the relatively shallowpenetration depth of electrons means that eukaryotic cellscan only be imaged after they have been fixed and sec-tioned to be less than 0.5 lm thick (McDonald, 2007). Sec-tioning is frequently a frustrating and time-consumingprocess, and requires the use of protocols—such as plasticembedding—that have the potential to significantly impactthe fidelity of the cell ultrastructure (Perktold et al., 2007).In this paper, we present a method for quantitatively imag-ing the organelles in whole eukaryotic cells using soft X-raymicroscopy. By using X-rays with wavelengths in the‘water window’ the resultant 3-dimensional (3-D) recon-structions of the cell have excellent natural contrast (Guet al., 2007; Larabell and Le Gros, 2004). Consequently,the cells are not exposed to potentially damaging stainingor fixing protocols prior to imaging with this technique.

D.Y. Parkinson et al. / Journal of Structural Biology 162 (2008) 380–386 381

Most molecular interactions that occur in a normallyfunctioning eukaryotic cell are location specific. Conse-quently, a number of methods have been developed totrack the precise location of proteins inside the cell. In thisregard, genetically encodable fluorescent proteins (FP)have been phenomenally successful, and have been devel-oped for a wide range of applications, including measuringthe distance between differently labeled molecules (Giep-mans et al., 2006) and as a means of obtaining relativelyhigh spatial resolution localization information (Betziget al., 2006; Gustafsson, 2005; Hell, 2007; Rust et al.,2006). That said, FP-based imaging methods have an obvi-ous shortcoming—they can only image the fluorescent sig-nal provided by the tagged molecules, so no information isobtained on the internal organization of the cell. Learningabout the internal organization requires the use of a com-plimentary imaging technique, typically electron micros-copy. It has become commonplace to determine thelocation of a particular protein in a cell using the fluores-cence signal from a FP tag, and use this knowledge as aguide for collecting higher resolution images using electronmicroscopy (Sosinsky et al., 2007). While the approach ofusing complementary imaging techniques provides valu-able insights into the details of cellular processes, it wouldbe preferable to obtain this information using a singleimaging method. Soft X-ray microscopy has the demon-strated potential to meet this need. For example, in previ-ous work we have already demonstrated the practicalityof using electron dense labels to localize proteins in mouse3T3 cells (Meyer-Ilse et al., 2001). In this paper, we showthe ability of soft X-ray tomography to examine the struc-tural composition of cells, which is critical in order toextend protein localization studies into three dimensions.

In addition to imaging cells that are minimally per-turbed, a further strength of soft X-ray tomography isthe throughput; a complete set of projection images canbe collected in less than 3 min. The throughput is furtherenhanced in the case of yeast because a number of cellsare in the field of view at the same time. This means thateach data collection run results in tomograms of three tofive cells. Consequently, cells can be imaged in statisticallysignificant numbers. This allows for an accurate correlationof phenotypic and morphological changes in the cellularstructure with genetic and biochemical data. This is of par-ticular importance for studies focused on subtle changes inorganelle morphology.

In many types of cells, the number and organization ofcertain organelles can change quickly in response to envi-ronmental factors such as cell density, temperature, oxygentension, and the availability of nutrients (Conibear and Ste-vens, 1995; Egner et al., 2002; Weisman et al., 1987). Yeastis a particularly good model system for studying the size,shape, and distribution of organelles such as mitochondriaas a means of coping with changes in their environment(Anesti and Scorrano, 2006; Hales, 2004; Hermann andShaw, 1998; Hoog et al., 2007; Jakobs et al., 2003; Jensen,2005; Logan, 2003, 2006; Sun et al., 2007; Yaffe et al.,

1996). For example, during logarithmic growth the mito-chondria in yeast constantly undergo fusion and fission.If the cells exhaust the available nutrients, the cells entera stationary phase characterized by cell cycle arrest andother specific physiological, biochemical, and morphologi-cal changes. One such change is the cessation of mitochon-drial fission, resulting in unbalanced fusion and theformation of ‘giant’ mitochondria. Yeast in the stationaryphase can remain viable in this state for very long periodsof time. When their environment becomes more suited togrowth, yeast in the stationary phase can quickly revitalizeand resume the normal cell cycle, including metabolic pro-cessing. As a consequence, when cells move from the sta-tionary phase to logarithmic growth, the size, location,and mobility of the mitochondria rapidly reverts to thattypically seen in cells growing logarithmically.

