Download toward a `visible cell`… and beyond

Special Technical Feature
TOWARD A 'VISIBLE CELL'… AND BEYOND
Brad Marsh1,2*
1Institute for Molecular Bioscience, University of Queensland, QLD 4072
2Centre for Microscopy & Microanalysis and School of Molecular & Microbial Sciences,
University of Queensland, QLD 4072
*Corresponding author: [email protected]
Introduction
Elucidating the three-dimensional (3D) fine structure of
molecules, cells and tissues is pivotal to the development of
a complete understanding of basic cell and molecular
biology, and thus requisite to successfully translating that
knowledge to efficacious clinical application and
therapeutic intervention in the prevention and/or
treatment of disease. If current efforts are successful with
respect to integrating data related to the 3D structurefunction relationships that underpin intra- and inter-cellular
interactions, networks and signalling − from molecule-tocell and from cell-to-tissue − new generations of biologists
will benefit from a more 'holistic' understanding of
molecular organisation and biochemistry in situ.
One such effort with an underlying focus on mammalian
cell imaging is the Visible CellTM Project, based at the
Institute for Molecular Bioscience and the ARC Centre in
Bioinformatics at the University of Queensland. Over the
past three years, this program has evolved beyond what
was originally simply an ambitious undertaking in
structural cell biology, that is, to reconstruct entire
mammalian cells in 3D at the electron microscopy (EM)
level with sufficient resolution to distinguish all subcellular
compartments and filaments of interest at once. The
program has matured into what is now a large-scale, crossdisciplinary and multi-institutional initiative that
ultimately aims to provide a computational and
visualisation framework with which to explore
mathematical models embedded onto real 3D cellular
structure data obtained from mammalian cells using a
variety of imaging methods, including high-resolution 3D
imaging techniques such as cellular electron tomography
(ET) (1,2). Indeed, the Visible CellTM Project has already
been described by Professor Anne Trefethen (former
Deputy Director of the UK's e-Science Core Program) as a
new way of doing biological science, and the program has
been held up as an exemplar of e-research in Australia
[www.arc.gov.au/pdf/e-Research_slides3.pdf].
Background and Significance
The first incarnation of a 'Visible Cell' per se arose from
work carried out at the Boulder Laboratory for 3D
Electron Microscopy of Cells, a National Biomedical
Technology Research Resource originally established
through the National Institutes of Health in the US so
that one of the 'founding fathers' of cell biology − Keith
Porter − could pursue the use of higher accelerating
voltages for imaging whole cells by EM. Over half a
century after the images from Porter's seminal
publication had demonstrated the power of the EM to
clearly resolve cellular fine structure in mammalian cells
and consequently revolutionised the study of
mammalian cell biology forever (3), a single highresolution 3D tomographic reconstruction of the Golgi
region in an insulin-secreting beta cell derived from the
'islets of Langerhans' of the endocrine pancreas (Fig. 1)
provided new and unanticipated insights into 3D cellular
organisation that has impacted substantially on how
scientists, students and the public alike picture life at the
subcellular level (Fig. 2) (4,5).
Fig. 1. A 3D reconstruction of an
intact pancreatic 'islet' isolated from
rodent pancreas and imaged by
multi-colour immunofluorescence
confocal laser scanning microscopy.
Shown are the glucagon-secreting alpha cells
(blue), somatostatin-secreting delta cells
(mauve) and insulin-secreting beta cells
(green). Reproduced with permission from
Robert Sorenson, Department of Genetics, Cell
Biology and Development, University of
Minnesota Medical School, USA (17)
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Special Technical Feature
ET uses mathematical techniques to computationally
reconstruct a 3D volume from a set of accurately aligned
two-dimensional (2D) images collected in the EM as the
specimen is serially tilted by small, regular increments
(e.g. 1° or 1.5°) over a relatively large angular tilt range
(e.g. ±60-70°). Because the resolution of an object within
the plane of the specimen with a slab geometry − such as
a thick slice ('section') cut from plastic-embedded cells
and/or tissue − also depends on its orientation relative to
the axis around which the specimen is tilted, cellular 'tilt
series' data are often collected around a second tilt axis
orthogonal to the first for improved resolution and
symmetry of cellular structures in 3D in the final 'dualaxis' tomographic reconstruction (1). Cellular ET in
particular has emerged as a powerful method for
obtaining useful 3D structural information about cells
and organelles at resolutions that typically range
between 5 and 10 nm, thus providing a 'resolution
bridge' for studying molecular cell biology in situ.
