The technological landscape and applications of single-cell multi-omics

Abstract

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Epigenetics in obesity: Mechanisms and advances in therapies based on natural products

“…developed spatially resolved, genome‐wide comapping of the epigenome and transcriptome by concurrent profiling of chromatin accessibility and messenger RNA expression (spatial assay for transposase‐accessible chromatin and RNA using sequencing, or spatial assay of cleavage unc‐seq), histone modifications and mRNA expression (spatial ATAC‐RNA‐seq), or histone modifications and mRNA expression on the same tissue segment at the cellular level to combine the chemistry of spatial‐ATAC‐seq3 or spatial‐CUT&Tag2 with that for spatial transcriptomics (spatial CUT&Tag‐RNA‐seq; applied to H3K27me3, H3K27ac, or H3K4me3 histone modifications) 126 . In the future, we hope this will be the impetus for application in adult human fat tissue to analyze how epigenetic and transcriptional changes affect the dynamics of different cell types in a tissue to help us better understand the epigenetic mechanisms of obesity 127 …”

Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA-sequencing

“…We have demonstrated that mechanistic models can be used to overcome the challenge of technical noise across modalities commonly suffered by multi-view statistical methods [75, 76]. Data integration across conditions, time and modalities is an important challenge for single-cell multi-omics, including epigenome and proteome [77–79], and mechanistic models like ours could provide a versatile tool for integrating these data [80].…”

Unveiling the Spatiotemporal Diversity of the Endothelium in Development: A Multi-Omics Approach

“…8 By offering detailed gene expression profiles at the cellular level, the field of single-cell omics has greatly enhanced our understanding of the variability and heterogeneity of cell populations in healthy and diseased tissues. 9 For instance, recent findings from single-cell sequencing studies bolster the idea that the transformation of endothelial cells from venous to arterial characteristics occurs at the beginning of mammalian development. Despite these technological advances, the challenge of studying biological samples in a manner that preserves their spatial and temporal integrity persists.…”