Noise is a significant part within a millimeter-wave molecular line datacube.Analyzing the noise improves our understanding of noise characteristics,and further contributes to scientific discoveries.We measure the noi...Noise is a significant part within a millimeter-wave molecular line datacube.Analyzing the noise improves our understanding of noise characteristics,and further contributes to scientific discoveries.We measure the noise level of a single datacube from MWISP and perform statistical analyses.We identified major factors which increase the noise level of a single datacube,including bad channels,edge effects,baseline distortion and line contamination.Cleaning algorithms are applied to remove or reduce these noise components.As a result,we obtained the cleaned datacube in which noise follows a positively skewed normal distribution.We further analyzed the noise structure distribution of a 3 D mosaicked datacube in the range l=40°7 to 43°3 and b=-2°3 to 0°3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.展开更多
Recent advances in spatial and single-cell omics have significantly revolutionized biomarker discovery in tumor immunotherapy by addressing critical challenges such as tumor heterogeneity,immune evasion,and variabilit...Recent advances in spatial and single-cell omics have significantly revolutionized biomarker discovery in tumor immunotherapy by addressing critical challenges such as tumor heterogeneity,immune evasion,and variability within the tumor microenvironment(TME).Immunotherapeutic strategies,including immune checkpoint in-hibitors and adoptive T-cell transfer,have demonstrated promising clinical outcomes;however,their efficacy is limited by low response rates and the incidence of immune-related adverse events(irAEs).Therefore,the identification of reliable biomarkers is essential for predicting treatment efficacy,minimizing irAEs,and facili-tating patient stratification.Spatial omics integrates molecular profiling with spatial localization,thereby providing comprehensive insights into the cellular organization and functional states within the TME.By elucidating the spatial patterns of immune cell infiltration and tumor heterogeneity,this approach enhances the prediction of therapeutic responses.Similarly,single-cell omics enables high-resolution analysis of cellular heterogeneity by capturing transcriptomic,epigenomic,and metabolic signatures at the single-cell level.The integrated application of spatial and single-cell omics has enabled the identification of previously undetected biomarkers,including rare immune cell subsets implicated in resistance mechanisms.In addition to spatial transcriptomics(ST),this technological landscape also includes spatial proteomics(SP)and spatial metab-olomics,which further facilitate the study of dynamic tumor-immune interactions.Multi-omics integration provides a comprehensive overview of biomarker landscapes,while the rapid evolution of artificial intelligence(AI)-based approaches enhances the analysis of complex,multidimensional datasets to ultimately enhance pre-dictive potential and clinical utility.Despite substantial progress,several challenges remain in the context of standardization,data integration,and real-time monitoring.Nevertheless,the incorporation of spatial and single-cell omics into biomarker research holds transformative potential for advancing personalized cancer immuno-therapy.These emerging strategies pave the way for the development of innovative diagnostic and therapeutic interventions,thereby enabling precision oncology and improving treatment outcomes across a wide range of tumor profiles.This review aims to provide a comprehensive overview of the integration of spatial omics with single-cell omics in the discovery of biomarkers for tumor immunotherapy.Specifically,it examines the strategies by which these emerging technologies address the challenges related to tumor heterogeneity,immune evasion,and the dynamic nature of the TME.By elaborating on the principles,applications,and clinical potential of these technologies,this review also critically evaluates their limitations,challenges,and the current gaps in clinical translation.展开更多
Objective:This study analysed the causal relationships between free cholesterol-to-total lipid ratios in different very low-density lipoprotein(VLDL)subfractions and the risk of colorectal cancer(CRC)via a two-sample ...Objective:This study analysed the causal relationships between free cholesterol-to-total lipid ratios in different very low-density lipoprotein(VLDL)subfractions and the risk of colorectal cancer(CRC)via a two-sample Mendelian randomization(MR)approach.Methods:Genetic variants associated with free cholesterol-to-total lipid ratios in chylomicrons,as well as extremely large,very large,large,medium,small,and very small VLDL subfractions,were identified from genome-wide association studies and selected as instrumental variables.Five analytical methods,including inverse variance weighted(IVW),MR‒Egger,weighted median,weighted mode,and simple mode,were implemented for MR analysis.Results:Elevated free cholesterol-to-total lipid ratios in very large VLDL(odds ratio(OR)=1.25;95%CI:1.06-1.49;P=0.01),medium VLDL(OR=1.20;95%CI:1.04-1.40;P=0.015),small VLDL(OR=1.17;95%CI:1.01-1.35;P=0.038),and very small VLDL(OR=1.29;95%CI:1.08-1.54;P=0.005)subfractions were found to be associated with an increased risk of CRC.No significant associations were detected between the risk of CRC and the free cholesterol-to-total lipid ratios in chylomicrons or the extremely large VLDL subfraction.Conclusions:MR analysis provided evidence for a causal association between elevated free cholesterol-to-total lipid ratios in very large,medium,small,and very small VLDL subfractions and an increased risk of CRC.These findings suggest the potential mechanistic role of altered VLDL metabolism in colorectal carcinogenesis and may inform novel therapeutic and preventive strategies.展开更多
基金supported by the National Key R&D Program of China(2017YFA0402701)Key Research Program of Frontier Sciences of CAS(QYZDJ-SSW-SLH047)partially supported by the National Natural Science Foundation of China(Grant No.U2031202)。
文摘Noise is a significant part within a millimeter-wave molecular line datacube.Analyzing the noise improves our understanding of noise characteristics,and further contributes to scientific discoveries.We measure the noise level of a single datacube from MWISP and perform statistical analyses.We identified major factors which increase the noise level of a single datacube,including bad channels,edge effects,baseline distortion and line contamination.Cleaning algorithms are applied to remove or reduce these noise components.As a result,we obtained the cleaned datacube in which noise follows a positively skewed normal distribution.We further analyzed the noise structure distribution of a 3 D mosaicked datacube in the range l=40°7 to 43°3 and b=-2°3 to 0°3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.
