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Luo, S.

Publications and source records attributed to Luo, S..

At least 73 records · Page 4Linked to original sources
bioRxiv2024

Targeted LNPs deliver mRNA encoding IL-15 superagonists to balance efficacy and toxicity in cancer therapy

Interleukin-15 (IL-15) emerges as a promising immunotherapeutic candidate in oncology because of its pivotal role in modulating both innate and adaptive immunity. However, the therapeutic utility remains concern due to the unexpected toxicity. We propose here that the mRNA lipid nanoparticle (mRNA-LNP) system can balance the issue through targeted delivery to increase IL-15 concentration in the tumor area and reduce leakage into the circulation. Utilizing the Structure-driven TARgeting (STAR) platform, we acquired intellectual property LNP vectors for effective and selective mRNA delivery to local (LNPLocal) and to pulmonary (LNPLung). Then the promising IL-15 superagonists mRNAs were obtained through structural optimization and sequence screening, showing better activity compared with benchmarker N-803. Subsequently, the anti-tumor efficacy of IL-15 superagonists mRNAs were evaluated by intratumoural (i.t.) injection and intravenous (i.v.) injection via LNPLocal and LNPLung, respectively. As a result, such superagonists exhibited better anti-tumor activity, less systematic exposure, and less cytokine related risks than N-803. We finally verified the selective delivery and well tolerability of LNPLung in non-human primates (NHPs), confirming the potential for clinical application. This finding may open up new possibilities for the treatment of lung cancers and lung metastasis cancers.

bioengineering↗
bioRxiv2023

Comparative analysis of methodologies for detecting extrachromosomal circular DNA

Extrachromosomal circular DNA (eccDNA) is crucial in oncogene amplification, gene transcription regulation, and intratumor heterogeneity. While various analysis pipelines and experimental methods have been developed for eccDNA identification, their detection efficiencies have not been systematically assessed. To address this, we evaluated the performance of 7 analysis pipelines using three simulated datasets, in terms of accuracy, similarity, duplication rate, and computational resource consumption. We also compared the eccDNA detection efficiency of 7 experimental methods through twenty-one real sequencing datasets. Our results identified Circle-Map and CReSIL as the most effective pipelines for eccDNA detection through short-read and long-read sequencing data, respectively. Moreover, third-generation sequencing-based Circle-Seq showed superior efficiency in detecting copy number-amplified eccDNA over 10 kb in length. These results offer valuable insights for researchers in choosing the most suitable methods for eccDNA research.

cancer biology↗
bioRxiv2023

Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction

When an important event occurs, the observer should identify which features of the environment might have caused it. This is the latent cause inference problem, and it must be solved if observers are to understand their environments. The problem is acute in social settings where individuals may not make equal contributions to the outcomes they achieve together. Here, we designed a novel task in which participants inferred which of two characters was the more likely to have been responsible for outcomes achieved by working together. Using computational modelling, univariate and multivariate analysis of human fMRI, and continuous theta burst stimulation we identified two brain regions that solved the task. Notably, as each outcome occurred, it was possible to decode inference of its cause (the responsible character) from activity in hippocampus. Activity in dorsomedial prefrontal cortex updated estimates of association between cause -- the responsible character - and the outcome.

neuroscience↗
bioRxiv2023

Search and Match across Spatial Omics Samples at Single-cell Resolution

Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match, and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (Cross-sample Alignment of SpaTial omics), a deep graph neural network (GNN)-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST enables spatially resolved differential analysis ({Delta}Analysis) to pinpoint and visualize disease-associated molecular pathways and cell-cell interactions, and single-cell relative translational efficiency (scRTE) profiling to reveal variations in translational control across cell types and regions. CAST serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities, and disease conditions, analogous to BLAST in sequence alignment.

bioinformatics↗
bioRxiv2023

Benchmarking computational methods for single-cell chromatin data analysis

Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices. We benchmarked 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluated the performance of each method at different data processing stages. This comprehensive approach allowed us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection. Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.

bioinformatics↗
bioRxiv2023

Drosophila Amus and Bin3 methylases functionally replace mammalian MePCE for capping and the stabilization of U6 and 7SK snRNAs

