Biology subjects

Dai, Q.

Publications and source records attributed to Dai, Q..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Splenic red pulp macrophages eliminate the liver-resistant Streptococcus pneumoniae during bloodstream infection

The spleen is well-known for defense against invasive infections of encapsulated bacteria, particularly Streptococcus pneumoniae (pneumococcus). However, the precise mechanism of the splenic anti-bacterial immunity remains elusive. Here we report that red pulp (RP) macrophages execute the splenic defense against S. pneumoniae in mice, with the help of natural antibodies (nAbs) and complement system. The spleen slowly but substantially cleared encapsulated pneumococci from the bloodstream in the early phase of blood infection, especially the serotypes that resist to liver filtration. Among splenic macrophage subpopulations, only the lack of RP macrophages led to the complete loss of the splenic immunity. Intravital microscopy detected direct capture and phagocytic killing of circulating pneumococci by RP macrophages in the absence of neutrophils and inflammatory monocytes. Likewise, plasma nAbs targeting pneumococcal cell wall phosphocholine and complement protein C3 were essential for RP macrophage-mediated immune clearance. Our findings have thus uncovered the long-sought immune pathway against invasive pneumococcal infections in the spleen. Short summaryThe spleen plays a crucial role in defending against invasive infections caused by Streptococcus pneumoniae (pneumococcus) and other encapsulated bacteria in humans, but the mechanisms underlying this organ-specific immunity remain largely elusive. This study has revealed that the spleen eliminates pneumococci through a coordinated interplay among red pulp macrophages, serum nAbs targeting phosphocholine and the complement system.

microbiology↗

bioRxiv2024

The PR factor Hamlet controls heterotypic epithelial tissue assembly in Drosophila reproduction system

Epithelial tissue fusion requires coordinated molecular events at the ends of two epithelial structures. Regulatory mechanisms controlling these events remain largely elusive. In the Drosophila reproductive system (RS), this fusion unites the gonad and the genital disc-derived tissues, into a continuous tube. This study unveils the pivotal role of Hamlet (Ham), a Drosophila PR-domain containing transcription factor, in orchestrating epithelial tissue fusion in the RS. Loss of ham leads to sterility and disconnection between the testes (TE) and seminal vesicles (SV). Systematic analysis of Ham-downstream genes reveals cytoskeletal, metabolic regulators and signaling pathway components. Ham activates genes for epithelial differentiation and remodeling, while repressing genes required for tissue growth and patterning. Using multiplexed in situ hybridization, we demonstrate spatial-temporal gene expression dynamics in contacting epithelia. Key Ham downstream effectors include E-Cadherin (E-Cad), Toll (Tl) and Wnt2 signaling pathways, regulating tissue interaction and fusion. Our findings present a comprehensive gene network crucial for heterotypic epithelial tissue fusion. Mammalian Ham orthologs PRDM3 and PRDM16 are highly expressed in epithelial tissues, suggesting a conserved role across species.

developmental biology↗

bioRxiv2024

Prominent involvement of acetylcholine in shaping stable olfactory representation across the Drosophila brain

Despite the vital role of neuromodulation in the neural system, the specific spatiotemporal dynamics of neuromodulators and their interactions with neuronal activities in vivo are still unclear, hampering our understanding of their information representation and functional contributions systemically. To address this problem, we employed two-photon synthetic aperture microscopy (2pSAM) to record long-term neuronal and neuromodulatory olfactory responses across the Drosophila brain at high speed. Our results revealed distinct response properties, global information propagation, functional connectivity, and odor identity representation among neuronal, cholinergic, and serotoninergic dynamics across multiple brain regions. We discovered the compensation between neuronal activity and cholinergic dynamics, both in the odor identity representation across the brain and the functional connectivity network structures of specific brain regions. Moreover, employing low-dimensional manifold and functional connectivity network analyses, we characterized the stable representation of cholinergic dynamics over a long term. Collectively, our unbiased and comprehensive investigation unveiled the prominent involvement of acetylcholine (ACh) in shaping olfactory representation across the brain, underscoring the inadequacy of solely considering neuronal activities when examining information representation of the brain.

