1Cell brings single-cell multi-omics to precision oncology — resolving tumor heterogeneity, clonal evolution and the microenvironment one cell at a time, so therapy can follow the biology.
the unit of resolution
RNA · chromatin · protein · genome · space
of cells profiled per tumor sample
therapy tailored to the individual
A tumor is not one disease — it is an evolving ecosystem of malignant subclones, immune cells, fibroblasts and vasculature. Averaging them hides the rare resistant clone that decides whether a treatment works or fails.
Single-cell technologies profile each cell on its own, revealing the populations, states and interactions that bulk assays smooth away.
Measuring several molecular layers in the same cell links genotype to regulation to phenotype — the connections that explain how a tumor behaves.
scRNA-seq captures gene expression programs that define cell identity, state and response to therapy.
Chromatin accessibility maps the regulatory landscape and the transcription factors driving malignant states.
CITE-seq pairs RNA with antibody-derived tags to anchor cell types in the markers clinicians already use.
Single-cell DNA and mutation calling trace copy-number changes and clonal lineages through the tumor.
Paired TCR/BCR sequencing reveals clonally expanded T and B cells reacting to the tumor.
Spatial transcriptomics places each cell in its neighborhood, showing who sits next to whom in the tissue.
Biopsy, resection or blood — dissociated to viable single cells or nuclei.
Barcoded droplet or plate-based assays across RNA, chromatin and protein.
Quality control, batch correction and joint embedding of modalities.
Clustering, clone inference, pathway and cell–cell communication analysis.
Biomarkers and hypotheses that inform therapy selection and trial design.
Identify subclones, track how they expand under treatment pressure, and find the vulnerabilities shared across them.
Detect rare drug-tolerant cell states before they drive relapse, and design combinations that close the escape routes.
Map exhausted T cells, myeloid suppressors and stromal niches to understand — and predict — checkpoint blockade response.
Follow disease burden and cell-state shifts over time to detect progression earlier than imaging alone.
Precision oncology asks which therapy for which patient.
Single-cell multi-omics shows which cells decide the answer.
Interested in single-cell multi-omics for translational oncology research? Reach out.
hello@1cell.io