[Methods in Cell Biology] Cellular Electron Microscopy Volume 79 || Reconstructing Mammalian...

CHAPTER 8

METHODS IN CELL BIOLCopyright 2007, Elsevier Inc.

Reconstructing Mammalian MembraneArchitecture by Large AreaCellular Tomography

Brad J. MarshInstitute for Molecular Bioscience, Queensland Bioscience PrecinctThe University of Queensland, BrisbaneQueensland 4072, Australia

Centre for Microscopy and MicroanalysisThe University of Queensland, BrisbaneQueensland 4072, Australia

School of Molecular and Microbial SciencesThe University of Queensland, BrisbaneQueensland 4072, Australia

I. In

OGY,All rig

troduction and Rationale

VOL. 79 0091hts reserved. 193 DOI: 10.1016/S0091

-679X-679X

II. M
ethods and Materials A. M ammalian Cell and Tissue Culture B. F ast-Freezing and Freeze-Substitution C. M icrotomy and Grid Preparation D. B asic Instrumentation Requirements for ET of Thick Plastic Sections E. A cquisition of Digital Tilt Series from Large Cellular Areas by
Automated IVEM/HVEM Montaging Tomography

(

F.
3 D Reconstruction of Large Cellular Areas G. 3 D Segmentation and Quantitative Analysis
III. D
iscussion A. F uture Directions
IV. S
ummary R eferences
Microscopy has provided crucial insights into the fundamental features and

architecture of mammalian cells and organelles for now over a century. These

glimpses of cellular fine structure have thus guided our impressions—as molecular

/07 $35.0006)79008-2

194 Brad J. Marsh

cell biologists—of how mammalian cells and their numerous membrane-bound

internal compartments are organized within three dimensions (3D), although for

the most part these extrapolations have come from static two-dimensional (2D)

images taken from the light or electron microscope. Recently, however, we have

finally been aVorded the chance to dissect subcellular membrane architecture and

dynamics at the nanoanatomical level in 3D through development of the tech-

nique referred to as electron microscope (EM) tomography, also frequently

termed ‘‘cellular tomography’’ (ET). With ET now in hand as a tool that eVec-tively allows one to study molecular membrane traYc ‘‘in context’’ (in situ), it has

become increasingly important to push the continued advancement of this method

so that large cellular volumes can be reconstructed without sacrificing the ability to

clearly resolve structures of interest. Likewise, it has become critical to do this as

quickly and eYciently as possible in order to generate statistically significant

sample sizes that oVer reliable insights into cell organization under diVerentphysiological or experimental conditions. In this chapter, some of the technical

developments, as well as key biological questions that have driven the development

of large area ET, will be presented and discussed.

I. Introduction and Rationale

The mammalian Golgi complex was first observed by light microscopy over

100 years ago by Camillo Golgi (Golgi, 1898) and visualized by electron micros-

copy by Ernest Fullam, Albert Claude, and Keith Porter in 1944 (Porter et al.,

1945). Despite this, the limitations of current and/or conventional cell biological

and biochemical techniques for elucidating the structure–function relationships of

such a complicated organelle can be evidenced by the controversies which remain

today with respect to even the most fundamental mechanisms of transport to,

through and from this organelle (Elsner et al., 2003; Gu et al., 2001; Lippincott-

Schwartz et al., 2000; Marsh and Howell, 2002; Storrie and Nilsson, 2002).

A myriad of approaches ranging from conventional two-dimensional (2D) EM

of thin-sectioned material, in vitro functional (cell free) assays of reconstituted

Golgi membranes, live-cell imaging studies to organellar proteomics analyses have

so far failed to satisfactorily answer basic questions related to molecular mem-

brane traYc through the Golgi (Breuza et al., 2004; Farquhar and Palade, 1998;

Mellman and Simons, 1992; Orci et al., 1989a; Palade, 1988; Rothman and

Orci, 1992; Wu et al., 2004; Yates et al., 2005).

Part of the problem lies in the fact that during the 1980s, as progress was

accelerating in molecular biological and live-cell imaging studies of the Golgi

and intracellular membrane traYc, conventional fine structure studies of intracel-

lular membranes in mammalian cells were for the most part considered passe due

both to their static nature and the limitations of 2D analysis, and resources for

EM in general were significantly downgraded (GriYths, 2004; McIntosh, 2001).

At the same time, high-resolution EM studies that focused on structure were for

8. Reconstructing Mammalian Cytomembranes by 3D EM 195

the most part dismissed in favor of alternative EM approaches that for the first

time allowed the immunolocalization of an antigen or antigens of interest on

standard thin sections (typically 60–100 nm), and thus provided crucial informa-

tion regarding the subcellular distributions of proteins (GriYths et al., 1984; Orci

et al., 1984; Slot and Geuze, 1983). However, as with any other method, techniques

for immunolabeling suVer from their own limitations. As noted above, Golgi

membranes in particular are often highly convoluted, and so it frequently becomes

diYcult to unambiguously follow connectivity from one section to the next. Other

limitations stem from the fact that only antigens exposed at the section surface can

be labeled, and that steric hindrance due to the immunogold itself can limit spatial

resolution to between 10 and 20 nm in some cases; labeling eYciency is also

compromised. In addition, immunolabeling studies of mammalian membrane

traYc have relied primarily on classical chemical fixation techniques to stabilize

membranes for visualization in the EM. As this process takes anywhere from

seconds to minutes, labile structures and highly dynamic events, which we now

know from live-cell imaging studies to be typical for many of the steps in intracel-

lular membrane traYc, are unlikely to be reliably or reproducibly captured (Gilkey

and Staehelin, 1986; Lippincott-Schwartz et al., 2000, 2001). It is now more

apparent than ever that conventional chemical fixation often captures the cell’s

(and/or organelle’s) response to the fixative as opposed to immobilizing cellular

structures in a ‘‘close-to-native’’ state (Biel et al., 2003; Dubochet, 1995). Thus,

it follows that reliable descriptions of morphology for the membranes of a

rapidly changing organelle such as the Golgi can only be achieved through the

use of methods for preservation that immobilize all cellular activity within milli-

seconds, such as plunge freezing or high-pressure freezing. To be supremely useful,

however, such methods for improved ultrastructural preservation of biological

specimens will eventually have to incorporate a capacity for the precise localiza-

tion and identification of proteins, lipids, and inorganic ions of interest within

fast/high-pressure frozen cells or tissues (see Chapters in Part IV, this volume).

Three-dimensional (3D) studies of Golgi organization by stereo-scanning EM

(Ho et al., 1999; Rambourg et al., 1974), together with ‘‘pseudo’’ 3D studies by

analysis of stereo pairs of 2D images of thick specimens tilted in the EM—carried

out from the early 1970s through the late 1990s—contributed enormously to the

body of knowledge about the 3D organization of Golgi and endoplasmic reticulum

(ER) membranes (Hama et al., 1994; Lindsey and Ellisman, 1985; Rambourg and

Clermont, 1997). However, all of these studies were ultimately frustrated by the

fact that the superimposition of 3D information in the 2D images restricted

resolution along the z-axis to the thickness of the section at best (Marsh, 2005).

