Biology subjects

Qiao, C.

Publications and source records attributed to Qiao, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural basis of self-loading by replicative helicases

DNA replication is conserved in all forms of life on earth, from subcellular structures such as viruses to human cells. Here we study a group of virus parasites whose replication is initiated by helicases like those found in simian viruses and eukaryotic cells. The process by which these viral helicases initiate the replication of the DNA remains poorly understood. Here, we show how the helicase binds the parasite's origin of replication with its carboxy terminal domains (CTDs) adopting flexible and different conformations. The CTD's flexibility moves the DNA towards the inside of the helicase's ring and bends the DNA at three discrete positions curving it. Our cryo-EM snapshots show that the helicase deforms the DNA while self-loading onto it as the first stage of the initiation of DNA replication, suggesting that initial melting and self-loading are coupled processes in this type of virus parasites.

biochemistry↗

bioRxiv2026

Redox-dependent dimerization of PolDIP2 and a conserved ApaG-domain motif required for CHCHD2 interaction

PolDIP2 is a multifunctional mitochondrial protein implicated in redox regulation, mitochondrial proteostasis, and diverse mtDNA-associated processes, yet the principles underlying its regulation remain unclear. Crystallographic analysis revealed that PolDIP2 forms a redox-dependent disulfide-linked homodimer via a conserved Cys143 residue within its N-terminal YccV-like domain, and cellular and in vitro assays confirmed that this residue is essential for dimer formation. Oxidative stress enhanced dimerization of endogenous and ectopically expressed PolDIP2, and dimers were detected exclusively within mitochondria, requiring proper mitochondrial import. WT and C143A PolDIP2 overexpression produced similarly modest effects on mtDNA replication in cells, suggesting that dimerization has limited impact on mtDNA-associated processes. Proteomic analysis and biochemical validation identified both previously known and not yet characterized mitochondrial interactors of PolDIP2, and highlighted CHCHD2 as a specific binding partner. A conserved glycine-rich motif in the C-terminal ApaG/DUF525-like domain proved essential for this interaction, and disruption of the motif enhanced Cys143-dependent dimerization while abolishing CHCHD2 association, which preferentially occurs with monomeric PolDIP2. These findings define redox-controlled dimerization and a conserved ApaG-domain motif as key structural features shaping PolDIP2s interaction state within mitochondria and provide a basis for exploring its roles in redox-sensitive mitochondrial pathways.

cell biology↗

bioRxiv2025

A bottom-up approach to understanding ultrasound mediated blood-brain barrier opening using long and short pulses

Ultrasound-mediated blood-brain barrier (BBB) opening is promising for non-invasive, localized, and reversible drug delivery to the brain. However, the underlying mechanisms remain unclear, particularly at microscopic level in real time. Recently developed short-pulse ultrasound enhances safety by reducing side effects compared to conventional long-pulse ultrasound. Using vessel-mimicking microchannels cultured with brain endothelial monolayers, we directly observed bubble dynamics and cellular bioeffects under flow conditions. Intriguingly, cyclic jetting during stable cavitation occurs at long-pulse mode, accompanied by a few localized cell detachments and extensive sonoporation. In contrast, short-pulse ultrasound induced milder, uniform bubble dynamics, reversible sonoporation and calcium signaling, promoting safer BBB modulation. In vivo two-photon imaging and histology in mice confirmed these findings, showing that long-pulse ultrasound enabled higher drug delivery efficiency but caused localized endothelial damage, while short-pulse ultrasound facilitated uniform delivery and faster BBB recovery. The differential bubble dynamics and cellular responses correlated well between in vitro and in vivo models. This study establishes a cross-scale framework for real-time analysis of ultrasound-mediated BBB opening, revealing key biophysical factors governing safety and efficacy. The findings provide guidance for the optimization of ultrasound protocols for vascular drug delivery, with potential applications in treating brain tumors, neurodegenerative diseases, and other disorders.

biophysics↗

bioRxiv2025

Ultrasonic Modulation of Astrocytic and Neuronal Calcium Dynamics in Mouse Cortex

