Biology subjects

Ni, Y.

Publications and source records attributed to Ni, Y..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Low-intensity transcranial focused ultrasound changes pain-associated behaviors by modulating pain processing brain circuits

There is an urgent and unmet clinical need to develop non-pharmacological interventions for chronic pain management due to the critical side effects of opioids. Low-intensity transcranial focused ultrasound is an emerging non-invasive neuromodulation technology with high spatial specificity and deep brain penetration. Here, we developed a tightly-focused 128-element ultrasound transducer to specifically target small mouse brains, employing dynamic focus steering. We demonstrate that transcranial focused ultrasound stimulation at pain processing brain circuits can significantly alter pain-associated behaviors in mouse models in vivo. Our findings indicate that a single-session focused ultrasound stimulation to the primary somatosensory cortex (S1) significantly attenuates heat pain sensitivity in wild-type mice and modulates heat and mechanical hyperalgesia in a humanized mouse model of chronic pain in sickle cell disease. Results further revealed a sustained behavioral change associated with heat hypersensitivity by targeting deeper cortical structures (e.g., insula) and multi-session focused ultrasound stimulation to S1 and insula. Analyses of brain electrical rhythms through electroencephalography demonstrated a significant change in noxious heat hypersensitive- and chronic hyperalgesia-associated neural signals following focused ultrasound treatment. Validation of efficacy was carried out through control experiments, tuning ultrasound parameters, adjusting inter-experiment intervals, and investigating effects on age, gender, genotype, and in a head-fixed awake model. Importantly, transcranial focused ultrasound was shown to be safe, causing no adverse effects on motor function and brain neuropathology. In conclusion, the rich experimental evidence validates the ability of novel focused ultrasound neuromodulation to suppress pain, presenting significant translational potential for next-generation chronic pain treatment without adverse effects. Key pointsO_LINovel non-invasive neuromodulation of brains pain processing circuits with submillimeter spatial precision for pain management C_LIO_LITranscranial focused ultrasound significantly modulates pain-related behaviors and brain electrical rhythms of pain in humanized SCD mice C_LI

bioengineering↗

bioRxiv2022

The shadowing effect of initial expectation on learning asymmetry

Evidence for positivity and optimism bias abounds in high-level belief updates. However, no consensus has been reached regarding whether learning asymmetries exists in more elementary forms of updates such as reinforcement learning (RL). In RL, the learning asymmetry concerns the sensitivity difference in incorporating positive and negative prediction errors (PE) into value estimation, namely the asymmetry of learning rates associated with positive and negative PEs. Although RL has been established as a canonical framework in interpreting agent and environment interactions, the direction of the learning rate asymmetry remains controversial. Here, we propose that part of the controversy stems from the fact that people may have different value expectations before entering the learning environment. Such default value expectation influences how PEs are calculated and consequently biases subjects choices. We test this hypothesis in two learning experiments with stable or varying reinforcement probabilities, across monetary gains, losses and gain-loss mixtures environments. Our results consistently support the model incorporating asymmetric learning rates and initial value expectation, highlighting the role of initial expectation in value update and choice preference. Further simulation and model parameter recovery analyses confirm the unique contribution of initial value expectation in accessing learning rate asymmetry. Author SummaryWhile RL model has long been applied in modeling learning behavior, where value update stands in the core of the learning process, it remains controversial whether and how learning is biased when updating from positive and negative PEs. Here, through model comparison, simulation and recovery analyses, we show that accurate identification of learning asymmetry is contingent on taking into account of subjects default value expectation in both monetary gain and loss environments. Our results stress the importance of initial expectation specification, especially in studies investigating learning asymmetry.

neuroscience↗

bioRxiv2022

Identification and calibration of ultrabright localizations to eliminate quantification error in SMLM

Single molecule localization microscopy (SMLM) is irreplaceable among super-resolution microscopies in revealing biological ultra-structures, given its unmatched high resolution. However, its sub-optimal quantitative capability, which is critical for characterizing true biomolecular organization of ultra-structures in cells, has hindered its widest application in biomedical research. Here, in SMLM imaging of cellular structures such as lipid rafts and microtubules with saturation labelling, we identified ultra-bright localizations, each of which is contributed by simultaneous emission of multiple molecules within a diffraction-limit region and has been regarded before as a regular localization from single molecule. Consistently, ultra-bright localizations are also observed in simulated SMLM imaging of endoplasmic reticulum or microtubules from public resource. Furthermore, after calibrating each ultrabright localization into multiple single-molecule localizations using the photon-number-based models, the density of total localizations shows linear correlation with the true molecule density, presenting SMLM with new reconstruction method as a quantitative analysis approach. Therefore, identification and dissection of ultra-bright localizations in SMLM enable the close and quantitative estimate of the true biomolecular organization.

