{"title":"空间多组学:研究心血管疾病复杂性的新工具。","authors":"Paul Kiessling, Christoph Kuppe","doi":"10.1186/s13073-024-01282-y","DOIUrl":null,"url":null,"abstract":"<p><p>Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":null,"pages":null},"PeriodicalIF":10.4000,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10795303/pdf/","citationCount":"0","resultStr":"{\"title\":\"Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases.\",\"authors\":\"Paul Kiessling, Christoph Kuppe\",\"doi\":\"10.1186/s13073-024-01282-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.</p>\",\"PeriodicalId\":12645,\"journal\":{\"name\":\"Genome Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.4000,\"publicationDate\":\"2024-01-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10795303/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genome Medicine\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s13073-024-01282-y\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Medicine","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13073-024-01282-y","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases.
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
期刊介绍:
Genome Medicine is an open access journal that publishes outstanding research applying genetics, genomics, and multi-omics to understand, diagnose, and treat disease. Bridging basic science and clinical research, it covers areas such as cancer genomics, immuno-oncology, immunogenomics, infectious disease, microbiome, neurogenomics, systems medicine, clinical genomics, gene therapies, precision medicine, and clinical trials. The journal publishes original research, methods, software, and reviews to serve authors and promote broad interest and importance in the field.