STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

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.

STATegra: Multi-omics data integration - A conceptual scheme and a bioinformatics pipeline

“…This analysis provided qualitative and quantitative insights into how much the different data-types (e.g., different omics) and their features were “ coordinated ”. Moreover, the analysis provided useful information for targeting specific omics combinations (2). In the third step, for those combinations of omics characterized as coordinated , NPC analysis allowed increasing the statistical power to identify significant features as we have recently demonstrated (40,41).…”

“…Thus, instead of looking at the PCA-derived components of mRNA and miRNA separately, we investigated the existence of components (or factors) shared by both omics (47). Intuitively, while in PCA we projected using the main components per omic (refer to Supplementary Figures 2B, 3A as examples), we next aimed to identify projections where the components are informative for more than one data-type simultaneously (refer to Figures 2A, C).…”

“…Computational and experimental developments have enabled the profiling of multiple layers of cell regulation: genome, transcriptome, epigenome, chromatin conformation or metabolome, among many others globally known “omics” (1,2). The development of such technologies was driven by the understanding that a single-omic does not provide enough information to allow dissecting biological mechanisms (3,4).…”

VIA: Generalized and scalable trajectory inference in single-cell omics data

“…Cells are renormalized by library count and scaled to unit variance and zero mean. VIA identifies the terminal state at 18-24 h and accurately recapitulates the gene expression trends 26 along inferred pseudotime of IgII1 , Slc7a5 , Fox01 , Myc , Ldha and Lig4 . ( Supplementary Fig.…”

Multi-omic analysis unveils biological pathways in peripheral immune system associated to minimal hepatic encephalopathy appearance in cirrhotic patients

“…Here, we have adopted a multi-omic approach to investigate the set of molecular and cellular events that are associated with MHE. Multi-omic technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics are increasingly being used to profile the multi-layered component of living cells and pathological processes 9 – 12 . The rationale behind this strategy is that disease usually impacts different types of biomolecules and by expanding the molecular space under study, the likelihood of identifying relevant biomarkers will increase.…”