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1339 Janelia Publications

Showing 1-10 of 1339 results
06/01/18 | Adaptive optical microscopy for neurobiology.
Rodriguez C, Ji N
Current Opinion in Neurobiology. 2018 Jun;50:83-91. doi: 10.1016/j.conb.2018.01.011

Highlights:

  • Biological specimens introduce wavefront aberrations and deteriorate the image quality of optical microscopy.
  • Adaptive optics is used in optical microscopy to recover ideal imaging performance.
  • Adaptive optical imaging improves structural imaging of neurons, allowing for synaptic-level resolution at depth.
  • Adaptive optical imaging leads to a more accurate characterization of the functional properties of neurons.

With the ability to correct for the aberrations introduced by biological specimens, adaptive optics—a method originally developed for astronomical telescopes—has been applied to optical microscopy to recover diffraction-limited imaging performance deep within living tissue. In particular, this technology has been used to improve image quality and provide a more accurate characterization of both structure and function of neurons in a variety of living organisms. Among its many highlights, adaptive optical microscopy has made it possible to image large volumes with diffraction-limited resolution in zebrafish larval brains, to resolve dendritic spines over 600μm deep in the mouse brain, and to more accurately characterize the orientation tuning properties of thalamic boutons in the primary visual cortex of awake mice.

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04/18/18 | Genetic dissection of neural circuits: a decade of progress
Luo L, Callaway EM, Svoboda K
Neuron. 2018 Apr 18;98(2):256-81. doi: 10.1016/j.neuron.2018.03.040

Tremendous progress has been made since Neuron published our Primer on genetic dissection of neural circuits 10 years ago. Since then, cell-type-specific anatomical, neurophysiological, and perturbation studies have been carried out in a multitude of invertebrate and vertebrate organisms, linking neurons and circuits to behavioral functions. New methods allow systematic classification of cell types and provide genetic access to diverse neuronal types for studies of connectivity and neural coding during behavior. Here we evaluate key advances over the past decade and discuss future directions.

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04/16/18 | Measuring the global substrate specificity of mycobacterial serine hydrolases using a library of fluorogenic ester substrates.
Bassett B, Waibel B, White A, Hansen H, Stephens D, Koelper A, Larsen EM, Kim C, Glanzer A, Lavis LD, Hoops GC, Johnson RJ
ACS Infectious Diseases. 2018 Apr 16:. doi: 10.1021/acsinfecdis.7b00263

Among the proteins required for lipid metabolism in Mycobacterium tuberculosis are a significant number of uncharacterized serine hydrolases, especially lipases and esterases. Using a streamlined synthetic method, a library of immolative fluorogenic ester substrates was expanded to better represent the natural lipidomic diversity of Mycobacterium. This expanded fluorogenic library was then used to rapidly characterize the global structure activity relationship (SAR) of mycobacterial serine hydrolases in M. smegmatis under different growth conditions. Confirmation of fluorogenic substrate activation by mycobacterial serine hydrolases was performed using nonspecific serine hydrolase inhibitors and reinforced the biological significance of the SAR. The hydrolases responsible for the global SAR were then assigned using gel-resolved activity measurements, and these assignments were used to rapidly identify the relative substrate specificity of previously uncharacterized mycobacterial hydrolases. These measurements provide a global SAR of mycobacterial hydrolase activity, a picture of cycling hydrolase activity, and a detailed substrate specificity profile for previously uncharacterized hydrolases.

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04/10/18 | A community-developed Open-Source computational ecosystem for big neuro data.
Burns R, Perlman E, Baden A, Roncal WG, Falk B, Chandrashekhar V, Collman F, Seshamani S, Patsolic J, Lillaney K, Kazhdan M, Hider Jr. R, Pryor D, Matelsky J, Gion T, Manavalan P, Wester B, Chevillet M, Trautman ET, Khairy K
arXiv. 2018 Apr 10:1804.02835

Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar spanning 20+ publications and 100+ terabytes including nanoscale ultrastructure, microscale synaptogenetic diversity, and mesoscale whole brain connectivity, making NeuroData the largest and most diverse open repository of brain data.

