Advances in next-generation sequencing and mass spectrometry technologies have revolutionized our ability to determine the cellular and molecular landscapes of cardiac development, homeostasis and disease mechanisms. Multi-omics approaches incorporating the transcriptome, metabolome, proteome, and DNA methylome now allow for the tissue-specific and patient-specific expression profiles of genes, transcripts, proteins, and metabolites to be queried on a large scale and connected to higher phenotypes to omics changes to decipher the underlying regulatory networks.
The advent of single-cell sequencing and single-cell proteomics methods is further extending these investigation to individual cell resolution to discern the contribution of individual cells or cell types to whole-organ physiology. In parallel, systems biology and computational approaches are being developed to harness large-scale data to model intercellular communication across individual cells. These developments hold the promise of transforming our understanding of tissue physiology, homeostasis, and disease pathological mechanisms involving multiple cell types.
This Research Topic aims to bring investigators from multi-disciplinary backgrounds together to share expertise in diverse aspects of multi-omics data integration and analysis with the overarching goal in deciphering and understanding secretome, interactome, and intercellular communication critical for cardiovascular development, homeostasis, and disease.
We welcome multiple types of manuscripts in the areas of:
1) Single-cell or bulk analyses of multi-omics datasets (such as transcriptomic, epigenomic, metabolomic, and proteomic) from cardiovascular tissues or derivatives in normal and pathological conditions.
2) Prediction and extraction of cellular interaction and secretome landscape in cardiovascular tissues and pluripotent stem cell derivatives
3) Development of novel computational tools, pipelines, and resources for cardiovascular cellular interaction.
4) Use of artificial intelligence, machine learning, and/or deep learning algorithms in understanding interactome, secretome, and intercellular communication.
Advances in next-generation sequencing and mass spectrometry technologies have revolutionized our ability to determine the cellular and molecular landscapes of cardiac development, homeostasis and disease mechanisms. Multi-omics approaches incorporating the transcriptome, metabolome, proteome, and DNA methylome now allow for the tissue-specific and patient-specific expression profiles of genes, transcripts, proteins, and metabolites to be queried on a large scale and connected to higher phenotypes to omics changes to decipher the underlying regulatory networks.
The advent of single-cell sequencing and single-cell proteomics methods is further extending these investigation to individual cell resolution to discern the contribution of individual cells or cell types to whole-organ physiology. In parallel, systems biology and computational approaches are being developed to harness large-scale data to model intercellular communication across individual cells. These developments hold the promise of transforming our understanding of tissue physiology, homeostasis, and disease pathological mechanisms involving multiple cell types.
This Research Topic aims to bring investigators from multi-disciplinary backgrounds together to share expertise in diverse aspects of multi-omics data integration and analysis with the overarching goal in deciphering and understanding secretome, interactome, and intercellular communication critical for cardiovascular development, homeostasis, and disease.
We welcome multiple types of manuscripts in the areas of:
1) Single-cell or bulk analyses of multi-omics datasets (such as transcriptomic, epigenomic, metabolomic, and proteomic) from cardiovascular tissues or derivatives in normal and pathological conditions.
2) Prediction and extraction of cellular interaction and secretome landscape in cardiovascular tissues and pluripotent stem cell derivatives
3) Development of novel computational tools, pipelines, and resources for cardiovascular cellular interaction.
4) Use of artificial intelligence, machine learning, and/or deep learning algorithms in understanding interactome, secretome, and intercellular communication.
