An Integrated Micro- and Macroarchitectural Analysis of
the Drosophila Brain by Computer-Assisted Serial
Section Electron Microscopy
Albert Cardona1, Stephan Saalfeld2, Stephan Preibisch2, Benjamin Schmid3, Anchi Cheng4, Jim Pulokas4,
Pavel Tomancak2, Volker Hartenstein5,6*
1Institute of Neuroinformatics, ETH/University of Zu ¨rich, Zu ¨rich, Switzerland, 2Max Plank Institute for Molecular Cell Biology and Genetics, Dresden, Germany, 3Lehrstuhl
fu ¨r Genetik und Neurobiologie, University of Wu ¨rzburg, Wu ¨rzburg, Germany, 4Automated Molecular Imaging Group, The Scripps Research Institute (TSRI), San Diego,
California, United States of America, 5Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, California, United
States of America, 6Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
The analysis of microcircuitry (the connectivity at the level of individual neuronal processes and synapses), which is
indispensable for our understanding of brain function, is based on serial transmission electron microscopy (TEM) or one of
its modern variants. Due to technical limitations, most previous studies that used serial TEM recorded relatively small stacks
of individual neurons. As a result, our knowledge of microcircuitry in any nervous system is very limited. We applied the
software package TrakEM2 to reconstruct neuronal microcircuitry from TEM sections of a small brain, the early larval brain of
Drosophila melanogaster. TrakEM2 enables us to embed the analysis of the TEM image volumes at the microcircuit level into
a light microscopically derived neuro-anatomical framework, by registering confocal stacks containing sparsely labeled
neural structures with the TEM image volume. We imaged two sets of serial TEM sections of the Drosophila first instar larval
brain neuropile and one ventral nerve cord segment, and here report our first results pertaining to Drosophila brain
microcircuitry. Terminal neurites fall into a small number of generic classes termed globular, varicose, axiform, and
dendritiform. Globular and varicose neurites have large diameter segments that carry almost exclusively presynaptic sites.
Dendritiform neurites are thin, highly branched processes that are almost exclusively postsynaptic. Due to the high
branching density of dendritiform fibers and the fact that synapses are polyadic, neurites are highly interconnected even
within small neuropile volumes. We describe the network motifs most frequently encountered in the Drosophila neuropile.
Our study introduces an approach towards a comprehensive anatomical reconstruction of neuronal microcircuitry and
delivers microcircuitry comparisons between vertebrate and insect neuropile.
Citation: Cardona A, Saalfeld S, Preibisch S, Schmid B, Cheng A, et al. (2010) An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by
Computer-Assisted Serial Section Electron Microscopy. PLoS Biol 8(10): e1000502. doi:10.1371/journal.pbio.1000502
Academic Editor: Kristen M. Harris, University of Texas at Austin, United States of America
Received March 31, 2010; Accepted August 19, 2010; Published October 5, 2010
Copyright: ? 2010 Cardona et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by National Institutes of Health grant R01 NS054814 to VH, and the EU grant 216593 Self Construction (SECO). The authors thank
Kevan Martin and Rodney Douglas at the Institute of Neuroinformatics, University of Zurich and ETH Zurich, for initiating and generously funding TrakEM2
software. The authors also thank Howard Hughes Medical Institute Janelia Farm Research Campus, the Max Plank Institute for Molecular Cell Biology and Genetics
(Dresden, Germany), and the Institute of Neuroinformatics for funding development sprints. Some of the work presented here was conducted at the National
Resource for Automated Molecular Microscopy, which is supported by the National Institutes of Health though the National Center for Research Resources’ P41
program (RR17573). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Abbreviations: BF/SEM, block face scanning electron microscopy; CPLd, centro-posterior-lateral compartment, dorsal domain; dePF, descending protocerebral
fascicle; EM, electron microscopy; FIB/SEM, focused ion beam scanning electron microscopy; GFP, green fluorescent protein; GUI, graphical user interface; L1,
Drosophila first larval instar; LM, light microscopy; MEF, medial equatorial fascicle; MRI, magnetic resonance imaging; PAT, primary axon tract; PB, phosphate
buffer; PBS, phosphate buffered saline; PBT, phosphate buffered saline with Triton X-100; PDF, pigment dispersing factor; PET, positron emission tomography;
SIFT, scale-invariant-feature-transform; TEM, transmission electron microscopy; VNC, ventral nerve cord
* E-mail: email@example.com
The brain of all higher animals is formed by a large number of
interconnected neurons. Typically, neurons are grouped into
larger assemblies (‘‘brain compartments’’), such as brain stem
nuclei or cortical layers in the vertebrate brain, or neural lineages
in the insect brain [1,2]. The analysis of the structure,
development, and function of the brain can therefore proceed at
two levels: the level of individual neurons and synapses, and the
level of brain compartments. Compartments represent structural
and functional modules; interconnected by bundles of axons, they
form ‘‘macro-circuits’’ that control certain aspects of behavior.
Unraveling macro-circuits has been the mainstay of classical
vertebrate neuroanatomy and physiology. Present-day studies
employing functional imaging (e.g., [3,4]) walk in the foot steps of
this approach, given that the signals registered by MRI or PET
scanners (for these and all other abbreviations, see Table 1) reflect
the activity of large numbers of contiguous cells .
The study of macrocircuitry informs us of how the brain is built,
which ‘‘packets of information’’ may interact, where in the brain
this interaction takes place, and what output channels are
activated to elicit a behavior that is correlated with the observed
PLoS Biology | www.plosbiology.org1 October 2010 | Volume 8 | Issue 10 | e1000502
macroscopic brain activity. Addressing macrocircuitry leaves the
question of how nervous tissue operates in processing information
unanswered. To tackle this problem, an approach is required that
considers the structure and connectedness of the building blocks of
the brain—i.e., the neurons, neurites, and synapses (‘‘microcir-
cuitry’’). The way in which a given neuron is tuned to a specific
input stimulus, or the pattern of activity triggered in this neuron
when providing a specific input, depends on the distribution of
excitatory and inhibitory synapses that connect the neuron with its
neighbors [6–9]. Given the small size of neurites and synapses, the
number and density of connections is immense. Calculations based
on both light- and electron microscopic preparations point out that
in mammalian brain, 1 mm3contains more than 105neurons,
more than 4 km of axon and 500 m of dendrite, and more than
700 million synapses . The goal of the analysis of
microcircuitry is to elucidate the geometric algorithms that
describe the connectivity within small volumes of brains; based
on these algorithms, one may hope we will be eventually able to
model the neuronal activity and information flow pervading brain
tissue while controlling sensory and motor activity of an organism.
Due to the small size of synapses and terminal neurites (0.1–
0.5 mm), structural aspects of microcircuitry can be conclusively
analyzed only electron microscopically. Traditionally, the acquisi-
tion and analysis of complete series of TEM sections required a
considerable effort; as a consequence, studies of microcircuitry have
mostly been restricted to small parts of neurons or neuropile
compartments in (e.g., [11–14]). The problem is now becoming
solvable, at least for small brains (or small volumes of large brains)
withdigital imagerecordingand specialized softwareforbothimage
acquisition and post-processing [15–18]. The resulting stacks of
registered digital sections can be segmented and analyzed in their
entirety. For the implementation of such a neuronal reconstruction
pipeline, aimed at the analysis of microcircuitry of the Drosophila
larval brain, we used the TrakEM2 open source software.
Drosophila serves as a favorable model system in which molecular
pathways involved in a wide range of events, from neural stem cell
proliferation, cell fate determination, neurite pathfinding, and
neurite connectivity, can be studied (e.g.,[19–22]). With the help of
sophisticated transgenic constructs (e.g., Gal4 lines; ), one can
target specific cell types, labeling these cells, or manipulating them
genetically. As a result, Drosophila also represents a great model
system to study systems-level questions, such as how neural circuits
develop or control behavior. Finally, Drosophila (and insects in
general) offers the advantage that its nervous system is formed by a
relatively small number of genetically and structurally defined
modules, the neural lineages [1,24–25]. Early in development, a
set of dedicated neural progenitors, called neuroblasts, segregate
from the ectoderm and subsequently proliferate to form the
neurons and glial cells of the CNS. Each neuroblast produces an
invariant set (‘‘lineage’’) of neurons; these neurons form a genetic
as well as a structural ‘‘module’’ of the brain (Figure 1). Thus,
axons of neurons belonging to the same lineage form a cohesive
tract and arborize within discrete compartments of the brain. The
axon tracts formed by neural lineages, and recognized from early
larval stages into the adult brain, represent the structural/
developmental units of brain macrocircuitry.
Lineage tracts represent an invariant, easily recognizable system
of structural landmarks onto which smaller elements—i.e.,
microcircuits or individual neurons—can be mapped. We argue
that for the efficient analysis of microcircuit data, it is
advantageous to follow an approach that integrates light
microscopic landmark structures, such as lineage tracts (the
‘‘macrocircuitry’’), with the analysis of TEM datasets. The
reconstruction of microcircuitry from serial TEM material
involves two main operations. The first is to segment the profiles
of neurites through many consecutive sections to establish branch
points and synaptic contacts. Given the amount of time involved in
manual segmentation (or proofreading of automatically segmented
material; ), there are currently two different strategies of serial
TEM data analysis. One (‘‘sparse strategy’’) is to focus on a
defined, relatively small set of neurons, such as sensory afferents,
or motor neurons, and trace their profiles and synaptic outputs/
inputs in their entirety. The other strategy, introduced in this
article, is to break down the overall TEM stack volume into
smaller ‘‘microvolumes,’’ in the range of 26261.5 mm up to
56565 mm, and reconstruct all profiles contained within these
microvolumes (‘‘dense strategy’’). From these small volumes,
structural parameters of terminal neurite connectivity (e.g.,
diameter, orientation, and density of terminal neurites; structure,
size, and density of synapses) can be extracted. Irrespective of the
strategy (sparse versus dense segmentation) chosen, an additional
step is to put the connectivity data resulting from the segmentation
into the context of brain macrocircuitry: what brain compartment
is the microcircuit located in, what are its input and output
channels, how does it compare to microcircuits located at other
positions in the brain. To be able to make these connections, the
operator has to be able to repeatedly switch between the EM and
LM level and to rapidly navigate from one location/compartment
The TrakEM2 software used in this study combines all the
components required for data acquisition, registration, naviga-
tion, and segmentation ([26,27]; http://www.ini.uzh.ch/˜acardona/
trakem2. html). TrakEM2 enables the analysis of a brain volume at
the microcircuit level (synapses, individual neurite branches) along
with the macrocircuit level (brain compartments, axon fascicles). We
illustrate the use of TrakEM2 towards this end by applying it to the
brain of the first instar (L1) larval Drosophila brain. As raw data we use
two stacks of stitched and registered EM sections, one containing 500
sections that include most of the neuropile of one brain hemisphere,
and another one of 250 sections including one complete abdominal
neuromere of the ventral nerve cord. We have cropped out several
small volumes and evaluated the structure and connectivity of the
Brains contain a vast number of connections between
neurons, termed synapses. The precise patterns of these
synaptic contacts form the structural underpinning of
electrical microcircuits responsible for animal behavior.
