use of negative stain and single-particle image processing to...

Journal of

Structural

Journal of Structural Biology 147 (2004) 247–258

Biology

www.elsevier.com/locate/yjsbi

Use of negative stain and single-particle image processingto explore dynamic properties of flexible macromolecules

Stan A. Burgess,* Matt L. Walker, Kavitha Thirumurugan, John Trinick,and Peter J. Knight

School of Biomedical Sciences and Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds LS2 9JT, UK

Received 13 January 2004, and in revised form 7 April 2004

Available online 6 May 2004

Abstract

Flexible macromolecules pose special difficulties for structure determination by crystallography or NMR. Progress can be made

by electron microscopy, but electron cryo-microscopy of unstained, hydrated specimens is limited to larger macromolecules because

of the inherently low signal-to-noise ratio. For three-dimensional structure determination, the single particles must be invariant in

structure. Here, we describe how we have used negative staining and single-particle image processing techniques to explore the

structure and flexibility of single molecules of two motor proteins: myosin and dynein. Critical for the success of negative staining is

a hydrophilic, thin carbon film, because it produces a low noise background around each molecule, and stabilises the molecule

against damage by the stain. The strategy adopted for single-particle image processing exploits the flexibility available within the

SPIDER software suite. We illustrate the benefits of successive rounds of image alignment and classification, and the use of whole

molecule averages and movies to analyse and display both structure and flexibility within the dynein motor.

� 2004 Elsevier Inc. All rights reserved.

Keywords: Negative stain; Single-particle image processing; SPIDER; Dynein; Myosin; Macromolecule; Fleximer; Flexibility

1. Introduction

The high contrast and �1.5 nm resolution achievable

by negative staining mean that this simple method still

has great utility in macromolecular microscopy. Al-

though cryo-electron microscopy followed by single-

particle image processing allows 3D structure to be

determined, many objects pose problems for this ap-

proach. For instance where the mass of the object isbelow �500 kDa, which includes most individual pro-

tein molecules or small complexes, beam damage may

make it impossible to gain enough information from

each weakly contrasted object to enable the molecular

orientation to be determined (Henderson, 1995).

Prominent substructural features reduce this limit (Orl-

ova et al., 2000), but do not abolish it. The higher

contrast of negative stain images can therefore allow

* Corresponding author. Fax: +44-113-343-4228.

E-mail address: [email protected] (S.A. Burgess).

1047-8477/$ - see front matter � 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.jsb.2004.04.004

progress to be made in discovering the structure of thesesmaller objects.

Another challenge arises where the object exists in

solution in several discrete structural states. If only a

small number of different conformations exist, and

these can be identified during preliminary processing,

it is possible to derive the 3D structure of each

conformer (Schoehn et al., 2000). However, this ap-

proach fails where the molecule is flexible, since thereis then essentially a continuum of conformers. We

refer to these flexible conformers as f leximers, to

distinguish them from conformational variants in-

duced by, for example, substrate binding by an en-

zyme. Fleximers produce ambiguity in the assignment

of orientation versus conformation for any given

image. Progress in describing the structure in two-

dimensional projections is still possible, however, us-ing single-particle processing methods. As long as one

part of the object allows alignment of the images

flexibility in other parts can then be described. Such

mail to: [email protected]

248 S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258

alignment requires a good signal-to-noise ratio in theraw images, and this is where negative staining can

bring benefits.

We have used negative staining and single-particle

processing to investigate the structure and action of the

motor proteins myosin and dynein. Except for the head

domain of dynein, the component parts of both these

molecules are too small for unstained cryo-EM to have

much chance of success, and there is evidence frommany techniques for considerable flexibility within mo-

tor proteins (Howard, 2001). Metal shadowing was used

successfully to provide initial descriptions of the struc-

ture and flexibility of the molecules (Elliott and Offer,

1978; Goodenough et al., 1987; Slayter and Lowey,

1967), but negative staining is capable of revealing

greater detail. Myosin is normally poorly preserved in

negative stain, but we found good preservation by pre-treatment of a thin carbon support film with ultraviolet

(UV) light prior to specimen application (Knight and

Trinick, 1984; Walker et al., 1985). Myosin was found to

be flexible, and we therefore adapted procedures within

the single-particle processing methodology to describe

this (Burgess et al., 1997). More recently we have ex-

tended the same general methods to describe and

quantitate acto-myosin interactions (Burgess et al.,2002; Walker et al., 2000) and dynein flexibility (Burgess

et al., 2003, 2004).

