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2496 Janelia Publications

Showing 51-60 of 2496 results
04/12/24 | Leptin Activated Hypothalamic BNC2 Neurons Acutely Suppress Food Intake
Han L. Tan , Luping Yin , Yuqi Tan , Jessica Ivanov , Kaja Plucinska , Anoj Ilanges , Brian R. Herb , Putianqi Wang , Christin Kosse , Paul Cohen , Dayu Lin , Jeffrey M. Friedman
bioRxiv. 12 Apr 2024:. doi: 10.1101/2024.01.25.577315

Leptin is an adipose tissue hormone that maintains homeostatic control of adipose tissue mass by regulating the activity of specific neural populations controlling appetite and metabolism1. Leptin regulates food intake by inhibiting orexigenic agouti-related protein (AGRP) neurons and activating anorexigenic pro-opiomelanocortin (POMC) neurons2. However, while AGRP neurons regulate food intake on a rapid time scale, acute activation of POMC neurons has only a minimal effect3–5. This has raised the possibility that there is a heretofore unidentified leptin-regulated neural population that suppresses appetite on a rapid time scale. Here, we report the discovery of a novel population of leptin-target neurons expressing basonuclin 2 (Bnc2) that acutely suppress appetite by directly inhibiting AGRP neurons. Opposite to the effect of AGRP activation, BNC2 neuronal activation elicited a place preference indicative of positive valence in hungry but not fed mice. The activity of BNC2 neurons is finely tuned by leptin, sensory food cues, and nutritional status. Finally, deleting leptin receptors in BNC2 neurons caused marked hyperphagia and obesity, similar to that observed in a leptin receptor knockout in AGRP neurons. These data indicate that BNC2-expressing neurons are a key component of the neural circuit that maintains energy balance, thus filling an important gap in our understanding of the regulation of food intake and leptin action.

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04/11/24 | A blue-shifted genetically encoded Ca2+ indicator with enhanced two-photon absorption
Abhi Aggarwal , Smrithi Sunil , Imane Bendifallah , Michael Moon , Mikhail Drobizhev , Landon Zarowny , Jihong Zheng , Sheng-Yi Wu , Alexander W. Lohman , Alison G. Tebo , Valentina Emiliani , Kaspar Podgorski , Yi Shen , Robert E. Campbell
bioRxiv. 2024 Apr 11:. doi: https://doi.org/10.1117/1.NPh.11.2.024207

Significance: Genetically encoded calcium ion (Ca2+) indicators (GECIs) are powerful tools for monitoring intracellular Ca2+ concentration changes in living cells and model organisms. In particular, GECIs have found particular utility for monitoring the transient increase of Ca2+concentration that is associated with the neuronal action potential. However, the palette of highly optimized GECIs for imaging of neuronal activity remains relatively limited. Expanding the selection of available GECIs to include new colors and distinct photophysical properties could create new opportunities for in vitro and in vivo fluorescence imaging of neuronal activity. In particular, blue-shifted variants of GECIs are expected to have enhanced two-photon brightness, which would facilitate multiphoton microscopy.

Aim: We describe the development and applications of T-GECO1-a high-performance blue-shifted GECI based on the Clavularia sp.-derived mTFP1.

Approach: We use protein engineering and extensive directed evolution to develop T-GECO1. We characterize the purified protein and assess its performance in vitro using one-photon excitation in cultured rat hippocampal neurons, in vivo using one-photon excitation fiber photometry in mice, and ex vivo using two-photon Ca2+ imaging in hippocampal slices.

Results: The Ca2+-bound state of T-GECO1 has an excitation peak maximum of 468 nm, an emission peak maximum of 500 nm, an extinction coefficient of 49,300M−1cm−1, a quantum yield of 0.83, and two-photon brightness approximately double that of EGFP. The Ca2+-dependent fluorescence increase is 15-fold, and the apparent Kd for Ca2+ is 82 nM. With two-photon excitation conditions at 850 nm, T-GECO1 consistently enabled the detection of action potentials with higher signal-to-noise (SNR) than a late generation GCaMP variant.

Conclusions: T-GECO1 is a high-performance blue-shifted GECI that, under two-photon excitation conditions, provides advantages relative to late generation GCaMP variants.

Keywords: blue-shifted fluorescence; genetically encoded calcium ion indicator; neuronal activity imaging; protein engineering; two-photon excitation.