Yeast vacuoles also respond to environmental changes,in particular changes in osmolarity. When fission yeastcells, Schizosaccharomyces pombe, are taken from mediaand placed in water, for example, the smaller vacuolesundergo rapid fusion to form much larger vacuoles (Boneet al., 1998).

In the work described here, we demonstrate the powerof soft X-ray tomography by quantifiably determiningthe size, shape, and organization of S. pombe organellesin stationary phase cells using soft X-ray tomography. Inparticular, we quantified and characterized the mitochon-dria and vacuoles in cells that had become stationary dur-ing different stages of the cell cycle—freshly buddeddaughter cells, single mature cells, and mother–daughtercells. This work follows our previous research on S. pombe

(Gu et al., 2007), in which we studied the process of celldivision.

2. Materials and methods

2.1. Cell culture

Wild-type S. pombe cells (strain #972 h) were grownwith rotary shaking at 30 �C in YES media (yeastextract + adenine + casamino acids) supplemented withleucine, histidine, uracil, and dextrose.

2.2. Light microscopy

Mitochondria and vacuoles were stained by adding fluo-rescent dyes to cells undergoing logarithmic growth. Thesestains had no apparent effect on cell growth when com-pared with unstained controls (data not shown). The dyesremained active for upwards of 72 h after incubation withcells, and images taken after long incubation appeared verysimilar to images taken at the cessation of logarithmicgrowth (data not shown). Vacuoles were visualized by add-ing 5-chloromethyl fluorescein diacetate (CMFDA; Molec-ular Probes) at a final concentration of 125 nm in growthmedia. MitoTracker Red 580 (Molecular Probes) was usedat a final concentration of 100 nm in the growth media.

382 D.Y. Parkinson et al. / Journal of Structural Biology 162 (2008) 380–386

Cells were imaged using the appropriate filters on a ZeissAxiovert M200 microscope.

2.3. X-ray tomography

Stationary phase S. pombe were pelleted at 3000g and re-suspended in PBS before being transferred to capillarysample tubes. The cells were then rapidly frozen in a cryo-genic gas stream prior to tomographic data collection (see(Le Gros et al., 2005) for a description of the sample tubesand cryogenic sample stage).

Projection images were collected using a transmissionsoft X-ray microscope (XM-1; beamline 6.1.2 at theAdvanced Light Source, Lawrence Berkeley National Lab-oratory, Berkeley, CA). The microscope was equipped withFresnel zone plate condenser and objective (with 55 and45 nm outer zone widths, respectively; the latter being theresolution-defining optical element). Data were collectedusing X-rays with an energy of 517 eV (2.4 nm), and 16-bit images were recorded using a Peltier-cooled, back-illu-minated, 1024 � 1024 soft X-ray CCD camera (Roper Sci-entific Instruments Micromax system with SIT chip; RoperIndustries Inc., Duluth, GA). Projection images were col-lected using exposure times ranging from 0.25 to 1.5 s. Afull tomographic data set consisted of 90 images collectedat 2� increments over 180� of rotation. In addition to thedata images, 10–12 background images were collected(with the sample moved out of the field of view). Each dataimage was divided by the average of the backgroundimages, and the negative logarithm of the quotient calcu-lated to give images whose gray values correlated directlywith the X-ray absorption coefficients.

We assume that the images we collect are good approx-imations to projections of the absorption coefficients of oursample. This means we neglect any phase effects due to thelimited aperture of the optics. Since XM-1 uses a bendmagnet X-ray source and uses a Fresnel zone plate con-denser, we believe that our assumption of an incoherentsource is valid.

As collected, each pixel in the projection image has awidth of 10 nm. Prior to alignment and calculation of thetomographic volumes, the images were down-sampled to512 � 512, and therefore a pixel corresponded to 20 nm(approximately half the spatial resolution).

The projection image series were aligned using fiducialmarkers (60 nm gold particles) with the IMOD softwarepackage (Mastronarde, 1997). Reconstruction was carriedout using the ‘‘Algebraic Reconstruction Technique withblobs” in the XMIPP software package (Marabini et al.,1998; Sorzano et al., 2004). ‘‘ART with blobs” is morecomputationally intensive than the more commonly usedfiltered backprojection method. However, by using a 20-node cluster of Apple X-serve computers each with twodual-core Intel Xeon processors the reconstructions are cal-culated in under 1 h. After reconstruction, the images weresegmented manually, measured, and visualized with Amira(Mercury Computer Systems).

