The Hippo Pathway Kinases LATS1/2 Suppress Cancer Immunity.

Poorly immunogenic tumor cells evade host immunity and grow even in the presence of an intact immune system, but the complex mechanisms regulating tumor immunogenicity have not been elucidated. Here, we discovered an unexpected role of the Hippo pathway in suppressing anti-tumor immunity. We demonstrate that, in three different murine syngeneic tumor models (B16, SCC7, and 4T1), loss of the Hippo pathway kinases LATS1/2 (large tumor suppressor 1 and 2) in tumor cells inhibits tumor growth. Tumor regression by LATS1/2 deletion requires adaptive immune responses, and LATS1/2 deficiency enhances tumor vaccine efficacy. Mechanistically, LATS1/2-null tumor cells secrete nucleic-acid-rich extracellular vesicles, which induce a type I interferon response via the Toll-like receptors-MYD88/TRIF pathway. LATS1/2 deletion in tumors thus improves tumor immunogenicity, leading to tumor destruction by enhancing anti-tumor immune responses. Our observations uncover a key role of the Hippo pathway in modulating tumor immunogenicity and demonstrate a proof of concept for targeting LATS1/2 in cancer immunotherapy.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Robust multivariable Mendelian randomization based on constrained maximum likelihood.

Mendelian randomization (MR) is a powerful tool for causal inference with observational genome-wide association study (GWAS) summary data. Compared to the more commonly used univariable MR (UVMR), multivariable MR (MVMR) not only is more robust to the notorious problem of genetic (horizontal) pleiotropy but also estimates the direct effect of each exposure on the outcome after accounting for possible mediating effects of other exposures. Despite promising applications, there is a lack of studies on MVMR's theoretical properties and robustness in applications. In this work, we propose an efficient and robust MVMR method based on constrained maximum likelihood (cML), called MVMR-cML, with strong theoretical support. Extensive simulations demonstrate that MVMR-cML performs better than other existing MVMR methods while possessing the above two advantages over its univariable counterpart. An application to several large-scale GWAS summary datasets to infer causal relationships between eight cardiometabolic risk factors and coronary artery disease (CAD) highlights the usefulness and some advantages of the proposed method. For example, after accounting for possible pleiotropic and mediating effects, triglyceride (TG), low-density lipoprotein cholesterol (LDL), and systolic blood pressure (SBP) had direct effects on CAD; in contrast, the effects of high-density lipoprotein cholesterol (HDL), diastolic blood pressure (DBP), and body height diminished after accounting for other risk factors.

Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.

Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.

Are trait-associated genes clustered together in a gene network?

Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES) data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.

MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits.

The Transcriptome-Wide Association Study (TWAS) is a widely used approach which integrates gene expression and Genome Wide Association Study (GWAS) data to study the role of cis-regulated gene expression (GEx) in complex traits. However, the genetic architecture of GEx varies across populations, and recent findings point to possible ancestral heterogeneity in the effects of GEx on complex traits, which may be amplified in TWAS by modeling GEx as a function of cis-eQTLs. Here, we present a novel extension to TWAS to account for heterogeneity in the effects of cis-regulated GEx which are correlated with ancestry. Our proposed Multi-Ancestry TwaS (MATS) framework jointly analyzes samples from multiple populations and distinguishes between shared, ancestry-specific and/or subject-specific expression-trait associations. As such, MATS amplifies power to detect shared GEx associations over ancestry-stratified TWAS through increased sample sizes, and facilitates the detection of genes with subgroup-specific associations which may be masked by standard TWAS. Our simulations highlight the improved Type-I error conservation and power of MATS compared with competing approaches. Our real data applications to Alzheimer's disease (AD) case-control genotypes from the Alzheimer's Disease Sequencing Project (ADSP) and continuous phenotypes from the UK Biobank (UKBB) identify a number of unique gene-trait associations which were not discovered through standard and/or ancestry-stratified TWAS. Ultimately, these findings promote MATS as a powerful method for detecting and estimating significant gene expression effects on complex traits within multi-ancestry cohorts and corroborates the mounting evidence for inter-population heterogeneity in gene-trait associations.

Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation.