Negative staining following adsorption onto carbon

has attendant concerns that the structure seen is dis-

torted or disrupted by the preparative procedure. For

example, interactions between protein and the charged

surface of the substrate may distort protein conforma-

tion. Further distortion may occur as the concentrationof salts and stain increase during drying with the addi-

tional possibility of drying induced collapse of structure.

Nevertheless, even large complexes that might be ex-

pected to collapse can show a surprising degree of fi-

delity between negative stained structure and that

determined by other techniques (e.g., Frank et al., 1991).

Our observation in raw images that the myosin head had

a large distal domain and two smaller ones in tandemcloser to the tail (Walker and Trinick, 1988), was con-

firmed when the atomic structure was solved by crys-

tallography (Rayment et al., 1993). Image processing of

such images has since shown the smaller subdomains

within the motor domain, such as the SH3 and converter

subdomains, as well as the N- and C-terminal lobes of

the light chains, each of which is about 9 kDa as re-

vealed by crystallography (Fig. 1). Moreover, confor-mational changes induced by nucleotide binding and

hydrolysis are preserved during specimen preparation,

allowing the study of myosin conformations in the

presence of ATP directly (Burgess et al., 2002), an ad-

vantage over crystallography which has been limited to

the use of ATP analogues. For dynein, images from

negative stain are compatible with preliminary data

obtained from frozen hydrated specimens (Burgesset al., 2004), indicating little gross distortion of the

molecule.

What can we learn about flexibility from negatively

stained molecules? First, drying forces may be useful in

revealing the location of weak points that deform under

stresses in vivo, which may not be detected in frozen-

hydrated specimens where such forces are absent. For

instance, negative staining of myosin molecules allowedus to conclude that the light chain domain was flexibly

attached to the motor domain (Burgess et al., 1997)

before a pliant region at their junction was identified by

crystallography (Dominguez et al., 1998). Second, ad-

sorption to the substrate may perturb a flexible molecule

in ways which provide important structural insights. For

instance, the helical order of myosin thick filaments

becomes disrupted (Knight and Trinick, 1984), and theextent of flexibility of dynein molecules depends on their

orientation (Burgess et al., 2004). These effects reveal the

reversal of polarity of myosin molecules in thick fila-

ments and an otherwise unseen component of dynein.

Third, changes in flexibility can be detected by com-

paring molecules prepared under different conditions. In

dynein molecules the stalk domain is stiffer in the ab-

sence of nucleotide than in the presence of ADP andvanadate (Burgess et al., 2003). And finally, in some

cases, where existing data are available for molecules in

solution, the flexibility deduced from electron micro-

graphs has been found to be compatible. Thus, the

persistence length of the giant muscle protein titin

(Tskhovrebova and Trinick, 2001) adsorbed to mica

agrees with that determined from light scattering solu-

tion studies, indicating that in this case where the mol-ecule is like a string of beads, its flexibility is faithfully

represented. However, with other molecular architec-

tures such good quantitative correspondence may not be

found.

Flexibility within motor proteins has long been re-

cognised as an important functional characteristic,

providing for example the in-series elastic component of

the force-generating interactions of myosin heads withactin in muscle (Huxley and Simmons, 1971). Flexibility

also allows myosin heads attached to thick filaments to

locate and form stereospecific interactions with actin

subunits in thin filaments despite the incommensurate

structures of the two filaments (Huxley, 1969). However,

an empirical description of flexibility has been long in

coming. Flexibility is generally anathema to crystallog-

raphers, and whilst structure determination by NMR isexcellent at describing inter-atomic spatial relations, it

has difficulty where domains move against one another.

Analysis of EM images by single-particle techniques

offers a way forward, and in what follows we describe

the methods we have employed both to get highly de-

tailed images of molecules in negative stain and to an-

alyse their structure and flexibility.

Fig. 1. Heads of myosin molecules. (A) Atomic structures of scallop

myosin 2 molecules with ADP and vanadate bound in the active site

(upper panel, PDB Accession No. 1DFL) or with no nucleotide bound

(lower panel, Accession No. 1DFK). Light chains are coloured yellow

and orange. The heavy chain is coloured blue with additional col-

ouring to show various motor subdomains, including the SH3 sub-

domain (red), upper (cyan) and lower (green) 50k subdomains, and the

converter subdomain (black). (B) Example class averages of negatively

stained recombinant myosin 5 heads after single-particle image pro-

cessing showing a close correspondence of motor structure to appro-

priately orientated atomic structures (A). Differences in the orientation

of the light chain-binding domain are seen, which may be due to dif-

ferences between myosins 2 and 5. Myosin 5 heads were prepared in

the presence of ATP. Scale bar: 10 nm.