The application of high-resolution ET to the study of Golgi membranes and

associated tubules and vesicles in mammalian cells, first in conventionally

prepared (Ladinsky et al., 1994), then in fast-frozen/freeze-substituted normal rat

kidney (NRK) cells (Ladinsky et al., 1999), convincingly demonstrated the utility

of this approach for providing novel insight into dynamic traYcking events at the

Golgi complex and, in particular, at the exit face of the Golgi—the trans-Golgi

196 Brad J. Marsh

network (TGN) (GriYths and Simons, 1986). This ability to truly dissect the

resultant 3D information voxel-by-voxel and/or in any desired orientation (see

Fig. 2 in Ladinsky et al., 1994) revealed the underlying power of this technique for

starting to address long-standing questions related to Golgi structure–function

relationships.

At the inception of our own work, and as noted above, 3D EM studies of the

Golgi and associated subcellular compartments had been performed in a number

of cell types (Katsumoto et al., 1991; Ladinsky et al., 1994; Rambourg and

Clermont, 1997; Tanaka et al., 1986), but not for the insulin-secreting beta cells

of the pancreas. Most of what is currently accepted as ‘‘conventional wisdom’’

regarding the mechanisms for insulin granule formation/maturation, traYcking

and release emerged from conventional 2D EM surveys of thin sections cut from

chemically fixed beta cells in the 1970s and 1980s (Howell and Bird, 1989; Howell

and Tyhurst, 1984; Orci, 1986; Orci et al., 1988). However, disparities had started

to appear between some of the key concepts regarding structure–function relation-

ships in the beta cell that came out of those early structural studies, and more

recent advances in understanding the molecular mechanisms underlying insulin

granule biogenesis and exocytosis (Arvan and Castle, 1998; Goodge and Hutton,

2000; Guest et al., 1997; Kowluru and Morgan, 2002; Kuliawat and Arvan, 1994;

Molinete et al., 2000; Rutter, 1999; Tsuboi and Rutter, 2003).

Thus, in light of the dramatic improvements that had taken place since the late

1980s in terms of methods for high-quality/reliable sample preparation (Gilkey

and Staehelin, 1986; McDonald and Morphew, 1993), instrumentation for EM in

general and ET in particular, and hardware/software for complex 3D analysis of

biological data (Kremer et al., 1996; Mastronarde, 1997), we felt the time was right

to revisit questions regarding precisely where and how are insulin secretory gran-

ules formed, and how changes in the organization of the Golgi and other subcel-

lular membranes under key physiological conditions relate to structure–function

relationships in the beta cells of the endocrine pancreas.

II. Methods and Materials

For our own studies of the insulin biosynthetic pathway, it was clear from the

outset that to attain the most reliable insights into the membrane traYc itinerary

of insulin in the pancreatic beta cell, our best approach would be to closely follow

the example of Ladinsky et al. (1999). Ladinsky and colleagues employed a

combination of the then disparate techniques of fast-freezing/freeze-substitution

(followed by plastic embedment) with dual-axis ET of four serial semithick

(250 nm) sections. This modification of the standard single-axis tomographic

approach improves the resolution and symmetry of cellular structures in all 3D

(Mastronarde, 1997; Penczek et al., 1995; Taylor et al., 1984). The end result was

a comparatively large (�1 � 1 � 4 mm3) volume of the Golgi ribbon in an NRK

cell reconstructed at high (�7 nm) resolution, which demonstrated near optimal


structural preservation and clarity throughout. From this, Ladinsky et al. were

able to computationally dissect Golgi membranes and associated vesicle and

tubule budding profiles with maximal confidence, and were aVorded a reasonably

large portion of the NRK Golgi ribbon within a single cell for analysis.

A. Mammalian Cell and Tissue Culture

Since ET of large cellular regions using the techniques that will be described below

still remains a relatively painstaking and labor-intensive process in comparison to

conventional ET, it has been crucial for us to ensure that cells and/or tissue to be

studied in detail are as healthy and as physiologically viable as possible. We initiated

our studies with the Syrian hamster-derived beta cell line, HIT-T15, because at early

passages it retains the capacity to synthesize and secrete insulin in response to

extracellular glucose (Fig. 1) (Marsh et al., 2001a; Santerre et al., 1981).

The fact that these cells are modestly granulated compared with beta cells in vivo

provided an advantage; it allowed us to conduct a detailed analysis of interac-

tions between the microtubule cytoskeleton and the key organelles involved in

membrane traYc/secretion of insulin (i.e., the ER, Golgi, and insulin granules)

(NovikoV et al., 1975; Rios and Bornens, 2003; Yorde and KalkhoV, 1987; seeFig. 2). We initially tried various techniques for rapid freezing, since immortalized

beta cell lines are usually cultured as a crude 2D monolayer in the absence of

supplementation with growth factors and/or extracellular matrix (Ohgawara et al.,

1995), and thus are theoretically thin enough to be amenable to plunge freezing.

However, we consistently found that a higher fraction of beta cells were well

preserved by high-pressure freezing. This technique is one of a number of diVerentfast-freezing methods that can be used to vitrify cells/tissue, so as to capture

cellular events in a nativ e, frozen- hyd rated stat e (see Chapt er 1 by Dubochet,

Chapter 2 by McDon ald, an d Chapt er 3 by Hess , this volume ).

Although immortalized beta cell lines are easily obtained and cultured, and

continue to serve as useful models for the study of the insulin secretory pathway

and diabetes (Gleason et al., 2000; Poitout et al., 1995), there are obvious and

inherent limitations to their usefulness. In general, they contain relatively low

levels of insulin (and low numbers of insulin granules) compared to normal adult

pancreas (Breant et al., 1992), and they respond poorly (in terms of granule release)

to physiologically relevant levels of glucose (Breant et al., 1992; Poitout et al.,

1996), suggesting that their mechanisms for sorting/processing insulin might diVersubstantially from beta cells in situ in intact islets. Additionally, islet beta cells are

polarized in vivo (Bonner-Weir, 1988; Orci et al., 1989b). Although transformed

beta cell lines’ cells can be induced to polarize experimentally (Cortizo et al., 1990),

they are typically not polarized when grown in culture. Such a loss of cell polarity

undoubtedly aVects cellular traYcking pathways (Lombardi et al., 1986).

Consequently, we modified our cryopreparative techniques for use with intact

islets of Langerhans obtained from the pancreata of adult, female Balb/c mice.

Care was taken to ensure that islets were as healthy as possible at the point of

Fig. 1 Organization of the Golgi region at high (�6 nm) resolution and in 3D in a mammalian cell

that makes and secretes insulin in a regulated manner (Marsh et al., 2001a) (Copyright 2001, National

Academy of Sciences, USA). (A) A single 2D image of a 400-nm plastic section cut from an immorta-

lized rodent beta cell line (HIT-T15) prepared by high-pressure freezing and freeze-substitution,

followed by plastic infiltration/embedding. This image is one of 80 such images that together comprise

a ‘‘tilt series,’’ collected for each axis by imaging the specimen every 1.5�as it was tilted over a range of

�60�. These images were aligned using 10-nm gold fiducials, as described in the chapter by O’Toole, this

volume. (B) The tomograms calculated from each set of aligned tilts are then brought into register and

combined in 3D to produce a single, dual-axis reconstruction. To enable the study of a larger portion of

the Golgi ribbon this process was repeated for multiple 400-nm sections, and the serial

reconstructed volumes were aligned with each other to create a single, large (�3.1 � 3.2 � 1.2 mm3)

volume reconstruction. Once all of the visible structures in the Golgi region had been modeled using the

IMOD software package (Kremer et al., 1996), any given (modeled) object could be studied closely

in 3D either alone or in context with any other object(s). (C) The Golgi complex with seven cisternae

198 Brad J. Marsh


cryopreservation by first culturing them under normal conditions either for several

hours or overnight after isolation. This step allows the isolated tissue to recover

following the isolation procedure itself (Sandler and Andersson, 1984), and

promotes both the viability (e.g., glucose responsiveness, protein synthesis, and

insulin secretory capacity) and integrity of isolated islets by allowing repair/

reformation of the connective tissue/collagen capsule at the periphery of rodent

islets (Bonner-Weir, 1989; Wang and Rosenberg, 1999). Islets were not cultured or

used for experiments more than 72 h following isolation.

