Dataset Information

Efficient labeling and imaging of protein-coding genes in living cells using CRISPR-Tag.

ABSTRACT: The lack of efficient tools to image non-repetitive genes in living cells has limited our ability to explore the functional impact of the spatiotemporal dynamics of such genes. Here, we addressed this issue by developing a CRISPR-Tag system using one to four highly active sgRNAs to specifically label protein-coding genes with a high signal-to-noise ratio for visualization by wide-field fluorescence microscopy. Our approach involves assembling a CRISPR-Tag within the intron region of a fluorescent protein and then integrating this cassette to N- or C-terminus of a specific gene, which enables simultaneous real-time imaging of protein and DNA of human protein-coding genes, such as HIST2H2BE, LMNA and HSPA8 in living cells. This CRISPR-Tag system, with a minimal size of ~250?bp DNA tag, represents an easily and broadly applicable technique to study the spatiotemporal organization of genomic elements in living cells.

SUBMITTER: Chen B

PROVIDER: S-EPMC6265289 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Efficient labeling and imaging of protein-coding genes in living cells using CRISPR-Tag.

Chen Baohui B Zou Wei W Xu Haiyue H Liang Ying Y Huang Bo B

Nature communications 20181129 1

The lack of efficient tools to image non-repetitive genes in living cells has limited our ability to explore the functional impact of the spatiotemporal dynamics of such genes. Here, we addressed this issue by developing a CRISPR-Tag system using one to four highly active sgRNAs to specifically label protein-coding genes with a high signal-to-noise ratio for visualization by wide-field fluorescence microscopy. Our approach involves assembling a CRISPR-Tag within the intron region of a fluorescen ...[more]

PMID: 30498221

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Project description:Malaria is a vector-borne disease that is a great burden on the poorest and most marginalized communities of the tropical and subtropical world. Approximately 41 species of Anopheline mosquitoes can effectively spread species of Plasmodium parasites that cause human malaria. Proposing a natural classification for the subfamily Anophelinae has been a continuous effort, addressed using both morphology and DNA sequence data. The monophyly of the genus Anopheles, and phylogenetic placement of the genus Bironella, subgenera Kerteszia, Lophopodomyia and Stethomyia within the subfamily Anophelinae, remain in question. To understand the classification of Anophelinae, we inferred the phylogeny of all three genera (Anopheles, Bironella, Chagasia) and major subgenera by analysing the amino acid sequences of the 13 protein coding genes of 150 newly sequenced mitochondrial genomes of Anophelinae and 18 newly sequenced Culex species as outgroup taxa, supplemented with 23 mitogenomes from GenBank. Our analyses generally place genus Bironella within the genus Anopheles, which implies that the latter as it is currently defined is not monophyletic. With some inconsistencies, Bironella was placed within the major clade that includes Anopheles, Cellia, Kerteszia, Lophopodomyia, Nyssorhynchus and Stethomyia, which were found to be monophyletic groups within Anophelinae. Our findings provided robust evidence for elevating the monophyletic groupings Kerteszia, Lophopodomyia, Nyssorhynchus and Stethomyia to genus level; genus Anopheles to include subgenera Anopheles, Baimaia, Cellia and Christya; Anopheles parvus to be placed into a new genus; Nyssorhynchus to be elevated to genus level; the genus Nyssorhynchus to include subgenera Myzorhynchella and Nyssorhynchus; Anopheles atacamensis and Anopheles pictipennis to be transferred from subgenus Nyssorhynchus to subgenus Myzorhynchella; and subgenus Nyssorhynchus to encompass the remaining species of Argyritarsis and Albimanus Sections.

Dataset Information

Efficient labeling and imaging of protein-coding genes in living cells using CRISPR-Tag.

Publications

Efficient labeling and imaging of protein-coding genes in living cells using CRISPR-Tag.

Similar Datasets

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