Due to their small size, synaptic contacts can be
conclusively shown using only high-resolution electron
microscopy (EM). Therefore, complete series of ultrathin
sections are required to reconstruct neuronal microcircuit-
ry. The acquisition and analysis of EM sections (with 15,000
sections per millimeter of tissue) is practical only by
computer-assisted means. In this article, we demonstrate
the utility of the software package TrakEM2 to model
interconnections of nerve fibers from consecutive EM
sections and to efficiently reconstruct the neural networks
encountered in different parts of a small brain, the early
larval brain of the fruit fly Drosophila melanogaster.
Neuronal networks are composed of patterns of axons
and dendrites (neuronal extensions that transmit and
receive signals, respectively), and using TrakEM2, we
describe the most common motifs they form. Our study
introduces an approach towards a comprehensive ana-
tomical reconstruction of neuronal microcircuitry and
delivers microcircuitry comparisons between vertebrate
and insect brains.
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org2 October 2010 | Volume 8 | Issue 10 | e1000502
terminal neurites contained within these volumes. Furthermore, we
have registered a confocal stack with the brain TEM stack and
evaluated the accuracy with which we can translate light microscopic
features from the confocal stack to the TEM stack. Our study is
intended to develop an approach towards a comprehensive
anatomical reconstruction of neuronal microcircuitry.
Materials and Methods
Freshly dissected first instar fly brains were fixed in 4%
paraformaldehyde and 2.5% glutaraldehyde in 0.1 M freshly made
phosphate buffer (PB; ph 7.3) for 24 h at 4uC. Brains were then
rinsed 5610 min in 0.1 M PB, postfixed in 1% osmium tetraoxyde
in 0.2 M PB for 60 min at 4uC, and rinsed 4610 min in distilled
water. Dehydration was done through an acetone series (10 min
50%, 10 min 70%, 10 min 96%, 3610 min 100%). Preparations
were embedded in Epon resin at room temperature as follows: 2 h
in 1:3 Epon:acetone, 3 h 2:2, overnight 3:1, overnight pure Epon.
Blocks were cured for 16 h at 60uC. 60 nm serial sections were cut
on a Leica UC8 ultratome and collected in ribbons onto pioloform-
coated single slot copper grids. Grids were contrasted in 8% uranyl
acetate (30 min at 60uC) and in Reynold’s lead citrate .
Immunohistochemistry and Confocal Imaging
Larval brains of a line in which the neurons expressing pigment
dispersing factor (PDF) were labeled by a Gal4-driven GFP reporter
were dissected in PBS and fixed in PBT (PBS with 0.1% Triton X-
100) containing 4% formaldehyde for 30 min at room temperature.
A mouse anti-Neurotactin antibody (; 1:10; Hybridoma Bank)
was used to label neurons and axon tracts. Secondary antibodies
were purchased from Jackson Laboratory and used at the
manufacturer’s recommended concentrations. Stained brains were
mounted in Vectashield (Vector Laboratory; H-1000). Confocal
images were taken on a Biorad MRC1024ES microscope using
Laser sharp version 3.2 software. Complete series of optical sections
were taken with a 406oil immersion lens at 1 mm intervals. Images
were analyzed using the ImageJ software.
The imaging of hundreds of sections at sufficient resolution to
resolve details like synapses used to pose a major problem in high-
throughput transmission electron microscopy (TEM). The soft-
ware package Leginon  automates TEM image acquisition of
multiple sections and large tissue areas. We used an FEI electron
microscope equipped with a Tietz camera and a goniometer-
powered mobile grid stage. Leginon software automatically
Figure 1. Drosophila first instar larval brain. (A) Schematic depiction of cross-section of one brain hemisphere, showing outer cortex of neuronal
cell bodies in central neuropile, formed by neuronal processes (neurites). One lineage of primary neurons is highlighted in purple color. Processes of
glial cells (green) surround the cortex and neuropile and form boundaries around compartments within the neuropile. (B) Schematic of larval brain
and ventral nerve cord, indicating positioning and orientation of serial sections. The first stack contains the neuropile of one brain hemisphere in two
closely adjacent sets of 400 and 100 sections, respectively. The second stack includes 250 sections of the ventral nerve cord (corresponding to
approximately two consecutive neuromeres). Colored lines represent axon tracts connecting brain and ventral nerve cord (after 69). (C, D) Electron
micrographs of sections of neuropile illustrating resolution that can be achieved at 5,0006primary magnification. Arrow in (C) points at synapse;
arrow in (D) indicates presynaptic vesicles; arrowhead shows obliquely sectioned microtubule. At a resolution of 4 nm per pixel, these structures can
be clearly resolved. Scale bars: 1 mm (C); 350 nm (D).
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org3October 2010 | Volume 8 | Issue 10 | e1000502
controls every component of the electron microscope and attached
camera. The acquisition starts by inserting a grid with about 10
serial sections into the microscope. Leginon images the entire slot
of the grid at low resolution and offers a grid atlas for manually or
semi-automatically picking the tissue areas of interest in all
sections. Then Leginon automatically adjusts the stage, power,
magnification, and camera to acquire the necessary sets of high-
resolution image tiles that cover all areas of interest. For our larval
neuropile sections, we chose a magnification of 5,6006binned at
2, delivering a resolution of 4 nm/pixel.
Image Volume Composition: Montaging Image Tiles
Within and Across Serial Sections
Acquired image tiles carry associated stage position coordinates,
but these alone would result in suboptimal montages. First, we
correct for lens deformations, which are constant to all tiles. For
the purpose, we extract scale-invariant-feature-transform (SIFT)
features  from nine heavily overlapping images and then
estimate and apply the correcting transformation to all tiles .
The correction of lens deformation greatly facilitates the
montaging of image tiles. Each section presents independent
non-linear deformations, generated during sectioning and also
induced by heat while imaging. No ground-truth is available for
the correction of these deformations. Our observations indicate
that, generally, section-wide gross deformations in consecutive
sections are mostly independent and that local non-linear
deformations contain translations smaller than the dimensions of
the image tiles. An approach that registers image tiles all-to-all
within and across sections would, to a considerable extent, cancel
out all independent deformations. We approximate this ideal
registration with an as-rigid-as-possible tile-wise registration
method. We extract SIFT features from all image tiles and search
for correspondences with their neighboring tiles within the section
and with tiles in the previous and subsequent sections in the series.
We estimate a rigid transformation model for each tile,
simultaneously from all tile-to-tile feature correspondences. An
iterative optimizer relaxes all tile-to-tile correspondences until the
sum of their square inter-distances is minimal. From this
configuration, we estimate a non-linear transformation for each
individual section using the Moving Least Squared method 
with tile centers as control points. With our approach, imaged
objects (such as sectioned neural arbors) that span over multiple
tiles within one tissue section are as smoothly continuous over tile
boundaries as possible, while preserving maximum continuity
across sections with minimal deformation applied to each
individual image .
Data Storage, Retrieval, and Navigation
The storage, retrieval, and navigation of a terabyte-sized dataset
of TEM images by a human operator in real time is a difficult and
time-consuming task, even when considering small objects like the
Drosophila L1 brain. We have found a successful approach in the
combination of two main factors: (1) the means to browse through
large areas and volumes at sufficiently high speed, and (2) the
ability to zoom in and out at very high speed, for the purpose of
obtaining a positional reference. We approach browsing with
mipmaps, that is, precomputed multiresolution images, which
match the current view magnification and are thus optimal for
data transfer and display [28,34].
Data Analysis: Segmentation Tools
Our immediate goal in analyzing the TEM stack was 2-fold: on
the one hand, we want to crop out small volumes, in the range of
5 mm across, for which we manually dense-segment every process
and synapse. Secondly, we wanted to identify and segment larger
structures, such as axon bundles and compartment boundaries,
which together form a framework of landmarks to which the
microvolumes could be related. To efficiently segment these
different structures by hand, we employed a small set of
segmentation data types: an ‘‘area list’’ (a list of 2D multi-area
objects, one per section, can represent individual neurites but also
large objects like compartments), a ‘‘ball’’ (a list of x,y points with a
radius, sitting on any section, can represent a specific landmark, like
point of intersection of tracts), a ‘‘pipe’’ (a serial sequence of points,
each point with an x,y coordinate on a specific section, and with a
radius that defines a tube passing through all points, can represent
axon tract), and a series of other convenience objects like floating
text labels (with associated x,y coordinates and on a specific section).
Data Analysis: Object Hierarchy
Densely segmented volumes, even at the scale of microvolumes,
contain a large number of separate objects that stand in specific
relationships to each other. We call the objects of a given brain the
hierarchy are user-provided; the L1 brain, for example, contains,
among others, the high-order elements ‘‘neuropile compartments,’’
‘‘neuropile tracts,’’ ‘‘lineages,’’ and ‘‘neurons.’’ High-order elements
include further, lower tiers of elements; a neuron, for example, forms
primary branches A, B; these in turn have secondary branches A1,
A2, … and B1, B2 … etc. Each branch forms a set of presynaptic
and/or postsynaptic sites, through which a given branch of a given
neuron communicates with a certain branch of another neuron. In
other words, the effective manipulation of the multitude of objects
included in a segmented brain (microvolume) demands a strategy
that groups these objects into recursively smaller and smaller groups,
effectively collapsing the complexity to an arbitrary level at which
high-order elements (like ‘‘neurons’’ or ‘‘branches’’) become the
elements to operate on: to measure, to hide/show,to visualizein3D,
to color, to highlight, etc. The result is a hierarchal tree of abstract
objects: a ‘‘neuron’’ is represented as a composition of lower-level
objects like ‘‘soma’’ and ‘‘arbor,’’ each of which in turn is composed
of further lower-level objects like ‘‘nucleus,’’ ‘‘cytoplasm,’’ ‘‘axon,’’
‘‘dendrite,’’ and so on, until reaching the level of primitive object
instances (whose terminology follows a controlled vocabulary, such
as‘‘area list’’or‘‘pipe’’).An ‘‘area list,’’for example,would represent
the series of 2D sections of a nucleus or synapse, which has been
created by manually or semi-automatically painting with mouse
movements over TEM images.
