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Zeng, S.

Publications and source records attributed to Zeng, S..

65 records · Page 4Linked to original sources
bioRxiv2020

Transmission dynamics of 2019 novel coronavirus (2019-nCoV)

RationaleSeveral studies have estimated basic production number of novel coronavirus pneumonia (NCP). However, the time-varying transmission dynamics of NCP during the outbreak remain unclear. ObjectivesWe aimed to estimate the basic and time-varying transmission dynamics of NCP across China, and compared them with SARS. MethodsData on NCP cases by February 7, 2020 were collected from epidemiological investigations or official websites. Data on severe acute respiratory syndrome (SARS) cases in Guangdong Province, Beijing and Hong Kong during 2002-2003 were also obtained. We estimated the doubling time, basic reproduction number (R0) and time-varying reproduction number (Rt) of NCP and SARS. Measurements and main resultsAs of February 7, 2020, 34,598 NCP cases were identified in China, and daily confirmed cases decreased after February 4. The doubling time of NCP nationwide was 2.4 days which was shorter than that of SARS in Guangdong (14.3 days), Hong Kong (5.7 days) and Beijing (12.4 days). The R0 of NCP cases nationwide and in Wuhan were 4.5 and 4.4 respectively, which were higher than R0 of SARS in Guangdong (R0=2.3), Hongkong (R0=2.3), and Beijing (R0=2.6). The Rt for NCP continuously decreased especially after January 16 nationwide and in Wuhan. The R0 for secondary NCP cases in Guangdong was 0.6, and the Rt values were less than 1 during the epidemic. ConclusionsNCP may have a higher transmissibility than SARS, and the efforts of containing the outbreak are effective. However, the efforts are needed to persist in for reducing time-varying reproduction number below one. At a Glance CommentaryO_ST_ABSScientific Knowledge on the SubjectC_ST_ABSSince December 29, 2019, pneumonia infection with 2019-nCoV, now named as Novel Coronavirus Pneumonia (NCP), occurred in Wuhan, Hubei Province, China. The disease has rapidly spread from Wuhan to other areas. As a novel virus, the time-varying transmission dynamics of NCP remain unclear, and it is also important to compare it with SARS. What This Study Adds to the FieldWe compared the transmission dynamics of NCP with SARS, and found that NCP has a higher transmissibility than SARS. Time-varying production number indicates that rigorous control measures taken by governments are effective across China, and persistent efforts are needed to be taken for reducing instantaneous reproduction number below one.

systems biology↗
bioRxiv2019

The Manifold Actions of Signaling Peptides on Subcellular Dynamics of a Receptor Specify Stomatal Cell Fate

Receptor endocytosis is important for signal activation and transduction. However, how a receptor interprets conflicting signals to adjust cellular output is not clearly understood. During plant development, the family of EPIDERMAL PATTERNING FACTOR (EPF) peptides fine-tunes stomatal patterning through ERECTA-family receptor kinases. Using genetic, cell biological, and pharmacological approaches, we report here that ERECTA-LIKE1 (ERL1), the major receptor restricting stomatal differentiation, undergoes dynamic subcellular behaviors in response to different signal inputs. ERL1 is constitutively recycled, whereas its activation by EPF1 peptide induces rapid internalization to multivesicular bodies (MVB). In contrast, the dominant-negative ERL1 resides predominantly in plasma membrane. The co-receptor, TOO MANY MOUTHS (TMM), is essential for EPF1-induced ERL1 internalization but dispensable for EPFL6-induced ERL1 internalization. The peptide antagonist of EPF1, Stomagen/EPFL9, triggers retention of ERL1 in the endoplasmic reticulum. Our study elucidates that multiple related yet unique peptides specify cell fate by deploying the differential subcellular dynamics of a single receptor.

plant biology↗
bioRxiv2019

A 3D High Resolution Generative Deep-learning Network for Fluorescence Microscopy Image