S.A. Burgess et al. / Journal of Structural Biology 147 (2004) 247–258 249

1.1. Negative staining

We use a single, continuous carbon film method for

negative staining. In general this produces less even

staining than the double-carbon �sandwich� techniqueused by others (Frank, 1996), but an advantage is that it

is relatively quick and easy. In our hands it produces a

higher yield of useable grids. For large proteins it typi-

cally produces single-sided staining of the molecule

(Frank, 1996). This is a severe shortcoming for three-dimensional reconstruction but in other situations, sin-

gle-sided staining can provide useful information about

the �face� of the molecule in contact with the carbon

(Verschoor et al., 1989). Commonly, the orientation of

molecules on carbon is heavily biased in favour of par-

ticular views.

1.1.1. Preparation of continuous carbon support films

The preparation of grids containing thin and intact

carbon films is a very important part of the procedure.

Carbon is evaporated from 1.0mm carbon thread (Agar

Scientific) at a height of �17 cm onto freshly cleaved

mica in a coating unit with an operating vacuum of

�10�6 torr. The current passed through the carbon

thread is increased rapidly to maximum, which ruptures

the thread. This speed helps create carbon films withconsistent thickness. A thickness of �8 nm provides a

good compromise between strength and low electron

scattering. Care is taken to avoid oil contamination of

the vacuum by minimising the duration of direct

pumping by the rotary pump and by cooling the diffu-

sion pump baffles with liquid nitrogen (a cold trap in the

vacuum chamber may also be used). Freshly made car-

bon films are collected onto 300 or 400 mesh coppergrids by floating the carbon onto the surface of a bath of

distilled water and then raising prearranged grids from

under the water surface to collect the carbon. The

�rough� side of the carbon (i.e., the surface not in contact

with mica) is therefore uppermost on the grid and is the

side to which the specimen is applied. Grids are then left

to dry overnight at room temperature. They may also be

dried more quickly in a warm oven although this tendsto cause more breakage.

1.1.2. Treatment with ultraviolet light

Grids are rendered hydrophilic by treatment with UV

light from a lamp emitting over a broad range of wave-

lengths. The lamp we use (low pressure mercury vapour

type R51, UV Products, Pasadena, CA) has had the low-

pass (black light) filter removed and generates ozone inthe air around it which may be involved in the modifi-

cation of the carbon surface. Grids are placed about 5 cm

from the lamp in an enclosed environment (e.g., card-

board box) which retains the ozone. Grids are typically

irradiated for about 40min, although this depends on the

specimen to be examined; filamentous proteins (e.g., F-

actin) require less time than single molecules. Also,

freshly made carbon may need less time as it is alreadyslightly hydrophilic. After UV irradiation, grids remain

hydrophilic for around 4 h. Details of the surface

chemistry of this process are poorly understood. How-

ever, it leads to a thin, even layer of stain upon drying,

and may promote interactions between the specimen

protein molecules and the carbon substrate which sta-

bilises them against any adverse effects of the stain.

1.1.3. Buffers and protein concentration

For good quality negative staining, attention must be

given to the constituents of the specimen buffer. Milli-

molar concentrations of reducing agents such as dithi-

othreitol and mercaptoethanol adversely affect the

quality of staining and increase the occurrence of stain

artefacts. If reducing agents cannot be avoided, micro-


molar amounts can be tolerated. The choice of bufferanion is not critical, although we have obtained partic-

ularly good results with MOPS, which is also a conve-

nient buffer for specimen storage by drop-freezing in

liquid nitrogen. In general, high concentrations of salt

(0.5M) and nucleotide (millimolar) also impair the

quality of staining, as do detergents, glycerol, and su-

crose. If these buffer constituents cannot be avoided or

removed by dilution before application to the grid, awashing step can be introduced before staining. The

wash buffer should be similar to the specimen buffer, but

lacking the undesirable constituents. A typical wash

buffer we use is 25mM KCl, 10mM MOPS, 1mM

EGTA, and 1mM MgCl2, pH 7.0. However, a washing

step does raise the possibility of some rearrangement of

the protein on the carbon substrate (Walker et al., 2000).

Suspensions of single molecules are applied to thegrid at a concentration of between 20 and 100 nM,

which usually gives an appropriate distribution of par-

ticles for subsequent windowing and single-particle

analysis. Particle density also seems to affect stain depth.

If the particle density is too low, the depth of stain is

often too shallow for good staining.

