Perspectives of Molecular and Cellular Electron Tomography

Perspectives of Molecular and Cellular Electron Tomography

Abraham J. Koster, Rudo Grimm, Dieter Typke, Reiner Hegerl, Arne Stoschek,Jochen Walz, and Wolfgang Baumeister

Abteilung Molekulare Strukturbiologie, Max-Planck-Institut fur Biochemie, 82152 Martinsried, Germany

Received October 6, 1997

After a general introduction to three-dimensionalelectron microscopy and particularly to electrontomography (ET), the perspectives of applying ET tonative (frozen–hydrated) cellular structures are dis-cussed. In ET, a set of 2-D images of an object isrecorded at different viewing directions and is thenused for calculating a 3-D image. ET at a resolutionof 2–5 nm would allow the 3-D organization ofstructural cellular components to be studied andwould provide important information about spatialrelationships and interactions. The question ofwhether it is a realistic long-term goal to visualizeor—by sophisticated pattern recognition methods—identify macromolecules in cells frozen in toto or infrozen sections of cells is addressed. Because of theradiation sensitivity of biological specimens, a pre-requisite of application of ET is the automation ofthe imaging process. Technical aspects of automatedET as realized in Martinsried and experiences arepreresented, and limitations of the technique areidentified, both theoretically and experimentally.Possible improvements of instrumentation to over-come at least part of the limitations are discussed insome detail. Those means include increasing theaccelerating voltage into the intermediate voltagerange (300 to 500 kV), energy filtering, the use of afield emission gun, and a liquid-helium-cooled speci-men stage. Two additional sections deal with ET ofisolated macromolecules and of macromolecularstructures in situ, and one section is devoted topossible methods for the detection of structures involume data. r 1997 Academic Press

Key Words: three-dimensional transmission elec-tron microscopy; cryomicroscopy; low-dose; ice-embedding; energy filtering.

1. INTRODUCTION

Many cells or tissues are too large to be transpar-ent to electrons, and must be cut into thin sectionsfor investigation by transmission electron micros-copy. As early as 1948, Pease and Baker were able toproduce 0.1- to 0.2-µm-thick sections of biological

material for electron microscopy (Pease and Baker,1948). Soon after the development of the first widelyused ultramicrotome (Porter and Blum, 1953) serialsections were prepared for the examination of the3-D organization of cellular structures (Gay andAnderson, 1954). Serial sectioning was further devel-oped by Sjøstrand (1958), and an impressive large-scale application of the technique in the analysis ofthe ‘‘circuitry’’ of the outer plexiform layer of therabbit retina can be found in Sjøstrand (1974).Meanwhile, serial section images can be combined inthe computer, and elaborate programs have beendeveloped to minimize cutting artifacts. The resolu-tion that can be obtained with serial sectioning isabout 4 nm in x- and y-directions and 30 nm in thez-direction at best. Although most of our knowledgeof the ultrastructure of eukaryotic and prokaryoticcells has been obtained by this method, the resolu-tion is obviously too low to reveal molecular struc-tures.

An electron microscopic image is essentially atwo-dimensional projection of the object along theprimary beam, in which features from differentlevels within the object overlap and cannot be sepa-rated in a single image. Recording of stereo pairs willnot help much in separating features of a continu-ously varying function (e.g., structure with variationin mass density), such as the electric potentialdistribution, which is the physical quantity that isimaged in a TEM.

A remedy to these shortcomings is the ‘‘opticalsectioning’’ approach. There are in fact two types ofoptical sectioning, which may be designated asthrough-focusing and tomography. Through-focus-ing is the acquisition of images, in which only oneparticular section of the specimen is recorded ‘‘sharp’’or ‘‘in focus,’’ while all other parts contribute only to ablurred or defocused background. One has to sepa-rately record images of many slices, thereby illumi-nating the object several times in full depth, in orderto obtain the 3-D information.

Tomographic techniques acquire projections of thespecimen from different directions, which are later

JOURNAL OF STRUCTURAL BIOLOGY 120, 276–308 (1997)ARTICLE NO. SB973933

2761047-8477/97 $25.00Copyright r 1997 by Academic PressAll rights of reproduction in any form reserved.

merged computationally to obtain a ‘‘reconstruction’’of the volume. This approach requires a much lowertotal exposure of the specimen than the through-focusing techniques.

Incidentally, an X-ray ‘‘tomogram’’ in its originalconception (from Greek Temnein, meaning to sec-tion) was a recording technique of the through-focustype, which could be described as ‘‘blur-tomography’’:It was recorded by moving the source and thephotographic film in an antiparallel way, with thespecimen between them, such that only a thin sec-tion of the object became sharp in the image, whilethe rest of the structure showed as a blurred back-ground (Oldendorf, 1961). The depth of the sectionwas chosen by adjusting the speed. Computer tomog-raphy with X-rays, which firmly established themeaning of tomography as it is used today, uses adifferent approach (Cormack (1980), Hounsfield(1980); also see Bracewell and Riddle (1967) forapplications in astronomy). Only a thin fan-beam isused for illuminating a slice of the object; the sourcetogether with a linear detector array is moved aroundthe object to obtain a set of 1-D projections indifferent directions. A 2-D image of the slice is thencalculated in the computer. To obtain a 3-D imageone has to record and evaluate similar projectionsets of many slices.

Electron tomography (ET) uses a set of 2-D im-ages, recorded under different orientations of theobject with respect to the primary beam, for calculat-ing a 3-D image. The basic concepts of this approach,i.e., of the 3-D reconstruction from projections, wereput forward in 1968 in three independent papers(Hart, 1968; Hoppe et al., 1968; DeRosier and Klug,1968). This method provides the basis for the devel-opment of electron crystallography, which has be-come a powerful tool for analyzing molecular struc-tures with near-atomic resolution (Henderson et al.,1990; Kuhlbrandt et al., 1994). While electron crystal-lography, based on the same principles, but techni-cally less demanding, has already led to remarkableachievements, the progress in applying electron to-mography to large individual structures has beenless impressive to date. A comprehensive account ofthe development of this particular application ofelectron microscopy has been given by Frank (1995).

Electron tomography can be used for 3-D imagingof macromolecular as well as of cellular structures.In this review we will give an account of recentdevelopments in automation of electron tomography,which we see as a key feature for a widespreadapplication of the method. Particularly, we will ad-dress the question of whether it is a realistic long-term goal to visualize or identify macromolecularstructures in cells frozen in toto or in frozen sectionsof cellular structures. Obviously, given the low con-

trast of electron micrographs of ice-embedded biologi-cal samples and the ‘‘crowdedness’’ of cellular vol-umes with proteins, DNA, RNA, and othercomponents, such a goal may not easily be attained.The major technical obstacle is the high sensitivity ofthe unstained ice-embedded samples to the electronirradiation; therefore it is essential to perform elec-tron tomographic data collection under extreme low-dose conditions.

One should bear in mind that it is not necessarilyrequired to identify the 3-D molecular structures bydirect visual inspection. The identification of a givenmolecular structure could be based on 3-D correla-tion or on other sophisticated pattern recognitionmethods. A 3-D cross-correlation peak may be signifi-cant even if the particle is hardly recognizable.Suitable reference structures can be derived fromdata obtained with other high-resolution techniquessuch as X-ray crystallography, nuclear magneticresonance (NMR), or electron crystallography. Alter-natively, the identification of molecules in a cellularvolume can be facilitated by suitable labeling withnanometer-sized high-density particles.

Electron tomography of cellular structures hasgreat potential to bridge the resolution gap betweenmethods providing near-atomic resolution and micro-scopic techniques which allow the examination ofliving cells such as confocal or dark field lightmicroscopy. ET of cellular structures embedded invitrified ice at a resolution of 2–5 nm would allow thestudy of the 3-D organization of structural compo-nents at a level that is sufficient to identify singlemacromolecules. Important information about supra-molecular organization and interactions could thusbe obtained by analyzing their spatial relationshipsinside a cell.

We will first discuss the technical aspects of elec-tron tomography in some detail, identifying thelimitations of the technique both theoretically andexperimentally. Next, current developments in elec-tron microscope instrumentation will be discussed,in particular automated data collection and theapplication of energy filtering to increase the imagecontrast of ice-embedded samples. Finally, the chal-lenge of identifying macromolecules in their cellularenvironment will be addressed and possible solu-tions will be discussed.

2. THREE-DIMENSIONAL ELECTRON MICROSCOPY

2.1. Basic Concepts of 3-D Electron Microscopy

As the fundamentals of the tomographic approachof optical sectioning, i.e., the reconstruction from aset of projections, can be found in several reviewsand books (Hoppe and Hegerl, 1980; Amos et al.,1982; Frank, 1992, 1996), we only briefly summarizethe underlying ideas. Three-dimensional electron

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microscopy in its wider sense includes electron crys-tallography of proteins, helical reconstructions, quasi-tomographic single-particle methods such as ‘‘ran-dom-conical tilting’’ or ‘‘angular reconstitution’’ (seebelow), and electron tomography in a strict sense,where all projections are recorded of the same speci-men by physical tilting.

The assumption that an electron microscopic im-age can be considered as a projection of the objectalong the direction of the primary beam is justifiedby the large depth of focus which is of the order of 100or more resolution elements (due to the short wave-length of electrons in the EM). From a set of projec-tions of an object, taken at many different views, a3-D image can be calculated either by one of theavailable methods in real space or via interpolationin Fourier space (or reciprocal space) and subse-quent backtransformation. The description in Fou-rier space helps to understand the requirements fordata recording depending on the specimen size andthe desired resolution. According to the projectiontheorem, a projection in real space corresponds to acentral section perpendicular to the projection direc-tion in the three-dimensional Fourier space. Record-ing a set of projections distributed over a large rangeof projection directions is equivalent to scanning thespecimen information in Fourier space by the corre-sponding set of sections.

For a finite specimen, the full structural informa-tion to a given resolution can be recorded by tiltingabout a single axis over an angular range of 180°.The relation between resolution d and tilt angleincrement Da (in radians) is given by the Crowtherrelation (Bracewell and Riddle, 1967; Hoppe, 1969;Crowther et al., 1970): d 5 D Da 5 pD/N, where D isthe diameter of the object, and N the number ofprojections recorded at equally spaced tilt anglesover a range of 180°. For reconstruction of an objectof 50 nm diameter with a resolution of 2 nm onewould need 75 projections and thus a tilt incrementof 2.4°. Most specimens in electron microscopy arequite extended in the x- and y-directions, but have alimited thickness in the z-direction. For optimalsampling of specimens of constant thickness, non-equidistant tilt angles may be advantageous (Saxtonet al., 1984; Olins et al., 1989). It is not possible to tiltsuch samples over the full angular range of 180°. Thelimited tilt range implies that data are missing in awedge-shaped region of the Fourier space, causingdistortions.

There are data collection geometries other thansingle-axis tilting: In ‘‘conical tilting’’ the specimen isrotated about an axis that is oblique to the beamdirection. With this geometry one has to rotate thespecimen over the full range of 360°. Nevertheless,no full data sets of individual objects can be recorded;

data are missing in a conical region of the reciprocalspace. The distortions caused by the missing dataare smaller than for single-axis tilting. Anothergeometry is tilting about two axes perpendicular tothe beam. In this case data are missing in a pyrami-dal region of the reciprocal space, and the distortionsare similar to those of conical tilting. Effective tiltinggeometries occurring in quasi-tomographic ap-proaches will be discussed in Section 2.3.

Regardless of the specimen and the type of datarecording, the 2-D images obtained experimentallyneed to be processed to obtain a 3-D image. There aremainly two tasks: First, the images have to bealigned with respect to each other in order to referthem to a common 3-D coordinate system describingthe object to be reconstructed. Second, a 3-D image iscomputed using a reconstruction algorithm of whichseveral types are available such as Fourier recon-struction methods, weighted backprojection meth-ods, or iterative direct space methods. The mostoften applied method is weighted backprojection,illustrated in Fig. 2.1. For a detailed discussion ofthe image processing involved we refer to Frank(1992).

