Parallel circuits throughout the CNS exhibit distinct sensitivities and responses to sensory stimuli. Ambiguities in the source and properties of signals elicited by physiological stimuli, however, frequently obscure the mechanisms underlying these distinctions. We found that differences in the degree to which activity in two classes of Off retinal ganglion cell (RGC) encode information about light stimuli near detection threshold were not due to obvious differences in the cells’ intrinsic properties or the chemical synaptic input the cells received; indeed, differences in the cells’ light responses were largely insensitive to block of fast ionotropic glutamate receptors. Instead, the distinct responses of the two types of RGCs likely reflect differences in light-evoked electrical synaptic input. These results highlight a surprising strategy by which the retina differentially processes and routes visual information and provide new insight into the circuits that underlie responses to stimuli near detection threshold.

Site-specific recombinases have been used for two decades to manipulate the structure of animal genomes in highly predictable ways and have become major research tools. However, the small number of recombinases demonstrated to have distinct specificities, low toxicity, and sufficient activity to drive reactions to completion in animals has been a limitation. In this report we show that four recombinases derived from yeast-KD, B2, B3, and R-are highly active and nontoxic in Drosophila and that KD, B2, B3, and the widely used FLP recombinase have distinct target specificities. We also show that the KD and B3 recombinases are active in mice.

MOTIVATION: Automatic recognition of cell identities is critical for quantitative measurement, targeting, and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages, and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable. RESULTS: We have developed a new method, called SRS (for Simultaneous Recognition and Segmentation of cells), and applied it to 3D image stacks of the model organism C. elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a data set of 175 C. elegans image stacks containing 14,118 manually curated BWM cells providing the "ground-truth" for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving ±90-degree rotations. With these stacks excluded from the data set, the recognition accuracy rose to 99.1%. We also show SRS is generally applicable to other cell-types, e.g. intestinal cells. AVAILABILITY: The supplementary movies can be downloaded from our website http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d) and will be released in the V3D plugin source code repository.

Pavlovian olfactory learning in Drosophila produces two genetically distinct forms of intermediate-term memories: anesthesia-sensitive memory, which requires the amnesiac gene, and anesthesia-resistant memory (ARM), which requires the radish gene. Here, we report that ARM is specifically enhanced or inhibited in flies with elevated or reduced serotonin (5HT) levels, respectively. The requirement for 5HT was additive with the memory defect of the amnesiac mutation but was occluded by the radish mutation. This result suggests that 5HT and Radish protein act on the same pathway for ARM formation. Three supporting lines of evidence indicate that ARM formation requires 5HT released from only two dorsal paired medial (DPM) neurons onto the mushroom bodies (MBs), the olfactory learning and memory center in Drosophila: (i) DPM neurons were 5HT-antibody immunopositive; (ii) temporal inhibition of 5HT synthesis or release from DPM neurons, but not from other serotonergic neurons, impaired ARM formation; (iii) knocking down the expression of d5HT1A serotonin receptors in α/β MB neurons, which are innervated by DPM neurons, inhibited ARM formation. Thus, in addition to the Amnesiac peptide required for anesthesia-sensitive memory formation, the two DPM neurons also release 5HT acting on MB neurons for ARM formation.

The ways in which cells set the size of intracellular structures is an important but largely unsolved problem [1]. Early embryonic divisions pose special problems in this regard. Many checkpoints common in somatic cells are missing from these divisions, which are characterized by rapid reductions in cell size and short cell cycles [2]. Embryonic cells must therefore possess simple and robust mechanisms that allow the size of many of their intracellular structures to rapidly scale with cell size.

Bacterial Rho-independent terminators (RITs) are important genomic landmarks involved in gene regulation and terminating gene expression. In this investigation we present RNIE, a probabilistic approach for predicting RITs. The method is based upon covariance models which have been known for many years to be the most accurate computational tools for predicting homology in structural non-coding RNAs. We show that RNIE has superior performance in model species from a spectrum of bacterial phyla. Further analysis of species where a low number of RITs were predicted revealed a highly conserved structural sequence motif enriched near the genic termini of the pathogenic Actinobacteria, Mycobacterium tuberculosis. This motif, together with classical RITs, account for up to 90% of all the significantly structured regions from the termini of M. tuberculosis genic elements. The software, predictions and alignments described below are available from http://github.com/ppgardne/RNIE.

Light sheet-based fluorescence microscopy (LSFM) is emerging as a powerful imaging technique for the life sciences. LSFM provides an exceptionally high imaging speed, high signal-to-noise ratio, low level of photo-bleaching and good optical penetration depth. This unique combination of capabilities makes light sheet-based microscopes highly suitable for live imaging applications. There is an outstanding potential in applying this technology to the quantitative study of embryonic development. Here, we provide an overview of the different basic implementations of LSFM, review recent technical advances in the field and highlight applications in the context of embryonic development. We conclude with a discussion of promising future directions.

Multiphoton imaging (MPI) is widely used for recording activity simultaneously from many neurons in superficial cortical layers in vivo. We combined regenerative amplification multiphoton microscopy (RAMM) with genetically encoded calcium indicators to extend MPI of neuronal population activity into layer 5 (L5) of adult mouse somatosensory cortex. We found that this approach could be used to record and quantify spontaneous and sensory-evoked activity in populations of L5 neuronal somata located as much as 800 μm below the pia. In addition, we found that RAMM could be used to simultaneously image activity from large (80) populations of apical dendrites and follow these dendrites down to their somata of origin.