U6 and 7SK snRNAs process a 5 cap, believed to be essential for their stability and maintained by mammalian MePCE or Drosophila Bin3 enzymes. Although loss of either protein results in 7SK instability, loss of neither is associated with U6 reduction. Their yeast homolog is also not required for U6 stability, casting further doubts on the function of capping U6. Here we show that the Drosophila Amus protein, homologous to both Bin3 and MePCE, is essential for U6 but not 7SK stability. A full function of Amus is required for Drosophila development, and that rests primarily on Amuss methylase activity. Remarkably, the loss of U6 is rescued by the expression of an Amus-MePCE hybrid protein harboring the methyltransferase domain from MePCE, highlighting the conserved function of the two proteins as the U6 capping enzyme. Our new investigations in human cells establish a dependence of both U6 and 7SK stability on MePCE, resolving a long-standing uncertainty. While uncovering an interesting division of labor of Bin3/MePCE/Amus proteins, we discovered a "Bin3-Box" domain present only in enzymes associated with 7SK regulation. Targeted mutagenesis in Drosophila confirmed its importance for Bin3 function, revealing a possible conserved element in 7SK but not U6 biology.

genetics↗
bioRxiv2023

A novel gapmer nanobiosensor for probing long noncoding RNAs (lncRNA) expression dynamics during osteogenic and adipogenic differentiation

Long non-coding RNAs (lncRNA) are non-protein coding RNA molecules that are longer than 200 nucleotides. lncRNA plays diverse roles in gene regulation, chromatin remodeling, and cellular processes, influencing various biological pathways. However, probing the complex dynamics of lncRNA in live cells is a challenging task. In this study, a double-stranded gapmer locked nucleic acid (ds-GapM-LNA) nanobiosensor is designed for visualizing the abundance and expression of lncRNA in live human bone-marrow-derived mesenchymal stem cells (hMSCs). The sensitivity, specificity, and stability were characterized. The results showed that this ds-GapM-LNA nanobiosensor has very good sensitivity, specificity, and stability, which allows for dissecting the regulatory roles of cellular processes during dynamic physiological events. By incorporating this nanobiosensor with living hMSCs imaging, we elucidated lncRNA MALAT1 expression dynamics during osteogenic and adipogenic differentiation. The data reveals that lncRNA MALAT1 expression is correlated with distinct sub-stages of osteogenic and adipogenic differentiation.

bioengineering↗
bioRxiv2023

A partial Drp1 knockout improves autophagy flux independent of mitochondrial function

Dynamin-related protein 1 (Drp1) is typically known for its role in mitochondrial fission. A partial inhibition of this protein has been reported to be protective in experimental models of neurodegenerative diseases. The protective mechanism has been attributed primarily to improved mitochondrial function. Herein, we provide evidence showing that a partial Drp1-knockout improves autophagy flux independent of mitochondria. First, we characterized in cell and animal models that at low non-toxic concentrations, manganese (Mn), which causes parkinsonian-like symptoms in humans, impaired autophagy flux but not mitochondrial function and morphology. Furthermore, nigral dopaminergic neurons were more sensitive than their neighbouring GABAergic counterparts. Second, in cells with a partial Drp1-knockdown and Drp1+/- mice, autophagy impairment induced by Mn was significantly attenuated. This study demonstrates that autophagy is a more vulnerable target than mitochondria to Mn toxicity. Furthermore, improving autophagy flux is a separate mechanism conferred by Drp1 inhibition independent of mitochondrial fission.

cell biology↗
bioRxiv2023

Pan-genome analysis highlights the role of structural variation in the evolution and environmental adaptation of Asian honeybees