2.2. Single-Axis Tilting

For a unique structure, such as a cellular organ-elle, single-axis tilting is the data collection geom-etry of choice to record the set of projections requiredto compute the 3-D reconstruction (Frank, 1992). Forthe collection of a single-axis tilt series, the specimenis tilted over a range of typically 670° in small tiltincrements (1°–5°), and an image of the same objectarea is recorded at each tilt angle.

The computation of a distortion-free 3-D recon-struction from a single axis tilt series would requirethat all projections of the sample over a full (690°)tilt range are available (Barnard et al., 1992). Due tophysical limitations imposed by the specimen hold-ers and samples, it is generally not possible to collectsuch data sets. The angular tilt range is limited andtherefore the 3-D reconstruction will suffer fromimperfections. Two types of distortions can be distin-guished. First, the resolution in the 3-D reconstruc-tion will be direction dependent, such that two layersof a membrane which may be resolved in one direc-tion, might appear fused in another (for an illustra-tion see Fig. 2.2). Second, features will becomeelongated in the direction of the angular gap. Forexample, a spherically shaped gold bead will beelongated in its 3-D reconstruction. Although thenature of these distortions is well understood, theydo complicate interpretation. To minimize the dete-riorating influence of the angular gap, the specimentilt range should be as large as possible. Recently,cryoholders allowing a tilt range of 670° have be-

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come commercially available. A fundamental limita-tion, however, is the increasing specimen thicknessat larger tilt angles. For example, a 100-nm-thicksample at a 70° tilt will be 2.7 times thicker (270 nm)and at a 80° tilt even 5.7 times thicker (570 nm).Thus, a consequence of high tilt is the loss of imagecontrast due to multiple electron scattering. A high-voltage, intermediate voltage, or energy filteringelectron microscope could offer a remedy (see Section4). Another way of increasing the image contrast isby the application of restoration techniques, whereimages taken at different defocus are combined(Typke et al., 1992). Reconstruction distortions canbe further reduced by merging two sets of projections(for example, collected separately over an angularrange of 670°) at orthogonal angles (see Manella etal. (1994) and Penczek et al. (1995)). Also, a poste-riori image processing methods imposing certainconstraints during the 3-D reconstruction have beenproposed, e.g., those using POCS (Carazo andCarrascosa, 1987; Bellon and Lanzavecchia, 1995) ormaximum entropy (Barth et al., 1988; Skoglund etal., 1995).

Because of the large number of projections re-quired in ET (typically 30 to 120), one might suspectthat a very high dose will accumulate during thecollection of the data set. Early theoretical consider-ations (Hegerl and Hoppe, 1976) have shown that, inprinciple, the electron dose required to visualizestructural details close to the resolution limit in a3-D image is about the same as that for a 2-D imageobtaining the equivalent information. The advan-tage of a visualization in 3-D is that fine detailsbecome separated while they overlap in 2-D and thuswill usually be obscured by other specimen features.Plausibility considerations were given in an earlierpaper (Hoppe et al., 1973), indicating that, in prin-ciple, one may fractionate the dose in the same wayin both the 3-D and the 2-D cases over many noisyimages. The signal-to-noise ratio (s/n) of the detail inthe single images will be the same, the only differ-ence being that all projection directions are equal inthe 2-D case. Adding up the information of allprojections increases the s/n in about the same wayin both cases. However, in the 3-D case the addi-tional information about the z-position is obtained. Apractical limitation to the number of projections overwhich the dose can be fractionated is given by therequirement that all projections must be properlyaligned to a common origin for 3-D reconstruction.This is usually done by cross-correlation which canbe facilitated by the use of high-density markers. Ina recent paper, McEwen et al. (1996) carried outdetailed computer simulations on dose fractionationand were able to show that the theorem holds, evenin the case of (not too strongly) absorbing specimens.

2.3. Quasi-tomographic Approaches

The first 3-D reconstruction of an individual mac-romolecule was based on a tomographic approachwith physical tilting of the specimen over 660°(Hoppe et al., 1974). Since then, quasi-tomographictechniques which do not require the recording of atilt series have been developed. These techniquesexploit the fact that a given preparation may containa large number of molecular structures, with differ-ent orientations, which are assumed to be identicalcopies of the ‘‘true’’ object and, consequently, thattheir images can be considered as independent projec-tion views. There are two extreme situations to beconsidered: The orientations of the molecules arerandomly distributed over the whole angular rangeor the particles are adsorbed to the grid with onepreferred orientation.

When the orientations of the molecules adsorbedto the grid are distributed completely randomly overthe whole angular range, the data collection experi-ment is straightforward: One exposure of the un-tilted specimen provides many different projectionviews of the object (Figs. 2.3a, 2.3d). The problem isto determine the orientation of each particle—expressed by three Euler angles—from the givenprojection data. Several techniques have been pro-posed which all are based on the central sectiontheorem and the common line principle (Crowther,1971). The name ‘‘angular reconstitution’’ has beenassigned to one of these techniques (van Heel, 1987),but may be used also for similar approaches (Vainsh-tein and Gontcharov, 1986; Gontcharov et al., 1987;Farrow and Ottensmeyer, 1992).

When the molecules have one preferred orienta-tion on the grid, the image of the untilted specimenshows the same projection view for all molecules,except for in-plane rotations. In order to obtaindifferent projection views, the specimen must betilted (Figs. 2.3b, 2.3c). Referring the resulting projec-tion directions to one particle in its untilted orienta-tion, the directions are distributed on a cone with itsaxis parallel to the electron beam in the untiltedview and a half angle equal to the tilt angle (Fig.2.3e). The distribution of the projection directions ismore straightforward to derive than for angularreconstitution because the tilt angle is defined by theexperiment and the azimuthal angles correspond tothe relative in-plane rotations which can be deter-mined from the untilted specimen. Regarding thegeometry of projection directions, ‘‘random conicaltilt reconstruction’’ was proposed as a name for thistechnique (Radermacher, 1987; Frank and Raderma-cher, 1992).

With quasi-tomographic approaches the 3-D recon-struction is performed with data obtained by expos-ing the specimen only once to the electron beam,


thus minimizing the specimen damage. This alsoholds for the random conical tilt technique becausethe micrograph of the tilted specimen is recordedfirst. Subsequent micrographs of the untilted speci-men are used only to determine the in-plane rotationand the azimuthal angle of the tilt axis and do notenter into the reconstruction. An intrinsic feature ofboth techniques is the averaging of many particles,as is the case in crystallographic methods.

In real-life experiments, the distribution of par-ticle orientations deviates from the ideal situation.Usually, particle orientations are neither completelyrandom nor strictly identical. When one orientationprevails, random conical tilt reconstruction is themethod of choice. Unless there is a single orienta-tion, the data sets must be classified and projectionviews which can be assigned to the same projectiondirection are combined. Angular reconstitution mayfail if the angular assignment is performed with

individual projection views especially when the dataare noisy, as is the case of samples embedded in ice.Again, the data set has to be broken down into a setof significant average images, each one representinga homogeneous class of projection views which corre-spond to the same projection direction. Image classi-fication thus becomes an indispensable element ofthe quasi-tomographic approaches. As shown re-cently for both techniques (Frank et al., 1995; Starket al., 1995), further improvement of the angularassignment is possible by an iterative refinementprocedure, using projections derived from a prelimi-nary 3-D reconstruction as references.

There is an important difference between bothquasi-tomographic approaches: While, in principle,the entire angular range of projection directions iscovered when using angular reconstitution (Fig.2.3d) a ‘‘missing cone’’ of projection directions isinherent to the random conical tilt technique (Fig.2.3e), rendering nonisotropic resolution. This prob-lem may be overcome if the molecules occur in morethan one preferred orientation. Two or more 3-Dreconstructions can be performed and merged, fillingup the missing cone (Frank et al., 1993). However,also with angular reconstitution, nonisotropic resolu-tion may occur if there is a nonuniform distributionof projection directions. Exploitation of the symme-try, or the additional recording of a few images fromthe tilted specimen, will allow a complete set ofprojection views to be obtained. Another differencebetween the two techniques is that in the case ofangular reconstitution, the basic requirement ofhaving ‘‘identical copies’’ of the object within thespecimen, is rather stringent. In contrast to therandom conical tilt method, one cannot apply classi-fication techniques to select those particles from theuntilted view which are more or less identical. Withangular reconstitution the structural homogeneity ofthe sample is particularly critical. At present, themost advanced applications, based on ice-embeddingand using approximately 5000 particles, have at-tained a resolution near 2 nm (Stark et al., 1997; Zhuet al., 1997).

3. AUTOMATED TOMOGRAPHY

In principle, the steps involved in collecting single-axis tilt tomographic data are straightforward: thespecimen is tilted over a large angular range andimages are recorded at a series of discrete tilt angles.The relation between resolution d and tilt angleincrement Da (in radians) is given by the Crowtherrelation (see Section 2.1).

In practice, manual data collection is very timeconsuming. Due to mechanical imperfections of thegoniometer, the tilt axis is not completely stable.Therefore, even if the specimen has been carefully

FIG. 2.1. (a) A 3-D object is projected at the various tilt anglesinto a series of 2-D images. As a first step in the reconstructionprocess, the projections are smeared out to form so called 3-Dbackprojection bodies. (b) To reconstruct the 3-D object, all thebackprojection bodies are summed. The backprojection algorithmcalculates a 3-D image representing an approximation of the 3-Dobject. The quality of this image improves when more projectionsare used over a large tilt range.

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adjusted to the correct z-position (the eucentricheight) and the tilt axis is well aligned to the opticalaxis of the microscope, the real tilt axis (and thusalso the specimen) may move by several 100 nmwhen the object is tilted. This movement producesimage shifts as well as focus changes. For cryohold-ers the movements are mostly more severe, perhapsbecause of the additional weight of the dewar vesselof the holder. Their design is apparently not fully

adapted yet to the needs of tilting at high tilt anglessince normally thermal drift is observed at high tiltangles. The larger the specimen tilt increments, thelarger the image shifts will be. Particularly at hightilt angles (.40°), one needs to recenter and refocusthe image after every tilt increment. Because of thebeam sensitivity of the specimen, data collection hasto be performed under strict low-dose imaging condi-tions. Under these conditions manual data collectionwould be extremely tedious and prone to failure.Moreover, photographic image recording requires alarge number of micrographs to be digitized andprocessed to obtain a 3-D reconstruction.

Two groups have pioneered the development ofdedicated instrumentation and methods for facilitat-ing tomographic data collection and processing, oneat the MPI in Martinsried (Typke et al., 1991;Dierksen et al., 1992, 1993) the other at UCSF (SanFrancisco) (Koster et al., 1992; Liu et al., 1995; Funget al., 1996). Although these systems have similarfeatures, they have been optimized with respect tosomewhat different goals and are used in differentranges of electron dose and resolution. Based onthese developments, other groups and companieshave started setting up similar systems (e.g., Rath etal., (1997)).

The system developed at UCSF is optimized tocollect data of relatively thick (0.1–0.3 µm) sectionsof plastic-embedded cellular structures. The micro-scope (Philips EM 430) is typically operated at 300

FIG. 2.2. Illustration of the missing wedge effect. The deteriorating influence of the missing gap depends on the tilt range and tiltincrement. With a tilt range of 690° and an increment of 2° the reconstruction is almost identical to the original image; with a tilt range of650° and an increment of 5° the similarity is very poor.

FIG. 2.3. Simulated data recording of the quasi-tomographictechniques using the 20S proteasome as object. (a) Image forangular reconstitution; (b and c) images of the untilted and tiltedspecimen required for random conical tilt reconstruction; (d and e)distribution of projection directions (F,Q) for both techniques.


kV and most data are collected at room temperature.Examples of this application can be found in Horow-itz et al. (1994) and Moritz et al. (1995). Both thedata collection and reconstruction processes arelargely automated and integrated in the programpackages EMACT and EMCAT, both running onSilicon Graphics workstations (Chen et al., 1996;Fung et al., 1996).