BackgroundAstrocytes are abundant in the brain and their calcium signaling is reported to have an important effect on neuronal activity in both physiological and pathological conditions. Low-frequency focused ultrasound (FUS) has recently emerged as a powerful noninvasive neuromodulation approach, yet its impact on astrocyte calcium dynamics in different brain states in vivo is poorly understood. ObjectiveThis study aimed to elucidate the effects of non-thermal FUS on astrocyte calcium dynamic with in vivo cellular-resolution and cell-type-specific recording and identify whether the influences of FUS on cortical astrocytes and neurons are distinctive and state dependent. MethodsHere we combined a customized 0.521MHz FUS transducer with two-photon microscopy, allowing simultaneous single-cell resoultion imaging and FUS stimulation at intensities of 0.91 or 1.5 W/cm2 to examine astrocyte and neuronal calcium responses in somatosensory cortex of both awake and lightly anesthetized mice. Functional clustering analysis was performed to identify calcium response activated or inhibited subpopulations. ResultsIn awake mice, FUS significantly enhanced the amplitude, frequency, and temporal integral of astrocyte calcium transients, while suppressing neuronal calcium activity and reducing the proportion of activated neuronal subpopulations. In contrast, lightly anesthetized mice displayed a blunted yet increased astrocyte response and negligible neuronal modulation under FUS, suggesting that baseline suppression from anesthesia partially masks FUS effects. ConclusionsOur study demonstrated that FUS elicited distinctive, state-dependent effects on cortical astrocytes and neurons, highlighting astrocytes as previously underappreciated targets of ultrasound neuromodulation. These findings will pave the way for FUS-based therapies targeting astrocyte-neuron interactions in conditions involving abnormal brain excitability. HighlightsO_LILow duty cycle low frequency focused ultrasound induced negligible heat effects. C_LIO_LIUltrasound markedly enhanced astrocyte calcium transients in awake mice. C_LIO_LIUltrasound suppressed neuronal calcium activity in awake mice. C_LIO_LIThe effects of ultrasound on both cell types were reduced under light anesthesia. C_LI

bioengineering↗

bioRxiv2024

Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity

BackgroundThe occurrence of brain disorders correlates with detectable dysfunctions in the specialization of brain connectomics. While extensive research has explored this relationship, there is a lack of studies specifically examining the statistical correlation between the integration and segregation of psychotic brain networks using high-order networks, given the limitations of low-order networks. Moreover, these dysfunctions are believed to be linked to information imbalances in brain functions. However, our understanding of how these imbalances give rise to specific psychotic symptoms remains limited. MethodsThis study aims to address this gap by investigating variations at the topological high-order level of the system with regard to specialization in both healthy individuals and those diagnosed with schizophrenia. By employing graph-theoretic brain network analysis, we systematically examine information integration and segregation to delineate system-level differences in the connectivity patterns of brain networks. ResultsThe findings indicate that topological high-order functional connectomics highlight differences in the connectome between healthy controls and schizophrenia, demonstrating increased cingulo-opercular task control and salience interactions, while the interaction between subcortical and default mode networks, dorsal attention and sensory/somatomotor mouth decreases in schizophrenia. Furthermore, we observed a reduction in the segregation of brain systems in healthy controls compared to individuals with schizophrenia, which means the balance between segregation and integration of brain networks is disrupted in schizophrenia, suggesting that restoring this balance may aid in the treatment of the disorder. Additionally, the increased segregation and decreased integration of brain systems in schizophrenia patients compared to healthy controls may serve as a novel indicator for early schizophrenia diagnosis. ConclusionWe discovered that topological high-order functional connectivity highlights brain network interactions compared to low-order functional connectivity. Furthermore, we observed alterations in specific brain regions associated with schizophrenia, as well as changes in brain network information integration and segregation in individuals with schizophrenia.

neuroscience↗

bioRxiv2024

Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging

Lattice light-sheet microscopy (LLSM) provides a crucial observation window into intra- and inter-cellular physiology of living specimens with high speed and low phototoxicity, however, at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present the meta-learning-empowered reflective lattice light-sheet virtual structured illumination microscopy (Meta-rLLS-VSIM), which instantly upgrades LLSM to a near-isotropic super resolution of [~]120-nm laterally and [~]160-nm axially, more than twofold improvement in each dimension, without any modification of the optical system or sacrifice of other imaging metrics. Moreover, to alleviate the tremendous demands on training data and time necessitated by existing deep-learning (DL) methods, we devised an adaptive online training approach by synergizing the front-end imaging system and back-end meta-learning framework, which reduced the total time for data acquisition and model training down to tens of seconds. With this method, a new model can be well-trained with tenfold less data and three orders of magnitude less time than current standard supervised learning. We demonstrate the versatile functionalities of Meta-rLLS-VSIM by imaging a variety of bioprocesses with ultrahigh spatiotemporal resolution for long duration of hundreds of multi-color volumes, characterizing the dynamic regulation of contractile ring filaments during mitosis and the growth of pollen tubes, and delineating the nanoscale distributions, dispersion, and interaction pattern of multiple organelles in embryos and eukaryotic cells.

biophysics↗

bioRxiv2024

Time-lapse Image Super-resolution Neural Network with Reliable Confidence Evaluation for Optical Microscopy