Fig. 2 3D display of part of the Golgi ribbon shown in Fig. 1 revealing the in situ physical relation-

ships between the cis-most cisterna and the microtubule cytoskeleton. (A) Microtubules closely follow

and occasionally form contacts with the membranes of the cis-most cisterna. Note that in the modeled

region, microtubules do not exhibit a radial organization. Scale bar ¼ 500 nm. (B) A higher-

magnification view oriented to show that the paths of some microtubules closely follow the membranes

over considerable distances. Microtubules traversing the Golgi stack can also be observed. Original

images are from Marsh et al. (2001a) and are reproduced with permission from Proceedings of the

National Academy of Sciences USA.

(cis–trans: C1–C7) is at the center. The color coding is as follows: C1, light blue; C2, pink; C3, cherry

red; C4, green; C5, dark blue; C6, gold; C7, bright red. The Golgi is displayed in the context of all

surrounding organelles, vesicles, ribosomes, and microtubules: ER, yellow; membrane-bound ribo-

somes, blue; free ribosomes, orange; microtubules, bright green; dense core vesicles, bright blue;

clathrin-negative vesicles, white; clathrin-positive compartments and vesicles, bright red; clathrin-

negative compartments and vesicles, purple; mitochondria, dark green. Two views of the modeled

(segmented) Golgi region are provided, rotated 180�with respect to each other around the vertical

axis. This figure has been reproduced with permission from: ‘‘Lessons from tomographic studies of the

mammalian Golgi, Biochim. Biophys. Acta (2005), 1744, 273–292.’’ Scale bars ¼ 500 nm.

200 Brad J. Marsh

B. Fast-Freezing and Freeze-Substitution

As noted earlier, to preserve membrane structure and organization in beta cells

with high fidelity, we initially employed two independent techniques for cryoim-

mobilization: plunge freezing (Ladinsky et al., 1999) and high-pressure freezing

(McDonald and Morphew, 1993). While plunge freezing yielded some improve-

ment in the preservation of cellular ultrastructure over conventional chemical

fixation methods, more reproducible results were generally obtained by high-

pressure freezing. This is likely due to the fact that this latter technique inhibits

ice crystal growth in thicker samples, even though the rate of cooling is lower than

with plunge freezing (Gilkey and Staehelin, 1986;McDonald andMorphew, 1993).

Importantly, no major diVerences could be discerned in secretory granule, vesicle

or Golgi morphologies between plunge and high-pressure frozen cells, assuring

us that high pressure per se did not appreciably perturb cellular membranes or

architecture.

For immortalized beta cell lines such as HIT-T15 that essentially grow as mono-

layers, cells were seeded onto small plastic chips cut from ThermanoxÔ coverslips

(Nalge Nunc International, Rochester, NY) and cultured for 2–3 days prior to

experiments (Marsh et al., 2001a). Immediately prior to freezing, each chip was

transferred into a small brass, sandwich device comprised of two, interlocking

halves referred to as a ‘‘planchette’’ (Swiss Precision, Inc., Millbrae, CA), also

prewarmed to 37 �C. The half of the planchette holding the plastic chip on which

the cells had grown was filled with regular medium warmed to 37 �C and buVeredwith HEPES (10 mM). The second half of the planchette was filled with medium

containing 10% dialyzed Ficoll (Sigma, St. Louis, MO) as an extracellular cryoprotec-

tant, and was placed on top of the half of the planchette containing the cells. After

brief (<10 sec) manipulation into the tip of the planchette holder, cells were frozen

within 10–20 msec under high pressure (�2100 atm) using a HPM 010 high-pressure

freezer (BAL-TEC AG, Balzers, Liechtenstein). Frozen specimens were stored under

liquid nitrogen. Water was removed from these specimens by freeze-substitution with

anhydrous acetone (Ernest F. Fullam, Inc., NY) containing 0.5% glutaraldehyde/0.1%

uranyl acetate (UA) at �90�C for 1–2 days, followed by substitution with acetone

containing 1% OsO4/0.1% UA (Electron Microscopy Sciences, PA) at �70 �C.Specimens were allowed to warm to 0 �C over 2–3 days, whereupon they were

rinsed five times with anhydrous acetone. Samples were then warmed to room

temperature, infiltrated with increasing concentrations of Embed812-Araldite

resin (Electron Microscopy Sciences, PA), and flat-embedded between two Teflon-

coated glass microscope slides (Miller-Stephenson Chemical Co., Inc., Sylmar,

CA; VWR Scientific, Inc., West Chester, PA). Resin was polymerized at 60 �C in

a dry oven over 1–2 days.

Isolated islets cultured overnight as a suspension of free-floating cell clusters were

similarly maintained at 37�C on a humidified heating block in culture medium

buVered by the addition of 10-mMHEPES immediately prior to freezing. Typically,

10–30 islets (depending on size) were gently manipulated into one-half of the


prewarmed holder prefilled with HEPES-buVered medium containing fetal bovine

serum (FBS) 10% (v/v) (Sigma, St. Louis, MO). All manipulations were carried out

on ParafilmÒ placed on top of an inverted heating block warmed to 37�C under a

dissecting microscope. The second half of the planchette, filled with RPMI con-

taining 10% dialyzed Ficoll (MW ¼ 70 kDa) and 0.5% Type IX ultra-low temper-

ature gelling agarose (Sigma, St. Louis, MO) as extracellular cryoprotectants, was

then placed on top of the half of the sample holder containing the islets. The pair of

interlocking hats was then secured into place at the tip of the specimen holder and

the islets frozen under high pressure, freeze-substituted, and plastic embedded

essentially as described previously and above (Marsh et al., 2001a, 2004).

C. Microtomy and Grid Preparation

Well-preserved beta cells (in the case of beta cell lines) or islets were individually

identified by phase-contrast light microscopy, excised from the resin, and remounted

onto plastic stubs.HIT-T15 cells on plastic chips weremounted in an orientation that

permitted en face sectioning; orientation is irrelevant for intact islets. Ribbons of

thin (40–60 nm) or thick (300–400 nm) sections were cut on a microtome (Leica

Microsystems, Vienna, Austria) for conventional 2D survey at 80–120 kilo electron

volt (keV) to assess the quality of cell/islet preservation and to select regions for

subsequent study or for high-tilt ET on instruments operating at higher voltages

(�300 keV) (see below), respectively (Marsh et al., 2001a, 2004). Ribbons of serial

thick sections were collected onto Formvar-coated copper (2 � 1 mm) slot grids

(Electron Microscopy Sciences, Hatfield, PA) and poststained with 2% aqueous

UA or 3% UA in 70% methanol (15 min) and Reynold’s lead citrate (3 min). These

samples often require an additional carbon-coating step to minimize charging/

movement in the electron beam, particularly when tilted for tomography (Marsh,

2005). Colloidal gold particles (10 nm) were then deposited on both surfaces of

these sections for use as fiducial markers during subsequent image alignment.