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04/10/18 | Structural Mechanism of Functional Modulation by Gene Splicing in NMDA Receptors.
Regan MC, Grant T, McDaniel MJ, Karakas E, Zhang J, Traynelis SF, Grigorieff N, Furukawa H
Neuron. 2018 Apr 10:. doi: 10.1016/j.neuron.2018.03.034

Alternative gene splicing gives rise to N-methyl-D-aspartate (NMDA) receptor ion channels with defined functional properties and unique contributions to calcium signaling in a given chemical environment in the mammalian brain. Splice variants possessing the exon-5-encoded motif at the amino-terminal domain (ATD) of the GluN1 subunit are known to display robustly altered deactivation rates and pH sensitivity, but the underlying mechanism for this functional modification is largely unknown. Here, we show through cryoelectron microscopy (cryo-EM) that the presence of the exon 5 motif in GluN1 alters the local architecture of heterotetrameric GluN1-GluN2 NMDA receptors and creates contacts with the ligand-binding domains (LBDs) of the GluN1 and GluN2 subunits, which are absent in NMDA receptors lacking the exon 5 motif. The unique interactions established by the exon 5 motif are essential to the stability of the ATD/LBD and LBD/LBD interfaces that are critically involved in controlling proton sensitivity and deactivation.

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04/09/18 | Odorant binding protein 69a connects social interaction to modulation of social responsiveness in Drosophila.
Bentzur A, Shmueli A, Omesi L, Ryvkin J, Knapp J, Parnas M, Davis FP, Shohat-Ophir G
PLoS Genetics. 2018 Apr 09;14(4):e1007328. doi: 10.1371/journal.pgen.1007328

Living in a social environment requires the ability to respond to specific social stimuli and to incorporate information obtained from prior interactions into future ones. One of the mechanisms that facilitates social interaction is pheromone-based communication. In Drosophila melanogaster, the male-specific pheromone cis-vaccenyl acetate (cVA) elicits different responses in male and female flies, and functions to modulate behavior in a context and experience-dependent manner. Although it is the most studied pheromone in flies, the mechanisms that determine the complexity of the response, its intensity and final output with respect to social context, sex and prior interaction, are still not well understood. Here we explored the functional link between social interaction and pheromone-based communication and discovered an odorant binding protein that links social interaction to sex specific changes in cVA related responses. Odorant binding protein 69a (Obp69a) is expressed in auxiliary cells and secreted into the olfactory sensilla. Its expression is inversely regulated in male and female flies by social interactions: cVA exposure reduces its levels in male flies and increases its levels in female flies. Increasing or decreasing Obp69a levels by genetic means establishes a functional link between Obp69a levels and the extent of male aggression and female receptivity. We show that activation of cVA-sensing neurons is sufficeint to regulate Obp69a levels in the absence of cVA, and requires active neurotransmission between the sensory neuron to the second order olfactory neuron. The cross-talk between sensory neurons and non-neuronal auxiliary cells at the olfactory sensilla, represents an additional component in the machinery that promotes behavioral plasticity to the same sensory stimuli in male and female flies.

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Riddiford LabTruman LabRubin Lab
04/04/18 | Juvenile hormone reveals mosaic developmental programs in the metamorphosing optic lobe of Drosophila melanogaster.
Riddiford LM, Truman JW, Nern A
Biology Open. 2018 Apr 04:. doi: 10.1242/bio.034025

The development of the adult optic lobe (OL) of is directed by a wave of ingrowth of the photoreceptors over a two day period at the outset of metamorphosis which is accompanied by the appearance of the pupal-specific transcription factor Broad-Z3 (Br-Z3) and expression of early drivers in OL neurons. During this time, there are pulses of ecdysteroids that time the metamorphic events. At the outset, the transient appearance of juvenile hormone (JH) prevents precocious development of the OL caused by the ecdysteroid peak that initiates pupariation, but the artificial maintenance of JH after this time misdirects subsequent development. Axon ingrowth, Br-Z3 appearance and the expression of early drivers were unaffected, but aspects of later development such as the dendritic expansion of the lamina monopolar neurons and the expression of late drivers were suppressed. This effect of the exogenous JH mimic (JHM) pyriproxifen is lost by 24 hr after pupariation. Part of this effect of JHM is due to its suppression of the appearance of ecdysone receptor EcR-B1 that occurs after pupation and during early adult development.

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04/04/18 | Opportunities and obstacles for deep learning in biology and medicine.
Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow P, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM
Journal of The Royal Society Interface. 2018 Apr 4:. doi: 10.1098/rsif.2017.0387

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

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04/03/18 | A deep (learning) dive into a cell.
Branson K
Nature Methods. 2018 Apr 03;15(4):253-4. doi: 10.1038/nmeth.4658
04/02/18 | Accurate and sensitive quantification of protein-DNA binding affinity.
Rastogi C, Rube HT, Kribelbauer JF, Crocker J, Loker RE, Martini GD, Laptenko O, Freed-Pastor WA, Prives C, Stern DL, Mann RS, Bussemaker HJ
Proceedings of the National Academy of Sciences of the United States of America. 2018 Apr 02:. doi: 10.1073/pnas.1714376115

Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes.

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