The object hierarchy window is set up in a manner that will
ultimately allow one to export the data in ‘‘sif’’ format into
programs designed to perform network analysis. Thus, tools like
‘‘Neuron’’ (http://www.neuron.yale.edu/neuron/) are designed
to accept data sets composed of large numbers of individual
elements connected via defined synapses and compute neural
circuit simulations using Hodgkin-Huxley models (integrate-and-
TrakEM2 Usage for Neuronal Reconstruction
TrakEM2 simultaneously presents two ways of browsing and
manipulating the data: on the one hand, the tree hierarchy of
abstract groupings, with the actual segmentation object instances at
the tips of the tree (‘‘object hierarchy window’’), and on the other
hand, a 2D display (‘‘canvas’’) for the manipulation of the data of
the segmentation objects (to fill in an area, to point-and-click to add
spheres, or lines, etc.) The 2D canvas is a view of the list of serial
sections, which can be any series of images such as image stacks
from confocal microscopy or registered histological or TEM serial
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org4 October 2010 | Volume 8 | Issue 10 | e1000502
sections. Via the 2D display, the images themselves are manipu-
lated. The key operations for serial section TEM are batch-
importing, image stitching within a section, registration of adjacent
sections, and contrast adjustment. Our image registration approach
preserves as much as possible the dimensions of each individual
image, avoiding the introduction of artifactual image deformations
. Both image volumes and segmented objects may be visualized
in 3D for spatial analysis, using the ImageJ ‘‘3D Viewer’’ .
Importing Extrinsic Objects
One of the requirements for an efficient serial section TEM
analysis is the recognition of structures that have already been
characterized at the light microscopic level. These structures
provide the context for the analysis of microstructures like synapses
and statistics of arborizations, etc. Currently, no algorithms exist for
generic automatic cross-modal image registration; that is, no
unsupervised algorithm can recognize structures both at the TEM
and at confocal images. TrakEM2 offers a simple 3D nonlinear
image registration approach based on user-picked fiduciary marks,
common between TEM and confocal images. From the fiduciary
marks, a nonlinear transformation is estimated using the Moving
Least Squares method  for 3D affine transformation. The
confocal image stack is thus brought into register with the TEM
sections. Then a re-sliced 50 nm section of the confocal image stack
is overlaid on top of the TEM section, using color composites.
An Integrated EM and LM Approach to the Analysis of
We prepared an uninterrupted series of TEM sections of a small
brain, the Drosophila first instar (L1) brain, recorded digital images
of a complete brain hemisphere at a high enough resolution to
reconstruct synapses and fine processes, and registered these
images so they can be navigated like a confocal stack. The
Drosophila L1 brain hemisphere has a diameter of approximately
50 mm. It consists of an outer cortex of neuronal cell bodies and a
central neuropile containing the branched neurites and synapses
(Figure 1A). The diameter of the neuropile measures 30 mm. In a
first series of TEM sections we focused on the neuropile; 500
sections of 60 nm thickness included the entire neuropile of one
brain hemisphere. In addition, we sectioned transverse slice of the
ventral nerve cord, containing 250 sections. For image capturing,
we used the software Leginon (Automated Molecular Imaging
group at the Scripps Institute, San Diego, CA). To clearly resolve
ultrastructural details such as neurotubules (about 25 nm diam-
eter) and synaptic vesicles (30–40 nm diameter), we aimed at a
resolution of approximately 3–4 nm per pixel, which can be
achieved at a magnification of 3,000–5,0006 (Figure 1). At that
resolution, the complete data stack including the neuropile of one
brain hemisphere amounts to approximately 1 terabyte; for the
entire L1 brain, the size would be approximately 5 terabyte.
Our strategy of ‘‘dense neuropile analysis’’ was to extract from
the TEM image volume multiple smaller ‘‘microvolumes,’’ in the
range of 26261.5 mm up to 56565 mm (Figure 2A), and
reconstruct all profiles contained within these microvolumes.
TrakEM2 allows us to efficiently crop out such microvolumes and
re-register them automatically. Contained within a microvolume
are the contiguous short segments and terminal branches of many
neurons and their synaptic contacts (Figure 2F–H; Figures S1, S2).
As shown below, the dense analysis of these objects can shed light
on a number of fundamental parameters of microcircuitry.
Furthermore, we argue that the reconstruction and analysis of
objects from stacked TEM images should be guided by known
‘‘macroarchitectural’’ brain landmark structures. In other words,
the interpretation of the pattern of neurites contained within a
given microvolume will be made easier by establishing position,
input, and output relationship of that volume (Figure 2C).
TrakEM2 embeds the analysis of the TEM stack at the
microcircuit level into a light microscopically derived three-
dimensional framework of landmark structures. This framework is
provided by the invariant pattern of compartment boundaries and
lineage-related axon tracts, many of which have been identified in
brains of all developmental stages ([36–40]; Figure 2D, E). Lineage
related primary axon tracts (PATs) in a L1 brain contain 5–20
tightly bundled, straight axons that originate and terminate at
defined positions. In many cases, PATs of several contiguous
lineages converge and form large fascicles, such as the antenno-
cerebral tract, peduncle, medial and lateral equatorial fascicle, or
posterior-lateral fascicle. Fascicles are all associated with high
concentrations of glial processes, which makes it easier to identify
them in the TEM stack (Figure 2B). Glial densities also accompany
many of the compartment boundaries . Fascicles and segment
boundaries (‘‘macrolandmarks’’) are segmented and become the
objects of an ‘‘intrinsic macro-model’’ of the brain contained
within the TEM stack.
TrakEM2 allowed us to seamlessly transition from the light-
microscopic (‘‘macrocircuitry’’) to ultrastructural level (‘‘microcir-
cuitry’’) of brain analysis. The graphic user interface of TrakEM2
features three simultaneously active windows: the object hierarchy
window, the 2D (raw data) canvas, and the 3D viewer (Figure 3).
The digitized TEM stack of the larval brain is opened as a
‘‘project’’ in TrakEM2 and appears in the 2D canvas, where it can
be navigated in the manner of a three-dimensional map .
Landmark structures that are segmented within the TEM stack are
represented as nodes of a hierarchical tree in the object hierarchy
window (Figure 3A). From within the object hierarchy window,
individual objects can be activated or inactivated, and properties
of their digital rendering, such as color or transparency, can be
changed. An object (or any number of objects) activated from
within in the object hierarchy window will appear as an outline
overlaid upon the electron microscopic images visible in the 2D
canvas window (Figure 3C). At the same time, a surface rendered
3D digital model of the object can be displayed in the 3D viewer
(Figure 3D). Alternatively, an object can be activated from within
the 2D canvas and is then identified in the object hierarchy
window. As a result, we were able to focus at the ultrastructural
level on any part of the neuropile in the 2D canvas and at the same
time get orienting cues from the intrinsic macro-model (displayed
in the 3D viewer) about the exact position relative to compartment
boundaries and axon fascicles.
TrakEM2 also offers the possibility of adding additional
macrostructures imaged from other (‘‘extrinsic’’) larval brains.
Thus, a confocal stack of a L1 brain in which a certain brain object
(e.g., cell type, or lineage), aside from the landmark structures, is
labeled by antibody or reporter construct is opened as an
additional, separate image volume. The landmark structures
define a set of fiduciary marks that allow one to register the
extrinsic confocal stack with the TEM stack. Following the
registration, all structures that are labeled, or manually segmented
as objects, in the extrinsic confocal project are overlaid with the
TEM project. In the example shown in Figure 3E–J, the confocal
stack of a brain in which the four neurons expressing the peptide
PDF (pigment dispersing factor) were labeled was merged with the
TEM stack. We used the points of intersection of 14 axon fascicles,
with each other or compartment boundaries, as fiduciary marks
for registering the confocal stack with the TEM stack (Figure 3E,
F). To assess the accuracy of the registration, we first determined
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org5October 2010 | Volume 8 | Issue 10 | e1000502
Figure 2. Microvolumes and neuropile landmarks. (A, B) Montage of electron micrographs of representative brain section (A; co, cortex; ne,
neuronal cell body; np, neuropile). Note that this and all other electron microscopic images of the figures presented in this article are a composite of
multiple tiled digital photographs stitched together with the TrakEM2 software, as explained in Materials and Methods. Occasional slight
discontinuities in image brightness (as for example at lower right corner of panel A) correspond to borders of tiles. Bundles of long axons can be
recognized and identified with specific, lineage-related fascicles known from confocal microscopy. Upper boxed area, shown at higher magnification
in panel B, contains branch of antenno-cerebral tract (APT; blue) and adjacent axon tract formed by one of the DPM lineages (PAT). Note glial
processes (gl) accompanying axon fascicles. Green box in (A) demarcates size and position of a microvolume that was cropped out of stack for further
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org6 October 2010 | Volume 8 | Issue 10 | e1000502
how closely the position of corresponding axon fascicles segmented
from the TEM stack and the confocal stack matched. Shown in
Figure 3G are the profiles of the MEF (medial equatorial fascicle)
and dePF (descending protocerebral fascicle) as they appear in the
TEM stack (outlined in magenta). Overlaid in blue (peduncle) and
red (MEF) are the profiles of the corresponding fascicles that were
segmented in the confocal stack and, following registration,
overlaid upon the TEM stack. In most sections, as the one shown
in Figure 3G, the profiles (which measure less than 2 mm in
diameter) overlap. Profiles of the mushroom body peduncle
(diameter: 5 mm) segmented from the confocal section overlap
more than 50% with the peduncle derived from the TEM stack
throughout all sections. The accuracy of registration can be further
improved by refining and adding more fiduciary marks. Of course,
one cannot expect that, employing the approach introduced here,
a single neurite (whose diameter averages around 0.25 mm)
segmented from a confocal stack will ‘‘fall’’ precisely over the
corresponding counterpart within the TEM stack. For that not to
happen, variability between brains (at the level of individual
neurons) in itself is reason enough. However, the import of labeled
objects from confocal stacks will assist significantly in identifying
specific neurons, as shown in the following for a small group of
neurons, the PDF neurons.
A GFP reporter expressed under the control of the PDF gene
promoter  labels four neurons located in the dorso-lateral L1
brain hemisphere. Figure 3H and 3I illustrate the PDF neurons as
they appear in a confocal section (3H) and overlaid upon the EM
stack (3I). The main axons of the four neurons converge and
fasciculate, extending as a thin transverse bundle along the dorso-
lateral neuropile boundary. Dendritic branches of the PDF
neurons branch off towards the larval optic neuropile (outside
the plane of section); numerous short secondary and tertiary
axonal branches are formed along the length of the PDF bundle
into the CPLd compartment. PDF neurons belong to the class of
peptidergic neurons, all of which are characterized ultrastructur-
ally by prominent dense core vesicles filling the entire neurite tree.