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04/06/24 | A tunable and versatile chemogenetic near infrared fluorescent reporter
Lina El Hajji , Benjamin Bunel , Octave Joliot , Chenge Li , Alison G. Tebo , Christine Rampon , Michel Volovitch , Evelyne Fischer , Nicolas Pietrancosta , Franck Perez , Xavier Morin , Sophie Vriz , Arnaud Gautier
bioRxiv. 2024 Apr 6:. doi: 10.1101/2024.04.05.588310

Near-infrared (NIR) fluorescent reporters provide additional colors for highly multiplexed imaging of cells and organisms, and enable imaging with less toxic light and higher contrast and depth. Here, we present the engineering of nirFAST, a small tunable chemogenetic NIR fluorescent reporter that is brighter than top-performing NIR fluorescent proteins in cultured mammalian cells. nirFAST is a small genetically encoded protein of 14 kDa that binds and stabilizes the fluorescent state of synthetic, highly cell-permeant, fluorogenic chromophores (so-called fluorogens) that are otherwise dark when free. Engineered to emit NIR light, nirFAST can also emit far-red or red lights through change of chromophore. nirFAST allows the imaging of proteins in live cultured mammalian cells, chicken embryo tissues and zebrafish larvae. Its near infrared fluorescence provides an additional color for high spectral multiplexing. We showed that nirFAST is well-suited for stimulated emission depletion (STED) nanoscopy, allowing the efficient imaging of proteins with subdiffraction resolution in live cells. nirFAST enabled the design of a chemogenetic green-NIR fluorescent ubiquitination-based cell cycle indicator (FUCCI) for the monitoring of the different phases of the cell cycle. Finally, bisection of nirFAST allowed the design of a fluorogenic chemically induced dimerization technology with NIR fluorescence readout, enabling the control and visualization of protein proximity.

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04/06/24 | Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation
Azaan Rehman , Alexander Zhovmer , Ryo Sato , Yosuke Mukoyama , Jiji Chen , Alberto Rissone , Rosa Puertollano , Harshad Vishwasrao , Hari Shroff , Christian A. Combs , Hui Xue
arXiv. 2024 Apr 6:

Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) it can then be used to enhance new images that are like the training data. In this study, we proposed a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN networks for image denoising. In our scheme we have trained a single CNNT based backbone model from pairwise high-low SNR images for one type of fluorescence microscope (instance structured illumination, iSim). Fast adaption to new applications was achieved by fine-tuning the backbone on only 5-10 sample pairs per new experiment. Results show the CNNT backbone and fine-tuning scheme significantly reduces the training time and improves the image quality, outperformed training separate models using CNN approaches such as - RCAN and Noise2Fast. Here we show three examples of the efficacy of this approach on denoising wide-field, two-photon and confocal fluorescence data. In the confocal experiment, which is a 5 by 5 tiled acquisition, the fine-tuned CNNT model reduces the scan time form one hour to eight minutes, with improved quality.

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04/06/24 | COPII with ALG2 and ESCRTs control lysosome-dependent microautophagy of ER exit sites.
Liao Y, Pang S, Li W, Shtengel G, Choi H, Schaefer K, Xu CS, Lippincott-Schwartz J
Dev Cell. 2024 Apr 06:. doi: 10.1016/j.devcel.2024.03.027

Endoplasmic reticulum exit sites (ERESs) are tubular outgrowths of endoplasmic reticulum that serve as the earliest station for protein sorting and export into the secretory pathway. How these structures respond to different cellular conditions remains unclear. Here, we report that ERESs undergo lysosome-dependent microautophagy when Ca is released by lysosomes in response to nutrient stressors such as mTOR inhibition or amino acid starvation in mammalian cells. Targeting and uptake of ERESs into lysosomes were observed by super-resolution live-cell imaging and focus ion beam scanning electron microscopy (FIB-SEM). The mechanism was ESCRT dependent and required ubiquitinated SEC31, ALG2, and ALIX, with a knockout of ALG2 or function-blocking mutations of ALIX preventing engulfment of ERESs by lysosomes. In vitro, reconstitution of the pathway was possible using lysosomal lipid-mimicking giant unilamellar vesicles and purified recombinant components. Together, these findings demonstrate a pathway of lysosome-dependent ERES microautophagy mediated by COPII, ALG2, and ESCRTS induced by nutrient stress.

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04/08/24 | Spike sorting with Kilosort4
Pachitariu M, Sridhar S, Pennington J, Stringer C
Nat Methods. 2024 Apr 08:. doi: 10.1038/s41592-024-02232-7

Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

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04/07/24 | Transformers do not outperform Cellpose
Carsen Stringer , Marius Pachitariu
bioRxiv. 2024 Apr 7:. doi: 10.1101/2024.04.06.587952

In a recent publication, Ma et al [1] claim that a transformer-based cellular segmentation method called Mediar [2] — which won a Neurips challenge — outperforms Cellpose [3] (0.897 vs 0.543 median F1 score). Here we show that this result was obtained by artificially impairing Cellpose in multiple ways. When we removed these impairments, Cellpose outperformed Mediar (0.861 vs 0.826 median F1 score on the updated test set). To further investigate the performance of transformers for cellular segmentation, we replaced the Cellpose backbone with a transformer. The transformer-Cellpose model also did not outperform the standard Cellpose (0.848 median F1 test score). Our results suggest that transformers do not advance the state-of-the-art in cellular segmentation.