The Asian honeybee, Apis cerana, is an ecologically and economically important pollinator. Mapping its genetic variation is key to understanding population-level health, histories, and potential capacities to respond to environmental changes. However, most efforts to date were focused on single nucleotide polymorphisms (SNPs) based on a single reference genome, thereby ignoring larger-scale genomic variation. We employed long-read sequencing technologies to generate a chromosome-scale reference genome for the ancestral group of A. cerana. Integrating this with 525 resequencing datasets, we constructed the first pan-genome of A. cerana, encompassing almost the entire gene content. We found that 31.32% of genes in the pan-genome were variably present across populations, providing a broad gene pool for environmental adaptation. We identified and characterized structural variations (SVs) and found that they were not closely linked with SNP distributions, however, the formation of SVs was closely associated with transposable elements. Furthermore, phylogenetic analysis using SVs revealed a novel A. cerana ecological group not recoverable from the SNP data. Performing environmental association analysis identified a total of 44 SVs likely to be associated with environmental adaptation. Verification and analysis of one of these, a 330 bp deletion in the Atpalpha gene, indicated that this SV may promote the cold adaptation of A. cerana by altering gene expression. Taken together, our study demonstrates the feasibility and utility of applying pan-genome approaches to map and explore genetic feature variations of honeybee populations, and in particular to examine the role of SVs in the evolution and environmental adaptation of A. cerana.

genomics↗
bioRxiv2023

Integrated multi-omics approach reveals the role of SPEG in skeletal muscle biology including its relationship with myospryn complex

Autosomal-recessive mutations in SPEG (striated muscle preferentially expressed protein kinase) have been linked to centronuclear myopathy. Loss of SPEG is associated with defective triad formation, abnormal excitation-contraction coupling, and calcium mishandling in skeletal muscles. To elucidate the underlying molecular pathways, we have utilized multi-omics tools and analysis to obtain a comprehensive view of the complex biological processes. We identified that SPEG interacts with myospryn complex proteins (CMYA5, FSD2, RyR1), and SPEG deficiency results in myospryn complex abnormalities. In addition, transcriptional and protein profiles of SPEG-deficient muscle revealed defective mitochondrial function including aberrant accumulation of enlarged mitochondria on electron microscopy. Furthermore, SPEG regulates RyR1 phosphorylation at S2902, and its loss affects JPH2 phosphorylation at multiple sites. On analyzing the transcriptome, the most dysregulated pathways affected by SPEG deficiency included extracellular matrix-receptor interaction and peroxisome proliferator-activated receptors signaling, which may be due to defective triad and mitochondrial abnormalities. In summary, we have elucidated the critical role of SPEG in triad as it works closely with myospryn complex, phosphorylates JPH2 and RyR1, and demonstrated that its deficiency is associated with mitochondrial abnormalities. This study emphasizes the importance of using multi-omics techniques to comprehensively analyze the molecular anomalies of rare diseases. Synopsis O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/538136v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@bc3cbborg.highwire.dtl.DTLVardef@37195dorg.highwire.dtl.DTLVardef@1624caeorg.highwire.dtl.DTLVardef@985a2c_HPS_FORMAT_FIGEXP M_FIG C_FIG We have previously linked mutations in SPEG (striated preferentially expressed protein) with a recessive form of centronuclear myopathy and/or dilated cardiomyopathy and have characterized a striated muscle-specific SPEG-deficient mouse model that recapitulates human disease with disruption of the triad structure and calcium homeostasis in skeletal muscles. In this study, we applied multi-omics approaches (interactomic, proteomic, phosphoproteomic, and transcriptomic analyses) in the skeletal muscles of SPEG-deficient mice to assess the underlying pathways associated with the pathological and molecular abnormalities. O_LISPEG interacts with myospryn complex proteins (CMYA5, FSD2, RyR1), and its deficiency results in myospryn complex abnormalities. C_LIO_LISPEG regulates RyR1 phosphorylation at S2902, and its loss affects JPH2 phosphorylation at multiple sites. C_LIO_LISPEG and SPEG{beta} have different interacting partners suggestive of differential function. C_LIO_LITranscriptome analysis indicates dysregulated pathways of ECM-receptor interaction and peroxisome proliferator-activated receptor signaling. C_LIO_LIMitochondrial defects on the transcriptome, proteome, and electron microscopy, may be a consequence of defective calcium signaling. C_LI

molecular biology↗
bioRxiv2023

A novel simian adenovirus vectored COVID-19 vaccine elicits effective mucosal and systemic immunity in mice by intranasal and intramuscular vaccination regimens

