Biology subjects

Fei, P.

Publications and source records attributed to Fei, P..

At least 19 recordsLinked to original sources

bioRxiv2026

PD-1 remodels SHP2 dynamics and drives the non-catalytic inhibition of T cell activation

The inhibitory receptor programmed cell death protein 1 (PD-1) suppresses T cell activation primarily by recruiting the src homology 2 domain-containing phosphatase 2 (SHP2), thereby unleashing its phosphatase activity. However, a purely enzymatic model struggles to fully explain the rapid and robust disruption of proximal T cell receptor (TCR) and CD28 signaling. Here, by integrating X-ray crystallography, single-molecule magnetic tweezers, supported lipid bilayers, TIRF imaging, liquid-liquid phase separation, and cellular assays, we uncover a dual-layered inhibitory mechanism of the PD-1/SHP2 axis. Mechanistically, we determine the crystal structure of SHP2 in complex with dually phosphorylated PD-1 cytoplasmic motifs (pPD-1), revealing a ligand-induced open conformation, which is distinct from the oncogenic E76K-associated state. We further directly resolve the closed-to-open conformational dynamics of individual SHP2 molecules, characterized by an 8-nm transition amplitude, and demonstrate that pPD-1 accelerates the transition by at least 94-fold. Crucially, the pPD-1-mediated highly open conformation exposes the tandem SH2 (t-SH2) domains to function as a biophysical barrier. Independent of phosphatase activity, these domains directly dismantle pTCR and pCD28 signaling condensates to suppress T cell activation. Together, our findings establish a model in which pPD-1 remodels SHP2 structural dynamics and drives a non-catalytic inhibitory mechanism of TCR and CD28 signaling, suggesting next-generation immunotherapy design to reverse T cell suppression.

immunology↗

bioRxiv2026

Quantitative extrapolation from single-tags (QuEST) immunofluorescence microscopy to derive TCR signalosome stoichiometries in human primary T cells

Upon T cell receptor (TCR) engagement, a T cell forms an immunological synapse (IS) with an antigen-presenting cell (APC), which can be mimicked by purified ligands on supported lipid bilayers (SLBs)1,2. Microvilli actively scan the surface; upon initial engagement, F-actin-dependent TCR microclusters form, and the central supramolecular activation cluster (cSMAC) sustains TCR signaling in CD8 T cells3,4. Although signaling activities within the IS have been observed qualitatively through total internal reflection immunofluorescence microscopy5-7, the stoichiometric relationships among the components of the TCR signalosome remain unknown. In this study, we employed a two-step approach to quantify the components of the TCR signalosome. First, Jurkat cell lines expressing GFP-tagged proteins on a knockout background were used to calibrate fluorescence intensity (IF) signals against molecular copy numbers, based on measurements of single-tag signals and multiple corrections. In the second step, this calibration was applied to determine the stoichiometries of key TCR signalosome components, including TCR, CD8, CD28, CD45, PD-1, Lck, ZAP-70, LAT, and PLC{gamma}1, across scanning, early activation, and sustained activation states in human primary T cells. We refer to the method as quantitative extrapolation from single-tags (QuEST) immunofluorescence microscopy. Applying the QuEST, we were surprised to find that the ZAP-70:TCR ratio in microclusters and the cSMAC was 1:1, far from the potential 10:1 ratio. Nanoscale structures of the TCR signalosome were further captured using direct stochastic optical reconstruction microscopy (dSTORM), confirming that ZAP-70 was strongly co-localized with the TCR. Moreover, we applied QuEST to confirm the presence of T cell intrinsic CD28 recruitment, independent of CD80 or CD86 on SLBs, during TCR activation. This T cell intrinsic CD28 recruitment could be disrupted through engagement of PD-1 with PD-L1 on SLBs. This shows that PD-1 engagement can disrupt T cell intrinsic CD28 costimulation. QuEST provides a broadly applicable pipeline for quantitative analysis of TCR signalosomes in human primary cells, enabling a quantitative basis for the rational manipulation and engineering of the TCR signalosome in immunotherapies.

immunology↗

bioRxiv2025

MAGNET: an all-in-one foundation model for cross-modal and cross-dimensional microscopic image restoration