biophysics↗

bioRxiv2021

Nanobody-Functionalized Cellulose for Capturing and Containing SARS-CoV-2

The highly transmissible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 217 million people, claiming ~ 4.5 million lives to date. Although mandatory quarantines, lockdowns, and vaccinations help curb viral transmission, safe and effective preventative measures remain urgently needed. Here, we present a generic strategy for containing SARS-CoV-2 by cellulose materials. Specifically, we developed a bifunctional fusion protein consisting of a cellulose-binding domain and a nanobody (Nb) targeting the receptor-binding domain of SARS-CoV-2. The immobilization of the fusion proteins on cellulose substrates enhanced the capture efficiency of Nbs against SARS-CoV-2 pseudoviruses of the wildtype and the D614G variant, the latter of which has been shown to confer higher infectivity. Furthermore, the fusion protein was integrated into a customizable chromatography with highly porous cellulose for neutralizing virus from contaminated fluids in a continuous and cost-effective fashion. Taken together, our work leverages low-cost cellulose materials and recently developed Nbs to provide a complementary approach to addressing the pandemic. IMPORTANCEThe ongoing efforts to address the COVID-19 pandemic center around the development of point-of-care diagnostics, preventative measures, and therapeutic strategies against COVID-19. In contrast to existing work, we have provided a complementary approach to target and contain SARS-CoV-2 from contaminated fluids and surfaces. Specifically, we present a generic strategy for the capture and containing of SARS-CoV-2 by cellulose-based substrates. This was archived by developing a bifunctional fusion protein consisting of both a cellulose-binding domain and a nanobody specific for the receptor-binding domain of SARS-CoV-2. As a proof-of-concept, our fusion protein-coated cellulose substrates exhibited enhanced capture efficiency against SARS-CoV-2 pseudovirus of both wildtype and the D614G mutant variants, the latter of which has been shown to confer higher infectivity. Furthermore, the fusion protein was integrated into a customizable chromatography with highly porous cellulose for neutralizing the virus from contaminated fluids in a highly continuous and cost-effective fashion.

bioengineering↗

bioRxiv2021

Spatial transcriptome sequencing revealed spatial trajectory in the Non-Small Cell Lung Carcinoma

Deepening understanding in the heterogeneity of tumors is critical for clinical treatment. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal lung cancer using the spatial transcriptomics (ST) technology. We identified gene expression gradients in stroma adjacent to tumor regions that allow for re-understanding of the tumor micro-environment. The establishment of these profiles was the first step towards an unbiased view of lung cancer and can serve as a dictionary for future studies. Tumor subclones were detected by ST technology in our research, while we contrast the EMT ability among in subclones which inferred the possible trajectory of tumor metastasis and invasion, which was confirmed by constructing the pseudo-time model of spatial transition within subclones. Together, these results uncovered lung cancer spatial heterogeneity, highlight potential tumor micro-environment differences and spatial evolution trajectory, and served as a resource for further investigation of tumor microenvironment.

cancer biology↗

bioRxiv2021

New metabolic alterations and predictive marker pipecolic acid in sera for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a major histological subtype of esophageal cancer with dismal prognosis. Although several serum metabolomic investigations have been reported, ESCC tumor-associated metabolic alterations along with predictive biomarkers in sera were not defined. Here we enrolled 34 treatment-naive ESCC patients and collected their pre-and post-esophagectomy sera together with sera from 34 healthy volunteers for metabolomic survey. Our comprehensive analysis discerned ESCC tumor-associated metabolic alterations as represented by a panel of 12 serum metabolites. Notably, postoperative abrosia and parenteral nutrition significantly perturbed the serum metabolome. Furthermore, we performed examination using sera from carcinogen-induced mice at dysplasia and ESCC stages, and identified three ESCC tumor-associated metabolites conserved between mice and humans. Notably, among these metabolites, pipecolic acid was progressively increased in mouse sera from dysplasia to cancerization, and it could accurately discriminate between mice at dysplasia stage and healthy control mice. Furthermore, this metabolite was essential for ECSS cells to oppose oxidative stress-induced DNA damage and cell proliferation arrest. Together, this study uncovered 12 ESCC tumor-associated serum metabolites with potential for monitoring therapeutic efficacy and disease relapse, presented evidence for refining parenteral nutrition composition, and highlighted serum pipecolic acid as an attractive biomarker for prediction of ESCC tumorigenesis.

cancer biology↗

bioRxiv2021

Enhancing the localization precision of single molecule localization microscopy by analyzing Brownian motion of individual fluorescent probe

