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187 Janelia Publications

Showing 141-150 of 187 results
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    04/03/17 | In vivo patch-clamp recording in awake head-fixed rodents.
    Lee D, Lee AK
    Cold Spring Harbor Protocols. 2017 Apr 03;2017(4):pdb.prot095802. doi: 10.1101/pdb.prot095802

    Whole-cell recording has been used to measure and manipulate a neuron's spiking and subthreshold membrane potential, allowing assessment of the cell's inputs and outputs as well as its intrinsic membrane properties. This technique has also been combined with pharmacology and optogenetics as well as morphological reconstruction to address critical questions concerning neuronal integration, plasticity, and connectivity. This protocol describes a technique for obtaining whole-cell recordings in awake head-fixed animals, allowing such questions to be investigated within the context of an intact network and natural behavioral states. First, animals are habituated to sit quietly with their heads fixed in place. Then, a whole-cell recording is obtained using an efficient, blind patching protocol. We have successfully applied this technique to rats and mice.

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    04/03/17 | Sensorimotor neuroscience: motor precision meets vision.
    Longden KD, Huston SJ, Reiser MB
    Current Biology : CB. 2017 Apr 03;27(7):R261-R263. doi: 10.1016/j.cub.2017.02.047

    Visual motion sensing neurons in the fly also encode a range of behavior-related signals. These nonvisual inputs appear to be used to correct some of the challenges of visually guided locomotion.

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    04/03/17 | Whole-cell recording in the awake brain.
    Lee D, Lee AK
    Cold Spring Harbor Protocols. 2017 Apr 03;2017(4):pdb.top087304. doi: 10.1101/pdb.top087304

    Intracellular recording is an essential technique for investigating cellular mechanisms underlying complex brain functions. Despite the high sensitivity of the technique to mechanical disturbances, intracellular recording has been applied to awake, behaving, and even freely moving, animals. Here we summarize recent advances in these methods and their application to the measurement and manipulation of membrane potential dynamics for understanding neuronal computations in behaving animals.

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    Ji Lab
    04/02/17 | Near-infrared fluorescent protein iRFP713 as a reporter protein for optogenetic vectors, a transgenic Cre-reporter rat, and other neuronal studies.
    Richie CT, Whitaker LR, Whitaker KW, Necarsulmer J, Baldwin HA, Zhang Y, Fortuno L, Hinkle JJ, Koivula P, Henderson MJ, Sun W, Wang K, Smith JC, Pickel J, Ji N, Hope BT, Harvey BK
    Journal of Neuroscience Methods. 2017 Apr 02;284:1-14. doi: 10.1016/j.jneumeth.2017.03.020

    BACKGROUND: The use of genetically-encoded fluorescent reporters is essential for the identification and observation of cells that express transgenic modulatory proteins. Near-infrared (NIR) fluorescent proteins have superior light penetration through biological tissue, but are not yet widely adopted.

    NEW METHOD: Using the near-infrared fluorescent protein, iRFP713, improves the imaging resolution in thick tissue sections or the intact brain due to the reduced light-scattering at the longer, NIR wavelengths used to image the protein. Additionally, iRFP713 can be used to identify transgenic cells without photobleaching other fluorescent reporters or affecting opsin function. We have generated a set of adeno-associated vectors in which iRFP713 has been fused to optogenetic channels, and can be expressed constitutively or Cre-dependently.

    RESULTS: iRFP713 is detectable when expressed in neurons both in vitro and in vivo without exogenously supplied chromophore biliverdin. Neuronally-expressed iRFP713 has similar properties to GFP-like fluorescent proteins, including the ability to be translationally fused to channelrhodopsin or halorhodopsin, however, it shows superior photostability compared to EYFP. Furthermore, electrophysiological recordings from iRFP713-labeled cells compared to cells labeled with mCherry suggest that iRFP713 cells are healthier and therefore more stable and reliable in an ex vivo preparation. Lastly, we have generated a transgenic rat that expresses iRFP713 in a Cre-dependent manner.

    CONCLUSIONS: Overall, we have demonstrated that iRFP713 can be used as a reporter in neurons without the use of exogenous biliverdin, with minimal impact on viability and function thereby making it feasible to extend the capabilities for imaging genetically-tagged neurons in slices and in vivo.

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    04/01/17 | Optogenetic control with a photocleavable protein, PhoCl.
    Zhang W, Lohman AW, Zhuravlova Y, Lu X, Wiens MD, Hoi H, Yaganoglu S, Mohr MA, Kitova EN, Klassen JS, Pantazis P, Thompson RJ, Campbell RE
    Nature Methods. 2017 Apr;14(4):391-394. doi: 10.1038/nmeth.4222

    To expand the range of experiments that are accessible with optogenetics, we developed a photocleavable protein (PhoCl) that spontaneously dissociates into two fragments after violet-light-induced cleavage of a specific bond in the protein backbone. We demonstrated that PhoCl can be used to engineer light-activatable Cre recombinase, Gal4 transcription factor, and a viral protease that in turn was used to activate opening of the large-pore ion channel Pannexin-1.

