Biology subjects

Sheng, K.

Publications and source records attributed to Sheng, K..

At least 19 recordsLinked to original sources

bioRxiv2026

High-plex protein imaging across thick tissue by synchronized amplification

High-plex protein imaging is essential for decoding complex tissue organization, yet it remains largely confined to thin sections. Current approaches in millimeter-thick samples suffer from poor signal penetration and depth-dependent, uneven signal amplification. Even with specialized instrumentation to facilitate probe diffusion, iterative millimeter-thick tissue workflows can take more than four days per staining round, and reported eleven-marker experiments extend beyond a month. We introduce Amplification by Cyclic Extension (ACE), a DNA-barcoded, synchronized in situ amplification strategy that uses thermally controlled cyclic extension to coordinate DNA concatemer growth across tissue depths. By synchronously activating and coordinating reaction progression throughout the sample, ACE enables controlled, uniform signal amplification across tissue depths. Using ACE, we demonstrate 63-plex protein and RNA imaging in thin sections, 22-plex protein imaging in 200 m mouse brain sections, and 16-plex protein imaging in 1 mm-thick brain tissue encompassing over three million cells. For millimeter-thick tissues, ACE reduces per-round target-switching time from multiple days to less than eight hours, shifting volumetric high-plex protein imaging from months to days. Together, these results establish ACE as a synchronized amplification strategy that enables scalable high-plex protein imaging in thick tissues.

bioengineering↗

bioRxiv2026

ESCAPE: assigning site-specific activity to covalent ligands in cells by prime editing

Chemical proteomics has identified covalent ligands targeting cysteine residues across many hundreds of human proteins. The functional effects of these liganding events, however, remain challenging to assign at scale. Here we describe ESCAPE (Endogenous Site-specific Competition Assays using Prime Editors), a platform for the site-resolved functional analysis of covalent ligands in cells. In this method, cysteine-to-serine substitutions are generated by prime editing to abrogate covalent ligand-protein interactions, and the impact of these edits on ligand-induced cellular phenotypes is quantified through allele frequency-based resistance scores. Applied to ligandable cysteines mapped by activity-based protein profiling in 50+ proteins, ESCAPE identified multiple covalent ligand-protein interactions that impair cancer cell growth, including azetidine butynamides that target a non-orthosteric cysteine in the RNA helicase DDX49 to disrupt 18S rRNA processing, 40S ribosome assembly, and protein synthesis. ESCAPE thus provides a scalable framework for the functional characterization of covalent ligands targeting structurally and mechanistically diverse proteins.

biochemistry↗

bioRxiv2026

Causal and directional elements of global brain dynamics

Mammalian cognition appears to involve coordinated neural activity within and across brain-spanning networks. However, the governing principles of large-scale integration remain largely unknown. Here we developed an approach to record spontaneous cortex-wide neural activity (including with novel genetically encoded activity sensors), over very long timescales, and applied unbiased computation to capture conserved spatiotemporal regularities in these data. Initial screening extracted a set of directional elements: consistent spatiotemporal patterns of cortical activity that generalized across excitatory and inhibitory neuronal cell types as revealed by genetic targeting of activity sensors. In particular, novel genetically encoded voltage-sensing strategies revealed that the directional elements were not only highly conserved, but also were represented across the full neural activity frequency spectrum including the fastest timescales (gamma rhythms) enabled by voltage-sensing. Exploiting the spatiotemporal structure revealed by these directional elements, we tested for causal rules governing cortical network activation, using patterned optogenetic stimulation combined with activity imaging. We found that the directional propagation structure of these elements encoded a causal control hierarchy, as source regions (but not sink regions) sufficed to drive full element recruitment--a principle that held across all tested directional elements. Employing a panel of psychotropic drugs, we showed that directional element structure and excitability were robust to manipulations of neural and behavioral state, even as spontaneous network dynamics were reshaped in compound-specific ways. Finally, we developed and applied an all-optical sensing/control approach targeting the directional elements in a behavioral visual detection paradigm, revealing contributions of these conserved large-scale dynamics to elevated sensorimotor behavioral performance.

