Periodic Reporting for period 4 - IntScOmics (A single-cell genomics approach integrating gene expression, lineage, and physical interactions)
Período documentado: 2022-07-01 hasta 2022-12-31
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.
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.
During the first 2,5 years this ERC grant has been used to develop new single-cell sequencing methods to integrate multiple measurements from the same cell. This work resulted in seven published papers in Nature (3), Science, Cell, Cell Stem Cell, and Nature Methods for which I am the senior corresponding author. First we developed ProximID (Nature Methods 2018) to determine the physical interaction network of cells in the mouse bone marrow. Next we developed ScarTrace to perform lineage tracing using single-cell sequencing (Nature 2018). Additionally we developed GateID allowing sorting of cells based on single-cell transcriptomes. Finally, we developed a new method to quantify nascent transcripts in single cells (Science 2020) en developed new tecnologies to characterize and grow mouse (Nature 2020a) and human gastruloids (Nature 2020b). During the second phase of the project we expanded our single-cell sequencing methods to quantify translation and epigenetic properties of single cells. This led to the development of scRibo-seq (Nature 2021), VASA-seq (Nature Biotechnology 2022), sortChIC (Nature Genetics 2022) and scChiX-seq (Nature Biotechnology 2023). Additionally we developed novel methods to integrate multiple lineage measurements from the same cel (Cell Genomics 2022).
Many novel methods were created that go beyond the start of the art:
- ProximID (Nature Methods, 2018)
- ScarTrace (Nature, 2018)
- GateID (Cell, 2019)
- scEU-seq (Science, 2020)
- integration of single-cell transcriptomics and tomo-seq in mouse gastruloids (Nature 2020a)
- generation of human gastruloids (Nature, 2020b)
- integration of multiple lineage measurements from the same cell (Cell Genomics, 2021)
- scRibo-seq (Nature, 2021)
- VASA-seq (Nature Biotechnology, 2022)
- sortChIC (Nature Genetics, 2022)
- scChiX-seq (Nature Biotechnology, 2023)
- ProximID (Nature Methods, 2018)
- ScarTrace (Nature, 2018)
- GateID (Cell, 2019)
- scEU-seq (Science, 2020)
- integration of single-cell transcriptomics and tomo-seq in mouse gastruloids (Nature 2020a)
- generation of human gastruloids (Nature, 2020b)
- integration of multiple lineage measurements from the same cell (Cell Genomics, 2021)
- scRibo-seq (Nature, 2021)
- VASA-seq (Nature Biotechnology, 2022)
- sortChIC (Nature Genetics, 2022)
- scChiX-seq (Nature Biotechnology, 2023)