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Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars

ABSTRACT: Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars

PROVIDER: PRJNA415636 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR6211470_1.fastq.gz	Fastqsanger.gz
	SRR6211470_2.fastq.gz	Fastqsanger.gz
	SRR6211480_1.fastq.gz	Fastqsanger.gz
	SRR6211480_2.fastq.gz	Fastqsanger.gz
	SRR6211490_1.fastq.gz	Fastqsanger.gz

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Massively parallel whole-organism lineage tracing using CRISPR/Cas9 induced genetic scars

Project description:A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many cells. Although impressive progress has been made in lineage tracing using imaging approaches, analysis of vertebrate lineage trees has mostly been limited to relatively small subsets of cells. Here we present scar-trace, a strategy for massively parallel whole-organism lineage tracing based on Cas9 induced genetic scars in the zebrafish.

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Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars

Project description:A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many different cell types. Single-cell RNA-sequencing (scRNA-seq) has become a widely-used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However, a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here, we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses, LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences) can be used as a systematic approach for identifying the lineage origin of novel cell types, or of known cell types under different conditions.

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Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars.

Project description:A key goal of developmental biology is to understand how a single cell is transformed into a full-grown organism comprising many different cell types. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the developmental origin of cells remains challenging. Here we present LINNAEUS (lineage tracing by nuclease-activated editing of ubiquitous sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae, and in heart, liver, pancreas, and telencephalon of adult fish. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

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Endogenous CRISPR/Cas9 arrays for scalable whole-organism lineage tracing

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Dual nuclease single-cell lineage tracing by Cas9 and Cas12a

Project description:Single-cell lineage tracing based on CRISPR/Cas9 gene editing enables the simultaneous linkage of cell states and lineage history at a high resolution. Despite its immense potential in resolving the cell fate determination and genealogy within an organism, existing implementations of this technology suffers from the limitations in recording capabilities and considerable information dropout. Here, we introduce a versatile tool, DuTracer, which utilizes two orthogonal gene editing systems to record deep cell lineage history at single-cell resolution in an inducible manner. DuTracer shows the ability of enhanced lineage recording with minimized target dropouts and deepened tree depth. Application of DuTracer in mouse embryoid bodies and neuromesodermal organoids illustrates the transition pattern of the lineage relationship of different cell types and proposes potential lineage-biased molecular drivers. Moreover, we have developed an entropy-based approach to quantify the lineage recording ability of DuTracer in cell differentiation models. Together, DuTracer facilitates the precise and regulatory interrogation of cellular lineages of complex biological processes.

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Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data.

Project description:Recent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a species-invariant lineage tree. To address these issues we developed a statistical method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of an invariant lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.

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Inference of single-cell phylogenies from lineage tracing data using Cassiopeia

Project description:The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia - a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together these should broadly enable large-scale mammalian lineage tracing efforts.Cassiopeia and its benchmarking resources are publicly available at https://www.github.com/YosefLab/Cassiopeia.

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