Investigation of human aging at the single-cell level.

Human aging is characterized by a gradual decline in physiological functions and an increased susceptibility to various diseases. The complex mechanisms underlying human aging are still not fully elucidated. Single-cell sequencing (SCS) technologies have revolutionized aging research by providing unprecedented resolution and detailed insights into cellular diversity and dynamics. In this review, we discuss the application of various SCS technologies in human aging research, encompassing single-cell, genomics, transcriptomics, epigenomics, and proteomics. We also discuss the combination of multiple omics layers within single cells and the integration of SCS technologies with advanced methodologies like spatial transcriptomics and mass spectrometry. These approaches have been essential in identifying aging biomarkers, elucidating signaling pathways associated with aging, discovering novel aging cell subpopulations, uncovering tissue-specific aging characteristics, and investigating aging-related diseases. Furthermore, we provide an overview of aging-related databases that offer valuable resources for enhancing our understanding of the human aging process.

SMARTdb: An Integrated Database for Exploring Single-cell Multi-omics Data of Reproductive Medicine.

Single-cell multi-omics sequencing has greatly accelerated reproductive research in recent years, and the data are continually growing. However, utilizing these data resources is challenging for wet-lab researchers. A comprehensive platform for exploring single-cell multi-omics data related to reproduction is urgently needed. Here, we introduce the single-cell multi-omics atlas of reproduction (SMARTdb), an integrative and user-friendly platform for exploring molecular dynamics of reproductive development, aging, and disease, which covers multi-omics, multi-species, and multi-stage data. We curated and analyzed single-cell transcriptomic and epigenomic data of over 2.0 million cells from 6 species across the entire lifespan. A series of powerful functionalities are provided, such as "Query gene expression", "DIY expression plot", "DNA methylation plot", and "Epigenome browser". With SMARTdb, we found that the male germ cell-specific expression pattern of RPL39L and RPL10L is conserved between human and other model animals. Moreover, DNA hypomethylation and open chromatin may collectively regulate the specific expression pattern of RPL39L in both male and female germ cells. In summary, SMARTdb is a powerful platform for convenient data mining and gaining novel insights into reproductive development, aging, and disease. SMARTdb is publicly available at https://smart-db.cn.

Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders.

Complex structural variations (cxSVs) are often overlooked in genome analyses due to detection challenges. We developed ARC-SV, a probabilistic and machine-learning-based method that enables accurate detection and reconstruction of cxSVs from standard datasets. By applying ARC-SV across 4,262 genomes representing all continental populations, we identified cxSVs as a significant source of natural human genetic variation. Rare cxSVs have a propensity to occur in neural genes and loci that underwent rapid human-specific evolution, including those regulating corticogenesis. By performing single-nucleus multiomics in postmortem brains, we discovered cxSVs associated with differential gene expression and chromatin accessibility across various brain regions and cell types. Additionally, cxSVs detected in brains of psychiatric cases are enriched for linkage with psychiatric GWAS risk alleles detected in the same brains. Furthermore, our analysis revealed significantly decreased brain-region- and cell-type-specific expression of cxSV genes, specifically for psychiatric cases, implicating cxSVs in the molecular etiology of major neuropsychiatric disorders.

Innate immune response in COVID-19: single-cell multi-omics profile of NK lymphocytes in a clinical case series.

BACKGROUND: COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) represents the biggest global health emergency in recent decades. The host immune response to SARS-CoV-2 seems to play a key role in disease pathogenesis and clinical manifestations, with Natural Killer (NK) lymphocytes being among the targets of virus-induced regulation. METHODS: This study performed a single-cell multi-omics analysis of transcripts and proteins of NK lymphocytes in COVID-19 patients, for the characterization of the innate immunological response to infection. NK cells were isolated from peripheral blood samples collected from adult subjects divided into 3 study groups: (1) non-infected subjects (Naïve group, n = 3), (2) post COVID-19 convalescent subjects (Healed group, n = 3) and (3) patients that were vaccinated against SARS-CoV-2 (Vaccine group, n = 3). Cells were then analysed by the BD Rhapsody System for the single-cell multi-omics investigation of transcriptome and membrane proteins. RESULTS: The bioinformatic analysis identified 5 cell clusters which differentially expressed gene/protein markers, defining NK cell subsets as "Active NK cells" and "Mature NK cells". Calculating the relative proportion of each cluster within patient groups, more than 40% of the Naïve group cell population was found to belong to Mature NKs, whereas more than 75% of the Vaccine group cell population belonged to the cluster of Active NKs. Regarding the Healed group, it seemed to show intermediate phenotype between Active and Mature NK cells. Differential expression of specific genes, proteins and signaling pathways was detected comparing the profile of the 3 experimental groups, revealing a more activated NK cell phenotype in vaccinated patients versus recovered individuals. CONCLUSIONS: The present study detected differential expression of NK cell markers in relation to SARS-CoV-2 infection and vaccine administration, suggesting the possibility to identify key molecular targets for clinical-diagnostic use of the individual response to viral infection and/or re-infection.

Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease.

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

CXC chemokine receptor 4 - mediated immune modulation and tumor microenvironment heterogeneity in gastric cancer: Utilizing multi-omics approaches to identify potential therapeutic targets.

G-protein-coupled receptors (GPRs) are critical regulators of various biological behaviors, and their role in gastric cancer (GC) progression is gaining increasing attention. Among them, the immune regulatory mechanisms mediated by chemokine receptor 4 (CXCR4) remain insufficiently understood. This study aims to explore the immune regulatory functions of CXCR4 and the heterogeneity of the tumor microenvironment (TME) by examining GPR-related gene expression in GC. Through multi-omics approaches, including spatial transcriptomics and single-cell RNA sequencing, we investigated the oncogenic mechanisms of CXCR4, particularly its role in T cell immune exhaustion. In vitro experiments, including ELISA, PCR, CCK8 assays, cell scratch assays, and colony formation assays, were used to validate the role of CXCR4 in the migration and invasion of AGS and SNU-1 cell lines. CXCR4 silencing using siRNA further demonstrated its regulatory effects on these cellular processes. Our results revealed a strong correlation between elevated CXCR4 expression and increased exhaustion of regulatory T cells (Tregs) in the TME. Furthermore, heightened CXCR4 expression was linked to increased TME heterogeneity, driven by oxidative stress and activation of the NF-κB pathway, promoting immune evasion and tumor progression. Silencing CXCR4 significantly inhibited the invasive and proliferative abilities of AGS and SNU-1 cells, while also reducing the expression of pro-inflammatory cytokines IL-1ß and interleukin-6, thus alleviating chronic inflammation and improving TME conditions. In conclusion, our comprehensive investigation highlights CXCR4 as a key mediator of TME dynamics and immune modulation in GC. Targeting CXCR4 presents a promising therapeutic strategy to slow tumor progression by reducing Tregs-mediated immune exhaustion and TME heterogeneity, positioning it as a novel therapeutic target in GC treatment.

Single-cell sequencing to multi-omics: technologies and applications.

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

The multicellular compartmentation of plant specialized metabolism.

The phenomenon of multicellular compartmentation in biosynthetic pathways has been documented for only a limited subset of specialized metabolites, despite its hypothesized significance in facilitating plant survival and adaptation to environmental stress. Transporters that shuttle metabolic intermediates between cells are hypothesized to be integral components enabling compartmentalized biosynthesis. Nevertheless, our understanding of the multicellular compartmentation of plant specialized metabolism and the associated intermediate transporters remains incomplete. The emergence of single-cell and spatial multiomics techniques holds promise for shedding light on unresolved questions in this field, such as the prevalence of multicellular compartmentation across the plant kingdom and the specific types of specialized metabolites whose biosynthetic pathways are prone to compartmentation. Advancing our understanding of the mechanisms underlying multicellular compartmentation will contribute to improving the production of specialized target metabolites through metabolic engineering or synthetic biology.

Decoding molecular features of bovine oocyte fate during antral follicle growth via single-cell multi-omics analysis†.

Antral follicle size is a useful predictive marker of the competency of enclosed oocytes for yielding an embryo following in vitro maturation and fertilization. However, the molecular mechanisms underpinning oocyte developmental potential during bovine antral follicle growth are still unclear. Here, we used a modified single-cell multi-omics approach to analyze the transcriptome, DNA methylome, and chromatin accessibility in parallel for oocytes and cumulus cells collected from bovine antral follicles of different sizes. Transcriptome profiling identified three types of oocytes (small, medium, and large) that underwent different developmental trajectories, with large oocytes exhibiting the largest average follicle size and characteristics resembling metaphase-II oocytes. Differential expression analysis and real-time polymerase chain reaction assay showed that most replication-dependent histone genes were highly expressed in large oocytes. The joint analysis of multi-omics data revealed that the transcription of 20 differentially expressed genes in large oocytes was associated with both DNA methylation and chromatin accessibility. In addition, oocyte-cumulus interaction analysis showed that inflammation, DNA damage, and p53 signaling pathways were active in small oocytes, which had the smallest average follicle sizes. We further confirmed that p53 pathway inhibition in the in vitro maturation experiments using oocytes obtained from small antral follicles could improve the quality of oocytes and increased the blastocyte rate after in vitro fertilization and culture. Our work provides new insights into the intricate orchestration of bovine oocyte fate determination during antral folliculogenesis, which is instrumental for optimizing in vitro maturation techniques to optimize oocyte quality.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling.