D. Basic Instrumentation Requirements for ET of Thick Plastic Sections

Atvarious points in our studies,wehave successfully viewed and collected tilt series

images from ribbons of equivalently stained 300- to 400-nm-thick sections using (in

decreasing order of operating voltage) a JEM-1000 high-voltage EM (HVEM)

operated at either 500 keV, 750 keV, or 1 million electron volt (MeV) (JEOL USA,

Inc., Peabody,MA), a JEM-4000FX intermediate-voltageEM(IVEM) (JEOLUSA,

Inc.) operated at 400 keV, and with a Tecnai F30 field emission gun (FEG) IVEM

(FEI) operated at 300 keV. Regardless of operating voltage, the grid holding the

specimen was tilted in a eucentric goniometer typically at either 1� or 1.5� intervalsover a range of 120�–140� about two orthogonal axes (Mastronarde, 1997), as

summarized in the legend that accompanies Fig. 1. In earlier tomographic studies

of the Golgi region in mammalian cells (Ladinsky et al., 1994, 1999; Marsh et al.,

2001a), single images taken at each tilt were collected on film (SO163, Kodak

Eastman Company, or 23D56, Agfa-Gevaert NV), with the negatives subsequently

202 Brad J. Marsh

digitized on a motorized light table using a high-resolution STAR-1 cooled charge-

coupled device (CCD) camera (Photometrics, Ltd., Tucson, AZ), yielding a final

pixel size of �2.3 nm. Although the processing of film and digitizing of EM

negatives is labor intensive and time consuming, this procedure inherently aVordedthose investigating extended membrane structures, like the Golgi, a much larger

field of view for a ‘‘single snapshot’’ at each tilted view than could be accomplished

otherwise without sacrificing resolution by imaging at a lower magnification to

overcome the smaller field of view of a typical CCD (Ladinsky et al., 1999; Marsh

et al., 2001a). Even with film, however, to take advantage of as much of the image

area on the EM negative as possible required the user to digitally tile a larger image

from ‘‘montaged’’ image arrays in x and y with defined areas of overlap (typically

10% of the pixel dimensions of each piece of the montage), using software to

precisely control the digitizing camera as well as movement of the motorized

light table (Fig. 1).

E. Acquisition of Digital Tilt Series from Large Cellular Areas by Automated IVEM/HVEMMontaging Tomography

Thick sections of 300–400 nm were typically imaged at 12,000� using a JEM-

1000 HVEM operating at 750 keV (JEOL USA) or at 15,500�, 20,000�, or

23,000� using a Tecnai F30 IVEM (FEI). Motorized, tilt-rotate specimen holders

(Models 650 and CT3500TR; Gatan, Pleasanton, CA) were available on both

kinds of microscopes. Tilt series data were digitally recorded using semiautomated

methods for CCD data acquisition, image focus, and alignment as the sections

were serially tilted through 1� or 1.5� increments over a range of 120�–140� abouttwo orthogonal axes. The camera and microscope were controlled by the program,

SerialEM (Mastronarde, 2005).

To best understand complex spatial and structural relationships for extended

cytomembranes, such as the Golgi or ER, it is important to examine a large

cellular area. We overcame the limited field of view typically aVorded by CCD

imaging on the EM by using a variation of the methods for digital montaging of

tiled image arrays, discussed above. This required the automation of image

montaging using image shift directly in the EM, which is now implemented in

the SerialEM data collection package (Mastronarde, 2005). Briefly, a set of images

was collected at diVerent coordinates in x and y at each tilt, using the software to

automatically reposition and capture each individual image piece, yielding a

montage of overlapping frames at each tilt (Fig. 3A). For our studies of the

Golgi region in insulin-secreting cells, we have typically used montages of either

2 � 2 or 3 � 3 panels in x and y (depending on the magnification and thus the final

CCD pixel size) to accommodate a total cellular area �4 mm2 per reconstruction

(Fig. 3).

Although there is a concomitant increase in the time required to digitally collect

a montaged tilt series, overall the yield of data for the time invested is higher with

this procedure (Mastronarde, 2005). For example, a 3 � 3 montage with images


collected every 1.5� over an angular tilt range of 120� (�60�) means an increase in

the number of individual image frames from 81 to 729, but the steps of tilting,

focusing, and tracking are performed more or less as they would be for a single

panel tilt series rather than for every frame. The largest additional temporal cost

related to data collection/image acquisition—aside from the CCD acquisition

time—is usually due to the additional brief (anywhere from 0.5 to 4 sec) preirra-

diation required just prior to image capture for each piece of the montage, to

combat specimen charging that is often inherent to tomography of thick, stained,

plastic sections.

F. 3D Reconstruction of Large Cellular Areas

The computer software package IMOD comprises a set of image processing,

modeling and display programs used for tomographic reconstruction from EM tilt

series, as well as for 3D reconstruction of EM serial sections and optical sections

(Kremer et al., 1996). Briefly, the package contains tools for assembling and

aligning data within multiple types and sizes of image stacks, viewing 3D data from

any orientation, modeling and display of the image files, and to obtain accurate

quantitative information in either 2D or 3D. IMOD was developed primarily by

David Mastronarde, Rick Gaudette, Sue Held, and Jim Kremer at the Boulder

Laboratory for 3D Electron Microscopy of Cells.

Using the IMOD package that incorporates the eTomo graphical user interface

for the alignment and tomographic reconstruction of digitally tiled images, indi-

vidual pieces of the montaged tilt series images acquired for each of the two ortho-

gonal axes were first brought into register with one another by cross-correlation of

overlapping image data to determine the relative displacements in x and y between

adjacent frames for a given tilted view (Fig. 3A, B, and B0). Occasionally it was

necessary to manually correct errors in correlation-based frame registration using

the computer tool MIDAS to ensure precise positioning of individual pieces of the

montage, and/or to account for any shifts in x and/or y that were too large to be

handled automatically. Likewise, cross-correlation was used to calculate the set of

translations in x and y that needed to be applied from one tilted view to the next

through the stack of tilt series images to generate a crudely prealigned stack.