The application of emerging Foundation models in current microscopy has remained limited due to task-specific model designs, strong modality dependence and insufficient robustness under complex degradations. Here, we introduce MAGNET (Microscopic All-in-one General fouNdation model for imagE resToration), the first end-to-end All-in-One foundation model designed for universal microscopic image restoration. MAGNET integrates multi-task, cross-modal and cross-dimensional (2D + 3D) image restoration capabilities within a single unified architecture, and it is specifically designed to handle composite degradations in practical imaging systems. The framework comprises three components: (i) a task-aware, prompt-guided feature enhancement module for adaptive learning across diverse degradations; (ii) an LIIF-based reconstruction module enabling continuous, resolution-adaptive restoration; and (iii) a dimension-compatible triple-plane projection module supporting both 2D and 3D data. Trained on large-scale datasets spanning 5 major microscopy modalities (structured illumination, confocal, light-sheet, wide-field and two-photon) and 8 representative restoration tasks, MAGNET achieves state-of-the-art performance across super-resolution, denoising, deblurring, background suppression, isotropic reconstruction, aberration correction, de-scattering and virtual staining. Beyond supervised training, MAGNET supports direct inference on unseen systems, efficient few-shot fine-tuning and self-supervised test-time optimization, enabling robust performance across diverse data and imaging conditions. MAGNET serves as a unified, generalizable, and scalable computational framework for microscopy, enabling more efficient and reliable downstream analyses in cell biology, neuroscience, and pathology.

cell biology↗

bioRxiv2025

Fast in vivo deep-tissue 3D imaging with selective-illumination NIR-II light-field microscopy and aberration-corrected implicit neural representation

Near-infrared II (NIR-II) microscopy, which enables in vivo deep-tissue visualization of vasculature and cell activities, has been a promising tool for understanding physiological mechanisms. However, the volumetric image speed of the NIR-II microscopy is hindered by scanning strategy, causing limitations for observing instantaneous biological dynamics in 3D space. Here, we developed a NIR-II light-field microscopy (LFM) based on selective-illumination and self-supervised implicit neural representation (INR)-reconstruction, which allows ultra-fast 3D in vivo imaging (100 volumes/s). Through integrating INR with view-wise aberration correction, we could conquer the artifacts induced by the angular subsampling and refractive index variation, achieving single-cell resolution at a volume of 550 m diameter and 200 m thickness. The volumetric selective-illumination overcomes the influence of out-of-focus background, together with the low scattering advantage of NIR-II wavelength, extending the imaging depth to 600 m. The developed aberration-corrected implicit neural representation reconstruction (AIR) NIR-II LFM showcases its capability by monitoring hemodynamics of mouse brain under norepinephrine and flow redistribution of ischemic stroke in 3D vasoganglion, as well as noninvasively tracking immune cell activities inside subcutaneous solid tumor through intact skin. This approach represents a significant advancement in 3D in vivo imaging, holding great potential in biomedical research and preclinical studies.

neuroscience↗

bioRxiv2024

PRISM: Multiplexed Profiling of RNA In-Situ through Single-round of Imaging in Three-Dimensional Tissue

Spatial RNA imaging has not been widely adopted because conventional fluorescence microscopy is limited to only a few channels, and the cyclic reactions needed to increase multiplexing in techniques such as sequential fluorescence in-situ hybridization (FISH) require sophisticated instrumentation. Here, we introduce Profiling of RNA In-situ through Single-round iMaging (PRISM), a method that expands coding capacity through color intensity grading. Using a radius vector filtering strategy to ensure the distinguishability of codewords in color space, PRISM achieves up to 64-plex color-barcoded RNA imaging in a single imaging round with conventional microscopes. We validate PRISMs versatility across various tissues by generating a 3D atlas of mouse embryonic development from E12.5 to E14.5, a quasi-3D tumor-normal transition landscape of human hepatocellular carcinoma (HCC), and a 3D cell atlas and subcellular RNA localization landscapes of mouse brain. Additionally, we show the critical role of cancer-associated fibroblasts (CAFs) in mediating immune infiltration and immune response heterogeneity within and between tumor microenvironments.

bioengineering↗

bioRxiv2024

3D live imaging and phenotyping of the subcellular cytotoxicity in cancer immunotherapy using event-triggered Bessel oblique plane microscopy

Clarification of the cytotoxic function of T cells is crucial for understanding human immune responses and immunotherapy procedures. Here, we report an event-triggered Bessel oblique plane microscopy (EBOPM) platform capable of smart 3D live imaging and phenotyping of chimeric antigen receptor (CAR)-modified T-cell cytotoxicity in cancer immunotherapy; the EBOPM platform has the following characteristics: an isotropic subcellular resolution of 320 nm, large-scale scouting over 400 interacting cell pairs, long-term observation across 5 hours, and quantitative analysis of the Terabyte-scale 3D, multichannel, time-lapse image datasets. Using this advanced microscopy platform, several key subcellular events in CAR-T cells were captured and comprehensively analyzed; these events included the instantaneous formation of immune synapses and the sustained changes in the microtubing morphology. Furthermore, we identified the actin retrograde flow speed, the actin depletion coefficient, the microtubule polarization and the contact area of the CAR-T/target cell conjugates as essential parameters strongly correlated with CAR-T-cell cytotoxic function. Our approach will be useful for establishing criteria for quantifying T-cell function in individual patients for all T-cell-based immunotherapies.

