Abstract
Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.
Highlights
Background and SummaryThe concept of multi-omics and data-integration has been increasingly used during the last 5 years to describe the multitude of high-throughput molecular technologies that can be applied to the study and analysis of biological systems[1]
We present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation
Proteins are measured by proteomics and phosphoproteomics approaches, based on Liquid Chromatography (LC) and Isotope-coded affinity tag labeling coupled to Mass Spectrometry (MS)
Summary
The concept of multi-omics and data-integration has been increasingly used during the last 5 years to describe the multitude of high-throughput molecular technologies that can be applied to the study and analysis of biological systems[1] Such techniques hold the promise to uncover the different biological processes and layers of regulatory complexity within biological systems. By collecting data over time, where as a rule the different molecular entities are correlated, it is much more amenable to extract key processes from each data-type as well as uncovering dependencies between different regulatory layers These technical and conceptual advances are currently being transferred into the vibrant single-cell biology community. We still lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems This state-of-affairs has been the rationale underpinning the formulation of what is referred to as the STATegra project (http://www.stategra.eu/).
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