Recently, a non-parametric method has been proposed to impute the genetic component of a trait for a large set of genotyped individuals based on a separate genome-wide association study (GWAS) summary dataset of the same trait (from the same population). The imputed trait may contain linear, non-linear and epistatic effects of genetic variants, thus can be used for downstream linear or non-linear association analyses and machine learning tasks. Here, we propose an extension of the method to impute both genetic and environmental components of a trait using both single nucleotide polymorphism (SNP)-trait and omics-trait association summary data. We illustrate an application to a UK Biobank subset of individuals (n ≈ 80K) with both body mass index (BMI) GWAS data and metabolomic data. We divided the whole dataset into two equally sized and non-overlapping training and test datasets; we used the training data to build SNP- and metabolite-BMI association summary data and impute BMI on the test data. We compared the performance of the original and new imputation methods. As by the original method, the imputed BMI values by the new method largely retained SNP-BMI association information; however, the latter retained more information about BMI-environment associations and were more highly correlated with the original observed BMI values.

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.

Kielin/chordin-like protein deficiency aggravates pressure overload-induced cardiac dysfunction and remodeling via P53/P21/CCNB1 signaling in mice.

Targeting cardiac remodeling is regarded as a key therapeutic strategy for heart failure. Kielin/chordin-like protein (KCP) is a secretory protein with 18 cysteine-rich domains and associated with kidney and liver fibrosis. However, the relationship between KCP and cardiac remodeling remains unclear. Here, we aimed to investigate the role of KCP in cardiac remodeling induced by pressure overload and explore its potential mechanisms. Left ventricular (LV) KCP expression was measured with real-time quantitative PCR, western blotting, and immunofluorescence staining in pressure overload-induced cardiac remodeling in mice. Cardiac function and remodeling were evaluated in wide-type (WT) mice and KCP knockout (KO) mice by echocardiography, which were further confirmed by histological analysis with hematoxylin and eosin and Masson staining. RNA sequence was performed with LV tissue from WT and KO mice to identify differentially expressed genes and related signaling pathways. Primary cardiac fibroblasts (CFs) were used to validate the regulatory role and potential mechanisms of KCP during fibrosis. KCP was down-regulated in the progression of cardiac remodeling induced by pressure overload, and was mainly expressed in fibroblasts. KCP deficiency significantly aggravated pressure overload-induced cardiac dysfunction and remodeling. RNA sequence revealed that the role of KCP deficiency in cardiac remodeling was associated with cell division, cell cycle, and P53 signaling pathway, while cyclin B1 (CCNB1) was the most significantly up-regulated gene. Further investigation in vivo and in vitro suggested that KCP deficiency promoted the proliferation of CFs via P53/P21/CCNB1 pathway. Taken together, these results suggested that KCP deficiency aggravates cardiac dysfunction and remodeling induced by pressure overload via P53/P21/CCNB1 signaling in mice.

Robust inference of bi-directional causal relationships in presence of correlated pleiotropy with GWAS summary data.

To infer a causal relationship between two traits, several correlation-based causal direction (CD) methods have been proposed with the use of SNPs as instrumental variables (IVs) based on GWAS summary data for the two traits; however, none of the existing CD methods can deal with SNPs with correlated pleiotropy. Alternatively, reciprocal Mendelian randomization (MR) can be applied, which however may perform poorly in the presence of (unknown) invalid IVs, especially for bi-directional causal relationships. In this paper, first, we propose a CD method that performs better than existing CD methods regardless of the presence of correlated pleiotropy. Second, along with a simple but yet effective IV screening rule, we propose applying a closely related and state-of-the-art MR method in reciprocal MR, showing its almost identical performance to that of the new CD method when their model assumptions hold; however, if the modeling assumptions are violated, the new CD method is expected to better control type I errors. Notably bi-directional causal relationships impose some unique challenges beyond those for uni-directional ones, and thus requiring special treatments. For example, we point out for the first time several scenarios where a bi-directional relationship, but not a uni-directional one, can unexpectedly cause the violation of some weak modeling assumptions commonly required by many robust MR methods. We also offer some numerical support and a modeling justification for the application of our new methods (and more generally MR) to binary traits. Finally we applied the proposed methods to 12 risk factors and 4 common diseases, confirming mostly well-known uni-directional causal relationships, while identifying some novel and plausible bi-directional ones such as between body mass index and type 2 diabetes (T2D), and between diastolic blood pressure and stroke.