Peptidergic neurites innervate the neuropile quite sparsely; the
overall number of peptidergic neurons in the L1 brain is in the
order of 50 per hemisphere (reviewed in ), out of 1,500
neurons. Therefore, the profiles with dense core vesicles (Figure 3J)
that we identify in the TEM stack in the domain occupied by the
overlaid profiles of PDF neurons imported from the confocal stack
most likely belong to the PDF neurons inherent to the TEM stack.
Drosophila Brain Microarchitecture: Generic Classes of
We have segmented and reconstructed neurites in five
microvolumes located in different compartments (calyx and spur
of mushroom body, dorso-lateral protocerebrum, and dorso-
lateral domain of ventral nerve cord). As outlined above, the dense
reconstruction of microvolumes yields information regarding
structural parameters such as neurite diameters, directionality,
branching, and synapse placement. Our data show that brain
ultrastructure is conserved in several aspects in all regions sampled.
We were able to identify four classes of neurites in each of the
microvolumes (Figure 4). The first class, termed ‘‘axiform
neurites,’’ is comprised of straight, unbranched processes of even
diameter, ranging between 0.2 and 0.4 mm (Figure 4B, D).
Axiform neurites form bundles originating in the cortex; they
correspond to the primary axon tracts (PATs) emitted by the
neurons belonging to one lineage. Within the neuropile, groups of
PATs typically coalesce to form larger assemblies (fascicles; see
examples of MEF or APT in Figure 3C). The second class of
neurites (‘‘varicose neurites’’) consists of branched processes that
alternately decrease and increase in diameter along their trajectory
(Figure 4A, D). Thick segments (swellings or ‘‘varicosities’’)
measure between 0.5 and 1.5 mm; thin segments 0.15–0.4 mm.
Varicose neurites account for most of the volume of the neuropile.
A variant of the varicose neurites are ‘‘globular neurites’’;
principally similar in shape than the former, they have swellings
(‘‘globules’’ or ‘‘boutons’’) that are more voluminous than
varicosities, exceeding 1.5 mm in diameter. The globules of these
central nerve fibers resemble the endplates, or ‘‘boutons,’’ of
peripheral motor axons. The fourth class of neurites (‘‘dendriti-
form neurites’’) is formed by highly branched, thin (less than
0.2 mm) fibers that frequently change direction (Figure 4C, D).
Synapses are ultrastructurally defined by their characteristic
presynaptic membrane specializations, consisting of an electron
dense membrane thickening bordered by synaptic vesicles and the
T-bar (also called synaptic ribbon), a cytoplasmic specialization
involved in tethering and docking of synaptic vesicles ([44,45];
Figure 4F, G). We observed that, independent of location within
the brain, presynaptic sites are highly uniform in size, ranging
from 0.15–0.3 mm, and are always found on the swellings of
varicose and globular neurites (Figure 4E). These then constitute
the terminal axons of the brain. In several cases, multiple
presynaptic sites (individually defined by the synaptic ribbon)
were confluent and formed band-like synaptic conglomerates.
Postsynaptic sites, characterized by (relatively inconspicuous)
membrane densities lacking T-bars or synaptic vesicles, are found
almost exclusively on dendritiform neurites (Figure 4E) and
(occasionally) on thin side branches of varicose neurites, implying
that this class of fibers represents (terminal branches of) dendrites.
Note that in insect neurons, the relatively strict distinction between
dendrites and axons as two different types of neurites (a distinction
that is quite typical for vertebrate brains) does not exist. Thus, the
neuronal cell body emits a single neurite, which in the neuropile
forms numerous branches that may be dendritic (i.e., postsynaptic)
or axonal (i.e., presynaptic). Our data show that at the level of
terminal branches, dendritic and axonal processes are mostly
separate (pre- and postsynaptic sites rarely occur on the same
preterminal process) and structurally distinct (presynaptic sites on
large diameter neurites, postsynaptic sites on small diameter
analysis (see panels F–H). Dotted purple line demarcates calyx (CX) compartment. (C) Schematic drawing showing L1 brain hemisphere with circuit
consisting of antennal lobe-calyx-spur/lobes of mushroom body. Fascicles forming this circuit (e.g., APT, mushroom body) can be identified in TEM
stack and help to interpret short segments of neurites contained within microvolumes (e.g., microvolume from within the calyx). (D, E) Confocal cross-
sections of first instar larval brain hemisphere (D) and adult brain hemisphere (E) labeled with anti-Brp to visualize neuropile. Set of lineage-related
axon fascicles that can be followed from first instar to adult are rendered in different colors. These fascicles (among them the APT shown in panels A,
B) form a system of invariant landmarks that can be recognized in confocal stacks and TEM stacks. Abbreviations: APT, antenno-protocerebral tract;
DPC, dorso-posterior commissure; loSM, longitudinal superior-medial fascicle; MEF, medial equatorial fascicle; obP, oblique posterior fascicle; SLP,
superior lateral protocerebrum; VLP, ventro-lateral protocerebrum. (F–H) 3D views of selected objects within the calyx microvolume. Microvolume
contained 42 large diameter (.0.2 mm) neurite segments. Of these, 14 were randomly picked and are shown individually in panels F1–F14 (volume in
side view; y and z indicate direction of axes of volume). (G, H) Top view of large diameter neurite segments combined (G: all 42 segments; H: 14
segments shown individually in panel F). Red dots indicate position of presynaptic sites. Scale bars: 4 mm (A); 0.5 mm (B); 10 mm (C, D); 1 mm (F, G, H).
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org7 October 2010 | Volume 8 | Issue 10 | e1000502
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org8 October 2010 | Volume 8 | Issue 10 | e1000502
The disparity in size between terminal axons and dendrites also
accounts for the fact that most, if not all, synapses of the Drosophila
brain are of the polyadic type, where a single (large) presynaptic
profile contacts multiple postsynaptic sites. The pre- to postsyn-
aptic ratio in most synapses ranges between three and five (see
examples shown in Figure 4F, G).
Drosophila Brain Microarchitecture: Domain-Specific
Patterns of Neurite Morphology
Although the types of neurites depicted above are encountered
in every region of the brain and ventral nerve cord, their relative
numbers, directionality, branching density, and placement of
synapses differ from one microvolume to the next. Two
microvolumes, taken from the calyx of the mushroom body and
the dorso-lateral column of the ventral nerve cord, are presented
as examples (Figures 2, 5, S1).
The calyx represents the input region of the mushroom body
that receives the axons of antennal projection neurons . The
microvolume extracted from the center of the calyx is character-
ized by a largely parallel array of varicose neurites carrying the
majority of presynaptic sites. These neurites then most likely
represent segments of the terminal axonal branches of antennal
projection neurons, which project, via the antenno-protocerebral
tract, to the calyx (Figure 2B). In total, the 20 mm3calyx
microvolume contained 33 varicose and two globular neurite
segments. These neurites have an average diameter (reducing the
shape of neurites to that of a smooth cylinder) of 0.38 mm. The
overall ‘‘cable length’’ (adding together all fragments of varicose/
globular neurites within the microvolume) amounted to 48 mm.
There were 16 branch points, corresponding to a density of
branch points of one every 3 mm of axonal cable. We find a
density of about 1.8 presynaptic sites per 1 mm3. Dendritiform
neurites form bundles of 5–10 thin fibers winding around the
varicosities of preterminal axons to whom they are postsynaptic.
Synapses are mostly dyadic-tetradic. Frequently, an individual
dendritiform neurite participates in two or more synaptic contact
made with the same axon (see also below for the VNC
Axonal cable length, branch density, and synapse density were
similar in two other brain microvolumes, one in the spur (an
output region of the mushroom body; ) and the other in the
CPLd compartment of the protocerebrum. In the spur, we
counted one branch point for every 4 mm of axonal cable; in the
CPLd, a branching occurred every 3.2 mm. The synapse density in
the spur and CPLd was 3.3 and 1.2, respectively.
The patterning of presynaptic (varicose/globular) neurites in the
dorso-lateral domain of the ventral nerve cord is characterized by
a lower density of branch points (approximately one branch every
7.5 mm) and synapses per volume unit (0.8/mm3) when compared
to the brain. Cable length and average diameter of neurites, on the
other hand, equals that in the brain. All types of neurites (varicose/
globular, axiform, and dendritiform) are oriented preferentially
along the longitudinal axis of the ventral nerve cord (VNC;
Figure 5A–F; Figure 1S). The high quality of the VNC stack (only
two out of 300 sections missing) made it possible to reliably
reconstruct the pattern of thin dendritiform neurites. Similar to
what is seen for the brain microvolumes, these neurites have a
higher branch density than varicose/globular neurites (one branch
every 4.1 mm). Also, the overall dendritic cable length exceeds that
of axonal elements by a factor of almost 2. Thus, the 85 mm3VNC
microvolume contained 247 mm of axiform/varicose/globular
neurites (‘‘axons’’) and 420 mm of dendritiform neurites (‘‘den-
drites’’). When following individual
throughout the microvolume, an interesting convergence-diver-
gence pattern becomes apparent (Figure 4H–J). Thus, at any given
level, dendritiform processes form aggregates of 5–10 processes
each. However, aggregates, when following the dendritiform
processes along the z-axis, do not translate into bundles: processes
forming an aggregate at a given level stay together only for a short
interval (,1 mm), after which they diverge and redistribute. This
pattern reflects the fact (mentioned above) that dendritiform
neurites do not follow a straight course but change direction
constantly and abruptly (Figure 4C, Figure S2). By contrast,
axonal processes have relatively straight trajectories: axiform
neurites (long axons without synapses) form tight bundles where
neighborhood relationships between neurites is maintained over
manymm (Figures 4D, H–J, 5A–C); terminal varicose/globular
axons are more loosely packed but, like axiform processes, extend
more or less parallel, maintaining their position relative to each
other (Figures 4B, D, H–J, 5A–C).