1.1.4. Staining

After application of a drop of the sample (and a

possible washing step with 2–3 drops of wash buffer), the

grid is stained with 2–3 drops of 1% aqueous uranyl

acetate previously passed through a 0.2 lm filter (Ac-

rodisc, Gelman Laboratory Sciences). Using a pasteur

pipette each drop of solution (sample, wash, and stain)

Fig. 2. Variations in stain depth affect the appearance of molecules. (A) Grada

heads. Enlarged regions (boxes) from deep (B) and shallow (C) stain show

contrast balancing show accumulation of stain around particles in shallow sta

is touched onto the grid surface and then �flicked� offrapidly to avoid migration onto the underside of the

grid. Excess stain is then removed by touching the torn

edge of a piece of filter paper (Grade 1) to the edge of

the grid.

1.2. Microscopy

Low-dose images (�300 e�/nm2) undoubtedly offerhigher resolution (Unwin, 1974). However, upon irra-

diation stain migration can improve the definition

against the background of fine structures like the coiled-

coil tail of myosin (Walker et al., 1991). Although this

improvement in signal-to-noise ratio occurs at the ex-

pense of resolution, molecules imaged under high dose

reveal structures as small as 9 kDa (Fig. 1B) and single

a-helices can be seen in raw micrographs (Walker andTrinick, 1986). For our recent work on dynein mole-

cules, we wanted to image its flexible coiled-coil stalk

with optimal clarity for subsequent image processing

(see below). In a previous study (Samso et al., 1998)

using low-dose conditions the stalk was not seen either

in raw images or after single-particle image processing.

Therefore, as for myosin, we use high dose conditions.

We do not routinely monitor the dose of each micro-graph, but as a guide, viewing for approximately 10 s at

a magnification of 40 000� followed by a 1 s exposure

corresponds to a dose of between about 5000 and

100 000 e�/nm2.

The quality of staining between different grids is

always variable, even when they are prepared under the

tion of stain depth across a single micrograph of recombinant myosin 5

ing individual molecules. Same regions (D and E, respectively) after

in (E), but not in deeper stain (D). Scale bars: (A) 200 nm; (B–E) 50 nm.


same conditions and on the same day. Grids aresearched at low magnification to locate areas which are

negatively stained (appear darker). Usually, these areas

show a gradation in stain depth (Fig. 2). Molecules in

deeper stain appear surrounded by a fairly uniform

darker background (Figs. 2B and D), whereas those in

shallower stain appear outlined by an accumulation of

stain (Figs. 2C and E). Molecules in extremely shallow

stain (not shown) are visibly damaged by the beam andare avoided. To ensure that particles selected for image

processing are as homogeneous as possible, we choose

Fig. 3. Negatively stained dynein molecules. (A) Field showing particles in

dynein. The stem is �25 nm long, the stalk is �15 nm long, and the head dom

visible in well-stained areas. (B) Gallery of selected particles. Each particle is

outer rings are indicated by circles in the lower right panel. Scale bars: (A)

them from regions of similar stain depth. In our ex-perience, those from shallow stain (Figs. 2C and E)

show the greatest detail after image processing

(Fig. 1B).

1.3. Image processing

All image processing described in this section is per-

formed using procedures written in the SPIDER suite ofprograms (Frank et al., 1996). SPIDER procedures are

batch files which implement SPIDER commands within

shallow stain. Inset: Cartoon showing the arrangement of domains in

ain is �13 nm in diameter. Stems and stalks as well as heads are directly

shown in a window of the size used for the initial alignment. Inner and

100nm; (B) 50 nm.


loops and with conditional statements. As such, they areextremely flexible allowing the image processing strategy

to be customised to suit molecules with unusual shapes

and behaviours. One such example is the microtubule

motor protein dynein.

Dynein has two extended and flexible structures,

called the stalk and stem, which emerge from a ring-like

head (Fig. 3). Of the two structures, the stalk is the

smaller, and predicted to consist of an a-helical coiledcoil. Understanding this structure is vital to under-

standing the mechanism of dynein since this coiled coil

carries at its distal end the all-important ATP-sensitive

microtubule-binding domain. Currently, the three-di-

mensional structure of the entire dynein molecule is

unknown, and the mechanism of this motor is poorly

understood. What follows is a description of the image

processing strategies we have used to align, classify, andanalyse images of negatively stained dynein molecules.

The resulting two-dimensional images have provided

new insights into dynein�s structure and mechanism

(Burgess et al., 2003, 2004).

1.3.1. Micrograph digitisation

Micrographs are digitised using a Leafscan 45 (Leaf

Systems, Southborough, MA) or an Imacon Flextight848 (Imacon A/S, Copenhagen, Denmark) film scanner

at 16-bit optical resolution, and with a pixel size of

20 lm, corresponding to �0.5 nm at the specimen.

1.3.2. Particle picking and windowing

Particle picking involves positioning a cursor over

each particle and recording its image coordinates.

