Data acquisition of cryo-electron tomography (CET) samples described in previous chapters involves relatively imprecise mechanical motions: the tilt series has shifts, rotations, and several other distortions between projections. Alignment is the procedure of correcting for these effects in each image and requires the estimation of a projection model that describes how points from the sample in three-dimensions are projected to generate two-dimensional images. This estimation is enabled by finding corresponding common features between images. This chapter reviews several software packages that perform alignment and reconstruction tasks completely automatically (or with minimal user intervention) in two main scenarios: using gold fiducial markers as high contrast features or using relevant biological structures present in the image (marker-free). In particular, we emphasize the key decision points in the process that users should focus on in order to obtain high-resolution reconstructions.

The golden age of DNA: We describe a strategy for engineering bifunctional proteins that simultaneously associate with metals and DNA to create self-assembled nanostructures. A DNA binding protein engineered with a gold binding peptide arranges colloidal gold particles along a DNA guide by virtue of its introduced peptide motif. These self-assembled complexes represent a step toward constructing nanoarchitectures with potential in nanoelectronic and photonic devices.

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.

Mitochondria are organelles that have been primarily known as the powerhouse of the cell. However, recent advances in the field have revealed that mitochondria are also involved in many other cellular activities like lipid modifications, redox balance, calcium balance, and even controlled cell death. These multifunctional organelles are motile and highly dynamic in shapes and forms; the dynamism is brought about by the mitochondria's ability to undergo fission and fusion with each other. Therefore, it is very important to be able to image mitochondrial shape changes to relate to the variety of cellular functions these organelles have to accomplish. The protocols described here will enable researchers to perform steady-state and time-lapse imaging of mitochondria in live cells by using confocal microscopy. High-resolution three-dimensional imaging of mitochondria will not only be helpful in understanding mitochondrial structure in detail but it also could be used to analyze their structural relationships with other organelles in the cell. FRAP (fluorescence recovery after photobleaching) studies can be performed to understand mitochondrial dynamics or dynamics of any mitochondrial molecule within the organelle. The microirradiation assay can be performed to study functional continuity between mitochondria. A protocol for measuring mitochondrial potential has also been included in this unit. In conclusion, the protocols described here will aid the understanding of mitochondrial structure-function relationship.

The surface layers (S layers) of those bacteria and archaea that elaborate these crystalline structures have been studied for 40 years. However, most structural analysis has been based on electron microscopy of negatively stained S-layer fragments separated from cells, which can introduce staining artifacts and allow rearrangement of structures prone to self-assemble. We present a quantitative analysis of the structure and organization of the S layer on intact growing cells of the Gram-negative bacterium Caulobacter crescentus using cryo-electron tomography (CET) and statistical image processing. Instead of the expected long-range order, we observed different regions with hexagonally organized subunits exhibiting short-range order and a broad distribution of periodicities. Also, areas of stacked double layers were found, and these increased in extent when the S-layer protein (RsaA) expression level was elevated by addition of multiple rsaA copies. Finally, we combined high-resolution amino acid residue-specific Nanogold labeling and subtomogram averaging of CET volumes to improve our understanding of the correlation between the linear protein sequence and the structure at the 2-nm level of resolution that is presently available. The results support the view that the U-shaped RsaA monomer predicted from negative-stain tomography proceeds from the N terminus at one vertex, corresponding to the axis of 3-fold symmetry, to the C terminus at the opposite vertex, which forms the prominent 6-fold symmetry axis. Such information will help future efforts to analyze subunit interactions and guide selection of internal sites for display of heterologous protein segments.

Vocal learning in songbirds requires an anatomically discrete and functionally dedicated circuit called the anterior forebrain pathway (AFP). The AFP is homologous to cortico-basal ganglia-thalamo-cortical loops in mammals. The basal ganglia portion of this pathway, Area X, shares many features characteristic of the mammalian striatum and pallidum, including cell types and connectivity. The AFP also deviates from mammalian basal ganglia circuits in fundamental ways. In addition, the microcircuitry, role of neuromodulators, and function of Area X are still unclear. Elucidating the mechanisms by which both mammalian-like and unique features of the AFP contribute to vocal learning may help lead to a broad understanding of the sensorimotor functions of basal ganglia circuits.

To compare appetitive and aversive visual memories of the fruit fly Drosophila melanogaster, we developed a new paradigm for classical conditioning. Adult flies are trained en masse to differentially associate one of two visual conditioned stimuli (CS) (blue and green light as CS) with an appetitive or aversive chemical substance (unconditioned stimulus or US). In a test phase, flies are given a choice between the paired and the unpaired visual stimuli. Associative memory is measured based on altered visual preference in the test. If a group of flies has, for example, received a sugar reward with green light in the training, they show a significantly higher preference for the green stimulus during the test than another group of flies having received the same reward with blue light. We demonstrate critical parameters for the formation of visual appetitive memory, such as training repetition, order of reinforcement, starvation, and individual conditioning. Furthermore, we show that formic acid can act as an aversive chemical reinforcer, yielding weak, yet significant, aversive memory. These results provide a basis for future investigations into the cellular and molecular mechanisms underlying visual memory and perception in Drosophila.

Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections.

Axial collision induced dissociation (CID) and high-pressure resonance CID were implemented and compared with normal low-pressure resonance CID in a miniature ion trap mass spectrometer to obtain more complete fragmentation spectra. Axial CID was realized simply by applying a potential to the discontinuous atmospheric pressure interface (DAPI) capillary without performing parent ion isolation before dissociation. High-pressure resonance CID employed a double-introduction pulse scan function, by means of which precursor ions isolated at low-pressure (<10(-3) torr) were dissociated at high-pressure (0.1 torr-1 torr) with higher excitation energy, so that tandem MS of isolated precursor ions was achieved and extensive fragmentation was obtained. A simple peptide (Leu-enkephalin) and dye molecule (rhodamine B) ionized by ESI were used to investigate both methods and compare them with normal low-pressure resonance CID.

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.