Cross-modal integration processes are essential for service robots to reliably perceive relevant parts of the partially known unstructured environment, We demonstrate how multimodal integration on different abstractio...Cross-modal integration processes are essential for service robots to reliably perceive relevant parts of the partially known unstructured environment, We demonstrate how multimodal integration on different abstraction levels leads to reasonable behavior that would be difficult to achieve with unimodal approaches. Sensing and acting modalities are composed to multimodal robot skills via a fuzzy multisensor fusion approach. Single modalities constitute basic robot skills that can dynamically be composed to appropriate behavior by symbolic planning. Furthermore, multimodal integration is exploited to answer relevant queries about the partially known environment. All these approaches are successfully implemented and tested on our mobile service robot platform TASER,展开更多
Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,rad...Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.展开更多
Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution predi...Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution prediction of RNA transcripts.These methods usually only offer a partial analysis of ST data,neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk.Here,we present eMCI,an explainable multimodal correlation integration model based on deep neural network framework.eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level.First,eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets.Second,eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication,by employing an attribution algorithm to dissect the visual input.Especially,eMCI has been applied to 3 cross-species datasets,including zebrafish melanomas,soybean nodule maturation,and human embryonic lung,which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context.In summary,eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference.This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.展开更多
With the modernization and internationalization of traditional Chinese medicine(TCM),the requirement for quality control has increased.The quality marker(Q-marker)is an important standard in this field and has been im...With the modernization and internationalization of traditional Chinese medicine(TCM),the requirement for quality control has increased.The quality marker(Q-marker)is an important standard in this field and has been implemented with remarkable success in recent years.However,the establishment of Qmarkers remains fragmented and the process lacks systematicity,resulting in inconsistent quality control and insufficient correlation with clinical efficacy and safety of TCM.This review introduces four multimodal integrated approaches that contribute to the discovery of more comprehensive and accurate Qmarkers,thus aiding in the establishment of new quality control patterns based on the characteristics and principles of TCM.These include the whole-process quality control strategy,chemical-activity-based screening method,efficacy,safety,and consistent combination strategy,and TCM theory-guided approach.Furthermore,methodologies and representative examples of these strategies are described,and important future directions and questions in this field are also proposed.展开更多
BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is one of the most common disorders in child and adolescent psychiatry,with a prevalence of more than 5%.Despite extensive research on ADHD in the last 10 to 20...BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is one of the most common disorders in child and adolescent psychiatry,with a prevalence of more than 5%.Despite extensive research on ADHD in the last 10 to 20 years,effective treatments are still lacking.Instead,the concept of ADHD seems to have become broader and more heterogeneous.Therefore,the diagnosis and treatment of ADHD remains challenging for clinicians.AIM To investigate the effects of a multimodal integrated intervention for children with ADHD.METHODS Between March 2019 and September 2020,a total of 100 children with ADHD who were diagnosed and treated at our hospital were assessed for eligibility,two of whom revoked their consent.A case-control study was conducted in which the children were equally assigned,using a randomized number table,to either a medication group(methylphenidate hydrochloride extended-release tablets and atomoxetine hydrochloride tablets)or a multimodal integrated intervention group(medication+parent training+behavior modification+sensory integration therapy+sand tray therapy),with 49 patients in each group.The clinical endpoint was the efficacy of the different intervention modalities.RESULTS The two groups of children with ADHD had comparable patient characteristics(P>0.05).Multimodal integrated intervention resulted in a significantly higher treatment efficacy(91.84%)than medication alone(75.51%)(P<0.05).Children who received the multimodal integrated intervention showed lower scores in the Conners Parent Symptom Questionnaire and the Weiss Functional Impairment Rating Scale than those treated with medication alone(P<0.05).The Sensory Integration Scale scores of children in the multimodal integrated intervention group were higher than those of children in the medication group(P<0.05).Children who received the multimodal integrated intervention had higher compliance and family satisfaction and a lower incidence of adverse events than those treated with medication alone(P<0.05).CONCLUSION Multimodal integrated intervention effectively alleviated symptoms associated with ADHD in children.It enhanced their memory and attention with high safety and parental satisfaction,demonstrating good potential for clinical promotion.展开更多
BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision therapies.AIM To investigate whether ...BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision therapies.AIM To investigate whether iCEMIGE(integration of CEll-morphometrics,MIcro-biome,and GEne biomarker signatures)improves risk stratification of breast cancer(BC)patients.METHODS We used our recently developed machine learning technique to identify cellular morphometric biomarkers(CMBs)from the whole histological slide images in The Cancer Genome Atlas(TCGA)breast cancer(TCGA-BRCA)cohort.Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score(CMPS)and our previously reported 12-gene expression prognosis score(GEPS)and 15-microbe abundance prognosis score(MAPS)were independent prognostic factors.iCEMIGE was built upon the sparse representation learning technique.The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS,GEPS,or MAPS alone.Nomogram models were created to predict overall survival(OS)and progress-free survival(PFS)rates at 5-and 10-year in the TCGA-BRCA cohort.RESULTS We identified 39 CMBs that were used to create a CMPS system in BCs.CMPS,GEPS,and MAPS were found to be significantly independently associated with OS.We then established an iCEMIGE scoring system for risk stratification of BC patients.The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors(age,stage,and estrogen and progesterone receptor status)and PAM50-based molecular subtype.Importantly,the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS,GEPS,or MAPS alone.CONCLUSION Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients,which can be extended to other types of cancer.展开更多
Human skin perceives external environmental stimulus by the synergies between the subcutaneous tactile corpuscles.Soft electronics with multiple sensing capabilities by mimicking the function of human skin are of sign...Human skin perceives external environmental stimulus by the synergies between the subcutaneous tactile corpuscles.Soft electronics with multiple sensing capabilities by mimicking the function of human skin are of significance in health monitoring and artificial sensation.The last decade has witnessed unprecedented development and convergence between multimodal tactile sensing devices and soft bioelectronics.Despite these advances,traditional flexible electronics achieve multimodal tactile sensing for pressure,strain,temperature,and humidity by integrating monomodal sensing devices together.This strategy results in high energy consumption,limited integration,and complex manufacturing process.Various multimodal sensors and crosstalk-free sensing mechanisms have been proposed to bridge the gap between natural sensory system and artificial perceptual system.In this review,we provide a comprehensive summary of tactile sensing mechanism,integration design principles,signal-decoupling strategies,and current applications for multimodal tactile perception.Finally,we highlight the current challenges and present the future perspectives to promote the development of multimodal tactile perception.展开更多
Joint analysis of multimodalities in spatial mass spectrometry imaging(SMSI)data,including histology,spatial location,and molecule data,allows us to gain novel insights into tissue structures.However,the significant d...Joint analysis of multimodalities in spatial mass spectrometry imaging(SMSI)data,including histology,spatial location,and molecule data,allows us to gain novel insights into tissue structures.However,the significant differences in characteristics such as scale and heterogeneity among the multimodal data,coupled with the high noise levels and uneven quality of MSI data,severely hinder their comprehensive analysis.Here,we introduce a cross-graph cycle attention model,MSCG,to learn efficient joint embeddings for multimodalities of SMSI data by integrating graph attention autoencoders and attention-transfer.Specifically,MSCG enables leveraging one modality(e.g.,histology)to fine-tune the graph neural network trained for another modality(e.g.,MSI).Our study on real datasets from different platforms highlights the superior capacities of MSCG in dissecting cellular heterogeneity,as well as in denoising and aggregating MSI data.Notably,MSCG demonstrates versatile applicability across MSI data from various platforms,showcasing its potential for broad utility in this field.展开更多
Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellula...Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellular and niche events at the precancerous tipping point of early gastric cancer(EGC),we aimed to develop more precise screening tools and design targeted interventions to prevent malignant transformation at this stage.We utilized our AI models to integrate spatial multimodal data from nine EGC endoscopic submucosal dissection(ESD)samples(covering sequential stages from normal to cancer),construct a spatial-temporal profile of disease progression,and identify a critical tipping point(PMC_P)characterized by an immune-suppressive microenvironment during early cancer development.At this stage,inflammatory pit mucous cells with stemness(PMC_2)interact with fibroblasts via NAMPT→ITGA5/ITGB1 and with macrophages via AREG→EGFR/ERBB2 signaling,fostering cancer initiation.We established gastric precancerous cell lines and organoids to demonstrate that NAMPT and AREG promote cellular proliferation in vitro.Furthermore,in the transgenic CEA-SV40 mouse model,targeting AREG and/or NAMPT disrupted key cell interactions,inhibited the JAK-STAT,MAPK,and NFκB pathways,and reduced PD-L1 expression,which was also confirmed by western blot in vitro.These interventions delayed disease progression,reversed the immunosuppressive microenvironment,and prevented malignant transformation.Clinical validation was conducted using endoscopically resected EGC specimens.Our study provides a precise spatiotemporal depiction of EGC development and identifies novel diagnostic markers and therapeutic targets for early intervention.展开更多
Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and lo...Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.展开更多
基金Supported by the German Research Foundation(No. GRK 1247/1)
文摘Cross-modal integration processes are essential for service robots to reliably perceive relevant parts of the partially known unstructured environment, We demonstrate how multimodal integration on different abstraction levels leads to reasonable behavior that would be difficult to achieve with unimodal approaches. Sensing and acting modalities are composed to multimodal robot skills via a fuzzy multisensor fusion approach. Single modalities constitute basic robot skills that can dynamically be composed to appropriate behavior by symbolic planning. Furthermore, multimodal integration is exploited to answer relevant queries about the partially known environment. All these approaches are successfully implemented and tested on our mobile service robot platform TASER,
基金Supported by the Natural Science Foundation of Jilin Province,No.YDZJ202401182ZYTSJilin Provincial Key Laboratory of Precision Infectious Diseases,No.20200601011JCJilin Provincial Engineering Laboratory of Precision Prevention and Control for Common Diseases,Jilin Province Development and Reform Commission,No.2022C036.
文摘Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.
基金supported by the National Key R&D Program of China(Nos.2023YFF1204700 and 2022YFF1202100)the National Natural Science Foundation of China(Nos.12371485,62201150,T2341022,62172164,12322119,and 12271180)the Natural Science Foundation of Guangdong Province of China(Nos.2022A1515110759,2023A1515110558,and 2024A1515011797).
文摘Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution prediction of RNA transcripts.These methods usually only offer a partial analysis of ST data,neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk.Here,we present eMCI,an explainable multimodal correlation integration model based on deep neural network framework.eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level.First,eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets.Second,eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication,by employing an attribution algorithm to dissect the visual input.Especially,eMCI has been applied to 3 cross-species datasets,including zebrafish melanomas,soybean nodule maturation,and human embryonic lung,which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context.In summary,eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference.This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.
基金supported by the National Natural Science Foundation of China(Grant No.:81973701)Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(Grant No.:ZYYCXTD-D-202002)Westlake Laboratory(Westlake Laboratory of Life Sciences and Biomedicine).
文摘With the modernization and internationalization of traditional Chinese medicine(TCM),the requirement for quality control has increased.The quality marker(Q-marker)is an important standard in this field and has been implemented with remarkable success in recent years.However,the establishment of Qmarkers remains fragmented and the process lacks systematicity,resulting in inconsistent quality control and insufficient correlation with clinical efficacy and safety of TCM.This review introduces four multimodal integrated approaches that contribute to the discovery of more comprehensive and accurate Qmarkers,thus aiding in the establishment of new quality control patterns based on the characteristics and principles of TCM.These include the whole-process quality control strategy,chemical-activity-based screening method,efficacy,safety,and consistent combination strategy,and TCM theory-guided approach.Furthermore,methodologies and representative examples of these strategies are described,and important future directions and questions in this field are also proposed.
基金Supported by Ningbo Medical Key Fostering Discipline Child Health Science,No.2022-F26Ningbo Science and Technology Plan Project Public Welfare Plan,No.2019C50099.