immunology↗

bioRxiv2023

High-fidelity, high-resolution light-field reconstruction with physics-informed neural representation learning

Light-field microscopy (LFM) captures 3D biological dynamics by single 2D snapshots but suffers from limited resolution and artifacts during 2D-to-3D inversion. Here, we introduce light-field meta neural representation (LFMNR), a novel self-supervised paradigm that utilizes physics-informed light-field implicit neural representation (LFINR) and meta learning for high-quality 3D reconstruction in Fourier-LFM. By developing a physics-based hybrid-rendering model, LFINR achieves artifact-free light-field reconstruction with enhanced spatial resolution (>1.4-fold improvement). Additionally, the integration of meta-learning and progressive sampling strategies mitigates INRs intrinsic limitations in low reconstruction speed caused by scene-specific optimization, enabling a [~]100-fold acceleration in the representation of consecutive volumes and facilitating the visualization of sustained 3D dynamics. These advancements enable LFMNR to deliver superior imaging with high spatiotemporal resolution and low phototoxicity, as demonstrated by capturing instantaneous voltage signals in C. elegans at 100 volumes per second and recording 6000 time points of organelle dynamics over 25 hours.

cell biology↗

bioRxiv2023

Implicit encoding via semantic redundancy elimination enabling adaptive compression of multidimensional biological data

With the rapid development in advanced imaging techniques, massive image data have been acquired for various biomedical applications, posing significant challenges to their efficient storage, transmission, and sharing. Classical model-or learning-based compression algorithms are optimized for specific dimensional data and neglect the semantic redundancy in multidimensional biomedical data, resulting limited compression performance. Here, we propose a Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) approach which achieves high-performance compression of various types of multi-dimensional biomedical images. Based on the first-proved semantic redundancy of biomedical data in the implicit neural function domain, we accomplished saliency-guided implicit neural compression, thereby notably improving the compression efficiency for large-scale image data in arbitrary dimensions. We have demonstrated that SINCS surpasses the alternative compression approaches in terms of image quality, compression ratio, and structure fidelity. Moreover, with using weight transfer and residual entropy coding strategies, SINCS improves compression speed while maintaining high-quality compression. It yields near-lossless compression with over 2000-fold compression ratio on 2D, 2D-T, 3D, 4D biomedical images of diverse targets ranging from single virus to entire human organs, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses, to be conducted at high efficiency.

bioengineering↗

bioRxiv2023

Optics-aware super-resolution light-field microscopy for long-term volumetric imaging of dynamic intracellular processes at millisecond timescales

Long-term and high-spatiotemporal-resolution 3D imaging of living cells remains an unmet challenge for super-resolution microscopy, owing to the noticeable phototoxicity and limited scanning speed. While emerging light-field microscopy can mitigate this issue through three-dimensionally capturing biological dynamics with merely single snapshot, it suffers from suboptimal resolution insufficient for resolving subcellular structures. Here we propose an Adaptive Learning PHysics-Aware Light-Field Microscopy (Alpha-LFM) with a physics-aware deep learning framework and adaptive-tuning strategies capable for highly-generalizable light-field reconstruction of diverse subcellular dynamics. Alpha-LFM delivers sub-diffraction-limit spatial resolution ([~]120 nm) while maintaining high temporal resolution and low phototoxicity. It enables rapid (at hundreds of volumes per second), long-term (up to 60 hours) 3D super-resolution imaging of diverse intracellular dynamics with exceptional details. Using Alpha-LFM approach, we finely resolve the lysosome-mitochondrial interactions, capture rapid motion of peroxisome and the endoplasmic reticulum, and reveal the variations in mitochondrial fission activity throughout two complete cell cycles.

microbiology↗

bioRxiv2022

Rapid and Signal Crowdedness-Robust In-Situ Sequencing through Hybrid Block Coding

Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening new possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108 gene panel from 453,843 cells from four mouse brain coronal slices in less than two days. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimers disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms.