Subsequently, individual frames or pieces of the montage were ‘‘blended’’ across

the zone of overlap, using redundant image data in the adjacent image pieces and

fused to yield a single image for each tilted view (Fig. 3C and C0).The images of the now-blended tilt series were then treated essentially like a

typical image stack and more accurately aligned by tracking the positions of �200

of the 10-nm gold fiducial markers on the top and bottom surface(s) of the

sections, preferentially with an equal number of fiducials on both surfaces. Ideally,

most of the fiducial points selected and tracked for the first axis are also tracked

for the second axis (Fig. 3C, C0, D, and D0). This step is accomplished using the

program transferfid, which automatically determines the most reliable estimate

of how fiducial markers correspond between the tilt series collected around the

Fig. 3 Digital montaging and image alignment of large areas for ET begins with data collected on the

IVEM as set of overlapping pieces or frames in x and y, collected over a large number of tilts over a

range of 120�–140

�, and around two orthogonal axes (Mastronarde, 2005). (A) This diagram illustrates

how image shift is used to move from one position to the next in a regular array in x and y (e.g., x1,y1,z1;

204 Brad J. Marsh


two axes. transferfid rotates the ‘‘seed’’ fiducial model generated for a single tilt

view (typically close to, or at, 0� tilt) from the first axis by 90� (trying first clockwisethen counterclockwise to find the best fit) and searches for the pair of views from

each tilt series that has the best fiducial correspondence. The program then

transfers the fiducial model from the first series to generate a seed model for the

second axis. The program also indicates which fiducials failed to transfer and

which corresponding fiducials should be specified when setting up to combine the

tomograms from each axis (Fig. 4), a particularly crucial step for dual-axis recon-

struction, since the coordinates of corresponding fiducials are used for the initial

calculation of 3D rotation, distortion, and shift between the volumes.

A large number of fiducials is required for accurately tracking and aligning large

area tilt series data because of the heterogeneous distortions that result from

specimen exposure to the electron beam. In our experience, the selection of

�200 fiducials for a cellular area �4 mm2 provides a suYciently high ratio of

known versus unknown parameters to accurately calculate image alignment while

accounting for diVerential section thinning, specimen shrinkage, and minor bend-

ing. While such distortions may be negligible when handling tilt series data collect-

ed from a relatively small area, they are critically important to solve for when

attempting to accurately reconstruct large cellular regions (i.e., >3 mm in x and y)

at high (5–7 nm) resolution (Marsh et al., 2001a). To deal with these issues, we

modified the tilt alignment procedure so that subsets of fiducial markers were used

to solve for local alignments in a limited area. tiltalign first calculates a single global

distortion solution by employing all of the fiducial markers to solve for tilt angle,

rotation, overall magnification, and linear distortion within the plane of the

section, including distortion due to specimen thinning in the electron beam. The

program next finds a series of solutions in a regular array of overlapping local areas

x1,y2,z1; x1,y3,z1; x2,y1,z1; x2,y2,z1; and so on), acquiring an image at each position, for a given tilt.

For a montage with an odd number of pieces such as a 3� 3 array, an initial image of the central piece in

the array (i.e., at position x2,y2) is taken and used as a reference for the accurate alignment/positioning

of each piece of the montage. Subsequent precise registration of individual pieces in IMOD is achieved

by the correlation of image data in zones of overlap (shaded, typically set at around 10% of the total

pixel area of each frame) between adjacent frames (see below). (B) Raw montaged images shown for

0�tilt. (B0) A comparable image from the second tilt series which is taken after the specimen has been

rotated 90�counterclockwise. When viewed in 3dmod, the crude registration of elements in the montage

can be seen because of the minor diVerences in contrast between adjacent pieces and/or minor displace-

ment by up to several pixels (compare B and B0). Once individual frames have been blended across the

regions of overlap and fused to generate a single image for each tilted view, such borders can be no

longer distinguished. A large number (�200) of colloidal gold markers are selected from images in the

first tilt series (C and D), then transferred to the corresponding view from the second (C0 and D0). Theseprovide the information necessary to solve for distortion (including rotation, translation, and magnifi-

cation) and accurately transform and align individual tilted views in the series. They also help in

matching and combining the volumes from each axis to make a dual-axis tomogram (see also Fig. 4).

Scale bars ¼ 1 mm.

Fig. 4 Matching and combining the tomograms generated from data collected around two orthogo-

nal tilt axes depends on knowing the displacement between corresponding subvolumes in the two

tomograms. The coordinates of the fiducials that correspond between the two tomograms allow an

initial determination of rotation, distortion, and translation between the two tomograms. However,

to combine two volumes precisely one must generate a set of nonlinear transformations by cross-

correlating ‘‘patches’’ of a defined size between the first volume and the transformed second volume

at an array of positions in 3D. The displacements between the two volumes at each position, which are

the output from this process, can be visually examined in the form of a ‘‘patch vector model.’’(A) Such

models are often highly informative, since each displacement vector is visualized as an exaggerated line

whose length is 10 times the actual length of the original displacement vector. Careful examination of

the patch vector model usually provides the user with visual cues as to the nature of the transformations

necessary to bring the volumes into register. When combining large cellular areas from montaged tilt

series, the large displacements seen in this figure typically result from the use of patches that are too

small to provide suYcient information for a good correlation between the corresponding subvolumes.

Although standard patches/subvolumes of 80 � 80 � 40 pixels would normally provide a good measure

of agreement between tomograms, for the large area generated from a 3 � 3 montage used in this

example, an increase in patch size in z (i.e., to 80 � 80 � 70 pixels) dramatically improved the resulting

displacement model (B). However, as noted in Section II, it is essential to exclude from the combine

206 Brad J. Marsh


or ‘‘patches’’ for each 2D projection. These local solutions are incremental to the

global solution, so all of the alignment parameters are constrained to vary slowly

through the tilt series. The positions of the fiducial markers in 3D are fixed at

the values found in the global solution, as the dual-axis tomographic approach

requires that the two reconstructed volumes must be accurately aligned with one

another (Mastronarde, 1997) (Fig. 4). These constraints dramatically reduce the

number of parameters that have to be solved for and increase the ratio of measure-

ments to unknowns, thus allowing accurate local solutions to be obtained from as

few as six gold fiducial particles. tiltalign then produces a set of linear transforma-

tions based on the global solution; these are used to produce globally aligned

images. For each local area, it also produces a set of refining transformations

based on accurately aligning the images for a reconstruction of that area alone.

An R-weighted back-projection program then uses the refining transformations

to determine which pixel in a projected image back projects to a given voxel in

the reconstructed volume (Wilson et al., 1992). Tomographic volumes calculated

by R-weighted back projection from each set of aligned tilts are then matched to

each other in 3D by nonlinear transformation using the program patchcrawl3d.

Basically, patchcrawl3d determines the 3D displacement between the two volumes

at a regular array of positions using sets of patches of defined sizes in x, y, and z,

and then runs the program corrsearch3d to determine the displacement of each

patch in one tomogram relative to the corresponding patch in the reference

tomogram (typically the tomogram from the first axis is used as the reference

volume) (Fig. 4). As for fiducial tracking and image alignment for montaged tilt

series data, we have had to modify the standard approaches to determine the

optimal displacement solutions for matching and combining tomograms of large

cellular areas.

Although relatively small patches of overlap will provide suYcient information

for accurate combine solutions for small image areas, larger patch sizes are gener-

ally required to accurately match one volume to another for the large image areas

that result from digitally montaged tilt series. For an example of the dramatic eVectof modest changes in patch size specification during tomogram matching and

combination, compare the ‘‘patch vector models’’ presented in Fig. 4A and B.