Two systems have been developed at the MPI(Martinsried). The first of these, consisting of aPhilips CM 200 FEG (with field emission gun),equipped with a charge-coupled device (CCD) cam-era and an external computer (TVIPS GmbH, Gaut-ing, Germany) for on-line control, is described insome detail below. The second system, a Philips CM120 Biofilter, is equipped with a Gatan postcolumnenergy filter and a CCD camera, and is described inSection 4.3 (de Jong et al., 1996). The setup based onthe CM 200 FEG is optimized for low-dose datacollection of frozen–hydrated specimens or for plas-tic sections cooled to liquid nitrogen temperature. Sofar, data has been collected of frozen hydratedsamples embedded in 50- to 100-nm-thin ice films(Dierksen et al., 1995; Horowitz et al., 1997; Walz etal., 1998; Nitsch et al., 1998) but also on negativestain preparations (Sperling et al., 1997) and onresin-embedded sections (Shillito et al., 1996). In thedesign of the data collection program, special atten-tion has been given to low-dose data collection, e.g.,recording of complete tilt series with a total dose ofabout 2000 e nm22. One of the long-term goals is thecollection of 3-D data sets of relatively large struc-tures (up to 0.2 µm) embedded in vitrified ice. Sincethe original description of the system (Typke et al.,1991; Dierksen et al., 1992), the hardware (see Fig.3.1) and software of this setup have been improved:measures have been added to compensate moreeffectively for specimen drift during data collection,and procedures required for calibrating the micro-scope control at various magnifications and hightensions have been made more flexible and user-friendly.

3.1. Digital Data Collection

In automated electron tomography all microscopeoperations required for recording data sets, includ-ing focusing and correction of image shifts, are doneautomatically. The images are usually recorded bymeans of a slow-scan CCD camera in a digitalformat, thus avoiding the time-consuming steps offilm development and digitization required withphotographic image recording. An additional advan-tage over photographic film is that, because of thelinear relation between electron exposure and read-out of the CCD camera (in counts, or analog-digitalunits (ADUs)), there is no need to correct for nonlin-

earities. Furthermore, the images recorded with aCCD camera are directly available for visual inspec-tion at the microscope, which simplifies low-doseelectron microscopy of low-contrast specimens. Adisadvantage of the present generation of cooledslow-scan CCD cameras is their relative small size(10242 pixels), or, in other terms, their low resolu-tion, compared to photographically recorded electronmicrographs (Downing et al., 1992). Aspects of digi-tal imaging will be discussed in more detail inSection 4.6.

3.2. Tracking Image Shifts and Focus Changes

In order not to lose the specimen area underinvestigation during recording of a tilt series, imageshifts and focus changes have to be measured andcorrected. For the correction, either the electron-

FIG. 3.1. The setup for automated data acquisition consists ofa Philips CM 200 FEG TEM, a slow-scan CCD camera (10242

pixels, 19 µm pixel size) and an on-site computer (TVIPS, Gaut-ing, Germany) that controls both the camera and the microscope.The on-site computer is equipped with a 160 Mflops arrayprocessor (Supercard 2XL, CSPI) which is mainly used for thecomputation of (filtered) Fourier transforms, diffractograms, andcross-correlation functions. A DMA-interface transfers the datafrom the CCD camera at a rate of 500 kpixels/s. At a nominal EMmagnification of 327 500, the pixel size referred to the specimenplane is 0.34 nm, and the total field of view of the camerameasures 3502 nm2. At 33800, the pixel size is 2.5 nm and thefield of view 2.52 µm2. The camera has a sensitivity of approxi-mately 10 counts per impinging electron. Thus a dose of 100 enm22 corresponds to 120 counts per pixel at 327 500 nominal EMmagnification. A tomographic data set may consist of more than140 images (1024 3 1024 3 2 bytes) which would equal 280Mbytes of data. The tomographic data collection procedures areembedded in the program package ‘‘MENU’’ of TVIPS GmbH(Gauting, Germany). The MENU program is written in TCL(Technical Command Language, TPD, Delft, The Netherlands),which is a general purpose image processing language. TVIPSadded command libraries to TCL to allow the control of electronmicroscopes, CCD cameras, image display boards, and arrayprocessors. TCL runs on a various computer platforms, includingOS-9 (version 3.0, Microware), a real-time dialect of Unix, theoperating system used on the external computer. Image reconstruc-tion is done on an off-site workstation (Unix, Silicon Graphics)using the EM software package (Hegerl and Altbauer, 1982;Hegerl, 1996).

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optical controls, i.e., the deflection coils and theobjective lens current, or, alternatively, the mechani-cal specimen stage controls (x–y–z controls) can beused. The electron-optical control is fast, convenient,accurate, and reproducible and thus preferable tothe control of the mechanical stage as long as theimage shift is within the accessible range. In thePhilips CM series microscopes the user has access tothe image shift in the SEARCH state of the low-dosepage. In the CM 200 FEG the total shift range islimited to about 66.5 µm at magnifications between31500 and 327 500 and to about 61.5 µm at highermagnifications, in order to allow higher stability ofthe coils at higher magnifications. Usually the elec-tron-optical control is sufficient, but in some casesboth types of control have to be used in combination.

The amount of shift and focus change that occursduring data collection depends on the goniometerand the specimen holder. Fig. 3.2 shows a typicalspecimen movement curve (shifts and focus changes)as measured with a Gatan 676 high-tilt cryoholder atlow temperature in combination with the PhilipsCompu-Stage. In most experiments, accumulatedspecimen shifts of 1–3 µm occur during a tilt series,provided that the specimen has carefully been set tothe eucentric height.

Image shifts are measured by determining thepeak position in the cross-correlation function (xcf )of an image recorded after setting a new tilt anglewith a reference image that was recorded previously.One should realize that the two correlated imagesare recorded at different tilt angles. Thus the twoimages will not only be shifted but also differ to some

extent due to the different projection angles. There-fore, prior to the computation of the cross-correlationfunction, the image recorded at the higher modulusof the tilt angle is stretched perpendicular to the tiltaxis by a factor cos(a 2 Da)/cos(a) (or a similarexpression depending on the sign of a and Da) withDa being the tilt angle increment. To compensate forthe image shift, the current in the image shift coils ischanged (using an appropriate calibration). At mag-nifications above ca. 310 000, the image shift per tiltincrement may be larger than half the field of viewrecorded with the CCD camera; as a consequence,the measurement will fail. For these larger displace-ments the maximum found in the xcf is not a correctmeasure of the image shift and it is then inevitableto do the shift measurement at a lower magnifica-tion.At 33800, the field of view increases to 2.52 µm2.

To minimize the electron dose that is delivered tothe specimen area of interest, the images requiredfor focusing and tracking at the higher magnificationare recorded on areas which do not coincide with thearea of interest (Fig. 3.3). In most cases, the optionsfor data collection are chosen such that after incre-menting the tilt angle, the image shift is first mea-sured and corrected for at low magnification(SEARCH). Fine image positioning is then done atan increased magnification (TRACK). In the follow-ing step, the specimen is automatically focused(FOCUS) using the principle of the wobbler: whenthe microscope is not in focus, the effect of tilting theilluminating beam is an image displacement. Theamount of image shift, measured by means of cross-correlation, is proportional to the defocus and canthus be used for focus correction by changing theobjective lens current (Koster et al., 1989; Koster andde Ruijter, 1992). After compensation of the shift andfocus changes, a 2-D image, i.e., one projection of thetilt series, is recorded (EXPOSURE). These foursteps are repeated until the full angular tilt range iscovered. Following this data collection scheme, theillumination and magnification conditions arechanged during data collection. When minimum-dose imaging conditions are chosen, meaning thatthe coarse image shift correction is performed at theSEARCH magnification on the same area as EXPO-SURE, but TRACK and FOCUS on different areas,then the additional dose applied to the EXPOSUREarea can be less than 3% of the total dose used fordata collection. When less strict low-dose imagingconditions are chosen, where SEARCH, TRACK, andFOCUS are all performed on the same area, but withdifferent settings of the illumination and exposuretime, the dose required for the SEARCH, TRACK,and FOCUS positions can amount to 11% or more ofthe total dose. Presently, the time required forSEARCH, TRACK, FOCUS, and EXPOSURE is

FIG. 3.2. Focus change (dots) and image shift (squares) as afunction of the specimen tilt (dashes) for a Gatan 676 high-tiltcryoholder in combination with the Philips Compu-Stage. Typi-cally, object shifts of 1–3 µm occur during a tilt series, sufficientlysmall to be tracked with the electron-optical controls. Largershifts are corrected using the mechanical specimen controls. Inthe experiment shown, the specimen is tilted at 20 k3 nominalEM magnification from 0° to 70°, then, at low magnification, it isreset from 70° to 0°. Finally it is tilted from 0° to 262.5°. Theaccumulated specimen movement is smaller when a room-temperature holder is used.


shown in Fig. 3.4. An example of an automaticallyacquired data set is given in Fig. 3.5.

In the current setup, one major factor affecting theperformance of recording tilt series of specimens atliquid nitrogen temperature is the drift of the speci-men holder occurring after each tilt increment. Thedrift can be initially as large as 10 nm/s, and it cantake 15 s or more before an acceptable drift rate of0.5 nm/s has been reached. The modules of both thetracking of image shifts and autofocusing have beenmodified recently to assure that these functions areperformed accurately, even in the case that thespecimen is drifting. The tracking modules inSEARCH and TRACK re-align each new reference inthe computer memory to the corresponding referencetaken in the previous cycle, to compensate for theamount of image drift which has occurred betweenthe moment the image displacement is measured (bytaking an image and comparing it to the previouslytaken reference) and the moment that the newreference image is recorded. The accuracy in compensat-ing the image shift is better than 0.5% of the fullimage size. The accuracy in obtaining a prealigneddata set is less, usually due to sudden specimenmovements (‘‘jumps’’) that occur when the tilt angleis incremented; typically it lies in the range of 2% ofthe full image size. The FOCUS module has alsobeen modified to function accurately in the presenceof specimen drift. In addition to the two imagesneeded for the determination of the amount ofdefocus (from images recorded with oppositely tilted

illumination), a third image is taken under the sameconditions as the first image. The first and thirdimages are used to measure the amount of specimendrift. An account of the accuracy of autofocusing isgiven in Table 3.1.

4. IMPROVEMENTS OF EM INSTRUMENTATION

Although the strong scattering of electrons byatoms allows the investigation of single biologicalmacromolecules, it also limits the thickness of speci-mens for structural investigations at a resolutionsufficient for visualizing or at least identifying, singlemacromolecular particles. In vitrified ice, the ‘‘total’’mean free path, Ltot (indicating the path length afterwhich an electron has been scattered once on aver-age either elastically or inelastically), is about 100nm at 100 kV accelerating voltage. Thus, for speci-men thicknesses in the range of several 100 nm,multiple scattering is inevitable. For biological speci-mens and vitrified ice, which are almost exclusivelycomposed of light atoms, inelastic scattering is muchstronger than elastic scattering. Of the elasticallyscattered electrons, the majority is normally inter-cepted by the objective aperture, while those scat-tered once and passing the aperture produce imagecontrast by interference with the primary beam.Inelastic scattering has a strong forward characteris-tic (see, e.g., Reimer (1989)), such that the majorityof the inelastically scattered electrons reaches the

FIG. 3.3. Automated data collection is carried out in four steps: SEARCH, TRACK, FOCUS, and EXPOSURE. To allow low-doseimaging, we can select for SEARCH a magnification of 33800 with low-intensity illumination and a very large defocus to enhance contrast,and an exposure time of 0.5 s in combination with a CCD image size of 5122 pixels (using 2 3 2 binning of pixels). With these settings, anexposure in SEARCH with an average readout of 1000 counts corresponds to an electron dose of 0.4 e nm22. For TRACK and FOCUS, thesame magnification used in EXPOSURE (e.g., 27.5 k3) can be chosen, and the beam can be adjusted such that a small nonoverlapping areais illuminated at a distance of 2 µm from the EXPOSURE area. Using a short exposure time (0.1 s) and low beam intensity, together with asetting of the CCD camera to 2562 pixels with 4 3 4 binning, an average readout of 500 counts is possible, which corresponds to a dose of 25e nm22. Finally, for the EXPOSURE area an exposure time of 0.5 s and a setting of the CCD camera to 10242 pixels (1 3 1 binning) may beused; an average readout of 500 counts then corresponds to a dose of 390 e nm22.

