Biology subjects

Wei, L.

Publications and source records attributed to Wei, L..

At least 109 records · Page 6Linked to original sources

bioRxiv2023

Oxidation of hemoproteins by Streptococcus pneumoniae collapses the cell cytoskeleton and disrupts mitochondrial respiration leading to cytotoxicity of human lung cells

Streptococcus pneumoniae (Spn) causes pneumonia that kills millions through acute toxicity and invasion of the lung parenchyma. During aerobic respiration, Spn releases hydrogen peroxide (Spn-H2O2), as a by-product of enzymes SpxB and LctO, and causes cell death with signs of both apoptosis and pyroptosis by oxidizing unknown cell targets. Hemoproteins are molecules essential for life and prone to oxidation by H2O2. We recently demonstrated that during infection-mimicking conditions, Spn-H2O2 oxidizes the hemoprotein hemoglobin (Hb), releasing toxic heme. In this study, we investigated details of the molecular mechanism(s) by which the oxidation of hemoproteins by Spn-H2O2 causes human lung cell death. Spn strains, but not H2O2-deficient Spn{Delta}spxB{Delta}lctO strains caused time-dependent cell cytotoxicity characterized by the rearrangement of the actin, the loss of the microtubule cytoskeleton and nuclear contraction. Disruption of the cell cytoskeleton correlated with the presence of invasive pneumococci and an increase of intracellular reactive oxygen species. In cell culture, the oxidation of Hb or cytochrome c (Cytc) caused DNA degradation and mitochondrial dysfunction from inhibition of complex I-driven respiration, which was cytotoxic to human alveolar cells. Oxidation of hemoproteins resulted in the creation of a radical, which was identified as a protein derived side chain tyrosyl radical by using electron paramagnetic resonance (EPR). Thus, we demonstrate that Spn invades lung cells, releasing H2O2 that oxidizes hemoproteins, including Cytc, catalyzing the formation of a tyrosyl side chain radical on Hb and causing mitochondrial disruption, that ultimately leads to the collapse of the cell cytoskeleton.

microbiology↗

bioRxiv2023

Consensus peaks of chromatin accessibility in the human genome

The rapid advancement of transposase-accessible chromatin using sequencing (ATAC-seq) technology, particularly with the emergence of single-cell ATAC-seq (scATAC-seq), has accelerated the studies of gene regulation. However, the absence of a generic feature reference for ATAC-seq data limits single-cell analyses and hinders the development of comprehensive cell atlases. To address this, we constructed a generic chromatin accessibility reference by aggregating peaks from 624 high-quality bulk ATAC-seq datasets, defining more than 1 million consensus peaks (cPeaks). Leveraging a deep neural network model, we expanded cPeaks to include previously unobserved regions, enhancing their coverage across diverse tissues and cell types. cPeaks exhibit consistent shapes across tissue types, sequencing technologies, and peak-calling methods, indicating that they represent inherent genomic features. Compared to existing feature defining methods and references, cPeaks show superior performance in scATAC-seq analyses, improving cell annotation and rare cell type identification. Additionally, cPeaks provide insights into chromatin dynamics during cellular differentiation and tumor progression. cPeaks can serve as a robust reference for chromatin accessibility studies to promote cross-dataset consistency and accelerate biological discoveries.

bioinformatics↗

bioRxiv2023

StructuralDPPIV: A novel deep learning model based on atom-structure for predicting dipeptidyl peptidase-IV inhibitory peptides

