Dataset Information

High Throughput Analysis of Golgi Structure by Imaging Flow Cytometry.

ABSTRACT: The Golgi apparatus is a dynamic organelle, which regulates the vesicular trafficking. While cellular trafficking requires active changes of the Golgi membranes, these are not accompanied by changes in the general Golgi's structure. However, cellular processes such as mitosis, apoptosis and migration require fragmentation of the Golgi complex. Currently, these changes are most commonly studied by basic immunofluorescence and quantified by manual and subjective classification of the Golgi structure in 100-500 stained cells. Several other high-throughput methods exist as well, but those are either complicated or do not provide enough morphological information. Therefore, a simple and informative high content methodology should be beneficial for the study of Golgi architecture. Here we describe the use of high-throughput imaging flow cytometry for quantification of Golgi fragmentation, which provides a simple way to analyze the changes in an automated, quantitative and non-biased manner. Furthermore, it provides a rapid and accurate way to analyze more than 50,000 cells per sample. Our results demonstrate that this method is robust and statistically powerful, thus, providing a much-needed analytical tool for future studies on Golgi dynamics, and can be adapted to other experimental systems.

SUBMITTER: Wortzel I

PROVIDER: S-EPMC5429768 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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High Throughput Analysis of Golgi Structure by Imaging Flow Cytometry.

Wortzel Inbal I Koifman Gabriela G Rotter Varda V Seger Rony R Porat Ziv Z

Scientific reports 20170411 1

The Golgi apparatus is a dynamic organelle, which regulates the vesicular trafficking. While cellular trafficking requires active changes of the Golgi membranes, these are not accompanied by changes in the general Golgi's structure. However, cellular processes such as mitosis, apoptosis and migration require fragmentation of the Golgi complex. Currently, these changes are most commonly studied by basic immunofluorescence and quantified by manual and subjective classification of the Golgi structu ...[more]

PMID: 28400563

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Project description:Mitochondria are highly dynamic organelles whose fusion and fission play an increasingly important role in a number of both normal and pathological cellular functions. Despite the increased interest in mitochondrial dynamics, robust, and quantitative methods to analyze mitochondrial fusion and fission activity in intact cells have not been developed. The current state-of-the art method to measure mitochondrial fusion activity is the polyethylene glycol (PEG) fusion assay in which cells expressing distinct mitochondrially-targeted fluorescent proteins (FPs) are fused together and mitochondrial fusion activity is determined by the rate at which color mixing occurs. Although this assay is useful, cell-cell fusion events are rare, and finding the number of fused cells required to generate statistically rigorous data is both tedious and time-consuming. Furthermore, the data-collection methods available for fluorescence microscopy lead to inherent selection biases that are difficult to control for. To that end, we have developed an unbiased and high-throughput method to detect, image, and analyze fused cells using the Amnis ImagestreamX™ MKII. With IDEAS™ software, we developed algorithms for identifying the fused cells (two nuclei within a single cell), distinguishing them from cell aggregates. Additionally, using the fluorescence localization of the mitochondrially-targeted fluorescent proteins (YFP and DsRed), we applied a modified co-localization algorithm to identify those cells that had a high co-localization score indicating mitochondrial fusion activity. These algorithms were tested using negative controls (FPs associated with fusion deficient mitochondria) and positive controls (cells expressing both FPs in the same mitochondria). Once validated these algorithms could be applied to test samples to evaluate the degree of mitochondrial fusion in cells with various genetic mutations. Ultimately, this new method is the first robust, high-throughput way to directly measure mitochondrial fusion in intact cells. Given how many cellular processes are being linked mitochondrial dynamics, this technique will provide a powerful new tool in the study of this important organelle. © 2016 International Society for Advancement of Cytometry.

Project description:BackgroundIn a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.ResultsWe compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. We find that parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts.ConclusionsOur results indicate that the preferred transformation for fluorescence channels is a parameter- optimized biexponential or generalized Box-Cox, in accordance with current best practices. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. However, generally speaking, the choice of transformation remains data-dependent. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available.

Dataset Information

High Throughput Analysis of Golgi Structure by Imaging Flow Cytometry.

Publications

High Throughput Analysis of Golgi Structure by Imaging Flow Cytometry.

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