3. Results

For the reconstructed 3-D volumes from soft X-raytomography, we used two methods to visualize organelles.The most basic method is a grayscale image of a one-voxel-thick slice through the volume, with the gray value of eachvoxel corresponding to the soft X-ray absorption coeffi-cient of the material. Fig. 1a–c shows this type of visualiza-tion for four cells (including in a mother and daughter thathave not yet separated, Fig. 1c). In Fig. 1d–u, we displaythe boundaries between structures that have been digitallysegmented from each other. In addition to the plasmamembrane of the cells, these images show the borders oforganelles within the cell.

Supplementary Movies 1–3 are available online. In themthe cells in Fig. 1d–u rotate, and the different organelles areshown one color at a time, to better indicate the distribu-tion of organelles.

In total, we segmented 50–100 organelles in each cell.We included only organelles with diameters of at least100 nm, approximately twice the observed spatial resolu-tion in the reconstructions. Table 1 lists the dimensionsand volumes of the organelles and cells shown in Fig. 1.The cells varied from 4.0 to 6.6 lm long, with volumes of17–31 lm3. Despite this variation in size, the volume ofthe nucleus in these cells appeared to comprise between4% and 6% of the total cellular volume, and the cumulativevolume of other segmented organelles was between 14%and 16% of the total cellular volume. Many of these statis-tics were based on boundaries that were hand-drawnaround the organelles. We subsequently used digital seg-mentation (boundary drawing) of some of the features mul-tiple times to determine the accuracy with which theboundaries had been drawn, and found that the relativestandard deviation was less than 5%.

As mentioned in Section 2, by collecting images with thesample in and out of the field, we quantitatively measuredprojections of the absorption coefficients of our sample,and can then determine these 3-D absorption coefficients.The values of the voxels in our reconstructed volume thushave value not only because of their contrast with respectto each other, but also because of their absolute values.Every material has a characteristic absorption coefficient(for example, protein, lipid, carbohydrate, water, or glass),so the measured X-ray absorption coefficient can aid in theidentification of organelles by giving some indication oftheir composition. We determined the average X-rayabsorption coefficient of each organelle and divided theminto five categories, corresponding to the X-ray absorptioncoefficients indicated in the figure legend (from least tomost X-ray dense, the colors are black, blue, green, yellow,and red). The nuclei, which would have an average X-rayabsorption coefficient corresponding to blue on the samecolor scale, are shown in orange to differentiate them fromother organelles. Fig. 1d–f shows all of the color-codedorganelles, while Fig. 1g–u shows the five groups of coloredorganelles in separate panels so that the distributions can

Fig. 1. Cells imaged by soft X-ray tomography. (a–c) One-voxel-thick slices through the reconstructed volumes of four cells (including mother anddaughter cells which have not separated), with the gray value of each voxel corresponding to the X-ray absorption coefficient of the material (darkergray = higher absorption). (d–f) Colored surfaces representing the boundaries of the organelles and the plasma membrane. The colors correspond to theaverage X-ray absorption coefficients inside the organelles. The scale is shown at the bottom of the figure. The nuclei are colored orange to distinguishthem, but have an average X-ray absorption coefficient corresponding to blue. The cell in (a and d) is 5 lm in length. (g–u) The same surfaces as shown in(d–f), but with each color shown isolated from the others. On each row in this section, there is also a cross-sectional image of an organelle characteristic ofthose displayed in that row. The arrows point to mitochondria, and the wedges point to other organelles. There are corresponding marks in Fig. 3, andthese are intended to aid in viewing the correspondence between the figures.

D.Y. Parkinson et al. / Journal of Structural Biology 162 (2008) 380–386 383

be more easily seen. On each row there is also an image of across-sectional slice of an organelle characteristic of thatgroup.