neuroscience↗

bioRxiv2024

A Systematically Optimized Miniaturized Mesoscope (SOMM) for large-scale calcium imaging in freely moving mice

Understanding how neuronal dynamics gives rise to ethologically relevant behavior requires recording of neuronal population activity via technologies that are compatible with unconstrained animal behavior. However, realizations of cellular resolution head-mounted microscopes for mice have been based on conventional microscope designs that feature various forms of ad-hoc miniaturization and weight reduction measures necessary for compatibility with the weight-limits for free animal behavior. As a result, they have typically remained limited to a small field of view (FOV) or low resolution, a shallow depth range and often remain susceptible to motion-induced artifacts. Here, we present a systematically optimized miniaturized mesoscope (SOMM), a widefield, head-mounted fluorescent mesoscope based on a principled optimization approach that allows for mesoscale, cellular resolution imaging of neuroactivity while offering robustness against motion-induced artifacts. This is achieved by co-optimization of a compact diffractive optical element and the associated computational algorithm under form-factor and weight constraints while maximizing the obtainable FOV, depth of field (DOF), and resolution. SOMM enables recordings of neuronal population activity at up to 16 Hz within a FOV of 3.6 x 3.6 mm2 in the cortex of freely moving mice while featuring 4-{micro}m resolution, a DOF of 300 {micro}m at a weight of less than 2.5 g. We show SOMMs performance of recording large-scale neuronal population activity during social interactions, during conditioning-type experiments and by investigating neurovascular coupling using dual-color imaging.

neuroscience↗

bioRxiv2023

Neural mechanism of attention modulation during non-movement decision-making

Decision-making is a cognitive process, in which participants need to attend to relevant information and ignore the irrelevant information. Previous studies have described a set of cortical areas important for attention. It is unclear whether subcortical areas also serve a role. The subthalamic nucleus (STN), a part of basal ganglia, is traditionally considered a critical node in the cortico-basal ganglia-thalamus-cortico network. Given the location of the STN and its widespread connections with cortical and subcortical brain regions, the STN plays an important role in motor and non-motor cognitive processing. We would like to know if STN is also related to fluctuations in attentional task performance, and how the STN interacts with prefrontal cortical regions during the process. We examined neural activities within STN covaried with lapses of attention (defined as behavior error). We found that decreased neural activities in STN were associated with sustained attention. By examining connectivity across STN and various sub-regions of the prefrontal cortex (PFC), we found that decreased connectivity across areas was associated with sustained attention. Our results indicated that decreased STN activities were associated with sustained attention, and the STN-PFC circuit supported this process. Significance StatementAttention is a core internal state variable that governs the allocation of limited resources depending on the task demands in various cognitive processes. If the subcortical area, subthalamic nucleus (STN), related to attentional fluctuations, and how the STN interacted with cortical regions during the process remains unclear. In this study, we examined neural activities within STN, and connectivity between STN and various prefrontal sub-regions during sustained attention and lapses of attention. We found both neural activities within STN and connectivity between STN-PFC circuit decreased during sustained attention. These findings indicated that decreased STN activities were associated with sustained attention, and the STN-PFC circuit supported this process.

neuroscience↗

bioRxiv2023

Optical segmentation-based compressed readout of neuronal voltage dynamics

Functional imaging of biological dynamics generally begins with acquiring time-series images, followed by quantifying spatially averaged intensity traces for the regions of interest (ROIs). The conventional pipeline discards a substantial portion of the acquired data when quantifying intensity traces, indicative of inefficient data acquisition. Here we propose a conceptually novel acquisition pipeline that assigns each ROI to a single pixel in the detector, enabling optimally compressed acquisition of the intensity traces. As a proof-of-principle, we implemented a detection module composed of a pair of spatial light modulators and a microlens array, which segments the original image into multiple subimages by introducing distinct angular shifts to each ROI. Each subimage exclusively encodes the signal for the corresponding ROI, facilitating the compressed readout of its intensity trace using a single pixel. This spatial compression allowed for maximizing the temporal information without compromising the spatial information on ROIs. Harnessing our novel approach, we demonstrate the recording of circuit-scale neuronal voltage dynamics at over 5 kHz sampling rate, revealing the individual action potential waveforms within subcellular structures, as well as their submillisecond-scale temporal delays.