Single image super-resolution (SISR) neural networks for optical microscopy have shown great capability to directly transform a low-resolution (LR) image into its super-resolution (SR) counterpart, enabling low-cost long-term live-cell SR imaging. However, when processing time-lapse data, current SISR models failed to exploit the important temporal dependencies between neighbor frames, often resulting in temporally inconsistent outputs. Besides, SISR models are subject to inference uncertainty that is hard to accurately quantify, therefore it is difficult to determine to what extend can we trust the inferred SR images. Here, we first build a large-scale, high-quality fluorescence microscopy dataset for the time-lapse image super-resolution (TISR) task, and conducted a comprehensive evaluation on two essential components of TISR neural networks, i.e., propagation and alignment. Second, we devised a deformable phase-space alignment (DPA) based TISR neural network (DPA-TISR), which adaptively enhances the cross-frame alignment in the phase domain and outperforms existing state-of-the-art SISR and TISR models. Third, we combined the Bayesian training scheme and Monte Carlo dropout with DPA-TISR, developing Bayesian DPA-TISR, and designed an expected calibration error (ECE)minimization framework to obtain a well-calibrated confidence map along with each output SR image, which reliably implicates potential inference errors. We demonstrate the unique characteristics of Bayesian DPA-TISR underlie the ultralong-term live-cell SR imaging capability with high spatial fidelity, superb temporal consistency, and accurate confidence quantification on a wide variety of bioprocesses.

biophysics↗

bioRxiv2023

Deep Learning-based Optical Aberration Estimation Enables Offline Digital Adaptive Optics and Super-resolution Imaging

Optical aberrations degrade the performance of fluorescence microscopy. Conventional adaptive optics (AO) leverages specific devices, such as the Shack-Hartmann wavefront sensor and deformable mirror, to measure and correct optical aberrations. However, conventional AO requires either additional hardware or a more complicated imaging procedure, resulting in higher cost or a lower acquisition speed. In this study, we proposed a novel space-frequency encoding network (SFE-Net) that can directly estimate the aberrated point spread functions (PSFs) from biological images, enabling fast optical aberration estimation with high accuracy without engaging extra optics and image acquisition. We showed that with the estimated PSFs, the optical aberration can be computationally removed by deconvolution algorithm. Furthermore, to fully exploit the benefits of SFE-Net, we incorporated the estimated PSF with neural network architecture design to devise an aberration-aware deep-learning super-resolution (DLSR) model, dubbed SFT-DFCAN. We demonstrated that the combination of SFE-Net and SFT-DFCAN enables instant digital AO and optical aberration-aware super-resolution reconstruction for live-cell imaging.

bioengineering↗

bioRxiv2023

A novel MARV glycoprotein-specific antibody with potentials of broad-spectrum neutralization to filovirus

Marburg virus (MARV) is one of the filovirus species that cause deadly hemorrhagic fever in humans, with mortality rates up to 90%. Neutralizing antibodies represent ideal candidates to prevent or treat virus disease. However, no antibody has been approved for MARV treatment to date. In this study, we identified a novel human antibody named AF-03 that targeted MARV glycoprotein (GP). AF-03 possessed a high binding affinity to MARV GP and showed neutralizing and protective activities against the pseudotyped MARV in vitro and in vivo. Epitope identification, including molecular docking and experiment-based analysis of mutated species, revealed that AF-03 recognized the Niemann-Pick C1 (NPC1) binding domain within GP1. Interestingly, we found the neutralizing activity of AF-03 to pseudotyped Ebola viruses (EBOV, SUDV, and BDBV) harboring cleaved GP instead of full-length GP. Furthermore, NPC2-fused AF-03 exhibited neutralizing activity to several filovirus species and EBOV mutants via binding to CI-MPR. In conclusion, this work demonstrates that AF-03 represents a promising therapeutic cargo for filovirus-caused disease.

immunology↗

bioRxiv2023

RAPSYN-Mediated Neddylation of BCR-ABL Alternatively Determines the Fate of Philadelphia Chromosome-positive Leukemia

Philadelphia chromosome-positive (Ph+) leukemia is a fatal hematological malignancy. Although standard treatments with tyrosine kinase inhibitors (TKI) have achieved remarkable success in prolonging patient survival, intolerance, relapse and TKI resistance remain serious issues for patients with Ph+ leukemia. Here, we report a new leukemogenic process in which RAPSYN and BCR-ABL co-occur in Ph+ leukemia, and RAPSYN mediates the neddylation of BCR-ABL. Consequently, neddylated BCR-ABL enhances the stability by competing its c-CBL-mediated degradation. Furthermore, SRC phosphorylates RAPSYN to activate its NEDD8 E3 ligase activity, promoting BCR-ABL stabilization and disease progression. Moreover, in contrast to in vivo ineffectiveness of PROTAC-based degraders, depletion of RAPSYN expression or its ligase activity decreased BCR-ABL stability and, in turn, inhibited tumor formation and growth. Collectively, these findings represent an alternative to tyrosine kinase activity for the oncoprotein and leukemogenic cells and generate a rationale of targeting RAPSYN-mediated BCR-ABL neddylation for the treatment of Ph+ leukemia.