Flexibility within dynein molecules and differences inorientation on the grid cause the particles to appear

rather variable in raw micrographs (Fig. 3A). For

elongated, multi-domain particles like these, it is neces-

sary to pick the same feature within each molecule since

this determines its position within the window which

affects the subsequent alignment process. For dynein we

pick the centre of the head domain, placing this feature

at the centre of each resulting windowed image(Fig. 3B).

The window size is chosen to include the stem and

stalk plus an additional border of background. During

subsequent alignment, image transformations (i.e., ro-

tations and translations) introduce artefacts into each

image near its edges. Therefore, the window must be

sufficiently large to ensure that these artefacts occur only

in the image background and not within the particleitself. For particles that are variable in length, like dy-

nein, particular care must be given to this when choos-

ing the window size. Larger windows are therefore

better but they reduce the speed of alignment, which

may need to be performed several times with different

parameters before it is successful. Larger windows also

increase the likelihood of containing neighbouring par-

ticles, although these can be obscured by applying acircular mask to each window (see below). To minimise

both the window size and the subsequent image trans-

lations during alignment, the centre of each head is

picked with greater precision than would otherwise be

required.

All windowed images undergo pre-processing steps

before alignment to minimise differences between them

arising from residual differences in stain depth and even-ness. A ramp of pixel densities is calculated and removed

from each image and then the pixel density distribution is

�floated� using an arithmetic operation to adjust the mean

pixel density to zero and the standard deviation to one. A

circular mask, if necessary, is applied at this stage. In this

case, particles are sufficiently well separated that the

majority of windows contain no intruding particles

(Fig. 3B), avoiding the need for masking.The number of particles required for successful

alignment and classification is difficult to predict be-

cause it depends on the homogeneity of the preparation

(orientation and conformation). However, we generally

aim to start with a minimum of 2000–4000 particles

which allows us subsequently to remove misaligned or

poorly stained molecules after initial classification yet

leaves enough particles for further analysis.

1.3.3. Initial alignment

Image alignment is critical since all subsequent pro-

cessing depends upon it. In our experience, some parti-

cles always fail to align, particularly if they are

heterogeneous in conformation. Therefore it is impor-

tant both to minimise these by exploring a range of

alignment strategies and to discard the failures that re-main. The high contrast of negative stain facilitates this

step because the morphology is easier to see in individ-

ual images. Reference-free strategies are used in general,

unless otherwise stated.

Successful alignment of dynein images requires the

use of a modified iterative procedure in which a rota-

tional alignment is performed first in each cycle, fol-

lowed by a translational alignment. This is the oppositeorder to that normally employed with roughly globular

particles which benefit from translational alignment

first. Another crucial parameter for alignment is the

choice of inner and outer radii for rotational alignment.

For rotational alignment of the stems it is necessary to

choose an outer radius large enough to encircle all the

stems. It is also absolutely necessary to exclude the head

from rotational alignment by using a larger than usualinner ring radius (Fig. 3B, circles). The stem is a small

feature within a large region of background staining.

Apparently, including the head makes the head compete

with the much weaker signal arising from the stem,

leading to many rotational misalignments of the stem.

After each iteration of rotational and translational

alignment we re-centre the aligned images using a


model image generated from a previous alignmentattempt. This is necessary to prevent images from

becoming translated substantially within the image

window. When this does occur, molecules escape the

inner and outer radii defined for rotational alignment

(which remain fixed relative to the image window)

thereby decreasing the likelihood of successful rota-

tional alignment. Re-centring also allows for error

checking. Any image translated by more than a user-defined value can be considered erroneous and is

reset to its original position. This can prevent the

accumulation of translational errors in the early it-

erative cycles. Therefore, an image with a final

translation of precisely (0, 0) indicates a misaligned

particle and this value can be used as a criterion to

eliminate the image at the end of alignment. The

value of the maximum permissible translation vector

Fig. 4. Initial alignment and classification of dynein particles. (A) Global aver

global variance here and in all subsequent figures, the contrast has been inv

variances are shown in darker shades, lower variances in paler shades. (C) M

averages showing flexibility within the most common orientations: (D) left vi

class is shown in the lower right corner of each panel. Each panel is 72 nm w

is chosen empirically and depends not only on thesize of the window, but also on the accuracy with

which the centre of each particle is identified during

particle picking.

The success of alignment can be assessed in a number

of ways. Re-centring (described above) permits, in

principle, a quantitative assessment of convergence of

alignment. The common alignment between iterations

means that the magnitude of translations and rotationsfor each image should tend towards zero as alignment

proceeds. However, for heterogeneous particles we

prefer to assess the success of alignment more directly by

examining class averages produced by a subsequent

classification step and also by examining all individual

aligned images on the screen. The stem and stalk of

dynein provide useful landmarks on the molecule for

this purpose.

age and (B) global variance of 3057 images after seven iterations. In the

erted from the usual appearance produced in SPIDER. Thus, higher

ask used for initial classification into 150 classes. (D–F) Selected class

ews, (E) side views, and (F) right views. The number of images in each

ide.