neuroscience↗

bioRxiv2023

Synthetic Frizzled agonist and LRP antagonist for high-efficiency Wnt/β-catenin signaling manipulation in organoid cultures and in vivo

Wnt/{beta}-catenin signaling and its dysregulation play critical roles in the fate determination of stem cells and the pathology of various diseases. However, the application of translated Wnt ligand in regenerative medicine is hampered by its hydrophobicity and cross-reactivity with Frizzled (FZD) receptors. Here, we generate an engineered water-soluble, FZD subtype-specific agonist, RRP-pbFn, for high-efficiency Wnt/{beta}-catenin signaling activation. In the absence of direct binding to LRP5/6, RRP-pbFn stimulates Wnt/{beta}-catenin signaling more potently than surrogate Wnt. RRP-pbFn supports the growth of a variety of mouse and human organoids, and induces the expansion of liver and intestine progenitors in vivo. Meanwhile, we develop a synthetic LRP antagonist, RRP-Dkk1c, which exhibits heightened effectiveness in attenuating Wnt/{beta}-catenin signaling activity compared to Dkk1, thereby abolishing the formation of CT26-derived colon cancer xenograft in vivo. Together, these two paired Wnt/{beta}-catenin signaling manipulators hold great promise for biomedical research and potential therapeutics.

synthetic biology↗

bioRxiv2023

Enzyme-mediated alkynylation enables transcriptome-wide identification of pseudouridine modifications

Pseudouridine ({Psi}) is one of the most abundant chemical modifications that exists in various types of RNA species and is known to play important roles in RNA function. The advances in studies of {Psi} in less abundant messenger RNA species have been hindered by a lack of suitable methods to precisely and sensitively map their distributions. Here we show that a methyltransferase from Methanocaldococcus jannaschii can label RNA {Psi} efficiently and specifically with various functional groups, both in isolated RNA and inside cells. We leveraged this enzymatic labeling strategy to develop ELAP-seq as a facile method to enrich {Psi}-modified transcripts for the detection of {Psi} modifications at single base resolution with high sensitivity and low background. Using this method, we identified over 10, 000 candidate {Psi} sites from human transcripts, which provides new insights into {Psi} biosynthesis and function. Our study provides a chemical biology method that specifically labels {Psi} for its detection and functional alteration.

biochemistry↗

bioRxiv2023

Spatial redundancy transformer for self-supervised fluorescence image denoising

Fluorescence imaging with high signal-to-noise ratios has become the foundation of accurate visualization and analysis of biological phenomena. However, the inevitable photon shot noise poses a formidable challenge on imaging sensitivity. In this paper, we provide a spatial redundancy denoising transformer (SRDTrans) to remove noise from fluorescence images in a self-supervised manner. First, a sampling strategy based on spatial redundancy is proposed to extract adjacent orthogonal training pairs, which eliminates the dependence on high imaging speed. Secondly, to break the performance bottleneck of convolutional neural networks (CNNs), we designed a lightweight spatiotemporal transformer architecture to capture long-range dependencies and high-resolution features at a low computational cost. SRDTrans can overcome the inherent spectral bias of CNNs and restore high-frequency information without producing over-smoothed structures and distorted fluorescence traces. Finally, we demonstrate the state-of-the-art denoising performance of SRDTrans on single-molecule localization microscopy and two-photon volumetric calcium imaging. SRDTrans does not contain any assumptions about the imaging process and the sample, thus can be easily extended to a wide range of imaging modalities and biological applications.

biophysics↗

bioRxiv2023

Self-supervised denoising for structured illumination microscopy enables long-term super-resolution live-cell imaging

Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of high-quality training data severely limits their applications. Here we developed a pixel-realignment-based self-supervised denoising framework for SIM (PRS-SIM) that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction artifacts. We demonstrated that PRS-SIM generates artifact-free images with 10-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth (GT). Moreover, the proposed method is compatible with multiple SIM modalities such as total internal reflective fluorescence SIM (TIRF-SIM), three-dimensional SIM (3D-SIM), lattice light-sheet SIM (LLS-SIM), and non-linear SIM (NL-SIM). With PRS-SIM, we achieved long-term super-resolution live-cell imaging of various bioprocesses, revealing the clustered distribution of clathrin coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

cell biology↗

bioRxiv2023

Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy

Computational super-resolution (SR) methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding SR performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary SR imaging conditions in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional (3D) wide-field microscopy, confocal microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy (SIM), which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

bioengineering↗

bioRxiv2023

PRDM16 restricts stem cell proliferation by integrating BMP and Wnt signalling in the developing choroid plexus

BMP signalling acts as an instructive cue in various developmental processes such as tissue patterning, stem cell proliferation, and differentiation. However, it is not fully understood how this signalling pathway generates different cell-specific outputs. Here we have identified PRDM16 as a key co-factor for BMP signalling. PRDM16 contributes to a repressive role of BMP signalling on neural stem cell (NSC) proliferation. We demonstrate that PRDM16 regulates the genomic distribution of BMP pathway transcription factors, the SMAD4/pSMAD complex, preventing the activation of cell proliferation genes. When Prdm16 is lost, the SMAD complex relocates to nearby genomic regions, leading to abnormal upregulation of BMP target genes. This function of PRDM16 is also required for the specification of choroid plexus (ChP) epithelial cells. Through a single-cell resolution fluorescent in situ approach, we have observed that genes co-repressed by SMAD and PRDM16, such as Wnt7b and several cell cycle regulators, become overexpressed in Prdm16 mutant ChP. Our findings elucidate a mechanism through which SMAD4 and pSMAD1/5/8 repress gene expression. Moreover, our study suggests a regulatory circuit composed of BMP and Wnt signaling, along with PRDM16, in controlling stem cell behaviors.

developmental biology↗

bioRxiv2022

Sharing Massive Biomedical Data at Magnitudes Lower Bandwidth Using Implicit Neural Function

Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning based methods demand huge training data and are difficult to generalize. Here we propose to conduct Biomedical data compRession with Implicit nEural Function (BRIEF) by representing the original data with compact neural networks, which are data specific and thus have no generalization issues. Benefiting from the strong representation capability of implicit neural function, BRIEF achieves 2 [~] 3 orders of magnitude compression on diverse biomedical data at significantly higher fidelity than existing techniques. Besides, BRIEF is of consistent performance across the whole data volume, supports customized spatially-varying fidelity. BRIEFs multi-fold advantageous features also serve reliable downstream tasks at low bandwidth. Our approach will facilitate low-bandwidth data sharing, and promote collaboration and progress in the biomedical field.

bioinformatics↗

bioRxiv2022

Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy

Fluorescence microscopy has become an indispensable tool for revealing the dynamic regulations of cells and organelles in high resolution noninvasively. However, stochastic noise inherently restricts the upper bonds of optical interrogation quality and exacerbates the observation fidelity in encountering joint demand of high frame rate, long-term, and low photobleaching and phototoxicity. Here, we propose DeepSeMi, a self-supervised-learning-based denoising framework capable of increasing SNR by over 12 dB across various conditions. With the introduction of newly designed eccentric blind-spot convolution filters, DeepSeMi accomplished efficacious denoising requiring no clean data as references and no compromise of spatiotemporal resolution on diverse imaging systems. The computationally 15-fold multiplied photon budget in a standard confocal microscope by DeepSeMi allows for recording organelle interactions in four colors and high-frame-rate across tens of thousands of frames, monitoring migrasomes and retractosomes over a half day, and imaging ultra-phototoxicity-sensitive Dictyostelium cells over thousands of frames, all faithfully and sample-friendly. Through comprehensive validations across various cells and species over various instruments, we prove DeepSeMi is a versatile tool for reliably and bio-friendly breaking the shot-noise limit, facilitating automated analysis of massive data about cell migrations and organelle interactions.

