Tumor heterogeneity has always been a limiting factor for the effects of therapeutic treatments for cancer. This heterogeneity includes not only genomic differences, but also transcriptome heterogeneity and immune microenvironment heterogeneity. Single-cell sequencing technology can define different cell types and cell states, including tumor cells and tumor-related immune cells, from the level of individual cells. It is a powerful method for studying tumor heterogeneity and immune microenvironment characteristics, and helps to understand diseases mechanisms, discover the causes of drug resistance and develop new treatments.
Systematic analysis, holistic view
Single-cell sequencing technology can enable systematic study of important cellular events such as cell differentiation, reprogramming, communication networks, developmental dynamics and deficiencies, especially immune cells, from a wide range of cells and tissues. The holistic picture is important towards the understanding of complex diseases.
Danuta R. Gawel, Jordi Serra-Musach,Sandra Lilja, et al. A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. Genome Med. 2019.
Tumor heterogeneity is categorized into inter-tumor heterogeneity and intra-tumor heterogeneity. Single-cell sequencing technology can sequence the genome and transcriptome at the single-cell level to analyze the heterogeneity of tumors. For example, researchers used scRNA-seq technology to describe the heterogeneous landscape of cells in late-stage non-small cell lung cancer (NSCLC) for the first time.
Single-cell sequencing can finely classify the cell types in the tumor immune microenvironment, and individually define the composition, distribution, function and development status of cell subpopulations based on heterogeneity. It can also discover new cell subpopulations. In the following case study, researchers used scRNA-seq technology to describe the immune microenvironment characteristics of osteosarcoma for the first time, and found that in the myeloid cell group of osteosarcoma, M2 tumor-associated macrophages occupy a dominant position. The researchers also found pro-inflammatory FABP4+ macrophages in lung metastasis tissues.
Zhou, Y. et al. Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma. Nature Communications. 2020.
Single-cell analysis can enable comprehensive analysis of tumor progression with unprecedented resolution, identify the true biomarkers of individual cancer cells, reveal tumor heterogeneity, and identify the characteristics of drug-resistant cancer cells with phenotypic significance. For example, in patients with drug-resistant melanoma, researchers used scRNA-seq technology to discover a tumor-infiltrating immunosuppressive cell that can lead to drug resistance. With this immuno-suppressive cell as the target, the researchers successfully reversed tumor drug resistance by using a combination of drugs.
Livnat Jerby-Arnon, Parin Shah, Michael S. Cuoco, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018.
Single-cell sequencing can monitor the dynamic changes of single-cell gene expression (for example with Singleron product DynaSCOPE), as well as its response to changes in the microenvironment and drug resistance. Currently, scRNAseq has been applied in discovery and development of disease-specific biomarkers and therapeutic targets. In one case study, researchers used scRNA-seq technology to find that Treg cells express NK/T cell inhibitory receptor TIGIT in osteosarcoma. Blocking TIGIT significantly enhanced the toxic effect of primary T cells on osteosarcoma cell lines, suggesting that targeting TIGIT Treatment has potential therapeutic value for osteosarcoma.
