Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Abstract 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 th...
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Autor*in:	Kiessling, Paul [verfasserIn] Kuppe, Christoph

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Spatial multi-omics Spatial transcriptomics In situ sequencing Multiplex in situ FISH MALDI Spatial proteomics Data integration Spatial neighborhood analysis

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Genome medicine - London : BioMed Central, 2009, 16(2024), 1 vom: 18. Jan.
Übergeordnetes Werk:	volume:16 ; year:2024 ; number:1 ; day:18 ; month:01

Links:	Volltext

DOI / URN:	10.1186/s13073-024-01282-y

Katalog-ID:	SPR054439760

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allfields	10.1186/s13073-024-01282-y doi (DE-627)SPR054439760 (SPR)s13073-024-01282-y-e DE-627 ger DE-627 rakwb eng Kiessling, Paul verfasserin aut Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract 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. Spatial multi-omics (dpeaa)DE-He213 Spatial transcriptomics (dpeaa)DE-He213 In situ sequencing (dpeaa)DE-He213 Multiplex in situ FISH (dpeaa)DE-He213 MALDI (dpeaa)DE-He213 Spatial proteomics (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Spatial neighborhood analysis (dpeaa)DE-He213 Kuppe, Christoph (orcid)0000-0003-4597-9833 aut Enthalten in Genome medicine London : BioMed Central, 2009 16(2024), 1 vom: 18. Jan. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:18 month:01 https://dx.doi.org/10.1186/s13073-024-01282-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 18 01
spelling	10.1186/s13073-024-01282-y doi (DE-627)SPR054439760 (SPR)s13073-024-01282-y-e DE-627 ger DE-627 rakwb eng Kiessling, Paul verfasserin aut Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract 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. Spatial multi-omics (dpeaa)DE-He213 Spatial transcriptomics (dpeaa)DE-He213 In situ sequencing (dpeaa)DE-He213 Multiplex in situ FISH (dpeaa)DE-He213 MALDI (dpeaa)DE-He213 Spatial proteomics (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Spatial neighborhood analysis (dpeaa)DE-He213 Kuppe, Christoph (orcid)0000-0003-4597-9833 aut Enthalten in Genome medicine London : BioMed Central, 2009 16(2024), 1 vom: 18. Jan. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:18 month:01 https://dx.doi.org/10.1186/s13073-024-01282-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 18 01
allfields_unstemmed	10.1186/s13073-024-01282-y doi (DE-627)SPR054439760 (SPR)s13073-024-01282-y-e DE-627 ger DE-627 rakwb eng Kiessling, Paul verfasserin aut Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract 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. Spatial multi-omics (dpeaa)DE-He213 Spatial transcriptomics (dpeaa)DE-He213 In situ sequencing (dpeaa)DE-He213 Multiplex in situ FISH (dpeaa)DE-He213 MALDI (dpeaa)DE-He213 Spatial proteomics (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Spatial neighborhood analysis (dpeaa)DE-He213 Kuppe, Christoph (orcid)0000-0003-4597-9833 aut Enthalten in Genome medicine London : BioMed Central, 2009 16(2024), 1 vom: 18. Jan. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:18 month:01 https://dx.doi.org/10.1186/s13073-024-01282-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 18 01
allfieldsGer	10.1186/s13073-024-01282-y doi (DE-627)SPR054439760 (SPR)s13073-024-01282-y-e DE-627 ger DE-627 rakwb eng Kiessling, Paul verfasserin aut Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract 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. Spatial multi-omics (dpeaa)DE-He213 Spatial transcriptomics (dpeaa)DE-He213 In situ sequencing (dpeaa)DE-He213 Multiplex in situ FISH (dpeaa)DE-He213 MALDI (dpeaa)DE-He213 Spatial proteomics (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Spatial neighborhood analysis (dpeaa)DE-He213 Kuppe, Christoph (orcid)0000-0003-4597-9833 aut Enthalten in Genome medicine London : BioMed Central, 2009 16(2024), 1 vom: 18. Jan. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:18 month:01 https://dx.doi.org/10.1186/s13073-024-01282-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 18 01
allfieldsSound	10.1186/s13073-024-01282-y doi (DE-627)SPR054439760 (SPR)s13073-024-01282-y-e DE-627 ger DE-627 rakwb eng Kiessling, Paul verfasserin aut Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract 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. Spatial multi-omics (dpeaa)DE-He213 Spatial transcriptomics (dpeaa)DE-He213 In situ sequencing (dpeaa)DE-He213 Multiplex in situ FISH (dpeaa)DE-He213 MALDI (dpeaa)DE-He213 Spatial proteomics (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Spatial neighborhood analysis (dpeaa)DE-He213 Kuppe, Christoph (orcid)0000-0003-4597-9833 aut Enthalten in Genome medicine London : BioMed Central, 2009 16(2024), 1 vom: 18. Jan. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:16 year:2024 number:1 day:18 month:01 https://dx.doi.org/10.1186/s13073-024-01282-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 18 01
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author	Kiessling, Paul
spellingShingle	Kiessling, Paul misc Spatial multi-omics misc Spatial transcriptomics misc In situ sequencing misc Multiplex in situ FISH misc MALDI misc Spatial proteomics misc Data integration misc Spatial neighborhood analysis Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases
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topic_title	Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases Spatial multi-omics (dpeaa)DE-He213 Spatial transcriptomics (dpeaa)DE-He213 In situ sequencing (dpeaa)DE-He213 Multiplex in situ FISH (dpeaa)DE-He213 MALDI (dpeaa)DE-He213 Spatial proteomics (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Spatial neighborhood analysis (dpeaa)DE-He213
topic	misc Spatial multi-omics misc Spatial transcriptomics misc In situ sequencing misc Multiplex in situ FISH misc MALDI misc Spatial proteomics misc Data integration misc Spatial neighborhood analysis
topic_unstemmed	misc Spatial multi-omics misc Spatial transcriptomics misc In situ sequencing misc Multiplex in situ FISH misc MALDI misc Spatial proteomics misc Data integration misc Spatial neighborhood analysis
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title	Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases
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title_sort	spatial multi-omics: novel tools to study the complexity of cardiovascular diseases
title_auth	Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases
abstract	Abstract 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. © The Author(s) 2024
abstractGer	Abstract 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. © The Author(s) 2024
abstract_unstemmed	Abstract 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. © The Author(s) 2024
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title_short	Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases
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Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

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