Single-molecule localization microscopy (SMLM) boosts its applications when combined with the studies of cells, in which nanometer-sized biomolecules are irresolvable due to diffraction limit unless being subjected to SMLM. Although being well immobilized, given the nanometer sizes of biological molecules, they are still capable of movement stochastically around their immobilized sites. The influence of such motion on image quality and possible improvements have not yet been systematically investigated. Here, we accessed the biomolecule stochastic motion in SMLM by calculating the displacements between different localizations from the same molecule in single-molecule samples of Alexa Fluor-647-conjugated oligonucleotides. We found that, for most molecules, localization displacements at random frame intervals are remarkably larger than those between temporally neighbouring frames despite of drift correction, showing that biomolecule stochastic motion is involved in SMLM. Furthermore, the localization displacements were observed to increase with frame intervals and then saturate, suggesting biomolecule stochastic motion is confined within a finite area. Moreover, we showed that the localization precision is deteriorated by enlarging molecule sizes and improved by sample post-fixation. This study reveals confined stochastic motion of biomolecules increase localization uncertainty in SMLM, and improved localization precision can be achieved via restricting biomolecule stochastic motion.

biophysics↗

bioRxiv2021

Engineering the Immune Adaptor Protein STING as a Biologic

Activation of the stimulator of interferon genes (STING) pathway through cyclic dinucleotides (CDNs) has been explored extensively as potent vaccine adjuvants against infectious diseases as well as to increase tumor immunogenicity towards cancer immunotherapy in solid tumors. Over the last decade, a myriad of synthetic vehicles, including liposomes, polymers, and other nanoparticle platforms, have been developed to improve the bioavailability and therapeutic efficacy of STING agonists in preclinical mouse models. In comparison to synthetic materials, protein-based carriers represent an attractive delivery platform owing to their biocompatibility, amenability to genetic engineering, and intrinsic capacity to form well-defined structures. In the present work, we have engineered the immune adaptor STING as a protein-based delivery system for efficient encapsulation and intracellular delivery of CDNs. Through genetic fusion with a protein transduction domain, the recombinant STING can spontaneously penetrate cells to markedly enhance the delivery of CDNs in a mouse vaccination model and a syngeneic mouse melanoma model. Moreover, motivated by recent findings that certain tumor cells can evade immune surveillance via loss of STING expression, we further unveiled that our STING platform can serve as a functional vehicle to restore the STING signaling in a panel of lung and melanoma cell lines with impaired STING expression. Taken together, our STING-based protein delivery platform may offer a unique direction towards targeting STING-silenced tumors as well as augmenting the efficacy of STING-based vaccine adjuvants.

bioengineering↗

bioRxiv2020

DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity

T-cells play an essential role in the adaptive immune system by seeking out, binding and destroying foreign antigens presented on the cell surface of diseased cells. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native epitopes to elicit a T cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen (HLA) alleles, for both synthetic biological applications and to augment real training datasets. Here, we proposed a beta-binomial distribution approach to derive epitope immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, KNN, SVM, Random Forest, AdaBoost) and three deep learning models (CNN, ResNet, GNN) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-Cov-2). We chose the CNN model as the best prediction model based on its adaptivity for small and large datasets, and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepHLApan and IEDB, DeepImmuno-CNN further correctly predicts which residues are most important for T cell antigen recognition. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physiochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface. Data AvailabilityDeepImmuno Python3 code is available at https://github.com/frankligy/DeepImmuno. The DeepImmuno web portal is available from https://deepimmuno.herokuapp.com. The data in this article is available in GitHub and supplementary materials.

bioinformatics↗

bioRxiv2020

Dissecting human skeletal stem cell ontogeny by single-cell transcriptomic and functional analyses

Human skeletal stem cells (SSCs) have been discovered in fetal and adult bones. However, the spatiotemporal ontogeny of human SSCs during embryogenesis has been elusive. Here we map the transcriptional landscape of human embryonic skeletogenesis at single-cell resolution to address this fundamental question. We found remarkable heterogeneity within human limb bud mesenchyme and epithelium, as well as the earliest osteo-chondrogenic progenitors. Importantly, embryonic SSCs (eSSCs) were found in the perichondrium of human long bones, which self-renew and generate osteochondral lineage cells, but not adipocytes or hematopoietic stroma. eSSCs are marked by the adhesion molecule CADM1 and highly enrich FOXP1/2 transcriptional network. Interestingly, neural crest-derived cells with similar phenotypic markers and transcriptional network were also found in the sagittal suture of human embryonic calvaria. Taken together, this study revealed the cellular heterogeneity and lineage hierarchy during human embryonic skeletogenesis, and identified distinct skeletal stem/progenitor cells that orchestrate endochondral and intramembranous ossification.

