Explore applications that benefit from single-cell multiomics
Each application below represents a research or development context where direct single-cell genotype-to-phenotype characterization — and the per-cell visibility it provides into therapy safety and drug development — answers questions that cell-averaged methods cannot.
Clinical Oncology
Single-cell multiomics resolves co-mutation status, clonal architecture, and protein co-expression directly in patient samples to support measurable residual disease (MRD) profiling, treatment stratification, and clinical trial biomarker readouts.
Cell & Gene Therapy
Single-cell multiomics characterizes on- and off-target editing events, co-occurrence of edits within the same cell, vector copy number (VCN), and in vivo biodistribution at per-cell resolution. For cell therapy, it profiles product identity, purity, and potency across the full edited population — giving safety and efficacy data that FACS or bulk NGS report only as population averages.
Oncology Research
Single-cell multiomics maps co-mutation frequencies, clonal evolution trajectories, and genotype-linked transcriptional states in AML, multiple myeloma, and solid tumor models. Per-cell genotype-to-phenotype resolution reveals mechanism of action and resistance at the clonal level rather than inferred from population averages.
Disease Modeling
Single-cell multiomics characterizes and validates disease models by linking introduced mutations to downstream transcriptional and protein phenotypes within each individual cell. Per-cell measurement confirms that iPSC-derived systems, organoids, and patient-derived xenograft models recapitulate the genotype-to-phenotype relationships observed in primary patient material.
Functional Genomics
Single-cell multiomics validates secondary CRISPR screens by confirming editing outcomes and measuring the downstream impact of each perturbation at the genotypic, transcriptional, and protein level within the same cell. Per-cell resolution removes the population averaging that obscures variant-level functional effects in pooled screen readouts.
The latest from Mission Bio
Technical notes, blogs, posters, recordings, and guides to support how you use single-cell for your application.
