Advances and challenges in epigenomic single-cell sequencing applications
Understanding multicellular physiology and pathobiology requires analysis of the relationship between genotype, chromatin organisation and phenotype. In the multi-omics era, many methods exist to investigate biological processes across the genome, transcriptome, epigenome, proteome and metabolome. U...
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Veröffentlicht in: | Current opinion in chemical biology 2020-08, Vol.57, p.17-26 |
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creator | Philpott, Martin Cribbs, Adam P. Brown, Tom Brown, Tom Oppermann, Udo |
description | Understanding multicellular physiology and pathobiology requires analysis of the relationship between genotype, chromatin organisation and phenotype. In the multi-omics era, many methods exist to investigate biological processes across the genome, transcriptome, epigenome, proteome and metabolome. Until recently, this was only possible for populations of cells or complex tissues, creating an averaging effect that may obscure direct correlations between multiple layers of data. Single-cell sequencing methods have removed this averaging effect, but computational integration after profiling distinct modalities separately may still not completely reflect underlying biology. Multiplexed assays resolving multiple modalities in the same cell are required to overcome these shortcomings and have the potential to deliver unprecedented understanding of biology and disease.
•Progress in microfluidics, imaging and chemistry permits multiple single-cell assays.•Multiplexing is possible including epigenetic or chromatin assays.•Spatial transcriptomic methods allow identification of cell states in tissues.•Computational solutions are developed to achieve integration of different data types. |
doi_str_mv | 10.1016/j.cbpa.2020.01.013 |
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subjects | Animals Epigenesis, Genetic Epigenomics - methods High-Throughput Nucleotide Sequencing - methods Humans Multiplexed single-cell assays Next-generation sequencing Single cell Single-Cell Analysis - methods Single-cell epigenomics |
title | Advances and challenges in epigenomic single-cell sequencing applications |
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