Dataset Information

Dynamics of CDKN1A in Single Cells Defined by an Endogenous Fluorescent Tagging Toolkit.

ABSTRACT: Observing the endogenous abundance, localization, and dynamics of proteins in mammalian cells is crucial to understanding their function and behavior. Currently, there is no systematic approach for the fluorescent tagging of endogenous loci. Here, we used Cas9-catalyzed DNA breaks, short homology arms, and a family of donor plasmids to establish endogenous Fluorescent tagging (eFlut): a low-cost and efficient approach to generating endogenous proteins with fluorescent labels. We validated this protocol on multiple proteins in several cell lines and species and applied our tools to study the cell-cycle inhibitor CDKN1A in single cells. We uncover heterogeneity in the timing and rate of CDKN1A induction post-DNA damage and show that this variability is post-transcriptionally regulated, depends on cell-cycle position, and has long-term consequences for cellular proliferation. The tools developed in this study should support widespread study of the dynamics and localization of diverse proteins in mammalian cells.

SUBMITTER: Stewart-Ornstein J

PROVIDER: S-EPMC5154611 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Dynamics of CDKN1A in Single Cells Defined by an Endogenous Fluorescent Tagging Toolkit.

Stewart-Ornstein Jacob J Lahav Galit G

Cell reports 20160211 7

Observing the endogenous abundance, localization, and dynamics of proteins in mammalian cells is crucial to understanding their function and behavior. Currently, there is no systematic approach for the fluorescent tagging of endogenous loci. Here, we used Cas9-catalyzed DNA breaks, short homology arms, and a family of donor plasmids to establish endogenous Fluorescent tagging (eFlut): a low-cost and efficient approach to generating endogenous proteins with fluorescent labels. We validated this p ...[more]

PMID: 26876176

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Project description:BackgroundMultiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.ResultsCytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset.ConclusionCytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit .

Dataset Information

Dynamics of CDKN1A in Single Cells Defined by an Endogenous Fluorescent Tagging Toolkit.

Publications

Dynamics of CDKN1A in Single Cells Defined by an Endogenous Fluorescent Tagging Toolkit.

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