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175 Janelia Publications

Showing 31-40 of 175 results
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    10/10/22 | Structured random receptive fields enable informative sensory encodings.
    Pandey B, Pachitariu M, Brunton BW, Harris KD
    PLoS Computational Biology. 2022 Oct 10;18(10):e1010484. doi: 10.1371/journal.pcbi.1010484

    Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

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    10/06/22 | Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.
    Qiao C, Li D, Liu Y, Zhang S, Liu K, Liu C, Guo Y, Jiang T, Fang C, Li N, Zeng Y, He K, Zhu X, Lippincott-Schwartz J, Dai Q, Li D
    Nature Biotechnology. 2022 Oct 06:. doi: 10.1038/s41587-022-01471-3

    The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.

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    10/05/22 | Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas.
    Avitan L, Stringer C
    Neuron. 2022 Oct 05;110(19):3064. doi: 10.1016/j.neuron.2022.06.019

    Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

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    Svoboda Lab
    10/04/22 | The Neurodata Without Borders ecosystem for neurophysiological data science.
    Rubel O, Tritt A, Ly R, Dichter BK, Ghosh S, Niu L, Baker P, Soltesz I, Ng L, Svoboda K, Frank L, Bouchard KE
    eLife. 2022 Oct 04;11:. doi: 10.7554/eLife.78362

    The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

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    09/29/22 | De novo protein identification in mammalian sperm using high-resolution in situ cryo-electron tomography
    Zhen Chen , Momoko Shiozaki , Kelsey M. Haas , Shumei Zhao , Caiying Guo , Benjamin J. Polacco , Zhiheng Yu , Nevan J. Krogan , Robyn M. Kaake , Ronald D. Vale , David A. Agard
    bioRxiv. 2022 Sep 29:. doi: 10.1101/2022.09.28.510016

    Understanding molecular mechanisms of cellular pathways requires knowledge of the identities of participating proteins, their cellular localization and their 3D structures. Contemporary workflows typically require multiple techniques to identify target proteins, track their localization using fluorescence microscopy, followed by in vitro structure determination. To identify mammal-specific sperm proteins and understand their functions, we developed a visual proteomics workflow to directly address these challenges. Our in situ cryo-electron tomography and subtomogram averaging provided 6.0 Å resolution reconstructions of axonemal microtubules and their associated proteins. The well-resolved secondary and tertiary structures allowed us to computationally match, in an unbiased manner, novel densities in our 3D reconstruction maps with 21,615 AlphaFold2-predicted protein models of the mouse proteome. We identified Tektin 5, CCDC105 and SPACA9 as novel microtubule inner proteins that form an extensive network crosslinking the lumen of microtubule and existing proteins. Additional biochemical and mass spectrometry analyses helped validate potential candidates. The novel axonemal sperm structures identified by this approach form an extensive interaction network within the lumen of microtubules, suggesting they have a role in the mechanical and elastic properties of the microtubule filaments required for the vigorous beating motions of flagella.

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    09/27/22 | A scalable implementation of the recursive least-squares algorithm for training spiking neural networks
    Benjamin J. Arthur , Christopher M. Kim , Susu Chen , Stephan Preibisch , Ran Darshan
    bioRxiv. 2022 Sep 27:. doi: 10.1101/2022.09.26.509578

    Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation allows training networks to reproduce neural activity of an order of millions neurons at order of magnitude times faster than the CPU implementation. We demonstrate this by applying our algorithm to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables efficient training of large-scale spiking models, thus allowing for in-silico study of the dynamics and connectivity underlying multi-area computations.

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    09/20/22 | A proliferative to invasive switch is mediated by srGAP1 downregulation through the activation of TGF-β2 signaling.
    Mondal C, Gacha-Garay MJ, Larkin KA, Adikes RC, Di Martino JS, Chien C, Fraser M, Eni-Aganga I, Agullo-Pascual E, Cialowicz K, Ozbek U, Naba A, Gaitas A, Fu T, Upadhyayula S, Betzig E, Matus DQ, Martin BL, Bravo-Cordero JJ
    Cell Reports. 2022 Sep 20;40(12):111358. doi: 10.1016/j.celrep.2022.111358

