Single-Cell Multiomics Is Advancing Cancer Research

Single-cell multiomics analysis—making measurements in more than one modality simultaneously from single cells (DNA and RNA, or RNA and protein expression, for example) —is quickly becoming an important cancer-fighting tool. Single-cell multiomics is important for revealing cellular heterogeneity in the tumor microenvironment (TME), a sophisticated ecosystem of interacting tumor cells, immune and non-immune cells, and extracellular matrix. Cellular heterogeneity is one of cancer’s secret weapons, perhaps enabling rare cancer cells to preserve genes conferring resistance to chemotherapy. Unveiling this heterogeneity within tumor cells may be a start to curing even the most challenging and deadly cancers. Single-cell multiomics is also an important tool for tracing cell lineages and constructing cell atlases. This article discusses how single-cell multiomics is being used in cancer research today to advance our understanding of cancer biology.

Multiomics for multiple sample types

10x Genomics supports multiple ‘omics modalities with the Chromium Single Cell platform. Their Chromium Single Cell Immune Profiling and Single Cell Gene Expression solutions are newly powered by GEM-X technology, for faster and more efficient single-cell partitioning. They offer the Chromium Single Cell Multiome Gene Expression + ATAC assay for epigenetic researchers to profile gene expression and open chromatin simultaneously. Also, a new option to profile intracellular proteins “allows researchers to simultaneously analyze gene expression and intracellular proteins, including cytokines and phospho-proteins,” says Angela Churchill, Product Marketing Manager for Single Cell Applications at 10x Genomics.

In addition to multiple ‘omics, 10x Genomics accommodates multiple sample types, recently expanded to include formalin-fixed paraffin-embedded (FFPE) tissue. A common sample type, FFPE tissues can be studied retrospectively to assess disease progression and response to treatment. “Researchers can now analyze these archived FFPE tissue blocks to study cancer using [the Chromium Single Cell Gene Expression Flex solution], and examine cell types and gene expression patterns in tumors,” says Churchill. Combining Chromium with other 10x Genomics spatial platforms such as Visium Spatial or Xenium In Situ offers further avenues of study, especially in the TME, and/or immune cell infiltration into tumors.

Multiomic single-cell analysis is a powerful tool against cancer that researchers are currently using to investigate disease progression, tumor heterogeneity, TME organization, and responses to cancer therapies. “By using a multiomic approach, including V(D)J analysis with Single Cell Immune Profiling, clonotypes of B and T cells can be explored for immuno-oncology applications,” says Churchill. “Acquiring a comprehensive view of individual cells with multiomic methods can reveal novel insights on cell populations and sample heterogeneity.”

Spatial transcriptomics and proteomics

Recent advances in spatial biology contribute additional context to single-cell analysis. Miltenyi Biotec’s MACSima™ Platform offers a spatial biology solution with single-cell resolution for multiomics using cyclic fluorescent staining for 100s of proteins and dozens of RNA molecules in a single sample. The platform detects proteins with fluorochrome-conjugated primary antibodies, and RNA molecules with RNAsky™ Detection Probes—which use targeted rolling circle amplification and iterative staining—to spatially resolve proteins and RNA molecules on the same sample.

According to Felix Eppler, Global Marketing Manager for Spatial Biology at Miltenyi Biotec, the MACSima Platform’s Imaging Cyclic Staining (MICS) technology supports single-cell multiomics with its ability to cross-validate upstream screening methods and analyze protein-RNA co-expression. “This is where RNAsky technology complements the MACSima Platform’s spatial proteomics workflow, so researchers [can] target any genes of interest, and all on the exact same tissue section,” he says.

The ability to use proteomics and/or RNA detection as needed offers flexibility to researchers when designing experiments. “We have observed a strong trend toward spatial multiomics technologies in cancer research,” says Eppler. “Several target applications can take advantage of these technologies, such as the characterization of disease mechanisms, biomarker identification, or revealing the mode of action of certain drugs.” Recent research led by Lai Guan Ng used the MACSima Platform to study the adaptability and reprogramming capability of neutrophils in the TME, using measurements of marker intensities and distances to analyze neutrophil spatial organization.¹ Eppler also notes ongoing research using Miltenyi’s multiomics MICS workflow to characterize immune-oncology markers at subcellular resolution in healthy and cancerous tissues.²

Identifying rare pre-cancerous cells

Mission Bio’s single-cell multiomics Tapestri platform detects both DNA lesions (such as single nucelotide and copy number variations, indels, duplications, and V(D)J clonotyping) and surface protein expression of single cells. Researchers are using Tapestri to study many types of cancers, detecting populations of pre-cancerous clonal cells that can suddenly expand to cause disease.

The Tapestri platform is especially strong in identifying rare cancer cell populations that may impact therapeutic decisions, such as in acute myeloid leukemia (AML), says Todd Druley, Chief Medical Officer at Mission Bio. Tapestri’s ability to target specific leukemia clones allows their detection and monitoring throughout the course of therapy to help eradicate the disease.³ The Tapestri platform can also detect residual myeloid leukemia after treatment, helping to prevent relapse.⁴

A “nip-it-in-the-bud” approach may be successful in preventing worse disease outcomes, by monitoring specific subclones that can quickly progress to cancer.⁵ “Treating individuals with these high-risk, pre-leukemic clones with targeted therapy [to prevent] full-blown AML expansion” may be successful, he says. This is not possible using flow cytometry or bulk NGS, even though these methods are comparable in sensitivity, says Druley. For example, when assessing the measurable residual disease in AML patients after cancer treatment, rare leukemia subclones are simply missed by the lower-resolution, bulk NGS; and residual leukemia cells can change their surface protein expression after treatment, eluding detection by flow cytometry. However, a single-cell multiomics assay can detect protein and genetic markers in rare leukemia cells.

Transcriptomics are essential for discovering the locations and timing of gene expression, while spatial methods provide biological context for gene and protein expression. The combination of DNA and protein analysis can track clonal evolution and expansion during entire courses of cancer treatments. “These technologies have drastically improved our temporal understanding of disease biology because we can track clonal evolution, epigenetic modifications, and cellular mechanisms before, during, and after cancer and its treatment,” says Druley. Single-cell multiomics are expanding beyond the traditional triad of DNA, RNA, and protein measurements to include epigenomics, epitranscriptomics, metabolomics, and more. Time will tell whether single-cell multiomics will soon yield diagnostic tools, but the technologies have momentum.

References

1. Melissa S. F. Ng et al., Deterministic reprogramming of neutrophils within tumors. Science 383, (2024).

2. Emily Neil et al., Spatial protein and RNA analysis on the same tissue section using MICS technology. bioRxiv 2023.10.27.564191; 

3. Toma, M.M., Karami, A., Nieborowska-Skorska, M. et al. Clonal medicine targeting DNA damage response eradicates leukemia. Leukemia 38, 671–675 (2024). 

4. Troy M. Robinson et al., Single-cell genotypic and phenotypic analysis of measurable residual disease in acute myeloid leukemia. Science Adv.9, (2023).

5. Maslah, N., Benajiba, L., Giraudier, S. et al. Clonal architecture evolution in Myeloproliferative Neoplasms: from a driver mutation to a complex heterogeneous mutational and phenotypic landscape. Leukemia 37, 957–963 (2023).