bioengineering↗

bioRxiv2022

Mapping global land sharing-sparing patterns between human and wildlife

Understanding the global patterns of land sharing-sparing between humans and wildlife is essential for pragmatic conservation implementation, yet analytical foundations and indicator-based assessments are still lacking. By integrating distributions of 30,664 terrestrial vertebrates and human pressures, we provide a series of spatial explicit Human-Nature Indices (HNIs) before classifying the global lands into four categories. We found that the Co-occurring (C) regions, where lands are shared by humans and wildlife, are not insignificant (16.91% of global land). For land-sparing, the Diversity-intact (D) and the Anthropic (A) regions account for 45.64% and 1.41% of the land, respectively. The patterns of HNIs, varying considerably among taxonomic groups, are determined mainly by the expansion of human-dominated land use. Land sharing and sparing could work as complementary strategies to support biodiversity and human development toward ambitious and pragmatic 30 by 30 goals. Our results highlight that those regions should adopt different conservation strategies according to their sharing-sparing patterns and distribution of protected areas.

ecology↗

bioRxiv2022

habCluster: Identifying Geographical Boundary among Intraspecific Units Using Community Detection Algorithm in R

Conservation management for a species generally rests on intraspecific units, while identification of their geographic boundaries is necessary for the implementation. Intraspecific units can be discriminated using population genetic methods, yet an analytical approach is still lacking for detecting their geographic boundaries. Here, based on landscape connectivity, we present a geographical boundary delineation method, habCluster, using community detection algorithm. Community detection is an algorithm in graph theory used to identify clusters of highly connected nodes within a network. We assume that the habitat raster cells with better connections tend to form a continuous habitat patch than the others, thus making the range of an intraspecific unit. The method is tested on grey wolf (Canis lupus) habitat in Europe and on giant panda (Ailuropoda melanoleuca) habitat in China. The habitat suitability for grey wolves and giant pandas were evaluated using species distribution modeling. Each cell in the habitat suitability index (HSI) raster is treated as a node and directly connected with its eight neighbor cells. The edge weight between nodes is the distance between the center of them weighted by the average of their HSI values. We implement habCluster using R programming language with inline C++ code to speed up the computing. The geographical clusters detected were compared with the HSI maps for both species and with the nature reserves for giant panda. We found the boundaries of the clusters delineated using habCluster could serve as a good indicator of habitat patches, and they match generally well with nature reserves in the giant panda case. habCluster can provide spatial analysis basis for conservation management plans such as monitoring, translocation and reintroduction, as well as for population structure research.

ecology↗

bioRxiv2022

AI inspired discovery of new biomarkers for clinical prognosis of liver cancer

Tissue biomarkers are crucial for cancer diagnosis, prognosis assessment, and treatment planning. However, few of current biomarkers used in clinics are robust enough to show a true analytical and clinical value. Thus the search for additional tissue biomarkers, including the strategies to identify them, is imperative. Recently, the capabilities of deep learning (DL)-based computational pathology in cancer diagnosis and prognosis have been explored, but the limited interpretability and generalizability make the results difficult to be accepted in clinical practice. Here we present an interpretable human-centric DL-guided framework--PathFinder (Pathological-biomarker-finder)-- that can inspire pathologists to discover new tissue biomarkers from well-performing DL models, which bridges the gap between DL and clinical prognosis. By combining sparse multi-class tissue spatial distribution information of whole slide images (WSIs) with attribution methods, PathFinder can achieve localization, characterization, and verification of potential biomarkers, while guaranteeing state-of-the-art prognostic performance. With the inspiration of PathFinder, we discovered that tumor necrosis in liver cancer, a long-neglected factor, has a strong relationship with patient prognosis. Thus we proposed two clinically independent indicators, including necrosis area fraction and tumor necrosis distribution, for practical prognosis, and verified their potentials in clinical prognosis according to Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)-derived criteria. Our work demonstrates a successful example of introducing artificial intelligence (AI) into clinical practice in a knowledge discovery way, which can be adopted in identifying biomarkers in various cancer types and modalities.

cancer biology↗

bioRxiv2022

OTTERS: A powerful TWAS framework leveraging summary-level reference data

Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies.

genetics↗