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Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo.

ABSTRACT: High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations of vertebrate development and disease. Here we applied single-cell RNA sequencing to >92,000 cells from zebrafish embryos during the first day of development. Using a graph-based approach, we mapped a cell-state landscape that describes axis patterning, germ layer formation, and organogenesis. We tested how clonally related cells traverse this landscape by developing a transposon-based barcoding approach (TracerSeq) for reconstructing single-cell lineage histories. Clonally related cells were often restricted by the state landscape, including a case in which two independent lineages converge on similar fates. Cell fates remained restricted to this landscape in embryos lacking the chordin gene. We provide web-based resources for further analysis of the single-cell data.

SUBMITTER: Wagner DE

PROVIDER: S-EPMC6083445 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo.

Wagner Daniel E DE Weinreb Caleb C Collins Zach M ZM Briggs James A JA Megason Sean G SG Klein Allon M AM

Science (New York, N.Y.) 20180426 6392

High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations of vertebrate development and disease. Here we applied single-cell RNA sequencing to >92,000 cells from zebrafish embryos during the first day of development. Using a graph-based approach, we mapped a cell-state landscape that describes axis patterning, germ layer formation, and organogenesis. We tested how clonally related cells traverse this landscape by developin ...[more]

PMID: 29700229

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Project description:Sensitivity and throughput of transcriptomics and proteomomics technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNAseq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and on any organism for which genomic data is available. Although these technologies are currently applied to many research questions in various model systems ranging from cell cultures to the entire organism level, there are few comparative studies of these technologies in the same system, let alone on the same samples. Here we present a comparison between gene and protein expression in zebrafish embryos, which is an upcoming model in disease studies. We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 15,927 genes. Gene expression was also found to correlate with the abundance of 1,017 quantified proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed. By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories. Our datasets submitted to public repositories are a good starting point for researchers interested in disease progression in zebrafish at a stage of development highly suited for high throughput screening technologies. The transcriptomics data is deposited in NCBI GEO. Proteomics informatics: Spectra were identified by Mascot and validated by PeptideProphet and ProteinProphet in the Trans-Proteomic Pipeline (TPP). The main search parameters are: full tryptic specificity, precursor mass searched within -0.5 to +2.5 Da, product ions within [-0.5,0.5] Da and allowing for one missed cleavage. Carbamidomethylation was assumed as the only and fixed PTM.

Dataset Information

Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo.

Publications

Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo.

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