If we take the microvolume-based measurements of cable
length, branch density, and synapse density, complement them
with light microscopic findings, and extrapolate to the brain as a
whole, we arrive at conclusions that are both interesting and
Figure 3. The graphical user interface of TrakEM2. (A) The object hierarchy window displays segmentation data types (left column, green), all
segmented objects captured with these types (center column, magenta), and all layers along the z-axis (right column, blue). (B) ImageJ tool box. (C)
The 2D canvas presents the TEM (or any other) raw data stack. The user can scroll and navigate through all sections displayed in this window. The
yellow grid at bottom left of panel shows all of the image tiles that constitute the montage of the section shown. When zoomed in, a red frame
indicates the area shown on canvas; by dragging the red frame, the user can navigate to the desired parts of the montage. Segmentation and
annotation of identified structures (e.g., compartments and lineage tracts) is performed in the 2D canvas window. Shown are outlines of four
identified axon fascicle (APT, antenno-protocerebral tract, blue; deCP, descending central protocerebral fascicle, yellow; MEF, medial equatorial
fascicle, red; p, peduncle of mushroom body, light blue). Points of intersections of fascicles, such as the one between deCP and MEF (blue circle),
serve as fiduciary marks with which different stacks are registered. (D) The interactive 3D viewer windows show selected objects segmented from the
TEM stack. Shown as examples are the same elements depicted as sections in panel C [mushroom body (CX, calyx; ml, medial lobe), MEF fascicle,
deCP fascicle]. (E–J) Registration of confocal stack to TEM stack. (E) 3D digital model of larval brain hemisphere derived from confocal stack. Shown
are brain surface (light gray), mushroom body (light blue), and axon fascicles (dark gray). Set of fascicles highlighted in TEM stack (see panels C, D) are
shown in corresponding colors. (F) 3D digital model of mushroom body, showing location of fiduciary marks (yellow) identified in both TEM stack and
confocal stack. (G) Representative section of TEM stack. Shaded in purple are profiles of two axon fascicles (p, peduncle; MEF, medial equatorial
fascicle) as they appear in the TEM section. Superimposed in red and blue are profiles of corresponding fascicles from confocal stack that was
registered with TEM stack (using fiduciary marks shown in F) and digitally re-sliced along the plane of sectioning of the TEM stack. Note high degree
of overlap between TEM profiles and ‘‘imported’’ confocal profiles. (H) Representative section of confocal stack of one brain hemisphere. Shown in
green are GFP-labeled PDF neurons. The cell bodies of these neurons are located in the dorso-lateral cortex; axons form bundle that extends along
the dorso-lateral cortex-neuropile boundary (white dotted line). Large arrowhead points at one cell body, small arrowhead at axon. (I) Section of
registered, re-sliced confocal stack projected upon corresponding EM section. Cell body and axon are indicated by large and small arrowhead,
respectively; boundary between dorso-lateral cortex and neuropile is shown as white dotted line. (J) Magnified view of area boxed in (I). Arrows point
at profiles of neurites containing (neuro-secretory) dense-core vesicles. Based on their proximity to the position of PDF neurons, it is probable that
these profiles correspond to branches of PDF neurites. Scale bars: 5 mm (C, G, I); 10 mm (H); 1 mm (J).
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org9 October 2010 | Volume 8 | Issue 10 | e1000502
Figure 4. Drosophila neuropile ultrastructure. (A–C) Generic types of neurites (globular/varicose, axiform, dendritiform) encountered in all
regions of the neuropile. Upper panels of columns A, B, C show schematic representations of these neurites; lower panels show 3D digital models of
representative neurite segments for corresponding classes, segmented from VNC microvolume (G1, globular neurite; V1, V2, varicose neurites; A1-3,
axiform neurites; D1-3, dendritiform neurites). Elements are shown in lateral view; arrows indicate orientation (ant, anterior; dor, dorsal) relative to
body axes. Red dots indicate position of presynaptic sites. (D) 3D digital model of all neurite segments shown in (A–C) as they are situated in VNC
microvolume. Volume is shown in dorso-posterior view; arrows indicate axes (ant, anterior; dor, dorsal; lat, lateral). (E) Correlation between frequency
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org 10October 2010 | Volume 8 | Issue 10 | e1000502
helpful for further functional/developmental analyses. The L1
brain hemisphere has a volume of approximately 20,000 mm3
. Differentiated primary neurons whose processes make up the
volume of the brain neuropile number approximately 1,500 per
hemisphere . The average values of presynapse density, axonal
branch point density, and axonal cable length (taken from the four
microvolumes) are 2/mm3, 0.8/mm3, and 2.9/mm3. Extrapolated
to an entire brain hemisphere, this would amount to a total of
40,000 presynaptic sites, 16,000 axonal branch points, and
58,000 mm axonal cable length. For the average neuron, that
means approximately 40 mm axonal length, 11 axonal branches,
and 27 presynaptic sites. These estimates go along well with light
microscopic data based on DiI fills (or other labelings) of L1 brain
neurons. We randomly sampled L1 neuron shapes by injecting
single cells with DiI and imaging neurons by fluorescence
microscopy (unpublished data). It is possible to roughly estimate
neurite length, and numbers of varicosities and branches from
fluorescent images of labeled neurons, and obtain numbers that
match closely our extrapolations from the microvolume analysis.
Connectivity and Network Motifs in the Drosophila Larval
An important aspect of Drosophila neuronal architecture is its
‘‘ultradense’’ neuropile architecture, for which three major
structural features are responsible. First, neuronal cell bodies, in
terms of volume a considerable part of the brain, are located
outside of the neuropile and therefore do not form part of circuits
(i.e., do not carry synapses). Second, dendritiform processes are
extremely thin and highly branched. Third, synapses are polyadic,
with each presynaptic site attaching to an average of four
postsynaptic sites. As a result of these factors, neurites within a
microvolume are highly interconnected, and one can detect in
high numbers certain types of network motifs [48,49] that may
determine the function of the corresponding neuropile micro-
volume. We will in the following summarize our first data
concerning connectivity and network motifs for the microvolume
generated for the dorso-lateral VNC.
From the 85 mm3VNC microvolume we reconstructed 170
elements over 1 mm length (Figures S1, S2). Of these, 39 were varicose
neurites; one was globular, 25 axiform, and 105 dendritiform. We
counted 68 presynaptic sites, all concentrated on the large diameter
segments of the globular neurite and of 24 varicose neurites. Only a
single presynaptic site was found on an axiform process.Among the 24
neurites with synapses, the synapse number per neurite ranged
between one and nine (average 2.7). The dendritiform processes
formed 256 postsynaptic sites, yielding an average ratio of four
postsynaptic sites to every one presynaptic site. In turn, each
dendritiform process contacted an average of 2.1 presynaptic neurites.
Contacts between neurites of the VNC microvolume are highly
concentrated along the longitudinal (z) axis. In other words,
postsynaptic partners of a given, longitudinally oriented presyn-
aptic process are clustered closely around this process (Figures 5M,
6). To quantify the correlation between the likelihood of forming a
synaptic contact and distance between two processes, we visualized
the envelopes that included a given axon and the dendritiform
neurites (Figure 6A) and then estimated in a pairwise fashion the
amount of overlap between axonal and dendritic envelope
(complete overlap, more than 50%, 10%–50%, less than 10%).
For each class we determined the frequency at which a synaptic
contact is made (Figure 6E). This frequency is a function of
overlap (which in turn reflects distance): if the overlap exceeds
50%, more than half of the dendritic processes form synapses with
the corresponding axon; this drops to about a quarter at an
overlap between 50% and 10% and to 17% at less than 10%
overlap (Figure 6B).
Most of the interconnected pre- and postsynaptic elements of
the VNC microvolume engaged in what is called a ‘‘dense
overlapping regulon motif’’ , defined as a network where a
given input element (axon) diverges onto multiple targets, and at
the same time, each target element (dendrite) receives input from
multiple presynaptic elements (Figure 5M, O). In the example
shown in Figure 5G–I, one varicose neurite possesses three
presynaptic sites on two varicosities. Contacting these sites are 12
postsynaptic dendritiform processes. Each dendrite in turn receives
input from up to five presynaptic partners. Most of the 24 axonal
processes within the VNC microvolume formed part of such a
dense overlapping regulon network.
A second type of network, encountered much less frequently, is
the feed forward motif. Here, a neurite, ‘‘A,’’ receives input from a
second neurite, ‘‘B,’’ and both ‘‘A’’ and ‘‘B’’ provide output to a
common target, ‘‘C’’ (Figure 5M, N). Thus, feed forward motifs
involve instances of neurites that serve as presynaptic partners to
some and postsynaptic partners to other fibers. In the VNC
microvolume, we encountered five cases where varicose neurites,
which are mostly presynaptic, also formed a postsynaptic site. In
all cases, this postsynaptic site was located not on the varicosity
itself, but the thin segment or a finger-like side branch of the
neurite (Figure 5J–L). In two cases, axiform neurites had
postsynaptic sites. Interestingly, fibers that, based on the presence
of characteristic dense core vesicles, belong to peptidergic neurons
all form part of axo-axonal contacts. In three cases, they served as
presynaptic partners, and in two cases as postsynaptic partners.
The approach towards the analysis of microcircuitry introduced
here makes it possible to analyze and compare ultrastructural
there may exist a number of generic properties of neuropiles, which
in part have already been observed in previous studies of insect
brain neuropile. In all parts of the central nervous system, we could
distinguish between four different types of neurite profiles,globular/
varicose, axiform, and dendritiform. The swellings of globular/
sites; these presynapses are contacted by multiple, small-diameter
dendritiform processes. Out of the several hundred synapses
observed, less than a percent did not conform to this polyadic type.
It is noteworthy that representative sections of late larval and adult
central brain neuropile (VH, unpublished), as well as TEM sections
of neuropile documented in the literature [13,14,50–54], showed
that the diameters of the large majority of presynaptic and
postsynaptic profiles, and the structure of synapses, are very similar
to what we see in the early larval brain of Drosophila.
of presynaptic and postsynaptic sites and neurite diameter. Presynaptic sites (blue; right) are predominantly found on large diameter profiles, which
correspond to thick segments of varicose and globular neurites. Postsynaptic profiles (yellow, left) almost exclusively belong to thin dendritiform
neurites. (F, G) Sections of two typical polyadic synapses. Green dots indicate presynaptic profile, characterized by T-bar (synaptic ribbon) and
synaptic vesicles. Presynapses are contacted by multiple, thin branches of dendritiform processes. (H–J) Representative EM sections of VNC
microvolume at three different levels along the antero-posterior-axis; levels are indicated by gray lines in panel A. Profiles of neurites shown in (A–D)
are shaded in the corresponding colors and identified by the corresponding annotations (G1, V1-2, A1-3, D1-3). Scale bars: 0.1 mm (F, G); 1 mm (H–J).
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org11 October 2010 | Volume 8 | Issue 10 | e1000502
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org12 October 2010 | Volume 8 | Issue 10 | e1000502
It is a widely reported finding that the dendrites and axons of
invertebrate neurons are not spatially separated as in most
vertebrate neurons but occur intermingled. Thus, a typical insect
neuron may produce one or a few major stem branches that give
off higher order branches with either dendritic or axonal
properties. What emerges from the literature is that in most cases,
the arbor of a given neuron can be subdivided into territories that
are predominantly (but not exclusively) presynaptic or postsynap-
tic. For example, synaptic sites of antennal projection neurons are
predominantly postsynaptic in the antennal lobe (close to the cell
bodies of these neurons), and presynaptic in the calyx (where they
form the massive ‘‘microglomeruli = globular endings; [13,55]).