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    04/01/17 | Time-accuracy tradeoffs in kernel prediction: controlling prediction quality.
    Kpotufe S, Verma N
    Journal of Machine Learning Research. 2017 Apr 1 ;18(44):1-29

    Kernel regression or classification (also referred to as weighted ε-NN methods in Machine Learning) are appealing for their simplicity and therefore ubiquitous in data analysis. How- ever, practical implementations of kernel regression or classification consist of quantizing or sub-sampling data for improving time efficiency, often at the cost of prediction quality. While such tradeoffs are necessary in practice, their statistical implications are generally not well understood, hence practical implementations come with few performance guaran- tees. In particular, it is unclear whether it is possible to maintain the statistical accuracy of kernel prediction—crucial in some applications—while improving prediction time.

    The present work provides guiding principles for combining kernel prediction with data- quantization so as to guarantee good tradeoffs between prediction time and accuracy, and in particular so as to approximately maintain the good accuracy of vanilla kernel prediction.

    Furthermore, our tradeoff guarantees are worked out explicitly in terms of a tuning parameter which acts as a knob that favors either time or accuracy depending on practical needs. On one end of the knob, prediction time is of the same order as that of single-nearest- neighbor prediction (which is statistically inconsistent) while maintaining consistency; on the other end of the knob, the prediction risk is nearly minimax-optimal (in terms of the original data size) while still reducing time complexity. The analysis thus reveals the interaction between the data-quantization approach and the kernel prediction method, and most importantly gives explicit control of the tradeoff to the practitioner rather than fixing the tradeoff in advance or leaving it opaque.

    The theoretical results are validated on data from a range of real-world application domains; in particular we demonstrate that the theoretical knob performs as expected. 

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    Ji Lab
    03/31/17 | Adaptive optical fluorescence microscopy.
    Ji N
    Nature Methods. 2017 Mar 31;14(4):374-380. doi: 10.1038/nmeth.4218

    The past quarter century has witnessed rapid developments of fluorescence microscopy techniques that enable structural and functional imaging of biological specimens at unprecedented depth and resolution. The performance of these methods in multicellular organisms, however, is degraded by sample-induced optical aberrations. Here I review recent work on incorporating adaptive optics, a technology originally applied in astronomical telescopes to combat atmospheric aberrations, to improve image quality of fluorescence microscopy for biological imaging.

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    03/31/17 | Automatic tracing of ultra-volumes of neuronal images.
    Peng H, Zhou Z, Meijering E, Zhao T, Ascoli GA, Hawrylycz M
    Nature Methods. 2017 Mar 31;14(4):332-333. doi: 10.1038/nmeth.4233
    Zlatic Lab
    03/31/17 | Facilitating neuron-specific genetic manipulations in Drosophila using a split GAL4 repressor.
    Dolan M, Luan H, Shropshire WC, Sutcliffe B, Cocanougher B, Scott RL, Frechter S, Zlatic M, Jefferis GS, White BH
    Genetics. 2017 Mar 31;206(2):775-84. doi: 10.1534/genetics.116.199687

    Efforts to map neural circuits have been galvanized by the development of genetic technologies that permit the manipulation of targeted sets of neurons in the brains of freely behaving animals. The success of these efforts relies on the experimenter's ability to target arbitrarily small subsets of neurons for manipulation, but such specificity of targeting cannot routinely be achieved using existing methods. In Drosophila melanogaster, a widely used technique for refined cell-type specific manipulation is the Split GAL4 system, which augments the targeting specificity of the binary GAL4-UAS system by making GAL4 transcriptional activity contingent upon two enhancers, rather than one. To permit more refined targeting, we introduce here the "Killer Zipper" (KZip(+)), a suppressor that makes Split GAL4 targeting contingent upon a third enhancer. KZip(+) acts by disrupting both the formation and activity of Split GAL4 heterodimers, and we show how this added layer of control can be used to selectively remove unwanted cells from a Split GAL4 expression pattern or to subtract neurons of interest from a pattern to determine their requirement in generating a given phenotype. To facilitate application of the KZip(+) technology, we have developed a versatile set of LexAop-KZip(+) fly lines that can be used directly with the large number of LexA driver lines with known expression patterns. The Killer Zipper significantly sharpens the precision of neuronal genetic control available in Drosophila and may be extended to other organisms where Split GAL4-like systems are used.

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    Cardona Lab
    03/29/17 | Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.
    Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Seung HS
    Bioinformatics (Oxford, England). 2017 Mar 29;33(15):2424-6. doi: 10.1093/bioinformatics/btx180

    Summary: State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This processis time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leveragesa limited number of manual annotations in order to train a classifier and segment the remaining dataautomatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.

    Availability and Implementation: TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation.

    Contact: ignacio.arganda@ehu.eus.

    Supplementary information: Supplementary data are available at Bioinformatics online.

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