neuroscience↗

bioRxiv2025

Beyond Correlation: An Ultra-Large Physics-Driven Vascularized Tumor Model to Bridge Feature Formation with Underlying Biology

Radiomics provides an appealing, non-invasive approach to probing tumor biology for potential diagnostic and prognostic applications. However, its clinical adoption is limited by challenges in interpretability, which in turn compromise its robustness. To uncover the underlying causation, we developed an ultra-large-scale (ULS) computational model that simulates heterogeneous, vascularized tumor growth under physical constraints to a scale that can be visualized in medical images. Our study revealed the pivotal role of tumor proliferation rate in driving necrosis and tissue heterogeneity and the dominant impact of oxygen consumption rate on vascularization level. Analysis of the resultant tumor Radiomics shows a causal relationship between tumor biophysical parameters and imaging features. Specifically, differences in proliferation and oxygen consumption rates result in distinct changes in radiomic image features, identifying suitable imaging modalities and quantitative imaging metrics for studying these biophysical parameters. This work thus reverse-engineers the building blocks of Radiomics as a means to understand their respective biological underpinnings.

bioinformatics↗

bioRxiv2025

Extracting task-relevant preserved dynamics from contrastive aligned neural recordings

Recent work indicates that low-dimensional dynamics of neural and behavioral data are often preserved across days and subjects. However, extracting these preserved dynamics remains challenging: high-dimensional neural population activity and the recorded neuron populations vary across recording sessions. While existing modeling tools can improve alignment between neural and behavioral data, they often operate on a per-subject basis or discretize behavior into categories, disrupting its natural continuity and failing to capture the underlying dynamics. We introduce Contrastive Aligned Neural DYnamics (CANDY), an end-to-end framework that aligns neural and behavioral data using rank-based contrastive learning, adapted for continuous behavioral variables, to project neural activity from different sessions onto a shared low-dimensional embedding space. CANDY fits a shared linear dynamical system to the aligned embeddings, enabling an interpretable model of the conserved temporal structure in the latent space. We validate CANDY on synthetic and real-world datasets spanning multiple species, behaviors, and recording modalities. Our results show that CANDY is able to learn aligned latent embeddings and preserved dynamics across neural recording sessions and subjects, and it achieves improved cross-session behavior decoding performance. We further show that the latent linear dynamical system generalizes to new sessions and subjects, achieving comparable or even superior behavior decoding performance to models trained from scratch. These advances enable robust cross-session behavioral decoding and offer a path towards identifying shared neural dynamics that underlie behavior across individuals and recording conditions. The code and two-photon imaging data of striatal neural activity that we acquired here are available at https://github.com/schnitzer-lab/CANDY-public.git.

neuroscience↗

bioRxiv2025

A parallelly distributed microscope and software system for scalable high-throughput multispectral 3D imaging

Recent advances in high throughput optical microscopy have achieved whole-organism scale imaging at diffraction-limited resolutions. Current microscopes, however, require making compromises between achieving the optimal resolution, imaging depth, multispectral capability, and data throughput due to limitations in optical design and data stream handling. We have created a parallel-line scanning confocal microscope (plSCM) which provides 1.1 Gigavoxels/second high speed imaging while achieving an optical resolution of [~]180 x 220 x 650 nm with 2 millimeters of imaging depth in 3 simultaneous spectral channels. To handle such a massive imaging data stream, we have engineered a scalable network-distributed image acquisition/processing framework (SNDiF), which allows continuous capture, real-time processing, and cluster storage of petabyte-scale single image datasets in days. Together, our parallelly distributed microscope and software system presents a general solution for large-scale high-throughput and high-resolution multicolor imaging which can operate for days at a time.

