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1. Mapping cell fate decisions in a single cell resolution
1. Mapping cell fate decisions in a single cell resolution
The highly choreographed lineage hierarchy of developing cells has been painstakingly characterized over several decades by marker gene analysis. Recent technological developments in single-cell transcriptomics and lineage tracing now enable characterization of cell states and transitions at unprecedented resolution. We are using the organoid model system of of embryogenesis, intestinal regeneration and developing cancers to map a developmental trajectory and single cell transcriptional atlas. We further nominate and validate transcriptional and epigenetic regulators of key fate decisions by perturbing transcriptional and epigenetic programs that underlie cell fate specification. We are aiming to provide resolved key mechanisms for charting cell-state hierarchies, cell fate decisions, and the factors that regulate fate choices.
The highly choreographed lineage hierarchy of developing cells has been painstakingly characterized over several decades by marker gene analysis. Recent technological developments in single-cell transcriptomics and lineage tracing now enable characterization of cell states and transitions at unprecedented resolution. We are using the organoid model system of of embryogenesis, intestinal regeneration and developing cancers to map a developmental trajectory and single cell transcriptional atlas. We further nominate and validate transcriptional and epigenetic regulators of key fate decisions by perturbing transcriptional and epigenetic programs that underlie cell fate specification. We are aiming to provide resolved key mechanisms for charting cell-state hierarchies, cell fate decisions, and the factors that regulate fate choices.
2. Epigenomic regulation of lineage priming
2. Epigenomic regulation of lineage priming
We are finding the evidence that cell fate choices at the critical specification event is tightly controlled by epigenetic regulations such as DNA methylation, which silences one of the programs in a precise temporal window of bifurcating cells. Indeed, we reported that promoter methylation suppresses naive pluripotency and PGC transcriptional programs in EB preimplantation epiblast-like cells and favors postimplantation and primed pluripotency programs. Our findings support the hypothesis that naive preimplantation epiblast cells are epigenetically primed for different cell fates by their differential DNA methylation. In line with this, we are using scRNA-seq, scATAC-seq and further single cell multiomics approaches to chart genetic and epigenetic cell states at the event of cell fate choices to evaluate and validate lineage priming. In addition, we are trying to modulate cell states by epigenome editing and then develop key methods to build alternative trajectories against cell fate.
We are finding the evidence that cell fate choices at the critical specification event is tightly controlled by epigenetic regulations such as DNA methylation, which silences one of the programs in a precise temporal window of bifurcating cells. Indeed, we reported that promoter methylation suppresses naive pluripotency and PGC transcriptional programs in EB preimplantation epiblast-like cells and favors postimplantation and primed pluripotency programs. Our findings support the hypothesis that naive preimplantation epiblast cells are epigenetically primed for different cell fates by their differential DNA methylation. In line with this, we are using scRNA-seq, scATAC-seq and further single cell multiomics approaches to chart genetic and epigenetic cell states at the event of cell fate choices to evaluate and validate lineage priming. In addition, we are trying to modulate cell states by epigenome editing and then develop key methods to build alternative trajectories against cell fate.
3. Single cell multiomics technology development
3. Single cell multiomics technology development
With providing insights into lineage priming concepts to decide cell fate, we also develop a set of tools for mapping lineage relationships, that is compatible with rapidly differentiating biological systems. We innovated a timestamp genetic recording system to generate cell-specific barcodes in narrow temporal windows and validate key developmental branchpoints in this highly dynamic system. We also developed a key method for single cell multiomics to read out the transcriptome and timestamped barcode from the same single cells by combining scRNA-seq and long-read DNA sequencing. A key innovation was inclusion of an additional UCI that enabled us to identify and control for overly frequent recombination events and, thus, reduce false positives. Our method is readily extensible to other rapidly differentiating systems or single-cell sequencing technologies
With providing insights into lineage priming concepts to decide cell fate, we also develop a set of tools for mapping lineage relationships, that is compatible with rapidly differentiating biological systems. We innovated a timestamp genetic recording system to generate cell-specific barcodes in narrow temporal windows and validate key developmental branchpoints in this highly dynamic system. We also developed a key method for single cell multiomics to read out the transcriptome and timestamped barcode from the same single cells by combining scRNA-seq and long-read DNA sequencing. A key innovation was inclusion of an additional UCI that enabled us to identify and control for overly frequent recombination events and, thus, reduce false positives. Our method is readily extensible to other rapidly differentiating systems or single-cell sequencing technologies
4. Developing algorithms of multimodal analysis
4. Developing algorithms of multimodal analysis
Multimodality of scRNA-seq, scATAC-seq and lineage tracing data by genetic barcode over a real-time of differentiation, require newly developed computational approaches to extract and integrate multimodal dataset. We have used machine learning based, trajectory reconstruction algorithms to reconstruct differentiation trajectories with bifurcating lineages. And also, we develop analysis packages and integrate public algorithms to extract the barcodes from the long sequencing reads. For the consistency of cell state annotation, we have used the prediction algorithms (random forest etc.) to classify cells from this experiment by their similarity to the cell states annotated in the original time course data. Now, we are trying to integrate multimodal data into 2D or 3D space with combined modules used for dimensionality reduction using our methods and public algorithms.
Multimodality of scRNA-seq, scATAC-seq and lineage tracing data by genetic barcode over a real-time of differentiation, require newly developed computational approaches to extract and integrate multimodal dataset. We have used machine learning based, trajectory reconstruction algorithms to reconstruct differentiation trajectories with bifurcating lineages. And also, we develop analysis packages and integrate public algorithms to extract the barcodes from the long sequencing reads. For the consistency of cell state annotation, we have used the prediction algorithms (random forest etc.) to classify cells from this experiment by their similarity to the cell states annotated in the original time course data. Now, we are trying to integrate multimodal data into 2D or 3D space with combined modules used for dimensionality reduction using our methods and public algorithms.
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