genomics↗

bioRxiv2021

Escape Steering by Cholecystokinin Peptidergic Signaling

Escape is an evolutionarily conserved and essential avoidance response. Considered to be innate, most studies on escape responses focused on hard-wired circuits. We report here that peptidergic signaling is an integral and necessary component of the Caenorhabditis elegans escape circuit. Combining genetic screening, electrophysiology and calcium imaging, we reveal that a neuropeptide NLP-18 and its cholecystokinin receptor CKR-1 enable the escape circuit to execute a full omega ({Omega}) turn, the last motor step where the animal robustly steers away from its original trajectory. We demonstrate in vivo and in vitro that CKR-1 is a Gq protein coupled receptor for NLP-18. in vivo, NLP-18 is mainly secreted by the gustatory sensory neuron (ASI) to activate CKR-1 in the head motor neuron (SMD) and the turn-initiating interneuron (AIB). Removal of NLP-18, removal of CKR-1, or specific knockdown of CKR-1 in SMD or AIB neurons lead to shallower turns hence less robust escape steering. Consistently, elevation of head motor neuron (SMD)s Ca2+ transients during escape steering is attenuated upon the removal of NLP-18 or CKR-1. in vitro, synthetic NLP-18 directly evokes CKR-1-dependent currents in oocytes and CKR-1-dependent Ca2+ transients in SMD. Thus, cholecystokinin signaling modulates an escape circuit to generate robust escape steering.

neuroscience↗

bioRxiv2021

Representation of Drosophila larval behaviors by muscle activity patterns

How muscle actions are coordinated to realize animal movement is a fundamental question in behavioral study. To obtain the overall muscular activity patterns accompanying behaviors at high spatiotemporal resolution is technically difficult. In this work, we used light sheet microscopy to simultaneously image and analyze the activity, length and orientation of Drosophila larval muscles across body segments at single muscle resolution in nearly free behaviors. For typical behavioral modes such as peristalsis, head cast and turning, larval muscles showed behavioral mode specific activity patterns. Unexpectedly, reorientation of larval head involves muscle tone in the apparently motionless posterior segments. With a STGCN(spatial temporal graph convolution neural network)-Generator model, sequence of larval behavioral poses outlined by morphological patterns of muscles could be accurately predicted based on the time series of ventral but not dorsal muscle activities, and vice versa. Laser ablation of ventral but not dorsal muscles interrupted peristaltic wave and undermined head cast in both frequency and amplitude. Our results provide a simplified muscle activity representation of soft body motion that can be used for probing the key components of animal motor control.

neuroscience↗

bioRxiv2021

Deep-learning on-chip DSLM enabling video-rate volumetric imaging of neural activities in moving biological specimens

Volumetric imaging of dynamic signals in a large, moving, and light-scattering specimen is extremely challenging, owing to the requirement on high spatiotemporal resolution and difficulty in obtaining high-contrast signals. Here we report that through combing a microfluidic chip-enabled digital scanning light-sheet illumination strategy with deep-learning based image restoration, we can realize isotropic 3D imaging of crawling whole Drosophila larva on an ordinary inverted microscope at single-cell resolution and high volumetric imaging rate up to 20 Hz. Enabled with high performances even unmet by current standard light-sheet fluorescence microscopes, we intoto record the neural activities during the forward and backward crawling of 1st instar larva, and successfully correlate the calcium spiking of motor neurons with the locomotion patterns.

bioengineering↗

bioRxiv2021

Multi-color 4D superresolution light-sheet microscopy reveals organelle interactions at isotropic 100-nm resolution and sub-second timescales

Long-term visualization of the dynamic organelle-organelle or protein-organelle interactions throughout the three-dimensional space of whole live cells is essential to better understand their functions, but this task remains challenging due to the limitations of existing three-dimensional fluorescence microscopy techniques, such as an insufficient axial resolution, low volumetric imaging rate, and photobleaching. Here, we present the combination of a progressive deep-learning superresolution strategy with a dual-ring-modulated SPIM design capable of visualizing the dynamics of intracellular organelles in live cells for hours at an isotropic spatial resolution of [~]100 nm in three dimensions and a temporal resolution up to [~]17 Hz. With a compelling spatiotemporal resolution, we substantially reveal the complex spatial relationships and interactions between the endoplasmic reticulum (ER) and mitochondria throughout live cells, providing new insights into ER-mediated mitochondrial division. We also localized the motion of Drp1 oligomers in three dimensions and observed Drp1-mediated mitochondrial branching for the first time.

biophysics↗

bioRxiv2021

Automatic segmentation and cardiac mechanics analysis of evolving zebrafish using deep-learning