284 KOSTER ET AL.

image, forming a strong inelastic background. Eventhough this background carries only little informa-tion about the specimen, its noise contribution signifi-cantly obscures fine details of the image. With in-creasing thickness the disturbing influence ofinelastic scattering increases drastically.

Several measures may be taken to improve thissituation: In order to reduce the disturbing influenceof the inelastic background, the accelerating voltagemay be increased to the intermediate-voltage range(300 to 500 kV), or inelastically scattered electronscan be filtered off by the use of an imaging energyfilter. A FEG may be used to increase the beamcoherence, enabling the use of relatively large defo-cus values without losing contrast transfer. Thesesteps will help to increase the contrast betweenbiological material and the surrounding ice. Theoscillations of the transfer function, occurring atrelatively large defocus, can then be corrected byimage restoration techniques (Typke et al., 1992;Kleinz et al., 1993). Lowering the specimen tempera-

ture from about 90 K (liquid nitrogen) to less than 10K by cooling with liquid helium (Fujiyoshi, 1989) willdecrease sensitivity to radiation damage (Zemlin etal., 1996) and thus increase the dose that can beapplied to data recording. Since each of these mea-sures can provide only a partial improvement, itbecomes necessary to use them in combination tomaximize the thickness of specimens in which macro-molecules can be meaningfully investigated withelectron tomography. In the following subsections wefirst compile some formulas that are useful whendealing with multiple scattering and apply them tomultiple scattering in ice; then we discuss what canbe gained by these measures.

4.1. Multiple Scattering

The scattering behavior of a homogeneous me-dium that is composed of several types of atoms isbest described by the mean free paths for elastic andinelastic scattering, Lel and Lin, respectively, which

FIG. 3.4. The time currently required for each tilt increment while recording a tomographic data set. The total time per tilt incrementis 110 s, of which 30% is spent during SEARCH (5122 images), 30% during TRACK (5122 images), 32% during FOCUS (5122 images), and18% during EXPOSURE (10242 images). About 26% of the total data collection time is spent on microscope control and 11% on data transferfrom the CCD camera to the computer system. Also indicated is the time required by the central processing unit and array processor(maximum search in cross-correlation fields, scaling of data for display, flatfielding of CCD images, Fourier transformations includingfiltering, and cross-correlation), which corresponds to about 46% of the total data collection time. The actual exposure time of the specimenis 3%. It is expected that in the near future the time required for the computations will become negligible and that microscope control anddata transfer will become faster.


are given by

Lel 5 1ol

rlsel,lNA/Al221

and

Lin 5 1ol

rlsin,lNA/Al221 (4.1)

Here rl is the partial mass density of one type of atom(indicated by subscript I), Al is its the atomic mass

number, sel,l and sin,l are its cross sections of elasticand inelastic scattering, respectively, and NA isAvogadro’s number. The total mean free path, Ltot,follows from

Ltot21 5 Lel

21 1 Lin21. (4.2)

For a specimen of thickness t, the average distribu-tion of electrons over the different ‘‘scattering chan-nels’’ of elastic and inelastic scattering is describedby the equation (Brunger and Menz, 1965)

Nµn 5N0

µ!n!· 1 t

Lel2µ

· 1 t

Lin2n

· e2t/Ltot, (4.3)

where N0 is the number of incoming electrons (e.g.,per unit area) and Nµn is the number of electrons thathave undergone µ elastic and n inelastic scatteringevents.

For vitreous ice, the distribution of scattered elec-trons into different scattering channels is shown as afunction of t/Ltot,H2O in Fig. 4.1a; for 300 kV accelerat-ing voltage the thickness in nm is also indicated. Thedistribution was calculated using the following data(at 100 kV): r 5 0.93 g cm23, sel,H 5 3.65 pm2, sel,O 590.7 pm2 (Bonham and Schafer, 1974), so Lel,H2O 5320 nm; Lin,H2O 5 175 nm (Grimm et al., 1996a). Thenumber of electrons that arrive in the image dependson the size of the objective aperture and thus on thedesired resolution. To a good approximation, the

FIG. 3.5. A tilt series of an 80-nm-thin section of the cup-shaped chitin microfibril formation system (Shillito et al., 1996) taken with theCM 200 FEG system described in the text. Displayed are 21 projections: from 260° to 160° with 6° increments. The original seriesconsisted out of 90 projections: from 161.5° to 272° with 1.5° intervals. The direction of the tilt axis is vertical. The dense particlesscattered over the images are 5-nm gold beads which were deposited on the surface of the section, and which are used for aligning theprojections of the tilt series. In the center of the structure is an electron-translucent object, the chitin microfibril tip.

TABLE 3.1Measured Accuracy in Automatic Focusing as a Function

of Magnifiction

Magnification

Targetdefocus

(nm)

Offsetdefocus

(nm)PCTF

fit (nm)Reproducibility

sd (nm)

11.5k 22000 25000 22000 1920 k 22000 22000 22300 1527.5k 21000 21000 21120 22

2200 21000 222038 k 2300 2500 2330 14115 k 2100 2200 2120 19

Note. The measurements were performed on a carbon film onwhich isolated negatively stained proteasomes were deposited.The reproducibility was estimated after repeating the automaticfocusing procedure 15 times. Focus was determined by measuringthe positions of the electron diffractogram rings.

286 KOSTER ET AL.

fraction passing the aperture is given by (Scherzer,1970)

sA/sel 5 [1 1 (0.6l/deqmax)2]21

5 [1 1 (0.6dcryst/de)2]21,(4.4)

where de 5 2.5 Å · Z 21/3 is a characteristic length ofthe atom and dcryst 5 l/qmax is the crystallographicresolution limit that corresponds to the aperture.For ice (essentially oxygen, de 5 1.25), the fraction ofelectrons passing the aperture is, e.g., 32.5% (14.8%,4.2%) if an aperture corresponding to 0.3 nm (0.5 nm,

1 nm) resolution is used; this is independent of theaccelerating voltage. For electron tomography ofrelatively thick ice-embedded specimens, the resolu-tion will at best be in the range of 1 to 3 nm, and thusa very small aperture could be utilized. For example,to obtain 1 nm resolution at 300 kV, the diametercould be 10 µm for a focal length of the objective lensof 2.5 mm. However, for practical reasons, e.g., inorder to facilitate autofocusing by the beam tiltmethod (Section 3.2), not too small an aperture sizeshould be chosen. Without energy filtering, a substan-tial part of the inelastically scattered electrons passthe aperture, forming a quasi-uniform background

FIG. 4.1. Distribution of scattered electrons for vitreous ice. (a) Distribution (without an aperture) over the elastic (lower diagonallyhatched area), inelastic (upper diagonally hatched area), and mixed (horizontally hatched area) scattering channels for vitreous ice as afunction of thickness (in multiples of the total mean free path Ltot and, for 300 kV, in nm). The dashed lines mark fractions of single (elasticor inelastic) scattering. (b) Distribution of electrons hitting or passing the objective aperture (diameter corresponding to 0.4 nm resolution).

FIG. 4.2. (a) Squared ratio of electron velocity to the speed of light, b2, which is a measure of the penetration power of electrons, andelectron wave length, l, as functions of the accelerating voltage. (b) Ratios of the maximum transferable and mean transferred energy tothe critical energy and ratio of the critical to the elastic cross section of knock-on events for carbon, assuming 10 eV critical energy, asfunctions of the accelerating voltage.


that reduces image contrast and enhances noisecontrast. Under the condition that 20% of the elasti-cally scattered electrons pass the aperture (corre-sponding to 0.4 nm resolution at 300 kV), the distri-bution of electrons into those reaching the image andthose cut off by the aperture is shown in Fig. 4.1b(see also Langmore and Smith, 1992).

4.2. Higher Accelerating Voltage

Increasing the accelerating voltage U increasesthe penetration power of the electron beam and thusreduces the influence of multiple scattering. On theother hand, it enhances the radiation damage due toknock-on events, i.e., elastic scattering events, inwhich the energy transferred to an atom is greaterthan its binding energy. The possible gain in penetra-tion power is limited, because the elastic as well asthe inelastic scattering cross sections, sel and sin,respectively, are—to a good approximation—propor-tional to 1/b2 (see, e.g., Scherzer, 1970), where b 5 n/cis the ratio of the electron velocity to that of light.The mean free paths for elastic and inelastic scatter-

ing, Lel and Lin, respectively, are thus proportional tob2. In Fig. 4.2a, b2 and the electron wavelength l aredisplayed as functions of the accelerating voltage U.While b2 approaches unity for U = `, l decreasescontinuously (l ~ U21/2 for U 9 m0c2 and l ~ U 21 forU : m0c2). As b2 equals 0.31 at 100 kV, the maxi-mum gain in penetration power for U = ` would bea factor of 3.2. Increasing the accelerating voltagefrom 100 to 300 kV gives already a factor of 2, whilethe additional gain by a further increase from 300 kVto 1.2 MV is only a factor 1.5. An accelerating voltageof 300 kV is thus a good compromise, an additionaladvantage being that field emission guns are commer-cially available up to this voltage.

It should be noted that for comparing imaging atdifferent accelerating voltages, the product of thedose and the elastic cross section has to be keptconstant. The reason is that the dose (in electronsper unit area) for obtaining a certain s/n at a desiredresolution and for equivalent imaging conditions is,for dark-field as well as for bright-field imaging,proportional to 1/seff (Scherzer, 1970) and thus also

FIG. 4.3. Three ways of looking at the effect of energy filtering. Schematic: An almost monochromatic beam is scattered in thespecimen. The majority of elastically scattered electrons (no change in energy) is intercepted by the objective aperture, whereas mostinelastically scattered electrons pass the aperture. The filter is set to select only the electrons not suffering an energy loss. Spectrum: Thebeam intensity that was originally contained in a single narrow peak is distributed into a wide spectrum due to inelastic scattering. Theplasmon peak and a characteristic edge are indicated. The energy filter is set to select only the zero-loss peak. Image: The zero-loss imagedisplays sharp details. If inelastically scattered electrons were allowed to contribute to the image, they would produce a low-resolutionbackground image that is further blurred due to the chromatic aberration of the imaging lenses. For thick amorphous specimens multiplescattering largely obscures structural information.

288 KOSTER ET AL.

proportional to b2. Here seff is the cross section thatcorresponds to electrons hitting the image patch ofan atom and passing the aperture.

The energy transferred to an atom of mass M2

during an elastic scattering event is given by (Scher-zer, 1970)

E 52m2v2

Msin2

q

25

4m0eU*

Msin2

q

2, (4.5)

where m 5 m0 /Î1 2 b2 is the mass, m0 the rest mass,2e the charge of the electron and U* 5 U(1 1 eU/2m0c2) the relativistically corrected accelerating volt-age. If, for scattering angles above a critical angleqcrit, the transferred energy E exceeds a criticalenergy Ecrit, the atom will be displaced and eventu-ally be ejected from the specimen (knock-on event).For q 5 p and a given accelerating voltage U, thetransferred energy becomes a maximum, Emax 5 4m0eU*/M. This means that atoms will not be dis-placed below a certain threshold voltage Ecrit; forinstance, a carbon atom (M 5 12u, u being theatomic mass unit) with Ecrit 5 10 eV binding energycan be displaced only for U* . 54.7 kV (or U . 52kV). Integrating the transferred energy of Eq. (4.5)over scattering angles q . qcrit, one obtains for themean transferred energy

Emean 5Ecrit · Emax

Emax 2 Ecritln

Emax

Ecrit. (4.6)

The mean energy transferred in a knock-on eventincreases more slowly with an increase in accelerat-ing voltage than the maximum transferable energy(see Fig. 4.2b).