The failure of COVID-19 vaccines to prevent SARS-CoV-2 infection and transmission, a possibly critical reason was the lack of protective mucosal immunity in respiratory tract. Here, we evaluated the effects of mucosal and systemic immunity from a novel simian adenovirus vectored COVID-19 vaccine (Sad23L- nCoV-S) in mice in comparison with Ad5-nCoV-S by intranasal (IN) drip and intramuscular (IM) injection vaccinations. As good as the well-known Ad5-nCoV-S vaccine, a single-dose IN inoculation of 1x109 PFU Sad23L-nCoV-S vaccine induced a similar level of IgG S-binding antibody (S-BAb) and neutralizing antibody (NAb) and higher IgA in serum, while IN route raised significantly higher IgG and IgA S- BAb and NAb in bronchoalveolar lavage (BAL), and specific IFN-{gamma} secreting T cell response in lung compared with IM route, but lower T cell response in spleen. By prime-boost vaccination regimens with different combination of IN and IM inoculations of Sad23L-nCoV-S vaccine, the IN involved vaccinations stimulated higher protective mucosal or local immunity in BAL and lung, while the IM involved immunizations induced higher systemic immunity in serum and spleen. A long-term sustained systemic and mucosal NAb and T cell immunity to SARS-CoV-2 was maintained at high levels over 32 weeks by prime-boost vaccination regimens with IN and IM routes. In conclusion, priming or boosting immunization with IN inoculation of Sad23L-nCoV-S vaccine could induced effective mucosal immunity and in combination of IM route could additionally achieve systemic immunity, which provided an important reference for vaccination regimens against respiratory virus infection. IMPORTANCEThe essential goal of vaccination is to generate potent and long-term protection against diseases. Several factors including type of vector, delivery route, boosting regimen influence the outcome of prime-boost immunization approaches. The immunization regimen by constructing a novel simian adenovirus vectored COVID-19 vaccine and employing combination of intranasal and intramuscular inoculations, could elicit mucosal neutralizing antibodies against five mutant strains in the respiratory tract, and strong systemic immunity. Immune protection could last for more than 32 weeks. Vectored vaccine construction and immunization regimens have positively impacted respiratory disease prevention.

immunology↗
bioRxiv2023

A supervised data-driven spatial filter denoising method for acoustic-induced artifacts in intracranial electrophysiological recordings

Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participants voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.

neuroscience↗
bioRxiv2023

Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein Interaction

AO_SCPLOWBSTRACTC_SCPLOWProtein-protein interactions are crucial to many biological processes, and predicting the effect of amino acid mutations on binding is important for protein engineering. While data-driven approaches using deep learning have shown promise, the scarcity of annotated experimental data remains a major challenge. In this work, we propose a new approach that predicts mutational effects on binding using the change in conformational flexibility of the protein-protein interface. Our approach, named Rotamer Density Estimator (RDE), employs a flow-based generative model to estimate the probability distribution of protein side-chain conformations and uses entropy to measure flexibility. RDE is trained solely on protein structures and does not require the supervision of experimental values of changes in binding affinities. Furthermore, the unsupervised representations extracted by RDE can be used for downstream neural network predictions with even greater accuracy. Our method outperforms empirical energy functions and other machine learning-based approaches.

bioinformatics↗
bioRxiv2023

Single cell DNA methylation ageing in mouse blood

Ageing is the accumulation of changes and overall decline of the function of cells, organs and organisms over time. At the molecular and cellular level, the concept of biological age has been established and biomarkers of biological age have been identified, notably epigenetic DNA-methylation based clocks. With the emergence of single-cell DNA methylation profiling methods, the possibility to study biological age of individual cells has been proposed, and a first proof-of-concept study, based on limited single cell datasets mostly from early developmental origin, indicated the feasibility and relevance of this approach to better understand organismal changes and cellular ageing heterogeneity. Here we generated a large single-cell DNA methylation and matched transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages (10-101 weeks of age). We observed that the number of genes expressed increased at older ages, but gene specific changes were small. We next developed a robust single cell DNA methylation age predictor (scEpiAge), which can accurately predict age in a broad range of publicly available datasets, including very sparse data and it also predicts age in single cells. Interestingly, the DNA methylation age distribution is wider than technically expected in 19% of single cells, suggesting that epigenetic age heterogeneity is present in vivo and may relate to functional differences between cells. In addition, we observe differences in epigenetic ageing between the major blood cell types. Our work provides a foundation for better single-cell and sparse data epigenetic age predictors and highlights the significance of cellular heterogeneity during ageing. Highlights- Model to estimate DNA methylation age in single cells - Large multi-omics dataset of single cells from murine blood - Epigenetic age deviations from chronological age are greater than technical expected from technical variability - Number of genes expressed increases with chronological and epigenetic age

cell biology↗
bioRxiv2022

Investigation of a mathematical model describing global cancer growth and treatment: An inhomogeneous model based on the generalized logistic equation