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

Sequential drug treatment targeting cell cycle and cell fate regulatory programs blocks non-genetic cancer evolution in acute lymphoblastic leukemia.

BACKGROUND: Targeted therapies exploiting vulnerabilities of cancer cells hold promise for improving patient outcome and reducing side-effects of chemotherapy. However, efficacy of precision therapies is limited in part because of tumor cell heterogeneity. A better mechanistic understanding of how drug effect is linked to cancer cell state diversity is crucial for identifying effective combination therapies that can prevent disease recurrence. RESULTS: Here, we characterize the effect of G2/M checkpoint inhibition in acute lymphoblastic leukemia (ALL) and demonstrate that WEE1 targeted therapy impinges on cell fate decision regulatory circuits. We find the highest inhibition of recovery of proliferation in ALL cells with KMT2A-rearrangements. Single-cell RNA-seq and ATAC-seq of RS4;11 cells harboring KMT2A::AFF1, treated with the WEE1 inhibitor AZD1775, reveal diversification of cell states, with a fraction of cells exhibiting strong activation of p53-driven processes linked to apoptosis and senescence, and disruption of a core KMT2A-RUNX1-MYC regulatory network. In this cell state diversification induced by WEE1 inhibition, a subpopulation transitions to a drug tolerant cell state characterized by activation of transcription factors regulating pre-B cell fate, lipid metabolism, and pre-BCR signaling in a reversible manner. Sequential treatment with BCR-signaling inhibitors dasatinib, ibrutinib, or perturbing metabolism by fatostatin or AZD2014 effectively counteracts drug tolerance by inducing cell death and repressing stemness markers. CONCLUSIONS: Collectively, our findings provide new insights into the tight connectivity of gene regulatory programs associated with cell cycle and cell fate regulation, and a rationale for sequential administration of WEE1 inhibitors with low toxicity inhibitors of pre-BCR signaling or metabolism.

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.

BACKGROUND: Advances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis. RESULTS: Extensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity. CONCLUSIONS: HyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.

A multi-omic single-cell landscape of cellular diversification in the developing human cerebral cortex.

The vast neuronal diversity in the human neocortex is vital for high-order brain functions, necessitating elucidation of the regulatory mechanisms underlying such unparalleled diversity. However, recent studies have yet to comprehensively reveal the diversity of neurons and the molecular logic of neocortical origin in humans at single-cell resolution through profiling transcriptomic or epigenomic landscapes, owing to the application of unimodal data alone to depict exceedingly heterogeneous populations of neurons. In this study, we generated a comprehensive compendium of the developing human neocortex by simultaneously profiling gene expression and open chromatin from the same cell. We computationally reconstructed the differentiation trajectories of excitatory projection neurons of cortical origin and inferred the regulatory logic governing lineage bifurcation decisions for neuronal diversification. We demonstrated that neuronal diversity arises from progenitor cell lineage specificity and postmitotic differentiation at distinct stages. Our data paves the way for understanding the primarily coordinated regulatory logic for neuronal diversification in the neocortex.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification.

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation.

Despite the success of antiretroviral therapy, human immunodeficiency virus (HIV) cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) approach to simultaneously profile the transcriptomic and epigenomic characteristics of ∼ 125,000 latently infected primary CD4+ T cells after reactivation using three different latency reversing agents. Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We identified cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrated that a machine learning model trained on these data was 75%-79% accurate at predicting viral reactivation. Finally, we validated the role of two candidate HIV-regulating factors, FOXP1 and GATA3, in viral transcription. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.

Unravelling B cell heterogeneity: insights into flow cytometry-gated B cells from single-cell multi-omics data.

Introduction: B cells play a pivotal role in adaptive immunity which has been extensively characterised primarily via flow cytometry-based gating strategies. This study addresses the discrepancies between flow cytometry-defined B cell subsets and their high-confidence molecular signatures using single-cell multi-omics approaches. Methods: By analysing multi-omics single-cell data from healthy individuals and patients across diseases, we characterised the level and nature of cellular contamination within standard flow cytometric-based gating, resolved some of the ambiguities in the literature surrounding unconventional B cell subsets, and demonstrated the variable effects of flow cytometric-based gating cellular heterogeneity across diseases. Results: We showed that flow cytometric-defined B cell populations are heterogenous, and the composition varies significantly between disease states thus affecting the implications of functional studies performed on these populations. Importantly, this paper draws caution on findings about B cell selection and function of flow cytometric-sorted populations, and their roles in disease. As a solution, we developed a simple tool to identify additional markers that can be used to increase the purity of flow-cytometric gated immune cell populations based on multi-omics data (AlliGateR). Here, we demonstrate that additional non-linear CD20, CD21 and CD24 gating can increase the purity of both naïve and memory populations. Discussion: These findings underscore the need to reconsider B cell subset definitions within the literature and propose leveraging single-cell multi-omics data for refined characterisation. We show that single-cell multi-omics technologies represent a powerful tool to bridge the gap between surface marker-based annotations and the intricate molecular characteristics of B cell subsets.

scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization.