cancer biology↗

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

Towards a reliable spatial analysis of missing features via spatially-regularized imputation

AO_SCPLOWBSTRACTC_SCPLOWImputation of missing features in spatial transcriptomics is urgently demanded due to technology limitations, while most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. To fill the research gaps, we introduce a computational model, TransImp, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference. Uniquely, we derived a set of attributes that can accurately predict imputation uncertainty, hence enabling us to select reliably imputed genes. Also, we introduced a spatial auto-correlation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms have demonstrated that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.

bioinformatics↗

bioRxiv2022

UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference

The recent breakthrough of single-cell RNA velocity methods brings attractive promises to reveal directed trajectory on cell differentiation, states transition and response to perturbations. However, the existing RNA velocity methods are often found to return erroneous results, partly due to model violation or lack of temporal regularization. Here, we present UniTVelo, a statistical framework of RNA velocity that models the dynamics of spliced and unspliced RNAs via flexible transcription activities. Uniquely, it also supports the inference of a unified latent time across the transcriptome. With ten datasets, we demonstrate that UniTVelo returns the expected trajectory in different biological systems, including hematopoietic differentiation and those even with weak kinetics or complex branches.

bioinformatics↗

bioRxiv2022

Munc13 supports vesicle fusogenicity after disrupting active zone scaffolds and synaptic vesicle docking

Active zones consist of protein scaffolds that are tightly attached to the presynaptic plasma membrane. They dock and prime synaptic vesicles, couple them to Ca2+ entry, and target neurotransmitter release to postsynaptic receptor domains. Simultaneous RIM+ELKS ablation disrupts these scaffolds, abolishes vesicle docking and removes active zone-targeted Munc13, but some vesicles remain releasable. This enduring vesicular fusogenicity may be Munc13-independent or be mediated by non-active zone-anchored Munc13. We tested its Munc13-dependence by ablating Munc13-1 and Munc13-2 on top of RIM+ELKS in cultured hippocampal neurons. The hextuple knockout synapses lacked docked vesicles, but other ultrastructural features were near-normal despite the strong genetic manipulation. Removing Munc13 in addition to RIM+ELKS further impaired action potential-evoked release by decreasing the remaining pool of releasable vesicles. We conclude that Munc13 can support some fusogenicity without RIM and ELKS, and that presynaptic recruitment of Munc13, even without active zone-anchoring, suffices to generate some fusion-competent vesicles.

neuroscience↗

bioRxiv2021

MQuad enables clonal substructure discovery using single cell mitochondrial variants

Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via https://github.com/single-cell-genetics/MQuad.

bioinformatics↗

bioRxiv2021

Representation learning of RNA velocity reveals robust cell transitions

AO_SCPLOWBSTRACTC_SCPLOWRNA velocity is a promising technique to reveal transient cellular dynamics among a heterogeneous cell population and quantify their transitions from single-cell transcriptome experiments. However, the cell transitions estimated from high dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present VeloAE, a tailored representation learning method to learn a low-dimensional representation of RNA velocity on which cell transitions can be robustly estimated. From various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture the expected cellular differentiation in different biological systems. VeloAE therefore enhances the usefulness of RNA velocity for studying a wide range of biological processes.

bioinformatics↗

bioRxiv2019

An engineered serum albumin-binding AAV9 capsid achieves improved liver transduction after intravenous delivery in mice

Recombinant adeno-associated viral (AAV) vectors are frequently used to deliver nucleic acids for in vivo applications and are currently the leading platform for therapeutic gene delivery in gene therapy clinical trials. Presently, there is a need for improved AAV vectors with optimized transduction efficiency in target tissues. In these studies, an engineered albumin-binding consensus domain (ABDCon) was incorporated into the AAV9 capsid via fusion to the N-terminus of the AAV9 VP2 capsid protein to generate a variant AAV9 capsid with albumin-binding properties. The variant capsid, called AAV9-ABDCon, formed viable genome-containing vector particles and exhibited binding to human serum albumin. The AAV9 capsid, on the other hand, was not found to bind to human serum albumin by the methods used in this study. Following intravenous administration, the modified AAV9-ABDCon vector was found to achieve higher levels of transduction in liver tissue compared to AAV9. These findings suggest that serum albumin-binding may be a potential method to augment AAV-mediated liver-directed gene delivery.

bioengineering↗