The likelihood of a critical collision can be esti-mated from the ‘‘critical cross section’’ (Scherzer,

1970):

scrit <4.7 eV

103 AEcritZ2/3sel · 11 2

Ecrit

Emax2 . (4.7)

Because the comparable dose is proportional to b2

and thus to 1/sel, a relevant quantity for consideringthe dependence of knock-on processes on the acceler-ating voltage is the ratio scrit/sel For the special caseof carbon (Z 5 6, A 5 12) and a critical energy Ecrit 510 eV, the ratios Emax/Ecrit, Emean/Ecrit and the ratio ofthe critical to the elastic cross section, scrit/sel, aredisplayed as functions of U in Fig. 4.2b. For anaccelerating voltage of 300 kV (100 kV), the maxi-mum transferable energy and the mean transferredenergy are Emax 5 71 eV (20 eV) and Emean 5 21 eV(14 eV), respectively.

It may be worthwhile to estimate the density ofknock-on events for low-dose imaging of ice-embed-ded samples. If the allowed dose at 300 kV is in therange of 2000 to 10 000 e nm22 (corresponding to1000 to 5000 e nm22 at 100 kV), the probability of aknock-on event is in the range 0.8 to 4 3 1025 (0.5 to2.5 3 1025 at 100 kV). This means that 0.6 to 3 suchknock-on events (0.4 to 2 at 100 kV) will happen onaverage during image recording in a 1 MDa protein(containing about 80 000 C, N, or O atoms). Thus asubstantial fraction of particles will be damaged bythese events.

As a result of these considerations we can statethat specimen damage due to knock-on events in-creases at higher accelerating voltages, because boththe probability of such events and the transferredenergy go up. However, the increase is not dramatic.It should be noted that the contribution of inelasticscattering remains almost constant in spite of appli-cation of a higher electron dose, since it scales in thesame way as elastic scattering with the acceleratingvoltage.

FIG. 4.4. Different types of energy filters. (a) Filter involvingan electrostatic mirror. (b) Omega-type filter. (c) Single magneticsegment filter. Types (a) and (b) are placed within the imaginglens system of the microscope column, whereas type (c) is mountedunderneath the microscope column.

FIG. 4.5. Setup of a TEM with postcolumn energy filter. Theessential parts of the filter are the magnetic prism, the energyselecting slit, and the filter optics: two CCD cameras (a video anda slow-scan camera) are adapted to the energy filter.


4.3. Energy Filtering

Energy filtering allows the selection of electronswith a specific kinetic energy for imaging. In prin-ciple, one can select energy losses that are specific tocertain elements, thereby visualizing elemental dis-tributions (Egerton and Leapman, 1995). The record-ing of elemental maps requires a very high electrondose of more than 100 000 e nm22. The mode ofimaging which is most useful for low-dose applica-tions is ‘‘zero-loss’’ energy filtering. For this, theenergy selecting window is positioned at the energyof the primary beam, selecting only unscattered andelastically scattered electrons for imaging. The en-ergy window has to be sufficiently wide to include thethermal energy spread of the electron gun, butsmaller than the smallest relevant energy loss in thesample, in most practical cases the 20- to 25-eVsingle plasmon loss, leading to a typical width of 10to 20 eV (Fig. 4.3).

Most energy filters currently available are basedon magnetic prisms, making use of the fact thatelectrons in a homogeneous magnetic field move incircles whose radius is dependent on the kineticenergy. A mechanical slit is placed in the plane ofdispersion to select electrons of a specific energy.In-column filters, which are placed above the projec-tor lenses, have been particularly popular for biologi-cal applications. While the first commercial instru-

ment used an electrostatic mirror that limited thehigh tension to 80 kV, a more recent design consistsof only magnetic elements, where the beam passesthrough four magnetic prism sectors in a path muchlike the Greek letter omega (Lanio, 1986). Alterna-tively, a filter consisting of a single magnetic seg-ment and a set of multiple lenses can be attached tothe lower end of the microscope column. This isreferred to as a postcolumn filter (Krivanek et al.,1991, 1995). This filter, which has a postmagnifica-tion of about 320, is particularly well suited forhigh-magnification work, as is typical for investiga-tions in materials science. In order to apply thefilter to biological specimens, the magnificationrange, in which the objective lens is still excited, hasto be extended to lower magnifications. The ar-rangement of deflecting elements of the differentfilter types is shown in Fig. 4.4. Our particular setupof a TEM with postcolumn filter (Philips CM 120Biofilter) is displayed in Fig. 4.5 (de Jong et al.,1996).

To evaluate the effect of zero-loss energy filteringon the quality of tomographic reconstructions wecompared images taken with and without energyfiltering. For comparison of different imaging modes,cross-correlation coefficients, calculated from twoimages that were recorded under the same imagingconditions, may be used as a criterion of image

FIG. 4.6. Improvement of image quality by zero-loss filtering as measured by cross-correlation. Images of an amorphous carbon film ofvarying thickness were recorded at 57 k3 magnification at a constant electron dose. (a) Fourier ring correlation coefficient (FRCC) for filmsof varying thickness. The overall height of the coefficient goes down as the specimen thickness increases, but the ratio of filtered tounfiltered coefficients increases. (b) Ratio of the coefficients of zero-loss filtered to unfiltered images. (c) Ratio of the integral of thecoefficients as shown in (a) integrated in the range 0.1 and 0.7 fNyq versus the thickness of the film.

290 KOSTER ET AL.

quality (Grimm et al., 1997). The cross-correlationcoefficient (XCC) can be written as

XCC 5s2

(s2 1 n2), (4.8)

where s designates the root-mean-square (rms) sig-

nal contrast common to both images and n the (rms)noise contrast of the images, containing shot noise ofthe imaging electrons, recording noise, and a contri-bution due to specimen changes. If one applies aFourier filter that contains only a narrow frequencyband to the images, the XCC equals the Fourier ringcorrelation coefficient (FRCC) of this frequency band.

FIG. 4.7. Filtered and unfiltered images of ice embedded specimens. (a) Actin filaments and vesicles (250 nm thick ice). The half of theimage below the diagonal is zero-loss filtered, while the upper half is unfiltered. Whereas contrast is comparable in both images, theresolution is improved in the case of the filtered image, such that individual actin filaments can be identified. (b) Multilamellar vesicles(600 nm thick ice). The division of the image is as in (a). Only a few lamella can be distinguished in the unfiltered case. (c) Filtered image ofa whole Sulfolobus cell (750 nm thick ice at the center, 200 nm at the edge). In order to visualize the full range of the CCD camera, theimages are scaled differently in the two halves. The S-layer as well as some inner structure of the cell can be seen. (d) Same cell as in (c),unfiltered.


Fourier ring correlation is normally used to estimatethe resolution of a structure obtained by averaging(Saxton and Baumeister, 1982). Here we use it as afrequency-dependent measure of similarity.

For the direct comparison of zero-loss filtered (zlf )with unfiltered (unf) imaging, three pairs of images,in the sequence zlf–unf–zlf, were recorded of thesame specimen area, and the third image pair wasused to verify that differences in the correlationcoefficients were not caused by specimen changes. Inorder to eliminate contributions of the CCD cameraitself to the cross-correlation peaks, the second im-age of each pair was slightly shifted against the firstone using the image shift facility of the microscope.Results of such an analysis, using a specimen ofamorphous carbon film, are shown in Fig. 4.6: Foreach image pair, the FRCCs as a function of spatial

frequency were calculated (Fig. 4.6a). The minimumat 0.7 fNyq reflects the first zero of the phase contrasttransfer function. Figure 4.6b displays the ratio ofFRCCs of zero-loss filtered images to those of unfil-tered images, while Fig. 4.6c shows the ratio ofFRCCs, integrated over the spatial frequency, as afunction of specimen thickness. This ratio may beinterpreted as gain due to zero-loss energy filtering.It should be pointed out that the improved cross-correlation will also improve the accuracy of align-ment of molecular images in the 2-D or 3-D analysisof single particles.

So far, energy filtering microscopes have onlyrarely been used for collecting high resolution struc-tural data of ice-embedded biological macromol-ecules. Smith and Langmore (1992) applied energyfiltering to quantify molecular densities of tobacco

FIG. 4.8. Sections from two reconstructions of a whole, ice-embedded cell. (a–c) Reconstructions using the logarithm of the intensitiesof the projection images. (d–f) Reconstruction using the original images. The cell of the genus Sulfolobus (HI15) was 750 nm thick.

FIG. 4.9. Long vesicle tube filled with round vesicles and actin filaments (2° steps between 258° and 156° at 33 k3 magnification and 5µm underfocus, 5000 e nm22 total dose, 100 nm thick embedding ice film). (a) Gallery of selected views from the tomographic tilt series. (b)Overview image with the envelope of the chain of small vesicles indicated by the dashed line (12 k3). The area chosen for tomography isindicated by the box. (c) Zero-loss filtered image at 0° tilt angle. (d) Slices from the three-dimensional reconstruction. The slices are 3 nmthick and are located 0, 14, 21, and 42 nm, respectively, above the central plane. (e) Surface rendering of the top half of the reconstructedvolume shows actin filaments along the direction of the vesicle ‘‘string.’’ (f ) Bottom half of the reconstruction indicates a membrane wallsurrounding the small vesicles (Grimm et al., 1997).

292 KOSTER ET AL.


mosaic virus (TMV). Schroder et al. (1993) collecteddata of actin decorated with myosin to calculate ahelical reconstruction at 2 nm resolution. Frank etal. (1995) carried out a 3-D reconstruction of theEscherichia coli ribosome at 2.5 nm resolution bymeans of single-particle averaging (Zhu et al., 1997).Since the structures investigated were relativelysmall (about 40 nm), the embedding ice film could bekept much thinner than the inelastic mean free path,so that zero-loss filtering gave only a minor improve-ment of image contrast in the reconstructions. Never-theless, zero-loss filtering allows the use of smallerdefocus values without impairing the accuracy ofalignment of the particles as required for averaging.

While it is not easily recognized upon direct visualinspection for thin specimens below ca. 100 nmthickness, the effect of zero-loss filtering becomesmore obvious for samples in the intermediate thick-ness range of 100 to 500 nm (at 120 kV accelerationvoltage). This is illustrated in Fig. 4.7 with threedifferent specimens. In those image regions, where asufficiently high number of electrons have pen-etrated the specimen without energy loss, details areobserved with higher resolution than in the unfil-tered image. Within thicker objects small details aresometimes more readily seen in the unfiltered imagedue to the low resolution information of the inelasti-cally scattered electrons. However, the resolution isbetter with zero-loss filtering. Thickness variationsin thick specimens may produce strong intensitydifferences in an image, and a CCD camera, due toits large dynamic range, is able to record properlysuch images.

In very thick specimens, in which most electronshave been inelastically scattered, it may be desirableto choose an energy selecting window close to themost probable, instead of the zero-loss. In tomo-graphic imaging of sections of constant thickness theposition of the most-probable loss can be computedfor every tilt angle. This led to a reconstruction ofBalbiani ring hnRNP in the plasmon peak (Olins etal., 1989). With ice-embedded specimens one canfirst make an automatic thickness measurement bymeans of the log-ratio technique (Malis et al., 1988)and subsequently compute the location of the plas-mon peak. This technique can be automated toinvestigate ice films of varying thickness. However,imaging using the most probable energy loss wasfound to produce results with a worse resolutionthan that of zero-loss filtering (Grimm et al., 1996).