Applied to images of dynein, the alignment strategydescribed above produces a global average image in

which the head and stem are visible (Fig. 4A). The stalk

is not seen but a faint indication of its presence is indi-

cated in the upper left region of the global variance

image (Fig. 4B).

1.3.4. Initial classification and segregation

For image classification our preferred method is K-means clustering. Previously, we found this method to

perform better than hierarchical ascendant classification

when molecules display continuous flexibility (Burgess

et al., 1997). Here, classification was done directly on all

image pixels within the mask (see below), that is, with-

out prior correspondence analysis and the selection of

factors. For dynein images a mask was chosen that en-

compasses the head and stem only (Fig. 4C). We basethe shape of the mask on the shape of the global vari-

ance image, rather than the global average, because the

former provides a better indication of the diversity of

fleximers than the latter. Attempts to classify the images

using masks also encompassing the stalks were unsuc-

cessful (see below). At this stage of processing we rou-

tinely create a series of classifications using different

masks and for each mask, different numbers of classes.This allows the investigator to assess the alignment

qualitatively and to build up a picture of the reliable

features in the images. The shape of the final mask

(Fig. 4C) is thus arrived at by trial and error to ensure

that the heads and stems of all molecules are included

within it, whilst including a minimum of background.

Having chosen a suitable mask for classification, it is

then necessary to choose an appropriate number ofclasses. There is no simple guide to do this, since every

data set is different in terms of stain variability, distri-

bution of particle orientations, particle heterogeneity,

and flexibility. One is trying to satisfy the competing

requirements of merging images to achieve noise re-

duction while preserving diversity. Starting with many

small classes allows the investigator to better detect the

diversity that exists within the data set. K-means clus-tering produces classes with broadly similar numbers of

particles (unlike some of the merging criteria in hierar-

chical ascendant classification), which can be helpful at

this stage. In general we find that 20–40 images per class

provides reasonable noise suppression of well-stained

molecules. Most classes thus produced are reliable,

however some can be misleading because they contain

significant numbers of misaligned, poorly stained orheterogeneous particles. Therefore, it is important to

check the individual images within each class. Careful

examination of individual images within heterogeneous

classes can sometimes reveal useful information. For

example, with dynein we found a perturbed structure

that was critical to our interpretation of the more usual

appearance (Burgess et al., 2003).

Dynein adopts three characteristic orientations of thehead on the carbon substrate, but multiple conforma-

tions exist within each of these (Figs. 4D–F). This ne-

cessitates the use of many classes (number of

classes¼ 150, average number of images per class �20).

Conformational variability is caused by flexibility within

the stem, near its connection with the head. The stalk is

also flexible as evidenced by its absence in these small

classes. Stalks were subsequently found to flex inde-pendently of the stem (see below) producing too many

different fleximer structures to form homogeneous clas-

ses. This problem is compounded by this alignment

which, for each orientation of the head fails to achieve a

precise alignment of the heads and hence the base of the

stalks. This happens because flexibility between the head

and stem cause both these domains (and hence the stalk)

to become slightly misaligned in favour of a global(head+ stem) alignment. To overcome this problem we

employed the following strategy. We identified classes

showing a particular head orientation and combined

them to form a new data set. To do this we used the

�markers� command in WEB to select each good class of

a particular head orientation from a gallery of all class

averages. Then, using a specially written procedure,

these coordinates were converted into class numbers andindividual particles from each class were extracted from

the document files produced by K-means clustering and

combined into a single, new document. Classes showing

poor detail were excluded at this stage. Thus, we created

three segregated data sets (Figs. 5A and B) which were

then processed separately in a second round of align-

ment and classification.

1.3.5. Second alignment and classification

Within each of the three data sets segregated on the

basis of orientation we obtain a common frame of refer-

ence by aligning them according to their head domains.

This is an entirely independent alignment from the first,

using re-windowed images, this time with a much smaller

window to exclude the stem and stalk. We also apply a

soft-edged circular mask to each image to further obscurethese structures. Reference-free alignment was performed

as before except with inner and outer radii for rotational

alignment modified to include only the head. Each ori-

entation-specific data set was aligned separately, but us-

ing the same parameters. Their global �head-aligned�image averages show considerably improved detail

(Fig. 5C) indicating the success of alignment. The align-

ment parameters (hrot, xsh, and ysh) thus determined werethen applied to windowed images large enough to contain

the whole molecule. This provides an alignment of one

part of themolecule (the head) allowing us to examine the

flexibility of the other parts (stem and stalk) relative to it.