developmental biology↗

bioRxiv2020

RAPPID: a platform of ratiometric bioluminescent sensors for homogeneous immunoassays

Heterogeneous immunoassays such as ELISA have become indispensable in modern bioanalysis, yet translation into easy-to-use point-of-care assays is hindered by their dependence on external calibration and multiple washing and incubation steps. Here, we introduce RAPPID (Ratiometric Plug-and-Play Immunodiagnostics), a "mix-and-measure" homogeneous immunoassay platform that combines highly specific antibody-based detection with a ratiometric bioluminescent readout that can be detected using a basic digital camera. The concept entails analyte-induced complementation of split NanoLuc luciferase fragments, photoconjugated to an antibody sandwich pair via protein G adapters. We also introduce the use of a calibrator luciferase that provides a robust ratiometric signal, allowing direct in-sample calibration and quantitative measurements in complex media such as blood plasma. We developed RAPPID sensors that allow low-picomolar detection of several protein biomarkers, anti-drug antibodies, therapeutic antibodies, and both SARS-CoV-2 spike protein and anti-SARS-CoV-2 antibodies. RAPPID combines ratiometric bioluminescent detection with antibody-based target recognition into an easy-to-implement standardized workflow, and therefore represents an attractive, fast, and low-cost alternative to traditional immunoassays, both in an academic setting and in clinical laboratories for point-of-care applications.

bioengineering↗

bioRxiv2020

mbkmeans: fast clustering for single cell data using mini-batch k-means

Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k - means. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.

bioinformatics↗

bioRxiv2020

Dissecting endothelial to haematopoietic stem cell transition by single-cell transcriptomic and functional analyses

Haematopoietic stem cells (HSCs) in adults are believed to be born from hemogenic endothelial cells (HECs) in mid-gestational mouse embryos. Due to rare and transient nature, the HSC-competent ECs have never been stringently identified and accurately captured, let alone their genuine vasculature precursors. Here, we firstly used high-precision single-cell transcriptomics to unbiasedly examine relevant EC populations at continuous developmental stages and transcriptomically identified putative HSC-primed HECs. Combining computational prediction and in vivo functional validation, we precisely captured HSC-competent HECs by newly constructed Neurl3-EGFP reporter mouse model, and realized enrichment further by surface marker combination. Surprisingly, endothelial-haematopoietic bi-potential was rarely but reliably witnessed in culture of single HECs. Noteworthy, primitive vascular ECs experienced two-step fate choices to become HSC-primed HECs, resolving several previously observed contradictions. Taken together, comprehensive understanding of endothelial evolutions and molecular programs underlying HSC-primed HEC specification in vivo will facilitate future investigations directing HSC production in vitro.

developmental biology↗

bioRxiv2019

An RNA-based system to study hepatitis B virus replication and select drug-resistance mutations

Hepatitis B virus (HBV) chronically infects over 250 million people worldwide, increasing their risk of liver cirrhosis and hepatocellular carcinoma. There is a vaccine to prevent new infections, but no efficient cure for chronic infection. New insights into HBV biology are needed to improve cure rates for this widespread devastating disease. We describe a method to initiate replication of HBV, a DNA virus, using synthetic RNA. This approach has several advantages over existing systems: it eliminates contaminating background signal from input virus or plasmid DNA and can be easily adapted to multiple genotypes and mutants. Further, it can be applied to identify anti-HBV compounds, measure anti-HBV drug efficiency, study virus evolution, and, as we demonstrate, it can be uniquely applied to predict antiviral drug resistance.

microbiology↗

bioRxiv2019

Single-cell tracing of the first hematopoietic stem cell generation in human embryos

Tracing the emergence of the first hematopoietic stem cells (HSCs) in human embryos, particularly the scarce and transient precursors thereof, is so far challenging, largely due to technical limitations and material rarity. Here, using single-cell RNA sequencing, we constructed the first genome-scale gene expression landscape covering the entire course of endothelial-to-HSC transition during human embryogenesis. The transcriptomically defined HSC-primed hemogenic endothelial cells (ECs) were captured at Carnegie stage 12-14 in an unbiased way, showing an unambiguous arterial EC feature with the up-regulation of RUNX1, MYB and ANGPT1. Importantly, subcategorizing CD34+CD45- ECs into CD44+ population strikingly enriched hemogenic ECs by over 10-fold. We further mapped the developmental path from arterial ECs via HSC-primed hemogenic ECs to hematopoietic stem progenitor cells, and revealed a distinct expression pattern of genes that were transiently over-represented upon the hemogenic fate choice of arterial ECs, including EMCN, PROCR and RUNX1T1. We also uncovered another temporally and molecularly distinct intra-embryonic hemogenic EC population, which was detected mainly at earlier CS 10 and lacked the arterial feature. Finally, we revealed the cellular components of the putative aortic niche and potential cellular interactions acting on the HSC-primed hemogenic ECs. The cellular and molecular programs and interactions that underlie the generation of the first HSCs from hemogenic ECs in human embryos, together with distinguishing HSC-primed hemogenic ECs from others, will shed light on the strategies for the production of clinically useful HSCs from pluripotent stem cells.

