Query：
学者姓名：郭磊
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the biological tissue into distinct regions with unique characteristics. However, current methods struggle due to the insufficient incorporation of biological context and high computational demand. To address these challenges, a novel deep learning-based approach is proposed, GraphMSI, which integrates metabolic profiles with spatial information to enhance MSI data analysis. Our comparative results demonstrate GraphMSI outperforms commonly used segmentation methods in both visual inspection and quantitative evaluation. Moreover, GraphMSI can incorporate partial or coarse biological contexts to improve segmentation results and enable more effective three-dimensional MSI segmentation with reduced computational requirements. These are facilitated by two optional enhanced modes: scribble-interactive and knowledge-transfer. Numerous results demonstrate the robustness of these two modes, ensuring that GraphMSI consistently retains its capability to identify biologically relevant sub-regions in complex practical applications. It is anticipated that GraphMSI will become a powerful tool for spatial heterogeneity analysis in MSI data.
Keyword ：
deep learning deep learning graph convolutional network graph convolutional network mass spectrometry imaging mass spectrometry imaging spatial heterogeneity spatial heterogeneity
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Guo, Lei , Xie, Peisi , Shen, Xionghui et al. Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI [J]. | ADVANCED SCIENCE , 2025 , 12 (8) . |
| MLA | Guo, Lei et al. "Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI" . | ADVANCED SCIENCE 12 . 8 (2025) . |
| APA | Guo, Lei , Xie, Peisi , Shen, Xionghui , Lam, Thomas Ka Yam , Deng, Lingli , Xie, Chengyi et al. Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI . | ADVANCED SCIENCE , 2025 , 12 (8) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients. © 2024 by the authors.
Keyword ：
liver cancer liver cancer metabolic reprogramming metabolic reprogramming prognostic risk model prognostic risk model single-cell RNA-seq single-cell RNA-seq
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Xiong, Z. , Li, L. , Wang, G. et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model [J]. | Genes , 2024 , 15 (6) . |
| MLA | Xiong, Z. et al. "Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model" . | Genes 15 . 6 (2024) . |
| APA | Xiong, Z. , Li, L. , Wang, G. , Guo, L. , Luo, S. , Liao, X. et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model . | Genes , 2024 , 15 (6) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients.
Keyword ：
liver cancer liver cancer metabolic reprogramming metabolic reprogramming prognostic risk model prognostic risk model single-cell RNA-seq single-cell RNA-seq
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Xiong, Zhuang , Li, Lizhi , Wang, Guoliang et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model [J]. | GENES , 2024 , 15 (6) . |
| MLA | Xiong, Zhuang et al. "Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model" . | GENES 15 . 6 (2024) . |
| APA | Xiong, Zhuang , Li, Lizhi , Wang, Guoliang , Guo, Lei , Luo, Shangyi , Liao, Xiangwen et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model . | GENES , 2024 , 15 (6) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.
