From populations of unicellular organisms to complex tissues, cell-to-cell variability in phenotypic traits seems to be universal. To study this heterogeneity and its biological consequences, researchers have used advanced microscopy-based approaches that provide exquisite spatial and temporal resolution, but these methods are typically limited to measuring a few properties in parallel. On the other hand, next generation sequencing technologies allow for massively parallel genome-wide approaches but have, until recently, relied on studying population averages obtained from pooling thousands to millions of cells, precluding genome-wide analysis of cell-to-cell variability. Very excitingly, in the last few years there has been a revolution in single-cell sequencing technologies allowing genome-wide quantification of mRNA and genomic DNA in thousands of individual cells leading to the convergence of genomics and single-cell biology. However, during this convergence the spatial and temporal information, easily accessed by microscopy-based approaches, is often lost in a single-cell sequencing experiment. The overarching goal of this proposal was to develop single-cell sequencing technology that retains important aspects of the spatial-temporal information and to integrate multiple measurements in the same cell. In the long-term these new technologies might be used in the clinical to determine heterogeneity in human tissues, such as tumors. Understanding intra-tumor heterogeneity is key to design a successful therapy.
This ERC advanced grant allowed us to develop a wide range of novel single-cell sequencing methods that allow the detection of cell-cell interactions (ProximID), the detection of nascent RNA (scEU-seq), cell type purification (GateID), and determine the lineage history of thousands of single cells (ScarTrace). Additionally we expanded our single-cell sequencing methods to quantify translation and epigenetic properties of single cells. This led to the development of scRibo-seq, VASA-seq, sortChIC and scChiX-seq. Finally we developed novel methods to integrate multiple lineage measurements from the same cell.
The main conclusion of this work is that is possible to successfully integrate multiple "omics" measurements from the same cell. For example, our ScarTrace technology simultaneously measure lineage information and the transcriptome in the same single cell. Our ProximID method combines the measurement of cell-cell interaction and the transcriptome of the same cell. These combined measurements provide a wealth of information, which is impossible to collect with single "omics" measurements.
These novel integrated single-cell sequencing technologies will open up new avenues to understand the mechanisms underlying cell-to-cell variability in gene expression in healthy and diseased tissues.
