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

By Alev Baysoy, Zhiliang Bai, Rahul Satija, Rong Fan. Nature reviews. Molecular cell biology, 2023

814 citations recorded by the source corpus.

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. Single-cell multi-omics methods are essential for characterizing cell states and types. The past decade has ushered in improvements in spatial resolution and computational data integration and in new omics modalities. Consequently, single-cell multi-omics have advanced fundamental and translational research, including, for example, in production of cell atlases and in tumour immunology therapeutics.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/da7c7d871d905c60ababe567260d597cc07919f0

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Citations in the corpus

• Regulatory mechanisms of PD-1/PD-L1 in cancers (2024, background): “For example, in heterogeneous diseases such as bladder cancer, multi-omics sequencing and analysis may help provide stronger biomarkers for predicting chemosensitivity [459].”