Keyword ：
dimensionality reduction dimensionality reduction ensemble learning ensemble learning mass spectrometry imaging mass spectrometry imaging spatial segmentation spatial segmentation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Shah, Mudassir , Guo, Lei , Xu, Xiangnan et al. eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity [J]. | JOURNAL OF PROTEOME RESEARCH , 2024 , 23 (8) : 3088-3095 . |
| MLA | Shah, Mudassir et al. "eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity" . | JOURNAL OF PROTEOME RESEARCH 23 . 8 (2024) : 3088-3095 . |
| APA | Shah, Mudassir , Guo, Lei , Xu, Xiangnan , Deng, Lingli , Lu, Keyi , Dong, Jiyang et al. eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity . | JOURNAL OF PROTEOME RESEARCH , 2024 , 23 (8) , 3088-3095 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Guo, Lei , Xie, Chengyi , Miao, Rui et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging [J]. | ANALYTICAL CHEMISTRY , 2024 , 96 (9) : 3829-3836 . |
| MLA | Guo, Lei et al. "DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging" . | ANALYTICAL CHEMISTRY 96 . 9 (2024) : 3829-3836 . |
| APA | Guo, Lei , Xie, Chengyi , Miao, Rui , Xu, Jingjing , Xu, Xiangnan , Fang, Jiacheng et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging . | ANALYTICAL CHEMISTRY , 2024 , 96 (9) , 3829-3836 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Guo, Lei , Xie, Chengyi , Miao, Rui et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging [J]. | ANALYTICAL CHEMISTRY , 2024 , 96 (9) : 3829-3836 . |
| MLA | Guo, Lei et al. "DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging" . | ANALYTICAL CHEMISTRY 96 . 9 (2024) : 3829-3836 . |
| APA | Guo, Lei , Xie, Chengyi , Miao, Rui , Xu, Jingjing , Xu, Xiangnan , Fang, Jiacheng et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging . | ANALYTICAL CHEMISTRY , 2024 , 96 (9) , 3829-3836 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The shortage of food and freshwater sources threatens human health and environmental sustainability. Spirulina grown in seawater-based media as a healthy food is promising and environmentally friendly. This study used three machine learning techniques to identify important cultivation parameters and their hidden interrelationships and optimize the biomass yield of Spirulina grown in seawater-based media. Through optimization of hyperparameters and features, eXtreme Gradient Boosting, along with the recursive feature elimination (RFE) model demonstrated optimal performance and identified 28 important features. Among them, illumination intensity and initial pH value were critical determinants of biomass, which impacted other features. Specifically, high initial pH values (>9.0) mainly increased biomass but also increased nutrient sedimentation and ammonia (NH3) losses. Both batch and continuous additions could decrease nutrient losses by increasing their availability in the seawater-based media. When illumination intensity exceeded 200 mu mol photons/m(2)/s, it amplified the growth of Spirulina by mitigating the light attenuation caused by a high initial inoculum level and counteracted the negative effect of low temperature (<25 degrees C). In large-scale cultivation, production efficiency would be reduced if illumination was not maintained at a high level. High salinity and sodium bicarbonate (NaHCO3) addition promoted carbohydrate accumulation, but suitable dilution could keep the required protein content in Spirulina with relatively low media and production costs. These findings reveal the interactive influence of cultivation parameters on biomass yield and help us determine the optimal cultivation conditions for large-scale cultivation of Spirulina-based seawater system based on a developed graphical user interface website.
Keyword ：
Biomass yield Biomass yield Cultivation parameters Cultivation parameters Graphical user interface Graphical user interface Large-scale cultivation Large-scale cultivation Machine learning Machine learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Huankai , Guo, Lei , Chen, Leijian et al. Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production [J]. | JOURNAL OF ENVIRONMENTAL MANAGEMENT , 2024 , 369 . |
| MLA | Li, Huankai et al. "Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production" . | JOURNAL OF ENVIRONMENTAL MANAGEMENT 369 (2024) . |