bioengineering↗

bioRxiv2024

Brain-like learning with exponentiated gradients

Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models of the brain. Specifically, it violates Dales law, by allowing synapses to change from excitatory to inhibitory, and leads to synaptic weights that are not log-normally distributed, contradicting experimental data. Here, starting from first principles of optimisation theory, we present an alternative learning algorithm, exponentiated gradient (EG), that respects Dales Law and produces log-normal weights, without losing the power of learning with gradients. We also show that in biologically relevant settings EG outperforms GD, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.

neuroscience↗

bioRxiv2024

Integrated single-cell RNA-seq analysis revealed PTN secreted by fibroblasts acting on itself and macrophages via SDC4 ligand in myocardial hypertrophy

BackgroundHypertrophic cardiomyopathy (HCM) is characterized by massive myocardial hypertrophy, which is the most frequent cause of sudden death and can lead to heart failure (HF) or stroke. The objective of this study was to explore the communication network among various cells in the heart of pathological HCM derived from transverse aortic constriction (TAC) mouse model, and investigate the potential mechanism through data mining, biological informatics analysis, and experimental validation. Methods and ResultsThe integrated analyses including CellChat, Seurat, gene ontology (GO), pseudo-time trajectory analysis, and weighted gene co-expression network analysis (WGCNA) were performed based on the single-cell RNA-seq data (scRNA-seq). In vitro tests were conducted to verify bioinformatic analysis findings through enzyme-linked immunosorbent assay (ELISA), real-time quantitative PCR (RT-qPCR), Edu staining, and transwell assay. In vivo tests were also performed to further verify bioinformatic analysis findings by western blot and immunofluorescence assays based on our established TAC mouse model with myocardial hypertrophy. Our results showed that in the heart of TAC mouse, the interaction between cardiac fibroblasts and macrophages was most common, and the increasing pleiotrophin (PTN) secreted by cardiac fibroblasts could promote themselves proliferation or invasion as well as stimulate macrophage activation to release inflammatory cytokines, such as TNF-, IL-6, Cox-2, Cd83, Egr2, and IL-10 through acting on its ligand recombinant Syndecan 4 (SDC4), which may affect cardiomyocyte normal function and eventually cause HCM. This study first demonstrated that PTN derived from cardiac fibroblasts may act on SDC4 to play crucial role in myocardial hypertrophy, which may be a potential therapeutic targets for patients with pathological HCM. ConclusionsIn this study, the complex interaction network between cardiac fibroblasts and macrophages of TAC mice based on the scRNA-seq data was investigated, and we found that the increasing PTN secreted by cardiac fibroblasts under cardiac pressure overload could promote themselves proliferation or invasion as well as stimulate macrophage activation to release inflammatory cytokines through acting on SDC4 ligand, which may affect cardiomyocyte normal function and eventually cause HCM. In addition, our study suggested that PTN derived from cardiac fibroblasts may act on SDC4 ligand to play crucial role in myocardial hypertrophy, which may be a potential therapeutic targets for patients with pathological HCM.

cell biology↗

bioRxiv2024

Activity-Dependent Remodeling of Corticostriatal Axonal Boutons During Motor Learning

Motor skill learning induces long-lasting synaptic plasticity at not only the inputs, such as dendritic spines1-4, but also at the outputs to the striatum of motor cortical neurons5,6. However, very little is known about the activity and structural plasticity of corticostriatal axons during learning in the adult brain. Here, we used longitudinal in vivo two-photon imaging to monitor the activity and structure of thousands of corticostriatal axonal boutons in the dorsolateral striatum in awake mice. We found that learning a new motor skill induces dynamic regulation of axonal boutons. The activities of motor corticostriatal axonal boutons exhibited selectivity for rewarded movements (RM) and un-rewarded movements (UM). Strikingly, boutons on the same axonal branches showed diverse responses during behavior. Motor learning significantly increased the fraction of RM boutons and reduced the heterogeneity of bouton activities. Moreover, motor learning-induced profound structural dynamism in boutons. By combining structural and functional imaging, we identified that newly formed axonal boutons are more likely to exhibit selectivity for RM and are stabilized during motor learning, while UM boutons are selectively eliminated. Our results highlight a novel form of plasticity at corticostriatal axons induced by motor learning, indicating that motor corticostriatal axonal boutons undergo dynamic reorganization that facilitates the acquisition and execution of motor skills.