文摘BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is one of the most common disorders in child and adolescent psychiatry,with a prevalence of more than 5%.Despite extensive research on ADHD in the last 10 to 20 years,effective treatments are still lacking.Instead,the concept of ADHD seems to have become broader and more heterogeneous.Therefore,the diagnosis and treatment of ADHD remains challenging for clinicians.AIM To investigate the effects of a multimodal integrated intervention for children with ADHD.METHODS Between March 2019 and September 2020,a total of 100 children with ADHD who were diagnosed and treated at our hospital were assessed for eligibility,two of whom revoked their consent.A case-control study was conducted in which the children were equally assigned,using a randomized number table,to either a medication group(methylphenidate hydrochloride extended-release tablets and atomoxetine hydrochloride tablets)or a multimodal integrated intervention group(medication+parent training+behavior modification+sensory integration therapy+sand tray therapy),with 49 patients in each group.The clinical endpoint was the efficacy of the different intervention modalities.RESULTS The two groups of children with ADHD had comparable patient characteristics(P>0.05).Multimodal integrated intervention resulted in a significantly higher treatment efficacy(91.84%)than medication alone(75.51%)(P<0.05).Children who received the multimodal integrated intervention showed lower scores in the Conners Parent Symptom Questionnaire and the Weiss Functional Impairment Rating Scale than those treated with medication alone(P<0.05).The Sensory Integration Scale scores of children in the multimodal integrated intervention group were higher than those of children in the medication group(P<0.05).Children who received the multimodal integrated intervention had higher compliance and family satisfaction and a lower incidence of adverse events than those treated with medication alone(P<0.05).CONCLUSION Multimodal integrated intervention effectively alleviated symptoms associated with ADHD in children.It enhanced their memory and attention with high safety and parental satisfaction,demonstrating good potential for clinical promotion.
基金Supported by This work was supported by the Department of Defense(DoD)BCRP,No.BC190820the National Cancer Institute(NCI)at the National Institutes of Health(NIH),No.R01CA184476+1 种基金MCIN/AEI/10.13039/501100011039,No.PID2020-118527RB-I00,and No.PDC2021-121735-I00the“European Union Next Generation EU/PRTR.”the Regional Government of Castile and León,No.CSI144P20.Lawrence Berkeley National Laboratory(LBNL)is a multi-program national laboratory operated by the University of California for the DOE under contract DE AC02-05CH11231.
文摘BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision therapies.AIM To investigate whether iCEMIGE(integration of CEll-morphometrics,MIcro-biome,and GEne biomarker signatures)improves risk stratification of breast cancer(BC)patients.METHODS We used our recently developed machine learning technique to identify cellular morphometric biomarkers(CMBs)from the whole histological slide images in The Cancer Genome Atlas(TCGA)breast cancer(TCGA-BRCA)cohort.Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score(CMPS)and our previously reported 12-gene expression prognosis score(GEPS)and 15-microbe abundance prognosis score(MAPS)were independent prognostic factors.iCEMIGE was built upon the sparse representation learning technique.The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS,GEPS,or MAPS alone.Nomogram models were created to predict overall survival(OS)and progress-free survival(PFS)rates at 5-and 10-year in the TCGA-BRCA cohort.RESULTS We identified 39 CMBs that were used to create a CMPS system in BCs.CMPS,GEPS,and MAPS were found to be significantly independently associated with OS.We then established an iCEMIGE scoring system for risk stratification of BC patients.The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors(age,stage,and estrogen and progesterone receptor status)and PAM50-based molecular subtype.Importantly,the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS,GEPS,or MAPS alone.CONCLUSION Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients,which can be extended to other types of cancer.
基金the Taishan Young Scholar Program of Shandong Province(No.tsqnz20231235)National Natural Science Foundation of China(Grant Nos.22104021,52303075,22227804)+1 种基金Natural Science Foundation of Shandong Province(ZR2023QB227)Department of Science and Technology of Guangdong Province(2022A1515110014).