Tumor growth is manifestation of the evolution of a complex system. Researchers have limited scope of modelling studies on specific aspects or stages of the process. It has led to unsatisfactory explanation of clinical observations. We hereby demonstrated that an inhomogeneous model built on the generalized logistic equation could serve better. It was developed to describe the whole process of tumor progression, clinically observed independence of index tumor growth from spread of the disease and growth deceleration during early stage of solid tumors. It was validated by simulating the coexistence of exponential and sigmoidal growth in chronic lymphocytic leukaemia, theories of tumor heterogeneity, as well as by accommodating notions pertaining to tumor treatment and prognosis. We thought therefore it was an interesting and not unjustifiable description of actual tumor growth in human body and hoped it might encourage more researchers to look at tumor modelling from a clinical perspective.

cancer biology↗
bioRxiv2022

Quantifying B-cell Clonal Diversity In Repertoire Data

The adaptive immune system has the extraordinary ability to produce a broad range of immunoglobulins that can bind a wide variety of antigens. During adaptive immune responses, activated B cells duplicate and undergo somatic hypermutation in their B-cell receptor (BCR) genes, resulting in clonal families of diversified B-cells that can be related back to a common ancestor. Advances in high-throughput sequencing technologies have enabled the high-throughput characterization of B-cell repertoires, however, the accurate identification of clonally related BCR sequences remains a major challenge. In this study, we compare three different clone identification methods on both simulated and experimental data, and investigate their impact on the characterization of B-cell diversity. We find that different methods may lead to different clonal definitions, which in turn can affect the quantification of clonal diversity in repertoire data. Interestingly, we find the Shannon entropy to be overall the most robust diversity index in regard to different clonal identification. Our analysis also suggests that the traditional germline gene alignment-based method for clonal identification remains the most accurate when the complete information about the sequence is known, but that alignment-free methods may be preferred for shorter read length. We make our implementation freely available as a Python library cdiversity.

bioinformatics↗
bioRxiv2022

Integrated analysis of single-cell transcriptome of liver cancer and cirrhosis reveals cell lineage similarity and prognostic-associated subpopulations

Background & AimsLiver cancer is one of the most leading causes of cancer deaths. Cirrhosis is an important risk factor for liver cancer, which is the result of over-fibrosis caused by diffuse and long-term liver damage. Despite extensive research, a systematic study for characterizing similarity between liver cancer and cirrhosis at single cell resolution is still lacking. MethodsWe established a data analysis framework to elucidate cell lineage similarity between liver cancer and cirrhosis to discover prognostic-associated subpopulations. We integrated single-cell transcriptome data from liver samples at normal, cirrhotic and cancer conditions, which totally contained 78,000 cells. Gene regulation analysis, cellular interactions and trajectory analysis were performed to characterize cirrhosis-like cell subpopulations. Bulk transcriptomes were used to discover prognostic-associated subpopulations. ResultsBy aligning cellular subpopulations across different samples, we found remarkable similarity between KNG1+ hepatocytes in cirrhosis and PGA5+ hepatocytes in HCC. Furthermore, gene regulation analysis and cellular interactions implicated E2F1, FOXA2, EGF, CDH and ANGPTL signaling in maintaining cirrhosis-like ecosystem. Strikingly, subpopulations with higher expression of cirrhosis-like signatures were associated with patients worse survival. ConclusionsWe revealed a previously unexplored cirrhosis-like ecosystem of liver cancer, which could provide novel biomarkers for therapeutic interventions in HCC. Core analysis modules in this study were integrated into a user-friendly toolkit, SIMscRNA(https://github.com/xmuhuanglab/SIM-scRNA), which could facilitate the exploration of similarity and heterogeneity between precancerous diseases and solid tumors.

bioinformatics↗