| APA | Li, Huankai , Guo, Lei , Chen, Leijian , Zhang, Feng , Wang, Wei , Lam, Thomas Ka-Yam et al. Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production . | JOURNAL OF ENVIRONMENTAL MANAGEMENT , 2024 , 369 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Antibody-Drug Conjugates (ADCs) represent a rapidly advancing category of oncology therapeutics, spanning the targeted therapy for both hematologic malignancies and solid cancers. A crucial aspect of ADC research involves the identification of optimal surface antigens that can effectively differentiate target cells from most mammalian cell types. Herein, we have devised an algorithm and compiled an extensive dataset annotating cell membrane proteins. This dataset is derived from comprehensive transcriptomic, proteomic, and genomic data encompassing 19 types of solid cancer as well as normal tissues. The aim is to uncover potential therapeutic surface antigens for precise ADC targeting. The resulting target landscape comprises 165 combinations of targets and indications, along with 75 candidates of cell surface proteins. Notably, 35 of these candidates possess characteristics suitable for ADC targeting, and have not been previously reported in ADC research and development. Additionally, we have identified a total of 159 ADCs from a pool of 760 clinical trials. Of these, 72 ADCs are presently undergoing interventional evaluation for a variety of solid cancer types, targeting 36 unique antigens. We conducted an analysis of their expression in normal tissues using this comprehensive annotation dataset, revealing a diverse range of profiles for the current ADC targets. Moreover, we emphasize that the biological impacts of target antigens have the potential to enhance their clinical effectiveness. We propose a comprehensive assessment of the drugability of target antigens, considering multiple facets. This study represents a thorough exploration of pan-cancer ADC targets over the past two decades, underscoring the potential of a comprehensive solid cancer target atlas to broaden the scope of ADC therapies.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Fang, Jiacheng , Guo, Lei , Zhang, Yanhao et al. The target atlas for antibody-drug conjugates across solid cancers [J]. | CANCER GENE THERAPY , 2023 , 31 (2) : 273-284 . |
| MLA | Fang, Jiacheng et al. "The target atlas for antibody-drug conjugates across solid cancers" . | CANCER GENE THERAPY 31 . 2 (2023) : 273-284 . |
| APA | Fang, Jiacheng , Guo, Lei , Zhang, Yanhao , Guo, Qing , Wang, Ming , Wang, Xiaoxiao . The target atlas for antibody-drug conjugates across solid cancers . | CANCER GENE THERAPY , 2023 , 31 (2) , 273-284 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The emerging toxicant N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q) is of wide concern due to its ubiquitous occurrence and high toxicity. Despite regular human exposure, limited evidence exists about its presence in the body and potential health risks. Herein, we analyzed cerebrospinal fluid (CSF) samples from Parkinson's disease (PD) patients and controls. The CSF levels of 6PPD-Q were twice as high in PD patients compared to controls. Immunostaining assays performed with primary dopaminergic neurons confirm that 6PPD-Q at environmentally relevant concentrations can exacerbate the formation of Lewy neurites induced by alpha-synuclein preformed fibrils (alpha-syn PFF). Assessment of cellular respiration reveals a considerable decrease in neuronal spare respiratory and ATP-linked respiration, potentially due to changes in mitochondrial membrane potential. Moreover, 6PPD-Q-induced mitochondrial impairment correlates with an upsurge in mitochondrial reactive oxygen species (mROS), and Mito-TEMPO-driven scavenging of mROS can lessen the amount of pathologic phospho-serine 129 alpha-synuclein. Untargeted metabolomics provides supporting evidence for the connection between 6PPD-Q exposure and changes in neuronal metabolite profiles. In-depth targeted metabolomics further unveils an overall reduction in glycolysis metabolite pool and fluctuations in the quantity of TCA cycle intermediates. Given its potentially harmful attributes, the presence of 6PPD-Q in human brain could potentially be a risk factor for PD.
Keyword ：
alpha-synuclein preformed fibrils alpha-synuclein preformed fibrils Cerebrospinal fluid Cerebrospinal fluid Metabolomics Metabolomics Mitochondrial respiration Mitochondrial respiration phenylenediamine quinone phenylenediamine quinone
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Fang, Jiacheng , Wang, Xiaoxiao , Cao, Guodong et al. 6PPD-quinone exposure induces neuronal mitochondrial dysfunction to exacerbate Lewy neurites formation induced by α-synuclein preformed fibrils seeding [J]. | JOURNAL OF HAZARDOUS MATERIALS , 2023 , 465 . |
| MLA | Fang, Jiacheng et al. "6PPD-quinone exposure induces neuronal mitochondrial dysfunction to exacerbate Lewy neurites formation induced by α-synuclein preformed fibrils seeding" . | JOURNAL OF HAZARDOUS MATERIALS 465 (2023) . |
| APA | Fang, Jiacheng , Wang, Xiaoxiao , Cao, Guodong , Wang, Fuyue , Ru, Yi , Wang, Bolun et al. 6PPD-quinone exposure induces neuronal mitochondrial dysfunction to exacerbate Lewy neurites formation induced by α-synuclein preformed fibrils seeding . | JOURNAL OF HAZARDOUS MATERIALS , 2023 , 465 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
