single-cell omics

Definition

Single-cell omics encompasses high-throughput technologies that measure molecular profiles—including genomics, transcriptomics, proteomics, and epigenomics—at the resolution of individual cells rather than bulk tissue samples. This approach reveals cellular heterogeneity within tissues, identifies rare cell populations, and maps developmental trajectories or disease progression at unprecedented resolution. Single-cell RNA sequencing (scRNA-seq) is the most widely adopted technique, but the field has expanded to include single-cell ATAC-seq for chromatin accessibility, single-cell proteomics, and multi-omics approaches that simultaneously measure multiple molecular layers within the same cell, enabling researchers to understand cell-to-cell variability and cellular states in complex biological systems.

Visualize single-cell omics in Nodes Bio

Researchers can visualize single-cell omics data as cell-cell similarity networks, where nodes represent individual cells and edges connect cells with similar expression profiles. Network clustering reveals distinct cell types and states, while trajectory analysis maps differentiation pathways. Gene regulatory networks can be inferred from single-cell data to identify master regulators controlling cell fate decisions, and cell-cell communication networks can model ligand-receptor interactions between different cell populations.

Example Use Case

A cancer researcher analyzing tumor biopsies uses single-cell RNA-seq to profile 50,000 cells from patient samples. Network visualization reveals distinct clusters representing cancer cells, immune cells, and stromal populations. By mapping cell-cell communication networks, they discover that a rare cancer stem cell population secretes immunosuppressive signals that inhibit T-cell activity. This finding identifies potential therapeutic targets to disrupt the tumor microenvironment and enhance immunotherapy response, leading to combination treatment strategies.

Related Terms