However, although ET is now almost considered a
mainstream research tool on many campuses globally,
including the University of Queensland, the significant
challenges associated with tomographic reconstruction
of large cell volumes at high-resolution has restricted
serious attempts to generate accurate 3D cell atlases to
just a handful of groups worldwide (1,6). By way of
example, although the reconstructed region presented in
Fig. 2 was estimated to encompass only around 1% of
that cell's total volume, the technical significance as well
as biomedical relevance of the work remains a hallmark
of the US National Resource where it was carried out
[http://www.ncrr.nih.gov/ncrrprog/btdir/Microsco.asp
#finest].
Nonetheless, spurred on by the National Library of
Medicine's
'The
Visible
Human
Project®'
[http://www.nlm.nih.gov/research/visible/visible_
human.html] which created the first 3D atlases of entire
human cadavers using a combination of computed
Fig. 2. A 3D model of the Golgi region in an insulin-secreting rodent pancreatic beta cell.
From Marsh et al. (2001) Organellar relationships in the Golgi region of the pancreatic beta cell line, HIT-T15,
visualized by high-resolution electron tomography. Proc. Natl. Acad. Sci. USA 98, 2399-2406.
Three serial 400 nm-thick sections cut from a high-pressure frozen, freeze-substituted and plastic-embedded HITT15 cell were reconstructed by cellular ET, and all visible objects within the resulting reconstructed volume (3.1 x
3.2 x 1.2 µm3) were manually defined or 'segmented'. The Golgi complex with its seven cisternae (C1-C7) is at the
center: 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 ribosomes (blue), free ribosomes (orange), microtubules (bright green), dense-core vesicles
(bright blue), clathrin-negative vesicles (white), clathrin-positive compartments and vesicles (bright red), clathrinnegative compartments and vesicles (purple), mitochondria (dark green). Two model views of the Golgi region
rotated 180° around the vertical axis are displayed. Copyright 2001 National Academy of Sciences, USA.
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Special Technical Feature
Fig. 3. Template matching applied to electron tomograms.
From the cover: Böhm et al. (2000) Toward detecting and identifying macromolecules in a cellular context:
template matching applied to electron tomograms. Proc. Natl. Acad. Sci. USA. 97, 14245-14250. ET currently
remains the only technique capable of reproducibly providing 3D subcellular data within the resolution range of
5-10 nm, with convincing prospects for the reproducible generation of cellular tomograms at resolutions better
than 5 nm on the immediate horizon. Thus, at least for the foreseeable future, it remains the only method available
for mapping the 3D territorial distribution of macromolecules in situ in a close-to-native environment. The
structural signatures of macromolecules can be visualised in tomographic slices from 3D cellular reconstructions
as black etchings on a background of gray density. Using a technique referred to as 'template matching', this
pioneering work from the group of Wolfgang Baumeister demonstrated that in principle the location of
macromolecules such as the thermosome and the 20S proteasome (shown on the left and right hand sides,
respectively, at 8 nm, 4 nm and 2 nm, viewed from back to front) could be identified within tomograms by virtue
of their structural signature alone. Copyright 2000 National Academy of Sciences, USA.
tomography (CT), magnetic resonance (MR) and serial
cryo-section imaging (7) − and is now considered a US
national resource in its own right − interest has
continued to grow for the development of methods that
would allow mammalian cells to be imaged then
reconstructed in toto in 3D by ET. Indeed, much of the
drive and enthusiasm for the creation of Visible CellTM
atlases in 3D at ~5 nm resolution has stemmed from an
emerging desire and need within the international cell
biology and bioinformatics communities to understand
the bigger picture with respect to cells as complex
systems. Starting with a precise spatial framework is
considered by many in systems biology as a fundamental
prerequisite to developing the capacity to realistically
and accurately predict changes in the spatio-temporal
coordinates of complex molecular and membrane
trafficking events in silico (2,8,9).
