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An adapted novel flow cytometry methodology to delineate types of cell death in airway epithelial cells.

ABSTRACT: Current methodologies to measure apoptotic and necrotic cell death using flow cytometry do not adequately differentiate between the two. Here, we describe a flow cytometry methodology adapted to airway epithelial cells (AEC) to sufficiently differentiate apoptotic and necrotic AEC. Specifically, cell lines and primary AEC (n = 12) were permeabilized or infected with rhinovirus 1b (RV1b) over 48 h. Cell death was then measured via annexin V/propidium iodide (A5/PI) or annexin V/TO-PRO-3 (A5/TP3) staining using a novel flow cytometry and gating methodology adapted to AEC. We show that A5/PI staining could not sufficiently differentiate between types of cell death following RV1b infection of primary AEC. However, A5/TP3 staining was able to distinguish six cell death populations (viable, necrotic, debris, A5+ apoptotic, A5- apoptotic, apoptotic bodies) after permeabilization or infection with RV1b, with phenotypic differences were observed in apoptotic populations. Collectively, using a staining and gating strategy never adapted to AEC, A5/TP3 could accurately differentiate and quantify viable, necrotic, and apoptotic AEC following RV1b infection.

SUBMITTER: Montgomery ST

PROVIDER: S-EPMC7666329 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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An adapted novel flow cytometry methodology to delineate types of cell death in airway epithelial cells.

Montgomery Samuel T ST Stick Stephen M SM Kicic Anthony A

Journal of biological methods 20201111 4

Current methodologies to measure apoptotic and necrotic cell death using flow cytometry do not adequately differentiate between the two. Here, we describe a flow cytometry methodology adapted to airway epithelial cells (AEC) to sufficiently differentiate apoptotic and necrotic AEC. Specifically, cell lines and primary AEC (<i>n</i> = 12) were permeabilized or infected with rhinovirus 1b (RV1b) over 48 h. Cell death was then measured <i>via</i> annexin V/propidium iodide (A5/PI) or annexin V/TO-P ...[more]

PMID: 33204742

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Project description:Background and aimsFlow cytometry has been used to measure nuclear DNA content in pollen, mostly to understand pollen development and detect unreduced gametes. Published data have not always met the high-quality standards required for some applications, in part due to difficulties inherent in the extraction of nuclei. Here we describe a simple and relatively novel method for extracting pollen nuclei, involving the bursting of pollen through a nylon mesh, compare it with other methods and demonstrate its broad applicability and utility.MethodsThe method was tested across 80 species, 64 genera and 33 families, and the data were evaluated using established criteria for estimating genome size and analysing cell cycle. Filter bursting was directly compared with chopping in five species, yields were compared with published values for sonicated samples, and the method was applied by comparing genome size estimates for leaf and pollen nuclei in six species.Key resultsData quality met generally applied standards for estimating genome size in 81 % of species and the higher best practice standards for cell cycle analysis in 51 %. In 41 % of species we met the most stringent criterion of screening 10 000 pollen grains per sample. In direct comparison with two chopping techniques, our method produced better quality histograms with consistently higher nuclei yields, and yields were higher than previously published results for sonication. In three binucleate and three trinucleate species we found that pollen-based genome size estimates differed from leaf tissue estimates by 1·5 % or less when 1C pollen nuclei were used, while estimates from 2C generative nuclei differed from leaf estimates by up to 2·5 %.ConclusionsThe high success rate, ease of use and wide applicability of the filter bursting method show that this method can facilitate the use of pollen for estimating genome size and dramatically improve unreduced pollen production estimation with flow cytometry.

Project description:Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

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An adapted novel flow cytometry methodology to delineate types of cell death in airway epithelial cells.

Publications

An adapted novel flow cytometry methodology to delineate types of cell death in airway epithelial cells.

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