MOTIVATION: The technology for analyzing single-cell multi-omics data has advanced rapidly and has provided comprehensive and accurate cellular information by exploring cell heterogeneity in genomics, transcriptomics, epigenomics, metabolomics and proteomics data. However, because of the high-dimensional and sparse characteristics of single-cell multi-omics data, as well as the limitations of various analysis algorithms, the clustering performance is generally poor. Matrix factorization is an unsupervised, dimensionality reduction-based method that can cluster individuals and discover related omics variables from different blocks. Here, we present a novel algorithm that performs joint dimensionality reduction learning and cell clustering analysis on single-cell multi-omics data using non-negative matrix factorization that we named scMNMF. We formulate the objective function of joint learning as a constrained optimization problem and derive the corresponding iterative formulas through alternating iterative algorithms. The major advantage of the scMNMF algorithm remains its capability to explore hidden related features among omics data. Additionally, the feature selection for dimensionality reduction and cell clustering mutually influence each other iteratively, leading to a more effective discovery of cell types. We validated the performance of the scMNMF algorithm using two simulated and five real datasets. The results show that scMNMF outperformed seven other state-of-the-art algorithms in various measurements. AVAILABILITY AND IMPLEMENTATION: scMNMF code can be found at https://github.com/yushanqiu/scMNMF.

Unveiling aging dynamics in the hematopoietic system insights from single-cell technologies.

As the demographic structure shifts towards an aging society, strategies aimed at slowing down or reversing the aging process become increasingly essential. Aging is a major predisposing factor for many chronic diseases in humans. The hematopoietic system, comprising blood cells and their associated bone marrow microenvironment, intricately participates in hematopoiesis, coagulation, immune regulation and other physiological phenomena. The aging process triggers various alterations within the hematopoietic system, serving as a spectrum of risk factors for hematopoietic disorders, including clonal hematopoiesis, immune senescence, myeloproliferative neoplasms and leukemia. The emerging single-cell technologies provide novel insights into age-related changes in the hematopoietic system. In this review, we summarize recent studies dissecting hematopoietic system aging using single-cell technologies. We discuss cellular changes occurring during aging in the hematopoietic system at the levels of the genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial multi-omics. Finally, we contemplate the future prospects of single-cell technologies, emphasizing the impact they may bring to the field of hematopoietic system aging research.

Arenobufagin enhances T-cell anti-tumor immunity in colorectal cancer by modulating HSP90ß accessibility.

BACKGROUND: Colorectal cancer (CRC) is a significant public health issue, ranking as one of the predominant cancer types globally in terms of incidence. Intriguingly, Arenobufagin (Are), a compound extracted from toad venom, has demonstrated the potential to inhibit tumor growth effectively. PURPOSE: This study aimed to explore Are's molecular targets and unravel its antitumor mechanism in CRC. Specifically, we were interested in its impact on immune checkpoint modulation and correlations with HSP90ß-STAT3-PD-L1 axis activity. METHODS: We investigated the in vivo antitumor effects of Are by constructing a colorectalcancer subcutaneous xenograft mouse model. Subsequently, we employed single-cell multi-omics technology to study the potential mechanism by which Are inhibits CRC. Utilizing target-responsive accessibility profiling (TRAP) technology, we identified heatshock protein 90ß (HSP90ß) as the direct target of Are, and confirmed this through a microscale thermophoresis experiment (MST). Further downstream mechanisms were explored through techniques such as co-immunoprecipitation, Western blotting, qPCR, and immunofluorescence. Concurrently, we arrived at the same research conclusion at the organoid level by co-cultivating with immune cells. RESULTS: We observed that Are inhibits PD-Ll expression in CRC tumor xenografts at low concentrations. Moreover, TRAP revealed that HSP90ß's accessibility significantly decreased upon Are binding. We demonstrated a decrease in the activity of the HSP90ß-STAT3-PD-Ll axis following low-concentration Are treatment in vivo. The PDO analysis showed improved enrichment of lymphocytes, particularly T cells, on the PDOs following Are treatment. CONCLUSION: Contrary to previous research focusing on the direct cytotoxicity of Are towards tumor cells, our findings indicate that it can also inhibit tumor growth at lower concentrations through the modulation of immune checkpoints. This study unveils a novel anti-tumor mechanism of Are and stimulates contemplation on the dose-response relationship of natural products, which is beneficial for the clinical translational application of Are.