For thick plastic-embedded sections, energy filter-ing in combination with STEM has been appliedsuccessfully (Colliex et al., 1989). Sections of up to 8µm thickness have been investigated and it wasobserved that image quality of CTEM is comparableto STEM for sections between 0.5 and 3 µm (at 300

kV). However, STEM is superior for greater thick-nesses (Boerchia et al., 1993). This implies that at120 kV, specimens of up to 1 µm thickness can beimaged in CTEM with no disadvantage compared toSTEM.

For all reconstruction algorithms it is assumedthat the image intensity depends linearly on theintegrated specimen density along the beam. For thereconstruction of single ice-embedded molecules, asdescribed in Section 5, this condition is usuallyobserved in phase contrast. The original projectionscan be used for the application of backprojectiontechniques. In the case of thick specimens, which arepreferably imaged as stained sections or in an energyfiltering microscope, (elastic and inelastic) scatter-ing contrast, which varies exponentially with thedensity along the beam path, dominates image forma-tion. Therefore it is necessary to use the logarithm ofthe intensity of the original images for the backpro-jection.

Figure 4.8 shows the reconstruction of an ice-embedded 750-nm-thick cell assuming exponentialand linear contrast formation. In the reconstructionusing the logarithm of the original images (a–c), thecell membrane and a high density inside the cell isobserved. Gold particles, as in the top right handcorner of Fig. 4.8a, appear in all sections with thesame contrast. The reconstruction using the originalimages (d–f) mainly shows high frequency informa-tion, e.g., the cell membrane and the gold particlesand the cell appears empty. In addition, the contrastof the gold particles decreases strongly from thecentral section (d) toward the top of the cell (f ). For acorrect interpretation of the density in the volume itwould be necessary to separate the linear contrastcontribution by phase contrast from the nonlinearpart by scattering contrast. This can be achieved byrecording a through-focus series, such that the focus-dependent component, which follows the CTF, can beseparated in Fourier space (Han et al., 1996). In thecase of ice-embedded specimens, where the CTF isnot visible and low-dose imaging conditions arerequired, this approach is not feasible. One could, inprinciple, determine at some other area of the speci-men which frequency range at the chosen defocus isdominated by which type of contrast, in order toreconstruct both separately. In practice, this has notbeen carried out, since for most specimens one kindof contrast dominates—phase contrast for thin ice-embedded specimens and scattering contrast forthick specimens, particularly when stained withheavy atoms.

An example for the application of zero-loss filter-ing is the tomographic reconstruction of vesicles, inwhich actin has been polymerized, is shown in Fig.4.9 (Grimm et al., 1997).

294 KOSTER ET AL.

4.4. Field Emission Gun

Field emission guns provide a significantly bettercoherence than usual thermionic guns because oftheir smaller energy spread (0.8 eV FWHM width fora Schottky emitter ‘‘FEG’’ compared to about 2 eV fora LaB6 gun) and their increased brightness (108 to109 A/cm2 · sr at 100 kV instead of about 2 3 106

A/cm2 · sr). Both properties lead to an improvedenvelope of the contrast transfer function. At 200 kVaccelerating voltage the decrease of the envelope isless than 10% out to a spatial frequency of about (0.3nm)21, which is of particular importance for electroncrystallography of proteins (Baumeister and Typke,1993). More importantly for electron tomography ofice-embedded samples at intermediate resolution(i.e., 1 to 2 nm) is that it is possible to use a relativelylarge defocus without sacrificing good contrast trans-fer. This is demonstrated by a comparison of transferfunctions of a FEG with a LaB6 gun at 200 kV forseveral defocus values in Fig. 4.10.

4.5. Cooling with Liquid Helium

Because of the importance of the dose problem inelectron tomography, it should be mentioned that thesensitivity to radiation damage can be further re-duced by a factor of 2 to 3 by cooling with liquid

helium (i.e., a reduction of the specimen temperaturefrom about 90 K to below 10 K (Zemlin et al., 1996)).Being able to use twice the dose for imaging impliesan increase of the s/n in the image by the square rootof two. A top-entry specimen stage to be used at Hetemperature has been constructed by Fujiyoshi andcolleagues (Fujiyoshi, 1989).

4.6. Direct Digital Image Recording with Slow-ScanCCD Cameras

A prerequisite for automated data collection is—atleast as long as no high-precision goniometers havebeen developed—that images are immediately avail-able in a digital format for appropriate controlprocedures. A suitable device for this purpose is theslow-scan charge-coupled device (ssCCD) camera,which became available about 10 years ago and isnow being used, in addition to photographic film, forhigh-quality image recording in electron microscopy(Daberkow et al., 1991; Kujawa and Krahl, 1992;Krivanek and Mooney, 1993; de Ruijter, 1995; Weick-enmeier et al., 1995; Daberkow et al., 1996). Scientific-grade CCD cameras used for this purpose areequipped with large-area chips, containing 10242 ormore pixels with pixel sizes of 15–30 µm. The chip iscooled to about 230°C in order to reduce darkcurrent noise, and images are read out at rates of 2 3105 to 8 3 106 pixels/s. Additional advantages of theslow-scan charge-coupled device (ssCCD) are theexcellent linearity between input electron exposureand the output signal and its large dynamic range(i.e., the ratio between the maximum signal and therms noise level), which is about 104, while that ofphotographic film is about 102 (Zeitler, 1992). On theother hand, the number of useful pixels is by fargreater for photographic film, even in comparisonwith a 20482-pixel CCD camera. Storage densitiesand capacities, relative to photographic film, arecompiled in Table 4.1. They were calculated assum-ing that the storage density is proportional to sH

2 ,where sH is the spatial frequency, for which themodulation transfer function (MTF) is decreased to50% (the MTF data for film were taken from Down-ing and Grano (1982); the pixel sizes for digitizationare given in the table). The dynamic range was notincluded in this consideration, as it is of minorrelevance when recording image data; if it is in-cluded, the storage capacity, defined in this way, is ofthe same order of magnitude for the two devices.

In a CCD camera for transmission electron micros-copy, the electrons are first converted into light bymeans of a scintillation screen at the entrance of thecamera. The light is guided via fiber optic elementsto the CCD chip, where it produces electron-holepairs in the active area. The electrons are trappedand accumulated in potential wells, corresponding to

FIG. 4.10. Comparison of transfer functions. FEG versusLaB6 (a) FEG, U 5 200 kV (b). LaB6, U 5 200 kV. Defocus values1.0, 2.0, and 5.0 µm, respectively. An energy spread of 0.8 eV (2.0eV) and an emission angle of 0.02 mrad (0.3 mrad) was assumedfor the FEG (LaB6) cathode.


the pixels. For readout of an image, the potentialwells are shifted toward an on-chip preamplifier,which sequentially measures the charges trapped inthe pixels which are then converted into numbers tobe stored.

The performance of an image converter is usuallycharacterized by its response characteristic, its MTF,and its detective quantum efficiency (DQE). For theperformance of the ssCCD, all elements of the con-verter chain are important, whereby the scintillatorhas a key role since it primarily determines sensitiv-ity, resolution, and noise contribution. Presently,single crystal YAG (Y3Al5O12:Ce21) or polycrystallinephosphor (P20 (ZnS/CdS:Ag) or P43 (Gd2O2S:Tb31))scintillators are normally used. For good resolutionthe scintillator should be thin, while high sensitivityand low noise contribution require a somewhatgreater thickness, the optimum depending on theaccelerating voltage. While very thin YAG crystalsare difficult to produce, the thickness of polycrystal-line phosphors can be chosen arbitrarily. As P20 hasa high conversion efficiency, a thickness of about 10µm (3.5 mg/cm2) is an acceptable compromise for100–300 keV electrons. Due to variations in thetransmission of the fiber-optic coupling betweenscintillator and CCD chip, a raw image recordedwith the ssCCD is affected by a multiplicative fixed(‘‘chicken wire’’) pattern. In addition, it containsadditive dark current and bias contributions. On-line image processing systems provide a correctionprocedure to eliminate these influences, leaving onlythe noise contributions of the dark current and theelectronics.

Because of the finite reaction volume of incidentelectrons and the spreading of emitted photons, apoint-like input signal produces an output that isspread over a certain area of the CCD chip, and thedistribution is described by the point spread function(PSF). The MTF is the Fourier transform of the PSF;it describes the damping of sinusoidal spatial modu-lations of the input intensity as a function of thespatial frequency. Typical MTFs of a 1k31k and a2kx2k ssCCD camera equipped with P20 scintilla-tors for 120-keV and 300-keV electrons, respectively,are shown in Fig. 4.11; it was measured using thestatistical method, with a correction for aliasing

effects (Herrmann and Krahl, 1984; also see Brinkand Chiu, 1994; Sherman et al., 1996; van Zwet andZandbergen, 1996). The noise behavior is describedby the DQE which is defined as DQE 5 (s/n)out

2 /(s/n) in

2 . Essentially the DQE indicates how muchnoise a device adds to an input signal, the optimumbeing DQE 5 1. In a ssCCD camera used for elec-trons, there are essentially four sources of noise: (1)variations in the energy conversion of electrons intophotons; (2) fluctuations in the generation of electron-hole pairs on the chip; (3) dark current noise; (4)preamplifier noise. Several authors have measured

TABLE 4.1Storage Density and Storage Capacity of ssCCDs, Photographic Films, and Imaging Plate

Device sH (mm21) Pixel (µm) sNyquist (mm21) Rel. storage density Area (mm) Rel. storage capacity

Film (Agfa 23 D 56, 100 kV) 75 var. (5) var. (100) 1 100 3 80 1Film (Kodak 4463, 100 kV) 32 var. (10) var. (50) 1/5.5 100 3 80 1/5.5ssCCD (1k 3 1k, 120 kV) 14 19 26 1/29 19.5 3 19.5 1/600ssCCD (2k 3 2k, 300 kV) 11 24 21 1/46 49.2 3 49.2 1/150Imaging plate (100–1000 kV) 4.3 var. (25) var. (20) 1/300 94 3 75 1/340

FIG. 4.11. MTFs of CCD cameras at different voltages. Thevoltage, the camera size, and the pixel size are as indicated.

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the corresponding noise contributions and calculatedthe DQE of ssCCDs (Krivanek and Mooney, 1993; deRuijter, 1995; Weickenmeier et al., 1995; Zuo, 1996).

5. ELECTRON TOMOGRAPHY OF ISOLATEDMACROMOLECULES

5.1. Method of 3-D Alignment and Averaging

When strictly repetitive structures, such as iso-lated and purified macromolecules, are to be investi-gated by 3-D electron microscopy, quasi-tomographicmethods (see Section 2.3) like random conical tilt orangular reconstitution are the methods of choice.The rather simple data acquisition, the ability toapply the allowed electron dose to only one or twoimages (and thereby achieving the highest possiblesignal-to-noise ratio in the unprocessed data), aswell as the averaging of hundreds or thousands ofparticles are intrinsic to these approaches. However,in some cases the tomographic approach has someadvantages which can be valuable in the 3-D recon-struction of isolated macromolecular assemblies: Forinstance when only a few particles are available forimaging, more than one view can be obtained of eachparticle and be used for its 3-D reconstruction. Anadvantage of the tomographic approach will arisewith very large particles, that have an inherentflexibility which makes them behave more like indi-vidual objects. After reconstruction and alignment,variance calculations as well as 3-D MSA (multivari-ate statistical analysis) and classification can beused to identify parts with high variance or mobility.

The procedure for 3-D data acquisition and process-ing consists of the following steps:

—Preparation of the EM grid (e.g., negative stain,vitreous ice).

—Acquisition of the tomographic data set, possiblyautomatic, including correction for shift, defocus,and, if necessary, astigmatism.

