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.

Previously, we showed that the mouse LIM-domain only 4 (Lmo4) gene, which encodes a protein containing two zinc-finger LIM domains that interact with various DNA-binding transcription factors, attenuates behavioral sensitivity to repeated cocaine administration. Here we show that transcription of anaplastic lymphoma kinase (Alk) is repressed by LMO4 in the striatum and that Alk promotes the development of cocaine sensitization and conditioned place preference, a measure of cocaine reward. Since LMO4 is known to interact with estrogen receptor α (ERα) at the promoters of target genes, we investigated whether Alk expression might be controlled by a similar mechanism. We found that LMO4 and ERα are associated with the Alk promoter by chromatin immunoprecipitation and that Alk is an estrogen-responsive gene in the striatum. Moreover, we show that ERα knock-out mice exhibit enhanced cocaine sensitization and conditioned place preference and an increase in Alk expression in the nucleus accumbens. These data define a novel regulatory network involved in behavioral responses to cocaine. Interestingly, sex differences in several behavioral responses to cocaine in humans and rodents have been described, and estrogen is thought to mediate some of these differences. Our data suggest that estrogen regulation of Alk may be one mechanism responsible for sexually dimorphic responses to cocaine.

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.

The Wiener filter is a standard means of optimizing the signal in sums of aligned, noisy images obtained by electron cryo-microscopy (cryo-EM). However, estimation of the resolution-dependent ("spectral") signal-to-noise ratio (SSNR) from the input data has remained problematic, and error reduction due to specific application of the SSNR term within a Wiener filter has not been reported. Here we describe an adjustment to the Wiener filter for optimal summation of images of isolated particles surrounded by large regions of featureless background, as is typically the case in single-particle cryo-EM applications. We show that the density within the particle area can be optimized, in the least-squares sense, by scaling the SSNR term found in the conventional Wiener filter by a factor that reflects the fraction of the image field occupied by the particle. We also give related expressions that allow the SSNR to be computed for application in this new filter, by incorporating a masking step into a Fourier Ring Correlation (FRC), a standard resolution measure. Furthermore, we show that this masked FRC estimation scheme substantially improves on the accuracy of conventional SSNR estimation methods. We demonstrate the validity of our new approach in numeric tests with simulated data corresponding to realistic cryo-EM imaging conditions. This variation of the Wiener filter and accompanying derivation should prove useful for a variety of single-particle cryo-EM applications, including 3D reconstruction.

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.

Modern applications in the life sciences are frequently based on in vivo imaging of biological specimens, a domain for which light microscopy approaches are typically best suited. Often, quantitative information must be obtained from large multicellular organisms at the cellular or even subcellular level and with a good temporal resolution. However, this usually requires a combination of conflicting features: high imaging speed, low photobleaching and low phototoxicity in the specimen, good three-dimensional (3D) resolution, an excellent signal-to-noise ratio, and multiple-view imaging capability. The latter feature refers to the capability of recording a specimen along multiple directions, which is crucial for the imaging of large specimens with strong light-scattering or light-absorbing tissue properties. An imaging technique that fulfills these requirements is essential for many key applications: For example, studying fast cellular processes over long periods of time, imaging entire embryos throughout development, or reconstructing the formation of morphological defects in mutants. Here, we discuss digital scanned laser light sheet fluorescence microscopy (DSLM) as a novel tool for quantitative in vivo imaging in the post-genomic era and show how this emerging technique relates to the currently most widely applied 3D microscopy techniques in biology: confocal fluorescence microscopy and two-photon microscopy.

Embryonic development is one of the most complex processes encountered in biology. In vertebrates and higher invertebrates, a single cell transforms into a fully functional organism comprising several tens of thousands of cells, arranged in tissues and organs that perform impressive tasks. In vivo observation of this biological process at high spatiotemporal resolution and over long periods of time is crucial for quantitative developmental biology. Importantly, such recordings must be realized without compromising the physiological development of the specimen. In digital scanned laser light-sheet fluorescence microscopy (DSLM), a specimen is rapidly scanned with a thin sheet of light while fluorescence is recorded perpendicular to the axis of illumination with a camera. Combining light-sheet technology and fast laser scanning, DSLM delivers quantitative data for entire embryos at high spatiotemporal resolution. Compared with confocal and two-photon fluorescence microscopy, DSLM exposes the embryo to at least three orders of magnitude less light energy, but still provides up to 50 times faster imaging speeds and a 10–100-fold higher signal-to-noise ratio. By using automated image processing algorithms, DSLM images of embryogenesis can be converted into a digital representation. These digital embryos permit following cells as a function of time, revealing cell fate as well as cell origin. By means of such analyses, developmental building plans of tissues and organs can be determined in a whole-embryo context. This article presents a sample preparation and imaging protocol for studying the development of whole zebrafish and Drosophila embryos using DSLM.

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.