Methods toward Single-Cell Total-Analysis

Single-cell total-analysis aims to bridge the gap between cellular molecular makeup and functional phenotype, deciphering how genomic, transcriptomic, proteomic, and metabolic networks orchestrate phenotypic outcomes. Despite rapid omics advances, a critical disconnect persists: nucleic acid-based analyses (genomics, epigenomics, transcriptomics) are mature, while proteomic/metabolomic profiling is incomplete, and comprehensive phenomics (the direct readout of cellular function) lags due to dynamic cellular complexity. This raises a core question: how to transcend isolated molecular layers to capture the ″molecular-phenotypic correlation″ in single cells? Multimodal integration progresses but is constrained by technical incompatibilities, throughput-depth trade-offs, and poor temporal resolution. This review examines advances in five core omics domains, identifies bottlenecks, analyzes multiomics coanalysis strategies, and outlines a roadmap for true single-cell total-analysis, emphasizing breakthrough approaches to unify molecular and phenotypic landscapes. We propose that true single-cell total-analysis is defined not by the accumulation of multiomic molecular layers, but by the direct establishment of phenotype-component correlations within the same individual cell.