In recent years, several single-cell approaches have emerged that enable researchers to understand the tumor heterogeneity with greater resolution than conventional bulk approaches. Single-cell RNA sequencing (scRNA-seq), for instance, has taken biological research labs by storm. It is a powerful application for looking at the heterogeneity of gene expression and identifying key pathways being impacted by therapeutics or disease states.
In 2019, Anna Nam, Dan Landau, and colleagues at Weill Cornell Medicine up-leveled this technique by developing Genotyping of Transcriptomes (GoT) that combines scRNA-seq with genotyping. This approach assesses cDNA from the scRNAseq libraries to detect DNA-level variants, enabling DNA mutations and transcriptome data to be assessed from the same cells.
Single-cell DNA sequencing (scDNA-seq) is another methodology that has transformed our ability to profile cancer. This method assesses DNA variants by amplifying targeted regions of the genome for NGS. Technologies like Mission Bio’s Tapestri Platform also conduct multi-omic assays, where DNA variants and cell-surface proteins (immunophenotype) are simultaneously assessed in the same cells.
Recognizing which technology to use to answer a biological question becomes difficult when multiple options are available. Here, we describe applications for which GoT and scDNA are best suited.
When to use Genotyping of Transcriptomes
The primary use of the GoT method is to measure the transcriptome of cancer cells in the context of the mutations they carry. Because tumors are composed of a mix of genetically distinct subclones, correlating transcription data with the underlying mutational profile of each cell provides valuable information about cancer complexity.
For instance, Nam et al. (2019) profiled cells from patients with myeloproliferative neoplasms to understand how somatic mutations disrupt normal blood development [1]. Here, the researchers compared the transcriptional profiles of mutant and normal cells to gain insights into disease processes. Overall, the GoT method is applicable for cancer profiling studies where information on gene expression and somatic mutations are needed to answer the scientific question.
Although GoT showcases the powerful ability to extract DNA variant information from scRNA-seq assays, it has some limitations. For instance, scRNA-seq is a genome-wide application and as such may have low numbers of reads to support genotype data (example 2x coverage). Thus, it can only confidently measure DNA variants with relatively high (VAF >10%). Also, because only ~ 5% of the genome is transcribed, only a small proportion of the genome can be assessed for cancer-associated mutations.
When to use Single-cell DNA
The strengths of scDNA-seq and multi-omics are apparent for deeper assessments of clonal architecture. With GoT, neither non-coding variants nor rare subclonal variants will be detected. Yet, both are critical to understanding genetic heterogeneity. Single-cell DNA sequencing platforms like Tapestri are purpose-built for detecting DNA variants directly from the genome, even ones with very low VAF (0.1%). Because the assays are targeted (there is high read depth per amplicon region), rare variants are captured with high confidence.
Due to its high sensitivity, scDNA-seq is appropriate for cancer applications where identifying the occurrence of rare subclones is paramount, such as when investigating clonal architecture and therapy resistance, and the detection of measurable residual disease (MRD). For instance, Miles et al. (2020) leveraged scDNA-seq to describe the clonal composition of patients with hematological malignancies, and Peretz et al (2021) used the method to identify mechanisms of therapy resistance in patients with acute myeloid leukemia [1,2]. Other studies are well-suited for Tapestri multi-omics assays that detect genotype and immunophenotype. Such assays can determine the cell type in which the mutation is occurring. Dillon et al. (2021) utilized this capability to enhance the detection of MRD.
Overall, both GoT and scDNA-seq have aspects that lend themselves well to investigating cancer. While the former includes gene expression analysis, it has limitations in detecting rare DNA variants. Alternatively, scDNA-seq is highly sensitive to detection mutations but lacks expression data. The choice of which method to use is largely determined by the scientific question and research goals of your project.
References
1. Nam AS et al. Somatic mutations and cell identity linked by Genotyping of Transcriptomes. Nature. 2019. Jul;571(7765):355-360.
2. Miles LA et al. Single-cell mutation analysis of clonal evolution in myeloid malignancies. Nature. 2020 Nov;587(7834):477-482.
3. Peretz CAC. Single-cell DNA sequencing reveals complex mechanisms of resistance to quizartinib. Blood Adv. 2021 Mar 9;5(5):1437-1441.
4. Dillon LW et al. Personalized Single-Cell Proteogenomics to Distinguish Acute Myeloid Leukemia from Non-Malignant Clonal Hematopoiesis. Blood Cancer Discov. 2021 Jul;2(4):319-325.