    Many breast cancer (BC) patients suffer from complications of metastatic disease. To form metastases, cancer cells must become migratory and coordinate both invasive and proliferative programs at distant organs. Here, we identify srGAP1 as a regulator of a proliferative-to-invasive switch in BC cells. High-resolution light-sheet microscopy demonstrates that BC cells can form actin-rich protrusions during extravasation. srGAP1 cells display a motile and invasive phenotype that facilitates their extravasation from blood vessels, as shown in zebrafish and mouse models, while attenuating tumor growth. Interestingly, a population of srGAP1 cells remain as solitary disseminated tumor cells in the lungs of mice bearing BC tumors. Overall, srGAP1 cells have increased Smad2 activation and TGF-β2 secretion, resulting in increased invasion and p27 levels to sustain quiescence. These findings identify srGAP1 as a mediator of a proliferative to invasive phenotypic switch in BC cells in vivo through a TGF-β2-mediated signaling axis.

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    07/29/20 | Dense reconstruction of elongated cell lineages: overcoming suboptimum lineage encoding and sparse cell sampling
    Sugino K, Miyares RL, Espinosa-Medina I, Chen H, Potter CJ, Lee T
    bioRxiv. 07/2020:. doi: 10.1101/2020.07.27.223321

    Acquiring both lineage and cell-type information during brain development could elucidate transcriptional programs underling neuronal diversification. This is now feasible with single-cell RNA-seq combined with CRISPR-based lineage tracing, which generates genetic barcodes with cumulative CRISPR edits. This technique has not yet been optimized to deliver high-resolution lineage reconstruction of protracted lineages. Drosophila neuronal lineages are an ideal model to consider, as multiple lineages have been morphologically mapped at single-cell resolution. Here we find the parameter ranges required to encode a representative neuronal lineage emanating from 100 stem cell divisions. We derive the optimum editing rate to be inversely proportional to lineage depth, enabling encoding to persist across lineage progression. Further, we experimentally determine the editing rates of a Cas9-deaminase in cycling neural stem cells, finding near ideal rates to map elongated Drosophila neuronal lineages. Moreover, we propose and evaluate strategies to separate recurring cell-types for lineage reconstruction. Finally, we present a simple method to combine multiple experiments, which permits dense reconstruction of protracted cell lineages despite suboptimum lineage encoding and sparse cell sampling.Competing Interest StatementThe authors have declared no competing interest.

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    10/28/21 | TEMPO: A system to sequentially label and genetically manipulate vertebrate cell lineages
    Espinosa-Medina I, Feliciano D, Belmonte-Mateos C, Garcia-Marques J, Foster B, Miyares RL, Pujades C, Koyama M, Lee T
    bioRxiv. 10/2021:. doi: 10.1101/2021.10.27.466134

    During development, regulatory factors appear in a precise order to determine cell fates over time. To investigate complex tissue development, one should not just label cell lineages but further visualize and manipulate cells with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labelling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation/inactivation of reporters/effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.One-Sentence Summary Gaining sequential genetic access to vertebrate cell lineages.Competing Interest StatementThe authors have declared no competing interest.

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    04/14/19 | Theoretical modeling on CRISPR-coded cell lineages: efficient encoding and optimal reconstruction
    Sugino K, Garcia-Marques J, Espinosa-Medina I, Lee T
    bioRxiv. 04/2019:. doi: 10.1101/538488

    Delineating cell lineages is a prerequisite for interrogating the genesis of cell types. CRISPR/Cas9 can edit genomic sequence during development which enables to trace cell lineages. Recent studies have demonstrated the feasibility of this idea. However, the optimality of the encoding or reconstruction processes has not been adequately addressed. Here, we surveyed a multitude of reconstruction algorithms and found hierarchical clustering, with a metric based on the number of shared Cas9 edits, delivers the best reconstruction. However, the trackable depth is ultimately limited by the number of available coding units that typically decrease exponentially across cell generations. To overcome this limit, we established two strategies that better sustain the coding capacity. One involves controlling target availability via use of parallel gRNA cascades, whereas the other strategy exploits adjustable Cas9/gRNA editing rates. In summary, we provide a theoretical basis in understanding, designing, and analyzing robust CRISPR barcodes for dense reconstruction of protracted cell lineages.

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