It is also important to identify if there are regions within the areas to be combined

that should not be used for correlating the tomograms, either because they do not

contain suYcient material (e.g., the lumen of a vacuole) or because the reconstruc-

tion quality is poor (e.g., typically at the corners of large tomograms) (Fig. 4B).

solution erroneous vectors that result from areas in the tomogram that do not contain suYcient dense

material for correlation, or from areas at the edges or corners that demonstrate poor reconstruction

quality. (B) Such wayward vectors are evident in regions of the correlation model that correspond to the

lumens of vacuoles (e.g., Figs. 3B and C and 5A) and at the corners of the model. (C) After manually

editing out bad vectors from the model, only the remaining vectors will contribute to the correlated

combine solution for matching the two volumes accurately in 3D.

208 Brad J. Marsh

The patch vector model can then be edited so these areas are excluded from the

combine solution (see Fig. 4C and accompanying legend). Finally, volumetric

image data from each of the two tomograms is combined to produce a single,

high-resolution, dual-axis 3D reconstruction (Mastronarde, 1997) (Fig. 5A).

To further increase the cellular volume available for analysis, 3D reconstruc-

tions calculated in this manner from serial plastic sections can then be aligned to

one another in z as previously described (Ladinsky et al., 1999; Marsh et al.,

2001a), to produce a final single large volume for analysis and segmentation. The

interactive program MIDAS was used to generate a general linear transform that

accounted for rotation, distortion, and stretch from one section to its nearest

neighbor based on the best-fit alignment of subsets of slices extracted from the

bottom of one tomogram and the top of adjacent tomogram.

G. 3D Segmentation and Quantitative Analysis

Membranes of the Golgi complex, ER, compartments of the endosomal–

lysosomal pathway, and associated tubules/vesicles within the tomographic

volumes were segmented, extracted, and viewed with the IMOD package using

3dmod (Kremer et al., 1996). Typically, cellular tomograms are stored and viewed

as stacks of pixel-thick slices, oriented parallel to the plane of section. However,

one is also able to use the slicer tool that is part of the program 3dmod to arbitrarily

rotate the data in x, y, and z to visualize membranous connections between com-

partments or between membrane-bound compartments and the microtubule

cytoskeleton with minimal ambiguity (O’Toole et al., 1999; Marsh et al., 2004).

Although the pixel size—and hence the tomographic slice thickness—are deter-

mined by the magnification at which the data were collected (and whether the

image data were binned or interpolated), the actual depth of biological material to

which these data correspond is somewhat larger, since sections cut from plastic-

embedded specimens collapse on initial exposure to the electron beam (Kremer

et al., 1990; Luther et al., 1988; Mastronarde, 1997). Despite the fact that each

tomographic slice resembles a conventional EM image or ‘‘micrograph,’’ it corres-

ponds to slices of material far thinner than can be cut by ultramicrotomy. For

example, for a tomographic data set with a nominal pixel size of 2.3 nm, a single

pixel-thick slice parallel to the plane of section actually corresponds to �3.8 nm of

material prior to the specimen collapse (Marsh et al., 2001a). Consequently, we

typically rescale 3D surface-rendered models generated from segmented cellular

tomograms by an appropriate amount in the z dimension to account for this

phenomenon, and to more accurately represent and quantify the topology of the

structures in the specimen prior to data collection in the EM (Ladinsky et al., 1999;

Marsh, 2005).

Features of interest to us, such as membrane bilayers and microtubules con-

trasted with heavy metals, are usually modeled by an expert user who manually

segments/traces the membrane contours of an organelle or compartment through

Fig. 5 (A) Part of the extended Golgi ribbon of a glucose-stimulated islet beta cell is evident as the

‘‘stacks’’ of Golgi cisternae that wind through the cytoplasm in the dual-axis tomogram computed from

the data represented in Fig. 3, which yielded a reconstructed cellular volume of 5.92 � 5.92 � 0.35 mm3.

Tilt series images were acquired as a 3� 3 digital montage at 1�increments over approximately�63

�on

a Tecnai F30 IVEMoperated at 300 keV using a motorized tilt-rotate holder (Gatan). Individual images

at each tilt were brought into register by cross-correlation of image data in zones of overlap between

adjacent frames, and blended to generate a single image at each tilt. Tilt series images were first aligned

with one another by cross-correlation to yield a prealigned stack, and were then accurately align-

ed by tracking the positions of 10-nm-gold fiduciary markers as discussed in Section II. The tomograms

calculated from each set of aligned tilts were then brought into register and combined in 3D to produce

the single, dual-axis reconstruction presented here. (B) Membranes of the Golgi, insulin granules, and

other compartments were modeled by manual segmentation of the membrane bilayer through sequen-

tial tomographic slices (each 1.03-nm thick) in z using the program 3dmod that is part of the IMOD

software package. (C) Polygonal meshes fitted between adjacent contours provided surfaces both for

visualization and quantification in 3D. In addition to the stacked cisternal membranes of the Golgi that

are color coded as described in the legend for Fig. 1, a number of immature granules and/or condensing

vacuoles are visible (colored in light blue). Scale bars ¼ 1 mm.


210 Brad J. Marsh

successive slices in z, or along an arbitrary plane in slicer, using computer tools in

the 3dmod program. Since each tomographic data set is stored arbitrarily as a set

of 2D slices in x and y (1 pixel thick in z), the user physically traces a given

membrane-bound compartment through successive slices in z (Fig. 5B). Triangular

or polygonal meshes fitted between adjacent contours then provide data for surface

meshing and visualization (Kremer et al., 1996; Ladinsky et al., 1999). Typically,

diVerent colors are used to define membrane contours that belong either to a given

compartment or compartment type. For example, in the case of a typical Golgi

region in one of our beta cell tomograms, a particular color might be used to

identify the membranes of a single continuous cisterna that occupies a certain

position or hierarchical level in the stack. The same color would also be used to

identify cisternae that can be determined as either functionally equivalent or

occupying an equivalent position in stacks that are spatially distinct but remain

part of the same Golgi ribbon for a given cell (Ladinsky et al., 1999; Marsh et al.,

2001a) (Fig. 5C).

In addition to providing surface data that can be used to generate a ‘‘skin’’ that

can simply be visualized in 3D, triangular meshes fitted between contours allow

one to compute quantitative parameters, such as the surface area and volume for

any segmented compartment, including accurate measurement of fenestrations or

holes in Golgi membranes (Ladinsky et al., 1999; Marsh et al., 2001a,b; Otegui

et al., 2001). In our case, since contours were drawn down the middle of the

membrane bilayer, the interior compartment volumes were computed by subtract-

ing the surface area � 3.5 nm (half of the membrane’s thickness) from the volumes

inside the contours. To ensure that quantitative data generated from 3D models

are as accurate as possible, the ends of closed compartments and vesicles are

‘‘capped’’—so that they are closed oV completely with a surface mesh—as well as

‘‘smoothed.’’ Smoothing routines work by fitting local polynomials to the sur-

faces and replacing each point with a corresponding point from the fitted surface

(Ladinsky et al., 1999). This operation helps to compensate for minor irregularities

in hand-drawn contours, due to user error, and results in surfaces that shift

smoothly from the plane of one contour to the next.

As noted above, and presented in Figs. 1 and 2, individual microtubules could

also be tracked in 3D as a set of points with a curvilinear trajectory to characterize

and quantify in situ associations between the microtubule cytoskeleton and the

membrane surface of diVerent organelles and compartments. This process was

frequently aided by use of the slicer tool in 3dmod, which allows the user to reorient

the data until the microtubule can be viewed clearly en face (for more detailed

explanation of the use of slicer for tracking and visualizing microtubules, please see

Chapt er 5 by O’Tool e, Chapt er 6 by Ho o g and Antony , and Chapt er 9 by Ote gui

and Austin, this volume).

