Methods toward Single-Cell Total-Analysis

Area of Science:

• Single-cell biology
• Systems biology
• Omics technologies

Background:

• Single-cell analysis seeks to connect molecular profiles (genomics, transcriptomics, proteomics, metabolomics) with cellular function.
• Current omics technologies are mature for nucleic acids but incomplete for proteins/metabolites, hindering comprehensive phenomics.
• A significant challenge is correlating molecular data with direct functional phenotypes within individual cells.

Purpose of the Study:

• To review advances in single-cell omics and multiomics integration.
• To identify bottlenecks hindering comprehensive single-cell total-analysis.
• To propose a roadmap for unifying molecular and phenotypic landscapes in single cells.

Main Methods:

• Examination of advances across five core omics domains.
• Analysis of bottlenecks in current single-cell multiomics approaches.
• Review of multiomics co-analysis strategies and their limitations.

Main Results:

• Nucleic acid-based omics are advanced, but proteomic, metabolomic, and phenomic data remain incomplete.
• Multimodal integration faces technical, throughput, and temporal resolution challenges.
• Existing multiomics approaches often fail to establish direct molecular-phenotypic correlations.

Conclusions:

• True single-cell total-analysis requires direct correlation of phenotype with molecular components.
• Accumulating multiple molecular layers alone does not constitute comprehensive single-cell analysis.
• Future research should focus on breakthrough approaches to unify molecular and phenotypic data within single cells.