developmental biology↗

bioRxiv2019

MMCA: a Web-based Server for the Microbiome and Metabolome Correlation Analysis

BackgroundIn the last decade, integrative studies of microbiome and metabolome have experienced exponential growth in understanding their impact on human health and diseases. However, analyzing the resulting multi-omics data and their correlations remains a significant challenge in current studies due to the lack of a comprehensive computational tool to facilitate data integration and interpretation. In this study, we have developed a microbiome and metabolome integrative analysis pipeline (M2IA) to meet the urgent needs for tools that effectively integrate microbiome and metabolome data to derive biological insights.\n\nResultsM2IA streamlines the integrative data analysis between metabolome and microbiome, including both univariate analysis and multivariate modeling methods. KEGG-based functional analysis of microbiome and metabolome are provided to explore their biological interactions within specific metabolic pathways. The functionality of M2IA was demonstrated using TwinsUK cohort data with 1116 fecal metabolites and 16s rRNA microbiome from 786 individuals. As a result, we identified two important pathways, i.e., the benzoate degradation and phosphotransferase system, were closely associated with obesity from both metabolome and microbiome.\n\nConclusionsM2IA is a public available web server (http://m2ia.met-bioinformatics.cn) with the capability to investigate the complex interplay between microbiome and metabolome, and help users to develop mechanistic hypothesis for nutritional and personalized therapies of diseases.

bioinformatics↗

bioRxiv2018

Dynamic urinary proteomic analysis in a Walker 256 intracerebral tumor model

Patients with primary and metastatic brain cancer have an extremely poor prognosis, mostly due to the late diagnosis of disease. Urine, which lacks homeostatic mechanisms, is an ideal biomarker source that accumulates early and highly sensitive changes to provides information about the early stage of disease. A rat model mimicking the local tumor growth process in the brain was established with intracerebral Walker 256 (W256) cell injection. Urine samples were collected on days 3, 5 and 8 after injection and then analyzed by LC-MS/MS. In the intracerebral W256 model, no obvious clinical manifestations changes or abnormal MRI signals were found on days 3 and 5; at these time points, nine proteins were changed significantly in the urine of all 8 tumor rats. On day 8, when tumors were detected by MRI, twenty-five differential proteins were identified, including 10 proteins that have been reported to be closely related to tumor metastasis or brain tumors. The differential urinary proteomes were compared with those from the subcutaneous W256 model and the intracerebral C6 model. Few differential proteins overlapped. Specific differential protein patterns were observed among the three models, indicating that the urinary proteome can reflect the difference when tumor cells with different growth characteristics are inoculated into the brain and when identical tumor cells are inoculated into different areas, specifically, the subcutis and the brain.

cancer biology↗

bioRxiv2018

Decreased kinesin-1 mitigates NMDA-induced exicitotoxicity and ischemia-evoked neurodegeneration

N-methyl-D-aspartate receptor (NMDAR) is highly compartmentalized in neurons and the dysfunction has been implicated in various neuropsychiatric and neurodegenerative disorders. Recent failure to exploit NMDAR antagonization as a potential therapeutic target has driven the need to identify molecular mechanisms that regulate NMDAR compartmentalization. Here, we report that neural activity-dependent reduction of Kif5b, the heavy chain of kinesin-1, protected neurons against NMDA-induced excitotoxicity and ischemia-provoked neurodegeneration. Direct binding of Kinesin-1 to the GluN2B cytoplasmic tails regulated levels of NMDAR at extrasynaptic sites and the subsequent influx of calcium mediated by extrasynaptic NMDAR via regulating the insertion of NMDARs into neuronal surface. Transient increase of Kif5b restored the surface levels of NMDAR and the decreased neuronal susceptibility to NMDA-induced excitotoxicity. Our findings reveal that kinesin-1 regulates extrasynaptic NMDAR targeting and signaling, and the reduction of kinesin-1 could be regulated by neural activity and could be exploited to postpone or halt neurodegeneration.

neuroscience↗