Once all objects of interest have been segmented in the cellular reconstructions,

spatial relationships among the modeled objects in 3D can be quantified by

measuring distances between objects within a plane and computing an average

density of neighboring items as a function of distance between objects, using the


programs nda and mtk (Marsh et al., 2001a). This is similar to previous quantita-

tive morphometric approaches to study the distribution of insulin granules and

spatial relationships with the beta cell microtubule cytoskeleton (Yorde and

KalkhoV, 1987). This technique, referred to as ‘‘neighbor density analysis,’’ re-

quires that each object of a given geometrical type (i.e., sphere, line, or complex/

convoluted surface defined by a mesh of triangles) serve in turn as a reference from

which the distances to all nearby neighboring objects of a particular kind are

measured (McDonald et al., 1992). Quantification of neighbor density was obtained

by dividing the number of items at a given range of distances by the approximate

total volume at those distances from all such reference objects, where volume

was estimated from the size of a spherical shell at that distance from the object

(Marsh et al., 2001a).

III. Discussion

The development and continued advancement of methods for reconstructing large

cellular areas in 3D at comparatively high resolution (5–7 nm) have been a prerequisite

forEMtomographic studies of themammalianGolgi ribbon,which frequently extends

over distances greater than 10 mm (Cooper et al., 1990; Rambourg and Clermont,

1997). As discussed above, these methods evolved from earlier tomographic studies

of large regions of the mammalian Golgi that were originally acquired on film and

subsequently digitized to provide a large cellular area for 3D reconstruction and

analysis. Key to the success of this method is the use of protocols that retain

suYcient resolution to readily identify protein coats such as clathrin (Ladinsky

et al., 1999; Marsh et al., 2001a). Importantly, these studies forced the develop-

ment of new mathematical approaches for handling large area tilt series data and

attempting to deal with the heterogeneous changes in specimen geometry that

occur over large areas; these had previously made it diYcult to accurately align

the image data without significantly compromising tomogram quality and resolu-

tion (Marsh, 2005; Marsh et al., 2001a). These methods have led directly to

tomographic studies of other extended cytomembranes such as somatic and

syncytial-type cell plate formation in plants (Otegui et al., 2001; Segui-Simarro

et al ., 2004 ) (also , see Chapter 9 by Otegui and Austin, this volume ). With large

format (>2K � 2K pixels) CCD cameras now readily available—and in fact often

the default for ET—tomographic studies of large cellular areas (even without

image montaging) are now more commonplace. Consequently, the application of

methods for dealing with distortions in image data over large areas has become a

requisite for the generation of high-quality cellular tomograms (Marsh et al.,

2001a).

The specific application of these techniques to elucidate structure–function

relationships among the key organelles involved in the biosynthesis and traYcking

of insulin, first in an immortalized cell culture model (Marsh et al., 2001a,b) and

then in glucose-stimulated beta cells preserved within intact pancreatic islets

212 Brad J. Marsh

isolated frommice (Marsh et al., 2004), has provided a number of new insights into

mammalian cell biology more generally, and the insulin biosynthetic pathway in

particular. In some instances, these high-resolution 3D data have provided the first

unequivocal evidence for concepts that had been previously received with skepticism

by the molecular membrane traYc community. One such example is the idea that

multiple, distinct trans-cisternae are consumed in the process of sorting and pack-

aging cargo for exit, originally a cornerstone of NovikoV’s Golgi-ER-lysosome

(GERL) hypothesis (NovikoV, 1964). Associated data that have also provided

convincing and clear evidence for other key aspects of the GERL hypothesis, such

as the extent of intimacy between specialized regions of the ER and trans-Golgi

cisternae (Ladinsky et al., 1999; Marsh et al., 2001a,b), have thus provoked serious

reconsideration of the complexity of events at and near the trans-Golgi. Moreover,

these studies have provided compelling evidence that all three major mechanisms

of intra-Golgi transport (cisternal progression-maturation, vesicle- and tubule-

mediated traYcking) play important roles in intra-Golgi traYcking, demonstrat-

ing that they can act in concert in the same region of the Golgi ribbon, and

revealing that one mechanism may appear dominant over another depending on

the level of protein/lipid traYc and the physiological state of the cell/tissue (Marsh

et al., 2001b, 2004; Mironov et al., 1997). Tomographic studies remain underway

to elucidate whether this kind of structure–function variation occurs along the

Golgi ribbon within a single cell.

In some cases, data from our studies have raised important questions about

fundamental concepts regarding maintenance of the sequential processing hierarchy

that is a considered a hallmark of Golgi organization. Using physiologically relevant

concentrations of glucose to stimulate proinsulin biosynthesis and insulin secretion

from beta cells in situ in primary cultured mouse islets, we have unequivocally

demonstrated the existence of direct connections between nonequivalent cisternae

in islet beta cells stimulated to traYc large amounts of secretory protein cargo

(Marsh et al., 2004). With short-term stimulation by extracellular glucose (1 h),

proinsulin biosynthesis occurs exclusively at the translational level (Wicksteed

et al., 2003), resulting in a rapid wave (within �15–30 min) of proinsulin moving

into and through the Golgi (Alarcon et al., 1993). Although tubular connections

between Golgi cisternae at their periphery or at branches in the Golgi ribbon had

been described by others previously (Rambourg and Clermont, 1997; Weidman,

1995), we observed membrane tubules that connected nonadjacent cisternae by

reaching directly through holes or fenestrae in the neighboring cisterna to eVec-tively ‘‘bypass’’ interceding cisternae (Marsh et al., 2004). Such direct intra-Golgi

connections could provide a continuous lumen for the expedited forward transit of

large amounts of newly synthesized proinsulin between nonequivalent cisternae

that are normally distinct from one another, and may also facilitate the rapid

retrieval of retrograde traYc (Marsh et al., 2004). These data have provided some

of the strongest evidence to date for the concept of lateral transfer in the Golgi

complex, in contrast to sequential linear processing.


A. Future Directions

Despite the fact that our ‘‘large’’ reconstruction of a Golgi region (Fig. 1) only

covered �1% of the volume of a beta cell, it provided a rare opportunity to

visualize the dense packing of subcellular organelles, ribosomes, vesicles, and

microtubules in the cytoplasm of a mammalian cell. This view cannot yet be

aVorded by any other method (Marsh, 2005; Marsh et al., 2001a). The continued

development of extended methods for the accurate tomographic reconstruction of

large cellular areas, to the point where it becomes possible to generate a complete

set of 3D spatial coordinates for a mammalian cell in toto at high resolution, is

likewise expected to provide important and unique insights into cellular organiza-

tion that cannot come from other methods or even from lower resolution ET of

whole cells. Such a ‘‘Visible Cell’’ project would lead to the generation of a cellular

atlas with suYcient resolution to distinguish all subcellular compartments and

filaments of interest at once. Such a Visible Cell atlas will likely play a crucial

role in advancing our understanding of (mammalian) cellular systems biology

(Bork and Serrano, 2005; Lehner et al., 2005; Nickell et al., 2006), and should be

seen as a fundamental prerequisite to realistically simulating/predicting the spatio-

temporal coordinates of complex molecular membrane traYcking events in silico.