A practical problem with Egger regression in Mendelian randomization.

Mendelian randomization (MR) is an instrumental variable (IV) method using genetic variants such as single nucleotide polymorphisms (SNPs) as IVs to disentangle the causal relationship between an exposure and an outcome. Since any causal conclusion critically depends on the three valid IV assumptions, which will likely be violated in practice, MR methods robust to the IV assumptions are greatly needed. As such a method, Egger regression stands out as one of the most widely used due to its easy use and perceived robustness. Although Egger regression is claimed to be robust to directional pleiotropy under the instrument strength independent of direct effect (InSIDE) assumption, it is known to be dependent on the orientations/coding schemes of SNPs (i.e. which allele of an SNP is selected as the reference group). The current practice, as recommended as the default setting in some popular MR software packages, is to orientate the SNPs to be all positively associated with the exposure, which however, to our knowledge, has not been fully studied to assess its robustness and potential impact. We use both numerical examples (with both real data and simulated data) and analytical results to demonstrate the practical problem of Egger regression with respect to its heavy dependence on the SNP orientations. Under the assumption that InSIDE holds for some specific (and unknown) coding scheme of the SNPs, we analytically show that other coding schemes would in general lead to the violation of InSIDE. Other related MR and IV regression methods may suffer from the same problem. Cautions should be taken when applying Egger regression (and related MR and IV regression methods) in practice.

Reprogramming Tumor-Associated Macrophages with a Se-Based Core-Satellite Nanoassembly to Enhance Cancer Immunotherapy.

Tumor-associated macrophages (TAMs), as the most prevalent immune cells in the tumor microenvironment, play a pivotal role in promoting tumor development through various signaling pathways. Herein, we have engineered a Se@ZIF-8 core-satellite nanoassembly to reprogram TAMs, thereby enhancing immunotherapy outcomes. When the nanoassembly reaches the tumor tissue, selenium nanoparticles and Zn2+ are released in response to the acidic tumor microenvironment, resulting in a collaborative effort to promote the production of reactive oxygen species (ROS). The generated ROS, in turn, activate the nuclear factor κB (NF-κB) signaling pathway, driving the repolarization of TAMs from M2-type to M1-type, effectively eliminating cancer cells. Moreover, the nanoassembly can induce the immunogenic death of cancer cells through excess ROS to expose calreticulin and boost macrophage phagocytosis. The Se@ZIF-8 core-satellite nanoassembly provides a potential paradigm for cancer immunotherapy by reversing the immunosuppressive microenvironment.

ROCK1 is a multifunctional factor maintaining the primordial follicle reserve and follicular development in mice.

The follicle is the basic structural and functional unit of the ovary in female mammals. The excessive depletion of follicles will lead to diminished ovarian reserve or even premature ovarian failure, resulting in diminished ovarian oogenesis and endocrine function. Excessive follicular depletion is mainly due to loss of primordial follicles. Our analysis of published human ovarian single-cell sequencing results by others revealed a significant increase in rho-associated protein kinase 1 (ROCK1) expression during primordial follicle development. However, the role of ROCK1 in primordial follicle development and maintenance is not clear. This study revealed a gradual increase in ROCK1 expression during primordial follicle activation. Inhibition of ROCK1 resulted in reduced primordial follicle activation, decreased follicular reserve, and delayed development of growing follicles. This effect may be achieved through the HIPPO pathway. The present study indicates that ROCK1 is a key molecule for primordial follicular reserve and follicular development.NEW & NOTEWORTHY ROCK1, one of the Rho GTPases, plays an important role in primordial follicle reserve and follicular development. ROCK1 was primarily expressed in the cytoplasm of oocytes and granulosa cell in mice. Inhibition of ROCK1 significantly reduced the primordial follicle reserve and delayed growing follicle development. ROCK1 regulates primordial follicular reserve and follicle development through the HIPPO signaling pathway. These findings shed new lights on the physiology of sustaining female reproduction.

Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles.