MotivationDiabetes is a chronic metabolic disorder that has been a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb amputation across the world. To alleviate the impact of diabetes, researchers have developed the next generation of anti-diabetic drugs, known as dipeptidyl peptidase IV inhibitory peptides (DPP-IV-IPs). However, the discovery of these promising drugs has been restricted due to the lack of effective peptide-mining tools. ResultsHere, we presented StructuralDPPI V, a deep learning model designed for DPP-IV-IP identification, which takes advantage of both molecular graph features in amino acid and sequence information. Experimental results on the independent test dataset and two wet experiment datasets show that our model outperforms the other state-of-art methods. Moreover, to better study what StructuralDPPIV learns, we used CAM technology and perturbation experiment to analyze our model, which yielded interpretable insights into the reasoning behind prediction results. AvailabilityThe project code is available at https://github.com/WeiLab-BioChem/Structural-DPP-IV. Contactweileyi@sdu.edu.cn, ran.su@tju.edu.cn

bioinformatics↗

bioRxiv2023

Deep flanking sequence engineering for efficient promoter design

Human experts are good at summarizing explicit strong patterns from small samples, while deep learning models can learn implicit weak patterns from big data. Biologists have traditionally described the sequence patterns of promoters via transcription factor binding sites (TFBSs), while the flanking sequences among TFBSs, which can also significantly influence promoter activity and function, remain largely uncharacterized. Thus, current synthetic promoters are mainly designed by the manipulation of TFBSs, while the flanking sequence is often chosen arbitrarily or by previous experience due to a lack of well-summarized optimization criteria. Here, we introduced an AI-aided promoter design framework, DeepSEED, that employs expert knowledge and deep learning methods to efficiently design synthetic promoters that have various desirable functions. DeepSEED incorporates the user-defined cis-regulatory sequences as seeds and generates flanking sequences that match the seeds. We showed that DeepSEED can automatically capture k-mer frequencies and DNA shape features from active promoters in the training set and efficiently optimize the flanking sequences to better match desired properties in synthetic promoters. We validated the effectiveness of this framework for diverse synthetic promoter design tasks in both prokaryotic and eukaryotic cells. DeepSEED successfully designed E. coli constitutive, isopropyl-beta-D-1-thiogalactopyranoside (IPTG)-inducible, and mammalian cell doxycycline (Dox)-inducible promoters with significant performance improvements, suggesting that DeepSEED has potential as an efficient AI-aided flanking sequence optimization approach for promoter design that may be of great benefit in synthetic biology applications. Availabilityhttps://github.com/WangLabTHU/deepseed

bioengineering↗

bioRxiv2023

The immune receptor SNC1 monitors helper NLRs targeted by a bacterial effector

Plants deploy intracellular receptors to counteract pathogen effectors that suppress cell-surface receptor-mediated immunity. To what extent pathogens manipulate also immunity mediated by intracellular receptors, and how plants tackle such manipulation, remains unknown. Arabidopsis thaliana encodes three very similar ADR1 class helper NLRs (ADR1, ADR1-L1 and ADR1-L2), which play key roles in plant immunity initiated by intracellular receptors. Here, we report that Pseudomonas syringae AvrPtoB, an effector with E3 ligase activity, can suppress ADR1-L1- and ADR1-L2-mediated cell death. ADR1, however, evades such suppression by diversification of two ubiquitination sites targeted by AvrPtoB. The intracellular sensor NLR SNC1 interacts with and guards the CCR domains of ADR1-L1 and ADR-L2. Removal of ADR1-L1 and ADR1-L2 or delivery of AvrPtoB activates SNC1, which then signals through ADR1 to trigger immunity. Our work not only uncovers the long sought-after physiological function of SNC1 in pathogen defense, but also that reveals how plants can use dual strategies, sequence diversification and a multiple layered guard-guardee system, to counteract pathogen attack on core immunity functions.

plant biology↗

bioRxiv2023

Reconstruction of TrkB complex assemblies and localizing an-tidepressant targets using Artificial Intelligence