—Alignment of the projections of the tilt series.—Reconstruction of the entire 3-D volume via

filtered backprojection.—Selection and cutting out 3-D cubes, each con-

taining one particle from the 3-D volume reconstruc-tion.

—Three-dimensional alignment of the particles inthe cubes (correction for x-, y-, and z-shifts andalignment of the three Eulerian angles).

—Averaging of the particles. This step includesthe calculation of the position of the particles in theoriginal projections, cutting out the projections, ap-plying a weighting function calculated for the newset of projection angles and calculating the 3-Dreconstruction via backprojection. Note that in mostcases it would not be sufficient to simply align thecubes selected from the large volume and add them,because although the weighting function used in the

first reconstruction is valid for the geometry of thetilt series, it is not valid for the new projectiondirections resulting from the rotational alignmentapplied to the particles. Only for a very large numberof particles in random orientations would asymme-tries of data density in Fourier space average out.

To evaluate the performance of this method inpractice, we recorded and processed single-axis elec-tron tomography data sets of isolated macromol-ecules. Two examples will be discussed in the follow-ing part: the 20S and the 26S proteasome in negativestain preparations. Two additional examples are the3-D alignment of tricorn capsids (Walz et al., 1997)and the alignment of thermosomes (Nitsch et al.,1998). The method of 3-D alignment, averaging ofmacromolecules, as well as the application of 3-Dmultivariate statistical analysis and classification isdescribed in detail in Walz et al. (1998).

5.2. The 20S Proteasome

The 20S proteasome is the proteolytic core of the26S proteasome complex (Peters, 1994). It consists of28 subunits forming four seven-membered ringswhich form a barrel-shaped structure. The length is15 nm; the diameter 11 nm. This structure is particu-larly suitable for testing tomographic methods, be-cause the atomic structure was recently determinedby X-ray crystallography (Lowe et al., 1995).

A tilt series was recorded of negatively stained 20Sproteasomes of Thermoplasma acidophilum (tiltrange 272° to 72°, increment 3°, 27.5 kx magnifica-tion). Individual projections were aligned using cross-correlation, and R-weighted back-projection was usedto calculate the 3-D reconstruction. From a 512 3512 3 128 volume, 78 cubes, each containing one 20Sparticle in a side-on orientation, were manuallyselected and cut from the large 3-D volume. For x-,y-, and z-alignment, 3-D cross-correlation was used,alignment of the three Eulerian angles was carriedout by scanning. For each alignment, the referencewas 14-fold symmetrized to minimize the effect ofadsorption to the carbon film and partial staining.

Figure 5.1a shows the central slice of the large 3-Dvolume. In the section, the particles in side-onorientation can be recognized, as well as the cavitiesenclosed by inner and outer rings and by the twoinner rings. An isosurface view of the averagedstructure with and without 14-fold symmetrizationis presented in Figs. 5.1b and 5.1c. The 14-foldsymmetrized average (b) fits quite well to a modelcalculated from the known atomic structure of theproteasome revealed with X-ray crystallography (Fig.5.1d). The main difference in the electron micro-scopic structure is in the cap-like densities located atboth ends of the cylinder. The reason for this could be


the 84 N-terminal residues of the a-subunits whichare not resolved in the X-ray structure.

5.3. The 26S Proteasome

In the 26S proteasome two 19S cap complexesassociate with both ends of the 20S core complex.The cap complexes confer upon the 26S proteasomethe ability to recognize and unfold ubiquitinatedsubstrate proteins, thus rendering them degradableby the 20S core. The 26S complex is an elongatedstructure, about 45 nm long and 19 nm wide; themolecular mass of the complex is approximately 2MDa (Yoshimura et al., 1993).

From the 3-D reconstruction of five tilt series (tiltrange 260° to 60°, increment 4°, negatively stainedspecimen, 20 kx magnification), 102 cubes wereselected and cut out. Three-dimensional alignmentand averaging gave a first impression of the struc-

ture of the twofold symmetrical 26S particle. Figure5.2 shows a gallery of sections of the 3-D volume.There is a visible difference between the top and thebottom parts of the structure resulting from adsorp-tion forces between protein and the supporting car-bon film. Furthermore, the missing wedge with anopening angle of 60° introduces artifacts which com-plicates the interpretation of details of structuraldetails in the current data.

6. ELECTRON TOMOGRAPHY OF MACROMOLECULARSTRUCTURES IN SITU

To date, most studies on cellular organelles involvefixation and plastic embedding, followed by section-ing to the desired thickness. Artifacts due to theembedding procedure and the unclear relationshipbetween stain deposition and the structure of inter-est, limit the usefulness of plastic sections for struc-

FIG. 5.1. (a) Central slice of the reconstruction from a tilt series with negatively stained 20S proteasomes. (b, c) Isosurface view of theresulting average before and after 14-fold symmetrization. (d) Representation of the electron density calculated from the atomiccoordinates of the molecule.

298 KOSTER ET AL.

tural studies on a molecular level. Moreover, underthe influence of the electron beam the section canshrink as much as 30% from its original thicknesswith a small dose (3 3 105 e nm22) at room tempera-ture (Luther et al., 1988), while the fine structure ofthe specimen undergoes changes that are impossibleto correct for. More favorable conditions are createdwhen the section is imaged at liquid nitrogen tem-peratures (2180°C) where it has been shown thatshrinkage, and most likely also specimen changeduring data collection, is minimal (Braunfeld et al.,1994).

The optimal approach to preserve structural ar-rangements is cryofixation. For isolated molecularstructures this approach requires plunge-freezing(Lepault et al., 1982), slam-freezing against a cooledcopper block, or, for larger structures, high-pressurefreezing (Dahl and Staehlin, 1989) followed by elec-tron cryomicroscopy. It is necessary that the speci-men is cooled sufficiently fast in order that the iceremains amorphous. At all times the specimen mustbe kept at temperatures below 2140°C. Cells, as wellas the structure of macromolecules, can be fixed indifferent functional states (Dubochet et al., 1982).Due to the radiation sensitivity of the specimen, adata set must be recorded under low dose imagingconditions which is a fraction of the dose conventionallyused for taking a tomographic data set. The total doseavailable for imaging depends highly on the sample andon the resolution desired. A dose of 2000 to 3000 e nm22

is allowable for imaging structures embedded in ice at 2nm resolution (Conway et al., 1993).

6.1. Considerations of Crowdedness

Although eukaryotic and bacterial cells consist ofa large percentage of water (<70%), this does notimply that the cytosol is a dilute solution. Actually,the concentration of protein and RNA is extremelyhigh. The macromolecular concentration on the cyto-plasm of E. coli was estimated to be in the order of300–400 mg/ml (Zimmerman and Trach, 1991). Thismeans that a large proportion of the cell is occupiedby protein and RNA and that little empty spaceremains. The amount of DNA, however, is relativelylow; in the case of E. coli it occupies only 1% of thecell volume.

The molecular concentration given above leads toa protein density in the cell of 20–30% by volume(specific protein volume 0.7 cm3/g). If one assumes allproteins to be solid spheres with a certain radius andplaces them on a cubic lattice, the next-neighbordistance between the surfaces of the spheres wouldbe between 40 and 80% of the radius, e.g., 2–4 nm forproteins of 10 nm diameter. Even though proteinsand other cytosolic structures occur in a wide rangeof sizes, this would imply that macromolecular struc-tures are often in contact with each other in thecellular environment (Martin and Hartl, 1997).

Figure 6.1 shows the central sections of two recon-structions of whole cells that were recorded on an

FIG. 5.2. The 26S proteasome from Drosophila melanogaster. 32 x–y-slices of the averaged 3-D structure obtained from tomographicreconstructions.


energy filtering microscope at 120 kV accelerationvoltage (Grimm et al., 1998). Archaea cells of thegenus Sulfolobus were embedded in vitreous ice,whereby the cell body severely protrudes out of theotherwise about 100-nm-thick ice film. The sectionswere reconstructed using the logarithm of the inten-sities in the original projections, corresponding tothe assumption of image formation by scatteringcontrast. Therefore the relative density of the cellcan be calculated, leading to values similar to thoseobtained by biochemical methods as given above.Due to the low resolution of the reconstructions, it isnot possible to identify single molecules. However,the quasi-periplasmic space between the plasmamembrane and the S-layer (20 nm) can be identifiedin three dimensions.

6.2. Required Resolution

Assuming that the resolution of a 3-D image of acell or cellular compartment is sufficiently high toseparate the various molecules in the cytoplasm, itmay not be sufficient to identify single molecules.Figure 6.2 shows sections through the 3-D image ofthe 20S proteasome computed from its known crys-tal structure (Lowe et al., 1995) at different resolu-tion limits. These simulations show that a resolutionof at least 2 nm is required to recognize visually thesevenfold symmetry of the proteasome as well as itsassembly from four rings. After having obtained an

estimate of what resolution a 3-D image must havein order to be able to distinguish one subunit withina 20S proteasome from another, next, we need todetermine how large a volume we can possiblyreconstruct at that resolution.

To estimate the largest volume size we can recon-struct with the desired resolution (Grimm et al.,1998), we start with the Crowther criterion (seeSection 3), from which the number of images re-quired for a given resolution can be derived. For thetilt increment, the scheme proposed by Saxton et al.(1984) is chosen, where the increment becomessmaller at larger tilt angles. It was designed specifi-cally for crystalline specimens, but is advantageousfor nonperiodic specimens as well. It results in anincreased number of images for a given tilt range bytypically 20–30% as compared to a constant tiltincrement.Artifacts introduced by the missing wedgein single-axis tilt tomography can be minimized bymaking the tilt range as large as possible, but theycannot be described in terms of resolution. The dosethat is needed to image a specimen is determined bythe thickness of the specimen, and by the minimumnumber of electrons which must accumulate in apixel of the detector device (as the intensity de-creases exponentially with thickness). In other words,if the specimen is too thick, the images recorded bythe ssCCD camera will be too faint to be aligned toone another, and no 3-D reconstruction can be com-

FIG. 6.1. (a) Subarea of the central section from the tomographic reconstruction of a Sulfolobus HI15 PING cell. The cell was 750 nmthick at its highest point. Magnification 36200, 52 images with 2.5° increment between 60° and 267.5°, 6000 e nm22. (b) Central sectionfrom a reconstruction of a Sulfolobus NOB8H2 cell, maximum thickness 600 nm. 36200 magnification, 139 images with a variable tiltincrement (Saxton et al., 1984) starting with 1.1° at 0° tilt, range 50° to 264°, 7000 e nm22. The insets show the corresponding projectionsat 0° tilt. The cells were kindly provided by W. Zillig (Grimm et al., 1998).

300 KOSTER ET AL.

puted. For the results of calculations shown in Fig.6.3, a mean free path of 200 nm at 120 kV accelera-tion voltage was assumed, and a count of five elec-trons per pixel on the (CCD) detector was required inorder to ensure alignability of the individual projec-tions (five counts were taken as a reasonable lowerlimit of electrons from our experience with tomogra-phy of thick samples). The magnification was chosenin a way such that a reconstruction from imagesafter 2 3 2 pixel binning was able to display the fullresolution at the Nyquist frequency. The exposuretime should be varied such that the count rate on thedetector remains constant, even though the geomet-ric thickness of the specimen changes. This is givenfor a variation as t 5 t0 exp[T(cos21(a) 2 1)] (Tthickness in terms of the mean free path, a tilt angle)for homogeneously thick specimens. By adding allexposure times for all tilt angles, one obtains thetotal dose for the series for the given resolution.

Figure 6.3. shows the attainable resolution for theconditions given above at an equivalent dose of 6000e nm22 (at 120 kV) for different specimen thicknesses(Grimm et al., 1998). For specimens less than 100 nm

thick, the resolution is similar at 120 and 300 kV.However, at greater thicknesses, the resolution canbe considerably improved by using a higher accelera-tion voltage, with otherwise unchanged conditions.Even if the tilt range is extended from 60° to 70°,increasing the number of images, tomography at 300kV results in an improved resolution at the sametotal dose as compared to 120 kV. At 300 kV, themaximum thickness for which 2 nm resolution canbe attained at the given dose is 150 nm.