The weakness of the stem and stalk in the resulting global

averages (Fig. 5E) confirms their flexibility, while global

variances indicate their ranges of positions (Fig. 5F).

Fig. 5. Segregation of dynein molecules according to orientation, and second alignment. (A) Averages and (B) variances of combined classes in each

of the three typical views after initial alignment and classification. Note the poor detail in the heads and clear visibility of the stems. Number of

particles are: left, n ¼ 1658; side, n ¼ 219; right, n ¼ 346. (C) Averages and (D) variances after a second alignment of each segregated data set using

features within the head domain only (head alignment). (E) Averages and (F) variances after head-alignment is applied to the original, larger images.


For each orientation of dynein, classification of the

stem and stalk regions is performed separately and in-

dependently. Again we use K-means clustering. Masks

need to be sufficiently large to enclose all positions of thestalk (Figs. 6A and B) and stem (Figs. 6C and D). Since

this also encloses a considerable area of background,

which is also �seen� by the classification procedure, it is

vital for optimal classification that these domains be

maximally contrasted by stain (using high dose condi-

tions).

Classification is exquisitely sensitive to mask shape.

We therefore try several different, though related masks.For each mask we also classify into many different

numbers of classes before choosing the most useful one

(Figs. 6E and F). This choice is subjective. Our defini-

tion of the best classification in this context is that which

satisfies the following criteria simultaneously: it pro-

duces the most classes with clearly defined structures of

interest while displaying the widest variety of their

conformations and retaining the most individual images.Performing multiple classifications in this way helps to

avoid producing spurious results.

1.4. Quantitative measurements from class averages

Image averages of stalks and stems contain a wealth

of information about the structure and flexibility of

dynein molecules. First, however, it is necessary toidentify those �good� classes with meaningful informa-

tion to ensure that measurements taken from them are

representative of their constituent individual images.

Some stalk class averages show no clear stalk (Fig. 6E).

Others appear good but inspection of their constituent

images reveals a number of inappropriately assigned

images. This situation probably arises because of thelarge size of the mask compared to the size of the stalk

and its occasional poor staining. Therefore, we exam-

ine carefully individual images within each class to

assess the validity of that class average before taking

measurements from it. This is a subjective process.

Concerns about bias (e.g., weighting in favour of un-

representative fleximers) can be alleviated by starting

with a large data set. For example with dynein we haveanalysed two data sets of left views. One of these

(Fig. 6) was prepared in the absence of nucleotide

(apo-dynein) and consists of 1658 images. The other

(Burgess et al., 2003) was prepared in the presence of

ATP and vanadate which produces an ADP�Vi–dyneincomplex and consists of 1733 images.

In each good class we mark the position of the end

of the stalk interactively. We also mark the point atwhich it attaches to the head, which we do only once

because it is visible in the global average. The co-or-

dinates of the two ends of the stalk are then used to

calculate its chord length and angle (relative to an ar-

bitrary axis, see cartoon, Fig. 6G). The same process is

applied to the stems. Within the common frame of

reference of the aligned heads, this data can be plotted

as a histogram to reveal the distribution of stem andstalk angles (Fig. 6G) after first weighting each value

according to the number of images in the class from

which it was derived. Note that the number of

Fig. 6. Classification of stem and stalk positions in left views of dynein molecules. Masks used for classification, encompassing the stalks (A) and

stems (C) are indicated alongside corresponding variance images (B and D) showing in outline the position of the mask. Gallery of all 70 class

averages from (E) stalk and (F) stem classifications. Some stalk class averages show poor detail or no stalk, whereas the larger stem is seen in all

classes. (G) Histogram showing the distribution of stalk and stem chord angles measured according to the arbitrary vertical axis shown in the

cartoons. The distributions are continuous.


measurements thus determined is less for stalks than

stems, because many stalk classes are poor.

From this data set quantitative measurements were

obtained for both stem and stalk from a sizeable pro-portion of individual molecules (�45%). Combining

the information allowed us to describe several impor-

tant aspects of the whole molecule (Burgess et al.,

2003). For example, by plotting stem angle versus stalk

angle for individual molecules, we were able to show

that their movements are not coupled. By calculating

the end-to-end length of individual molecules we alsoshowed that the molecule shortens when prepared in

the absence of ATP and vanadate. And by plotting

histograms of the angle between stem and stalk in


individual images we showed a systematic change inangle between molecules in the two nucleotide condi-

tions.