Since Major Depressive Disorder (MDD) represents a neurological pathology caused by inter-synaptic messaging errors, membrane receptors, the source of signal cascades, constitute appealing drugs targets. G protein-coupled receptors (GPCRs) and ion channel receptors chelated antidepressants (ADs) high-resolution architectures were reported to realize receptors physical mechanism and design prototype compounds with minimal side effects. Tyrosine kinase receptor 2 (TrkB), a receptor that directly modulates synaptic plasticity, has a finite three-dimensional chart due to its high molecular mass and intrinsically disordered regions (IDRs). Leveraging breakthroughs in deep learning, the meticulous architecture of TrkB was projected employing Alphfold 2 (AF2). Furthermore, the Alphafold Multimer algorithm (AF-M) models the coupling of intra- and extra-membrane topologies to chaperones: mBDNF, SHP2, Etc. Conjugating firmly dimeric transmembrane helix with novel compounds like 2R,6R-hydroxynorketamine (2R,6R-HNK) expands scopes of drug screening to encompass all coding sequences throughout genomes. The operational implementation of TrkB kinase-SHP2, PLC{gamma}1, and SHC1 ensembles has paved the path for machine learning in which it can forecast structural transitions in the self-assembly and self-dissociation of molecules during trillions of cellular mechanisms. In silicon, the cornerstone of the alteration will be artificial intelligence (AI), empowering signal networks to operate at the atomic level and picosecond timescales.

bioinformatics↗

bioRxiv2023

Comprehensive Multi-omics Analysis Reveals the Core Role of Glycerophospholipid Metabolism in Rheumatoid Arthritis Development

ObjectivesRheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. MethodsA total of 162 subjects were included in our study, 121 subjects were used for 16S rRNA, ITS sequencing and metabolomics analysis and 41 subjects were used for transcriptomics analysis. Enrichment analysis was based on significant difference metabolites and genes. Protein-protein interaction network and correlation analyses were performed to interpret the interactions among intestinal flora, metabolites and genes. We applied three models to distinguish between NORA and CRA using computational OR values, LASSO and random forest, respectively. ResultsOur results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 28 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. ConclusionsThese findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.

immunology↗

bioRxiv2023

Development and Validation of an MRI-Derived Head-Neck Finite Element Model

PurposeThis study aimed to develop and validate a magnetic resonance imaging (MRI)-based biofidelic head-neck finite element (FE) model comprised of scalp, skull, CSF, brain, dura mater, pia mater, cervical vertebrae, and discs, 14 ligaments, and 42 neck muscles. MethodsWe developed this model using head and neck MRI images of a healthy male participant and by implementing a novel meshing algorithm to create finer hexahedral mesh structures of the brain. The model was validated by replicating four experimental studies: NBDLs high acceleration profile, Itos frontal impact cervical vertebrae study, Alshareefs brain sonomicrometry study, and Nahums impact study. ResultsThe results showed reasonable geometrical fidelity. Our simulated brain displacement and cervical disc strain results were close to their experimental counterparts. The intracranial pressure and brain stress data of our head-only model (excluding neck structures and constraining the base of the skull) were similar to Nahums reported results. As neck structures were not considered in Nahums study, the FE results of our head-neck model showed slight discrepancies. Notably, the addition of neck structures (head-neck model) reduced brain stress values and uncovered the brains intracranial pressure dynamics, which the head-only model failed to capture. Nevertheless, the FE simulation results showed a good agreement (r > 0.97) between the kinematic responses of the head-neck model and NBDLs experimental results. ConclusionThe developed head-neck model can accurately replicate the experimental results and has the potential to be used as an efficient computational tool for brain and head injury biomechanics research. Statements and DeclarationsThis work was primarily supported by the U.S. Department of Homeland Security (70RSAT21CB0000023). The MRI data acquisition was supported by the Texas Tech Neuroimaging Center.