Using high-dimensional genetic variants such as single nucleotide polymorphisms (SNP) to predict complex diseases and traits has important applications in basic research and other clinical settings. For example, predicting gene expression is a necessary first step to identify (putative) causal genes in transcriptome-wide association studies. Due to weak signals, high-dimensionality, and linkage disequilibrium (correlation) among SNPs, building such a prediction model is challenging. However, functional annotations at the SNP level (e.g., as epigenomic data across multiple cell- or tissue-types) are available and could be used to inform predictor importance and aid in outcome prediction. Existing approaches to incorporate annotations have been based mainly on (generalized) linear models. Bayesian additive regression trees (BART), in contrast, is a reliable method to obtain high-quality nonlinear out of sample predictions without overfitting. Unfortunately, the default prior from BART may be too inflexible to handle sparse situations where the number of predictors approaches or surpasses the number of observations. Motivated by our real data application, this article proposes an alternative prior based on the logit normal distribution because it provides a framework that is adaptive to sparsity and can model informative functional annotations. It also provides a framework to incorporate prior information about the between SNP correlations. Computational details for carrying out inference are presented along with the results from a simulation study and a genome-wide prediction analysis of the Alzheimer's Disease Neuroimaging Initiative data.

Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data.

We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform causal analysis and discover causal relationships among multiple traits; (2) account for the possible presence of some invalid IVs; (3) allow for data analysis using only genome-wide association studies (GWAS) summary statistics when individual-level data are not available; (4) consider the possibility of bidirectional relationships between traits. Our method employs a simple stepwise selection to identify invalid IVs, thus avoiding false positives while possibly increasing true discoveries based on two-stage least squares (2SLS). We use both real GWAS data and simulated data to demonstrate the superior performance of our method over the standard 2SLS/SEMs. For real data analysis, our proposed approach is applied to a human blood metabolite GWAS summary data set to uncover putative causal relationships among the metabolites; we also identify some metabolites (putative) causal to Alzheimer's disease (AD), which, along with the inferred causal metabolite network, suggest some possible pathways of metabolites involved in AD.

Clemastine-induced enhancement of hippocampal myelination alleviates memory impairment in mice with chronic pain.

Patients with chronic pain often experience memory impairment, but the underlying mechanisms remain elusive. The myelin sheath is crucial for rapid and accurate action potential conduction, playing a pivotal role in the development of cognitive abilities in the central nervous system. The study reveals that myelin degradation occurs in the hippocampus of chronic constriction injury (CCI) mice, which display both chronic pain and memory impairment. Using fiber photometry, we observed diminished task-related neuronal activity in the hippocampus of CCI mice. Interestingly, the repeated administration with clemastine, which promotes myelination, counteracts the CCI-induced myelin loss and reduced neuronal activity. Notably, clemastine specifically ameliorates the impaired memory without affecting chronic pain in CCI mice. Overall, our findings highlight the significant role of myelin abnormalities in CCI-induced memory impairment, suggesting a potential therapeutic approach for treating memory impairments associated with neuropathic pain.

Accounting for nonlinear effects of gene expression identifies additional associated genes in transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) integrate genome-wide association study (GWAS) data with gene expression (GE) data to identify (putative) causal genes for complex traits. There are two stages in TWAS: in Stage 1, a model is built to impute gene expression from genotypes, and in Stage 2, gene-trait association is tested using imputed gene expression. Despite many successes with TWAS, in the current practice, one only assumes a linear relationship between GE and the trait, which however may not hold, leading to loss of power. In this study, we extend the standard TWAS by considering a quadratic effect of GE, in addition to the usual linear effect. We train imputation models for both linear and quadratic gene expression levels in Stage 1, then include both the imputed linear and quadratic expression levels in Stage 2. We applied both the standard TWAS and our approach first to the ADNI gene expression data and the IGAP Alzheimer's disease GWAS summary data, then to the GTEx (V8) gene expression data and the UK Biobank individual-level GWAS data for lipids, followed by validation with different GWAS data, suitable model checking and more robust TWAS methods. In all these applications, the new TWAS approach was able to identify additional genes associated with Alzheimer's disease, LDL and HDL cholesterol levels, suggesting its likely power gains and thus the need to account for potentially nonlinear effects of gene expression on complex traits.