Caution should be taken when interpreting the ap-

parent flexibility seen in molecules adsorbed to a sub-

strate, since distortion during specimen preparation

(e.g., flattening) may have contributed to it or even

caused it. Therefore, it may not represent the true flex-

ibility of the molecule in solution. Indeed, we havefound that for dynein molecules adsorbed in different

head orientations, different extents of stem flexibility are

seen (Burgess et al., 2004). Nevertheless, these images

may still be useful because distortions by external forces

still show the locations within the molecule where flex-

ibility is most likely to occur.

1.5. Movie making

Quantitative information like the angle of the stalk

and stem can be used to sort a series of images into a

meaningful sequence suitable for playing as frames of

a movie. The advantage of movies is that they provide a

rapid and intuitive means of viewing large numbers of

images (class averages or individual images), so are

useful for comparing the images within a class to test forconsistency with the class average. They also enable the

identification of subtle differences between individual

frames which may be overlooked in a gallery. Image

sequences of dynein made using stem and stalk angles

produce movies illustrating differences in the way the

stalk flexes in molecules prepared in different nucleotide

conditions (Burgess et al., 2003). However, caution

should be taken to avoid spurious inferences being de-rived from the sequence in which the frames are played.

The sequence of frames is imposed by the investigator

and therefore does not, for instance, represent a se-

quence of events in time. For example, the order of

images in Fig. 7C should not be taken to indicate that

Fig. 7. Whole molecule averages of dynein. (A) Examples of individual head

this alignment. (C) Whole-molecule averages of molecules shown in (A) cre

erages, illustrated for the last panel in (D). (E) Whole-molecule averages sho

aligned images.

the stem moves progressively from one extreme positionto the other. When displayed as a movie (Burgess et al.,

2003) such a sequence looks like a pendulum swinging,

but there is no reason to suppose that the molecule ac-

tually behaves in this manner. Instead, we suppose that

it exhibits random, thermally driven fluctuations about

its lowest energy conformation.

1.6. Generating whole-molecule averages

So far classification of dynein has only been possible

for one part of the molecule at a time. For illustrative

purposes it is helpful to show fleximers in which both the

stem and stalk are visible simultaneously. To do this we

splice together image averages of stalk and stem classes

to create low-noise images of whole molecules (Figs. 7C

and D). Importantly, it is necessary to create whole-molecule images only for those fleximers that actually

exist, since not all combinations of stem and stalk occur

(Burgess et al., 2004). We do this by selecting each

molecule in which both stem and stalk were classified

successfully (Fig. 7A) and splicing together the two class

averages into which it had been assigned (Fig. 7C). For

dynein, this process is simple, requiring the splicing to-

gether of two entirely non-overlapping regions(Fig. 7D). For other molecular shapes, it may be nec-

essary to use masks to achieve this.

1.7. Alignment of class averages

To obtain a single frame of reference between apo-

and ADP�Vi–dynein data sets, which is meaningful in

terms of how the motor works, we decided to performan additional alignment of the molecules according to

the bases of their stems (cargo-binding domains). In-

stead of using images of individual dynein stems, we

used class averages showing noise-reduced stems from

head-aligned images (Burgess et al., 2004). This way we

-aligned dynein molecules and (B) corresponding global average from

ated by splicing together their corresponding stalk and stem class av-

wn in (C) after alignment of their stems. (F) Global average of stem-


avoided losing any images to misalignment, thereby al-lowing us to apply all the stem and stalk positional data

obtained previously to a newly transformed frame of

reference (i.e., stem alignment). Briefly, to each stem

class average a mask was applied to obscure the head

(the stalk is not visible). One class average was used as a

reference image and the others were aligned with respect

to it. This third alignment (Figs. 7E and F) provides a

basis from which to plot the two-dimensional distribu-tion of the stalk tip position (i.e., the microtubule-

binding domain) that results from the combined

flexibility of the stalk and stem (Burgess et al., 2004).

Since molecules in both apo- and ADP�Vi-conditionsare aligned to a common origin, the new alignment al-

lows us to see their overlapping distributions, suggesting

how dynein might operate in situ.

2. Conclusion

Negative stain images of molecules contain a wealth

of information which can be extracted by customising

the alignment and classification strategies within single-

particle image processing. Uniquely, large-scale flexi-

bility within macromolecules is amenable to study usingappropriately designed single-particle processing tech-

niques. Despite progress in cryo-electron microscopy we

envisage that negative staining will continue as a useful

additional technique for the study of macromolecular

function.

Acknowledgments

We thank J. Sellers for recombinant myosin 5 and K.

Oiwa and H. Sakakibara for dynein. This work was

supported by NIH (J.T.) and BBSRC (P.J.K.).

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