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Spatial protein analysis in developing tissues: a sampling-based image processing approach.

ABSTRACT: Advances in fluorescence microscopy approaches have made it relatively easy to generate multi-dimensional image volumes and have highlighted the need for flexible image analysis tools for the extraction of quantitative information from such data. Here we demonstrate that by focusing on simplified feature-based nuclear segmentation and probabilistic cytoplasmic detection we can create a tool that is able to extract geometry-based information from diverse mammalian tissue images. Our open-source image analysis platform, called 'SilentMark', can cope with three-dimensional noisy images and with crowded fields of cells to quantify signal intensity in different cellular compartments. Additionally, it provides tissue geometry related information, which allows one to quantify protein distribution with respect to marked regions of interest. The lightweight SilentMark algorithms have the advantage of not requiring multiple processors, graphics cards or training datasets and can be run even with just several hundred megabytes of memory. This makes it possible to use the method as a Web application, effectively eliminating setup hurdles and compatibility issues with operating systems. We test this platform on mouse pre-implantation embryos, embryonic stem cell-derived embryoid bodies and mouse embryonic heart, and relate protein localization to tissue geometry. This article is part of a discussion meeting issue 'Contemporary morphogenesis'.

SUBMITTER: Leonavicius K

PROVIDER: S-EPMC7482225 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Spatial protein analysis in developing tissues: a sampling-based image processing approach.

Leonavicius Karolis K Royer Christophe C Miranda Antonio M A AMA Tyser Richard C V RCV Kip Annemarie A Srinivas Shankar S

Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20200824 1809

Advances in fluorescence microscopy approaches have made it relatively easy to generate multi-dimensional image volumes and have highlighted the need for flexible image analysis tools for the extraction of quantitative information from such data. Here we demonstrate that by focusing on simplified feature-based nuclear segmentation and probabilistic cytoplasmic detection we can create a tool that is able to extract geometry-based information from diverse mammalian tissue images. Our open-source i ...[more]

PMID: 32829691

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Project description:Short half-life proteins regulate many essential processes, including cell cycle, transcription, and apoptosis. However, few well-characterized protein-turnover pathways have been identified because traditional methods to measure protein half-life are time and labor intensive. To overcome this barrier, we developed a protein stability probe and high-content screening pipeline for novel regulators of short half-life proteins using automated image analysis. Our pilot probe consists of the short half-life protein c-MYC (MYC) fused to Venus fluorescent protein (MYC-Venus). This probe enables protein half-life to be scored as a function of fluorescence intensity and distribution. Rapid turnover prevents maximal fluorescence of the probe due to the relatively longer maturation time of the fluorescent protein. Cells expressing the MYC-Venus probe were analyzed using a pipeline in which automated confocal microscopy and image analyses were used to score MYC-Venus stability by two strategies: assaying the percentage of cells with Venus fluorescence above background, and phenotypic comparative analysis. To evaluate this high-content screening pipeline and our probe, a kinase inhibitor library was screened by confocal microscopy to identify known and novel kinases that regulate MYC stability. Compounds identified were shown to increase the half-life of both MYC-Venus and endogenous MYC, validating the probe and pipeline. Fusion of another short half-life protein, myeloid cell leukemia 1 (MCL1), with Venus also demonstrated an increase in percent Venus-positive cells after treatment with inhibitors known to stabilize MCL1. Together, the results validate the use of our automated microscopy and image analysis pipeline of stability probe-expressing cells to rapidly and quantitatively identify regulators of short half-life proteins. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Dataset Information

Spatial protein analysis in developing tissues: a sampling-based image processing approach.

Publications

Spatial protein analysis in developing tissues: a sampling-based image processing approach.

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