But even the proximal neurites in the antennal lobe were found to
carry presynaptic sites . Significantly, these were found on the
proximal, large diameter neurites; as these neurites branched into
higher order, thinner terminal fibers, these were all postsynaptic.
This report is in line with our finding that almost without
exception, postsynaptic profiles were of very small diameter, and
Figure 5. Network motifs encountered in VNC microvolume. (A–F) Structure of the VNC microvolume. (A) Representative section of VNC stack;
profiles of all globular, varicose, and axiform neurites are tinted in shades of green and blue. (B, C) 3D digital model of these processes (B: frontal view; C:
fronto-lateral view; red lines in these and other panels show edges of VNC microvolume; yellow dots indicate corner points). Note bundle formed by
axiform neurites (arrow in A–C); another bundle of preterminal axons (light green; arrowhead) enters the microvolume from ventro-lateral. Presynaptic
sites are colored red. (D) Representative section of VNC microvolume; dendritiform neurites are colored in shades of yellow and brown. (E, F) 3D digital
model of all dendritiform processes in frontal view (E) and fronto-lateral view (F). (G–I) Dense overlapping regulon motif. One ‘‘primary’’ presynaptic
neurite (‘‘a’’; bright green) contacts 12 postsynaptic dendritiform neurites (yellow-brown) at three synapses. Seven other ‘‘secondary’’ presynaptic
elements (transparent green) are also presynaptic to these dendrites. Panel G shows representative section in which elements forming part of this motif
are shaded; (H) and (I) represent 3D digital models of these elements in frontal view (H) and lateral view (I). In (I), the ‘‘primary’’ axonal element is colored
red for bettervisibility. (J–L) Feed forwardmotif.The globularneurite (G) has thinbranch(Gd) thatis postsynaptictovaricoseneuritesegment(V; arrow in
K, L). The dendritiform neurite (D) shown in orange is postsynaptic to both G (arrowhead in J) and V (arrowhead in K). Panels J and K are representative
sections of VNCcube where elements ofthe feed forwardmotif are highlighted;(L) shows 3Dmodelofmotif in lateral view; levels ofsectionsshown in (J)
and (K) are indicated. (M) Schematic representation of connectivity encountered in VNC microvolume. (N, O) Schematic representation of feed forward
motif and dense overlapping regulon motif, respectively. Scale bar: 1 mm (A, D, G, J, K).
Figure 6. Correlation between spatial overlap of neurite segments and probability of synaptic contacts in VNC microvolume. Panels
A–D show 3D digital models of elements of VNC microvolume (frontal view). All dendritiform neurites are rendered in transparent shades of yellow-
brown. In panel A, one axiform neurite (a) and two dendritiform neurites (d1, d2) are rendered in solid colors. Red frames delineate boundaries of
envelope surrounding the three profiles. In this example, envelope of d1 and a overlap slightly (less than 10% of volume of envelope of a is shared
with envelope of d1); envelope of d2 and a overlap strongly (greater than 50% of volume of envelope of a is shared with envelope of d2). In each one
of panels B–D, one varicose neurite (a) is rendered in solid green or blue; the postsynaptic dendritiform neurites that actually form synaptic contacts
with the varicose neurite are rendered in solid colors (yellow-brown). Note tight clustering of denritiform neurites around the corresponding varicose
element. (E) Histogram depicting correlation between overlap of pre- and postsynaptic elements (x-axis) and frequency of synaptic contacts (y-axis).
Counted for the VNC microvolume; overall number of presynaptic elements over 1 mm length: 40; postsynaptic elements: 105. For example, only 17%
of the pre- and postsynaptic elements whose envelopes overlap less than 10% actually form synaptic contacts.
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org 13October 2010 | Volume 8 | Issue 10 | e1000502
presynaptic profiles of large diameters. It is reasonable to assume
that the generic neurite properties that we establish here for
different neuropile compartments of the larval central brain and
ventral nerve cord may find close parallels in other insect brains as
well. The computer-aided recording and reconstruction of serial
TEM sections applied in this work makes it feasible to generate
data sets in other species in a reasonable amount of time, which
gives good reason to anticipate that quantitative ultrastructural
data concerning neuropile architecture will be available soon for a
variety of different brains.
Neuropile Architecture in Mammalian Cortex and Fly
Brain: A First Comparison
Statistical analyses of light microscopic preparations (e.g., Golgi
stained preparations) and representative EM sections of mamma-
lian brains yielded estimates for parameters like synapse density,
cable length, and branch point density (e.g., ) that can be
compared to the data we present here for the fly larval brain.
Surprisingly, when considering the enormous difference in overall
size of individual neurons and the brain as a whole, a number of
parameters are very similar. For example, the average diameter of
axon shafts and synapse bearing varicosities, as well as presynaptic
sites themselves, is almost the same in Drosophila brain and
mammalian cortex. As a result, the overall axonal cable length per
neuropile volume is also very similar. One mm3of mouse cortex
contains an estimated 4,100 m of axon; translating this number to
a smaller cube of 10 mm length, more appropriate when dealing
with miniature brains, would yield 4.1 mm of axon. In the
microvolumes analyzed for the larval brain, axonal cable length
ranged from 1.5 mm to 4.0 mm in a cube of 10 mm length. A
conspicuous difference between the mammalian cortex and fly
brain lies in the size of dendrites. In mammals, dendrite shafts
have an average diameter of close to 1 micrometer , and many
dendrites are considerably thicker and less branched. As a result,
dendritic cable length per volume unit appears to be much lower
than axonal cable length in mammalian brain (0.5 versus 4.1 mm
in a 10610610 mm cube), whereas the opposite is true in fly brain.
Thus, when extrapolated to a 10610610 mm volume, the overall
length of varicose/globular neurites totaled 2.9 mm; that of
dendritiform neurites was 5.4 mm.
Another distinguishing characteristic of the Drosophila brain
appears to be the higher density of branch points, in particular for
dendrites. Statistical analyses in mammalian cortex yielded
average distances of 10 mm and higher between branch points
. Measured here for Drosophila, terminal axons had average
branch point intervals of 4 mm (mushroom body calyx), 2.8 mm
(mushroom body, spur), or 7.5 mm (VNC), respectively. The
density of branch points in dendrites appears to be even higher.
Thus, for the VNC cube where thin dendrites could be reliably
reconstructed, we measured an average interbranch point distance
of 4.9 mm.
In light of the higher branch point density, one might also
expect a higher number of synaptic contacts per volume unit in
Drosophila, compared to the mammalian brain. In mammalian
cortex, synaptic density has been estimated at 0.72/mm3.
Synapses are predominantly of the monadic type, where one
presynaptic site contacts a single postsynaptic site. By contrast,
synapses are polyadic in the Drosophila nervous system. For each
presynaptic site, one observes multiple postsynaptic sites. In terms
of total number of synaptic contacts (where one would count a
tetrad with three postsynaptic partners as three contacts), the
Drosophila brain does have a much higher synapse density than the
mammalian brain; when only counting presynaptic sites, numbers
are comparable. Thus, in the material analyzed for this study,
presynapse density falls within a range from 0.8/mm3(VNC) to
about 4/mm3(input region of mushroom body). We expect that for
mammalian brain, additional direct measurements will yield
values of synapse density that may considerably vary between
different neuropile compartments.
The higher density of branches and synaptic contacts in the
Drosophila brain, compared to mammalian brain, is correlated with
a larger degree of ‘‘connectedness’’ between neurites. As shown in
this study, the large majority of neurites contained within a
microvolume of less than 100 mm3are engaged in networks of the
type of dense overlapping regulon or feed forward motifs, which
simply reflects the fact that in average, terminal axons and
dendrites in such a small volume have multiple branches that
engage in synaptic contacts. This is not the case in mammalian
cortex. When considering the low average branch point density
(one every 10 mm; see above), it is unlikely that many neurite
segments included within a 100 mm3volume will have a branch,
and thereby more than one contact. This estimate is confirmed in
a recent microvolume reconstruction of rat hippocampus [18,56].
Here, the only type of connectivity observed consists in the
convergence of multiple axonal segments onto isolated dendritic
segments. However, very few of the (unbranched) axonal segments
give synaptic input to more than one (unbranched) dendritic
segment. As a result, network motifs like the dense overlapping
regulon motif or feed forward motif are not observed within
microvolumes of 5 mm diameter. In other words, the volumes of
mammalian brain that ‘‘contain’’ microcircuits are considerably
larger than in Drosophila: connectivity occupies more space. The
number and size of vertebrate neurons are typically much larger
than in insects. Other fundamental architectural features of
vertebrate brains are the inclusion of neuronal somata into the
neuropile (somata carry many postsynaptic sites), the large
diameter of dendrites, and concomitantly, the absence of polyadic
synapses. One may speculate that one of the prime driving forces
of the evolution of vertebrate brain architecture was the increasing
number of neurons. Consecutively, the average vertebrate neuron
also had to grow individually in axonal and dendritic length, in
order to accommodate the higher number of synapses needed to
connect a given neuron to a certain fraction of the (increasing)
pool of neurons. Finally, as a result of the increase in cell number,
cable length, and synapse number, branches of dendrites and
axons became spaced further apart. Typical ‘‘microcircuits’’ in the
mammalian brain, formed by multiple interconnected elements
providing for stimulus convergence and divergence and inhibito-
ry/excitatory feed forward and feed back loops, will occupy
volumes of (300–1,000 mm)3diameter [7,57,58]. In the Drosophila
brain, these volumes would be two orders of magnitude smaller,
which constitutes a significant advantage when reconstructing
circuitry from densely segmented serial EM sections.