Therefore, if macromolecules are to be identified intheir cellular environment, it is necessary to workwith cryosections instead of whole cells. The largestwhole cells suitable for investigation with electrontomography at 120 kV are about 600 nm in height,and they are compressed by the embedding ice layerto about 400 nm. At this thickness, the theoreticallyattainable resolution under low-dose conditions is 8nm at 120 kV and 5 nm at 300 kV. While the loweracceleration voltage will only be sufficient to studylarge structures, like the S-layer, large inclusionbodies, flagella etc., the resolution at 300 kV may besufficient to locate in situ large macromolecules. The

FIG. 6.2. Slices from a reconstruction of a 20S proteasome from projections in the angular range 660° that were computed from theatomic structure. (a) Resolution cutoff for the reconstruction set at 2 nm. (b) Cutoff at 3 nm. (c) Cutoff at 4 nm.


considerations discussed in this section provide anestimate of what resolution can be expected fromcryotomography and indicate the advantage of using300 kV instruments. Furthermore, if macromol-ecules are to be detected unambiguously in largecellular organelles or cells, it is inevitable to cutthem into, at most, 150-nm-thick slices.

7. DETECTION OF STRUCTURES

One of the most exciting prospects of electrontomography is the ability to investigate macromolecu-lar structures associated with other structures withincells or cellular organelles. For example, given aspecific type of molecule, one would like to identifyand, for multiple occurrences in the cell, locate theirposition, determine their orientation, and obtainquantitative information on their distribution withinthe cell.

To detect a molecule of interest in a tomographicreconstruction of a large cellular volume, the mol-ecule must be recognized directly by its structuralfeatures, or indirectly by means of a (high-densitygold) label. High-contrast labeling methods will facili-tate the 3-D mapping of molecular structures, butare unfortunately rarely quantitative. A limitation ofthe labeling techniques developed so far, such asimmunolabeling (Griffith, 1993), is that they aresurface techniques. For useful 3-D applications bulklabeling is required.

Detection of antigens in the intact cell can be doneby using preembedding labeling techniques. Thisapproach is less critical for the antibodies, which arerather small. They can be introduced by microinjec-tion techniques or by permeabilization of the cellmembrane. Small gold markers that have beenintroduced recently (Undecagold, Aurion, Nanogold;see Hainfeld (1988)), which can be enlarged bysilver-enhancement (Danscher, 1981) to allow visual-ization, seem to be promising. Whereas membraneproteins can be labeled using cell ghosts, in whichmost of the cytoplasm has been cleared by thepermeabilization of the cell, soluble macromoleculescan be investigated meaningfully only in ‘‘intact’’cells. It is at present unclear what effect the crowd-ing of the cells (Zimmerman and Trach, 1991) willhave on the labeling efficiency.

In conclusion, the application of immunogold label-ing in cell biology has been broadened by the ongoingdevelopment of preparation procedures, especiallythose of freeze substitution and the use of ultrasmallgold in preembedding labeling (Sibon et al., 1995).

In the following, we discuss possible approachesfor the automated recognition of macromolecules intomographic reconstructions of cells under the as-sumption that the structure of the molecule is knownand represented by a 3-D density map withoutresorting to labeling techniques.

The task of detection by image processing can be

FIG. 6.3. Theoretical resolution limit in electron tomography as a function of the specimen thickness under low-dose conditions using6000 e nm22 (Grimm et al., 1998).

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formulated as follows: a macromolecule (target) hasto be identified in a tomographic reconstruction (3-Dobservation space). The tomographic reconstructionis characterized by a s/n of less than one, by a lowand varying contrast, and by a multiple occurrenceof randomly distributed and randomly oriented cop-ies of the same macromolecule. Additionally, in thetomographic reconstruction macromolecules may bepresent which are deformed individual representa-tives of the macromolecule of interest, or which areindividual representatives of a similar—but not iden-tical—type of macromolecule. Furthermore, theremight be a very dense distribution of macromolecu-lar structures in the tomographic reconstruction.The macromolecule is represented by a 3-D densitymap, usually obtained from a previous structureanalysis, e.g., from its crystal structure. The s/n ofthe model representing the type of macromolecule isassumed to be considerably better than that of thetomographic reconstruction. The recognition proce-dure must function independent of translations androtations of the macromolecule in the tomographicreconstruction. Thus any possible approach mustdetermine six parameters describing the positionand orientation of the macromolecule in space. Mostof the research on 3-D object recognition (for a reviewsee Besl and Jain (1985)) is concerned with the taskof detection and identification of 3-D objects in a 2-Dimage or a set of 2-D images. Object recognition inreal volume data is mainly used in medical imagingin conjunction with tomographic reconstruction towhich we restrict ourseleves.

Three-dimensional object recognition methods canbe classified into two categories: those based on areduced description of the observation space (i.e.,tomographic reconstruction) and those using the fullgray-scale information.

The goal of reduced description is to describe allobjects in the tomographic reconstruction (observa-tion space), as well as the model, by suitable geom-etry features. The detection is carried out by identify-ing unique features of the model in the reconstructedvolume. For example, if the object were a 20Sproteasome, we could describe it as a stack of fourdisks collectively forming a cylinder, with 11 nmdiameter and a length of 15 nm. Any occurrence ofsuch a cylinder in the tomographic reconstructionwould indicate a 20S proteasome. Without attempt-ing to be comprehensive, some methods for featureextraction as proposed in the literature are men-tioned.

One group of methods is based on surface detec-tion (Besl and Jain, 1985; Gueziec, 1993; Hille andHastings, 1993; Neunschwander et al., 1995; Szeke-ley et al., 1995). Size, shape, and orientation ofsurfaces found in the observation space can be used

to identify objects. Other approaches use extractededges or lines to generate polyhedral models (Besland Jain, 1985; Yla-Jaaski and Kubler, 1988) orobject description by volume primitives (Bajcsy andSolina, 1987; Bertin et al., 1993). Methods based ongeometry features are quite common for 3-D objectrecognition. A major advantage is that the mappingof the model to the observation space is less complexwhen compared to detection based on the full gray-scale information discussed below. The main diffi-culty is to extract the geometry features in a distor-tion-robust manner, where the features have to besufficiently descriptive for detection and discrimina-tion. Especially in electron tomography, the featureextraction is expected to fail due to strong noise,object deformations, or inhomogeneous contrast.

Gray-scale oriented detection methods use the fullquasi-continuous information in any pixel or voxel ina data set for comparison with the model. A straight-forward method is the intensity-based object extrac-tion, where a threshold window is applied to imageintensity values in order to determine the volume ofobjects (Roll et al., 1994). This method is verysensitive to varying contrast in the image as well asto noise, and rather poor in discriminating differenttypes of objects having similar sizes. Also 3-D recog-nition based on moment invariants does not seem tobe feasible for detection of macromolecules in elec-tron tomographic reconstructions due to the highnoise in the data. Detection of homogeneous volumesin 3-D data can be achieved by texture segmentation(Muzzolini et al., 1994; Ip and Lam, 1995) or bymapping local variance gradients (for 2-D images seevan Heel (1982)). By extracting geometric momentsof the detected volumes, a simple comparison withthe model data can be performed. A limitation forthese methods are scenes with a high density ofobjects, which usually cannot be resolved as singleobjects.

Most often, biological macromolecules have arather complicated shape. Hence, the parametriza-tion of a model can be difficult. Therefore, theongoing research on deformable models will be ofinterest for our application.

The excellent performance of cross correlation fordetecting targets in noisy 2-D scenes suggests thatthis method can be extended to 3-D data. Whilecross-correlation is inherently shift invariant, onemust scan over the three rotational degrees of free-dom in the 3-D observation space. With the computa-tional power of today’s work-stations, a data volumeof 128 3 128 3 128 pixels can be scanned with asufficient angular resolution within a few days.Using symmetry properties of the object to detect,the number of angles can be decreased in manycases, thus reducing the processing time down to


several hours. Depending on the number of objects tobe detected in the data volume, an extraction ofregions of interest followed by 3-D correlation withthese small regions, can be preferable to scanningthe whole volume (parallel processing). Conven-tional cross-correlation is known to have the draw-backs of a relatively broad response in the correla-tion plane or space, together with a limiteddiscrimination toward unwanted objects. Extensionof the minimum noise and correlation energy(MINACE) correlator concept as utilized for 2-Ddetection (Stoschek and Hegerl, 1996) to 3-D wouldsignificantly improve the detection performance com-pared to conventional cross-correlation.

One approach to speeding up the process of scan-ning the rotational degrees of freedom in the 3-Dobservation space is to utilize steerable filters (alsocalled deformable filters) (Freeman and Adelson,1991). Using the concept of steerability, a small set ofbasis functions is calculated from the model. Thesebasis functions are applied to the observation spacein the sense of a filter. For any orientation of theobject, the actual correlation response is then ob-tained by a suitable linear combination of the filterresponses resulting from the basis functions. Com-pared to the correlation method discussed above, themajority of the correlation operations needed to scanthe angular range is replaced by much faster interpo-lation operations.

The choice of an optimum method for 3-D recogni-tion strongly depends on the specimen preparationand on the imaging conditions. In extremely noisyreconstructions, geometry feature extraction doesnot seem to be feasible. In this case the 3-D correla-tion technique will be preferable, although the dis-crimination of unwanted objects can be a limitingfactor. In images with relatively low noise, e.g., ofstained plastic sections, application of geometryfeature-based recognition can result in a good dis-crimination. The computational costs when usinggeometry feature-based recognition significantly in-crease with the number of different types of objectsas well as with the overall number of objects in the3-D data. However, the processing time of 3-D corre-lation is independent of the number of objects.

The limiting factor in detecting structures in elec-tron tomographic reconstructions is the strong noise,hampering the application of any detection method.Therefore, it seems to be worthwhile to considerpreprocessing the data using noise reduction prior tothe detection step. Adapting the ideas of the so-calledwavelet denoising technique (Donoho, 1993), werecently proposed a method for noise reduction ofelectron tomographic reconstructions of unique struc-tures, i.e. those not containing multiple copies of astructure (Stoschek and Hegerl, 1998). Intended as a

remedy to improve the visualization of such 3-Dreconstructions, the method shows very promisingresults in terms of preserving the signal whilereducing the noise considerably. The potential of thismethod as a preprocessor in the context of automaticobject detection in volume data should be exploredfurther in the future.

8. CONCLUSION

Although the basic concepts of single-axis electrontomography were put forward almost 30 years ago,the progress in applying this technique to largeindividual structures has been less impressive thanrelated 3-D imaging methods such as quasi-tomo-graphic approaches or electron crystallography. Inthis review we discussed the technical aspects ofelectron tomography in detail, identified some of theexperimental and theoretical limitations of the tech-nique and touched on current developments in elec-tron microscope instrumentation. In particular, auto-mated data collection, the application of energyfiltering to increase image contrast of ice-embeddedsamples, and the use of helium cooled specimenholders to increase the allowable electron dose, aremajor technical improvements which contribute tothe quality of 3-D data sets. In addition, improved3-D reconstruction procedures and the adaptation of3-D pattern recognition algorithms to electron tomo-graphic reconstructions will enhance the applicabil-ity of electron tomography in practice. During thepast 5 years, the development of high-resolutioncryo-electron tomography has narrowed the resolu-tion gap between atomic-resolution crystallographicapproaches and low-resolution light-microscopymethods. Electron tomography has matured signifi-cantly and is close to becoming a powerful tool forstructural biology, which allows identification ofmacromolecules in their cellular environment andanalysis of spatial relationships such as supramolecu-lar structure and organization within cells.

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