Loss of AR-regulated AFF3 contributes to prostate cancer progression and reduces ferroptosis sensitivity by downregulating ACSL4 based on single-cell sequencing analysis.

Prostate cancer (PCa) is one of the most common cancers affecting the health of men worldwide. Castration-resistant prostate cancer (CRPC), the advanced and refractory phase of prostate cancer, has multiple mechanisms of resistance to androgen deprivation therapy (ADT) such as AR mutations, aberrant androgen synthase, and abnormal expression of AR-related genes. Based on the research of the AR pathway, new drugs for the treatment of CRPC have been developed in clinical practice, such as Abiraterone and enzalutamide. However, many areas in this pathway are still worth exploring. In this study, single-cell sequencing analysis was utilized to scrutinize significant genes in the androgen receptor (AR) pathway related to CRPC. Our analysis of single-cell sequencing combined with bulk-cell sequencing revealed a substantial downregulation of AR-regulated AFF3 in CRPC. Overexpression of AFF3 restricted the proliferation and migration of prostate cancer cells whilst also increasing their sensitivity towards enzalutamide, while knockdown of AFF3 had the opposite effect. To elucidate the mechanism of tumor inhibition by AFF3, we applied GSVA and GSEA to investigate the metabolic pathways related to AFF3 and revealed that AFF3 had an impact on fatty acids metabolism and ferroptosis through the regulation of ACSL4 protein expression. Based on correlation analysis and flow cytometry, we can speculate that AFF3 can impact the sensitivity of the CRPC cell lines to the ferroptosis inducer (RSL3) by regulating ACSL4. Therefore, our findings may provide new insights into the mechanisms of drug resistance in CRPC, and AFF3 may serve as a novel prognostic biomarker in prostate cancer.

Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects.

With the increasing availability of large-scale GWAS summary data on various complex traits and diseases, there have been tremendous interests in applications of Mendelian randomization (MR) to investigate causal relationships between pairs of traits using SNPs as instrumental variables (IVs) based on observational data. In spite of the potential significance of such applications, the validity of their causal conclusions critically depends on some strong modeling assumptions required by MR, which may be violated due to the widespread (horizontal) pleiotropy. Although many MR methods have been proposed recently to relax the assumptions by mainly dealing with uncorrelated pleiotropy, only a few can handle correlated pleiotropy, in which some SNPs/IVs may be associated with hidden confounders, such as some heritable factors shared by both traits. Here we propose a simple and effective approach based on constrained maximum likelihood and model averaging, called cML-MA, applicable to GWAS summary data. To deal with more challenging situations with many invalid IVs with only weak pleiotropic effects, we modify and improve it with data perturbation. Extensive simulations demonstrated that the proposed methods could control the type I error rate better while achieving higher power than other competitors. Applications to 48 risk factor-disease pairs based on large-scale GWAS summary data of 3 cardio-metabolic diseases (coronary artery disease, stroke, and type 2 diabetes), asthma, and 12 risk factors confirmed its superior performance.

Glowing Octopus-Inspired Nanomachine: A Versatile Aptasensor for Efficient Capture, Imaging, Separation, and NIR-Triggered Release of Cancer Cells.

The separation and analysis of circulating tumor cells (CTCs) in liquid biopsy significantly facilitated clinical cancer diagnosis and personalized therapy. However, current methods face challenges in simultaneous efficient capturing, separation, and imaging of CTCs, and most of the devices cannot be reused/regenerated. We present here an innovative glowing octopus-inspired nanomachine (GOIN), capable of capturing, imaging, separating, and controlling the release of cancer cells from whole blood and normal cells. The GOIN comprises an aptamer-decorated magnetic fluorescent covalent organic framework (COF), which exhibits a strong affinity for nucleolin-overexpressed cancer cells through a multivalent binding effect. The captured cancer cells can be directly imaged using the intrinsic stable fluorescence of the COF layer in the GOIN. Employing magnet and NIR laser assistance enables easy separation and mild photothermal release of CTCs from the normal cells and the nanomachine without compromising cell viability. Moreover, the GOIN demonstrates a reusing capability, as the NIR-triggered CTC release is mild and nondestructive, allowing the GOIN to be reused at least three times.