Dataset Information

Super-resolution imaging of fluorescently labeled, endogenous RNA Polymerase II in living cells with CRISPR/Cas9-mediated gene editing.

ABSTRACT: Live cell imaging of mammalian RNA polymerase II (Pol II) has previously relied on random insertions of exogenous, mutant Pol II coupled with the degradation of endogenous Pol II using a toxin, ?-amanitin. Therefore, it has been unclear whether over-expression of labeled Pol II under an exogenous promoter may have played a role in reported Pol II dynamics in vivo. Here we label the endogenous Pol II in mouse embryonic fibroblast (MEF) cells using the CRISPR/Cas9 gene editing system. Using single-molecule based super-resolution imaging in the living cells, we captured endogenous Pol II clusters. Consistent with previous studies, we observed that Pol II clusters were short-lived (cluster lifetime ~8?s) in living cells. Moreover, dynamic responses to serum-stimulation, and drug-mediated transcription inhibition were all in agreement with previous observations in the exogenous Pol II MEF cell line. Our findings suggest that previous exogenously tagged Pol II faithfully recapitulated the endogenous polymerase clustering dynamics in living cells, and our approach may in principle be used to directly label transcription factors for live cell imaging.

SUBMITTER: Cho WK

PROVIDER: S-EPMC5080603 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Super-resolution imaging of fluorescently labeled, endogenous RNA Polymerase II in living cells with CRISPR/Cas9-mediated gene editing.

Cho Won-Ki WK Jayanth Namrata N Mullen Susan S Tan Tzer Han TH Jung Yoon J YJ Cissé Ibrahim I II

Scientific reports 20161026

Live cell imaging of mammalian RNA polymerase II (Pol II) has previously relied on random insertions of exogenous, mutant Pol II coupled with the degradation of endogenous Pol II using a toxin, α-amanitin. Therefore, it has been unclear whether over-expression of labeled Pol II under an exogenous promoter may have played a role in reported Pol II dynamics in vivo. Here we label the endogenous Pol II in mouse embryonic fibroblast (MEF) cells using the CRISPR/Cas9 gene editing system. Using single ...[more]

PMID: 27782203

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Project description:MotivationCRISPR/Cas9 technology has been revolutionizing the field of gene editing in recent years. Guide RNAs (gRNAs) enable Cas9 proteins to target specific genomic loci for editing. However, editing efficiency varies between gRNAs. Thus, computational methods were developed to predict editing efficiency for any gRNA of interest. High-throughput datasets of Cas9 editing efficiencies were produced to train machine-learning models to predict editing efficiency. However, these high-throughput datasets have low correlation with functional and endogenous editing. Another difficulty arises from the fact that functional and endogenous editing efficiency is more difficult to measure, and as a result, functional and endogenous datasets are too small to train accurate machine-learning models on.ResultsWe developed DeepCRISTL, a deep-learning model to predict the on-target efficiency given a gRNA sequence. DeepCRISTL takes advantage of high-throughput datasets to learn general patterns of gRNA on-target editing efficiency, and then uses transfer learning (TL) to fine-tune the model and fit it to the functional and endogenous prediction task. We pre-trained the DeepCRISTL model on more than 150 000 gRNAs, produced through the DeepHF study as a high-throughput dataset of three Cas9 enzymes. We improved the DeepHF model by multi-task and ensemble techniques and achieved state-of-the-art results over each of the three enzymes: up to 0.89 in Spearman correlation between predicted and measured on-target efficiencies. To fine-tune model weights to predict on-target efficiency of functional or endogenous datasets, we tested several TL approaches, with gradual learning being the overall best performer, both when pre-trained on DeepHF and when pre-trained on CRISPROn, another high-throughput dataset. DeepCRISTL outperformed state-of-the-art methods on all functional and endogenous datasets. Using saliency maps, we identified and compared the important features learned by the model in each dataset. We believe DeepCRISTL will improve prediction performance in many other CRISPR/Cas9 editing contexts by leveraging TL to utilize both high-throughput datasets, and smaller and more biologically relevant datasets, such as functional and endogenous datasets.Availability and implementationDeepCRISTL is available via github.com/OrensteinLab/DeepCRISTL.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Super-resolution imaging of fluorescently labeled, endogenous RNA Polymerase II in living cells with CRISPR/Cas9-mediated gene editing.

Publications

Super-resolution imaging of fluorescently labeled, endogenous RNA Polymerase II in living cells with CRISPR/Cas9-mediated gene editing.

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