ObjectiveIn the study of early cardiac development, it is important to acquire accurate volume changes of the heart chambers. Although advanced imaging techniques, such as light-sheet fluorescent microscopy (LSFM), provide an accurate procedure for analyzing the structure of the heart, rapid and robust segmentation is required to reduce laborious time and accurately quantify developmental cardiac mechanics. MethodsThe traditional biomedical analysis involving segmentation of the intracardiac volume is usually carried out manually, presenting bottlenecks due to enormous data volume at high axial resolution. Our advanced deep-learning techniques provide a robust method to segment the volume within a few minutes. Our U-net based segmentation adopted manually segmented intracardiac volume changes as training data and produced the other LSFM zebrafish cardiac motion images automatically. ResultsThree cardiac cycles from 2 days post fertilization (dpf) to 5 dpf were successfully segmented by our U-net based network providing volume changes over time. In addition to understanding the cardiac function for each of the two chambers, the ventricle and atrium were separated by 3D erode morphology methods. Therefore, cardiac mechanical properties were measured rapidly and demonstrated incremental volume changes of both chambers separately. Interestingly, stroke volume (SV) remains similar in the atrium while that of the ventricle increases SV gradually. ConclusionOur U-net based segmentation provides a delicate method to segment the intricate inner volume of zebrafish heart during development; thus providing an accurate, robust and efficient algorithm to accelerate cardiac research by bypassing the labor-intensive task as well as improving the consistency in the results.

bioengineering↗

bioRxiv2021

High-speed large-scale 4D activities mapping of moving C. elegans by deep-learning-enabled light-field microscopy on a chip

We report a novel fusion of microfluidics and light-field microscopy, to achieve high-speed 4D (space + time) imaging of moving C. elegans on a chip. Our approach combines automatic chip-based worm loading / compartmentalization / flushing / reloading with instantaneous deep-learning light-field imaging of moving worm. Taken together, we realized intoto image-based screening of wild-type and uncoordinated-type worms at a volume rate of 33 Hz, with sustained observation of 1 minute per worm, and overall throughput of 42 worms per hour. With quickly yielding over 80000 image volumes that four-dimensionally visualize the dynamics of all the worms, we can quantitatively analyse their behaviours as well as the neural activities, and correlate the phenotypes with the neuron functions. The different types of worms can be readily identified as a result of the high-throughput activity mapping. Our approach shows great potential for various lab-on-a-chip biological studies, such as embryo sorting and cell growth assays.

bioengineering↗

bioRxiv2020

Neurexins play a crucial role in cerebellar granule cell survival by organizing autocrine machinery for neurotrophins

Neurexins (NRXNs) are among the key presynaptic cell adhesion molecules that regulate synapse function and formation via trans-synaptic interaction with postsynaptic ligands. Here, we generated cerebellar granule cell (CGC)-specific Nrxn triple-knockout (TKO) mice to allow the deletion of all NRXNs. Unexpectedly, most CGCs died in these mice. The requirement of NRNXs for cell survival was reproduced in cultured CGCs. We showed that the axons of cultured Nrxn TKO CGCs that were not in contact with the postsynaptic structure had defects in the formation of presynaptic protein cluster and action potential-induced Ca2+ influx. Additionally, these cells were impaired in the secretion from axons of depolarization-induced fluorescence-tagged brain-derived neurotrophic factor (BDNF), and the cell-survival defect was rescued by the application of BDNF. Our results suggest that CGC survival is maintained by autocrine neurotrophic factors, and that NRXNs organize the presynaptic protein clusters and the autocrine neurotrophic factor secretory machinery independent of contact with postsynaptic ligands.

neuroscience↗

bioRxiv2020

A Hybrid of Light-Field and Light-Sheet Imaging to Decouple Myocardial Biomechanics from Intracardiac Flow Dynamics

Biomechanical forces intimately contribute to cardiac morphogenesis. However, 4-D (3-D space + time) imaging is needed to investigate the developmental cardiac mechanics with high temporal and spatial resolution. We hereby integrated light-sheet fluorescence microscopy (LSFM) with light-field microscopy (LFM), to simultaneously visualize myocardial contractility and intracardiac blood flow in three dimensions at 200 volumes per second (vps). LSFM allows for reconstruction of the myocardial contraction in zebrafish embryo; and LFM enables simultaneous tracking of the blood cells entering and leaving the contracting heart. We herein established particle tracking velocimetry to interrogate the trajectories of intracardiac blood cells, and we demonstrated deformable image registration to reveal a decrease in the myocardial contractility from atrioventricular (AV) canal to the outflow tract (OFT). We imaged myocardium undergoing torsional contraction and blood flow undergoing regurgitation. Taken together, the integration of light-field and light-sheet microscopy, followed by an image-based analysis pipeline, provides the biomechanical insights into coupling myocardial kinetics with rotational contraction along with intracardiac flow dynamics during development.

bioengineering↗