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Visible Proteomics and the Eukaryotic Cell
3D reconstructions of cells by ET at high-resolution have
been described as '3D images of the entire proteome of the
cell' (10,11). The term 'visual proteomics' − aptly coined by
Stephan Nickell and colleagues at the Max Planck Institute
for Biochemistry in Martinsried, Germany (6) − has already
started to gain favour within the systems biology as well as
molecular microscopy communities (8,9). For obvious
reasons, a number of major international efforts have been
launched to reconstruct entire cells at near-molecular
resolution since the proof-of-principle demonstration that
macromolecular complexes can be detected and identified
in situ in tomographic reconstructions by virtue of their 3D
structural 'signature' alone (Fig. 3) (12,13). However, for a
variety of reasons these projects have focused almost
exclusively on prokaryotic cells that more or less lend
themselves to the methods, in stark contrast to most
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Special Technical Feature
mammalian cells. Even where research groups have set
out to reconstruct eukaryotic cells by ET, they have so far
confined their attempts to simple eukaryotes (14).
We envisage an important biomedical goal of
accomplishing the same task for mammalian cells, whose
structure when mapped in 3D at high resolution may
inform efforts in public health. The first 'Visible Human
Dataset' created by Vic Spitzer and colleagues at the
Center for Human Simulation at the University of
Colorado [http://www.uchsc.edu/sm/chs/] garnered as
much attention and curiosity from the public due to its
enormous educational and visual appeal as it did from the
biomedical research and surgical communities because of
the wealth of fundamental scientific information it
encompassed (15). By analogy, in addition to serving as a
platform for 3D computer simulations of mammalian cell
'behaviour', a high resolution 3D atlas of even a single
mammalian cell in its entirety would in its own right
provide profound advances in our understanding and
appreciation of cellular architecture and organisation as it
relates to function when visualised in toto, particularly in
the context of providing unique insights into the basic
unit cell as a model for the study of complex systems
biology (6,8,9). Initially, we envisage that a Visible Cell
atlas will play a high-profile role in scientific educational
outreach at both the national and international levels − by
providing a quantum leap in terms of how scientists,
students and the public alike picture life at the subcellular
level in mammalian cells (5). This would have added
value if the cell type chosen for such a study had
immediate biomedical and/or clinical relevance as well as
fundamental scientific value at the basic science level.
Fig. 4. The application of
semi-automated tools for
pre-processing and 3D
segmentation of tomograms
generated by cellular ET.
The most time-consuming portion of
cellular ET currently resides at the
level of detailed 3D image analysis,
i.e. having to manually delineate or
'segment'
membrane-bound
compartments and other objects of
interest at the subcellular level.
However, preliminary efforts
towards the development and
application of fast and accurate
automated methods for detecting
and segmenting 3D cellular data, in
a manner analogous to the complex
3D algorithms routinely used to
segment EM density maps of 3D
molecular structure data (16), have
already proved fruitful. Typically,
cellular ET data require some
additional image 'pre-processing' to help the 'signal' of interest (e.g. membranes, filaments, vesicles) to stand out
against a sea of 'noise' in the background (e.g. cytoplasmic density). In a mathematical context, this enhancement of
signal-to-noise generally improves the performance of algorithms designed to accurately detect and define
structures of interest. (a) A tomographic slice is presented from the original reconstruction of one of the 400 nm-thick
plastic sections from which the surface-rendered 3D model shown in Fig. 2 was generated. (b) The same tomogram
was pre-processed with a three-pass iterative 3D median filtering algorithm that efficiently denoises the image data
while preserving the location of edges and enhancing contrast. In the example of semi-automated segmentation
using a novel 3D variant of the watershed transform (c-f), a slice of the original tomogram is shown at the far left (c).
In (d), the raw outcome of an application of the 3D watershed algorithm after pre-processing with a spatial
averaging routine is overlaid on the original, unfiltered data for reference, with very few falsely assigned structures
evident. These can be easily identified/eliminated during post-processing. (e) The segmentation after cleanup. Some
of the features display reversed contrast and can only be segmented correctly if this is taken into account. (f) The
combined result of the two segmentation runs with contrast reversal (first, black border; second, white border)
showing that practically all membrane-bound structures are identified and most of them are well delineated.
(a-b) Reproduced with permission from Niels Volkmann. (c-f) Reprinted from Fig. 6, first published in 'A novel
three-dimensional variant of the watershed transform for segmentation of electron density maps' by Niels
Volkmann, J. Struct. Biol. (2002) 138, 123-129, copyright Elsevier (16).
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Special Technical Feature
Fig. 5. Scientific artwork depicting the putative protein landscape in a macrophage
engulfing a bacterium.
