Research in the fruit fly Drosophila melanogaster has led to insights in neural development, axon guidance, ion channel function, synaptic transmission, learning and memory, diurnal rhythmicity, and neural disease that have had broad implications for neuroscience. Drosophila is currently the eukaryotic model organism that permits the most sophisticated in vivo manipulations to address the function of neurons and neuronally expressed genes. Here, we summarize many of the techniques that help assess the role of specific neurons by labeling, removing, or altering their activity. We also survey genetic manipulations to identify and characterize neural genes by mutation, overexpression, and protein labeling. Here, we attempt to acquaint the reader with available options and contexts to apply these methods.

Electron crystallography is a powerful technique for the study of membrane protein structure and function in the lipid environment. When well-ordered two-dimensional crystals are obtained the structure of both protein and lipid can be determined and lipid-protein interactions analyzed. Protons and ionic charges can be visualized by electron crystallography and the protein of interest can be captured for structural analysis in a variety of physiologically distinct states. This review highlights the strengths of electron crystallography and the momentum that is building up in automation and the development of high throughput tools and methods for structural and functional analysis of membrane proteins by electron crystallography.

Olfactory systems encode odours by which neurons respond and by when they respond. In mammals, every sniff evokes a precise, odour-specific sequence of activity across olfactory neurons. Likewise, in a variety of neural systems, ranging from sensory periphery to cognitive centres, neuronal activity is timed relative to sampling behaviour and/or internally generated oscillations. As in these neural systems, relative timing of activity may represent information in the olfactory system. However, there is no evidence that mammalian olfactory systems read such cues. To test whether mice perceive the timing of olfactory activation relative to the sniff cycle (’sniff phase’), we used optogenetics in gene-targeted mice to generate spatially constant, temporally controllable olfactory input. Here we show that mice can behaviourally report the sniff phase of optogenetically driven activation of olfactory sensory neurons. Furthermore, mice can discriminate between light-evoked inputs that are shifted in the sniff cycle by as little as 10 milliseconds, which is similar to the temporal precision of olfactory bulb odour responses. Electrophysiological recordings in the olfactory bulb of awake mice show that individual cells encode the timing of photoactivation in relation to the sniff in both the timing and the amplitude of their responses. Our work provides evidence that the mammalian olfactory system can read temporal patterns, and suggests that timing of activity relative to sampling behaviour is a potent cue that may enable accurate olfactory percepts to form quickly.

In the rodent vibrissal system, active sensation and sensorimotor integration are mediated in part by connections between barrel cortex and vibrissal motor cortex. Little is known about how these structures interact at the level of neurons. We used Channelrhodopsin-2 (ChR2) expression, combined with anterograde and retrograde labeling, to map connections between barrel cortex and pyramidal neurons in mouse motor cortex. Barrel cortex axons preferentially targeted upper layer (L2/3, L5A) neurons in motor cortex; input to neurons projecting back to barrel cortex was particularly strong. Barrel cortex input to deeper layers (L5B, L6) of motor cortex, including neurons projecting to the brainstem, was weak, despite pronounced geometric overlap of dendrites with axons from barrel cortex. Neurons in different layers received barrel cortex input within stereotyped dendritic domains. The cortico-cortical neurons in superficial layers of motor cortex thus couple motor and sensory signals and might mediate sensorimotor integration and motor learning.

Mitral/tufted cells of the olfactory bulb receive odorant information from receptor neurons and transmit this information to the cortex. Studies in awake behaving animals have found that sustained responses of mitral cells to odorants are rare, suggesting sparse combinatorial representation of the odorants. Careful alignment of mitral cell firing with the phase of the respiration cycle revealed brief transient activity in the larger population of mitral cells, which respond to odorants during a small fraction of the respiration cycle. Responses of these cells are therefore temporally sparse. Here, we propose a mathematical model for the olfactory bulb network that can reproduce both combinatorially and temporally sparse mitral cell codes. We argue that sparse codes emerge as a result of the balance between mitral cells’ excitatory inputs and inhibition provided by the granule cells. Our model suggests functional significance for the dendrodendritic synapses mediating interactions between mitral and granule cells.

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of their functioning. We have developed a novel method capable of automatically tracing neurons in three-dimensional microscopy data. In contrast to template-based methods, the proposed approach makes no assumptions about the shape or appearance of neurite structure. Instead, an efficient seeding approach is applied to capture complex neuronal structures and the tracing problem is solved by computing the optimal reconstruction with a weighted graph. The optimality is determined by the cost function designed for the path between each pair of seeds and by topological constraints defining the component interrelations and completeness. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient and has been validated using different types of microscopy data sets including Drosophila’s projection neurons and fly neurons with presynaptic sites. In all cases, the approach yielded promising results.

Research on the biology of addiction has advanced significantly over the last 50 years expanding our understanding of the brain mechanisms underlying reward, reinforcement and craving. Novel experimental approaches and techniques have provided an ever increasing armory of tools to dissect behavioral processes, neural networks and molecular mechanisms. The ultimate goal is to reintegrate this knowledge into a coherent, mechanistic framework of addiction to help identify new treatment. This can be greatly facilitated by using tools that allow, with great spatial and temporal specificity, to link molecular changes with altered activation of neural circuits and behavior. Such specificity can now be achieved by using optogenetic tools. Our review describes the general principles of optogenetics and its use to understand the links between neural activity and behavior. We also provide an overview of recent studies using optogenetic tools in addiction and consider some outstanding questions of addiction research that are particularly amenable for optogenetic approaches.