PPubMed21 Mar 2022Cited 64×

Single-Cell Multiomics Techniques: From Conception to Applications Dimitriu et al. Recent Advances in Single-Cell Multiomics

Maria A. Dimitriu, Irina Lazar-Contes, Martin Roszkowski, Isabelle M. Mansuy

Abstract

'Isabelle M. Mansuy'] Recent advances in methods for single-cell analyses and barcoding strategies have led to considerable progress in research. The development of multiplexed assays offers the possibility to conduct parallel analyses of multiple factors and processes for comprehensive characterization of cellular and molecular states in health and disease. These technologies have expanded extremely rapidly in the past years and constantly evolve and provide better specificity, precision and resolution. This review summarizes recent progress in single-cell multiomics approaches, and focuses, in particular, on the most innovative techniques that integrate genome, epigenome and transcriptome profiling. It describes the methodologies, discusses their advantages and limitations, and explains how they have been applied to studies on cell heterogeneity and differentiation, and epigenetic reprogramming.

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Single-cell multiomics techniques now enable simultaneous profiling of genome, epigenome, and transcriptome, advancing cell‑type characterization.

What’s new

i.Summarizes recent progress in single‑cell multiomics approaches.
ii.Highlights innovative techniques that integrate genome, epigenome, and transcriptome profiling.
iii.Describes methodologies, advantages, and limitations of each technique.
iv.Explains applications to cell heterogeneity, differentiation, and epigenetic reprogramming.

How they did it

The authors performed a comprehensive literature review of recent single‑cell multiomics methods, extracting details on molecular layers profiled, throughput, and special features. They compiled comparative tables summarizing performance metrics such as library complexity, coverage, and detection rates. The review synthesizes these findings to evaluate strengths and weaknesses of each approach.

What they found

Paired-seq achieves ultra‑high throughput with five rounds of combinatorial barcoding.
sn‑m3C‑seq provides 500,000 contacts per cell (4,200 cells) and deepest DNA‑methylation coverage.
scMethyl‑HiC yields 80,763 informative contacts per nucleus from 150 cells and highest genome mapping rate.
TARGET‑seq detects all mutations in 98.4% of cells and offers the highest throughput.
scSIDR‑seq achieves >90% alignment rate and captures total RNA.
scTrio‑seq2 attains the highest DNA‑methylome coverage with 16.4 million CpGs per cell.

Caveats

✕Data sparsity and noise due to limited coverage per molecular layer.
✕High sequencing costs create trade‑offs between throughput and information richness.
✕Low recovery efficiency and gene/allelic dropout in individual cells.
✕High‑throughput methods often have lower library complexity compared to low‑throughput approaches.

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