neuroscience↗

bioRxiv2024

Domain consolidation in Bacterial 50S assembly revealed by Anti-Sense Oligonucleotide Probing

Investigating the intricate and rapid folding kinetics of large RNA-protein complexes (RNPs), like the bacterial ribosome, remains a formidable challenge in structural biology. Previous genetic approaches to probe assembly have focused on modulating the expression of either r-proteins or assembly factors. Here, anti-sense oligonucleotides (ASOs) were used to disrupt native RNA/RNA and RNA/protein interactions, in order to generate novel folding intermediates. In an in vitro co-transcriptional assembly assay, 8 assembly inhibitor ASOs were identified. Using cryo-electron microscopy, 38 new intermediate structures were determined resulting from the specific inhibitions. In particular a novel intermediate class provided compelling evidence of independent rRNA domain folding before proper interdomain docking. Three PNAs targeting domain-I of 23S- rRNA further subdivided the previously identified assembly core into smaller blocks representing the earliest steps in assembly. The resulting assembly graph reveals template-directed RNA foldon docking and domain consolidation, which provides a hierarchical view of the RNP assembly process.

biochemistry↗

bioRxiv2024

Assembly of the Bacterial Ribosome with Circularly Permuted rRNA

Co-transcriptional assembly is an integral feature of the formation of RNA-protein complexes that mediate translation. For ribosome synthesis, prior studies have indicated that the strict order of transcription of rRNA domains may not be obligatory during bacterial ribosome biogenesis, since a series of circularly permuted rRNAs are viable. In this work, we report the insights into assembly of the bacterial ribosome large subunit (LSU) based on cryo-EM density maps of intermediates that accumulate during in vitro ribosome synthesis using a set of circularly permuted (CiPer) rRNAs. The observed ensemble of twenty-three resolved ribosome large subunit intermediates reveals conserved assembly routes with an underlying hierarchy among cooperative assembly blocks. There are intricate interdependencies for the formation of key structural rRNA helices revealed from the circular permutation of rRNA. While the order of domain synthesis is not obligatory, the order of domain association does appear to proceed with a particular order, likely due to the strong evolutionary pressure on efficient ribosome synthesis. This work reinforces the robustness of the known assembly hierarchy of the bacterial large ribosomal subunit, and offers a coherent view of how efficient assembly of CiPer rRNAs can be understood in that context.

biophysics↗

bioRxiv2024

The protective roles of Eugenol on type 1 diabetes mellitus through NRF2 mediated oxidative stress pathway

Type 1 diabetes mellitus (T1DM), known as insulin-dependent diabetes mellitus, is characterized by persistent hyperglycemia resulting from damage to the pancreatic {beta} cells and an absolute deficiency of insulin, leading to multi-organ involvement and a poor prognosis. The progression of T1DM is significantly influenced by oxidative stress and apoptosis. The natural compound eugenol (EUG) possesses anti-inflammatory, anti-oxidant, and anti-apoptotic properties. However, the potential effects of EUG on T1DM had not been investigated. In this study, we established the streptozotocin (STZ)-induced T1DM mouse model in vivo and STZ-induced pancreatic {beta} cell MIN6 cell model in vitro to investigate the protective effects of EUG on T1DM, and tried to elucidate its potential mechanism. Our findings demonstrated that the intervention of EUG could effectively induce the activation of nuclear factor E2-related factor 2 (NRF2), leading to an up-regulation in the expressions of downstream proteins NQO-1 and HO-1, which are regulated by NRF2. Moreover, this intervention exhibited a significant amelioration in pancreatic {beta} cell damage associated with T1DM, accompanied by an elevation in insulin secretion and a reduction in the expression levels of apoptosis and oxidative stress related markers. Furthermore, ML385, an NRF2 inhibitor, reversed these effects of EUG. The present study suggested that EUG exerted protective effects on pancreatic {beta} cells in T1DM by attenuating apoptosis and oxidative stress through the activation of the NRF2 signaling pathway. Consequently, EUG holds great promise as a potential therapeutic candidate for T1DM.