bioengineering↗

bioRxiv2023

scDecouple: Decoupling cellular response and infection bias in scCRISPR-seq

MotivationscCRISPR-seq is an emerging high-throughput CRISPR screening technology that combines CRISPR screening with single-cell sequencing technologies. It provides rich information on the mechanism of gene regulation. However, when scCRISPR-seq is applied in a population of heterogeneous cells, the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. ResultsWe developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries. AvailabilityscDecouple is freely available at https://github.com/MengQiuchen/scDecouple. Contactweilei92@tsinghua.edu.cn, yinqingl@tsinghua.edu.cn

bioinformatics↗

bioRxiv2023

Increments in Visual Motion Coherence are More Readily Detected than Decrements

Understanding the circuits that access and read out information in the cerebral cortex to guide behavior remains a challenge for systems-level neuroscience. Recent optogenetic experiments targeting specific cell classes in mouse primary visual cortex (V1) have shown that mice are sensitive to optically-induced increases in V1 spiking, but are relatively insensitive to decreases in neuronal spiking of similar magnitude and time course. This asymmetry suggests that the readout of signals from cortex depends preferentially on increases in spike rate. We investigated whether humans display a similar asymmetry by measuring thresholds for detecting changes in the motion coherence of dynamic random dot stimuli. The middle temporal visual area (MT) has been shown to play an important role in discriminating random dot stimuli, and the responses of its individual neurons to dynamic random dots are well characterized. While both increments and decrements in motion coherence have heterogeneous effects on MT responses, increments cause on average more increases in firing rates. Consistent with this, we found that subjects are far more sensitive to increments of random dot motion coherence than to decrements of coherence. The magnitude of the difference in detectability was largely consistent with the expected difference in effectiveness of coherence increments and decrements in producing increases in MT spike rates. The results add strength to the notion that the circuit mechanisms that read out cortical signals are relatively insensitive to decrements in cortical spiking.

neuroscience↗

bioRxiv2023

Differentiated genomic footprints and connections inferred from 440 Hmong-Mien genomes suggest their isolation and long-distance migration

BackgroundThe underrepresentation of Hmong-Mien (HM) people in Asian genomic studies has hindered our comprehensive understanding of population history and human health. South China is an ethnolinguistically diverse region and indigenously settled by ethnolinguistically diverse HM, Austroasiatic (AA), Tai-Kadai (TK), Austronesian (AN), and Sino-Tibetan (ST) people, which is regarded as East Asias initial cradle of biodiversity. However, previous fragmented genetic studies have only presented a fraction of the landscape of genetic diversity in this region, especially the lack of haplotype-based genomic resources. The deep characterization of demographic history and natural-selection-relevant architecture in HM people was necessary. ResultsWe comprehensively reported the population-specific genomic resources and explored the fine-scale genetic structure and adaptative features inferred from the high-density SNP data in 440 individuals from 34 ethnolinguistic populations, including previously unreported She. We identified solid genetic differentiation between inland (Miao/Yao) and coastal (She) southern Chinese HM people, and the latter obtained more gene flow from northern East Asians. Multiple admixture models further confirmed that extensive gene flow from surrounding ST, TK, and AN people entangled in forming the gene pool of coastal southeastern East Asian HM people. Population genetic findings of isolated shared unique ancestral components based on the sharing alleles and haplotypes deconstructed that HM people from Yungui Plateau carried the breadth of genomic diversity and previously unknown genetic features. We identified a direct and recent genetic connection between Chinese and Southeast Asian HM people as they shared the most extended IBD fragments, supporting the long-distance migration hypothesis. Uniparental phylogenetic topology and Network relationship reconstruction found ancient uniparental lineages in southwestern HM people. Finally, the population-specific biological adaptation study identified the shared and differentiated natural-selection signatures among inland and coastal HM people associated with physical features and immune function. The allele frequency spectrum (AFS) of clinical cancer susceptibility alleles and pharmacogenomic genes showed significant differences between HM and northern Chinese people. ConclusionsOur extensive genetic evidence combined with the historic documents supported the view that ancient HM people originated in Yungui regions associated with ancient Three-Miao tribes descended from the ancient Daxi-Qujialing-Shijiahe people. And then, some recently rapidly migrated to Southeast Asia, and some culturally dispersed eastward and mixed respectively with Southeast Asian indigenes, coastal Liangzhu-related ancient populations, and incoming southward Sino-Tibetan people. Generally, complex population migration, admixture, and adaptation history contributed to their specific patterns of non-coding or disease-related genetic variations.