文摘Human skin perceives external environmental stimulus by the synergies between the subcutaneous tactile corpuscles.Soft electronics with multiple sensing capabilities by mimicking the function of human skin are of significance in health monitoring and artificial sensation.The last decade has witnessed unprecedented development and convergence between multimodal tactile sensing devices and soft bioelectronics.Despite these advances,traditional flexible electronics achieve multimodal tactile sensing for pressure,strain,temperature,and humidity by integrating monomodal sensing devices together.This strategy results in high energy consumption,limited integration,and complex manufacturing process.Various multimodal sensors and crosstalk-free sensing mechanisms have been proposed to bridge the gap between natural sensory system and artificial perceptual system.In this review,we provide a comprehensive summary of tactile sensing mechanism,integration design principles,signal-decoupling strategies,and current applications for multimodal tactile perception.Finally,we highlight the current challenges and present the future perspectives to promote the development of multimodal tactile perception.
基金supported by the National Natural Science Foundation of China under Grant No.32300523the Shanghai Sailing Program under Grant No.22YF1401700+1 种基金the Fundamental Research Funds for the Central Universities of China under Grant No.2232022Dthe Shanghai Science and Technology Program under Grant No.20DZ2251400.
文摘Joint analysis of multimodalities in spatial mass spectrometry imaging(SMSI)data,including histology,spatial location,and molecule data,allows us to gain novel insights into tissue structures.However,the significant differences in characteristics such as scale and heterogeneity among the multimodal data,coupled with the high noise levels and uneven quality of MSI data,severely hinder their comprehensive analysis.Here,we introduce a cross-graph cycle attention model,MSCG,to learn efficient joint embeddings for multimodalities of SMSI data by integrating graph attention autoencoders and attention-transfer.Specifically,MSCG enables leveraging one modality(e.g.,histology)to fine-tune the graph neural network trained for another modality(e.g.,MSI).Our study on real datasets from different platforms highlights the superior capacities of MSCG in dissecting cellular heterogeneity,as well as in denoising and aggregating MSI data.Notably,MSCG demonstrates versatile applicability across MSI data from various platforms,showcasing its potential for broad utility in this field.
基金supported by Shanghai Oriental Talent Youth Program(QNKJ2024006)National Natural Science Foundation of China(82170555,32300523,32570769,and 62132015)+1 种基金Shanghai Academic/Technology Research Leader(22XD1422400)Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(22SG06).
文摘Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellular and niche events at the precancerous tipping point of early gastric cancer(EGC),we aimed to develop more precise screening tools and design targeted interventions to prevent malignant transformation at this stage.We utilized our AI models to integrate spatial multimodal data from nine EGC endoscopic submucosal dissection(ESD)samples(covering sequential stages from normal to cancer),construct a spatial-temporal profile of disease progression,and identify a critical tipping point(PMC_P)characterized by an immune-suppressive microenvironment during early cancer development.At this stage,inflammatory pit mucous cells with stemness(PMC_2)interact with fibroblasts via NAMPT→ITGA5/ITGB1 and with macrophages via AREG→EGFR/ERBB2 signaling,fostering cancer initiation.We established gastric precancerous cell lines and organoids to demonstrate that NAMPT and AREG promote cellular proliferation in vitro.Furthermore,in the transgenic CEA-SV40 mouse model,targeting AREG and/or NAMPT disrupted key cell interactions,inhibited the JAK-STAT,MAPK,and NFκB pathways,and reduced PD-L1 expression,which was also confirmed by western blot in vitro.These interventions delayed disease progression,reversed the immunosuppressive microenvironment,and prevented malignant transformation.Clinical validation was conducted using endoscopically resected EGC specimens.Our study provides a precise spatiotemporal depiction of EGC development and identifies novel diagnostic markers and therapeutic targets for early intervention.
基金supported by the National Key R&D Program of China(Grant Nos.2020YFA0712403 and 2021YFF1200901)the National Natural Science Foundation of China(Grant Nos.61922047,81890993,61721003,and 62133006)+1 种基金the Beijing National Research Centre for Information Science and Technology Young Innovation Fund,China(Grant No.BNR2020RC01009)the Science and Technology Commission of Shanghai Municipality,China(Grant No.20PJ1408300)。
文摘Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.