Light microscopy was performed for comparison withsoft X-ray tomography images. Fig. 2 shows these imagesof S. pombe under two different conditions. Fig. 2a–d

shows bright field (a), fluorescence (b, c), and overlay (d)images of log phase yeast in media, where the mitochondriahave been labeled with MitoTracker (c) and the vacuoleshave been labeled with CMFDA (b). These images showa mitochondrial network that is interconnected throughthe cell, and show a number of spherical vacuoles in each

Table 1Dimensions and volumes of selected organelles and cells from Fig. 1

Featurea Cell

Early Late Mother Daughter

Cell length 5.0 6.6 4.5 4.0Cell width 2.5 2.8 3.0 2.5Cell volume 25 31 25 17Vol. nucleus 1.0 1.3 1.1 0.9# Organelles 71 94 87 57Vol. organelles 4.2 4.7 3.9 2.4# Mitochondria 14 5 11 9Vol. mitochondria 1.9 1.4 1.7 0.8

a Lengths are in lm, volumes are in lm3.

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cell. Fig. 2e–h shows the corresponding series of images foryeast in PBS which have entered stationary phase. Asdescribed in Section 1, in stationary phase yeast there is acessation of mitochondrial fission, resulting in unbalancedfusion and the formation of a number of spherical mito-chondria. Because the cells are in PBS rather than water,thus maintaining the osmolarity of the environment, thevacuole size, and number remain approximately the samein these cells.

Fig. 2. Bright field, fluorescence, and overlay images of log phase yeast in medfluorescently labeled with MitoTracker (c and g), and the vacuoles were label

Fig. 3. The same cells as shown in Fig. 1, but in (b–d), only the nucleus and orwith a mitochondrial cross-section. The remaining organelles are shown in (f–organelles are vacuoles (compare to Fig. 2f). The arrows and wedges are desc

Our initial assignment of the organelles seen in the X-ray tomography images was carried out on the basis ofthe organelle appearance and the soft X-ray linearabsorption coefficient of the segmented objects. Mito-chondria can be readily identified in the tomograms bytheir characteristic appearance. Whereas most of theorganelles have relatively homogeneous absorption coeffi-cients, mitochondria have a thick outer layer that ishighly absorptive, while the interior has much lowerabsorption coefficients, and is very heterogeneous. Across-sectional image of a mitochondrion and anotherorganelle are displayed in Fig. 3a and e, respectively, toillustrate the differences between them. We believe thedark border of the organelle in Fig. 3a is the mitochon-drial double lipid membrane layers, and the highly heter-ogeneous interior is due to the cristae. Fig. 3b–d showssurface visualizations of the mitochondria in each cell(the mitochondria are shown in red).

We identified between 5 and 14 putative mitochondria ineach cell, and in each case these mitochondria were distrib-uted throughout the cell and accounted for between 4%and 8% of the cell volume (see Table 1). Because of theirheterogeneity, the average absorption of the various

ia (a–d) and stationary phase yeast in PBS (e–h). The mitochondria wereed with CMFDA (b and f). Scale bar is 10 lm.

ganelles identified as mitochondria are shown (compare to Fig. 2g), alongh), along with a characteristic cross-section in (e). Many of the remainingribed in Fig. 1 caption.

D.Y. Parkinson et al. / Journal of Structural Biology 162 (2008) 380–386 385

mitochondria corresponded to multiple different colors inthe scheme of Fig. 1. To show the correspondence betweenthe organelles in Figs. 1 and 3, matching arrows andwedges have been added to these figures, as described inthe figure captions.

4. Discussion

In this work, we examined the organelle structure, com-position, and distribution in stationary phase S. pombe

cells using soft X-ray tomography. As presented in Section3, we determined the sizes and volumes of cells and theirorganelles at different stages of the cell cycle, and in partic-ular we analyzed the mitochondria in each cell. In a com-parable analysis of a single S. pombe cell that had beengrowing in the log phase, the cell was 6.7 lm long andhad a volume of 33.5 lm3, of which the nucleus comprised3.16 lm3 (or 9% of the cell volume) and the mitochon-dria—which formed a linked network—comprised1.23 lm3 (3%) (Hoog et al., 2007).

The differences between the mitochondria in that studyand ours are likely based on the different phases in whichthe yeast were measured. The yeast cells we measured bysoft X-ray tomography (as shown in Figs. 1 and 3) wereplaced in PBS for imaging, and thus correspond to the lightmicroscopy images in Fig. 2e–h, while the study mentionedin the previous paragraph used yeast comparable to thatshown by light microscopy in Fig. 2a–d. In another recentreport, electron tomography was used to study the trans-formation of mitochondria during apoptosis (Sun et al.,2007). They found that the cristae matrix fragments andbecomes a small number of swollen compartments. Mito-chondria of yeast in the stationary phase share some char-acteristics of mitochondria in cells undergoing apoptosis(Hales, 2004).