Digital Serial EM Analysis as a Tool for the Genetic and
Cell Biological Analysis of Neuronal Development
The dense segmentation of serially sectioned neuropile plays a
pivotal role for reconstructing microcircuitry. However, the ability
to reconstruct ultrastructural features of neurites in all parts of the
neuropile of a small brain, such as the Drosophila larval brain, will
prove to be of great value for in vivo cell biological and genetic
studies of neuronal development as well. The complex shape and
connectivity of a neuron is a reflection of an intracellular
molecular machinery that places membrane proteins (e.g.,
adhesion molecules, receptors, channels) and cytoskeletal proteins
into the right position, such that pre- and postsynaptic sites,
branch points, and specific connections with targets are formed in
the right pattern. The analysis of these developmental phenomena
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org 14October 2010 | Volume 8 | Issue 10 | e1000502
is in its infancy. We know of many proteins that are differentially
expressed in specific membrane domains [59,60] and that the
directional protein transport in axons and dendrites is controlled
by different mechanisms. However, the exact mechanisms of
targeted protein expression are unknown. Models such as
Drosophila offer the opportunity of a genetic approach, that is,
study the phenotype following genetic manipulation. This
approach has been very successful to elucidate the development
of several types of (peripheral) sensory neurons and motor neurons
[61–63]. However, up until now, virtually no mutant phenotypes
have been established for central brain neurons or circuits on the
ultrastructural level, simply because the basic parameters of wild
type neuronal ultrastructure were not available, and the necessary
technology, serial EM, was too time-consuming. With the
computer-aided reconstruction of EM stacks, such an analysis is
now within reach. Thus, once parameters of neurite size,
branching, and connectivity have been established for a number
of compartments, it will be possible to generate stacks of EM
sections that contain specific brain compartments (such as the
calyx or lobes of the mushroom body) of genetically manipulated
animals, and carry out detailed, quantitative comparisons with the
wild-type. Such stacks (for the early larval brain) would contain as
little as 100 contiguous sections; their preparation, image
capturing, and analysis (when following the microvolume
approach) may require only a matter of weeks. It is therefore
realistic to generate EM stacks numerous specimens’ brains of
larvae carrying specific mutations, to then establish changes in
basic neurite parameters, such as diameters, branch and synapse
density, and synapse architecture.
Approaches to the Dense Reconstruction of Neuropile
Serial TEM is the classical approach for the reconstruction of
microcircuitry. In the days before digital photography and
computer-assisted image processing, this approach was extremely
labor intensive and was therefore utilized mostly for small parts of
individual neurons (‘‘sparse segmentation’’). The only exception
was the reconstruction of the C. elegans central nervous system
[64,65]. We predict that given the speed of imaging that is now
possible (and will certainly further increase), serial EM will
experience a renaissance as an approach for the reconstruction of
Two recent technological developments have reduced the
difficulty of large-scale serial section electron microscopy and
improved its reliability. Serial block-face scanning electron
microscopy  has proven useful in imaging relatively large
volumes of neural tissue at an isotropic resolution of about 20 nm/
pixel. The smaller dimensions of Drosophila neural tissue compo-
nents, compared to the vertebrate equivalents, require a resolution
of at least 8 nm/pixel (ideally 4 nm/pixel) for the reconstruction of
small terminal dendrites, which is necessary for the conclusive
elucidation of synaptic partners. The second novel technique,
focused ion beam (FIB) milling combined with block-face scanning
electron microscopy , delivers up to 5 nm/pixel resolution.
While FIB delivers images with the necessary resolution for the
reconstruction of Drosophila neuropile, its imaging field of view
is currently limited in practice to a window of 20620 microns
(Graham Knott, personal communication), which does not
enclose a transverse section of the entire nerve cord neuropile
of Drosophila larva. Traditional serial section transmission electron
microscopy, as employed in this article, delivers the required high
imaging resolution on the plane (4 nm/pixel or better) but
reduced resolution in the z-axis (50 nm, the thickness of the
section). However, our reconstructions indicate that, given
sufficient resolution in the XY plane, the 50 nm/pixel resolution
of the z-axis does not prevent the full reconstruction of even the
smallest terminal dendrites, measuring only about 60 nm in
diameter. We anticipate that for the immediate future, serial
TEM, BF/SEM, and FIB/SEM will coexist as more or less
equally valuable techniques for the electron microscopic recon-
struction of microcircuitry.
The generation of large EM image data sets has made
imperative the development of novel specialized software for its
processing, visualization, and analysis . The sheer size of the
data sets has prompted the development of novel algorithms for
the automatic segmentation and reconstruction of neural arbors
. Thus, given the current conditions, to manually segment an
entire L1 Drosophila brain would take one person in the order of
50 years, which means that the development of tools for automatic
segmentation is of high priority. It should be noted that the 50-
year projection does not take into account the fact that neuropile
ultrastructure, to some extent, is most likely modular. As a result,
the network diagram extracted from a given microvolume can be
extrapolated to neighboring volumes, as long as they fall within the
same compartment. In other words, the hope and anticipation (in
particular in regard to ‘‘big brains’’ of vertebrates) is that one does
not dense-segment every single ‘‘voxel’’ of a given compartment
but focus on a certain set of samples that are (manually or
automatically) densely segmented; then, using algorithms that
need to take into account data from many microvolumes, circuitry
in the samples can be extrapolated to a compartment as a whole.
Until recently, two software packages were primarily used for
visualization and analysis of TEM serial sections: IMOD  and
Reconstruct . IMOD is the current gold standard in EM
software for image composition and processing. Reconstruct
provides an efficient GUI for manual and semiautomatic image
stitching and is particularly noted for its manual image
segmentation toolkit. Neither of these programs handles data sets
that are considerably larger than RAM and have limited support
for large-scale image registration with nonlinear transformations.
The ir-tools and associated visualization applications , while
providing effective image registration and a comprehensive image
analysis toolkit, depend on RAM and lack ease of customization
for highly specialized tasks. We have found that TrakEM2, while
not yet fully independent of RAM, provides the means to register
hundreds of thousands of images, overcoming numerous limita-
tions imposed by computer hardware. TrakEM2 eases the
concatenation of image transformations, including polynomial
models for lens deformation correction . In contrast to the ir-
tools, TrakEM2 operates on image tiles that correspond to the
original acquired images and not on stitched large images.
Precomputed image pyramids for each tile enable enhanced
performance. We highlight the robustness of the image registration
library associated with TrakEM2, and the ability to manually
correct errors when these inevitably occur, an ability facilitated by
the tile-oriented approach. Finally, TrakEM2 is a (large)
component of Fiji , an ImageJ-based image processing
environment in active development and with thousands of image
analysis plugins readily available. We believe that TrakEM2
represents a useful addition to existing software packages that can
handle special tasks, in particular the segmentation and subse-
quent analysis of large data sets with high numbers of individual
elements. In summary, TrakEM2 improves over existing software
packages in being less dependent on scarce computer resources
and by bundling numerous image segmentation and analysis tools
within a unique graphical interface. TrakEM2 acknowledges that
any automatic procedure (such as image registration and image
segmentation) will eventually fail partially or fully and will require
manual correction by a human operator. The combination of both
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org 15 October 2010 | Volume 8 | Issue 10 | e1000502
manual and automatic procedures for neuronal reconstruction
makes TrakEM2 a practical application for the reconstruction of
large volumes of brain neuropiles.
and axiform elements over 1 mm length segmented from
VNC microvolume. All panels in lateral view; anterior to the
left, dorsal up. Rows 1–8 show globular/varicose elements; red
dots represent presynaptic sites. Rows 9–13 show axiform
elements. Scale bar: 1 mm.
Found at: doi:10.1371/journal.pbio.1000502.s001 (6.70 MB TIF)
Digital 3D models of all varicose/globular
ments over 1 mm length segmented from VNC micro-
volume. All panels in lateral view; anterior to the left, dorsal up.
Vertical elements of last row  formed a bundle of relatively
large diameter fibers that grazed lateral surface of VNC
microvolume; the basis for classifying them as dendritiform was
Digital 3D models of all dendritiform ele-
that they possessed short segments or branches approaching the
neuropile. Scale bar: 1 mm.
Found at: doi:10.1371/journal.pbio.1000502.s002 (9.61 MB TIF)
The authors are very grateful to Richard Fetter for generating the VNC
serial section set, to Julie Simpson for making the stay of A.C. as a visiting
scientist at Janelia Farm Research Campus possible, and to Wayne
Pereanu for assistance in imaging; all of these scientists are at the HHMI
Janelia Farm Research Campus. We thank Rita Bopp and John C.
Anderson at the Institute of Neuroinformatics for assistance in generating
the brain serial section set and German Koestinger and Nuno da Costa for
feedback on TrakEM2.
The author(s) have made the following declarations about their
contributions: Conceived and designed the experiments: AC VH.
Performed the experiments: AC. Analyzed the data: AC SS PT.
Contributed reagents/materials/analysis tools: SS SP BS AC JP PT.
Wrote the paper: AC VH.
1. Hartenstein V, Spindler S, Pereanu W, Fung S (2008) The development of the
Drosophila larval brain. Adv Exp Med Biol 628: 1–31.
2. Hartenstein V, Cardona A, Pereanu W, Younossi-Hartenstein A (2008)
Modeling the developing Drosophila brain: rationale, technique and application.
BioScience 58: 823–836.
3. Cropley VL, Fujita M, Innis RB, Nathan PJ (2006) Molecular imaging of the
dopaminergic system and its association with human cognitive function. Biol
Psychiatry 59: 898–907.
4. May A (2007) Neuroimaging: visualising the brain in pain. Neurol Sci 28 Suppl
5. Shibasaki H (2008) Human brain mapping: hemodynamic response and
electrophysiology. Clin Neurophysiol 119: 731–743.
6. Douglas RJ, Martin KA (2004) Neuronal circuits of the neocortex. Ann Rev
Neurosci 27: 419–451.
7. Silberberg G, Grillner S, LeBeau FE, Maex R, Markram H (2005) Synaptic
pathways in neural microcircuits. Trends Neurosci 28: 541–551.
8. Schubert D, Ko ¨tter R, Staiger JF (2007) Mapping functional connectivity in
barrel-related columns reveals layer- and cell type-specific microcircuits. Brain
Struct Funct 212: 107–119.
9. Strowbridge BW (2009) Role of cortical feedback in regulating inhibitory
microcircuits. Ann N Y Acad Sci 1170: 270–274.
10. Braitenberg V, Schu ¨z A (1998) Cortex: statistics and geometry of neuronal
connectivity (2nd edition). SpringerBerlin: Heidelberg.
11. Watson AH, Burrows M (1983) The morphology, ultrastructure, and
distribution of synapses on an intersegmental interneuron of the locust.
J Comp Neurol 214: 154–169.
12. Meinertzhagen IA, O’Neil SD (1991) Synaptic organization of columnar
elements in the lamina of the wild type in Drosophila melanogaster. J Comp Neurol
13. Yasuyama K, Meinertzhagen IA, Schurmann FW (2002) Synaptic organization
of the mushroom body calyx in Drosophila melanogaster. J Comp Neurol 445:
14. Yasuyama K, Meinertzhagen IA, Schurmann FW (2003) Synaptic connections
of cholinergic antennal lobe relay neurons innervating the lateral horn neuropile
in the brain of Drosophila melanogaster. J Comp Neurol 466: 299–315.