pharmacology and toxicology↗

bioRxiv2024

Senolytic Treatment for Low Back Pain.

Senescent cells (SnCs) accumulate due to aging and external cellular stress throughout the body. They adopt a senescence-associated secretory phenotype (SASP) and release inflammatory, and degenerative factors that actively contribute to age-related diseases such as low back pain (LBP). The senolytics, o-Vanillin and RG-7112, remove senescent human intervertebral (IVD) cells and reduce SASP release, but it is not known if they can treat LBP. sparc-/- mice, with LBP, were treated orally with o-Vanillin and RG-7112 as single or combination treatments. Treatment reduced LBP and SASP factor release and removed SnCs from the IVD and spinal cord. Treatment also lowered degeneration score in the IVDs, improved vertebral bone quality, and reduced the expression of pain markers in the spinal cord. The result indicates that RG-7112 and o-Vanillin with the combination treatment providing the strongest effect are potential disease-modifying drugs for LBP and other painful disorders where cell senescence is implicated. One Sentence Summary: Senolytics drugs can reduce back pain

cell biology↗

bioRxiv2023

Early HIV-1 Gag Assembly on Lipid Membrane with vRNA

Mass photometry (MP) was used to investigate the assembly of myristoylated full-length HIV-1 Gag (myr-Gag) and vRNA 5 UTR fragment in a supported lipid bilayer (SLB) model system. The MP trajectories demonstrated that Gag trimerization on the membrane is a key step of early Gag assembly in the presence of vRNA. Growth of myr-Gag oligomers requires vRNA, occuring by addition of 1 or 2 monomers at a time from solution. These data support a model where formation of the Gag hexamers characteristic of the immature capsid lattice occurs by a gradual edge expansion, following a trimeric nucleation event. These dynamic single molecule data involving protein, RNA, and lipid components together, provide novel and fundamental insights into the initiation of virus capsid assembly.

biophysics↗

bioRxiv2022

Unsupervised Voxel-based Segmentation reveals a Landscape of Bacterial Ribosome Large Subunit Early Assembly

Ribosome biogenesis is a complex but efficient process in rapidly growing bacteria, where assemble a functional 70S ribosome takes ~ 2 min, involving participation of 3 rRNAs, 50 r-proteins and dozens of assembly factors. In vitro reconstitution using different subsets of large subunit (50S, LSU) proteins with rRNAs, pioneered by Nierhaus lab, resulted in the Nierhaus assembly map, embodying the cooperativity and dependency for binding of LSU r-proteins to 23S rRNA. Critically absent from the Nierhaus map is the underlying folding of the rRNA that creates the binding sites for the r-proteins. In addition, the relationship of the observed cooperativity in vitro to the co-transcriptional assembly in cells remains to be determined. Pre-50S intermediates accumulate at low temperature in {Delta}deaD, a DEAD-box helicase implicated in 50S assembly. We solved 21 pre-50S density maps from intermediate-containing fractions using cryo-EM. In the newly solved maps, we discovered the earliest intermediate ever reported, consisting of domain I at the 5-end of 23S rRNA. To probe the mechanism behind the maps during assembly, we developed a novel density map segmentation and dependency analysis method. Ten cooperative assembly blocks were identified from segmentation of the maps, and these were organized into a block dependency map. This is the first time the dependencies on folding of rRNA helices and ribosomal protein binding could be integrated into a complete assembly map. In addition, we showed how the exit tunnel is folded on the solvent side, serving as a scaffold for 50S maturation. Using this new segmentation analysis method, we revisited previously reported bL17-depletion and {Delta}srmB datasets. Most remarkably, the other two datasets are also consistent with the block dependency, implying a unified early assembly pathway and flexible maturation landscape in early 50S biogenesis.