We cannot make definitive assignments for the remain-ing non-mitochondrial organelles without performing fur-ther experiments. The surfaces of the segmented non-mitochondrial organelles are shown in Fig. 3f–h, alongwith a characteristic cross-section of one of these organellesin Fig. 3e. This figure shows that the distribution and num-ber of these remaining organelles is in good agreement withthe fluorescence images in 2c and g, in which the vacuolesare labeled. Thus, we believe that a large percentage ofthese remaining organelles are vacuoles.

Supplementary Movies 4–6 are available online. In themthe cells in Fig. 3a–b rotate, and the mitochondria andnon-mitochondrial organelles are shown separately, to bet-ter indicate their distributions.

Based on the measured X-ray absorption coefficients ofeach organelle, we can also determine the likely contents ofmany of these organelles. For example, lipids have a char-acteristic linear absorption coefficient that is higher thanthat of proteins (Henke et al., 1993; Weiss et al., 2000).Thus, the organelles with the highest X-ray absorptioncoefficients—colored red in Fig. 1d–f and shown isolated

from other organelles in Fig. 1g–i—are most likely filledwith lipids.

In comparison with other microscopy techniques, softX-ray tomography has several advantages for studyingthe size, distribution, and density of organelles. New‘super-resolution’ techniques such as stimulated emissiondepletion microscopy (STED) (Hell, 2007), saturated struc-tured illumination microscopy (SSIM) (Gustafsson, 2005),stochastic optical reconstruction microscopy (STORM)(Rust et al., 2006), photoactivated localization microscopy(PALM) (Betzig et al., 2006), and 4PI confocal microscopy(Egner et al., 2002), can match the resolution of soft X-raytomography in two dimensions, but in the third dimensionthey have significantly lower spatial resolutions (�100 nm),or are limited to thin sections. In addition, these techniquesare limited to information from the fluorescent labels, anddo not have access to full structural information on the cell.Electron tomography has been used to analyze organellesin three dimensions at high resolution (Lucic et al., 2005),but in all cases these cells have been chemically fixed, dehy-drated, embedded in plastic, and sectioned. Soft X-raytomography overcomes many of the limitations inherentto the above techniques.

In future work, we will identify organelles through cor-relative fluorescence and soft X-ray microscopy, usinglabeling moieties as our guide (Alivisatos et al., 2005).Some organelles clearly have characteristic X-ray absorp-tion profiles, such as the lipid droplets and mitochondriashown here, and we expect that other structures will alsoshow characteristic X-ray absorption profiles.

Soft X-ray tomography is an ideal tool for studyingyeast as they respond to environmental changes, especiallywhen correlated with images obtained by light microscopy,such as those in Fig. 2. The imaging described in this paperwas carried out using a multi-purpose soft X-ray micro-scope while a new instrument specifically designed for bio-logical and biomedical imaging is being constructed at theAdvanced Light Source of Lawrence Berkeley NationalLaboratory. This new microscope, designated XM2, willproduce images with higher spatial resolution and greatlyimproved signal to noise. With this new microscope weanticipate being able to confidently visualize smaller fea-tures with subtle X-ray absorbance variations, for example,the microtubule cytoskeleton and territories in the nucleus.This microscope is near completion, and we will use it tocontinue this investigation of yeast ultrastructure.

Acknowledgments

We thank Zenaida Serrano for growing the yeast. Wethank the XMIPP developers for their assistance, in partic-ular C.O.S. Sorzano, R. Marabini, J.R. Bilbao-Castro, andJ.M. Carazo. This work was funded by the US Departmentof Energy, Office of Biological and Environmental Re-search (DE-AC02-05CH11231), the National Center forResearch Resources of the National Institutes of Health

386 D.Y. Parkinson et al. / Journal of Structural Biology 162 (2008) 380–386

(P41 RR019664) and the National Institutes of GeneralMedicine of the National Institutes of Health (GM63948).

Appendix A. Supplementary data

Supplementary data associated with this article can befound, in the online version, at doi:10.1016/j.jsb.2008.02.003.

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