Simultaneous Quantification of Spatial Genome Positioning and Transcriptomics in Single Cells with scDam&T-Seq.

Spatial genome organization is considered to play an important role in mammalian cells, by guiding gene expression programs and supporting lineage specification. Yet it is still an outstanding question in the field what the direct impact of spatial genome organization on gene expression is. To elucidate this relationship further, we have recently developed scDam&T-seq, a method that simultaneously quantifies protein-DNA interactions and transcriptomes in single cells. This method efficiently combines two preexisting methods: DamID for measuring protein-DNA contacts and CEL-Seq2 for quantification of the transcriptome in single cells. scDam&T-seq has been successfully applied to measure DNA contacts with the nuclear lamina, while at the same time revealing the effect of these contacts on gene expression. This method is applicable to many different proteins of interest and can thereby aid in studying the relationship between protein-DNA interactions and gene expression in single cells.

High-Throughput Single-Cell RNA Sequencing and Data Analysis.

Understanding biological systems at a single cell resolution may reveal several novel insights which remain masked by the conventional population-based techniques providing an average readout of the behavior of cells. Single-cell transcriptome sequencing holds the potential to identify novel cell types and characterize the cellular composition of any organ or tissue in health and disease. Here, we describe a customized high-throughput protocol for single-cell RNA-sequencing (scRNA-seq) combining flow cytometry and a nanoliter-scale robotic system. Since scRNA-seq requires amplification of a low amount of endogenous cellular RNA, leading to substantial technical noise in the dataset, downstream data filtering and analysis require special care. Therefore, we also briefly describe in-house state-of-the-art data analysis algorithms developed to identify cellular subpopulations including rare cell types as well as to derive lineage trees by ordering the identified subpopulations of cells along the inferred differentiation trajectories.