genetics↗

bioRxiv2023

BnIR: a multi-omics collaborative resource and tool platform for Brassica napus biological study

In the post-GWAS era, multi-omics techniques have shown great power and potential for candidate gene mining and functional genomics research. However, due to the lack of effective data integration and multi-omics analysis platforms, such techniques have not still been applied widely in rapeseed, an important oil crop worldwide. Here, we constructed a rapeseed multi-omics database (BnIR; http://yanglab.hzau.edu.cn/BnIR), which provides datasets of six omics including genomics, transcriptomics, variomics, epigenetics, phenomics and metabolomics, as well as numerous "variation-gene expression-phenotype" associations by using multiple statistical methods. In addition, a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets. BnIR is the most comprehensive multi-omics database for rapeseed so far, and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms. Short SummaryIn this study, we developed BnIR (http://yanglab.hzau.edu.cn/BnIR), a multi-omics database for rapeseed that integrates six omics datasets and "variation-gene expression-phenotype" associations. The database is equipped with a range of multi-omics tools for streamlined browsing and analysis. Through multiple case studies, we demonstrated BnIRs potential to identify candidate genes associated with specific traits and investigate their regulatory mechanisms.

bioinformatics↗

bioRxiv2023

IL-15 enhances functional properties and responses of CD28-negative CD4+ cytotoxic T cells expanded in Systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disorder that results in an attack by bodys immune system of its own tissues, causing chronic inflammation and tissue damage. T cells play a key role in the pathogenesis of SLE, as they secrete pro-inflammatory cytokines as well as mediate direct effects on target tissues. Recently, CD4-positive T cells with cytotoxic potential were showed to be involved in autoimmune disease progression and tissue damage. However, whether this cell type expands and plays effector functions in SLE patients remain to be elucidated. Analyzing single-cell RNA sequencing (scRNA-Seq) data and flow cytometry data, we find that cytotoxic CD4+CD28- T cells are present in SLE patients. We also show that these cells expand most prominently in patients affected by lupus nephritis, and they exhibit cytotoxic activity against human renal glomerular endothelial cells in vitro. In addition, our study suggests that Interleukin-15 (IL-15) promotes the expansion, proliferation and cytotoxic function of CD4+CD28- T cells in SLE patients. Tofacitinib, a selective JAK3 inhibitor, inhibits the effect of IL-15 on CD4+CD28- T cells. Together, our study clearly demonstrated that CD4+ CD28- T cells characterized by proinflammatory properties and cytolytic function expand in SLE patients. The pathogenic potential of these CD4+ CD28- T cells is driven by IL-15/IL-15R/JAK3/STAT5 signaling pathway, which may open new avenues for therapeutic intervention to prevent progression of SLE patients.

immunology↗

bioRxiv2022

DeepBIO is an automated and interpretable deep-learning platform for biological sequence prediction, functional annotation, and visualization analysis

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization, and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis, functional sequential region discovery, etc. Additionally, DeepBIO supports 9 base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust, and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists, and provide meaningful functional insights at both sequence-level and base-level from biological sequences alone. DeepBIO is publicly available at http://inner.wei-group.net/DeepBIO.

bioinformatics↗

bioRxiv2022

Learning Spatially-Aware Representations of Transcriptomic Data via Transfer Learning

Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues in health or diseases. Spatial transcriptomics (ST) data are powerful for depicting spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. On the other hand, single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We developed the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer the SC-ST mapping and predict pseudo-spatial adjacency between cells in the SC data. Semi-simulation and real data experiments verified that the embeddings preserved the spatial information and eliminated technical biases between SC and ST data. Besides, we can use attribution analysis in STEM to reveal genes whose expressions dominate spatial information. We applied STEM to data of human squamous cell carcinoma and of hepatic lobule to uncover the spatial localization of rare cell types data and reveal cell-type-specific gene expression variation along a spatial axis. STEM is a powerful tool for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.