biochemistry↗

bioRxiv2022

Near-Physiological in vitro Assembly of 50S Ribosomes Involves Parallel Pathways

Understanding the assembly principles of biological macromolecular complexes remains a significant challenge, due to the complexity of the systems and the difficulties in developing experimental approaches. As a ribonucleoprotein complex, the ribosome serves as an ideal model system for the profiling of macromolecular complex assembly. In this work, we report an ensemble of large ribosomal subunit intermediate structures that accumulate during synthesis in a near-physiological and co-transcriptional in vitro reconstitution system. Thirteen pre-50S intermediate maps covering the whole assembly process were resolved using cryo-EM single particle analysis and heterogeneous subclassification. Segmentation of the set of density maps reveals that the 50S ribosome intermediates assemble based on fourteen cooperative assembly blocks, including the smallest assembly core reported up to now, which is composed of a 600-nucleotide-long folded rRNA and three ribosomal proteins. The cooperative blocks assemble onto the assembly core following a defined set of dependencies, revealing the parallel assembly pathways at both early and late assembly stages of the 50S subunit.

biochemistry↗

bioRxiv2022

Domain Adaptive Neural Inference for Neurons, Microcircuits and Networks

Accurately inferring neural circuit connectivity from in vivo recordings is essential for understanding the computations that support behavior and cognition. However, current deep learning approaches are limited by incomplete observability and the lack of ground-truth labels in real experiments. Consequently, models are often trained on synthetic data, which leads to the well-known "model mismatch" problem when simulated dynamics diverge from true neural activity. To overcome these challenges, we present Deep Domain-Adaptive Matching (DeepDAM), a training framework that adaptively matches synthetic and in vivo data domains for neural connectivity inference. Specifically, DeepDAM fine-tunes deep neural networks on a combined dataset of synthetic simulations and unlabeled in vivo recordings, aligning the models feature representations with real neural dynamics to mitigate model mismatch. We demonstrate this approach in rodent hippocampal CA1 circuits as a proof-of-concept, achieving near-perfect connectivity inference performance (Matthews correlation coefficient [~]0.97-1.0) and substantially surpassing classical methods ([~]0.6-0.7). We further demonstrate robustness across multiple recording conditions within this hippocampal dataset. Additionally, to illustrate its broader applicability, we extend the framework to two distinct systems without altering the core methodology: a stomatogastric microcircuit in Cancer borealis (ex vivo) and single-neuron intracellular recordings in mouse, where DeepDAM significantly improves efficiency and accuracy over standard approaches. By effectively leveraging synthetic data for in vivo and ex vivo analysis, DeepDAM offers a generalizable strategy for overcoming model mismatch and represents a critical step towards data-driven reconstruction of functional neural circuits.

neuroscience↗

bioRxiv2022

Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system

As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop a fast and memory-efficient open-source GPU-based hybrid computational modeling platform Gell (GPU Cell), for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement. Author SummaryNumerical cell simulations provide indispensable insight into the cell-to-tumor tissue transition and help reduce biological experimental variables. However, the availability and practicality of large-scale cell simulation tools have been limited by high computational cost, slow performance, or proprietary. Recent developments in open-source simulation codes and GPU implementation have partially addressed the challenge. We further optimized the cell simulation platform for GPU implementation in this work. As a result, benchmark cell simulation experiments can be performed efficiently on a personal computer with a modern GPU. We made the platform open source to encourage community adoption and collective development.

systems biology↗