Dataset Information

New method to assess mitophagy flux by flow cytometry.

ABSTRACT: Mitochondrial autophagy, also known as mitophagy, is an autophagosome-based mitochondrial degradation process that eliminates unwanted or damaged mitochondria after cell stress. Most studies dealing with mitophagy rely on the analysis by fluorescence microscopy of mitochondrial-autophagosome colocalization. However, given the fundamental role of mitophagy in the physiology and pathology of organisms, there is an urgent need for novel quantitative methods with which to study this process. Here, we describe a flow cytometry-based approach to determine mitophagy by using MitoTracker Deep Red, a widely used mitochondria-selective probe. Used in combination with selective inhibitors it may allow for the determination of mitophagy flux. Here, we test the validity of the use of this method in cell lines and in primary cell and tissue cultures.

SUBMITTER: Mauro-Lizcano M

PROVIDER: S-EPMC4509449 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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New method to assess mitophagy flux by flow cytometry.

Mauro-Lizcano Marta M Esteban-Martínez Lorena L Seco Esther E Serrano-Puebla Ana A Garcia-Ledo Lucia L Figueiredo-Pereira Cláudia C Vieira Helena L A HL Boya Patricia P

Autophagy 20150101 5

Mitochondrial autophagy, also known as mitophagy, is an autophagosome-based mitochondrial degradation process that eliminates unwanted or damaged mitochondria after cell stress. Most studies dealing with mitophagy rely on the analysis by fluorescence microscopy of mitochondrial-autophagosome colocalization. However, given the fundamental role of mitophagy in the physiology and pathology of organisms, there is an urgent need for novel quantitative methods with which to study this process. Here, w ...[more]

PMID: 25945953

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Project description:BackgroundNucleic acids can fold into non-canonical secondary structures named G-quadruplexes (G4s), which consist of guanine-rich sequences stacked into guanine tetrads stabilized by Hoogsteen hydrogen bonding, ?-? interactions, and monovalent cations. G4 structure formation and properties are well established in vitro, but potential in vivo functions remain controversial. G4s are evolutionarily enriched at distinct, functional genomic loci, and both genetic and molecular findings indicate that G4s are involved in multiple aspects of cellular homeostasis. In order to gain a deeper understanding of the function of G4 structures and the trigger signals for their formation, robust biochemical methods are needed to detect and quantify G4 structures in living cells. Currently available methods mostly rely on fluorescence microscopy or deep sequencing of immunoprecipitated DNA or RNA using G4-specific antibodies. These methods provide a clear picture of the cellular or genomic localization of G4 structures but are very time-consuming. Here, we assembled a novel protocol that uses the G4-specific antibody BG4 to quantify G4 structures by flow cytometry (BG-flow).ResultsWe describe and validate a flow cytometry-based protocol for quantifying G4 levels by using the G4-specific antibody BG4 to label standard cultured cells (Hela and THP-1) as well as primary cells obtained from human blood (peripheral blood mononuclear cells (PBMCs)). We additionally determined changes in G4 levels during the cell cycle in immortalized MCF-7 cells, and validated changes previously observed in G4 levels by treating mouse macrophages with the G4-stabilizing agent pyridostatin (PDS).ConclusionWe provide mechanistic proof that BG-flow is working in different kinds of cells ranging from mouse to humans. We propose that BG-flow can be combined with additional antibodies for cell surface markers to determine G4 structures in subpopulations of cells, which will be beneficial to address the relevance and consequences of G4 structures in mixed cell populations. This will support ongoing research that discusses G4 structures as a novel diagnostic tool.

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.

Dataset Information

New method to assess mitophagy flux by flow cytometry.

Publications

New method to assess mitophagy flux by flow cytometry.

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