Inference of single-cell phylogenies from lineage tracing data using Cassiopeia
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA611743 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11357694.fastq.gz | Fastqsanger.gz | |||
| SRR11357695.fastq.gz | Fastqsanger.gz | |||
| SRR11357696.fastq.gz | Fastqsanger.gz | |||
| SRR11357697.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 4 of 4 |
Similar Datasets
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.
2020-03-11 | GSE146712 | GEO
Project description:Lineage Tracing of PSC-derived iAEC2 Differentiation Using Lentiviral Barcoding
| PRJNA573107 | ENA
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.
2019-05-17 | GSE81533 | GEO
Project description:Lineage tracing of individual cells during directed differentiation human iPSC into alveolospheres was performed using a lentiviral barcode labeling system (Weinreb et al., 2020) as described in Hurley et al., 2020.
2019-09-24 | GSE137805 | GEO
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 www.github.com/YosefLab/Cassiopeia.
| S-EPMC7155257 | biostudies-literature
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.
2018-04-04 | GSE106121 | GEO
Project description:Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars
| PRJNA415636 | ENA
Project description:Cardiac fibroblasts convert to myofibroblasts with injury to mediate healing after acute myocardial infarction and to mediate long-standing fibrosis with chronic disease. Myofibroblasts remain a poorly defined cell-type in terms of their origins and functional effects in vivo. Methods: Here we generate Postn (periostin) gene-targeted mice containing a tamoxifen inducible Cre for cellular lineage tracing analysis. This Postn allele identifies essentially all myofibroblasts within the heart and multiple other tissues. Results: Lineage tracing with 4 additional Cre-expressing mouse lines shows that periostin-expressing myofibroblasts in the heart derive from tissue-resident fibroblasts of the Tcf21 lineage, but not endothelial, immune/myeloid or smooth muscle cells. Deletion of periostin+ myofibroblasts reduces collagen production and scar formation after myocardial infarction. Periostin-traced myofibroblasts also revert back to a less activated state upon injury resolution. Conclusions: Our results define the myofibroblast as a periostin-expressing cell-type necessary for adaptive healing and fibrosis in the heart, which arises from Tcf21+ tissue-resident fibroblasts. Fluidigm C1 whole genome transcriptome analysis of lineage mapped cardiac myofibroblasts
2016-06-14 | E-GEOD-83337 | biostudies-arrayexpress
Project description:Retrospective lineage tracing harnesses naturally occurring mutations in cells to elucidate single cell development. Common single cell phylogenetic fate mapping methods have utilized highly mutable microsatellite loci found within the human genome. Such methods were limited by the introduction of in vitro noise through polymerase slippage inherent in DNA amplification, which we characterized to be approximately 10-100 higher than in vivo replication mutation rate. Here, we present RETrace, a method for simultaneously capturing both microsatellites and methylation-informative cytosines to characterize both lineage and cell type, respectively, from the same single cell. An important unique feature of RETrace was the introduction of linear amplification of microsatellites in order to reduce in vitro amplification noise. We further coupled microsatellite capture with single-cell reduced representation bisulfite sequencing (scRRBS), to measure the CpG methylation status on the same cell for cell type inference. When compared to existing retrospective lineage tracing methods, RETrace achieved higher accuracy (88% triplet accuracy from an ex vivo HCT116 tree) at a higher cell division resolution (lowering the required number of cell division difference between single cells by approximately 100 divisions). Simultaneously, RETrace demonstrated the ability to capture on average 150,000 unique CpGs per single cell in order to accurately determine cell type. We further formulated additional developments that would allow high-resolution mapping on microsatellite stable cells or tissues with RETrace. Overall, we present RETrace as a foundation for multi-omics lineage mapping and cell typing of single cells.
2020-02-23 | GSE136089 | GEO
Project description:Multicellular systems develop from single cells through a lineage, but current lineage tracing approaches scale poorly to whole organisms. Here we use genome editing to progressively introduce and accumulate diverse mutations in a DNA barcode over multiple rounds of cell division. The barcode, an array of CRISPR/Cas9 target sites, records lineage relationships in the patterns of mutations shared between cells. In cell culture and zebrafish, we show that rates and patterns of editing are tunable, and that thousands of lineage-informative barcode alleles can be generated. By sampling hundreds of thousands of cells from individual zebrafish, we find that most cells in adult zebrafish organs derive from relatively few embryonic progenitors. Genome editing of synthetic target arrays for lineage tracing (GESTALT) will help generate large-scale maps of cell lineage in multicellular systems.
2016-05-26 | GSE81713 | GEO