Deep learning technology enables us acquire high resolution image from low resolution image in biological imaging free from sophisticated optical hardware. However, current methods require a huge number of the precisely registered low-resolution (LR) and high-resolution (HR) volume image pairs. This requirement is challengeable for biological volume imaging. Here, we proposed 3D deep learning network based on dual generative adversarial network (dual-GAN) framework for recovering HR volume images from LR volume images. Our network avoids learning the direct mappings from the LR and HR volume image pairs, which need precisely image registration process. And the cycle consistent network makes the predicted HR volume image faithful to its corresponding LR volume image. The proposed method achieves the recovery of 20x/1.0 NA volume images from 5x/0.16 NA volume images collected by light-sheet microscopy. In essence our method is suitable for the other imaging modalities.

neuroscience↗
bioRxiv2019

Recombinase system-dependent copackaging strategy for highly efficient neurocircuit tracing

Mapping the detailed cell-type-specific input networks and neuronal projectomes are essential to understand brain function in normal and pathological states. However, several properties of current tracing systems, including labeling sensitivity, trans-synaptic efficiencies, reproducibility among different individuals and different Cre-driver animals, still remained unsatisfactory. Here, we developed MAP-ENVIVIDERS, a recombinase system-dependent vector mixing-based strategy for highly efficient neurocircuit tracing. MAP-ENVIVIDERS enhanced tracing efficiency of input networks across the whole brain, with over 10-fold improvement in diverse previously poor-labeled input brain regions and particularly, up to 70-fold enhancement in brainstem compared with the current standard rabies-virus-mediated systems. MAP-ENVIVIDERS was over 10-fold more sensitive for cell-type-specific labeling than previous strategies, enabling us to capture individual cell-type-specific neurons with extremely complex axonal branches and presynaptic axonal boutons, both about one order of magnitude than previously reported and considered. MAP-ENVIVIDERS provides powerful tools for deconstructing novel input/output circuitry towards functional studies and disorders-related mechanisms.

neuroscience↗
bioRxiv2019

Complete single neuron reconstruction reveals morphological diversity in molecularly defined claustral and cortical neuron types

Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits.

neuroscience↗
bioRxiv2019

RecV recombinase system for spatiotemporally controlled light-inducible genomic modifications.

Brain circuits are composed of vast numbers of intricately interconnected neurons with diverse molecular, anatomical and physiological properties. To allow highly specific targeting of individual neurons for structural and functional studies, we modified three site-specific DNA recombinases, Cre, Dre and Flp, by combining them with a fungal light-inducible protein, Vivid, so that their recombinase activities can be driven by blue light. We generated viral vectors to express these light-inducible recombinases and demonstrated that they can induce genomic modifications in dense or sparse populations of neurons in live mouse brains controlled by one-photon or two-photon light induction. As an important application, we showed that light-inducible recombinases can produce highly targeted, sparse and strong labeling of individual neurons thereby enabling whole-brain morphological reconstruction to identify their axonal projection specificity. In addition to targeting cortical brain areas, we applied the method in deep targets, with a demonstration of functional calcium imaging. These molecular tools enable spatiotemporally-precise, targeted genomic modifications that will greatly facilitate detailed analysis of neural circuits and linking genetic identity, morphology, connectivity and function.

neuroscience↗
bioRxiv2018

Narrowly confined and glomerulus-specific onset latencies of odor-evoked calcium transients in the periglomerular cells of the mouse main olfactory bulb

Odor information is transmitted from olfactory sensory neurons to principal neurons at the glomeruli of the olfactory bulb. The intraglomerular neuronal circuit also includes hundreds of GABAergic interneurons referred to as periglomerular (PG) cells. Stimulus selectivity is well correlated among PG cells that are associated with the same glomerulus, consistent with their highly homogeneous sensory inputs. However, much less is known about the temporal aspects of their activity, including the temporal coordination of their odor-evoked responses. As many PG cells within a glomerular module respond to the same stimulus, the extent to which their activity is temporally aligned will affect the temporal profile of their population inhibitory inputs. Using random-access high-speed two-photon microscopy, we recorded the odor-evoked calcium transients of mouse PG cells and compared the onset latency and rise time among neurons putatively associated with the same and different glomeruli. Whereas the overall onset latencies of odor-evoked transients were distributed across a ~150 ms time window, those from cells putatively associated with the same glomerulus were confined to a much narrower window of several tens of milliseconds. This result suggests that onset latency primarily depends on the associated glomerulus. We also observed glomerular specificity in the rise time. The glomerulus-specific temporal pattern of odor-evoked activity implies that the temporal patterns of inhibitory inputs are unique to individual glomerulus-odor pairs, which may contribute to efficient shaping of the temporal pattern of activity in the principal neurons.

