We describe a technique for imaging single mRNAs in living cells based on fluorescent protein (FP) complementation. We employ the high affinity interaction between the bacterial phage MS2/PP7 coat proteins and their respective RNA binding motifs as an RNA scaffold to bring two halves of a split-FP together to image single reporter mRNAs without background fluorescence.

When a perturbation is applied in a sensorimotor transformation task, subjects can adapt and maintain performance by either relying on sensory feedback, or, in the absence of such feedback, on information provided by rewards. For example, in a classical rotation task where movement endpoints must be rotated to reach a fixed target, human subjects can successfully adapt their reaching movements solely on the basis of binary rewards, although this proves much more difficult than with visual feedback. Here, we investigate such a reward-driven sensorimotor adaptation process in a minimal computational model of the task. The key assumption of the model is that synaptic plasticity is gated by the reward. We study how the learning dynamics depend on the target size, the movement variability, the rotation angle and the number of targets. We show that when the movement is perturbed for multiple targets, the adaptation process for the different targets can interfere destructively or constructively depending on the similarities between the sensory stimuli (the targets) and the overlap in their neuronal representations. Destructive interferences can result in a drastic slowdown of the adaptation. As a result of interference, the time to adapt varies non-linearly with the number of targets. Our analysis shows that these interferences are weaker if the reward varies smoothly with the subject's performance instead of being binary. We demonstrate how shaping the reward or shaping the task can accelerate the adaptation dramatically by reducing the destructive interferences. We argue that experimentally investigating the dynamics of reward-driven sensorimotor adaptation for more than one sensory stimulus can shed light on the underlying learning rules.

Perceptual decisions involve distributed cortical activity. Does information flow sequentially from one cortical area to another, or do networks of interconnected areas contribute at the same time? Here we delineate when and how activity in specific areas drives a whisker-based decision in mice. A short-term memory component temporally separated tactile "sensation" and "action" (licking). Using optogenetic inhibition (spatial resolution, 2 mm; temporal resolution, 100 ms), we surveyed the neocortex for regions driving behavior during specific behavioral epochs. Barrel cortex was critical for sensation. During the short-term memory, unilateral inhibition of anterior lateral motor cortex biased responses to the ipsilateral side. Consistently, barrel cortex showed stimulus-specific activity during sensation, whereas motor cortex showed choice-specific preparatory activity and movement-related activity, consistent with roles in motor planning and movement. These results suggest serial information flow from sensory to motor areas during perceptual decision making.

Sighted animals extract motion information from visual scenes by processing spatiotemporal patterns of light falling on the retina. The dominant models for motion estimation exploit intensity correlations only between pairs of points in space and time. Moving natural scenes, however, contain more complex correlations. We found that fly and human visual systems encode the combined direction and contrast polarity of moving edges using triple correlations that enhance motion estimation in natural environments. Both species extracted triple correlations with neural substrates tuned for light or dark edges, and sensitivity to specific triple correlations was retained even as light and dark edge motion signals were combined. Thus, both species separately process light and dark image contrasts to capture motion signatures that can improve estimation accuracy. This convergence argues that statistical structures in natural scenes have greatly affected visual processing, driving a common computational strategy over 500 million years of evolution.

Many different types of functional non-coding RNAs participate in a wide range of important cellular functions but the large majority of these RNAs are not routinely annotated in published genomes. Several programs have been developed for identifying RNAs, including specific tools tailored to a particular RNA family as well as more general ones designed to work for any family. Many of these tools utilize covariance models (CMs), statistical models of the conserved sequence, and structure of an RNA family. In this chapter, as an illustrative example, the Infernal software package and CMs from the Rfam database are used to identify RNAs in the genome of the archaeon Methanobrevibacter ruminantium, uncovering some additional RNAs not present in the genome’s initial annotation. Analysis of the results and comparison with family-specific methods demonstrate some important strengths and weaknesses of this general approach.

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

Drosophila type II neuroblasts (NBs), like mammalian neural stem cells, deposit neurons through intermediate neural progenitors (INPs) that can each produce a series of neurons. Both type II NBs and INPs exhibit age-dependent expression of various transcription factors, potentially specifying an array of diverse neurons by combinatorial temporal patterning. Not knowing which mature neurons are made by specific INPs, however, conceals the actual variety of neuron types and limits further molecular studies. Here we mapped neurons derived from specific type II NB lineages and found that sibling INPs produced a morphologically similar but temporally regulated series of distinct neuron types. This suggests a common fate diversification program operating within each INP that is modulated by NB age to generate slightly different sets of diverse neurons based on the INP birth order. Analogous mechanisms might underlie the expansion of neuron diversity via INPs in mammalian brain.

By generating and studying mosaic organisms, we are learning how intricate tissues form as cells proliferate and diversify through organism development. FLP/FRT-mediated site-specific mitotic recombination permits the generation of mosaic flies with efficiency and control. With heat-inducible or tissue-specific FLP transgenes at our disposal, we can engineer mosaics carrying clones of homozygous cells that come from specific pools of heterozygous precursors. This permits detailed cell lineage analysis followed by mosaic analysis of gene functions in the underlying developmental processes. Expression of transgenes (e.g., reporters) only in the homozygous cells enables mosaic analysis in the complex nervous system. Tracing neuronal lineages by using mosaics revolutionized mechanistic studies of neuronal diversification and differentiation, exemplifying the power of genetic mosaics in developmental biology. WIREs Dev Biol 2014, 3:69–81. doi: 10.1002/wdev.122

Many biomolecules in cells can be visualized with high sensitivity and specificity by fluorescence microscopy. However, the resolution of conventional light microscopy is limited by diffraction to ~200-250nm laterally and >500nm axially. Here, we describe superresolution methods based on single-molecule localization analysis of photoswitchable fluorophores (PALM: photoactivated localization microscopy) as well as our recent three-dimensional (3D) method (iPALM: interferometric PALM) that allows imaging with a resolution better than 20nm in all three dimensions. Considerations for their implementations, applications to multicolor imaging, and a recent development that extend the imaging depth of iPALM to ~750nm are discussed. As the spatial resolution of superresolution fluorescence microscopy converges with that of electron microscopy (EM), direct imaging of the same specimen using both approaches becomes feasible. This could be particularly useful for cross validation of experiments, and thus, we also describe recent methods that were developed for correlative superresolution fluorescence and EM.

The database iPfam, available at http://ipfam.org, catalogues Pfam domain interactions based on known 3D structures that are found in the Protein Data Bank, providing interaction data at the molecular level. Previously, the iPfam domain-domain interaction data was integrated within the Pfam database and website, but it has now been migrated to a separate database. This allows for independent development, improving data access and giving clearer separation between the protein family and interactions datasets. In addition to domain-domain interactions, iPfam has been expanded to include interaction data for domain bound small molecule ligands. Functional annotations are provided from source databases, supplemented by the incorporation of Wikipedia articles where available. iPfam (version 1.0) contains >9500 domain-domain and 15 500 domain-ligand interactions. The new website provides access to this data in a variety of ways, including interactive visualizations of the interaction data.