Meanwhile, comparatively large area cellular reconstructions carried out at rela-

tively high throughput/high resolution through the use of large format/fast readout

CCD cameras will provide important new insights into other key aspects of insulin

traYcking in the beta cell that occur after secretory cargo packaging and insulin

granule biogenesis at the trans-Golgi. Specifically, current and future studies are

aimed at elucidating the specifics of granule recruitment to the cell surface for

release into the bloodstream after glucose levels in the blood rise above normal.

Quantitatively determining how interactions between granules and the micro-

tubule and actin cytoskeletons change in islet beta cells exposed to low, normal,

and stimulatory concentrations of glucose for diVerent times will be essential to

develop a complete understanding of these events (Fig. 6). Microtubules play an

important role in potentiating the second, sustained phase of insulin release by

transporting insulin granules from the Golgi region (where they are formed) to the

plasma membrane, to replenish the readily releasable granule pool following a rise

in intracellular [Ca2þ] (Boyd et al., 1982; Donelan et al., 2002; Malaisse et al.,

1975). This process is believed to be dependent on the recruitment of the cyto-

plasmic motor protein, kinesin, to the granule membrane (Balczon et al., 1992;

Donelan et al., 2002; Varadi et al., 2002). Evidence also suggests that the binding of

small GTP-binding proteins such as Rab3A and Rab27A to yet uncharacterized

‘‘eVector’’ proteins plays a crucial role in regulating these events in islet beta cells

(Coppola et al., 2002; Kajio et al., 2001; Park et al., 2002; Yaekura et al., 2003;

Yi et al., 2002). By tracking the frequency of such interactions along the length of

any given microtubule over a relatively large distance, combined with a capacity

to scrutinize interactions between the microtubule and granule membrane at high

resolution, we expect to precisely establish the sequence of steps and molecular

Fig. 6 Our early study of the insulinoma cell line, HIT-T15, demonstrated that we could dissect the

beta cell modeled in 3D at�6-nm resolution as required, that is, each modeled object could be extracted

and viewed in any chosen orientation and in the context of any other object(s) to analyze structural

associations with confidence (Marsh et al., 2001a). (A) Shown are the membranes that comprise the

Golgi, together with the ER, ribosomes, microtubules, secretory granules, vesicles, endosomal–

lysosomal compartment, and mitochondria (for color key see legend for Fig. 1C). In that study,

microtubules with a total combined length of over 80 mm were segmented to accurately quantify

associations between the microtubule cytoskeleton and diVerent organelles and compartments in situ.

In that analysis each microtubule was divided into regularly spaced (10 nm) subsegments, and the

distances from each segment to all neighboring objects of a particular kind were measured. These data

revealed the distances of closest approaches from each subsegment to other objects, which in turn gave

indications of which objects interact with microtubules and which do not. (B) The paths of the

microtubules (bright green) can be followed more readily, as they are displayed only in the context of

insulin secretory granules (bright blue) and endosomal–lysosomal compartments (purple and red),

which are also likely to play an important role in proinsulin processing/insulin traYcking (Turner and

Arvan, 2000). Here, we demonstrate the ability to pinpoint sites of close approach betweenmicrotubules

and insulin containing compartments in tomograms of beta cells. Using the programmtk, microtubules

and insulin granules shown in (C) were analyzed in 3D to determine all distances of approach between

them. After examining the histogram of distances of close approach between all microtubules and

granules, we limited our search of the modeled data to approaches on the order of �20 nm. This

identified the subset of microtubules highlighted in red in (D). (E) Using visible ‘‘markers’’ placed by the

software between the granules and the nearby microtubules, we were able to identify the structures at

sites of close approach (�20 nm) in the modeled data (F). Such sites represent the best chance of directly

identifying microtubule-associated proteins and cytoplasmic motors involved in microtubule-dependent

insulin transport in situ. Scale bar ¼ 500 nm.

214 Brad J. Marsh


machinery involved in moving insulin from the peri-Golgi region to the cell

surface, and to better understand the nature of the subcellular defects that accom-

pany states of impaired insulin intracellular traYcking and exocytosis/secretion.

IV. Summary

In cooperation with David Mastronarde at the Boulder Laboratory for 3D Elec-

tron Microscopy of Cells, we have developed methods for high resolution, large

area ET of thick, stained plastic sections for the 3D reconstruction and analysis of

mammalian membrane architecture at high fidelity in the insulin-secreting beta cells

of the endocrine pancreas. This approach, when employed in conjunction with high-

pressure freezing and freeze-substitution, has allowed us to generate tomograms of

comparatively large cellular volumes (each typically measuring at least 4 � 4 �0.4 mm3) with which to study the key organelles and compartments involved in

the synthesis, processing, and traYcking of insulin with unprecedented reliability.

These data have aVorded us new insights into beta cell biology and function

that include: (1) the complexity of structural relationships among the Golgi, ER,

and compartments of the endosomal–lysosomal system (Marsh et al., 2001a);

(2) evidence that multiple transport mechanisms act in concert in the same region

of the Golgi ribbon (Marsh et al., 2001b); (3) a role for the ER in regulating

membrane traYc/sorting at the trans-Golgi, the presumptive site where proinsulin

is sorted and packaged into nascent secretory granules (Marsh et al., 2001a,b);

(4) evidence that multiple, distinct trans-cisternae, frequently referred to as the

TGN, detach and fragment as membrane is consumed in the process of packaging

secretory cargo for exit (Marsh et al., 2001a); and (5) the unequivocal demonstra-

tion of direct intra-Golgi connections that facilitate ‘‘cisternal bypass’’ to provide a

continuous lumen for the expeditious transit of Golgi traYc (Marsh et al., 2004).

Acknowledgments

We thank the entire staff of The Boulder Laboratory for 3D Electron Microscopy of Cells for their

personal support and professional assistance from the very onset of these studies, and Kathryn Howell

and John Hutton of the University of Colorado Health Sciences Center for additional tutelage and

intellectual support. This work was supported by P41-RR00592 to J.R.M., GM42629 and P01-

GM61306 to K. E. H., and a Juvenile Diabetes Research Foundation International (JDRF) Postdoc-

toral Fellowship (3-1999-538) to B.J.M. B.J.M. is currently supported by JDRF (2-2004-275) and NIH/

NIDDK (DK-71236) funding and is a senior research affiliate of the ARC Special Research Centre for

Functional and Applied Genomics. I would like to especially thank Adam Costin, Janette Galea, Garry

Morgan, and Peter van der Heide for generating and segmenting data that are presented here.

The Advanced Cryo-ElectronMicroscopy Laboratory housed at the Institute forMolecular Bioscience

is a major node of the federally funded Nanostructural Analysis Network Organisation’s Major National

Research Facility (NANO-MNRF), and is jointly supported by the Queensland State government’s

‘‘Smart State Strategy’’ initiative. We thank Dr. Jamie Riches of the NANO-MNRF for instrument

calibrations and upkeep, and nanoTechnology Systems (Greensborough, VIC, Australia) for critical

maintenance and upgrades of the 300 keV Tecnai F30 EM within the laboratory.

216 Brad J. Marsh

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