neuroscience↗
bioRxiv2017

Advanced NeuroGPS-Tree: dense reconstruction of brain-wideneuronal population close to ground truth

Recent progresses allow imaging specific neuronal populations at single-axon level across mouse brain. However, digital reconstruction of neurons in large dataset requires months of human labor. Here, we developed a tool to solve this problem. Our tool offers a special error-screening system for fast localization of submicron errors in densely packed neurites and along long projection across the whole brain, thus achieving reconstruction close to the ground-truth. Moreover, our tool equips algorithms that significantly reduce intensive manual interferences and achieve high-level automation, with speed 5 times faster compared to semi-automatic tools. We also demonstrated reconstruction of 35 long projection neurons around one injection site of a mouse brain at an affordable time cost. Our tool is applicable with datasets of 10 TB or higher from various light microscopy, and provides a starting point for the reconstruction of neuronal population for neuroscience studies at a single-cell level.

neuroscience↗
bioRxiv2017

Genetic Single Neuron Anatomy reveals fine granularity of cortical interneuron subtypes

Parsing diverse nerve cells into biological types is necessary for understanding neural circuit organization. Morphology is an intuitive criterion for neuronal classification and a proxy of connectivity, but morphological diversity and variability often preclude resolving the granularity of discrete cell groups from population continuum. Combining genetic labeling with high-resolution, large volume light microscopy, we established a platform of genetic single neuron anatomy that resolves, registers and quantifies complete neuron morphologies in the mouse brain. We discovered that cortical axo-axonic cells (AACs), a cardinal GABAergic interneuron type that controls pyramidal neuron (PyN) spiking at axon initial segment, consist of multiple subtypes distinguished by laminar position, dendritic and axonal arborization patterns. Whereas the laminar arrangements of AAC dendrites reflect differential recruitment by input streams, the laminar distribution and local geometry of AAC axons enable differential innervation of PyN ensembles. Therefore, interneuron types likely consist of fine-grained subtypes with distinct input-output connectivity patterns.

neuroscience↗
bioRxiv2017

Identifying weak signals in inhomogeneous neuronal images for large-scale tracing of neurites

Reconstructing neuronal morphology across different regions or even the whole brain is important in many areas of neuroscience research. Large-scale tracing of neurites constitutes the core of this type of reconstruction and has many challenges. One key challenge is how to identify a weak signal from an inhomogeneous background. Here, we addressed this problem by constructing an identification model. In this model, empirical observations made from neuronal images are summarized into rules, which are used to design feature vectors that display the differences between the foreground and background, and a support vector machine is used to learn these feature vectors. We embedded this identification model into a tool that we previously developed, SparseTracer, and termed this integration SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace neurites with extremely weak signals (signal-to-background-noise ratio <1.1) against an inhomogeneous background. By testing 12 sub-blocks extracted from a whole imaging dataset, ST-LFV can achieve an average recall rate of 0.99 and precision rate of 0.97, which is superior to that of SparseTracer (which has an average recall rate of 0.93 and average precision rate of 0.86), indicating that this method is well suited to weak signal identification. We applied ST-LFV to trace neurites from large-scale images (approximately 105 GB). During the tracing process, obtaining results equivalent to the ground truth required only one round of manual editing for ST-LFV compared to 20 rounds of manual editing for SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the scale of an entire brain.

bioinformatics↗