bioinformatics↗

bioRxiv2022

Temporally resolved kinase regulatory networks control endothelial barrier integrity

Vascular leak is a common disease complication, yet the signaling networks regulating barrier integrity are incompletely understood. We developed a novel methodology, Temporally REsolved KInase Network Generation (TREKING), which combines a 28-kinase inhibitor screen with machine learning and network reconstruction to build time-resolved, functional phosphosignaling networks. We demonstrated the utility of TREKING for identifying pathways mediating barrier integrity following thrombin stimulation with or without tumor necrosis factor (TNF) activation in brain endothelial cells. TREKING assigned distinct barrier phenotypes to mitogen-activated protein kinase (MAPK) pathways and revealed a condition-specific MAPKAPK2/MK2 switch kinase pathway with early barrier-disruptive activity in both inflammatory conditions, but late barrier-restorative activity exclusively in the absence of TNF pre-conditioning. MAPKAPK2/MK2 was activated with expected distinct kinetics under the two inflammatory conditions and late activation was linked to a MAP3K20/ZAK-MAPK14/p38-MAPKAPK2/MK2 pathway. Beyond MAPKs, TREKING predicts extensive interconnected networks that control barrier integrity and is a tool for dissecting complex temporal phosphosignaling networks across biological systems.

systems biology↗

bioRxiv2022

Cryptochrome Stabilization Ameliorates Chronic Pain

Physiological and pathological pain exhibits striking diurnal variation, but the underlying mechanisms are largely unknown. We now describe an independent molecular clock in peripheral sensory neurons and satellite glial cells of sensory ganglia. We show that it is the sensory neuron transcription-translation feedback loops (TTFLs) that are responsible for diurnal pain behaviors. This clock regulates diurnal neurophysiological responses to a range of ligands, as well as synaptic activities of primary nociceptors. Furthermore, we find that loss of Cry1 and Cry2, the repressive arm of the core TTFLs, intensifies pain responses associated with increased voltage-gated sodium channel currents. Conversely, stabilization of CRY1 and CRY2 using the small molecule KL001, reduces pain sensitivity. Our results highlight novel opportunities to address chronic pain by directly harnessing circadian mechanisms. One-Sentence SummaryA peripheral pain clock governs daily pain fluctuations, which can be harnessed for treating pain disorders.

neuroscience↗

bioRxiv2022

Integrated single-cell sequencing analysis reveals peripheral immune landscape across the human lifespan

Systematic understanding of immune dynamics across the entire human lifespan at single-cell resolution is currently lacking. Here, we performed single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor (TCR)/B cell receptor (BCR) sequencing (scTCR/BCR-seq) on over 380,000 peripheral blood mononuclear cells collected from 45 healthy participants aged 0 to over 90 years. We revealed that the functions of T cell subsets were most susceptible to senescence among all PBMCs, featured by increased NF-{kappa}B signaling and IFN-{gamma} responses pathways, while reduced telomere maintenance and energy metabolism. We subsequently explored the rewiring of cell-cell interactions among different immune cells across the lifespan and revealed the major alteration of immune checkpoints in T cells within the cellular interaction networks. By combined analysis of scRNA-seq and scTCR-seq, we revealed that 1) GNLY+ CD8 Effector T cells exhibited a high clonal expansion with distinct functional signatures in children and the elderly; 2) Naive CD4+ T and naive CD8+ T cells displayed different aging patterns in both transcriptomes and immune repertoires; and 3) CD8+ MAIT cells showed a higher cell abundance and clonal diversity in adolescents. Furthermore, we identified a unique cytotoxic B cell subset enriched in children by scRNA-seq and scBCR-seq analysis and experimental validations. In summary, our work provided valuable insights and rich resources for understanding the development and aging of the human immune system across the lifespan.

immunology↗