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04/10/24 | Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings
Zhiwen Ye , Andrew M Shelton , Jordan R Shaker , Julien M Boussard , Jennifer Colonell , Daniel Birman , Sahar Manavi , Susu Chen , Charlie Windolf , Cole Hurwitz , Tomoyuki Namima , Frederico Pedraja , Shahaf Weiss , Bogdan Raducanu , Torbjørn Ness , Xiaoxuan Jia , Giulia Mastroberardino , L. Federico Rossi , Matteo Carandini , Michael Hausser , Gaute T Einevoll , Gilles Laurent , Nathaniel B Sawtell , Wyeth Bair , Anitha Pasupathy , Carolina Mora-Lopez , Barun Dutta , Liam Paninski , Joshua H Siegle , Christof Koch , Shawn R Olsen , Timothy D Harris , Nicholas A Steinmetz
bioRxiv. 2024 Apr 10:. doi: 10.1101/2023.08.23.554527

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density ( 10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron’s electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to 3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

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01/10/24 | A split-GAL4 driver line resource for Drosophila CNS cell types
Geoffrey W Meissner , Allison Vannan , Jennifer Jeter , Kari Close , Gina M DePasquale , Zachary Dorman , Kaitlyn Forster , Jaye Anne Beringer , Theresa V Gibney , Joanna H Hausenfluck , Yisheng He , Kristin Henderson , Lauren Johnson , Rebecca M Johnston , Gudrun Ihrke , Nirmala Iyer , Rachel Lazarus , Kelley Lee , Hsing-Hsi Li , Hua-Peng Liaw , Brian Melton , Scott Miller , Reeham Motaher , Alexandra Novak , Omotara Ogundeyi , Alyson Petruncio , Jacquelyn Price , Sophia Protopapas , Susana Tae , Jennifer Taylor , Rebecca Vorimo , Brianna Yarbrough , Kevin Xiankun Zeng , Christopher T Zugates , Heather Dionne , Claire Angstadt , Kelly Ashley , Amanda Cavallaro , Tam Dang , Guillermo A Gonzalez III , Karen L Hibbard , Cuizhen Huang , Jui-Chun Kao , Todd Laverty , Monti Mercer , Brenda Perez , Scarlett Pitts , Danielle Ruiz , Viruthika Vallanadu , Grace Zhiyu Zheng , Cristian Goina , Hideo Otsuna , Konrad Rokicki , Robert R Svirskas , Han SJ Cheong , Michael-John Dolan , Erica Ehrhardt , Kai Feng , Basel El Galfi , Jens Goldammer , Stephen J Huston , Nan Hu , Masayoshi Ito , Claire McKellar , Ryo Minegishi , Shigehiro Namiki , Aljoscha Nern , Catherine E Schretter , Gabriella R Sterne , Lalanti Venkatasubramanian , Kaiyu Wang , Tanya Wolff , Ming Wu , Reed George , Oz Malkesman , Yoshinori Aso , Gwyneth M Card , Barry J Dickson , Wyatt Korff , Kei Ito , James W Truman , Marta Zlatic , Gerald M Rubin , FlyLight Project Team
bioRxiv. 2024 Jan 10:. doi: 10.1101/2024.01.09.574419

Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila central nervous system and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.

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04/01/24 | Cryo-electron tomographic investigation of native hippocampal glutamatergic synapses
Aya Matsui , Catherine Spangler , Johannes Elferich , Momoko Shiozaki , Nikki Jean , Xiaowei Zhao , Maozhen Qin , Haining Zhong , Zhiheng Yu , Eric Gouaux
bioRxiv. 2024 Apr 1:. doi: 10.1101/2024.04.01.587595

Chemical synapses are the major sites of communication between neurons in the nervous system and mediate either excitatory or inhibitory signaling. At excitatory synapses, glutamate is the primary neurotransmitter and upon release from presynaptic vesicles, is detected by postsynaptic glutamate receptors, which include ionotropic AMPA and NMDA receptors. Here we have developed methods to identify glutamatergic synapses in brain tissue slices, label AMPA receptors with small gold nanoparticles (AuNPs), and prepare lamella for cryo-electron tomography studies. The targeted imaging of glutamatergic synapses in the lamella is facilitated by fluorescent pre- and postsynaptic signatures, and the subsequent tomograms allow for identification of key features of chemical synapses, including synaptic vesicles, the synaptic cleft and AuNP-labeled AMPA receptors. These methods pave the way for imaging natively derived brain regions at high resolution, using unstained, unfixed samples preserved under near-native conditions.

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