15. Anderson JR, Jones BW, Yang JH, Shaw MV, Watt CB, et al. (2009) A
computational framework for ultrastructural mapping of neural circuitry. PLoS
Biology 7(3): e1000074. doi:10.1371/journal.pbio.1000074.
16. Fiala JC (2005) Reconstruct: a free editor for serial section microscopy. J Microsc
17. Kremer JR, Mastronarde DN, McIntosh JR (1996) Computer visualization of
three-dimensional image data using IMOD. J Struct Biol 116: 71–76.
18. Mishchenko Y (2009) Automation of 3D reconstruction of neural tissue from
large volume of conventional serial section transmission electron micrographs.
J Neurosci Meth 176: 276–289.
19. Cauchi RJ, van den Heuvel M (2006) The fly as a model for neurodegenerative
diseases: is it worth the jump? Neurodegenerative Disorders 3: 338–356.
20. Dodson MW, Guo M (2007) Pink1, Parkin, DJ-1 and mitochondrial dysfunction
in Parkinson’s disease. Curr Opin Neurobiol 17: 331–337.
21. Doronkin S, Reiter LT (2008) Drosophila orthologues to human disease genes: an
update on progress. Prog Nucleic Acid Res Mol Biol 82: 1–32.
22. Song J, Tanouye MA (2008) From bench to drug: human seizure modeling using
Drosophila. Prog Neurobiol 84: 182–191.
23. Brand AH, Perrimon N (1993) Targeted gene expression as a means of altering
cell fates and generating dominant phenotypes. Development 118: 401–415.
24. Truman JW, Bate M (1988) Spatial and temporal patterns of neurogenesis in the
central nervous system of Drosophila melanogaster. Dev Biol 125: 145–157.
25. Larsen C, Shy D, Spindler S, Fung S, Younossi-Hartenstein A, et al. (2009)
Patterns of growth, axonal extension and axonal arborization of neuronal
lineages in the developing Drosophila brain. Dev Biol 335: 289–304.
26. Saalfeld S, Cardona A, Hartenstein V, Tomanca ´k P (2009) CATMAID:
collaborative annotation toolkit for massive amounts of image data. Bioinfor-
matics 25: 1984–1986.
27. Saalfeld S, Cardona A, Hartenstein V, Tomancak P (2010) As-rigid-as-possible
2d-mosaicking and 3d-registration of large serial section TEM data sets.
Bioinformatics (in press).
28. Johnson EL, Fetter RD, Davis GW (2009) Negative regulation of active zone
assembly by a newly identified SR protein kinase. PLoS Biol 7: e1000193.
29. Hortsch M, Patel NH, Bieber AJ, Traquina ZR, Goodman CS (1990) Drosophila
neurotactin, a surface glycoprotein with homology to serine esterases, is
dynamically expressed during embryogenesis. Development 110: 1327–1340.
30. Suloway C, Pulokas J, Fellmann D, Cheng A, Guerra F, et al. (2005) Automated
molecular microscopy: the new Leginon system. J Struct Biol 151: 41–60.
31. Lowe DG (2004) Distinctive image features from scale-invariant keypoints.
International J Computer Vision 60: 91–110.
32. Kaynig V, Fischer B, Buhmann JB (2008) Probabilistic image registration and
anomaly detection by nonlinear warping, CVPR, 1-8, 2008 IEEE Conference
on Computer Vision and Pattern Recognition.
33. Schaefer S, McPhail T, Warren J (2006) Image deformation using moving least
squares. ACM Transactions on Graphics 25: 533–540.
34. Williams L (1983) Pyramidal parametrics. Proceedings of the 10th annual
conference on computer graphics and interactive techniques, p.1-11, July 25-29,
1983, Detroit, Michigan, United States [doi.10.1145/800059.801126].
35. Schmid B, Schindelin J, Cardona A, Longair M, Heisenberg M (2010) A high-
level 3D visualization API for Java and ImageJ. BMC Bioinformatics 11: 274.
36. Nassif C, Noveen A, Hartenstein V (2003) Early development of the Drosophila
brain III. The pattern of neuropile founder tracts during the larval period.
J Comp Neurol 455: 417–434.
37. Dumstrei K, Wang F, Nassif C, Hartenstein V (2003) Early development of the
Drosophila brain. V. Pattern of postembryonic neuronal lineages expressing Shg/
DE-cadherin. J Comp Neurol 455: 451–462.
38. Pereanu W, Hartenstein V (2006) Neural lineages of the Drosophila brain: a 3D
digital atlas of the pattern of lineage location and projection at the late larval
stage. J Neurosci 26: 5534–5555.
39. Pereanu W, Kumar A, Jennett A, Reichert H, Younossi-Hartenstein A, et al.
(2010) A development-based compartmentalization of the Drosophila central
brain. J Comp Neur 518: 2996–3023.
40. Younossi-Hartenstein A, Shy D, Hartenstein V (2006) The embryonic formation
of the Drosophila brain neuropile. J Comp Neur 497: 981–998.
41. Younossi-Hartenstein A, Salvaterra P, Hartenstein V (2003) Early development
of the Drosophila brain IV. Larval neuropile compartments defined by glial septa.
J Comp Neurol 455: 435–450.
42. Park JH, Helfrich-Fo ¨rster C, Lee G, Liu L, Rosbash M, et al. (2000) Differential
regulation of circadian pacemaker output by separate clock genes in Drosophila.
Proc Natl Acad Sci U S A 97: 3608–3613.
43. Na ¨ssel DR (2002) Neuropeptides in the nervous system of Drosophila and other
insects: multiple roles as neuromodulators and neurohormones. Prog Neurobiol
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org16October 2010 | Volume 8 | Issue 10 | e1000502
44. Feeney CJ, Karunanithi S, Pearce J, Govind CK, Atwood HL (1998) Motor Download full-text
nerve terminals on abdominal muscles in larval flesh flies, Sarcophaga bullata:
comparisons with Drosophila. J Comp Neurol 402: 197–209.
45. Kittel RJ, Wichmann C, Rasse TM, Fouquet W, Schmidt M, et al. (2006)
Bruchpilot promotes active zone assembly, Ca2+ channel clustering, and vesicle
release. Science 312: 1051–1054.
46. Ramaekers A, Magnenat E, Marin EC, Gendre N, Jefferis GS, et al. (2005)
Glomerular maps without cellular redundancy at successive levels of the
Drosophila larval olfactory circuit. Curr Biol 15: 982–992.
47. Fahrbach SE (2006) Structure of the mushroom bodies of the insect brain. Annu
Rev Entomol 51: 209–232.
48. Reigl M, Alon U, Chklovskii DB (2004) Search for computational modules in the
C. elegans brain. BMC Biol 2: 2–25.
49. Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev
Genet 8: 450–461.
50. Peters BH, Ro ¨mer H, Marquart V (1986) Spatial segregation of synaptic inputs
and outputs in a locust auditory interneurone. J Comp Neurol 254: 34–50.
51. Watson AH, Pflu ¨ger HJ (1989) Regional specialisation for synaptic input and
output on a locust intersegmental interneurone with multiple spike-initiating
zones. J Comp Neurol 279: 515–527.
52. Distler PG, Boeckh J (1997) Synaptic connections between identified neuron
types in the antennal lobe glomeruli of the cockroach, Periplaneta americana: I.
Uniglomerular projection neurons. J Comp Neurol 378: 307–319.
53. Distler PG, Gruber C, Boeckh J (1998) Synaptic connections between GABA-
immunoreactive neurons and uniglomerular projection neurons within the
antennal lobe of the cockroach, Periplaneta americana. Synapse 29: 1–13.
54. Watson AH, Schu ¨rmann FW (2002) Synaptic structure, distribution, and
circuitry in the central nervous system of the locust and related insects. Micr Res
Tech 56: 210–226.
55. Leiss F, Groh C, Butcher NJ, Meinertzhagen IA, Tavosanis G (2009) Synaptic
organization in the adult Drosophila mushroom body calyx. J Comp Neurol 517:
56. MishchenkoY,Spacek J,Mendenhall J,HarrisKM,ChklovskiiDB. Reconstruction
of hippocampal CA1 neuropil at nanometer resolution reveals disordered
packing of processes and dependence of synaptic connectivity on local environ-
ment and dendrite caliber. Preprint.
57. Krimer LS, Goldman-Rakic PS (2001) Prefrontal microcircuits: membrane
properties and excitatory input of local, medium, and wide arbor interneurons.
J Neurosci 21: 3788–3796.
58. Kalisman N, Silberberg G, Markram H (2005) The neocortical microcircuit as a
tabula rasa. Proc Natl Acad Sci U S A 102: 880–885.
59. Rolls MM, Satoh D, Clyne PJ, Henner AL, Uemura T, et al. (2007) Polarity and
intracellular compartmentalization of Drosophila neurons. Neural Dev 30; 2: 7.
60. Lasiecka ZM, Yap CC, Vakulenko M, Winckler B (2009) Compartmentalizing
the neuronal plasma membrane from axon initial segments to synapses. Int Rev
Cell Mol Biol 272: 303–389.
61. Ye B, Zhang Y, Song W, Younger SH, Jan LY, et al. (2007) Growing dendrites
and axons differ in their reliance on the secretory pathway. Cell 130: 717–729.
62. Stone MC, Roegiers F, Rolls MM (2008) Microtubules have opposite orientation
in axons and dendrites of Drosophila neurons. Mol Biol Cell 19: 4122–4129.
63. Zheng Y, Wildonger J, Ye B, Zhang Y, Kita A, et al. (2008) Dynein is required
for polarized dendritic transport and uniform microtubule orientation in axons.
Nat Cell Biol 10: 1172–1180.
64. White JG, Southgate E, Thomson JN, Brenner S (1986) The structure of the
nervous system of the nematode Caenorhabditis elegans. Phil Trans R Soc Lond B
65. Chen BL, Hall DH, Chklovskii DB (2006) Wiring optimization can relate
neuronal structure and function. Proc Natl Acad Sci U S A 103: 4723–4728.
66. Denk W, Horstmann H (2004) Serial block-face scanning electron microscopy to
reconstruct three-dimensional tissue nanostructure. PLoS Biol 2: e329.
67. Knott G, Marchman H, Wall D, Lich B (2008) Serial section scanning electron
microscopy of adult brain tissue using focused ion beam milling. J Neurosci 28:
68. Schindelin J (2008) ‘‘Fiji is just ImageJ (batteries included). ’’2ndImageJ User and
Developer Conference. Luxemburg.
Serial TEM Analysis of Drosophila Brain Circuitry
PLoS Biology | www.plosbiology.org17 October 2010 | Volume 8 | Issue 10 | e1000502