基金support from Hangzhou Institute of Medicine,China(No.2024ZZBS11)Chinese Academy of Sciences,China Postdoctoral Science Foundation(No.2024M763331)+1 种基金National Oncology Clinical Key Specialty of China(No.2023-GJZK-001)Zhejiang Provincial Natural Science Foundation of China(No.LQN25H160009).
文摘Recent advances in spatial and single-cell omics have significantly revolutionized biomarker discovery in tumor immunotherapy by addressing critical challenges such as tumor heterogeneity,immune evasion,and variability within the tumor microenvironment(TME).Immunotherapeutic strategies,including immune checkpoint in-hibitors and adoptive T-cell transfer,have demonstrated promising clinical outcomes;however,their efficacy is limited by low response rates and the incidence of immune-related adverse events(irAEs).Therefore,the identification of reliable biomarkers is essential for predicting treatment efficacy,minimizing irAEs,and facili-tating patient stratification.Spatial omics integrates molecular profiling with spatial localization,thereby providing comprehensive insights into the cellular organization and functional states within the TME.By elucidating the spatial patterns of immune cell infiltration and tumor heterogeneity,this approach enhances the prediction of therapeutic responses.Similarly,single-cell omics enables high-resolution analysis of cellular heterogeneity by capturing transcriptomic,epigenomic,and metabolic signatures at the single-cell level.The integrated application of spatial and single-cell omics has enabled the identification of previously undetected biomarkers,including rare immune cell subsets implicated in resistance mechanisms.In addition to spatial transcriptomics(ST),this technological landscape also includes spatial proteomics(SP)and spatial metab-olomics,which further facilitate the study of dynamic tumor-immune interactions.Multi-omics integration provides a comprehensive overview of biomarker landscapes,while the rapid evolution of artificial intelligence(AI)-based approaches enhances the analysis of complex,multidimensional datasets to ultimately enhance pre-dictive potential and clinical utility.Despite substantial progress,several challenges remain in the context of standardization,data integration,and real-time monitoring.Nevertheless,the incorporation of spatial and single-cell omics into biomarker research holds transformative potential for advancing personalized cancer immuno-therapy.These emerging strategies pave the way for the development of innovative diagnostic and therapeutic interventions,thereby enabling precision oncology and improving treatment outcomes across a wide range of tumor profiles.This review aims to provide a comprehensive overview of the integration of spatial omics with single-cell omics in the discovery of biomarkers for tumor immunotherapy.Specifically,it examines the strategies by which these emerging technologies address the challenges related to tumor heterogeneity,immune evasion,and the dynamic nature of the TME.By elaborating on the principles,applications,and clinical potential of these technologies,this review also critically evaluates their limitations,challenges,and the current gaps in clinical translation.
基金financially supported by the National Natural Science Foundation of China(No.82473158)the Fundamental Research Funds for the Central Universities,China(No.YG2024ZD05).
文摘Objective:This study analysed the causal relationships between free cholesterol-to-total lipid ratios in different very low-density lipoprotein(VLDL)subfractions and the risk of colorectal cancer(CRC)via a two-sample Mendelian randomization(MR)approach.Methods:Genetic variants associated with free cholesterol-to-total lipid ratios in chylomicrons,as well as extremely large,very large,large,medium,small,and very small VLDL subfractions,were identified from genome-wide association studies and selected as instrumental variables.Five analytical methods,including inverse variance weighted(IVW),MR‒Egger,weighted median,weighted mode,and simple mode,were implemented for MR analysis.Results:Elevated free cholesterol-to-total lipid ratios in very large VLDL(odds ratio(OR)=1.25;95%CI:1.06-1.49;P=0.01),medium VLDL(OR=1.20;95%CI:1.04-1.40;P=0.015),small VLDL(OR=1.17;95%CI:1.01-1.35;P=0.038),and very small VLDL(OR=1.29;95%CI:1.08-1.54;P=0.005)subfractions were found to be associated with an increased risk of CRC.No significant associations were detected between the risk of CRC and the free cholesterol-to-total lipid ratios in chylomicrons or the extremely large VLDL subfraction.Conclusions:MR analysis provided evidence for a causal association between elevated free cholesterol-to-total lipid ratios in very large,medium,small,and very small VLDL subfractions and an increased risk of CRC.These findings suggest the potential mechanistic role of altered VLDL metabolism in colorectal carcinogenesis and may inform novel therapeutic and preventive strategies.
