Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA415636 | ENA |
REPOSITORIES: ENA
Dataset's files
| 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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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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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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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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Project description:Endogenous CRISPR/Cas9 arrays for scalable whole-organism lineage tracing
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Project description:RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells
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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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Project description:We designed a simulation program that mimics the CRISPR-Cas9 editing on evolving barcode and double strand break repair procedure along with cell divisions. Emerging barcode mutations tend to build upon previously existing mutations, occurring sequentially with each generation. This process results in a unique mutation profile in each cell. We sample the barcodes in leaf cells and reconstruct the lineage, comparing it to the original lineage tree to test algorithm accuracy under different parameter settings. Our computational simulations validate the reasonable assumptions deduced from experimental observations, emphasizing that factors such as sampling size, barcode length, multiple barcodes, indel probabilities, and Cas9 activity are critical for accurate and successful lineage tracing. Among the many factors we found that sampling size and indel probabilities are two major ones that affect lineage tracing accuracy. Large segment deletions in early generations could greatly impact lineage accuracy. These simulation results offer insightful recommendations for enhancing the design and analysis of Cas9-mediated molecular barcodes in actual experiments.
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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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Project description:Tracing the lineage history of cells is key to answering diverse and fundamental questions in biology. Coupling of cell ancestry information with other molecular readouts represents an important goal in the field. Here, we describe the CARLIN (for CRISPR Array Repair LINeage tracing) mouse line and corresponding analysis tools that can be used to simultaneously interrogate the lineage and transcriptomic information of single cells in vivo. This model exploits CRISPR technology to generate up to 44,000 transcribed barcodes in an inducible fashion at any point during development or adulthood, is compatible with sequential barcoding, and is fully genetically defined. We have used CARLIN to identify intrinsic biases in the activity of fetal liver hematopoietic stem cell (HSC) clones and to uncover a previously unappreciated clonal bottleneck in the response of HSCs to injury. CARLIN also allows the unbiased identification of transcriptional signatures associated with HSC activity without cell sorting.
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