Single cell ‘omics
Single-cell RNA Sequencing to Elucidate Tumor and Tumor Subclone-Specific Cancer Phenotypes – Gabor Marth:
Massively parallel DNA/RNA sequencing has empowered detailed maps of clonal variation in human cancer, both through an inference of clonal substructure by analysis of variant allele frequencies in bulk tumor cell populations and direct sequencing of single cells. Bulk cell DNA-sequencing collected at multiple time points can be used to estimate the mutational, copy number, and subclone population evolution of a tumor over time or in response to treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) are providing opportunities to evaluate decouple the transcriptional behavior of tumor and normal cells, and explore the cellular diversity within the tumor. Here we present a methodology to evaluate the transcriptional effects of somatic genome alterations in cancer cells, and to explore the cellular diversity across subclonal cell subpopulations within an individual tumor, by linking single cells from scRNA-seq data to genetic variant profiles and subclone populations inferred using bulk (whole-genome or whole-exome) DNA-sequencing.
This workflow, implemented in our scBayes tool, consists of the following steps: (1) identification of somatically acquired mutations (either point mutations or copy number changes such as deletions/amplifications); (2) reconstruction of subclone structure within either one or a series of tumor biopsies from the patient; (3) extraction of subclone-defining “characters” i.e. somatic mutations or copy number events (deletions / amplifications); (4) genotyping of such subclone-defining characters in single cells, based on the single-cell RNA-Seq data; (5) the assignment of each cell to a subclone; (6) characterization of subclone-specific phenotypes based on the expression profiles / cell states of the single cells assigned to each subclone.
We present the application of this methodology to study subclone-specific evolution of transcriptional behaviour across disease progression in metastatic breast cancer and chronic ly
How to Perform Quantitative Single Cell Proteomics with SCoPE2 – Harrison Specht:
The fate and physiology of individual cells are controlled by protein interactions. We recently developed SCoPE-MS, a method for direct analysis of proteomes of single cells by LC-MS/MS. SCoPE-MS is enabled by isobaric-tag multiplexing of peptides from single cells together with carrier material, which serves both to minimize sample losses to equipment surfaces and enhance peptide identifications. Yet, our ability to quantitatively analyze proteins in single cells has remained limited. To overcome this barrier, we developed SCoPE2. SCoPE2 lowers cost and hands-on time by introducing automated and miniaturized sample preparation while increasing quantitative accuracy using only commercially-available equipment and reagents. Additionally, SCoPE2 accomplishes increased sample preparation throughput and increased measurement throughput. Using SCoPE2, we quantified over 2,700 proteins in 1,018 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allowed us to discern single cells by cell type. Parallel measurements of transcripts by 10x Genomics scRNA-seq indicate that most genes had similar responses at the protein and RNA levels, though the responses of hundreds of genes differed.
In this talk, we present users with how to begin adopting SCoPE2 in their lab. Executing a SCoPE2 experiment has similarities to conventional proteomics sample preparation as well as pitfalls and deviations unique to working with single cells. We discuss successful strategies, including study design and reagent selection, how to avoid common pitfalls, and the range of common equipment that can be used to execute SCoPE2 sample prep. Additionally, we discuss approaches to optimizing LC-MS/MS instrumentation for SCoPE2 samples. We hope to give users ideas about how to successfully design and execute a SCoPE2 experiment in order to facilitate the broad adoption of automated and quantitative single-cell analysis of proteins by mass-spectrometry.