This series of three watercolour paintings by David Goodsell that show a portion of each of the two cells
beautifully illustrates the distribution of macromolecules in the cells and the surrounding blood serum. Small
organic molecules and water, which fill the space between the macromolecules, are omitted. The colour coding is
as follows: cell membranes and their associated proteins (green), cytoplasm (blue and purple), nuclear material
(red and orange), blood serum proteins (yellow and brown). Some of the subcellular structures highlighted
include the nuclear pore complex, bacterial flagellum and motor, ribosomes, microtubule and tubulin subunits,
actin filament, antibody, nucleosome, fibrinogen, chaperonin, RNA polymerase, DNA polymerase, spectrin,
transfer RNA, topoisomerase, ATP synthase, glycolytic enzymes and insulin. Reproduced from 'Macrophage and
Bacterium 2,000,000X' with permission from David Goodsell, copyright 1999.
The Visible Insulin-Secreting Beta Cell
To this end, we have undertaken a multiresolution/multi-throughput
approach
toward
reconstructing mammalian cells in 3D at the EM level.
Our group's primary research remains focused on
elucidating key structure-function relationships related
to the biosynthesis, processing and trafficking of insulin
under normal versus abnormal conditions in beta cells
still resident in situ within intact pancreatic islets (Fig. 1)
isolated from mice and humans. Complementary to
these high-throughput/high-resolution studies of the
key organelles involved in insulin manufacture and
exocytosis, two separate yet overlapping projects aimed
at developing methods for imaging whole pancreatic
beta cells at different resolutions are currently well
underway in our laboratory; these projects reside at the
very heart of the Visible CellTM Project. Each in its own
way will provide crucial scaffold data essential for
setting up interactive platforms for computational
dissection of structure-function changes in silico.
The first of these two projects aims to reconstruct and
Vol 37 No 3 December 2006
segment an arbitrary volume (~400 µm3) of a single beta
cell by ET at approximately 5 nm resolution as proof-ofconcept. This project requires the development of novel
methods at almost every level, from efficient and precise
image acquisition, 3D reconstruction, segmentation and
navigation to data warehousing, management, archiving
and retrieval. Moreover, even though this proof-ofconcept dataset represents only a fraction (~10%) of the
data that will be required to reconstruct a whole cell in
3D at high resolution, it remains an incredible volume of
raw 3D cellular data (i.e. approximately 1 TB) and itself
represents a grand challenge in high-throughput ET,
since it will equate to around 405 'normal' high
resolution tomograms when complete. Consequently,
the successful analysis of this enormous volume
demands the concurrent development and application of
new algorithmic approaches for data mining, i.e., using
mathematical edge detection-, thresholding- and closedness-based techniques that utilise pixel intensity or
texture together with other parameters such as size and
shape, to automatically detect and identify structures
and compartments of interest (Fig. 4) (16).
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Special Technical Feature
Our second major undertaking directly relevant to the
Visible CellTM Project is focused on the rapid imaging
and reconstruction of whole mammalian cells at ~15-20
nm resolution using ET. This is accompanied by the
development of efficient yet accurate mark-up of whole
cell data, such that the 3D spatial coordinates and
approximate volumes and membrane surface areas of
organelles can be quantified, annotated and visualised in
3D, again with a particular focus on key compartments
of the insulin biosynthetic and secretory pathway. With
two whole cell reconstructions of glucose-stimulated
beta cells already in hand and with analysis of both
almost complete, we are on track to provide quantitative
4D measurements that will show the relative expansion
of the Golgi complex together with changes in the
distribution, size and morphology of mitochondria in
pancreatic beta cells following exposure to stimulatory
concentrations of extracellular glucose. Moreover, these
and sibling datasets will allow us to pilot the
development of basic methods for performing
rudimentary spatial analysis queries on different
organelles in 3D and 4D space, and to investigate the use
of different simplified representations in spatialtemporal modelling and computational simulation of
mammalian cells.
Over the longer term, by providing a prototype platform
for protein and organelle annotation and database
integration (see also article by Teasdale and Hamilton in
this Special Technical Feature), 3D visualisation and 4D
animations of cells, the Visible CellTM will emerge as
advanced data environment for e-research where
molecular, cell, developmental and genomic data from
multiple, geographically dispersed sources will be
managed, integrated, navigated and explored through
computational experimentation at 'pseudo' molecular
resolution (Fig. 5). Sophisticated cross-disciplinary
approaches such as these should eventually allow cell,
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molecular and computational biologists to model and
predict changes in cell structure-function relationships
that accompany key changes in cellular physiology and
pathology, such as the onset and progression of chronic
diseases such as Type 1 diabetes and cancer.
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